Repository: Comfy-Org/ComfyUI Branch: master Commit: 589228e671e8 Files: 718 Total size: 23.3 MB Directory structure: gitextract_r6aji11h/ ├── .ci/ │ ├── update_windows/ │ │ ├── update.py │ │ ├── update_comfyui.bat │ │ └── update_comfyui_stable.bat │ ├── windows_amd_base_files/ │ │ ├── README_VERY_IMPORTANT.txt │ │ ├── run_amd_gpu.bat │ │ └── run_amd_gpu_disable_smart_memory.bat │ ├── windows_nightly_base_files/ │ │ └── run_nvidia_gpu_fast.bat │ └── windows_nvidia_base_files/ │ ├── README_VERY_IMPORTANT.txt │ ├── advanced/ │ │ └── run_nvidia_gpu_disable_api_nodes.bat │ ├── run_cpu.bat │ ├── run_nvidia_gpu.bat │ └── run_nvidia_gpu_fast_fp16_accumulation.bat ├── .coderabbit.yaml ├── .gitattributes ├── .github/ │ ├── ISSUE_TEMPLATE/ │ │ ├── bug-report.yml │ │ ├── config.yml │ │ ├── feature-request.yml │ │ └── user-support.yml │ ├── PULL_REQUEST_TEMPLATE/ │ │ └── api-node.md │ ├── scripts/ │ │ └── check-ai-co-authors.sh │ └── workflows/ │ ├── api-node-template.yml │ ├── check-ai-co-authors.yml │ ├── check-line-endings.yml │ ├── pullrequest-ci-run.yml │ ├── release-stable-all.yml │ ├── release-webhook.yml │ ├── ruff.yml │ ├── stable-release.yml │ ├── stale-issues.yml │ ├── test-build.yml │ ├── test-ci.yml │ ├── test-execution.yml │ ├── test-launch.yml │ ├── test-unit.yml │ ├── update-api-stubs.yml │ ├── update-ci-container.yml │ ├── update-version.yml │ ├── windows_release_dependencies.yml │ ├── windows_release_dependencies_manual.yml │ ├── windows_release_nightly_pytorch.yml │ └── windows_release_package.yml ├── .gitignore ├── CODEOWNERS ├── CONTRIBUTING.md ├── LICENSE ├── QUANTIZATION.md ├── README.md ├── alembic.ini ├── alembic_db/ │ ├── README.md │ ├── env.py │ ├── script.py.mako │ └── versions/ │ ├── 0001_assets.py │ ├── 0002_merge_to_asset_references.py │ └── 0003_add_metadata_job_id.py ├── api_server/ │ ├── __init__.py │ ├── routes/ │ │ ├── __init__.py │ │ └── internal/ │ │ ├── README.md │ │ ├── __init__.py │ │ └── internal_routes.py │ ├── services/ │ │ ├── __init__.py │ │ └── terminal_service.py │ └── utils/ │ └── file_operations.py ├── app/ │ ├── __init__.py │ ├── app_settings.py │ ├── assets/ │ │ ├── api/ │ │ │ ├── routes.py │ │ │ ├── schemas_in.py │ │ │ ├── schemas_out.py │ │ │ └── upload.py │ │ ├── database/ │ │ │ ├── models.py │ │ │ └── queries/ │ │ │ ├── __init__.py │ │ │ ├── asset.py │ │ │ ├── asset_reference.py │ │ │ ├── common.py │ │ │ └── tags.py │ │ ├── helpers.py │ │ ├── scanner.py │ │ ├── seeder.py │ │ └── services/ │ │ ├── __init__.py │ │ ├── asset_management.py │ │ ├── bulk_ingest.py │ │ ├── file_utils.py │ │ ├── hashing.py │ │ ├── ingest.py │ │ ├── metadata_extract.py │ │ ├── path_utils.py │ │ ├── schemas.py │ │ └── tagging.py │ ├── custom_node_manager.py │ ├── database/ │ │ ├── db.py │ │ └── models.py │ ├── frontend_management.py │ ├── logger.py │ ├── model_manager.py │ ├── node_replace_manager.py │ ├── subgraph_manager.py │ └── user_manager.py ├── blueprints/ │ ├── .glsl/ │ │ ├── Brightness_and_Contrast_1.frag │ │ ├── Chromatic_Aberration_16.frag │ │ ├── Color_Adjustment_15.frag │ │ ├── Edge-Preserving_Blur_128.frag │ │ ├── Film_Grain_15.frag │ │ ├── Glow_30.frag │ │ ├── Hue_and_Saturation_1.frag │ │ ├── Image_Blur_1.frag │ │ ├── Image_Channels_23.frag │ │ ├── Image_Levels_1.frag │ │ ├── README.md │ │ ├── Sharpen_23.frag │ │ ├── Unsharp_Mask_26.frag │ │ └── update_blueprints.py │ ├── Brightness and Contrast.json │ ├── Canny to Image (Z-Image-Turbo).json │ ├── Canny to Video (LTX 2.0).json │ ├── Chromatic Aberration.json │ ├── Color Adjustment.json │ ├── Depth to Image (Z-Image-Turbo).json │ ├── Depth to Video (ltx 2.0).json │ ├── Edge-Preserving Blur.json │ ├── Film Grain.json │ ├── Glow.json │ ├── Hue and Saturation.json │ ├── Image Blur.json │ ├── Image Captioning (gemini).json │ ├── Image Channels.json │ ├── Image Edit (Flux.2 Klein 4B).json │ ├── Image Edit (Qwen 2511).json │ ├── Image Inpainting (Qwen-image).json │ ├── Image Levels.json │ ├── Image Outpainting (Qwen-Image).json │ ├── Image Upscale(Z-image-Turbo).json │ ├── Image to Depth Map (Lotus).json │ ├── Image to Layers(Qwen-Image Layered).json │ ├── Image to Model (Hunyuan3d 2.1).json │ ├── Image to Video (Wan 2.2).json │ ├── Pose to Image (Z-Image-Turbo).json │ ├── Pose to Video (LTX 2.0).json │ ├── Prompt Enhance.json │ ├── Sharpen.json │ ├── Text to Audio (ACE-Step 1.5).json │ ├── Text to Image (Z-Image-Turbo).json │ ├── Text to Video (Wan 2.2).json │ ├── Unsharp Mask.json │ ├── Video Captioning (Gemini).json │ ├── Video Inpaint(Wan2.1 VACE).json │ ├── Video Stitch.json │ ├── Video Upscale(GAN x4).json │ └── put_blueprints_here ├── comfy/ │ ├── audio_encoders/ │ │ ├── audio_encoders.py │ │ ├── wav2vec2.py │ │ └── whisper.py │ ├── cldm/ │ │ ├── cldm.py │ │ ├── control_types.py │ │ ├── dit_embedder.py │ │ └── mmdit.py │ ├── cli_args.py │ ├── clip_config_bigg.json │ ├── clip_model.py │ ├── clip_vision.py │ ├── clip_vision_config_g.json │ ├── clip_vision_config_h.json │ ├── clip_vision_config_vitl.json │ ├── clip_vision_config_vitl_336.json │ ├── clip_vision_config_vitl_336_llava.json │ ├── clip_vision_siglip2_base_naflex.json │ ├── clip_vision_siglip_384.json │ ├── clip_vision_siglip_512.json │ ├── comfy_types/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── examples/ │ │ │ └── example_nodes.py │ │ └── node_typing.py │ ├── conds.py │ ├── context_windows.py │ ├── controlnet.py │ ├── diffusers_convert.py │ ├── diffusers_load.py │ ├── extra_samplers/ │ │ └── uni_pc.py │ ├── float.py │ ├── gligen.py │ ├── hooks.py │ ├── image_encoders/ │ │ ├── dino2.py │ │ ├── dino2_giant.json │ │ └── dino2_large.json │ ├── k_diffusion/ │ │ ├── deis.py │ │ ├── sa_solver.py │ │ ├── sampling.py │ │ └── utils.py │ ├── latent_formats.py │ ├── ldm/ │ │ ├── ace/ │ │ │ ├── ace_step15.py │ │ │ ├── attention.py │ │ │ ├── lyric_encoder.py │ │ │ ├── model.py │ │ │ └── vae/ │ │ │ ├── autoencoder_dc.py │ │ │ ├── music_dcae_pipeline.py │ │ │ ├── music_log_mel.py │ │ │ └── music_vocoder.py │ │ ├── anima/ │ │ │ └── model.py │ │ ├── audio/ │ │ │ ├── autoencoder.py │ │ │ ├── dit.py │ │ │ └── embedders.py │ │ ├── aura/ │ │ │ └── mmdit.py │ │ ├── cascade/ │ │ │ ├── common.py │ │ │ ├── controlnet.py │ │ │ ├── stage_a.py │ │ │ ├── stage_b.py │ │ │ ├── stage_c.py │ │ │ └── stage_c_coder.py │ │ ├── chroma/ │ │ │ ├── layers.py │ │ │ └── model.py │ │ ├── chroma_radiance/ │ │ │ ├── layers.py │ │ │ └── model.py │ │ ├── common_dit.py │ │ ├── cosmos/ │ │ │ ├── blocks.py │ │ │ ├── cosmos_tokenizer/ │ │ │ │ ├── layers3d.py │ │ │ │ ├── patching.py │ │ │ │ └── utils.py │ │ │ ├── model.py │ │ │ ├── position_embedding.py │ │ │ ├── predict2.py │ │ │ └── vae.py │ │ ├── flux/ │ │ │ ├── controlnet.py │ │ │ ├── layers.py │ │ │ ├── math.py │ │ │ ├── model.py │ │ │ └── redux.py │ │ ├── genmo/ │ │ │ ├── joint_model/ │ │ │ │ ├── asymm_models_joint.py │ │ │ │ ├── layers.py │ │ │ │ ├── rope_mixed.py │ │ │ │ ├── temporal_rope.py │ │ │ │ └── utils.py │ │ │ └── vae/ │ │ │ └── model.py │ │ ├── hidream/ │ │ │ └── model.py │ │ ├── hunyuan3d/ │ │ │ ├── model.py │ │ │ └── vae.py │ │ ├── hunyuan3dv2_1/ │ │ │ └── hunyuandit.py │ │ ├── hunyuan_video/ │ │ │ ├── model.py │ │ │ ├── upsampler.py │ │ │ ├── vae.py │ │ │ └── vae_refiner.py │ │ ├── hydit/ │ │ │ ├── attn_layers.py │ │ │ ├── controlnet.py │ │ │ ├── models.py │ │ │ ├── poolers.py │ │ │ └── posemb_layers.py │ │ ├── kandinsky5/ │ │ │ └── model.py │ │ ├── lightricks/ │ │ │ ├── av_model.py │ │ │ ├── embeddings_connector.py │ │ │ ├── latent_upsampler.py │ │ │ ├── model.py │ │ │ ├── symmetric_patchifier.py │ │ │ ├── vae/ │ │ │ │ ├── audio_vae.py │ │ │ │ ├── causal_audio_autoencoder.py │ │ │ │ ├── causal_conv3d.py │ │ │ │ ├── causal_video_autoencoder.py │ │ │ │ ├── conv_nd_factory.py │ │ │ │ ├── dual_conv3d.py │ │ │ │ └── pixel_norm.py │ │ │ └── vocoders/ │ │ │ └── vocoder.py │ │ ├── lumina/ │ │ │ ├── controlnet.py │ │ │ └── model.py │ │ ├── mmaudio/ │ │ │ └── vae/ │ │ │ ├── __init__.py │ │ │ ├── activations.py │ │ │ ├── alias_free_torch.py │ │ │ ├── autoencoder.py │ │ │ ├── bigvgan.py │ │ │ ├── distributions.py │ │ │ ├── vae.py │ │ │ └── vae_modules.py │ │ ├── models/ │ │ │ └── autoencoder.py │ │ ├── modules/ │ │ │ ├── attention.py │ │ │ ├── diffusionmodules/ │ │ │ │ ├── __init__.py │ │ │ │ ├── mmdit.py │ │ │ │ ├── model.py │ │ │ │ ├── openaimodel.py │ │ │ │ ├── upscaling.py │ │ │ │ └── util.py │ │ │ ├── distributions/ │ │ │ │ ├── __init__.py │ │ │ │ └── distributions.py │ │ │ ├── ema.py │ │ │ ├── encoders/ │ │ │ │ ├── __init__.py │ │ │ │ └── noise_aug_modules.py │ │ │ ├── sdpose.py │ │ │ ├── sub_quadratic_attention.py │ │ │ └── temporal_ae.py │ │ ├── omnigen/ │ │ │ └── omnigen2.py │ │ ├── pixart/ │ │ │ ├── blocks.py │ │ │ └── pixartms.py │ │ ├── qwen_image/ │ │ │ ├── controlnet.py │ │ │ └── model.py │ │ ├── util.py │ │ └── wan/ │ │ ├── model.py │ │ ├── model_animate.py │ │ ├── model_multitalk.py │ │ ├── vae.py │ │ └── vae2_2.py │ ├── lora.py │ ├── lora_convert.py │ ├── memory_management.py │ ├── model_base.py │ ├── model_detection.py │ ├── model_management.py │ ├── model_patcher.py │ ├── model_sampling.py │ ├── nested_tensor.py │ ├── ops.py │ ├── options.py │ ├── patcher_extension.py │ ├── pinned_memory.py │ ├── pixel_space_convert.py │ ├── quant_ops.py │ ├── rmsnorm.py │ ├── sample.py │ ├── sampler_helpers.py │ ├── samplers.py │ ├── sd.py │ ├── sd1_clip.py │ ├── sd1_clip_config.json │ ├── sd1_tokenizer/ │ │ ├── merges.txt │ │ ├── special_tokens_map.json │ │ ├── tokenizer_config.json │ │ └── vocab.json │ ├── sdxl_clip.py │ ├── supported_models.py │ ├── supported_models_base.py │ ├── t2i_adapter/ │ │ └── adapter.py │ ├── taesd/ │ │ ├── taehv.py │ │ └── taesd.py │ ├── text_encoders/ │ │ ├── ace.py │ │ ├── ace15.py │ │ ├── ace_lyrics_tokenizer/ │ │ │ └── vocab.json │ │ ├── ace_text_cleaners.py │ │ ├── anima.py │ │ ├── aura_t5.py │ │ ├── bert.py │ │ ├── byt5_config_small_glyph.json │ │ ├── byt5_tokenizer/ │ │ │ ├── added_tokens.json │ │ │ ├── special_tokens_map.json │ │ │ └── tokenizer_config.json │ │ ├── cosmos.py │ │ ├── flux.py │ │ ├── genmo.py │ │ ├── hidream.py │ │ ├── hunyuan_image.py │ │ ├── hunyuan_video.py │ │ ├── hydit.py │ │ ├── hydit_clip.json │ │ ├── hydit_clip_tokenizer/ │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.txt │ │ ├── jina_clip_2.py │ │ ├── kandinsky5.py │ │ ├── llama.py │ │ ├── llama_tokenizer/ │ │ │ ├── tokenizer.json │ │ │ └── tokenizer_config.json │ │ ├── long_clipl.py │ │ ├── longcat_image.py │ │ ├── lt.py │ │ ├── lumina2.py │ │ ├── mt5_config_xl.json │ │ ├── newbie.py │ │ ├── omnigen2.py │ │ ├── ovis.py │ │ ├── pixart_t5.py │ │ ├── qwen25_tokenizer/ │ │ │ ├── merges.txt │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.json │ │ ├── qwen_image.py │ │ ├── qwen_vl.py │ │ ├── sa_t5.py │ │ ├── sd2_clip.py │ │ ├── sd2_clip_config.json │ │ ├── sd3_clip.py │ │ ├── spiece_tokenizer.py │ │ ├── t5.py │ │ ├── t5_config_base.json │ │ ├── t5_config_xxl.json │ │ ├── t5_old_config_xxl.json │ │ ├── t5_pile_config_xl.json │ │ ├── t5_pile_tokenizer/ │ │ │ └── tokenizer.model │ │ ├── t5_tokenizer/ │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer.json │ │ │ └── tokenizer_config.json │ │ ├── umt5_config_base.json │ │ ├── umt5_config_xxl.json │ │ ├── wan.py │ │ └── z_image.py │ ├── utils.py │ ├── weight_adapter/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── boft.py │ │ ├── bypass.py │ │ ├── glora.py │ │ ├── loha.py │ │ ├── lokr.py │ │ ├── lora.py │ │ └── oft.py │ └── windows.py ├── comfy_api/ │ ├── feature_flags.py │ ├── generate_api_stubs.py │ ├── input/ │ │ ├── __init__.py │ │ ├── basic_types.py │ │ └── video_types.py │ ├── input_impl/ │ │ ├── __init__.py │ │ └── video_types.py │ ├── internal/ │ │ ├── __init__.py │ │ ├── api_registry.py │ │ ├── async_to_sync.py │ │ └── singleton.py │ ├── latest/ │ │ ├── __init__.py │ │ ├── _caching.py │ │ ├── _input/ │ │ │ ├── __init__.py │ │ │ ├── basic_types.py │ │ │ └── video_types.py │ │ ├── _input_impl/ │ │ │ ├── __init__.py │ │ │ └── video_types.py │ │ ├── _io.py │ │ ├── _io_public.py │ │ ├── _ui.py │ │ ├── _ui_public.py │ │ ├── _util/ │ │ │ ├── __init__.py │ │ │ ├── geometry_types.py │ │ │ ├── image_types.py │ │ │ └── video_types.py │ │ └── generated/ │ │ └── ComfyAPISyncStub.pyi │ ├── torch_helpers/ │ │ ├── __init__.py │ │ └── torch_compile.py │ ├── util/ │ │ ├── __init__.py │ │ └── video_types.py │ ├── util.py │ ├── v0_0_1/ │ │ ├── __init__.py │ │ └── generated/ │ │ └── ComfyAPISyncStub.pyi │ ├── v0_0_2/ │ │ ├── __init__.py │ │ └── generated/ │ │ └── ComfyAPISyncStub.pyi │ └── version_list.py ├── comfy_api_nodes/ │ ├── __init__.py │ ├── apis/ │ │ ├── __init__.py │ │ ├── bfl.py │ │ ├── bria.py │ │ ├── bytedance.py │ │ ├── elevenlabs.py │ │ ├── gemini.py │ │ ├── grok.py │ │ ├── hitpaw.py │ │ ├── hunyuan3d.py │ │ ├── ideogram.py │ │ ├── kling.py │ │ ├── luma.py │ │ ├── magnific.py │ │ ├── meshy.py │ │ ├── minimax.py │ │ ├── moonvalley.py │ │ ├── openai.py │ │ ├── pixverse.py │ │ ├── recraft.py │ │ ├── reve.py │ │ ├── rodin.py │ │ ├── runway.py │ │ ├── stability.py │ │ ├── topaz.py │ │ ├── tripo.py │ │ ├── veo.py │ │ ├── vidu.py │ │ └── wavespeed.py │ ├── nodes_bfl.py │ ├── nodes_bria.py │ ├── nodes_bytedance.py │ ├── nodes_elevenlabs.py │ ├── nodes_gemini.py │ ├── nodes_grok.py │ ├── nodes_hitpaw.py │ ├── nodes_hunyuan3d.py │ ├── nodes_ideogram.py │ ├── nodes_kling.py │ ├── nodes_ltxv.py │ ├── nodes_luma.py │ ├── nodes_magnific.py │ ├── nodes_meshy.py │ ├── nodes_minimax.py │ ├── nodes_moonvalley.py │ ├── nodes_openai.py │ ├── nodes_pixverse.py │ ├── nodes_recraft.py │ ├── nodes_reve.py │ ├── nodes_rodin.py │ ├── nodes_runway.py │ ├── nodes_sora.py │ ├── nodes_stability.py │ ├── nodes_topaz.py │ ├── nodes_tripo.py │ ├── nodes_veo2.py │ ├── nodes_vidu.py │ ├── nodes_wan.py │ ├── nodes_wavespeed.py │ └── util/ │ ├── __init__.py │ ├── _helpers.py │ ├── client.py │ ├── common_exceptions.py │ ├── conversions.py │ ├── download_helpers.py │ ├── request_logger.py │ ├── upload_helpers.py │ └── validation_utils.py ├── comfy_config/ │ ├── config_parser.py │ └── types.py ├── comfy_execution/ │ ├── cache_provider.py │ ├── caching.py │ ├── graph.py │ ├── graph_utils.py │ ├── jobs.py │ ├── progress.py │ ├── utils.py │ └── validation.py ├── comfy_extras/ │ ├── chainner_models/ │ │ └── model_loading.py │ ├── nodes_ace.py │ ├── nodes_advanced_samplers.py │ ├── nodes_align_your_steps.py │ ├── nodes_apg.py │ ├── nodes_attention_multiply.py │ ├── nodes_audio.py │ ├── nodes_audio_encoder.py │ ├── nodes_camera_trajectory.py │ ├── nodes_canny.py │ ├── nodes_cfg.py │ ├── nodes_chroma_radiance.py │ ├── nodes_clip_sdxl.py │ ├── nodes_color.py │ ├── nodes_compositing.py │ ├── nodes_cond.py │ ├── nodes_context_windows.py │ ├── nodes_controlnet.py │ ├── nodes_cosmos.py │ ├── nodes_custom_sampler.py │ ├── nodes_dataset.py │ ├── nodes_differential_diffusion.py │ ├── nodes_easycache.py │ ├── nodes_edit_model.py │ ├── nodes_eps.py │ ├── nodes_flux.py │ ├── nodes_freelunch.py │ ├── nodes_fresca.py │ ├── nodes_gits.py │ ├── nodes_glsl.py │ ├── nodes_hidream.py │ ├── nodes_hooks.py │ ├── nodes_hunyuan.py │ ├── nodes_hunyuan3d.py │ ├── nodes_hypernetwork.py │ ├── nodes_hypertile.py │ ├── nodes_image_compare.py │ ├── nodes_images.py │ ├── nodes_ip2p.py │ ├── nodes_kandinsky5.py │ ├── nodes_latent.py │ ├── nodes_load_3d.py │ ├── nodes_logic.py │ ├── nodes_lora_debug.py │ ├── nodes_lora_extract.py │ ├── nodes_lotus.py │ ├── nodes_lt.py │ ├── nodes_lt_audio.py │ ├── nodes_lt_upsampler.py │ ├── nodes_lumina2.py │ ├── nodes_mahiro.py │ ├── nodes_mask.py │ ├── nodes_math.py │ ├── nodes_mochi.py │ ├── nodes_model_advanced.py │ ├── nodes_model_downscale.py │ ├── nodes_model_merging.py │ ├── nodes_model_merging_model_specific.py │ ├── nodes_model_patch.py │ ├── nodes_morphology.py │ ├── nodes_nag.py │ ├── nodes_nop.py │ ├── nodes_optimalsteps.py │ ├── nodes_pag.py │ ├── nodes_painter.py │ ├── nodes_perpneg.py │ ├── nodes_photomaker.py │ ├── nodes_pixart.py │ ├── nodes_post_processing.py │ ├── nodes_preview_any.py │ ├── nodes_primitive.py │ ├── nodes_qwen.py │ ├── nodes_rebatch.py │ ├── nodes_replacements.py │ ├── nodes_resolution.py │ ├── nodes_rope.py │ ├── nodes_sag.py │ ├── nodes_sd3.py │ ├── nodes_sdpose.py │ ├── nodes_sdupscale.py │ ├── nodes_slg.py │ ├── nodes_stable3d.py │ ├── nodes_stable_cascade.py │ ├── nodes_string.py │ ├── nodes_tcfg.py │ ├── nodes_textgen.py │ ├── nodes_tomesd.py │ ├── nodes_toolkit.py │ ├── nodes_torch_compile.py │ ├── nodes_train.py │ ├── nodes_upscale_model.py │ ├── nodes_video.py │ ├── nodes_video_model.py │ ├── nodes_wan.py │ ├── nodes_wanmove.py │ ├── nodes_webcam.py │ └── nodes_zimage.py ├── comfyui_version.py ├── cuda_malloc.py ├── custom_nodes/ │ └── example_node.py.example ├── execution.py ├── extra_model_paths.yaml.example ├── folder_paths.py ├── hook_breaker_ac10a0.py ├── latent_preview.py ├── main.py ├── manager_requirements.txt ├── middleware/ │ ├── __init__.py │ └── cache_middleware.py ├── new_updater.py ├── node_helpers.py ├── nodes.py ├── protocol.py ├── pyproject.toml ├── pytest.ini ├── requirements.txt ├── script_examples/ │ ├── basic_api_example.py │ ├── websockets_api_example.py │ └── websockets_api_example_ws_images.py ├── server.py ├── tests/ │ ├── README.md │ ├── __init__.py │ ├── compare/ │ │ ├── conftest.py │ │ └── test_quality.py │ ├── conftest.py │ ├── execution/ │ │ ├── test_async_nodes.py │ │ ├── test_execution.py │ │ ├── test_jobs.py │ │ ├── test_preview_method.py │ │ ├── test_progress_isolation.py │ │ ├── test_public_api.py │ │ └── testing_nodes/ │ │ └── testing-pack/ │ │ ├── __init__.py │ │ ├── api_test_nodes.py │ │ ├── async_test_nodes.py │ │ ├── conditions.py │ │ ├── flow_control.py │ │ ├── specific_tests.py │ │ ├── stubs.py │ │ ├── tools.py │ │ └── util.py │ └── inference/ │ ├── __init__.py │ ├── graphs/ │ │ └── default_graph_sdxl1_0.json │ └── test_inference.py ├── tests-unit/ │ ├── README.md │ ├── app_test/ │ │ ├── __init__.py │ │ ├── custom_node_manager_test.py │ │ ├── frontend_manager_test.py │ │ ├── model_manager_test.py │ │ ├── test_migrations.py │ │ └── user_manager_system_user_test.py │ ├── assets_test/ │ │ ├── conftest.py │ │ ├── helpers.py │ │ ├── queries/ │ │ │ ├── conftest.py │ │ │ ├── test_asset.py │ │ │ ├── test_asset_info.py │ │ │ ├── test_cache_state.py │ │ │ ├── test_metadata.py │ │ │ └── test_tags.py │ │ ├── services/ │ │ │ ├── __init__.py │ │ │ ├── conftest.py │ │ │ ├── test_asset_management.py │ │ │ ├── test_bulk_ingest.py │ │ │ ├── test_enrich.py │ │ │ ├── test_ingest.py │ │ │ ├── test_tag_histogram.py │ │ │ └── test_tagging.py │ │ ├── test_assets_missing_sync.py │ │ ├── test_crud.py │ │ ├── test_downloads.py │ │ ├── test_file_utils.py │ │ ├── test_list_filter.py │ │ ├── test_metadata_filters.py │ │ ├── test_prune_orphaned_assets.py │ │ ├── test_sync_references.py │ │ ├── test_tags_api.py │ │ └── test_uploads.py │ ├── comfy_api_test/ │ │ ├── input_impl_test.py │ │ └── video_types_test.py │ ├── comfy_extras_test/ │ │ ├── __init__.py │ │ ├── image_stitch_test.py │ │ └── nodes_math_test.py │ ├── comfy_quant/ │ │ └── test_mixed_precision.py │ ├── comfy_test/ │ │ ├── folder_path_test.py │ │ └── model_detection_test.py │ ├── execution_test/ │ │ ├── preview_method_override_test.py │ │ ├── test_cache_provider.py │ │ └── validate_node_input_test.py │ ├── feature_flags_test.py │ ├── folder_paths_test/ │ │ ├── __init__.py │ │ ├── filter_by_content_types_test.py │ │ ├── misc_test.py │ │ └── system_user_test.py │ ├── prompt_server_test/ │ │ ├── __init__.py │ │ ├── system_user_endpoint_test.py │ │ └── user_manager_test.py │ ├── requirements.txt │ ├── seeder_test/ │ │ └── test_seeder.py │ ├── server/ │ │ └── utils/ │ │ └── file_operations_test.py │ ├── server_test/ │ │ └── test_cache_control.py │ ├── utils/ │ │ ├── extra_config_test.py │ │ └── json_util_test.py │ └── websocket_feature_flags_test.py └── utils/ ├── __init__.py ├── extra_config.py ├── install_util.py ├── json_util.py └── mime_types.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .ci/update_windows/update.py ================================================ import pygit2 from datetime import datetime import sys import os import shutil import filecmp def pull(repo, remote_name='origin', branch='master'): for remote in repo.remotes: if remote.name == remote_name: remote.fetch() remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target merge_result, _ = repo.merge_analysis(remote_master_id) # Up to date, do nothing if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE: return # We can just fastforward elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD: repo.checkout_tree(repo.get(remote_master_id)) try: master_ref = repo.lookup_reference('refs/heads/%s' % (branch)) master_ref.set_target(remote_master_id) except KeyError: repo.create_branch(branch, repo.get(remote_master_id)) repo.head.set_target(remote_master_id) elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL: repo.merge(remote_master_id) if repo.index.conflicts is not None: for conflict in repo.index.conflicts: print('Conflicts found in:', conflict[0].path) # noqa: T201 raise AssertionError('Conflicts, ahhhhh!!') user = repo.default_signature tree = repo.index.write_tree() repo.create_commit('HEAD', user, user, 'Merge!', tree, [repo.head.target, remote_master_id]) # We need to do this or git CLI will think we are still merging. repo.state_cleanup() else: raise AssertionError('Unknown merge analysis result') pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0) repo_path = str(sys.argv[1]) repo = pygit2.Repository(repo_path) ident = pygit2.Signature('comfyui', 'comfy@ui') try: print("stashing current changes") # noqa: T201 repo.stash(ident) except KeyError: print("nothing to stash") # noqa: T201 except: print("Could not stash, cleaning index and trying again.") # noqa: T201 repo.state_cleanup() repo.index.read_tree(repo.head.peel().tree) repo.index.write() try: repo.stash(ident) except KeyError: print("nothing to stash.") # noqa: T201 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S')) print("creating backup branch: {}".format(backup_branch_name)) # noqa: T201 try: repo.branches.local.create(backup_branch_name, repo.head.peel()) except: pass print("checking out master branch") # noqa: T201 branch = repo.lookup_branch('master') if branch is None: try: ref = repo.lookup_reference('refs/remotes/origin/master') except: print("fetching.") # noqa: T201 for remote in repo.remotes: if remote.name == "origin": remote.fetch() ref = repo.lookup_reference('refs/remotes/origin/master') repo.checkout(ref) branch = repo.lookup_branch('master') if branch is None: repo.create_branch('master', repo.get(ref.target)) else: ref = repo.lookup_reference(branch.name) repo.checkout(ref) print("pulling latest changes") # noqa: T201 pull(repo) if "--stable" in sys.argv: def latest_tag(repo): versions = [] for k in repo.references: try: prefix = "refs/tags/v" if k.startswith(prefix): version = list(map(int, k[len(prefix):].split("."))) versions.append((version[0] * 10000000000 + version[1] * 100000 + version[2], k)) except: pass versions.sort() if len(versions) > 0: return versions[-1][1] return None latest_tag = latest_tag(repo) if latest_tag is not None: repo.checkout(latest_tag) print("Done!") # noqa: T201 self_update = True if len(sys.argv) > 2: self_update = '--skip_self_update' not in sys.argv update_py_path = os.path.realpath(__file__) repo_update_py_path = os.path.join(repo_path, ".ci/update_windows/update.py") cur_path = os.path.dirname(update_py_path) req_path = os.path.join(cur_path, "current_requirements.txt") repo_req_path = os.path.join(repo_path, "requirements.txt") def files_equal(file1, file2): try: return filecmp.cmp(file1, file2, shallow=False) except: return False def file_size(f): try: return os.path.getsize(f) except: return 0 if self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10: shutil.copy(repo_update_py_path, os.path.join(cur_path, "update_new.py")) exit() if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path): import subprocess try: subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', repo_req_path]) shutil.copy(repo_req_path, req_path) except: pass stable_update_script = os.path.join(repo_path, ".ci/update_windows/update_comfyui_stable.bat") stable_update_script_to = os.path.join(cur_path, "update_comfyui_stable.bat") try: if not file_size(stable_update_script_to) > 10: shutil.copy(stable_update_script, stable_update_script_to) except: pass ================================================ FILE: .ci/update_windows/update_comfyui.bat ================================================ @echo off ..\python_embeded\python.exe .\update.py ..\ComfyUI\ if exist update_new.py ( move /y update_new.py update.py echo Running updater again since it got updated. ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update ) if "%~1"=="" pause ================================================ FILE: .ci/update_windows/update_comfyui_stable.bat ================================================ @echo off ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --stable if exist update_new.py ( move /y update_new.py update.py echo Running updater again since it got updated. ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update --stable ) if "%~1"=="" pause ================================================ FILE: .ci/windows_amd_base_files/README_VERY_IMPORTANT.txt ================================================ As of the time of writing this you need this driver for best results: https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html HOW TO RUN: If you have a AMD gpu: run_amd_gpu.bat If you have memory issues you can try disabling the smart memory management by running comfyui with: run_amd_gpu_disable_smart_memory.bat IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors RECOMMENDED WAY TO UPDATE: To update the ComfyUI code: update\update_comfyui.bat TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI: In the ComfyUI directory you will find a file: extra_model_paths.yaml.example Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor. ================================================ FILE: .ci/windows_amd_base_files/run_amd_gpu.bat ================================================ .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build pause ================================================ FILE: .ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat ================================================ .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory pause ================================================ FILE: .ci/windows_nightly_base_files/run_nvidia_gpu_fast.bat ================================================ .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast pause ================================================ FILE: .ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt ================================================ HOW TO RUN: if you have a NVIDIA gpu: run_nvidia_gpu.bat if you want to enable the fast fp16 accumulation (faster for fp16 models with slightly less quality): run_nvidia_gpu_fast_fp16_accumulation.bat To run it in slow CPU mode: run_cpu.bat IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints You can download the stable diffusion 1.5 one from: https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors RECOMMENDED WAY TO UPDATE: To update the ComfyUI code: update\update_comfyui.bat To update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies. update\update_comfyui_and_python_dependencies.bat TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI: In the ComfyUI directory you will find a file: extra_model_paths.yaml.example Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor. ================================================ FILE: .ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat ================================================ ..\python_embeded\python.exe -s ..\ComfyUI\main.py --windows-standalone-build --disable-api-nodes echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe pause ================================================ FILE: .ci/windows_nvidia_base_files/run_cpu.bat ================================================ .\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build pause ================================================ FILE: .ci/windows_nvidia_base_files/run_nvidia_gpu.bat ================================================ .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe pause ================================================ FILE: .ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat ================================================ .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast fp16_accumulation echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe pause ================================================ FILE: .coderabbit.yaml ================================================ # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json language: "en-US" early_access: false tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code." reviews: profile: "chill" request_changes_workflow: false high_level_summary: false poem: false review_status: false review_details: false commit_status: true collapse_walkthrough: true changed_files_summary: false sequence_diagrams: false estimate_code_review_effort: false assess_linked_issues: false related_issues: false related_prs: false suggested_labels: false auto_apply_labels: false suggested_reviewers: false auto_assign_reviewers: false in_progress_fortune: false enable_prompt_for_ai_agents: true path_filters: - "!comfy_api_nodes/apis/**" - "!**/generated/*.pyi" - "!.ci/**" - "!script_examples/**" - "!**/__pycache__/**" - "!**/*.ipynb" - "!**/*.png" - "!**/*.bat" path_instructions: - path: "**" instructions: | IMPORTANT: Only comment on issues directly introduced by this PR's code changes. Do NOT flag pre-existing issues in code that was merely moved, re-indented, de-indented, or reformatted without logic changes. If code appears in the diff only due to whitespace or structural reformatting (e.g., removing a `with:` block), treat it as unchanged. Contributors should not feel obligated to address pre-existing issues outside the scope of their contribution. - path: "comfy/**" instructions: | Core ML/diffusion engine. Focus on: - Backward compatibility (breaking changes affect all custom nodes) - Memory management and GPU resource handling - Performance implications in hot paths - Thread safety for concurrent execution - path: "comfy_api_nodes/**" instructions: | Third-party API integration nodes. Focus on: - No hardcoded API keys or secrets - Proper error handling for API failures (timeouts, rate limits, auth errors) - Correct Pydantic model usage - Security of user data passed to external APIs - path: "comfy_extras/**" instructions: | Community-contributed extra nodes. Focus on: - Consistency with node patterns (INPUT_TYPES, RETURN_TYPES, FUNCTION, CATEGORY) - No breaking changes to existing node interfaces - path: "comfy_execution/**" instructions: | Execution engine (graph execution, caching, jobs). Focus on: - Caching correctness - Concurrent execution safety - Graph validation edge cases - path: "nodes.py" instructions: | Core node definitions (2500+ lines). Focus on: - Backward compatibility of NODE_CLASS_MAPPINGS - Consistency of INPUT_TYPES return format - path: "alembic_db/**" instructions: | Database migrations. Focus on: - Migration safety and rollback support - Data preservation during schema changes auto_review: enabled: true auto_incremental_review: true drafts: false ignore_title_keywords: - "WIP" - "DO NOT REVIEW" - "DO NOT MERGE" finishing_touches: docstrings: enabled: false unit_tests: enabled: false tools: ruff: enabled: false pylint: enabled: false flake8: enabled: false gitleaks: enabled: true shellcheck: enabled: false markdownlint: enabled: false yamllint: enabled: false languagetool: enabled: false github-checks: enabled: true timeout_ms: 90000 ast-grep: essential_rules: true chat: auto_reply: true knowledge_base: opt_out: false learnings: scope: "auto" ================================================ FILE: .gitattributes ================================================ /web/assets/** linguist-generated /web/** linguist-vendored comfy_api_nodes/apis/__init__.py linguist-generated ================================================ FILE: .github/ISSUE_TEMPLATE/bug-report.yml ================================================ name: Bug Report description: "Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)" labels: ["Potential Bug"] body: - type: markdown attributes: value: | Before submitting a **Bug Report**, please ensure the following: - **1:** You are running the latest version of ComfyUI. - **2:** You have your ComfyUI logs and relevant workflow on hand and will post them in this bug report. - **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing `--disable-all-custom-nodes` command line argument. If you have custom node try updating them to the latest version. - **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen. ## Very Important Please make sure that you post ALL your ComfyUI logs in the bug report **even if there is no crash**. Just paste everything. The startup log (everything before "To see the GUI go to: ...") contains critical information to developers trying to help. For a performance issue or crash, paste everything from "got prompt" to the end, including the crash. More is better - always. A bug report without logs will likely be ignored. - type: checkboxes id: custom-nodes-test attributes: label: Custom Node Testing description: Please confirm you have tried to reproduce the issue with all custom nodes disabled. options: - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help) required: false - type: textarea attributes: label: Expected Behavior description: "What you expected to happen." validations: required: true - type: textarea attributes: label: Actual Behavior description: "What actually happened. Please include a screenshot of the issue if possible." validations: required: true - type: textarea attributes: label: Steps to Reproduce description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author." validations: required: true - type: textarea attributes: label: Debug Logs description: "Please copy the output from your terminal logs here." render: powershell validations: required: true - type: textarea attributes: label: Other description: "Any other additional information you think might be helpful." validations: required: false ================================================ FILE: .github/ISSUE_TEMPLATE/config.yml ================================================ blank_issues_enabled: true contact_links: - name: ComfyUI Frontend Issues url: https://github.com/Comfy-Org/ComfyUI_frontend/issues about: Issues related to the ComfyUI frontend (display issues, user interaction bugs), please go to the frontend repo to file the issue - name: ComfyUI Matrix Space url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source). - name: Comfy Org Discord url: https://discord.gg/comfyorg about: The Comfy Org Discord is available for support and general discussion related to ComfyUI. ================================================ FILE: .github/ISSUE_TEMPLATE/feature-request.yml ================================================ name: Feature Request description: "You have an idea for something new you would like to see added to ComfyUI's core." labels: [ "Feature" ] body: - type: markdown attributes: value: | Before submitting a **Feature Request**, please ensure the following: **1:** You are running the latest version of ComfyUI. **2:** You have looked to make sure there is not already a feature that does what you need, and there is not already a Feature Request listed for the same idea. **3:** This is something that makes sense to add to ComfyUI Core, and wouldn't make more sense as a custom node. If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. - type: textarea attributes: label: Feature Idea description: "Describe the feature you want to see." validations: required: true - type: textarea attributes: label: Existing Solutions description: "Please search through available custom nodes / extensions to see if there are existing custom solutions for this. If so, please link the options you found here as a reference." validations: required: false - type: textarea attributes: label: Other description: "Any other additional information you think might be helpful." validations: required: false ================================================ FILE: .github/ISSUE_TEMPLATE/user-support.yml ================================================ name: User Support description: "Use this if you need help with something, or you're experiencing an issue." labels: [ "User Support" ] body: - type: markdown attributes: value: | Before submitting a **User Report** issue, please ensure the following: **1:** You are running the latest version of ComfyUI. **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics. If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. - type: checkboxes id: custom-nodes-test attributes: label: Custom Node Testing description: Please confirm you have tried to reproduce the issue with all custom nodes disabled. options: - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help) required: false - type: textarea attributes: label: Your question description: "Post your question here. Please be as detailed as possible." validations: required: true - type: textarea attributes: label: Logs description: "If your question relates to an issue you're experiencing, please go to `Server` -> `Logs` -> potentially set `View Type` to `Debug` as well, then copypaste all the text into here." render: powershell validations: required: false - type: textarea attributes: label: Other description: "Any other additional information you think might be helpful." validations: required: false ================================================ FILE: .github/PULL_REQUEST_TEMPLATE/api-node.md ================================================ ## API Node PR Checklist ### Scope - [ ] **Is API Node Change** ### Pricing & Billing - [ ] **Need pricing update** - [ ] **No pricing update** If **Need pricing update**: - [ ] Metronome rate cards updated - [ ] Auto‑billing tests updated and passing ### QA - [ ] **QA done** - [ ] **QA not required** ### Comms - [ ] Informed **Kosinkadink** ================================================ FILE: .github/scripts/check-ai-co-authors.sh ================================================ #!/usr/bin/env bash # Checks pull request commits for AI agent Co-authored-by trailers. # Exits non-zero when any are found and prints fix instructions. set -euo pipefail base_sha="${1:?usage: check-ai-co-authors.sh }" head_sha="${2:?usage: check-ai-co-authors.sh }" # Known AI coding-agent trailer patterns (case-insensitive). # Each entry is an extended-regex fragment matched against Co-authored-by lines. AGENT_PATTERNS=( # Anthropic — Claude Code / Amp 'noreply@anthropic\.com' # Cursor 'cursoragent@cursor\.com' # GitHub Copilot 'copilot-swe-agent\[bot\]' 'copilot@github\.com' # OpenAI Codex 'noreply@openai\.com' 'codex@openai\.com' # Aider 'aider@aider\.chat' # Google — Gemini / Jules 'gemini@google\.com' 'jules@google\.com' # Windsurf / Codeium '@codeium\.com' # Devin 'devin-ai-integration\[bot\]' 'devin@cognition\.ai' 'devin@cognition-labs\.com' # Amazon Q Developer 'amazon-q-developer' '@amazon\.com.*[Qq].[Dd]eveloper' # Cline 'cline-bot' 'cline@cline\.ai' # Continue 'continue-agent' 'continue@continue\.dev' # Sourcegraph 'noreply@sourcegraph\.com' # Generic catch-alls for common agent name patterns 'Co-authored-by:.*\b[Cc]laude\b' 'Co-authored-by:.*\b[Cc]opilot\b' 'Co-authored-by:.*\b[Cc]ursor\b' 'Co-authored-by:.*\b[Cc]odex\b' 'Co-authored-by:.*\b[Gg]emini\b' 'Co-authored-by:.*\b[Aa]ider\b' 'Co-authored-by:.*\b[Dd]evin\b' 'Co-authored-by:.*\b[Ww]indsurf\b' 'Co-authored-by:.*\b[Cc]line\b' 'Co-authored-by:.*\b[Aa]mazon Q\b' 'Co-authored-by:.*\b[Jj]ules\b' 'Co-authored-by:.*\bOpenCode\b' ) # Build a single alternation regex from all patterns. regex="" for pattern in "${AGENT_PATTERNS[@]}"; do if [[ -n "$regex" ]]; then regex="${regex}|${pattern}" else regex="$pattern" fi done # Collect Co-authored-by lines from every commit in the PR range. violations="" while IFS= read -r sha; do message="$(git log -1 --format='%B' "$sha")" matched_lines="$(echo "$message" | grep -iE "^Co-authored-by:" || true)" if [[ -z "$matched_lines" ]]; then continue fi while IFS= read -r line; do if echo "$line" | grep -iqE "$regex"; then short="$(git log -1 --format='%h' "$sha")" violations="${violations} ${short}: ${line}"$'\n' fi done <<< "$matched_lines" done < <(git rev-list "${base_sha}..${head_sha}") if [[ -n "$violations" ]]; then echo "::error::AI agent Co-authored-by trailers detected in PR commits." echo "" echo "The following commits contain Co-authored-by trailers from AI coding agents:" echo "" echo "$violations" echo "These trailers should be removed before merging." echo "" echo "To fix, rewrite the commit messages with:" echo " git rebase -i ${base_sha}" echo "" echo "and remove the Co-authored-by lines, then force-push your branch." echo "" echo "If you believe this is a false positive, please open an issue." exit 1 fi echo "No AI agent Co-authored-by trailers found." ================================================ FILE: .github/workflows/api-node-template.yml ================================================ name: Append API Node PR template on: pull_request_target: types: [opened, reopened, synchronize, ready_for_review] paths: - 'comfy_api_nodes/**' # only run if these files changed permissions: contents: read pull-requests: write jobs: inject: runs-on: ubuntu-latest steps: - name: Ensure template exists and append to PR body uses: actions/github-script@v7 with: script: | const { owner, repo } = context.repo; const number = context.payload.pull_request.number; const templatePath = '.github/PULL_REQUEST_TEMPLATE/api-node.md'; const marker = ''; const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: number }); let templateText; try { const res = await github.rest.repos.getContent({ owner, repo, path: templatePath, ref: pr.base.ref }); const buf = Buffer.from(res.data.content, res.data.encoding || 'base64'); templateText = buf.toString('utf8'); } catch (e) { core.setFailed(`Required PR template not found at "${templatePath}" on ${pr.base.ref}. Please add it to the repo.`); return; } // Enforce the presence of the marker inside the template (for idempotence) if (!templateText.includes(marker)) { core.setFailed(`Template at "${templatePath}" does not contain the required marker:\n${marker}\nAdd it so we can detect duplicates safely.`); return; } // If the PR already contains the marker, do not append again. const body = pr.body || ''; if (body.includes(marker)) { core.info('Template already present in PR body; nothing to inject.'); return; } const newBody = (body ? body + '\n\n' : '') + templateText + '\n'; await github.rest.pulls.update({ owner, repo, pull_number: number, body: newBody }); core.notice('API Node template appended to PR description.'); ================================================ FILE: .github/workflows/check-ai-co-authors.yml ================================================ name: Check AI Co-Authors on: pull_request: branches: ['*'] jobs: check-ai-co-authors: name: Check for AI agent co-author trailers runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v4 with: fetch-depth: 0 - name: Check commits for AI co-author trailers run: bash .github/scripts/check-ai-co-authors.sh "${{ github.event.pull_request.base.sha }}" "${{ github.event.pull_request.head.sha }}" ================================================ FILE: .github/workflows/check-line-endings.yml ================================================ name: Check for Windows Line Endings on: pull_request: branches: ['*'] # Trigger on all pull requests to any branch jobs: check-line-endings: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history to compare changes - name: Check for Windows line endings (CRLF) run: | # Get the list of changed files in the PR CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }}) # Flag to track if CRLF is found CRLF_FOUND=false # Loop through each changed file for FILE in $CHANGED_FILES; do # Check if the file exists and is a text file if [ -f "$FILE" ] && file "$FILE" | grep -q "text"; then # Check for CRLF line endings if grep -UP '\r$' "$FILE"; then echo "Error: Windows line endings (CRLF) detected in $FILE" CRLF_FOUND=true fi fi done # Exit with error if CRLF was found if [ "$CRLF_FOUND" = true ]; then exit 1 fi ================================================ FILE: .github/workflows/pullrequest-ci-run.yml ================================================ # This is the GitHub Workflow that drives full-GPU-enabled tests of pull requests to ComfyUI, when the 'Run-CI-Test' label is added # Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/ name: Pull Request CI Workflow Runs on: pull_request_target: types: [labeled] jobs: pr-test-stable: if: ${{ github.event.label.name == 'Run-CI-Test' }} strategy: fail-fast: false matrix: os: [macos, linux, windows] python_version: ["3.9", "3.10", "3.11", "3.12"] cuda_version: ["12.1"] torch_version: ["stable"] include: - os: macos runner_label: [self-hosted, macOS] flags: "--use-pytorch-cross-attention" - os: linux runner_label: [self-hosted, Linux] flags: "" - os: windows runner_label: [self-hosted, Windows] flags: "" runs-on: ${{ matrix.runner_label }} steps: - name: Test Workflows uses: comfy-org/comfy-action@main with: os: ${{ matrix.os }} python_version: ${{ matrix.python_version }} torch_version: ${{ matrix.torch_version }} google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} comfyui_flags: ${{ matrix.flags }} use_prior_commit: 'true' comment: if: ${{ github.event.label.name == 'Run-CI-Test' }} runs-on: ubuntu-latest permissions: pull-requests: write steps: - uses: actions/github-script@v6 with: script: | github.rest.issues.createComment({ issue_number: context.issue.number, owner: context.repo.owner, repo: context.repo.repo, body: '(Automated Bot Message) CI Tests are running, you can view the results at https://ci.comfy.org/?branch=${{ github.event.pull_request.number }}%2Fmerge' }) ================================================ FILE: .github/workflows/release-stable-all.yml ================================================ name: "Release Stable All Portable Versions" on: workflow_dispatch: inputs: git_tag: description: 'Git tag' required: true type: string jobs: release_nvidia_default: permissions: contents: "write" packages: "write" pull-requests: "read" name: "Release NVIDIA Default (cu130)" uses: ./.github/workflows/stable-release.yml with: git_tag: ${{ inputs.git_tag }} cache_tag: "cu130" python_minor: "13" python_patch: "11" rel_name: "nvidia" rel_extra_name: "" test_release: true secrets: inherit release_nvidia_cu128: permissions: contents: "write" packages: "write" pull-requests: "read" name: "Release NVIDIA cu128" uses: ./.github/workflows/stable-release.yml with: git_tag: ${{ inputs.git_tag }} cache_tag: "cu128" python_minor: "12" python_patch: "10" rel_name: "nvidia" rel_extra_name: "_cu128" test_release: true secrets: inherit release_nvidia_cu126: permissions: contents: "write" packages: "write" pull-requests: "read" name: "Release NVIDIA cu126" uses: ./.github/workflows/stable-release.yml with: git_tag: ${{ inputs.git_tag }} cache_tag: "cu126" python_minor: "12" python_patch: "10" rel_name: "nvidia" rel_extra_name: "_cu126" test_release: true secrets: inherit release_amd_rocm: permissions: contents: "write" packages: "write" pull-requests: "read" name: "Release AMD ROCm 7.2" uses: ./.github/workflows/stable-release.yml with: git_tag: ${{ inputs.git_tag }} cache_tag: "rocm72" python_minor: "12" python_patch: "10" rel_name: "amd" rel_extra_name: "" test_release: false secrets: inherit ================================================ FILE: .github/workflows/release-webhook.yml ================================================ name: Release Webhook on: release: types: [published] jobs: send-webhook: runs-on: ubuntu-latest env: DESKTOP_REPO_DISPATCH_TOKEN: ${{ secrets.DESKTOP_REPO_DISPATCH_TOKEN }} steps: - name: Send release webhook env: WEBHOOK_URL: ${{ secrets.RELEASE_GITHUB_WEBHOOK_URL }} WEBHOOK_SECRET: ${{ secrets.RELEASE_GITHUB_WEBHOOK_SECRET }} run: | # Generate UUID for delivery ID DELIVERY_ID=$(uuidgen) HOOK_ID="release-webhook-$(date +%s)" # Create webhook payload matching GitHub release webhook format PAYLOAD=$(cat <> ./python3${{ inputs.python_minor }}._pth curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py ./python.exe get-pip.py ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/* grep comfy ../ComfyUI/requirements.txt > ./requirements_comfyui.txt ./python.exe -s -m pip install -r requirements_comfyui.txt rm requirements_comfyui.txt sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space rm ./Lib/site-packages/torch/lib/libprotoc.lib rm ./Lib/site-packages/torch/lib/libprotobuf.lib fi cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable mv python_embeded ComfyUI_windows_portable mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI cd ComfyUI_windows_portable mkdir update cp -r ComfyUI/.ci/update_windows/* ./update/ cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./ cp ../update_comfyui_and_python_dependencies.bat ./update/ cd .. "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z - shell: bash if: ${{ inputs.test_release }} run: | cd .. cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu python_embeded/python.exe -s ./update/update.py ComfyUI/ ls - name: Upload binaries to release uses: softprops/action-gh-release@v2 with: files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z tag_name: ${{ inputs.git_tag }} draft: true overwrite_files: true ================================================ FILE: .github/workflows/stale-issues.yml ================================================ name: 'Close stale issues' on: schedule: # Run daily at 430 am PT - cron: '30 11 * * *' permissions: issues: write jobs: stale: runs-on: ubuntu-latest steps: - uses: actions/stale@v9 with: stale-issue-message: "This issue is being marked stale because it has not had any activity for 30 days. Reply below within 7 days if your issue still isn't solved, and it will be left open. Otherwise, the issue will be closed automatically." days-before-stale: 30 days-before-close: 7 stale-issue-label: 'Stale' only-labels: 'User Support' exempt-all-assignees: true exempt-all-milestones: true ================================================ FILE: .github/workflows/test-build.yml ================================================ name: Build package # # This workflow is a test of the python package build. # Install Python dependencies across different Python versions. # on: push: paths: - "requirements.txt" - ".github/workflows/test-build.yml" jobs: build: name: Build Test runs-on: ubuntu-latest strategy: fail-fast: false matrix: python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt ================================================ FILE: .github/workflows/test-ci.yml ================================================ # This is the GitHub Workflow that drives automatic full-GPU-enabled tests of all new commits to the master branch of ComfyUI # Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/ name: Full Comfy CI Workflow Runs on: push: branches: - master - release/** paths-ignore: - 'app/**' - 'input/**' - 'output/**' - 'notebooks/**' - 'script_examples/**' - '.github/**' - 'web/**' workflow_dispatch: jobs: test-stable: strategy: fail-fast: false matrix: # os: [macos, linux, windows] # os: [macos, linux] os: [linux] python_version: ["3.10", "3.11", "3.12"] cuda_version: ["12.1"] torch_version: ["stable"] include: # - os: macos # runner_label: [self-hosted, macOS] # flags: "--use-pytorch-cross-attention" - os: linux runner_label: [self-hosted, Linux] flags: "" # - os: windows # runner_label: [self-hosted, Windows] # flags: "" runs-on: ${{ matrix.runner_label }} steps: - name: Test Workflows uses: comfy-org/comfy-action@main with: os: ${{ matrix.os }} python_version: ${{ matrix.python_version }} torch_version: ${{ matrix.torch_version }} google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} comfyui_flags: ${{ matrix.flags }} # test-win-nightly: # strategy: # fail-fast: true # matrix: # os: [windows] # python_version: ["3.9", "3.10", "3.11", "3.12"] # cuda_version: ["12.1"] # torch_version: ["nightly"] # include: # - os: windows # runner_label: [self-hosted, Windows] # flags: "" # runs-on: ${{ matrix.runner_label }} # steps: # - name: Test Workflows # uses: comfy-org/comfy-action@main # with: # os: ${{ matrix.os }} # python_version: ${{ matrix.python_version }} # torch_version: ${{ matrix.torch_version }} # google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} # comfyui_flags: ${{ matrix.flags }} test-unix-nightly: strategy: fail-fast: false matrix: # os: [macos, linux] os: [linux] python_version: ["3.11"] cuda_version: ["12.1"] torch_version: ["nightly"] include: # - os: macos # runner_label: [self-hosted, macOS] # flags: "--use-pytorch-cross-attention" - os: linux runner_label: [self-hosted, Linux] flags: "" runs-on: ${{ matrix.runner_label }} steps: - name: Test Workflows uses: comfy-org/comfy-action@main with: os: ${{ matrix.os }} python_version: ${{ matrix.python_version }} torch_version: ${{ matrix.torch_version }} google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} comfyui_flags: ${{ matrix.flags }} ================================================ FILE: .github/workflows/test-execution.yml ================================================ name: Execution Tests on: push: branches: [ main, master, release/** ] pull_request: branches: [ main, master, release/** ] jobs: test: strategy: matrix: os: [ubuntu-latest, windows-latest, macos-latest] runs-on: ${{ matrix.os }} continue-on-error: true steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.12' - name: Install requirements run: | python -m pip install --upgrade pip pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu pip install -r requirements.txt pip install -r tests-unit/requirements.txt - name: Run Execution Tests run: | python -m pytest tests/execution -v --skip-timing-checks ================================================ FILE: .github/workflows/test-launch.yml ================================================ name: Test server launches without errors on: push: branches: [ main, master, release/** ] pull_request: branches: [ main, master, release/** ] jobs: test: runs-on: ubuntu-latest steps: - name: Checkout ComfyUI uses: actions/checkout@v4 with: repository: "Comfy-Org/ComfyUI" path: "ComfyUI" - uses: actions/setup-python@v4 with: python-version: '3.10' - name: Install requirements run: | python -m pip install --upgrade pip pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu pip install -r requirements.txt pip install wait-for-it working-directory: ComfyUI - name: Start ComfyUI server run: | python main.py --cpu 2>&1 | tee console_output.log & wait-for-it --service 127.0.0.1:8188 -t 30 working-directory: ComfyUI - name: Check for unhandled exceptions in server log run: | grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': True, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" console_output.log | grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': False, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" > console_output_filtered.log cat console_output_filtered.log if grep -qE "Exception|Error" console_output_filtered.log; then echo "Unhandled exception/error found in server log." exit 1 fi working-directory: ComfyUI - uses: actions/upload-artifact@v4 if: always() with: name: console-output path: ComfyUI/console_output.log retention-days: 30 ================================================ FILE: .github/workflows/test-unit.yml ================================================ name: Unit Tests on: push: branches: [ main, master, release/** ] pull_request: branches: [ main, master, release/** ] jobs: test: strategy: matrix: os: [ubuntu-latest, windows-2022, macos-latest] runs-on: ${{ matrix.os }} continue-on-error: true steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.12' - name: Install requirements run: | python -m pip install --upgrade pip pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu pip install -r requirements.txt - name: Run Unit Tests run: | pip install -r tests-unit/requirements.txt python -m pytest tests-unit ================================================ FILE: .github/workflows/update-api-stubs.yml ================================================ name: Generate Pydantic Stubs from api.comfy.org on: schedule: - cron: '0 0 * * 1' workflow_dispatch: jobs: generate-models: runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.10' - name: Install dependencies run: | python -m pip install --upgrade pip pip install 'datamodel-code-generator[http]' npm install @redocly/cli - name: Download OpenAPI spec run: | curl -o openapi.yaml https://api.comfy.org/openapi - name: Filter OpenAPI spec with Redocly run: | npx @redocly/cli bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components - name: Generate API models run: | datamodel-codegen --use-subclass-enum --input filtered-openapi.yaml --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel - name: Check for changes id: git-check run: | git diff --exit-code comfy_api_nodes/apis || echo "changes=true" >> $GITHUB_OUTPUT - name: Create Pull Request if: steps.git-check.outputs.changes == 'true' uses: peter-evans/create-pull-request@v5 with: commit-message: 'chore: update API models from OpenAPI spec' title: 'Update API models from api.comfy.org' body: | This PR updates the API models based on the latest api.comfy.org OpenAPI specification. Generated automatically by the a Github workflow. branch: update-api-stubs delete-branch: true base: master ================================================ FILE: .github/workflows/update-ci-container.yml ================================================ name: "CI: Update CI Container" on: release: types: [published] workflow_dispatch: inputs: version: description: 'ComfyUI version (e.g., v0.7.0)' required: true type: string jobs: update-ci-container: runs-on: ubuntu-latest # Skip pre-releases unless manually triggered if: github.event_name == 'workflow_dispatch' || !github.event.release.prerelease steps: - name: Get version id: version run: | if [ "${{ github.event_name }}" = "release" ]; then VERSION="${{ github.event.release.tag_name }}" else VERSION="${{ inputs.version }}" fi echo "version=$VERSION" >> $GITHUB_OUTPUT - name: Checkout comfyui-ci-container uses: actions/checkout@v4 with: repository: comfy-org/comfyui-ci-container token: ${{ secrets.CI_CONTAINER_PAT }} - name: Check current version id: current run: | CURRENT=$(grep -oP 'ARG COMFYUI_VERSION=\K.*' Dockerfile || echo "unknown") echo "current_version=$CURRENT" >> $GITHUB_OUTPUT - name: Update Dockerfile run: | VERSION="${{ steps.version.outputs.version }}" sed -i "s/^ARG COMFYUI_VERSION=.*/ARG COMFYUI_VERSION=${VERSION}/" Dockerfile - name: Create Pull Request id: create-pr uses: peter-evans/create-pull-request@v7 with: token: ${{ secrets.CI_CONTAINER_PAT }} branch: automation/comfyui-${{ steps.version.outputs.version }} title: "chore: bump ComfyUI to ${{ steps.version.outputs.version }}" body: | Updates ComfyUI version from `${{ steps.current.outputs.current_version }}` to `${{ steps.version.outputs.version }}` **Triggered by:** ${{ github.event_name == 'release' && format('[Release {0}]({1})', github.event.release.tag_name, github.event.release.html_url) || 'Manual workflow dispatch' }} labels: automation commit-message: "chore: bump ComfyUI to ${{ steps.version.outputs.version }}" ================================================ FILE: .github/workflows/update-version.yml ================================================ name: Update Version File on: pull_request: paths: - "pyproject.toml" branches: - master - release/** jobs: update-version: runs-on: ubuntu-latest # Don't run on fork PRs if: github.event.pull_request.head.repo.full_name == github.repository permissions: pull-requests: write contents: write steps: - name: Checkout repository uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v4 with: python-version: "3.11" - name: Install dependencies run: | python -m pip install --upgrade pip - name: Update comfyui_version.py run: | # Read version from pyproject.toml and update comfyui_version.py python -c ' import tomllib # Read version from pyproject.toml with open("pyproject.toml", "rb") as f: config = tomllib.load(f) version = config["project"]["version"] # Write version to comfyui_version.py with open("comfyui_version.py", "w") as f: f.write("# This file is automatically generated by the build process when version is\n") f.write("# updated in pyproject.toml.\n") f.write(f"__version__ = \"{version}\"\n") ' - name: Commit changes run: | git config --local user.name "github-actions" git config --local user.email "github-actions@github.com" git fetch origin ${{ github.head_ref }} git checkout -B ${{ github.head_ref }} origin/${{ github.head_ref }} git add comfyui_version.py git diff --quiet && git diff --staged --quiet || git commit -m "chore: Update comfyui_version.py to match pyproject.toml" git push origin HEAD:${{ github.head_ref }} ================================================ FILE: .github/workflows/windows_release_dependencies.yml ================================================ name: "Windows Release dependencies" on: workflow_dispatch: inputs: xformers: description: 'xformers version' required: false type: string default: "" extra_dependencies: description: 'extra dependencies' required: false type: string default: "" cu: description: 'cuda version' required: true type: string default: "130" python_minor: description: 'python minor version' required: true type: string default: "13" python_patch: description: 'python patch version' required: true type: string default: "11" # push: # branches: # - master jobs: build_dependencies: runs-on: windows-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }} - shell: bash run: | echo "@echo off call update_comfyui.bat nopause echo - echo This will try to update pytorch and all python dependencies. echo - echo If you just want to update normally, close this and run update_comfyui.bat instead. echo - pause ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 pause" > update_comfyui_and_python_dependencies.bat grep -v comfyui requirements.txt > requirements_nocomfyui.txt python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir python -m pip install --no-cache-dir ./temp_wheel_dir/* echo installed basic ls -lah temp_wheel_dir mv temp_wheel_dir cu${{ inputs.cu }}_python_deps tar cf cu${{ inputs.cu }}_python_deps.tar cu${{ inputs.cu }}_python_deps - uses: actions/cache/save@v4 with: path: | cu${{ inputs.cu }}_python_deps.tar update_comfyui_and_python_dependencies.bat key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }} ================================================ FILE: .github/workflows/windows_release_dependencies_manual.yml ================================================ name: "Windows Release dependencies Manual" on: workflow_dispatch: inputs: torch_dependencies: description: 'torch dependencies' required: false type: string default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128" cache_tag: description: 'Cached dependencies tag' required: true type: string default: "cu128" python_minor: description: 'python minor version' required: true type: string default: "12" python_patch: description: 'python patch version' required: true type: string default: "10" jobs: build_dependencies: runs-on: windows-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }} - shell: bash run: | echo "@echo off call update_comfyui.bat nopause echo - echo This will try to update pytorch and all python dependencies. echo - echo If you just want to update normally, close this and run update_comfyui.bat instead. echo - pause ..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2 pause" > update_comfyui_and_python_dependencies.bat grep -v comfyui requirements.txt > requirements_nocomfyui.txt python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir python -m pip install --no-cache-dir ./temp_wheel_dir/* echo installed basic ls -lah temp_wheel_dir mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps - uses: actions/cache/save@v4 with: path: | ${{ inputs.cache_tag }}_python_deps.tar update_comfyui_and_python_dependencies.bat key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }} ================================================ FILE: .github/workflows/windows_release_nightly_pytorch.yml ================================================ name: "Windows Release Nightly pytorch" on: workflow_dispatch: inputs: cu: description: 'cuda version' required: true type: string default: "129" python_minor: description: 'python minor version' required: true type: string default: "13" python_patch: description: 'python patch version' required: true type: string default: "5" # push: # branches: # - master jobs: build: permissions: contents: "write" packages: "write" pull-requests: "read" runs-on: windows-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 30 persist-credentials: false - uses: actions/setup-python@v5 with: python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }} - shell: bash run: | cd .. cp -r ComfyUI ComfyUI_copy curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip unzip python_embeded.zip -d python_embeded cd python_embeded echo 'import site' >> ./python3${{ inputs.python_minor }}._pth curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py ./python.exe get-pip.py python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir ls ../temp_wheel_dir ./python.exe -s -m pip install --pre ../temp_wheel_dir/* sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable_nightly_pytorch mv python_embeded ComfyUI_windows_portable_nightly_pytorch mv ComfyUI_copy ComfyUI_windows_portable_nightly_pytorch/ComfyUI cd ComfyUI_windows_portable_nightly_pytorch mkdir update cp -r ComfyUI/.ci/update_windows/* ./update/ cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./ cp -r ComfyUI/.ci/windows_nightly_base_files/* ./ echo "call update_comfyui.bat nopause ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 pause" > ./update/update_comfyui_and_python_dependencies.bat cd .. "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z cd ComfyUI_windows_portable_nightly_pytorch python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu ls - name: Upload binaries to release uses: svenstaro/upload-release-action@v2 with: repo_token: ${{ secrets.GITHUB_TOKEN }} file: ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z tag: "latest" overwrite: true ================================================ FILE: .github/workflows/windows_release_package.yml ================================================ name: "Windows Release packaging" on: workflow_dispatch: inputs: cu: description: 'cuda version' required: true type: string default: "129" python_minor: description: 'python minor version' required: true type: string default: "13" python_patch: description: 'python patch version' required: true type: string default: "6" # push: # branches: # - master jobs: package_comfyui: permissions: contents: "write" packages: "write" pull-requests: "read" runs-on: windows-latest steps: - uses: actions/cache/restore@v4 id: cache with: path: | cu${{ inputs.cu }}_python_deps.tar update_comfyui_and_python_dependencies.bat key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }} - shell: bash run: | mv cu${{ inputs.cu }}_python_deps.tar ../ mv update_comfyui_and_python_dependencies.bat ../ cd .. tar xf cu${{ inputs.cu }}_python_deps.tar pwd ls - uses: actions/checkout@v4 with: fetch-depth: 150 persist-credentials: false - shell: bash run: | cd .. cp -r ComfyUI ComfyUI_copy curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip unzip python_embeded.zip -d python_embeded cd python_embeded echo 'import site' >> ./python3${{ inputs.python_minor }}._pth curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py ./python.exe get-pip.py ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/* sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space rm ./Lib/site-packages/torch/lib/libprotoc.lib rm ./Lib/site-packages/torch/lib/libprotobuf.lib cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable mv python_embeded ComfyUI_windows_portable mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI cd ComfyUI_windows_portable mkdir update cp -r ComfyUI/.ci/update_windows/* ./update/ cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./ cp ../update_comfyui_and_python_dependencies.bat ./update/ cd .. "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu python_embeded/python.exe -s ./update/update.py ComfyUI/ ls - name: Upload binaries to release uses: svenstaro/upload-release-action@v2 with: repo_token: ${{ secrets.GITHUB_TOKEN }} file: new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z tag: "latest" overwrite: true ================================================ FILE: .gitignore ================================================ __pycache__/ *.py[cod] /output/ /input/ !/input/example.png /models/ /temp/ /custom_nodes/ !custom_nodes/example_node.py.example extra_model_paths.yaml /.vs .vscode/ .idea/ venv*/ .venv/ /web/extensions/* !/web/extensions/logging.js.example !/web/extensions/core/ /tests-ui/data/object_info.json /user/ *.log web_custom_versions/ .DS_Store openapi.yaml filtered-openapi.yaml uv.lock ================================================ FILE: CODEOWNERS ================================================ # Admins * @comfyanonymous @kosinkadink @guill ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing to ComfyUI Welcome, and thank you for your interest in contributing to ComfyUI! There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved. ## Asking Questions Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you. ## Providing Feedback Your comments and feedback are welcome, and the development team is available via a handful of different channels. See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord. ## Reporting Issues Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible. ### Look For an Existing Issue Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed. If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment: * 👍 - upvote * 👎 - downvote If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues. ### Creating Pull Requests * Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project. ## Thank You Your contributions to open source, large or small, make great projects like this possible. 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Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . ================================================ FILE: QUANTIZATION.md ================================================ # The Comfy guide to Quantization ## How does quantization work? Quantization aims to map a high-precision value x_f to a lower precision format with minimal loss in accuracy. These smaller formats then serve to reduce the models memory footprint and increase throughput by using specialized hardware. When simply converting a value from FP16 to FP8 using the round-nearest method we might hit two issues: - The dynamic range of FP16 (-65,504, 65,504) far exceeds FP8 formats like E4M3 (-448, 448) or E5M2 (-57,344, 57,344), potentially resulting in clipped values - The original values are concentrated in a small range (e.g. -1,1) leaving many FP8-bits "unused" By using a scaling factor, we aim to map these values into the quantized-dtype range, making use of the full spectrum. One of the easiest approaches, and common, is using per-tensor absolute-maximum scaling. ``` absmax = max(abs(tensor)) scale = amax / max_dynamic_range_low_precision # Quantization tensor_q = (tensor / scale).to(low_precision_dtype) # De-Quantization tensor_dq = tensor_q.to(fp16) * scale tensor_dq ~ tensor ``` Given that additional information (scaling factor) is needed to "interpret" the quantized values, we describe those as derived datatypes. ## Quantization in Comfy ``` QuantizedTensor (torch.Tensor subclass) ↓ __torch_dispatch__ Two-Level Registry (generic + layout handlers) ↓ MixedPrecisionOps + Metadata Detection ``` ### Representation To represent these derived datatypes, ComfyUI uses a subclass of torch.Tensor to implements these using the `QuantizedTensor` class found in `comfy/quant_ops.py` A `Layout` class defines how a specific quantization format behaves: - Required parameters - Quantize method - De-Quantize method ```python from comfy.quant_ops import QuantizedLayout class MyLayout(QuantizedLayout): @classmethod def quantize(cls, tensor, **kwargs): # Convert to quantized format qdata = ... params = {'scale': ..., 'orig_dtype': tensor.dtype} return qdata, params @staticmethod def dequantize(qdata, scale, orig_dtype, **kwargs): return qdata.to(orig_dtype) * scale ``` To then run operations using these QuantizedTensors we use two registry systems to define supported operations. The first is a **generic registry** that handles operations common to all quantized formats (e.g., `.to()`, `.clone()`, `.reshape()`). The second registry is layout-specific and allows to implement fast-paths like nn.Linear. ```python from comfy.quant_ops import register_layout_op @register_layout_op(torch.ops.aten.linear.default, MyLayout) def my_linear(func, args, kwargs): # Extract tensors, call optimized kernel ... ``` When `torch.nn.functional.linear()` is called with QuantizedTensor arguments, `__torch_dispatch__` automatically routes to the registered implementation. For any unsupported operation, QuantizedTensor will fallback to call `dequantize` and dispatch using the high-precision implementation. ### Mixed Precision The `MixedPrecisionOps` class (lines 542-648 in `comfy/ops.py`) enables per-layer quantization decisions, allowing different layers in a model to use different precisions. This is activated when a model config contains a `layer_quant_config` dictionary that specifies which layers should be quantized and how. **Architecture:** ```python class MixedPrecisionOps(disable_weight_init): _layer_quant_config = {} # Maps layer names to quantization configs _compute_dtype = torch.bfloat16 # Default compute / dequantize precision ``` **Key mechanism:** The custom `Linear._load_from_state_dict()` method inspects each layer during model loading: - If the layer name is **not** in `_layer_quant_config`: load weight as regular tensor in `_compute_dtype` - If the layer name **is** in `_layer_quant_config`: - Load weight as `QuantizedTensor` with the specified layout (e.g., `TensorCoreFP8Layout`) - Load associated quantization parameters (scales, block_size, etc.) **Why it's needed:** Not all layers tolerate quantization equally. Sensitive operations like final projections can be kept in higher precision, while compute-heavy matmuls are quantized. This provides most of the performance benefits while maintaining quality. The system is selected in `pick_operations()` when `model_config.layer_quant_config` is present, making it the highest-priority operation mode. ## Checkpoint Format Quantized checkpoints are stored as standard safetensors files with quantized weight tensors and associated scaling parameters, plus a `_quantization_metadata` JSON entry describing the quantization scheme. The quantized checkpoint will contain the same layers as the original checkpoint but: - The weights are stored as quantized values, sometimes using a different storage datatype. E.g. uint8 container for fp8. - For each quantized weight a number of additional scaling parameters are stored alongside depending on the recipe. - We store a metadata.json in the metadata of the final safetensor containing the `_quantization_metadata` describing which layers are quantized and what layout has been used. ### Scaling Parameters details We define 4 possible scaling parameters that should cover most recipes in the near-future: - **weight_scale**: quantization scalers for the weights - **weight_scale_2**: global scalers in the context of double scaling - **pre_quant_scale**: scalers used for smoothing salient weights - **input_scale**: quantization scalers for the activations | Format | Storage dtype | weight_scale | weight_scale_2 | pre_quant_scale | input_scale | |--------|---------------|--------------|----------------|-----------------|-------------| | float8_e4m3fn | float32 | float32 (scalar) | - | - | float32 (scalar) | You can find the defined formats in `comfy/quant_ops.py` (QUANT_ALGOS). ### Quantization Metadata The metadata stored alongside the checkpoint contains: - **format_version**: String to define a version of the standard - **layers**: A dictionary mapping layer names to their quantization format. The format string maps to the definitions found in `QUANT_ALGOS`. Example: ```json { "_quantization_metadata": { "format_version": "1.0", "layers": { "model.layers.0.mlp.up_proj": "float8_e4m3fn", "model.layers.0.mlp.down_proj": "float8_e4m3fn", "model.layers.1.mlp.up_proj": "float8_e4m3fn" } } } ``` ## Creating Quantized Checkpoints To create compatible checkpoints, use any quantization tool provided the output follows the checkpoint format described above and uses a layout defined in `QUANT_ALGOS`. ### Weight Quantization Weight quantization is straightforward - compute the scaling factor directly from the weight tensor using the absolute maximum method described earlier. Each layer's weights are quantized independently and stored with their corresponding `weight_scale` parameter. ### Calibration (for Activation Quantization) Activation quantization (e.g., for FP8 Tensor Core operations) requires `input_scale` parameters that cannot be determined from static weights alone. Since activation values depend on actual inputs, we use **post-training calibration (PTQ)**: 1. **Collect statistics**: Run inference on N representative samples 2. **Track activations**: Record the absolute maximum (`amax`) of inputs to each quantized layer 3. **Compute scales**: Derive `input_scale` from collected statistics 4. **Store in checkpoint**: Save `input_scale` parameters alongside weights The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters. ================================================ FILE: README.md ================================================
# ComfyUI **The most powerful and modular visual AI engine and application.** [![Website][website-shield]][website-url] [![Dynamic JSON Badge][discord-shield]][discord-url] [![Twitter][twitter-shield]][twitter-url] [![Matrix][matrix-shield]][matrix-url]
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ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS. ## Get Started ### Local #### [Desktop Application](https://www.comfy.org/download) - The easiest way to get started. - Available on Windows & macOS. #### [Windows Portable Package](#installing) - Get the latest commits and completely portable. - Available on Windows. #### [Manual Install](#manual-install-windows-linux) Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend). ### Cloud #### [Comfy Cloud](https://www.comfy.org/cloud) - Our official paid cloud version for those who can't afford local hardware. ## Examples See what ComfyUI can do with the [newer template workflows](https://comfy.org/workflows) or old [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/). ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. - Image Models - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)) - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/) - [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) - [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) - Pixart Alpha and Sigma - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/) - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/) - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/) - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/) - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/) - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/) - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/) - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/) - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/) - Image Editing Models - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/) - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model) - [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11) - [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model) - Video Models - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/) - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/) - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/) - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/) - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/) - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/) - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5) - Audio Models - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/) - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/) - 3D Models - [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2) - Asynchronous Queue system - Many optimizations: Only re-executes the parts of the workflow that changes between executions. - Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading. - Works even if you don't have a GPU with: ```--cpu``` (slow) - Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models. - Safe loading of ckpt, pt, pth, etc.. files. - Embeddings/Textual inversion - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/) - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/) - Loading full workflows (with seeds) from generated PNG, WebP and FLAC files. - Saving/Loading workflows as Json files. - Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones. - [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/) - [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models. - [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/) - [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/) - [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/) - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/) - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/) - Latent previews with [TAESD](#how-to-show-high-quality-previews) - Works fully offline: core will never download anything unless you want to. - Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview) disable with: `--disable-api-nodes` - [Config file](extra_model_paths.yaml.example) to set the search paths for models. Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/) ## Release Process ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories: 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)** - Releases a new stable version (e.g., v0.7.0) roughly every week. - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release. - Minor versions will be used for releases off the master branch. - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense. - Commits outside of the stable release tags may be very unstable and break many custom nodes. - Serves as the foundation for the desktop release 2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)** - Builds a new release using the latest stable core version 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** - Weekly frontend updates are merged into the core repository - Features are frozen for the upcoming core release - Development continues for the next release cycle ## Shortcuts | Keybind | Explanation | |------------------------------------|--------------------------------------------------------------------------------------------------------------------| | `Ctrl` + `Enter` | Queue up current graph for generation | | `Ctrl` + `Shift` + `Enter` | Queue up current graph as first for generation | | `Ctrl` + `Alt` + `Enter` | Cancel current generation | | `Ctrl` + `Z`/`Ctrl` + `Y` | Undo/Redo | | `Ctrl` + `S` | Save workflow | | `Ctrl` + `O` | Load workflow | | `Ctrl` + `A` | Select all nodes | | `Alt `+ `C` | Collapse/uncollapse selected nodes | | `Ctrl` + `M` | Mute/unmute selected nodes | | `Ctrl` + `B` | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | | `Delete`/`Backspace` | Delete selected nodes | | `Ctrl` + `Backspace` | Delete the current graph | | `Space` | Move the canvas around when held and moving the cursor | | `Ctrl`/`Shift` + `Click` | Add clicked node to selection | | `Ctrl` + `C`/`Ctrl` + `V` | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) | | `Ctrl` + `C`/`Ctrl` + `Shift` + `V` | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) | | `Shift` + `Drag` | Move multiple selected nodes at the same time | | `Ctrl` + `D` | Load default graph | | `Alt` + `+` | Canvas Zoom in | | `Alt` + `-` | Canvas Zoom out | | `Ctrl` + `Shift` + LMB + Vertical drag | Canvas Zoom in/out | | `P` | Pin/Unpin selected nodes | | `Ctrl` + `G` | Group selected nodes | | `Q` | Toggle visibility of the queue | | `H` | Toggle visibility of history | | `R` | Refresh graph | | `F` | Show/Hide menu | | `.` | Fit view to selection (Whole graph when nothing is selected) | | Double-Click LMB | Open node quick search palette | | `Shift` + Drag | Move multiple wires at once | | `Ctrl` + `Alt` + LMB | Disconnect all wires from clicked slot | `Ctrl` can also be replaced with `Cmd` instead for macOS users # Installing ## Windows Portable There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases). ### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z) Simply download, extract with [7-Zip](https://7-zip.org) or with the windows explorer on recent windows versions and run. For smaller models you normally only need to put the checkpoints (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints but many of the larger models have multiple files. Make sure to follow the instructions to know which subfolder to put them in ComfyUI\models\ If you have trouble extracting it, right click the file -> properties -> unblock The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start. #### Alternative Downloads: [Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z) [Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs). #### How do I share models between another UI and ComfyUI? See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor. ## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started) You can install and start ComfyUI using comfy-cli: ```bash pip install comfy-cli comfy install ``` ## Manual Install (Windows, Linux) Python 3.14 works but some custom nodes may have issues. The free threaded variant works but some dependencies will enable the GIL so it's not fully supported. Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12 torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old. ### Instructions: Git clone this repo. Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints Put your VAE in: models/vae ### AMD GPUs (Linux) AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version: ```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1``` This is the command to install the nightly with ROCm 7.2 which might have some performance improvements: ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2``` ### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only. These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware. RDNA 3 (RX 7000 series): ```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/``` RDNA 3.5 (Strix halo/Ryzen AI Max+ 365): ```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/``` RDNA 4 (RX 9000 series): ```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/``` ### Intel GPUs (Windows and Linux) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html) 1. To install PyTorch xpu, use the following command: ```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu``` This is the command to install the Pytorch xpu nightly which might have some performance improvements: ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu``` ### NVIDIA Nvidia users should install stable pytorch using this command: ```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130``` This is the command to install pytorch nightly instead which might have performance improvements. ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130``` #### Troubleshooting If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with: ```pip uninstall torch``` And install it again with the command above. ### Dependencies Install the dependencies by opening your terminal inside the ComfyUI folder and: ```pip install -r requirements.txt``` After this you should have everything installed and can proceed to running ComfyUI. ### Others: #### Apple Mac silicon You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version. 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly). 1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux. 1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies). 1. Launch ComfyUI by running `python main.py` > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux). #### Ascend NPUs For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method: 1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary. 2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform. 3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page. 4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier. #### Cambricon MLUs For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method: 1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html) 2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html) 3. Launch ComfyUI by running `python main.py` #### Iluvatar Corex For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method: 1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536) 2. Launch ComfyUI by running `python main.py` ## [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4) **ComfyUI-Manager** is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI. ### Setup 1. Install the manager dependencies: ```bash pip install -r manager_requirements.txt ``` 2. Enable the manager with the `--enable-manager` flag when running ComfyUI: ```bash python main.py --enable-manager ``` ### Command Line Options | Flag | Description | |------|-------------| | `--enable-manager` | Enable ComfyUI-Manager | | `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) | | `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) | # Running ```python main.py``` ### For AMD cards not officially supported by ROCm Try running it with this command if you have issues: For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py``` For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py``` ### AMD ROCm Tips You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default. ```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention``` You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run. # Notes Only parts of the graph that have an output with all the correct inputs will be executed. Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed. Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it. You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\). You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}. Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`. To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension): ```embedding:embedding_filename.pt``` ## How to show high-quality previews? Use ```--preview-method auto``` to enable previews. The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews. ## How to use TLS/SSL? Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"` Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`. > Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.

If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal. ## Support and dev channel [Discord](https://comfy.org/discord): Try the #help or #feedback channels. [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source). See also: [https://www.comfy.org/](https://www.comfy.org/) ## Frontend Development As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory. ### Reporting Issues and Requesting Features For any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently. ### Using the Latest Frontend The new frontend is now the default for ComfyUI. However, please note: 1. The frontend in the main ComfyUI repository is updated fortnightly. 2. Daily releases are available in the separate frontend repository. To use the most up-to-date frontend version: 1. For the latest daily release, launch ComfyUI with this command line argument: ``` --front-end-version Comfy-Org/ComfyUI_frontend@latest ``` 2. For a specific version, replace `latest` with the desired version number: ``` --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2 ``` This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes. ### Accessing the Legacy Frontend If you need to use the legacy frontend for any reason, you can access it using the following command line argument: ``` --front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest ``` This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend). # QA ### Which GPU should I buy for this? [See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI) ================================================ FILE: alembic.ini ================================================ # A generic, single database configuration. [alembic] # path to migration scripts # Use forward slashes (/) also on windows to provide an os agnostic path script_location = alembic_db # template used to generate migration file names; The default value is %%(rev)s_%%(slug)s # Uncomment the line below if you want the files to be prepended with date and time # see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file # for all available tokens # file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s # sys.path path, will be prepended to sys.path if present. # defaults to the current working directory. prepend_sys_path = . # timezone to use when rendering the date within the migration file # as well as the filename. # If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library. # Any required deps can installed by adding `alembic[tz]` to the pip requirements # string value is passed to ZoneInfo() # leave blank for localtime # timezone = # max length of characters to apply to the "slug" field # truncate_slug_length = 40 # set to 'true' to run the environment during # the 'revision' command, regardless of autogenerate # revision_environment = false # set to 'true' to allow .pyc and .pyo files without # a source .py file to be detected as revisions in the # versions/ directory # sourceless = false # version location specification; This defaults # to alembic_db/versions. When using multiple version # directories, initial revisions must be specified with --version-path. # The path separator used here should be the separator specified by "version_path_separator" below. # version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions # version path separator; As mentioned above, this is the character used to split # version_locations. The default within new alembic.ini files is "os", which uses os.pathsep. # If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas. # Valid values for version_path_separator are: # # version_path_separator = : # version_path_separator = ; # version_path_separator = space # version_path_separator = newline # # Use os.pathsep. Default configuration used for new projects. version_path_separator = os # set to 'true' to search source files recursively # in each "version_locations" directory # new in Alembic version 1.10 # recursive_version_locations = false # the output encoding used when revision files # are written from script.py.mako # output_encoding = utf-8 sqlalchemy.url = sqlite:///user/comfyui.db [post_write_hooks] # post_write_hooks defines scripts or Python functions that are run # on newly generated revision scripts. See the documentation for further # detail and examples # format using "black" - use the console_scripts runner, against the "black" entrypoint # hooks = black # black.type = console_scripts # black.entrypoint = black # black.options = -l 79 REVISION_SCRIPT_FILENAME # lint with attempts to fix using "ruff" - use the exec runner, execute a binary # hooks = ruff # ruff.type = exec # ruff.executable = %(here)s/.venv/bin/ruff # ruff.options = check --fix REVISION_SCRIPT_FILENAME ================================================ FILE: alembic_db/README.md ================================================ ## Generate new revision 1. Update models in `/app/database/models.py` 2. Run `alembic revision --autogenerate -m "{your message}"` ================================================ FILE: alembic_db/env.py ================================================ from sqlalchemy import engine_from_config from sqlalchemy import pool from alembic import context # this is the Alembic Config object, which provides # access to the values within the .ini file in use. config = context.config from app.database.models import Base, NAMING_CONVENTION target_metadata = Base.metadata # other values from the config, defined by the needs of env.py, # can be acquired: # my_important_option = config.get_main_option("my_important_option") # ... etc. def run_migrations_offline() -> None: """Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output. """ url = config.get_main_option("sqlalchemy.url") context.configure( url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={"paramstyle": "named"}, ) with context.begin_transaction(): context.run_migrations() def run_migrations_online() -> None: """Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. """ connectable = engine_from_config( config.get_section(config.config_ini_section, {}), prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: context.configure( connection=connection, target_metadata=target_metadata, render_as_batch=True, naming_convention=NAMING_CONVENTION, ) with context.begin_transaction(): context.run_migrations() if context.is_offline_mode(): run_migrations_offline() else: run_migrations_online() ================================================ FILE: alembic_db/script.py.mako ================================================ """${message} Revision ID: ${up_revision} Revises: ${down_revision | comma,n} Create Date: ${create_date} """ from typing import Sequence, Union from alembic import op import sqlalchemy as sa ${imports if imports else ""} # revision identifiers, used by Alembic. revision: str = ${repr(up_revision)} down_revision: Union[str, None] = ${repr(down_revision)} branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} def upgrade() -> None: """Upgrade schema.""" ${upgrades if upgrades else "pass"} def downgrade() -> None: """Downgrade schema.""" ${downgrades if downgrades else "pass"} ================================================ FILE: alembic_db/versions/0001_assets.py ================================================ """ Initial assets schema Revision ID: 0001_assets Revises: None Create Date: 2025-12-10 00:00:00 """ from alembic import op import sqlalchemy as sa revision = "0001_assets" down_revision = None branch_labels = None depends_on = None def upgrade() -> None: # ASSETS: content identity op.create_table( "assets", sa.Column("id", sa.String(length=36), primary_key=True), sa.Column("hash", sa.String(length=256), nullable=True), sa.Column("size_bytes", sa.BigInteger(), nullable=False, server_default="0"), sa.Column("mime_type", sa.String(length=255), nullable=True), sa.Column("created_at", sa.DateTime(timezone=False), nullable=False), sa.CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"), ) op.create_index("uq_assets_hash", "assets", ["hash"], unique=True) op.create_index("ix_assets_mime_type", "assets", ["mime_type"]) # ASSETS_INFO: user-visible references op.create_table( "assets_info", sa.Column("id", sa.String(length=36), primary_key=True), sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""), sa.Column("name", sa.String(length=512), nullable=False), sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False), sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True), sa.Column("user_metadata", sa.JSON(), nullable=True), sa.Column("created_at", sa.DateTime(timezone=False), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False), sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False), sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"), ) op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"]) op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"]) op.create_index("ix_assets_info_name", "assets_info", ["name"]) op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"]) op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"]) op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"]) # TAGS: normalized tag vocabulary op.create_table( "tags", sa.Column("name", sa.String(length=512), primary_key=True), sa.Column("tag_type", sa.String(length=32), nullable=False, server_default="user"), sa.CheckConstraint("name = lower(name)", name="ck_tags_lowercase"), ) op.create_index("ix_tags_tag_type", "tags", ["tag_type"]) # ASSET_INFO_TAGS: many-to-many for tags on AssetInfo op.create_table( "asset_info_tags", sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False), sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False), sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"), sa.Column("added_at", sa.DateTime(timezone=False), nullable=False), sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"), ) op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"]) op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"]) # ASSET_CACHE_STATE: N:1 local cache rows per Asset op.create_table( "asset_cache_state", sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True), sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False), sa.Column("file_path", sa.Text(), nullable=False), # absolute local path to cached file sa.Column("mtime_ns", sa.BigInteger(), nullable=True), sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")), sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"), sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"), ) op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"]) op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"]) # ASSET_INFO_META: typed KV projection of user_metadata for filtering/sorting op.create_table( "asset_info_meta", sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False), sa.Column("key", sa.String(length=256), nullable=False), sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"), sa.Column("val_str", sa.String(length=2048), nullable=True), sa.Column("val_num", sa.Numeric(38, 10), nullable=True), sa.Column("val_bool", sa.Boolean(), nullable=True), sa.Column("val_json", sa.JSON(), nullable=True), sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"), ) op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"]) op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"]) op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"]) op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"]) # Tags vocabulary tags_table = sa.table( "tags", sa.column("name", sa.String(length=512)), sa.column("tag_type", sa.String()), ) op.bulk_insert( tags_table, [ {"name": "models", "tag_type": "system"}, {"name": "input", "tag_type": "system"}, {"name": "output", "tag_type": "system"}, {"name": "configs", "tag_type": "system"}, {"name": "checkpoints", "tag_type": "system"}, {"name": "loras", "tag_type": "system"}, {"name": "vae", "tag_type": "system"}, {"name": "text_encoders", "tag_type": "system"}, {"name": "diffusion_models", "tag_type": "system"}, {"name": "clip_vision", "tag_type": "system"}, {"name": "style_models", "tag_type": "system"}, {"name": "embeddings", "tag_type": "system"}, {"name": "diffusers", "tag_type": "system"}, {"name": "vae_approx", "tag_type": "system"}, {"name": "controlnet", "tag_type": "system"}, {"name": "gligen", "tag_type": "system"}, {"name": "upscale_models", "tag_type": "system"}, {"name": "hypernetworks", "tag_type": "system"}, {"name": "photomaker", "tag_type": "system"}, {"name": "classifiers", "tag_type": "system"}, {"name": "encoder", "tag_type": "system"}, {"name": "decoder", "tag_type": "system"}, {"name": "missing", "tag_type": "system"}, {"name": "rescan", "tag_type": "system"}, ], ) def downgrade() -> None: op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta") op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta") op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta") op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta") op.drop_table("asset_info_meta") op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state") op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state") op.drop_constraint("uq_asset_cache_state_file_path", table_name="asset_cache_state") op.drop_table("asset_cache_state") op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags") op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags") op.drop_table("asset_info_tags") op.drop_index("ix_tags_tag_type", table_name="tags") op.drop_table("tags") op.drop_constraint("uq_assets_info_asset_owner_name", table_name="assets_info") op.drop_index("ix_assets_info_owner_name", table_name="assets_info") op.drop_index("ix_assets_info_last_access_time", table_name="assets_info") op.drop_index("ix_assets_info_created_at", table_name="assets_info") op.drop_index("ix_assets_info_name", table_name="assets_info") op.drop_index("ix_assets_info_asset_id", table_name="assets_info") op.drop_index("ix_assets_info_owner_id", table_name="assets_info") op.drop_table("assets_info") op.drop_index("uq_assets_hash", table_name="assets") op.drop_index("ix_assets_mime_type", table_name="assets") op.drop_table("assets") ================================================ FILE: alembic_db/versions/0002_merge_to_asset_references.py ================================================ """ Merge AssetInfo and AssetCacheState into unified asset_references table. This migration drops old tables and creates the new unified schema. All existing data is discarded. Revision ID: 0002_merge_to_asset_references Revises: 0001_assets Create Date: 2025-02-11 """ from alembic import op import sqlalchemy as sa revision = "0002_merge_to_asset_references" down_revision = "0001_assets" branch_labels = None depends_on = None def upgrade() -> None: # Drop old tables (order matters due to FK constraints) op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta") op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta") op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta") op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta") op.drop_table("asset_info_meta") op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags") op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags") op.drop_table("asset_info_tags") op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state") op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state") op.drop_table("asset_cache_state") op.drop_index("ix_assets_info_owner_name", table_name="assets_info") op.drop_index("ix_assets_info_last_access_time", table_name="assets_info") op.drop_index("ix_assets_info_created_at", table_name="assets_info") op.drop_index("ix_assets_info_name", table_name="assets_info") op.drop_index("ix_assets_info_asset_id", table_name="assets_info") op.drop_index("ix_assets_info_owner_id", table_name="assets_info") op.drop_table("assets_info") # Truncate assets table (cascades handled by dropping dependent tables first) op.execute("DELETE FROM assets") # Create asset_references table op.create_table( "asset_references", sa.Column("id", sa.String(length=36), primary_key=True), sa.Column( "asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False, ), sa.Column("file_path", sa.Text(), nullable=True), sa.Column("mtime_ns", sa.BigInteger(), nullable=True), sa.Column( "needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false"), ), sa.Column( "is_missing", sa.Boolean(), nullable=False, server_default=sa.text("false") ), sa.Column("enrichment_level", sa.Integer(), nullable=False, server_default="0"), sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""), sa.Column("name", sa.String(length=512), nullable=False), sa.Column( "preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True, ), sa.Column("user_metadata", sa.JSON(), nullable=True), sa.Column("created_at", sa.DateTime(timezone=False), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False), sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False), sa.Column("deleted_at", sa.DateTime(timezone=False), nullable=True), sa.CheckConstraint( "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg" ), sa.CheckConstraint( "enrichment_level >= 0 AND enrichment_level <= 2", name="ck_ar_enrichment_level_range", ), ) op.create_index( "uq_asset_references_file_path", "asset_references", ["file_path"], unique=True ) op.create_index("ix_asset_references_asset_id", "asset_references", ["asset_id"]) op.create_index("ix_asset_references_owner_id", "asset_references", ["owner_id"]) op.create_index("ix_asset_references_name", "asset_references", ["name"]) op.create_index("ix_asset_references_is_missing", "asset_references", ["is_missing"]) op.create_index( "ix_asset_references_enrichment_level", "asset_references", ["enrichment_level"] ) op.create_index("ix_asset_references_created_at", "asset_references", ["created_at"]) op.create_index( "ix_asset_references_last_access_time", "asset_references", ["last_access_time"] ) op.create_index( "ix_asset_references_owner_name", "asset_references", ["owner_id", "name"] ) op.create_index("ix_asset_references_deleted_at", "asset_references", ["deleted_at"]) # Create asset_reference_tags table op.create_table( "asset_reference_tags", sa.Column( "asset_reference_id", sa.String(length=36), sa.ForeignKey("asset_references.id", ondelete="CASCADE"), nullable=False, ), sa.Column( "tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False, ), sa.Column( "origin", sa.String(length=32), nullable=False, server_default="manual" ), sa.Column("added_at", sa.DateTime(timezone=False), nullable=False), sa.PrimaryKeyConstraint( "asset_reference_id", "tag_name", name="pk_asset_reference_tags" ), ) op.create_index( "ix_asset_reference_tags_tag_name", "asset_reference_tags", ["tag_name"] ) op.create_index( "ix_asset_reference_tags_asset_reference_id", "asset_reference_tags", ["asset_reference_id"], ) # Create asset_reference_meta table op.create_table( "asset_reference_meta", sa.Column( "asset_reference_id", sa.String(length=36), sa.ForeignKey("asset_references.id", ondelete="CASCADE"), nullable=False, ), sa.Column("key", sa.String(length=256), nullable=False), sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"), sa.Column("val_str", sa.String(length=2048), nullable=True), sa.Column("val_num", sa.Numeric(38, 10), nullable=True), sa.Column("val_bool", sa.Boolean(), nullable=True), sa.Column("val_json", sa.JSON(), nullable=True), sa.PrimaryKeyConstraint( "asset_reference_id", "key", "ordinal", name="pk_asset_reference_meta" ), ) op.create_index("ix_asset_reference_meta_key", "asset_reference_meta", ["key"]) op.create_index( "ix_asset_reference_meta_key_val_str", "asset_reference_meta", ["key", "val_str"] ) op.create_index( "ix_asset_reference_meta_key_val_num", "asset_reference_meta", ["key", "val_num"] ) op.create_index( "ix_asset_reference_meta_key_val_bool", "asset_reference_meta", ["key", "val_bool"], ) def downgrade() -> None: """Reverse 0002_merge_to_asset_references: drop new tables, recreate old schema. NOTE: Data is not recoverable. The upgrade discards all rows from the old tables and truncates assets. After downgrade the old schema will be empty. A filesystem rescan will repopulate data once the older code is running. """ # Drop new tables (order matters due to FK constraints) op.drop_index("ix_asset_reference_meta_key_val_bool", table_name="asset_reference_meta") op.drop_index("ix_asset_reference_meta_key_val_num", table_name="asset_reference_meta") op.drop_index("ix_asset_reference_meta_key_val_str", table_name="asset_reference_meta") op.drop_index("ix_asset_reference_meta_key", table_name="asset_reference_meta") op.drop_table("asset_reference_meta") op.drop_index("ix_asset_reference_tags_asset_reference_id", table_name="asset_reference_tags") op.drop_index("ix_asset_reference_tags_tag_name", table_name="asset_reference_tags") op.drop_table("asset_reference_tags") op.drop_index("ix_asset_references_deleted_at", table_name="asset_references") op.drop_index("ix_asset_references_owner_name", table_name="asset_references") op.drop_index("ix_asset_references_last_access_time", table_name="asset_references") op.drop_index("ix_asset_references_created_at", table_name="asset_references") op.drop_index("ix_asset_references_enrichment_level", table_name="asset_references") op.drop_index("ix_asset_references_is_missing", table_name="asset_references") op.drop_index("ix_asset_references_name", table_name="asset_references") op.drop_index("ix_asset_references_owner_id", table_name="asset_references") op.drop_index("ix_asset_references_asset_id", table_name="asset_references") op.drop_index("uq_asset_references_file_path", table_name="asset_references") op.drop_table("asset_references") # Truncate assets (upgrade deleted all rows; downgrade starts fresh too) op.execute("DELETE FROM assets") # Recreate old tables from 0001_assets schema op.create_table( "assets_info", sa.Column("id", sa.String(length=36), primary_key=True), sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""), sa.Column("name", sa.String(length=512), nullable=False), sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False), sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True), sa.Column("user_metadata", sa.JSON(), nullable=True), sa.Column("created_at", sa.DateTime(timezone=False), nullable=False), sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False), sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False), sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"), ) op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"]) op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"]) op.create_index("ix_assets_info_name", "assets_info", ["name"]) op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"]) op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"]) op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"]) op.create_table( "asset_cache_state", sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True), sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False), sa.Column("file_path", sa.Text(), nullable=False), sa.Column("mtime_ns", sa.BigInteger(), nullable=True), sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")), sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"), sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"), ) op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"]) op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"]) op.create_table( "asset_info_tags", sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False), sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False), sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"), sa.Column("added_at", sa.DateTime(timezone=False), nullable=False), sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"), ) op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"]) op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"]) op.create_table( "asset_info_meta", sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False), sa.Column("key", sa.String(length=256), nullable=False), sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"), sa.Column("val_str", sa.String(length=2048), nullable=True), sa.Column("val_num", sa.Numeric(38, 10), nullable=True), sa.Column("val_bool", sa.Boolean(), nullable=True), sa.Column("val_json", sa.JSON(), nullable=True), sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"), ) op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"]) op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"]) op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"]) op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"]) ================================================ FILE: alembic_db/versions/0003_add_metadata_job_id.py ================================================ """ Add system_metadata and job_id columns to asset_references. Change preview_id FK from assets.id to asset_references.id. Revision ID: 0003_add_metadata_job_id Revises: 0002_merge_to_asset_references Create Date: 2026-03-09 """ from alembic import op import sqlalchemy as sa from app.database.models import NAMING_CONVENTION revision = "0003_add_metadata_job_id" down_revision = "0002_merge_to_asset_references" branch_labels = None depends_on = None def upgrade() -> None: with op.batch_alter_table("asset_references") as batch_op: batch_op.add_column( sa.Column("system_metadata", sa.JSON(), nullable=True) ) batch_op.add_column( sa.Column("job_id", sa.String(length=36), nullable=True) ) # Change preview_id FK from assets.id to asset_references.id (self-ref). # Existing values are asset-content IDs that won't match reference IDs, # so null them out first. op.execute("UPDATE asset_references SET preview_id = NULL WHERE preview_id IS NOT NULL") with op.batch_alter_table( "asset_references", naming_convention=NAMING_CONVENTION ) as batch_op: batch_op.drop_constraint( "fk_asset_references_preview_id_assets", type_="foreignkey" ) batch_op.create_foreign_key( "fk_asset_references_preview_id_asset_references", "asset_references", ["preview_id"], ["id"], ondelete="SET NULL", ) batch_op.create_index( "ix_asset_references_preview_id", ["preview_id"] ) # Purge any all-null meta rows before adding the constraint op.execute( "DELETE FROM asset_reference_meta" " WHERE val_str IS NULL AND val_num IS NULL AND val_bool IS NULL AND val_json IS NULL" ) with op.batch_alter_table("asset_reference_meta") as batch_op: batch_op.create_check_constraint( "ck_asset_reference_meta_has_value", "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL", ) def downgrade() -> None: # SQLite doesn't reflect CHECK constraints, so we must declare it # explicitly via table_args for the batch recreate to find it. # Use the fully-rendered constraint name to avoid the naming convention # doubling the prefix. with op.batch_alter_table( "asset_reference_meta", table_args=[ sa.CheckConstraint( "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL", name="ck_asset_reference_meta_has_value", ), ], ) as batch_op: batch_op.drop_constraint( "ck_asset_reference_meta_has_value", type_="check" ) with op.batch_alter_table( "asset_references", naming_convention=NAMING_CONVENTION ) as batch_op: batch_op.drop_index("ix_asset_references_preview_id") batch_op.drop_constraint( "fk_asset_references_preview_id_asset_references", type_="foreignkey" ) batch_op.create_foreign_key( "fk_asset_references_preview_id_assets", "assets", ["preview_id"], ["id"], ondelete="SET NULL", ) with op.batch_alter_table("asset_references") as batch_op: batch_op.drop_column("job_id") batch_op.drop_column("system_metadata") ================================================ FILE: api_server/__init__.py ================================================ ================================================ FILE: api_server/routes/__init__.py ================================================ ================================================ FILE: api_server/routes/internal/README.md ================================================ # ComfyUI Internal Routes All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice. ================================================ FILE: api_server/routes/internal/__init__.py ================================================ ================================================ FILE: api_server/routes/internal/internal_routes.py ================================================ from aiohttp import web from typing import Optional from folder_paths import folder_names_and_paths, get_directory_by_type from api_server.services.terminal_service import TerminalService import app.logger import os class InternalRoutes: ''' The top level web router for internal routes: /internal/* The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only. Check README.md for more information. ''' def __init__(self, prompt_server): self.routes: web.RouteTableDef = web.RouteTableDef() self._app: Optional[web.Application] = None self.prompt_server = prompt_server self.terminal_service = TerminalService(prompt_server) def setup_routes(self): @self.routes.get('/logs') async def get_logs(request): return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()])) @self.routes.get('/logs/raw') async def get_raw_logs(request): self.terminal_service.update_size() return web.json_response({ "entries": list(app.logger.get_logs()), "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows} }) @self.routes.patch('/logs/subscribe') async def subscribe_logs(request): json_data = await request.json() client_id = json_data["clientId"] enabled = json_data["enabled"] if enabled: self.terminal_service.subscribe(client_id) else: self.terminal_service.unsubscribe(client_id) return web.Response(status=200) @self.routes.get('/folder_paths') async def get_folder_paths(request): response = {} for key in folder_names_and_paths: response[key] = folder_names_and_paths[key][0] return web.json_response(response) @self.routes.get('/files/{directory_type}') async def get_files(request: web.Request) -> web.Response: directory_type = request.match_info['directory_type'] if directory_type not in ("output", "input", "temp"): return web.json_response({"error": "Invalid directory type"}, status=400) directory = get_directory_by_type(directory_type) def is_visible_file(entry: os.DirEntry) -> bool: """Filter out hidden files (e.g., .DS_Store on macOS).""" return entry.is_file() and not entry.name.startswith('.') sorted_files = sorted( (entry for entry in os.scandir(directory) if is_visible_file(entry)), key=lambda entry: -entry.stat().st_mtime ) return web.json_response([entry.name for entry in sorted_files], status=200) def get_app(self): if self._app is None: self._app = web.Application() self.setup_routes() self._app.add_routes(self.routes) return self._app ================================================ FILE: api_server/services/__init__.py ================================================ ================================================ FILE: api_server/services/terminal_service.py ================================================ from app.logger import on_flush import os import shutil class TerminalService: def __init__(self, server): self.server = server self.cols = None self.rows = None self.subscriptions = set() on_flush(self.send_messages) def get_terminal_size(self): try: size = os.get_terminal_size() return (size.columns, size.lines) except OSError: try: size = shutil.get_terminal_size() return (size.columns, size.lines) except OSError: return (80, 24) # fallback to 80x24 def update_size(self): columns, lines = self.get_terminal_size() changed = False if columns != self.cols: self.cols = columns changed = True if lines != self.rows: self.rows = lines changed = True if changed: return {"cols": self.cols, "rows": self.rows} return None def subscribe(self, client_id): self.subscriptions.add(client_id) def unsubscribe(self, client_id): self.subscriptions.discard(client_id) def send_messages(self, entries): if not len(entries) or not len(self.subscriptions): return new_size = self.update_size() for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration if client_id not in self.server.sockets: # Automatically unsub if the socket has disconnected self.unsubscribe(client_id) continue self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id) ================================================ FILE: api_server/utils/file_operations.py ================================================ import os from typing import List, Union, TypedDict, Literal from typing_extensions import TypeGuard class FileInfo(TypedDict): name: str path: str type: Literal["file"] size: int class DirectoryInfo(TypedDict): name: str path: str type: Literal["directory"] FileSystemItem = Union[FileInfo, DirectoryInfo] def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]: return item["type"] == "file" class FileSystemOperations: @staticmethod def walk_directory(directory: str) -> List[FileSystemItem]: file_list: List[FileSystemItem] = [] for root, dirs, files in os.walk(directory): for name in files: file_path = os.path.join(root, name) relative_path = os.path.relpath(file_path, directory) file_list.append({ "name": name, "path": relative_path, "type": "file", "size": os.path.getsize(file_path) }) for name in dirs: dir_path = os.path.join(root, name) relative_path = os.path.relpath(dir_path, directory) file_list.append({ "name": name, "path": relative_path, "type": "directory" }) return file_list ================================================ FILE: app/__init__.py ================================================ ================================================ FILE: app/app_settings.py ================================================ import os import json from aiohttp import web import logging class AppSettings(): def __init__(self, user_manager): self.user_manager = user_manager def get_settings(self, request): try: file = self.user_manager.get_request_user_filepath( request, "comfy.settings.json" ) except KeyError as e: logging.error("User settings not found.") raise web.HTTPUnauthorized() from e if os.path.isfile(file): try: with open(file) as f: return json.load(f) except: logging.error(f"The user settings file is corrupted: {file}") return {} else: return {} def save_settings(self, request, settings): file = self.user_manager.get_request_user_filepath( request, "comfy.settings.json") with open(file, "w") as f: f.write(json.dumps(settings, indent=4)) def add_routes(self, routes): @routes.get("/settings") async def get_settings(request): return web.json_response(self.get_settings(request)) @routes.get("/settings/{id}") async def get_setting(request): value = None settings = self.get_settings(request) setting_id = request.match_info.get("id", None) if setting_id and setting_id in settings: value = settings[setting_id] return web.json_response(value) @routes.post("/settings") async def post_settings(request): settings = self.get_settings(request) new_settings = await request.json() self.save_settings(request, {**settings, **new_settings}) return web.Response(status=200) @routes.post("/settings/{id}") async def post_setting(request): setting_id = request.match_info.get("id", None) if not setting_id: return web.Response(status=400) settings = self.get_settings(request) settings[setting_id] = await request.json() self.save_settings(request, settings) return web.Response(status=200) ================================================ FILE: app/assets/api/routes.py ================================================ import asyncio import functools import json import logging import os import urllib.parse import uuid from typing import Any from aiohttp import web from pydantic import ValidationError import folder_paths from app import user_manager from app.assets.api import schemas_in, schemas_out from app.assets.services import schemas from app.assets.api.schemas_in import ( AssetValidationError, UploadError, ) from app.assets.helpers import validate_blake3_hash from app.assets.api.upload import ( delete_temp_file_if_exists, parse_multipart_upload, ) from app.assets.seeder import ScanInProgressError, asset_seeder from app.assets.services import ( DependencyMissingError, HashMismatchError, apply_tags, asset_exists, create_from_hash, delete_asset_reference, get_asset_detail, list_assets_page, list_tags, remove_tags, resolve_asset_for_download, update_asset_metadata, upload_from_temp_path, ) from app.assets.services.tagging import list_tag_histogram ROUTES = web.RouteTableDef() USER_MANAGER: user_manager.UserManager | None = None _ASSETS_ENABLED = False def _require_assets_feature_enabled(handler): @functools.wraps(handler) async def wrapper(request: web.Request) -> web.Response: if not _ASSETS_ENABLED: return _build_error_response( 503, "SERVICE_DISABLED", "Assets system is disabled. Start the server with --enable-assets to use this feature.", ) return await handler(request) return wrapper # UUID regex (canonical hyphenated form, case-insensitive) UUID_RE = r"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}" def get_query_dict(request: web.Request) -> dict[str, Any]: """Gets a dictionary of query parameters from the request. request.query is a MultiMapping[str], needs to be converted to a dict to be validated by Pydantic. """ query_dict = { key: request.query.getall(key) if len(request.query.getall(key)) > 1 else request.query.get(key) for key in request.query.keys() } return query_dict # Note to any custom node developers reading this code: # The assets system is not yet fully implemented, # do not rely on the code in /app/assets remaining the same. def register_assets_routes( app: web.Application, user_manager_instance: user_manager.UserManager | None = None, ) -> None: global USER_MANAGER, _ASSETS_ENABLED if user_manager_instance is not None: USER_MANAGER = user_manager_instance _ASSETS_ENABLED = True app.add_routes(ROUTES) def disable_assets_routes() -> None: """Disable asset routes at runtime (e.g. after DB init failure).""" global _ASSETS_ENABLED _ASSETS_ENABLED = False def _build_error_response( status: int, code: str, message: str, details: dict | None = None ) -> web.Response: return web.json_response( {"error": {"code": code, "message": message, "details": details or {}}}, status=status, ) def _build_validation_error_response(code: str, ve: ValidationError) -> web.Response: errors = json.loads(ve.json()) return _build_error_response(400, code, "Validation failed.", {"errors": errors}) def _validate_sort_field(requested: str | None) -> str: if not requested: return "created_at" v = requested.lower() if v in {"name", "created_at", "updated_at", "size", "last_access_time"}: return v return "created_at" def _build_preview_url_from_view(tags: list[str], user_metadata: dict[str, Any] | None) -> str | None: """Build a /api/view preview URL from asset tags and user_metadata filename.""" if not user_metadata: return None filename = user_metadata.get("filename") if not filename: return None if "input" in tags: view_type = "input" elif "output" in tags: view_type = "output" else: return None subfolder = "" if "/" in filename: subfolder, filename = filename.rsplit("/", 1) encoded_filename = urllib.parse.quote(filename, safe="") url = f"/api/view?type={view_type}&filename={encoded_filename}" if subfolder: url += f"&subfolder={urllib.parse.quote(subfolder, safe='')}" return url def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResult) -> schemas_out.Asset: """Build an Asset response from a service result.""" if result.ref.preview_id: preview_detail = get_asset_detail(result.ref.preview_id) if preview_detail: preview_url = _build_preview_url_from_view(preview_detail.tags, preview_detail.ref.user_metadata) else: preview_url = None else: preview_url = _build_preview_url_from_view(result.tags, result.ref.user_metadata) return schemas_out.Asset( id=result.ref.id, name=result.ref.name, asset_hash=result.asset.hash if result.asset else None, size=int(result.asset.size_bytes) if result.asset else None, mime_type=result.asset.mime_type if result.asset else None, tags=result.tags, preview_url=preview_url, preview_id=result.ref.preview_id, user_metadata=result.ref.user_metadata or {}, metadata=result.ref.system_metadata, job_id=result.ref.job_id, prompt_id=result.ref.job_id, # deprecated: mirrors job_id for cloud compat created_at=result.ref.created_at, updated_at=result.ref.updated_at, last_access_time=result.ref.last_access_time, ) @ROUTES.head("/api/assets/hash/{hash}") @_require_assets_feature_enabled async def head_asset_by_hash(request: web.Request) -> web.Response: hash_str = request.match_info.get("hash", "").strip().lower() try: hash_str = validate_blake3_hash(hash_str) except ValueError: return _build_error_response( 400, "INVALID_HASH", "hash must be like 'blake3:'" ) exists = asset_exists(hash_str) return web.Response(status=200 if exists else 404) @ROUTES.get("/api/assets") @_require_assets_feature_enabled async def list_assets_route(request: web.Request) -> web.Response: """ GET request to list assets. """ query_dict = get_query_dict(request) try: q = schemas_in.ListAssetsQuery.model_validate(query_dict) except ValidationError as ve: return _build_validation_error_response("INVALID_QUERY", ve) sort = _validate_sort_field(q.sort) order_candidate = (q.order or "desc").lower() order = order_candidate if order_candidate in {"asc", "desc"} else "desc" result = list_assets_page( owner_id=USER_MANAGER.get_request_user_id(request), include_tags=q.include_tags, exclude_tags=q.exclude_tags, name_contains=q.name_contains, metadata_filter=q.metadata_filter, limit=q.limit, offset=q.offset, sort=sort, order=order, ) summaries = [_build_asset_response(item) for item in result.items] payload = schemas_out.AssetsList( assets=summaries, total=result.total, has_more=(q.offset + len(summaries)) < result.total, ) return web.json_response(payload.model_dump(mode="json", exclude_none=True)) @ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}") @_require_assets_feature_enabled async def get_asset_route(request: web.Request) -> web.Response: """ GET request to get an asset's info as JSON. """ reference_id = str(uuid.UUID(request.match_info["id"])) try: result = get_asset_detail( reference_id=reference_id, owner_id=USER_MANAGER.get_request_user_id(request), ) if not result: return _build_error_response( 404, "ASSET_NOT_FOUND", f"AssetReference {reference_id} not found", {"id": reference_id}, ) payload = _build_asset_response(result) except ValueError as e: return _build_error_response( 404, "ASSET_NOT_FOUND", str(e), {"id": reference_id} ) except Exception: logging.exception( "get_asset failed for reference_id=%s, owner_id=%s", reference_id, USER_MANAGER.get_request_user_id(request), ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}/content") @_require_assets_feature_enabled async def download_asset_content(request: web.Request) -> web.Response: disposition = request.query.get("disposition", "attachment").lower().strip() if disposition not in {"inline", "attachment"}: disposition = "attachment" try: result = resolve_asset_for_download( reference_id=str(uuid.UUID(request.match_info["id"])), owner_id=USER_MANAGER.get_request_user_id(request), ) abs_path = result.abs_path content_type = result.content_type filename = result.download_name except ValueError as ve: return _build_error_response(404, "ASSET_NOT_FOUND", str(ve)) except NotImplementedError as nie: return _build_error_response(501, "BACKEND_UNSUPPORTED", str(nie)) except FileNotFoundError: return _build_error_response( 404, "FILE_NOT_FOUND", "Underlying file not found on disk." ) _DANGEROUS_MIME_TYPES = { "text/html", "text/html-sandboxed", "application/xhtml+xml", "text/javascript", "text/css", } if content_type in _DANGEROUS_MIME_TYPES: content_type = "application/octet-stream" safe_name = (filename or "").replace("\r", "").replace("\n", "") encoded = urllib.parse.quote(safe_name) cd = f"{disposition}; filename*=UTF-8''{encoded}" file_size = os.path.getsize(abs_path) size_mb = file_size / (1024 * 1024) logging.info( "download_asset_content: path=%s, size=%d bytes (%.2f MB), type=%s, name=%s", abs_path, file_size, size_mb, content_type, filename, ) async def stream_file_chunks(): chunk_size = 64 * 1024 with open(abs_path, "rb") as f: while True: chunk = f.read(chunk_size) if not chunk: break yield chunk return web.Response( body=stream_file_chunks(), content_type=content_type, headers={ "Content-Disposition": cd, "Content-Length": str(file_size), "X-Content-Type-Options": "nosniff", }, ) @ROUTES.post("/api/assets/from-hash") @_require_assets_feature_enabled async def create_asset_from_hash_route(request: web.Request) -> web.Response: try: payload = await request.json() body = schemas_in.CreateFromHashBody.model_validate(payload) except ValidationError as ve: return _build_validation_error_response("INVALID_BODY", ve) except Exception: return _build_error_response( 400, "INVALID_JSON", "Request body must be valid JSON." ) # Derive name from hash if not provided name = body.name if name is None: name = body.hash.split(":", 1)[1] if ":" in body.hash else body.hash result = create_from_hash( hash_str=body.hash, name=name, tags=body.tags, user_metadata=body.user_metadata, owner_id=USER_MANAGER.get_request_user_id(request), mime_type=body.mime_type, preview_id=body.preview_id, ) if result is None: return _build_error_response( 404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist" ) asset = _build_asset_response(result) payload_out = schemas_out.AssetCreated( **asset.model_dump(), created_new=result.created_new, ) return web.json_response(payload_out.model_dump(mode="json", exclude_none=True), status=201) @ROUTES.post("/api/assets") @_require_assets_feature_enabled async def upload_asset(request: web.Request) -> web.Response: """Multipart/form-data endpoint for Asset uploads.""" try: parsed = await parse_multipart_upload(request, check_hash_exists=asset_exists) except UploadError as e: return _build_error_response(e.status, e.code, e.message) owner_id = USER_MANAGER.get_request_user_id(request) try: spec = schemas_in.UploadAssetSpec.model_validate( { "tags": parsed.tags_raw, "name": parsed.provided_name, "user_metadata": parsed.user_metadata_raw, "hash": parsed.provided_hash, "mime_type": parsed.provided_mime_type, "preview_id": parsed.provided_preview_id, } ) except ValidationError as ve: delete_temp_file_if_exists(parsed.tmp_path) return _build_error_response( 400, "INVALID_BODY", f"Validation failed: {ve.json()}" ) if spec.tags and spec.tags[0] == "models": if ( len(spec.tags) < 2 or spec.tags[1] not in folder_paths.folder_names_and_paths ): delete_temp_file_if_exists(parsed.tmp_path) category = spec.tags[1] if len(spec.tags) >= 2 else "" return _build_error_response( 400, "INVALID_BODY", f"unknown models category '{category}'" ) try: # Fast path: hash exists, create AssetReference without writing anything if spec.hash and parsed.provided_hash_exists is True: result = create_from_hash( hash_str=spec.hash, name=spec.name or (spec.hash.split(":", 1)[1]), tags=spec.tags, user_metadata=spec.user_metadata or {}, owner_id=owner_id, mime_type=spec.mime_type, preview_id=spec.preview_id, ) if result is None: delete_temp_file_if_exists(parsed.tmp_path) return _build_error_response( 404, "ASSET_NOT_FOUND", f"Asset content {spec.hash} does not exist" ) delete_temp_file_if_exists(parsed.tmp_path) else: # Otherwise, we must have a temp file path to ingest if not parsed.tmp_path or not os.path.exists(parsed.tmp_path): return _build_error_response( 400, "MISSING_INPUT", "Provided hash not found and no file uploaded.", ) result = upload_from_temp_path( temp_path=parsed.tmp_path, name=spec.name, tags=spec.tags, user_metadata=spec.user_metadata or {}, client_filename=parsed.file_client_name, owner_id=owner_id, expected_hash=spec.hash, mime_type=spec.mime_type, preview_id=spec.preview_id, ) except AssetValidationError as e: delete_temp_file_if_exists(parsed.tmp_path) return _build_error_response(400, e.code, str(e)) except ValueError as e: delete_temp_file_if_exists(parsed.tmp_path) return _build_error_response(400, "BAD_REQUEST", str(e)) except HashMismatchError as e: delete_temp_file_if_exists(parsed.tmp_path) return _build_error_response(400, "HASH_MISMATCH", str(e)) except DependencyMissingError as e: delete_temp_file_if_exists(parsed.tmp_path) return _build_error_response(503, "DEPENDENCY_MISSING", e.message) except Exception: delete_temp_file_if_exists(parsed.tmp_path) logging.exception("upload_asset failed for owner_id=%s", owner_id) return _build_error_response(500, "INTERNAL", "Unexpected server error.") asset = _build_asset_response(result) payload_out = schemas_out.AssetCreated( **asset.model_dump(), created_new=result.created_new, ) status = 201 if result.created_new else 200 return web.json_response(payload_out.model_dump(mode="json", exclude_none=True), status=status) @ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}") @_require_assets_feature_enabled async def update_asset_route(request: web.Request) -> web.Response: reference_id = str(uuid.UUID(request.match_info["id"])) try: body = schemas_in.UpdateAssetBody.model_validate(await request.json()) except ValidationError as ve: return _build_validation_error_response("INVALID_BODY", ve) except Exception: return _build_error_response( 400, "INVALID_JSON", "Request body must be valid JSON." ) try: result = update_asset_metadata( reference_id=reference_id, name=body.name, user_metadata=body.user_metadata, owner_id=USER_MANAGER.get_request_user_id(request), preview_id=body.preview_id, ) payload = _build_asset_response(result) except PermissionError as pe: return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id}) except ValueError as ve: return _build_error_response( 404, "ASSET_NOT_FOUND", str(ve), {"id": reference_id} ) except Exception: logging.exception( "update_asset failed for reference_id=%s, owner_id=%s", reference_id, USER_MANAGER.get_request_user_id(request), ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}") @_require_assets_feature_enabled async def delete_asset_route(request: web.Request) -> web.Response: reference_id = str(uuid.UUID(request.match_info["id"])) delete_content_param = request.query.get("delete_content") delete_content = ( False if delete_content_param is None else delete_content_param.lower() not in {"0", "false", "no"} ) try: deleted = delete_asset_reference( reference_id=reference_id, owner_id=USER_MANAGER.get_request_user_id(request), delete_content_if_orphan=delete_content, ) except Exception: logging.exception( "delete_asset_reference failed for reference_id=%s, owner_id=%s", reference_id, USER_MANAGER.get_request_user_id(request), ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") if not deleted: return _build_error_response( 404, "ASSET_NOT_FOUND", f"AssetReference {reference_id} not found." ) return web.Response(status=204) @ROUTES.get("/api/tags") @_require_assets_feature_enabled async def get_tags(request: web.Request) -> web.Response: """ GET request to list all tags based on query parameters. """ query_map = dict(request.rel_url.query) try: query = schemas_in.TagsListQuery.model_validate(query_map) except ValidationError as e: return _build_error_response( 400, "INVALID_QUERY", "Invalid query parameters", {"errors": json.loads(e.json())}, ) rows, total = list_tags( prefix=query.prefix, limit=query.limit, offset=query.offset, order=query.order, include_zero=query.include_zero, owner_id=USER_MANAGER.get_request_user_id(request), ) tags = [ schemas_out.TagUsage(name=name, count=count, type=tag_type) for (name, tag_type, count) in rows ] payload = schemas_out.TagsList( tags=tags, total=total, has_more=(query.offset + len(tags)) < total ) return web.json_response(payload.model_dump(mode="json", exclude_none=True)) @ROUTES.post(f"/api/assets/{{id:{UUID_RE}}}/tags") @_require_assets_feature_enabled async def add_asset_tags(request: web.Request) -> web.Response: reference_id = str(uuid.UUID(request.match_info["id"])) try: json_payload = await request.json() data = schemas_in.TagsAdd.model_validate(json_payload) except ValidationError as ve: return _build_error_response( 400, "INVALID_BODY", "Invalid JSON body for tags add.", {"errors": ve.errors()}, ) except Exception: return _build_error_response( 400, "INVALID_JSON", "Request body must be valid JSON." ) try: result = apply_tags( reference_id=reference_id, tags=data.tags, origin="manual", owner_id=USER_MANAGER.get_request_user_id(request), ) payload = schemas_out.TagsAdd( added=result.added, already_present=result.already_present, total_tags=result.total_tags, ) except PermissionError as pe: return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id}) except ValueError as ve: return _build_error_response( 404, "ASSET_NOT_FOUND", str(ve), {"id": reference_id} ) except Exception: logging.exception( "add_tags_to_asset failed for reference_id=%s, owner_id=%s", reference_id, USER_MANAGER.get_request_user_id(request), ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}/tags") @_require_assets_feature_enabled async def delete_asset_tags(request: web.Request) -> web.Response: reference_id = str(uuid.UUID(request.match_info["id"])) try: json_payload = await request.json() data = schemas_in.TagsRemove.model_validate(json_payload) except ValidationError as ve: return _build_error_response( 400, "INVALID_BODY", "Invalid JSON body for tags remove.", {"errors": ve.errors()}, ) except Exception: return _build_error_response( 400, "INVALID_JSON", "Request body must be valid JSON." ) try: result = remove_tags( reference_id=reference_id, tags=data.tags, owner_id=USER_MANAGER.get_request_user_id(request), ) payload = schemas_out.TagsRemove( removed=result.removed, not_present=result.not_present, total_tags=result.total_tags, ) except PermissionError as pe: return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id}) except ValueError as ve: return _build_error_response( 404, "ASSET_NOT_FOUND", str(ve), {"id": reference_id} ) except Exception: logging.exception( "remove_tags_from_asset failed for reference_id=%s, owner_id=%s", reference_id, USER_MANAGER.get_request_user_id(request), ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.get("/api/assets/tags/refine") @_require_assets_feature_enabled async def get_tags_refine(request: web.Request) -> web.Response: """GET request to get tag histogram for filtered assets.""" query_dict = get_query_dict(request) try: q = schemas_in.TagsRefineQuery.model_validate(query_dict) except ValidationError as ve: return _build_validation_error_response("INVALID_QUERY", ve) tag_counts = list_tag_histogram( owner_id=USER_MANAGER.get_request_user_id(request), include_tags=q.include_tags, exclude_tags=q.exclude_tags, name_contains=q.name_contains, metadata_filter=q.metadata_filter, limit=q.limit, ) payload = schemas_out.TagHistogram(tag_counts=tag_counts) return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.post("/api/assets/seed") @_require_assets_feature_enabled async def seed_assets(request: web.Request) -> web.Response: """Trigger asset seeding for specified roots (models, input, output). Query params: wait: If "true", block until scan completes (synchronous behavior for tests) Returns: 202 Accepted if scan started 409 Conflict if scan already running 200 OK with final stats if wait=true """ try: payload = await request.json() roots = payload.get("roots", ["models", "input", "output"]) except Exception: roots = ["models", "input", "output"] valid_roots = tuple(r for r in roots if r in ("models", "input", "output")) if not valid_roots: return _build_error_response(400, "INVALID_BODY", "No valid roots specified") wait_param = request.query.get("wait", "").lower() should_wait = wait_param in ("true", "1", "yes") started = asset_seeder.start(roots=valid_roots) if not started: return web.json_response({"status": "already_running"}, status=409) if should_wait: await asyncio.to_thread(asset_seeder.wait) status = asset_seeder.get_status() return web.json_response( { "status": "completed", "progress": { "scanned": status.progress.scanned if status.progress else 0, "total": status.progress.total if status.progress else 0, "created": status.progress.created if status.progress else 0, "skipped": status.progress.skipped if status.progress else 0, }, "errors": status.errors, }, status=200, ) return web.json_response({"status": "started"}, status=202) @ROUTES.get("/api/assets/seed/status") @_require_assets_feature_enabled async def get_seed_status(request: web.Request) -> web.Response: """Get current scan status and progress.""" status = asset_seeder.get_status() return web.json_response( { "state": status.state.value, "progress": { "scanned": status.progress.scanned, "total": status.progress.total, "created": status.progress.created, "skipped": status.progress.skipped, } if status.progress else None, "errors": status.errors, }, status=200, ) @ROUTES.post("/api/assets/seed/cancel") @_require_assets_feature_enabled async def cancel_seed(request: web.Request) -> web.Response: """Request cancellation of in-progress scan.""" cancelled = asset_seeder.cancel() if cancelled: return web.json_response({"status": "cancelling"}, status=200) return web.json_response({"status": "idle"}, status=200) @ROUTES.post("/api/assets/prune") @_require_assets_feature_enabled async def mark_missing_assets(request: web.Request) -> web.Response: """Mark assets as missing when outside all known root prefixes. This is a non-destructive soft-delete operation. Assets and metadata are preserved, but references are flagged as missing. They can be restored if the file reappears in a future scan. Returns: 200 OK with count of marked assets 409 Conflict if a scan is currently running """ try: marked = asset_seeder.mark_missing_outside_prefixes() except ScanInProgressError: return web.json_response( {"status": "scan_running", "marked": 0}, status=409, ) return web.json_response({"status": "completed", "marked": marked}, status=200) ================================================ FILE: app/assets/api/schemas_in.py ================================================ import json from dataclasses import dataclass from typing import Any, Literal from app.assets.helpers import validate_blake3_hash from pydantic import ( BaseModel, ConfigDict, Field, conint, field_validator, model_validator, ) class UploadError(Exception): """Error during upload parsing with HTTP status and code.""" def __init__(self, status: int, code: str, message: str): super().__init__(message) self.status = status self.code = code self.message = message class AssetValidationError(Exception): """Validation error in asset processing (invalid tags, metadata, etc.).""" def __init__(self, code: str, message: str): super().__init__(message) self.code = code self.message = message @dataclass class ParsedUpload: """Result of parsing a multipart upload request.""" file_present: bool file_written: int file_client_name: str | None tmp_path: str | None tags_raw: list[str] provided_name: str | None user_metadata_raw: str | None provided_hash: str | None provided_hash_exists: bool | None provided_mime_type: str | None = None provided_preview_id: str | None = None class ListAssetsQuery(BaseModel): include_tags: list[str] = Field(default_factory=list) exclude_tags: list[str] = Field(default_factory=list) name_contains: str | None = None # Accept either a JSON string (query param) or a dict metadata_filter: dict[str, Any] | None = None limit: conint(ge=1, le=500) = 20 offset: conint(ge=0) = 0 sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = ( "created_at" ) order: Literal["asc", "desc"] = "desc" @field_validator("include_tags", "exclude_tags", mode="before") @classmethod def _split_csv_tags(cls, v): # Accept "a,b,c" or ["a","b"] (we are liberal in what we accept) if v is None: return [] if isinstance(v, str): return [t.strip() for t in v.split(",") if t.strip()] if isinstance(v, list): out: list[str] = [] for item in v: if isinstance(item, str): out.extend([t.strip() for t in item.split(",") if t.strip()]) return out return v @field_validator("metadata_filter", mode="before") @classmethod def _parse_metadata_json(cls, v): if v is None or isinstance(v, dict): return v if isinstance(v, str) and v.strip(): try: parsed = json.loads(v) except Exception as e: raise ValueError(f"metadata_filter must be JSON: {e}") from e if not isinstance(parsed, dict): raise ValueError("metadata_filter must be a JSON object") return parsed return None class UpdateAssetBody(BaseModel): name: str | None = None user_metadata: dict[str, Any] | None = None preview_id: str | None = None # references an asset_reference id, not an asset id @model_validator(mode="after") def _validate_at_least_one_field(self): if all( v is None for v in (self.name, self.user_metadata, self.preview_id) ): raise ValueError( "Provide at least one of: name, user_metadata, preview_id." ) return self class CreateFromHashBody(BaseModel): model_config = ConfigDict(extra="ignore", str_strip_whitespace=True) hash: str name: str | None = None tags: list[str] = Field(default_factory=list) user_metadata: dict[str, Any] = Field(default_factory=dict) mime_type: str | None = None preview_id: str | None = None # references an asset_reference id, not an asset id @field_validator("hash") @classmethod def _require_blake3(cls, v): return validate_blake3_hash(v or "") @field_validator("tags", mode="before") @classmethod def _normalize_tags_field(cls, v): if v is None: return [] if isinstance(v, list): out = [str(t).strip().lower() for t in v if str(t).strip()] seen = set() dedup = [] for t in out: if t not in seen: seen.add(t) dedup.append(t) return dedup if isinstance(v, str): return [t.strip().lower() for t in v.split(",") if t.strip()] return [] class TagsRefineQuery(BaseModel): include_tags: list[str] = Field(default_factory=list) exclude_tags: list[str] = Field(default_factory=list) name_contains: str | None = None metadata_filter: dict[str, Any] | None = None limit: conint(ge=1, le=1000) = 100 @field_validator("include_tags", "exclude_tags", mode="before") @classmethod def _split_csv_tags(cls, v): if v is None: return [] if isinstance(v, str): return [t.strip() for t in v.split(",") if t.strip()] if isinstance(v, list): out: list[str] = [] for item in v: if isinstance(item, str): out.extend([t.strip() for t in item.split(",") if t.strip()]) return out return v @field_validator("metadata_filter", mode="before") @classmethod def _parse_metadata_json(cls, v): if v is None or isinstance(v, dict): return v if isinstance(v, str) and v.strip(): try: parsed = json.loads(v) except Exception as e: raise ValueError(f"metadata_filter must be JSON: {e}") from e if not isinstance(parsed, dict): raise ValueError("metadata_filter must be a JSON object") return parsed return None class TagsListQuery(BaseModel): model_config = ConfigDict(extra="ignore", str_strip_whitespace=True) prefix: str | None = Field(None, min_length=1, max_length=256) limit: int = Field(100, ge=1, le=1000) offset: int = Field(0, ge=0, le=10_000_000) order: Literal["count_desc", "name_asc"] = "count_desc" include_zero: bool = True @field_validator("prefix") @classmethod def normalize_prefix(cls, v: str | None) -> str | None: if v is None: return v v = v.strip() return v.lower() or None class TagsAdd(BaseModel): model_config = ConfigDict(extra="ignore") tags: list[str] = Field(..., min_length=1) @field_validator("tags") @classmethod def normalize_tags(cls, v: list[str]) -> list[str]: out = [] for t in v: if not isinstance(t, str): raise TypeError("tags must be strings") tnorm = t.strip().lower() if tnorm: out.append(tnorm) seen = set() deduplicated = [] for x in out: if x not in seen: seen.add(x) deduplicated.append(x) return deduplicated class TagsRemove(TagsAdd): pass class UploadAssetSpec(BaseModel): """Upload Asset operation. - tags: optional list; if provided, first is root ('models'|'input'|'output'); if root == 'models', second must be a valid category - name: display name - user_metadata: arbitrary JSON object (optional) - hash: optional canonical 'blake3:' for validation / fast-path - mime_type: optional MIME type override - preview_id: optional asset_reference ID for preview Files are stored using the content hash as filename stem. """ model_config = ConfigDict(extra="ignore", str_strip_whitespace=True) tags: list[str] = Field(default_factory=list) name: str | None = Field(default=None, max_length=512, description="Display Name") user_metadata: dict[str, Any] = Field(default_factory=dict) hash: str | None = Field(default=None) mime_type: str | None = Field(default=None) preview_id: str | None = Field(default=None) # references an asset_reference id @field_validator("hash", mode="before") @classmethod def _parse_hash(cls, v): if v is None: return None s = str(v).strip() if not s: return None return validate_blake3_hash(s) @field_validator("tags", mode="before") @classmethod def _parse_tags(cls, v): """ Accepts a list of strings (possibly multiple form fields), where each string can be: - JSON array (e.g., '["models","loras","foo"]') - comma-separated ('models, loras, foo') - single token ('models') Returns a normalized, deduplicated, ordered list. """ items: list[str] = [] if v is None: return [] if isinstance(v, str): v = [v] if isinstance(v, list): for item in v: if item is None: continue s = str(item).strip() if not s: continue if s.startswith("["): try: arr = json.loads(s) if isinstance(arr, list): items.extend(str(x) for x in arr) continue except Exception: pass # fallback to CSV parse below items.extend([p for p in s.split(",") if p.strip()]) else: return [] # normalize + dedupe norm = [] seen = set() for t in items: tnorm = str(t).strip().lower() if tnorm and tnorm not in seen: seen.add(tnorm) norm.append(tnorm) return norm @field_validator("user_metadata", mode="before") @classmethod def _parse_metadata_json(cls, v): if v is None or isinstance(v, dict): return v or {} if isinstance(v, str): s = v.strip() if not s: return {} try: parsed = json.loads(s) except Exception as e: raise ValueError(f"user_metadata must be JSON: {e}") from e if not isinstance(parsed, dict): raise ValueError("user_metadata must be a JSON object") return parsed return {} @model_validator(mode="after") def _validate_order(self): if not self.tags: raise ValueError("at least one tag is required for uploads") root = self.tags[0] if root not in {"models", "input", "output"}: raise ValueError("first tag must be one of: models, input, output") if root == "models": if len(self.tags) < 2: raise ValueError( "models uploads require a category tag as the second tag" ) return self ================================================ FILE: app/assets/api/schemas_out.py ================================================ from datetime import datetime from typing import Any from pydantic import BaseModel, ConfigDict, Field, field_serializer class Asset(BaseModel): """API view of an asset. Maps to DB ``AssetReference`` joined with its ``Asset`` blob; ``id`` here is the AssetReference id, not the content-addressed Asset id.""" id: str name: str asset_hash: str | None = None size: int | None = None mime_type: str | None = None tags: list[str] = Field(default_factory=list) preview_url: str | None = None preview_id: str | None = None # references an asset_reference id, not an asset id user_metadata: dict[str, Any] = Field(default_factory=dict) is_immutable: bool = False metadata: dict[str, Any] | None = None job_id: str | None = None prompt_id: str | None = None # deprecated: use job_id created_at: datetime updated_at: datetime last_access_time: datetime | None = None model_config = ConfigDict(from_attributes=True) @field_serializer("created_at", "updated_at", "last_access_time") def _serialize_datetime(self, v: datetime | None, _info): return v.isoformat() if v else None class AssetCreated(Asset): created_new: bool class AssetsList(BaseModel): assets: list[Asset] total: int has_more: bool class TagUsage(BaseModel): name: str count: int type: str class TagsList(BaseModel): tags: list[TagUsage] = Field(default_factory=list) total: int has_more: bool class TagsAdd(BaseModel): model_config = ConfigDict(str_strip_whitespace=True) added: list[str] = Field(default_factory=list) already_present: list[str] = Field(default_factory=list) total_tags: list[str] = Field(default_factory=list) class TagsRemove(BaseModel): model_config = ConfigDict(str_strip_whitespace=True) removed: list[str] = Field(default_factory=list) not_present: list[str] = Field(default_factory=list) total_tags: list[str] = Field(default_factory=list) class TagHistogram(BaseModel): tag_counts: dict[str, int] ================================================ FILE: app/assets/api/upload.py ================================================ import logging import os import uuid from typing import Callable from aiohttp import web import folder_paths from app.assets.api.schemas_in import ParsedUpload, UploadError from app.assets.helpers import validate_blake3_hash def normalize_and_validate_hash(s: str) -> str: """Validate and normalize a hash string. Returns canonical 'blake3:' or raises UploadError. """ try: return validate_blake3_hash(s) except ValueError: raise UploadError(400, "INVALID_HASH", "hash must be like 'blake3:'") async def parse_multipart_upload( request: web.Request, check_hash_exists: Callable[[str], bool], ) -> ParsedUpload: """ Parse a multipart/form-data upload request. Args: request: The aiohttp request check_hash_exists: Callable(hash_str) -> bool to check if a hash exists Returns: ParsedUpload with parsed fields and temp file path Raises: UploadError: On validation or I/O errors """ if not (request.content_type or "").lower().startswith("multipart/"): raise UploadError( 415, "UNSUPPORTED_MEDIA_TYPE", "Use multipart/form-data for uploads." ) reader = await request.multipart() file_present = False file_client_name: str | None = None tags_raw: list[str] = [] provided_name: str | None = None user_metadata_raw: str | None = None provided_hash: str | None = None provided_hash_exists: bool | None = None provided_mime_type: str | None = None provided_preview_id: str | None = None file_written = 0 tmp_path: str | None = None while True: field = await reader.next() if field is None: break fname = getattr(field, "name", "") or "" if fname == "hash": try: s = ((await field.text()) or "").strip().lower() except Exception: raise UploadError( 400, "INVALID_HASH", "hash must be like 'blake3:'" ) if s: provided_hash = normalize_and_validate_hash(s) try: provided_hash_exists = check_hash_exists(provided_hash) except Exception as e: logging.exception( "check_hash_exists failed for hash=%s: %s", provided_hash, e ) raise UploadError( 500, "HASH_CHECK_FAILED", "Backend error while checking asset hash.", ) elif fname == "file": file_present = True file_client_name = (field.filename or "").strip() if provided_hash and provided_hash_exists is True: # Hash exists - drain file but don't write to disk try: while True: chunk = await field.read_chunk(8 * 1024 * 1024) if not chunk: break file_written += len(chunk) except Exception: raise UploadError( 500, "UPLOAD_IO_ERROR", "Failed to receive uploaded file." ) continue uploads_root = os.path.join(folder_paths.get_temp_directory(), "uploads") unique_dir = os.path.join(uploads_root, uuid.uuid4().hex) os.makedirs(unique_dir, exist_ok=True) tmp_path = os.path.join(unique_dir, ".upload.part") try: with open(tmp_path, "wb") as f: while True: chunk = await field.read_chunk(8 * 1024 * 1024) if not chunk: break f.write(chunk) file_written += len(chunk) except Exception: delete_temp_file_if_exists(tmp_path) raise UploadError( 500, "UPLOAD_IO_ERROR", "Failed to receive and store uploaded file." ) elif fname == "tags": tags_raw.append((await field.text()) or "") elif fname == "name": provided_name = (await field.text()) or None elif fname == "user_metadata": user_metadata_raw = (await field.text()) or None elif fname == "id": raise UploadError( 400, "UNSUPPORTED_FIELD", "Client-provided 'id' is not supported. Asset IDs are assigned by the server.", ) elif fname == "mime_type": provided_mime_type = ((await field.text()) or "").strip() or None elif fname == "preview_id": provided_preview_id = ((await field.text()) or "").strip() or None if not file_present and not (provided_hash and provided_hash_exists): raise UploadError( 400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'." ) if ( file_present and file_written == 0 and not (provided_hash and provided_hash_exists) ): delete_temp_file_if_exists(tmp_path) raise UploadError(400, "EMPTY_UPLOAD", "Uploaded file is empty.") return ParsedUpload( file_present=file_present, file_written=file_written, file_client_name=file_client_name, tmp_path=tmp_path, tags_raw=tags_raw, provided_name=provided_name, user_metadata_raw=user_metadata_raw, provided_hash=provided_hash, provided_hash_exists=provided_hash_exists, provided_mime_type=provided_mime_type, provided_preview_id=provided_preview_id, ) def delete_temp_file_if_exists(tmp_path: str | None) -> None: """Safely remove a temp file and its parent directory if empty.""" if tmp_path: try: if os.path.exists(tmp_path): os.remove(tmp_path) except OSError as e: logging.debug("Failed to delete temp file %s: %s", tmp_path, e) try: parent = os.path.dirname(tmp_path) if parent and os.path.isdir(parent): os.rmdir(parent) # only succeeds if empty except OSError: pass ================================================ FILE: app/assets/database/models.py ================================================ from __future__ import annotations import uuid from datetime import datetime from typing import Any from sqlalchemy import ( JSON, BigInteger, Boolean, CheckConstraint, DateTime, ForeignKey, Index, Integer, Numeric, String, Text, ) from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship from app.assets.helpers import get_utc_now from app.database.models import Base class Asset(Base): __tablename__ = "assets" id: Mapped[str] = mapped_column( String(36), primary_key=True, default=lambda: str(uuid.uuid4()) ) hash: Mapped[str | None] = mapped_column(String(256), nullable=True) size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0) mime_type: Mapped[str | None] = mapped_column(String(255)) created_at: Mapped[datetime] = mapped_column( DateTime(timezone=False), nullable=False, default=get_utc_now ) references: Mapped[list[AssetReference]] = relationship( "AssetReference", back_populates="asset", primaryjoin=lambda: Asset.id == foreign(AssetReference.asset_id), foreign_keys=lambda: [AssetReference.asset_id], cascade="all,delete-orphan", passive_deletes=True, ) # preview_id on AssetReference is a self-referential FK to asset_references.id __table_args__ = ( Index("uq_assets_hash", "hash", unique=True), Index("ix_assets_mime_type", "mime_type"), CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"), ) def __repr__(self) -> str: return f"" class AssetReference(Base): """Unified model combining file cache state and user-facing metadata. Each row represents either: - A filesystem reference (file_path is set) with cache state - An API-created reference (file_path is NULL) without cache state """ __tablename__ = "asset_references" id: Mapped[str] = mapped_column( String(36), primary_key=True, default=lambda: str(uuid.uuid4()) ) asset_id: Mapped[str] = mapped_column( String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False ) # Cache state fields (from former AssetCacheState) file_path: Mapped[str | None] = mapped_column(Text, nullable=True) mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True) needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False) is_missing: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False) enrichment_level: Mapped[int] = mapped_column(Integer, nullable=False, default=0) # Info fields (from former AssetInfo) owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="") name: Mapped[str] = mapped_column(String(512), nullable=False) preview_id: Mapped[str | None] = mapped_column( String(36), ForeignKey("asset_references.id", ondelete="SET NULL") ) user_metadata: Mapped[dict[str, Any] | None] = mapped_column( JSON(none_as_null=True) ) system_metadata: Mapped[dict[str, Any] | None] = mapped_column( JSON(none_as_null=True), nullable=True, default=None ) job_id: Mapped[str | None] = mapped_column(String(36), nullable=True, default=None) created_at: Mapped[datetime] = mapped_column( DateTime(timezone=False), nullable=False, default=get_utc_now ) updated_at: Mapped[datetime] = mapped_column( DateTime(timezone=False), nullable=False, default=get_utc_now ) last_access_time: Mapped[datetime] = mapped_column( DateTime(timezone=False), nullable=False, default=get_utc_now ) deleted_at: Mapped[datetime | None] = mapped_column( DateTime(timezone=False), nullable=True, default=None ) asset: Mapped[Asset] = relationship( "Asset", back_populates="references", foreign_keys=[asset_id], lazy="selectin", ) preview_ref: Mapped[AssetReference | None] = relationship( "AssetReference", foreign_keys=[preview_id], remote_side=lambda: [AssetReference.id], ) metadata_entries: Mapped[list[AssetReferenceMeta]] = relationship( back_populates="asset_reference", cascade="all,delete-orphan", passive_deletes=True, ) tag_links: Mapped[list[AssetReferenceTag]] = relationship( back_populates="asset_reference", cascade="all,delete-orphan", passive_deletes=True, overlaps="tags,asset_references", ) tags: Mapped[list[Tag]] = relationship( secondary="asset_reference_tags", back_populates="asset_references", lazy="selectin", viewonly=True, overlaps="tag_links,asset_reference_links,asset_references,tag", ) __table_args__ = ( Index("uq_asset_references_file_path", "file_path", unique=True), Index("ix_asset_references_asset_id", "asset_id"), Index("ix_asset_references_owner_id", "owner_id"), Index("ix_asset_references_name", "name"), Index("ix_asset_references_is_missing", "is_missing"), Index("ix_asset_references_enrichment_level", "enrichment_level"), Index("ix_asset_references_created_at", "created_at"), Index("ix_asset_references_last_access_time", "last_access_time"), Index("ix_asset_references_deleted_at", "deleted_at"), Index("ix_asset_references_preview_id", "preview_id"), Index("ix_asset_references_owner_name", "owner_id", "name"), CheckConstraint( "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg" ), CheckConstraint( "enrichment_level >= 0 AND enrichment_level <= 2", name="ck_ar_enrichment_level_range", ), ) def __repr__(self) -> str: path_part = f" path={self.file_path!r}" if self.file_path else "" return f"" class AssetReferenceMeta(Base): __tablename__ = "asset_reference_meta" asset_reference_id: Mapped[str] = mapped_column( String(36), ForeignKey("asset_references.id", ondelete="CASCADE"), primary_key=True, ) key: Mapped[str] = mapped_column(String(256), primary_key=True) ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0) val_str: Mapped[str | None] = mapped_column(String(2048), nullable=True) val_num: Mapped[float | None] = mapped_column(Numeric(38, 10), nullable=True) val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True) val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True) asset_reference: Mapped[AssetReference] = relationship( back_populates="metadata_entries" ) __table_args__ = ( Index("ix_asset_reference_meta_key", "key"), Index("ix_asset_reference_meta_key_val_str", "key", "val_str"), Index("ix_asset_reference_meta_key_val_num", "key", "val_num"), Index("ix_asset_reference_meta_key_val_bool", "key", "val_bool"), CheckConstraint( "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL", name="has_value", ), ) class AssetReferenceTag(Base): __tablename__ = "asset_reference_tags" asset_reference_id: Mapped[str] = mapped_column( String(36), ForeignKey("asset_references.id", ondelete="CASCADE"), primary_key=True, ) tag_name: Mapped[str] = mapped_column( String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True ) origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual") added_at: Mapped[datetime] = mapped_column( DateTime(timezone=False), nullable=False, default=get_utc_now ) asset_reference: Mapped[AssetReference] = relationship(back_populates="tag_links") tag: Mapped[Tag] = relationship(back_populates="asset_reference_links") __table_args__ = ( Index("ix_asset_reference_tags_tag_name", "tag_name"), Index("ix_asset_reference_tags_asset_reference_id", "asset_reference_id"), ) class Tag(Base): __tablename__ = "tags" name: Mapped[str] = mapped_column(String(512), primary_key=True) tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user") asset_reference_links: Mapped[list[AssetReferenceTag]] = relationship( back_populates="tag", overlaps="asset_references,tags", ) asset_references: Mapped[list[AssetReference]] = relationship( secondary="asset_reference_tags", back_populates="tags", viewonly=True, overlaps="asset_reference_links,tag_links,tags,asset_reference", ) __table_args__ = (Index("ix_tags_tag_type", "tag_type"),) def __repr__(self) -> str: return f"" ================================================ FILE: app/assets/database/queries/__init__.py ================================================ from app.assets.database.queries.asset import ( asset_exists_by_hash, bulk_insert_assets, get_asset_by_hash, get_existing_asset_ids, reassign_asset_references, update_asset_hash_and_mime, upsert_asset, ) from app.assets.database.queries.asset_reference import ( CacheStateRow, UnenrichedReferenceRow, bulk_insert_references_ignore_conflicts, bulk_update_enrichment_level, bulk_update_is_missing, bulk_update_needs_verify, convert_metadata_to_rows, delete_assets_by_ids, delete_orphaned_seed_asset, delete_reference_by_id, delete_references_by_ids, fetch_reference_and_asset, fetch_reference_asset_and_tags, get_or_create_reference, get_reference_by_file_path, get_reference_by_id, get_reference_with_owner_check, get_reference_ids_by_ids, get_references_by_paths_and_asset_ids, get_references_for_prefixes, get_unenriched_references, get_unreferenced_unhashed_asset_ids, insert_reference, list_all_file_paths_by_asset_id, list_references_by_asset_id, list_references_page, mark_references_missing_outside_prefixes, rebuild_metadata_projection, reference_exists, reference_exists_for_asset_id, restore_references_by_paths, set_reference_metadata, set_reference_preview, set_reference_system_metadata, soft_delete_reference_by_id, update_reference_access_time, update_reference_name, update_is_missing_by_asset_id, update_reference_timestamps, update_reference_updated_at, upsert_reference, ) from app.assets.database.queries.tags import ( AddTagsResult, RemoveTagsResult, SetTagsResult, add_missing_tag_for_asset_id, add_tags_to_reference, bulk_insert_tags_and_meta, ensure_tags_exist, get_reference_tags, list_tag_counts_for_filtered_assets, list_tags_with_usage, remove_missing_tag_for_asset_id, remove_tags_from_reference, set_reference_tags, validate_tags_exist, ) __all__ = [ "AddTagsResult", "CacheStateRow", "RemoveTagsResult", "SetTagsResult", "UnenrichedReferenceRow", "add_missing_tag_for_asset_id", "add_tags_to_reference", "asset_exists_by_hash", "bulk_insert_assets", "bulk_insert_references_ignore_conflicts", "bulk_insert_tags_and_meta", "bulk_update_enrichment_level", "bulk_update_is_missing", "bulk_update_needs_verify", "convert_metadata_to_rows", "delete_assets_by_ids", "delete_orphaned_seed_asset", "delete_reference_by_id", "delete_references_by_ids", "ensure_tags_exist", "fetch_reference_and_asset", "fetch_reference_asset_and_tags", "get_asset_by_hash", "get_existing_asset_ids", "get_or_create_reference", "get_reference_by_file_path", "get_reference_by_id", "get_reference_with_owner_check", "get_reference_ids_by_ids", "get_reference_tags", "get_references_by_paths_and_asset_ids", "get_references_for_prefixes", "get_unenriched_references", "get_unreferenced_unhashed_asset_ids", "insert_reference", "list_all_file_paths_by_asset_id", "list_references_by_asset_id", "list_references_page", "list_tag_counts_for_filtered_assets", "list_tags_with_usage", "mark_references_missing_outside_prefixes", "reassign_asset_references", "rebuild_metadata_projection", "reference_exists", "reference_exists_for_asset_id", "remove_missing_tag_for_asset_id", "remove_tags_from_reference", "restore_references_by_paths", "set_reference_metadata", "set_reference_preview", "set_reference_system_metadata", "soft_delete_reference_by_id", "set_reference_tags", "update_asset_hash_and_mime", "update_is_missing_by_asset_id", "update_reference_access_time", "update_reference_name", "update_reference_timestamps", "update_reference_updated_at", "upsert_asset", "upsert_reference", "validate_tags_exist", ] ================================================ FILE: app/assets/database/queries/asset.py ================================================ import sqlalchemy as sa from sqlalchemy import select from sqlalchemy.dialects import sqlite from sqlalchemy.orm import Session from app.assets.database.models import Asset, AssetReference from app.assets.database.queries.common import MAX_BIND_PARAMS, calculate_rows_per_statement, iter_chunks def asset_exists_by_hash( session: Session, asset_hash: str, ) -> bool: """ Check if an asset with a given hash exists in database. """ row = ( session.execute( select(sa.literal(True)) .select_from(Asset) .where(Asset.hash == asset_hash) .limit(1) ) ).first() return row is not None def get_asset_by_hash( session: Session, asset_hash: str, ) -> Asset | None: return ( (session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))) .scalars() .first() ) def upsert_asset( session: Session, asset_hash: str, size_bytes: int, mime_type: str | None = None, ) -> tuple[Asset, bool, bool]: """Upsert an Asset by hash. Returns (asset, created, updated).""" vals = {"hash": asset_hash, "size_bytes": int(size_bytes)} if mime_type: vals["mime_type"] = mime_type ins = ( sqlite.insert(Asset) .values(**vals) .on_conflict_do_nothing(index_elements=[Asset.hash]) ) res = session.execute(ins) created = int(res.rowcount or 0) > 0 asset = ( session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1)) .scalars() .first() ) if not asset: raise RuntimeError("Asset row not found after upsert.") updated = False if not created: changed = False if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0: asset.size_bytes = int(size_bytes) changed = True if mime_type and not asset.mime_type: asset.mime_type = mime_type changed = True if changed: updated = True return asset, created, updated def bulk_insert_assets( session: Session, rows: list[dict], ) -> None: """Bulk insert Asset rows with ON CONFLICT DO NOTHING on hash.""" if not rows: return ins = sqlite.insert(Asset).on_conflict_do_nothing(index_elements=[Asset.hash]) for chunk in iter_chunks(rows, calculate_rows_per_statement(5)): session.execute(ins, chunk) def get_existing_asset_ids( session: Session, asset_ids: list[str], ) -> set[str]: """Return the subset of asset_ids that exist in the database.""" if not asset_ids: return set() found: set[str] = set() for chunk in iter_chunks(asset_ids, MAX_BIND_PARAMS): rows = session.execute( select(Asset.id).where(Asset.id.in_(chunk)) ).fetchall() found.update(row[0] for row in rows) return found def update_asset_hash_and_mime( session: Session, asset_id: str, asset_hash: str | None = None, mime_type: str | None = None, ) -> bool: """Update asset hash and/or mime_type. Returns True if asset was found.""" asset = session.get(Asset, asset_id) if not asset: return False if asset_hash is not None: asset.hash = asset_hash if mime_type is not None and not asset.mime_type: asset.mime_type = mime_type return True def reassign_asset_references( session: Session, from_asset_id: str, to_asset_id: str, reference_id: str, ) -> None: """Reassign a reference from one asset to another. Used when merging a stub asset into an existing asset with the same hash. """ ref = session.get(AssetReference, reference_id) if ref and ref.asset_id == from_asset_id: ref.asset_id = to_asset_id session.flush() ================================================ FILE: app/assets/database/queries/asset_reference.py ================================================ """Query functions for the unified AssetReference table. This module replaces the separate asset_info.py and cache_state.py query modules, providing a unified interface for the merged asset_references table. """ from collections import defaultdict from datetime import datetime from decimal import Decimal from typing import NamedTuple, Sequence import sqlalchemy as sa from sqlalchemy import delete, select from sqlalchemy.dialects import sqlite from sqlalchemy.exc import IntegrityError from sqlalchemy.orm import Session, noload from app.assets.database.models import ( Asset, AssetReference, AssetReferenceMeta, AssetReferenceTag, Tag, ) from app.assets.database.queries.common import ( MAX_BIND_PARAMS, apply_metadata_filter, apply_tag_filters, build_prefix_like_conditions, build_visible_owner_clause, calculate_rows_per_statement, iter_chunks, ) from app.assets.helpers import escape_sql_like_string, get_utc_now def _check_is_scalar(v): if v is None: return True if isinstance(v, bool): return True if isinstance(v, (int, float, Decimal, str)): return True return False def _scalar_to_row(key: str, ordinal: int, value) -> dict: """Convert a scalar value to a typed projection row.""" if isinstance(value, bool): return {"key": key, "ordinal": ordinal, "val_bool": bool(value)} if isinstance(value, (int, float, Decimal)): num = value if isinstance(value, Decimal) else Decimal(str(value)) return {"key": key, "ordinal": ordinal, "val_num": num} if isinstance(value, str): return {"key": key, "ordinal": ordinal, "val_str": value} return {"key": key, "ordinal": ordinal, "val_json": value} def convert_metadata_to_rows(key: str, value) -> list[dict]: """Turn a metadata key/value into typed projection rows.""" if value is None: return [] if _check_is_scalar(value): return [_scalar_to_row(key, 0, value)] if isinstance(value, list): if all(_check_is_scalar(x) for x in value): return [_scalar_to_row(key, i, x) for i, x in enumerate(value) if x is not None] return [{"key": key, "ordinal": i, "val_json": x} for i, x in enumerate(value) if x is not None] return [{"key": key, "ordinal": 0, "val_json": value}] def get_reference_by_id( session: Session, reference_id: str, ) -> AssetReference | None: return session.get(AssetReference, reference_id) def get_reference_with_owner_check( session: Session, reference_id: str, owner_id: str, ) -> AssetReference: """Fetch a reference and verify ownership. Raises: ValueError: if reference not found or soft-deleted PermissionError: if owner_id doesn't match """ ref = get_reference_by_id(session, reference_id=reference_id) if not ref or ref.deleted_at is not None: raise ValueError(f"AssetReference {reference_id} not found") if ref.owner_id and ref.owner_id != owner_id: raise PermissionError("not owner") return ref def get_reference_by_file_path( session: Session, file_path: str, ) -> AssetReference | None: """Get a reference by its file path.""" return ( session.execute( select(AssetReference).where(AssetReference.file_path == file_path).limit(1) ) .scalars() .first() ) def reference_exists_for_asset_id( session: Session, asset_id: str, ) -> bool: q = ( select(sa.literal(True)) .select_from(AssetReference) .where(AssetReference.asset_id == asset_id) .where(AssetReference.deleted_at.is_(None)) .limit(1) ) return session.execute(q).first() is not None def reference_exists( session: Session, reference_id: str, ) -> bool: """Return True if a reference with the given ID exists (not soft-deleted).""" q = ( select(sa.literal(True)) .select_from(AssetReference) .where(AssetReference.id == reference_id) .where(AssetReference.deleted_at.is_(None)) .limit(1) ) return session.execute(q).first() is not None def insert_reference( session: Session, asset_id: str, name: str, owner_id: str = "", file_path: str | None = None, mtime_ns: int | None = None, preview_id: str | None = None, ) -> AssetReference | None: """Insert a new AssetReference. Returns None if unique constraint violated.""" now = get_utc_now() try: with session.begin_nested(): ref = AssetReference( asset_id=asset_id, name=name, owner_id=owner_id, file_path=file_path, mtime_ns=mtime_ns, preview_id=preview_id, created_at=now, updated_at=now, last_access_time=now, ) session.add(ref) session.flush() return ref except IntegrityError: return None def get_or_create_reference( session: Session, asset_id: str, name: str, owner_id: str = "", file_path: str | None = None, mtime_ns: int | None = None, preview_id: str | None = None, ) -> tuple[AssetReference, bool]: """Get existing or create new AssetReference. For filesystem references (file_path is set), uniqueness is by file_path. For API references (file_path is None), we look for matching asset_id + owner_id + name. Returns (reference, created). """ ref = insert_reference( session, asset_id=asset_id, name=name, owner_id=owner_id, file_path=file_path, mtime_ns=mtime_ns, preview_id=preview_id, ) if ref: return ref, True # Find existing - priority to file_path match, then name match if file_path: existing = get_reference_by_file_path(session, file_path) else: existing = ( session.execute( select(AssetReference) .where( AssetReference.asset_id == asset_id, AssetReference.name == name, AssetReference.owner_id == owner_id, AssetReference.file_path.is_(None), ) .limit(1) ) .unique() .scalar_one_or_none() ) if not existing: raise RuntimeError("Failed to find AssetReference after insert conflict.") return existing, False def update_reference_timestamps( session: Session, reference: AssetReference, preview_id: str | None = None, ) -> None: """Update timestamps and optionally preview_id on existing AssetReference.""" now = get_utc_now() if preview_id and reference.preview_id != preview_id: reference.preview_id = preview_id reference.updated_at = now def list_references_page( session: Session, owner_id: str = "", limit: int = 100, offset: int = 0, name_contains: str | None = None, include_tags: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, metadata_filter: dict | None = None, sort: str | None = None, order: str | None = None, ) -> tuple[list[AssetReference], dict[str, list[str]], int]: """List references with pagination, filtering, and sorting. Returns (references, tag_map, total_count). """ base = ( select(AssetReference) .join(Asset, Asset.id == AssetReference.asset_id) .where(build_visible_owner_clause(owner_id)) .where(AssetReference.is_missing == False) # noqa: E712 .where(AssetReference.deleted_at.is_(None)) .options(noload(AssetReference.tags)) ) if name_contains: escaped, esc = escape_sql_like_string(name_contains) base = base.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc)) base = apply_tag_filters(base, include_tags, exclude_tags) base = apply_metadata_filter(base, metadata_filter) sort = (sort or "created_at").lower() order = (order or "desc").lower() sort_map = { "name": AssetReference.name, "created_at": AssetReference.created_at, "updated_at": AssetReference.updated_at, "last_access_time": AssetReference.last_access_time, "size": Asset.size_bytes, } sort_col = sort_map.get(sort, AssetReference.created_at) sort_exp = sort_col.desc() if order == "desc" else sort_col.asc() base = base.order_by(sort_exp).limit(limit).offset(offset) count_stmt = ( select(sa.func.count()) .select_from(AssetReference) .join(Asset, Asset.id == AssetReference.asset_id) .where(build_visible_owner_clause(owner_id)) .where(AssetReference.is_missing == False) # noqa: E712 .where(AssetReference.deleted_at.is_(None)) ) if name_contains: escaped, esc = escape_sql_like_string(name_contains) count_stmt = count_stmt.where( AssetReference.name.ilike(f"%{escaped}%", escape=esc) ) count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags) count_stmt = apply_metadata_filter(count_stmt, metadata_filter) total = int(session.execute(count_stmt).scalar_one() or 0) refs = session.execute(base).unique().scalars().all() id_list: list[str] = [r.id for r in refs] tag_map: dict[str, list[str]] = defaultdict(list) if id_list: rows = session.execute( select(AssetReferenceTag.asset_reference_id, Tag.name) .join(Tag, Tag.name == AssetReferenceTag.tag_name) .where(AssetReferenceTag.asset_reference_id.in_(id_list)) .order_by(AssetReferenceTag.tag_name.asc()) ) for ref_id, tag_name in rows.all(): tag_map[ref_id].append(tag_name) return list(refs), tag_map, total def fetch_reference_asset_and_tags( session: Session, reference_id: str, owner_id: str = "", ) -> tuple[AssetReference, Asset, list[str]] | None: stmt = ( select(AssetReference, Asset, Tag.name) .join(Asset, Asset.id == AssetReference.asset_id) .join( AssetReferenceTag, AssetReferenceTag.asset_reference_id == AssetReference.id, isouter=True, ) .join(Tag, Tag.name == AssetReferenceTag.tag_name, isouter=True) .where( AssetReference.id == reference_id, AssetReference.deleted_at.is_(None), build_visible_owner_clause(owner_id), ) .options(noload(AssetReference.tags)) .order_by(Tag.name.asc()) ) rows = session.execute(stmt).all() if not rows: return None first_ref, first_asset, _ = rows[0] tags: list[str] = [] seen: set[str] = set() for _ref, _asset, tag_name in rows: if tag_name and tag_name not in seen: seen.add(tag_name) tags.append(tag_name) return first_ref, first_asset, tags def fetch_reference_and_asset( session: Session, reference_id: str, owner_id: str = "", ) -> tuple[AssetReference, Asset] | None: stmt = ( select(AssetReference, Asset) .join(Asset, Asset.id == AssetReference.asset_id) .where( AssetReference.id == reference_id, AssetReference.deleted_at.is_(None), build_visible_owner_clause(owner_id), ) .limit(1) .options(noload(AssetReference.tags)) ) pair = session.execute(stmt).first() if not pair: return None return pair[0], pair[1] def update_reference_access_time( session: Session, reference_id: str, ts: datetime | None = None, only_if_newer: bool = True, ) -> None: ts = ts or get_utc_now() stmt = sa.update(AssetReference).where(AssetReference.id == reference_id) if only_if_newer: stmt = stmt.where( sa.or_( AssetReference.last_access_time.is_(None), AssetReference.last_access_time < ts, ) ) session.execute(stmt.values(last_access_time=ts)) def update_reference_name( session: Session, reference_id: str, name: str, ) -> None: """Update the name of an AssetReference.""" now = get_utc_now() session.execute( sa.update(AssetReference) .where(AssetReference.id == reference_id) .values(name=name, updated_at=now) ) def update_reference_updated_at( session: Session, reference_id: str, ts: datetime | None = None, ) -> None: """Update the updated_at timestamp of an AssetReference.""" ts = ts or get_utc_now() session.execute( sa.update(AssetReference) .where(AssetReference.id == reference_id) .values(updated_at=ts) ) def rebuild_metadata_projection(session: Session, ref: AssetReference) -> None: """Delete and rebuild AssetReferenceMeta rows from merged system+user metadata. The merged dict is ``{**system_metadata, **user_metadata}`` so user keys override system keys of the same name. """ session.execute( delete(AssetReferenceMeta).where( AssetReferenceMeta.asset_reference_id == ref.id ) ) session.flush() merged = {**(ref.system_metadata or {}), **(ref.user_metadata or {})} if not merged: return rows: list[AssetReferenceMeta] = [] for k, v in merged.items(): for r in convert_metadata_to_rows(k, v): rows.append( AssetReferenceMeta( asset_reference_id=ref.id, key=r["key"], ordinal=int(r["ordinal"]), val_str=r.get("val_str"), val_num=r.get("val_num"), val_bool=r.get("val_bool"), val_json=r.get("val_json"), ) ) if rows: session.add_all(rows) session.flush() def set_reference_metadata( session: Session, reference_id: str, user_metadata: dict | None = None, ) -> None: ref = session.get(AssetReference, reference_id) if not ref: raise ValueError(f"AssetReference {reference_id} not found") ref.user_metadata = user_metadata or {} ref.updated_at = get_utc_now() session.flush() rebuild_metadata_projection(session, ref) def set_reference_system_metadata( session: Session, reference_id: str, system_metadata: dict | None = None, ) -> None: """Set system_metadata on a reference and rebuild the merged projection.""" ref = session.get(AssetReference, reference_id) if not ref: raise ValueError(f"AssetReference {reference_id} not found") ref.system_metadata = system_metadata or {} ref.updated_at = get_utc_now() session.flush() rebuild_metadata_projection(session, ref) def delete_reference_by_id( session: Session, reference_id: str, owner_id: str, ) -> bool: stmt = sa.delete(AssetReference).where( AssetReference.id == reference_id, build_visible_owner_clause(owner_id), ) return int(session.execute(stmt).rowcount or 0) > 0 def soft_delete_reference_by_id( session: Session, reference_id: str, owner_id: str, ) -> bool: """Mark a reference as soft-deleted by setting deleted_at timestamp. Returns True if the reference was found and marked deleted. """ now = get_utc_now() stmt = ( sa.update(AssetReference) .where( AssetReference.id == reference_id, AssetReference.deleted_at.is_(None), build_visible_owner_clause(owner_id), ) .values(deleted_at=now) ) return int(session.execute(stmt).rowcount or 0) > 0 def set_reference_preview( session: Session, reference_id: str, preview_reference_id: str | None = None, ) -> None: """Set or clear preview_id and bump updated_at. Raises on unknown IDs.""" ref = session.get(AssetReference, reference_id) if not ref: raise ValueError(f"AssetReference {reference_id} not found") if preview_reference_id is None: ref.preview_id = None else: if not session.get(AssetReference, preview_reference_id): raise ValueError(f"Preview AssetReference {preview_reference_id} not found") ref.preview_id = preview_reference_id ref.updated_at = get_utc_now() session.flush() class CacheStateRow(NamedTuple): """Row from reference query with cache state data.""" reference_id: str file_path: str mtime_ns: int | None needs_verify: bool asset_id: str asset_hash: str | None size_bytes: int | None def list_references_by_asset_id( session: Session, asset_id: str, ) -> Sequence[AssetReference]: return ( session.execute( select(AssetReference) .where(AssetReference.asset_id == asset_id) .where(AssetReference.is_missing == False) # noqa: E712 .where(AssetReference.deleted_at.is_(None)) .order_by(AssetReference.id.asc()) ) .scalars() .all() ) def list_all_file_paths_by_asset_id( session: Session, asset_id: str, ) -> list[str]: """Return every file_path for an asset, including soft-deleted/missing refs. Used for orphan cleanup where all on-disk files must be removed. """ return list( session.execute( select(AssetReference.file_path) .where(AssetReference.asset_id == asset_id) .where(AssetReference.file_path.isnot(None)) ) .scalars() .all() ) def upsert_reference( session: Session, asset_id: str, file_path: str, name: str, mtime_ns: int, owner_id: str = "", ) -> tuple[bool, bool]: """Upsert a reference by file_path. Returns (created, updated). Also restores references that were previously marked as missing. """ now = get_utc_now() vals = { "asset_id": asset_id, "file_path": file_path, "name": name, "owner_id": owner_id, "mtime_ns": int(mtime_ns), "is_missing": False, "created_at": now, "updated_at": now, "last_access_time": now, } ins = ( sqlite.insert(AssetReference) .values(**vals) .on_conflict_do_nothing(index_elements=[AssetReference.file_path]) ) res = session.execute(ins) created = int(res.rowcount or 0) > 0 if created: return True, False upd = ( sa.update(AssetReference) .where(AssetReference.file_path == file_path) .where( sa.or_( AssetReference.asset_id != asset_id, AssetReference.mtime_ns.is_(None), AssetReference.mtime_ns != int(mtime_ns), AssetReference.is_missing == True, # noqa: E712 AssetReference.deleted_at.isnot(None), ) ) .values( asset_id=asset_id, mtime_ns=int(mtime_ns), is_missing=False, deleted_at=None, updated_at=now, ) ) res2 = session.execute(upd) updated = int(res2.rowcount or 0) > 0 return False, updated def mark_references_missing_outside_prefixes( session: Session, valid_prefixes: list[str], ) -> int: """Mark references as missing when file_path doesn't match any valid prefix. Returns number of references marked as missing. """ if not valid_prefixes: return 0 conds = build_prefix_like_conditions(valid_prefixes) matches_valid_prefix = sa.or_(*conds) result = session.execute( sa.update(AssetReference) .where(AssetReference.file_path.isnot(None)) .where(AssetReference.deleted_at.is_(None)) .where(~matches_valid_prefix) .where(AssetReference.is_missing == False) # noqa: E712 .values(is_missing=True) ) return result.rowcount def restore_references_by_paths(session: Session, file_paths: list[str]) -> int: """Restore references that were previously marked as missing. Returns number of references restored. """ if not file_paths: return 0 total = 0 for chunk in iter_chunks(file_paths, MAX_BIND_PARAMS): result = session.execute( sa.update(AssetReference) .where(AssetReference.file_path.in_(chunk)) .where(AssetReference.is_missing == True) # noqa: E712 .where(AssetReference.deleted_at.is_(None)) .values(is_missing=False) ) total += result.rowcount return total def get_unreferenced_unhashed_asset_ids(session: Session) -> list[str]: """Get IDs of unhashed assets (hash=None) with no active references. An asset is considered unreferenced if it has no references, or all its references are marked as missing. Returns list of asset IDs that are unreferenced. """ active_ref_exists = ( sa.select(sa.literal(1)) .where(AssetReference.asset_id == Asset.id) .where(AssetReference.is_missing == False) # noqa: E712 .where(AssetReference.deleted_at.is_(None)) .correlate(Asset) .exists() ) unreferenced_subq = sa.select(Asset.id).where( Asset.hash.is_(None), ~active_ref_exists ) return [row[0] for row in session.execute(unreferenced_subq).all()] def delete_assets_by_ids(session: Session, asset_ids: list[str]) -> int: """Delete assets and their references by ID. Returns number of assets deleted. """ if not asset_ids: return 0 total = 0 for chunk in iter_chunks(asset_ids, MAX_BIND_PARAMS): session.execute( sa.delete(AssetReference).where(AssetReference.asset_id.in_(chunk)) ) result = session.execute(sa.delete(Asset).where(Asset.id.in_(chunk))) total += result.rowcount return total def get_references_for_prefixes( session: Session, prefixes: list[str], *, include_missing: bool = False, ) -> list[CacheStateRow]: """Get all references with file paths matching any of the given prefixes. Args: session: Database session prefixes: List of absolute directory prefixes to match include_missing: If False (default), exclude references marked as missing Returns: List of cache state rows with joined asset data """ if not prefixes: return [] conds = build_prefix_like_conditions(prefixes) query = ( sa.select( AssetReference.id, AssetReference.file_path, AssetReference.mtime_ns, AssetReference.needs_verify, AssetReference.asset_id, Asset.hash, Asset.size_bytes, ) .join(Asset, Asset.id == AssetReference.asset_id) .where(AssetReference.file_path.isnot(None)) .where(AssetReference.deleted_at.is_(None)) .where(sa.or_(*conds)) ) if not include_missing: query = query.where(AssetReference.is_missing == False) # noqa: E712 rows = session.execute( query.order_by(AssetReference.asset_id.asc(), AssetReference.id.asc()) ).all() return [ CacheStateRow( reference_id=row[0], file_path=row[1], mtime_ns=row[2], needs_verify=row[3], asset_id=row[4], asset_hash=row[5], size_bytes=int(row[6]) if row[6] is not None else None, ) for row in rows ] def bulk_update_needs_verify( session: Session, reference_ids: list[str], value: bool ) -> int: """Set needs_verify flag for multiple references. Returns: Number of rows updated """ if not reference_ids: return 0 total = 0 for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS): result = session.execute( sa.update(AssetReference) .where(AssetReference.id.in_(chunk)) .values(needs_verify=value) ) total += result.rowcount return total def bulk_update_is_missing( session: Session, reference_ids: list[str], value: bool ) -> int: """Set is_missing flag for multiple references. Returns: Number of rows updated """ if not reference_ids: return 0 total = 0 for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS): result = session.execute( sa.update(AssetReference) .where(AssetReference.id.in_(chunk)) .values(is_missing=value) ) total += result.rowcount return total def update_is_missing_by_asset_id( session: Session, asset_id: str, value: bool ) -> int: """Set is_missing flag for ALL references belonging to an asset. Returns: Number of rows updated """ result = session.execute( sa.update(AssetReference) .where(AssetReference.asset_id == asset_id) .where(AssetReference.deleted_at.is_(None)) .values(is_missing=value) ) return result.rowcount def delete_references_by_ids(session: Session, reference_ids: list[str]) -> int: """Delete references by their IDs. Returns: Number of rows deleted """ if not reference_ids: return 0 total = 0 for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS): result = session.execute( sa.delete(AssetReference).where(AssetReference.id.in_(chunk)) ) total += result.rowcount return total def delete_orphaned_seed_asset(session: Session, asset_id: str) -> bool: """Delete a seed asset (hash is None) and its references. Returns: True if asset was deleted, False if not found or has a hash """ asset = session.get(Asset, asset_id) if not asset: return False if asset.hash is not None: return False session.execute( sa.delete(AssetReference).where(AssetReference.asset_id == asset_id) ) session.delete(asset) return True class UnenrichedReferenceRow(NamedTuple): """Row for references needing enrichment.""" reference_id: str asset_id: str file_path: str enrichment_level: int def get_unenriched_references( session: Session, prefixes: list[str], max_level: int = 0, limit: int = 1000, ) -> list[UnenrichedReferenceRow]: """Get references that need enrichment (enrichment_level <= max_level). Args: session: Database session prefixes: List of absolute directory prefixes to scan max_level: Maximum enrichment level to include (0=stubs, 1=metadata done) limit: Maximum number of rows to return Returns: List of unenriched reference rows with file paths """ if not prefixes: return [] conds = build_prefix_like_conditions(prefixes) query = ( sa.select( AssetReference.id, AssetReference.asset_id, AssetReference.file_path, AssetReference.enrichment_level, ) .where(AssetReference.file_path.isnot(None)) .where(AssetReference.deleted_at.is_(None)) .where(sa.or_(*conds)) .where(AssetReference.is_missing == False) # noqa: E712 .where(AssetReference.enrichment_level <= max_level) .order_by(AssetReference.id.asc()) .limit(limit) ) rows = session.execute(query).all() return [ UnenrichedReferenceRow( reference_id=row[0], asset_id=row[1], file_path=row[2], enrichment_level=row[3], ) for row in rows ] def bulk_update_enrichment_level( session: Session, reference_ids: list[str], level: int, ) -> int: """Update enrichment level for multiple references. Returns: Number of rows updated """ if not reference_ids: return 0 result = session.execute( sa.update(AssetReference) .where(AssetReference.id.in_(reference_ids)) .values(enrichment_level=level) ) return result.rowcount def bulk_insert_references_ignore_conflicts( session: Session, rows: list[dict], ) -> None: """Bulk insert reference rows with ON CONFLICT DO NOTHING on file_path. Each dict should have: id, asset_id, file_path, name, owner_id, mtime_ns, etc. The is_missing field is automatically set to False for new inserts. """ if not rows: return enriched_rows = [{**row, "is_missing": False} for row in rows] ins = sqlite.insert(AssetReference).on_conflict_do_nothing( index_elements=[AssetReference.file_path] ) for chunk in iter_chunks(enriched_rows, calculate_rows_per_statement(14)): session.execute(ins, chunk) def get_references_by_paths_and_asset_ids( session: Session, path_to_asset: dict[str, str], ) -> set[str]: """Query references to find paths where our asset_id won the insert. Args: path_to_asset: Mapping of file_path -> asset_id we tried to insert Returns: Set of file_paths where our asset_id is present """ if not path_to_asset: return set() pairs = list(path_to_asset.items()) winners: set[str] = set() # Each pair uses 2 bind params, so chunk at MAX_BIND_PARAMS // 2 for chunk in iter_chunks(pairs, MAX_BIND_PARAMS // 2): pairwise = sa.tuple_(AssetReference.file_path, AssetReference.asset_id).in_( chunk ) result = session.execute( select(AssetReference.file_path).where(pairwise) ) winners.update(result.scalars().all()) return winners def get_reference_ids_by_ids( session: Session, reference_ids: list[str], ) -> set[str]: """Query to find which reference IDs exist in the database.""" if not reference_ids: return set() found: set[str] = set() for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS): result = session.execute( select(AssetReference.id).where(AssetReference.id.in_(chunk)) ) found.update(result.scalars().all()) return found ================================================ FILE: app/assets/database/queries/common.py ================================================ """Shared utilities for database query modules.""" import os from decimal import Decimal from typing import Iterable, Sequence import sqlalchemy as sa from sqlalchemy import exists from app.assets.database.models import AssetReference, AssetReferenceMeta, AssetReferenceTag from app.assets.helpers import escape_sql_like_string, normalize_tags MAX_BIND_PARAMS = 800 def calculate_rows_per_statement(cols: int) -> int: """Calculate how many rows can fit in one statement given column count.""" return max(1, MAX_BIND_PARAMS // max(1, cols)) def iter_chunks(seq, n: int): """Yield successive n-sized chunks from seq.""" for i in range(0, len(seq), n): yield seq[i : i + n] def iter_row_chunks(rows: list[dict], cols_per_row: int) -> Iterable[list[dict]]: """Yield chunks of rows sized to fit within bind param limits.""" if not rows: return yield from iter_chunks(rows, calculate_rows_per_statement(cols_per_row)) def build_visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement: """Build owner visibility predicate for reads. Owner-less rows are visible to everyone. """ owner_id = (owner_id or "").strip() if owner_id == "": return AssetReference.owner_id == "" return AssetReference.owner_id.in_(["", owner_id]) def build_prefix_like_conditions( prefixes: list[str], ) -> list[sa.sql.ColumnElement]: """Build LIKE conditions for matching file paths under directory prefixes.""" conds = [] for p in prefixes: base = os.path.abspath(p) if not base.endswith(os.sep): base += os.sep escaped, esc = escape_sql_like_string(base) conds.append(AssetReference.file_path.like(escaped + "%", escape=esc)) return conds def apply_tag_filters( stmt: sa.sql.Select, include_tags: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, ) -> sa.sql.Select: """include_tags: every tag must be present; exclude_tags: none may be present.""" include_tags = normalize_tags(include_tags) exclude_tags = normalize_tags(exclude_tags) if include_tags: for tag_name in include_tags: stmt = stmt.where( exists().where( (AssetReferenceTag.asset_reference_id == AssetReference.id) & (AssetReferenceTag.tag_name == tag_name) ) ) if exclude_tags: stmt = stmt.where( ~exists().where( (AssetReferenceTag.asset_reference_id == AssetReference.id) & (AssetReferenceTag.tag_name.in_(exclude_tags)) ) ) return stmt def apply_metadata_filter( stmt: sa.sql.Select, metadata_filter: dict | None = None, ) -> sa.sql.Select: """Apply filters using asset_reference_meta projection table.""" if not metadata_filter: return stmt def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement: return sa.exists().where( AssetReferenceMeta.asset_reference_id == AssetReference.id, AssetReferenceMeta.key == key, *preds, ) def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement: if value is None: return sa.not_( sa.exists().where( AssetReferenceMeta.asset_reference_id == AssetReference.id, AssetReferenceMeta.key == key, ) ) if isinstance(value, bool): return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value)) if isinstance(value, (int, float, Decimal)): num = value if isinstance(value, Decimal) else Decimal(str(value)) return _exists_for_pred(key, AssetReferenceMeta.val_num == num) if isinstance(value, str): return _exists_for_pred(key, AssetReferenceMeta.val_str == value) return _exists_for_pred(key, AssetReferenceMeta.val_json == value) for k, v in metadata_filter.items(): if isinstance(v, list): ors = [_exists_clause_for_value(k, elem) for elem in v] if ors: stmt = stmt.where(sa.or_(*ors)) else: stmt = stmt.where(_exists_clause_for_value(k, v)) return stmt ================================================ FILE: app/assets/database/queries/tags.py ================================================ from dataclasses import dataclass from typing import Iterable, Sequence import sqlalchemy as sa from sqlalchemy import delete, func, select from sqlalchemy.dialects import sqlite from sqlalchemy.exc import IntegrityError from sqlalchemy.orm import Session from app.assets.database.models import ( Asset, AssetReference, AssetReferenceMeta, AssetReferenceTag, Tag, ) from app.assets.database.queries.common import ( apply_metadata_filter, apply_tag_filters, build_visible_owner_clause, iter_row_chunks, ) from app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags @dataclass(frozen=True) class AddTagsResult: added: list[str] already_present: list[str] total_tags: list[str] @dataclass(frozen=True) class RemoveTagsResult: removed: list[str] not_present: list[str] total_tags: list[str] @dataclass(frozen=True) class SetTagsResult: added: list[str] removed: list[str] total: list[str] def validate_tags_exist(session: Session, tags: list[str]) -> None: """Raise ValueError if any of the given tag names do not exist.""" existing_tag_names = set( name for (name,) in session.execute(select(Tag.name).where(Tag.name.in_(tags))).all() ) missing = [t for t in tags if t not in existing_tag_names] if missing: raise ValueError(f"Unknown tags: {missing}") def ensure_tags_exist( session: Session, names: Iterable[str], tag_type: str = "user" ) -> None: wanted = normalize_tags(list(names)) if not wanted: return rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))] ins = ( sqlite.insert(Tag) .values(rows) .on_conflict_do_nothing(index_elements=[Tag.name]) ) session.execute(ins) def get_reference_tags(session: Session, reference_id: str) -> list[str]: return [ tag_name for (tag_name,) in ( session.execute( select(AssetReferenceTag.tag_name) .where(AssetReferenceTag.asset_reference_id == reference_id) .order_by(AssetReferenceTag.tag_name.asc()) ) ).all() ] def set_reference_tags( session: Session, reference_id: str, tags: Sequence[str], origin: str = "manual", ) -> SetTagsResult: desired = normalize_tags(tags) current = set(get_reference_tags(session, reference_id)) to_add = [t for t in desired if t not in current] to_remove = [t for t in current if t not in desired] if to_add: ensure_tags_exist(session, to_add, tag_type="user") session.add_all( [ AssetReferenceTag( asset_reference_id=reference_id, tag_name=t, origin=origin, added_at=get_utc_now(), ) for t in to_add ] ) session.flush() if to_remove: session.execute( delete(AssetReferenceTag).where( AssetReferenceTag.asset_reference_id == reference_id, AssetReferenceTag.tag_name.in_(to_remove), ) ) session.flush() return SetTagsResult(added=sorted(to_add), removed=sorted(to_remove), total=sorted(desired)) def add_tags_to_reference( session: Session, reference_id: str, tags: Sequence[str], origin: str = "manual", create_if_missing: bool = True, reference_row: AssetReference | None = None, ) -> AddTagsResult: if not reference_row: ref = session.get(AssetReference, reference_id) if not ref: raise ValueError(f"AssetReference {reference_id} not found") norm = normalize_tags(tags) if not norm: total = get_reference_tags(session, reference_id=reference_id) return AddTagsResult(added=[], already_present=[], total_tags=total) if create_if_missing: ensure_tags_exist(session, norm, tag_type="user") current = set(get_reference_tags(session, reference_id)) want = set(norm) to_add = sorted(want - current) if to_add: with session.begin_nested() as nested: try: session.add_all( [ AssetReferenceTag( asset_reference_id=reference_id, tag_name=t, origin=origin, added_at=get_utc_now(), ) for t in to_add ] ) session.flush() except IntegrityError: nested.rollback() after = set(get_reference_tags(session, reference_id=reference_id)) return AddTagsResult( added=sorted(((after - current) & want)), already_present=sorted(want & current), total_tags=sorted(after), ) def remove_tags_from_reference( session: Session, reference_id: str, tags: Sequence[str], ) -> RemoveTagsResult: ref = session.get(AssetReference, reference_id) if not ref: raise ValueError(f"AssetReference {reference_id} not found") norm = normalize_tags(tags) if not norm: total = get_reference_tags(session, reference_id=reference_id) return RemoveTagsResult(removed=[], not_present=[], total_tags=total) existing = set(get_reference_tags(session, reference_id)) to_remove = sorted(set(t for t in norm if t in existing)) not_present = sorted(set(t for t in norm if t not in existing)) if to_remove: session.execute( delete(AssetReferenceTag).where( AssetReferenceTag.asset_reference_id == reference_id, AssetReferenceTag.tag_name.in_(to_remove), ) ) session.flush() total = get_reference_tags(session, reference_id=reference_id) return RemoveTagsResult(removed=to_remove, not_present=not_present, total_tags=total) def add_missing_tag_for_asset_id( session: Session, asset_id: str, origin: str = "automatic", ) -> None: select_rows = ( sa.select( AssetReference.id.label("asset_reference_id"), sa.literal("missing").label("tag_name"), sa.literal(origin).label("origin"), sa.literal(get_utc_now()).label("added_at"), ) .where(AssetReference.asset_id == asset_id) .where( sa.not_( sa.exists().where( (AssetReferenceTag.asset_reference_id == AssetReference.id) & (AssetReferenceTag.tag_name == "missing") ) ) ) ) session.execute( sqlite.insert(AssetReferenceTag) .from_select( ["asset_reference_id", "tag_name", "origin", "added_at"], select_rows, ) .on_conflict_do_nothing( index_elements=[ AssetReferenceTag.asset_reference_id, AssetReferenceTag.tag_name, ] ) ) def remove_missing_tag_for_asset_id( session: Session, asset_id: str, ) -> None: session.execute( sa.delete(AssetReferenceTag).where( AssetReferenceTag.asset_reference_id.in_( sa.select(AssetReference.id).where(AssetReference.asset_id == asset_id) ), AssetReferenceTag.tag_name == "missing", ) ) def list_tags_with_usage( session: Session, prefix: str | None = None, limit: int = 100, offset: int = 0, include_zero: bool = True, order: str = "count_desc", owner_id: str = "", ) -> tuple[list[tuple[str, str, int]], int]: counts_sq = ( select( AssetReferenceTag.tag_name.label("tag_name"), func.count(AssetReferenceTag.asset_reference_id).label("cnt"), ) .select_from(AssetReferenceTag) .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id) .where(build_visible_owner_clause(owner_id)) .where( sa.or_( AssetReference.is_missing == False, # noqa: E712 AssetReferenceTag.tag_name == "missing", ) ) .where(AssetReference.deleted_at.is_(None)) .group_by(AssetReferenceTag.tag_name) .subquery() ) q = ( select( Tag.name, Tag.tag_type, func.coalesce(counts_sq.c.cnt, 0).label("count"), ) .select_from(Tag) .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True) ) if prefix: escaped, esc = escape_sql_like_string(prefix.strip().lower()) q = q.where(Tag.name.like(escaped + "%", escape=esc)) if not include_zero: q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0) if order == "name_asc": q = q.order_by(Tag.name.asc()) else: q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc()) total_q = select(func.count()).select_from(Tag) if prefix: escaped, esc = escape_sql_like_string(prefix.strip().lower()) total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc)) if not include_zero: visible_tags_sq = ( select(AssetReferenceTag.tag_name) .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id) .where(build_visible_owner_clause(owner_id)) .where( sa.or_( AssetReference.is_missing == False, # noqa: E712 AssetReferenceTag.tag_name == "missing", ) ) .where(AssetReference.deleted_at.is_(None)) .group_by(AssetReferenceTag.tag_name) ) total_q = total_q.where(Tag.name.in_(visible_tags_sq)) rows = (session.execute(q.limit(limit).offset(offset))).all() total = (session.execute(total_q)).scalar_one() rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows] return rows_norm, int(total or 0) def list_tag_counts_for_filtered_assets( session: Session, owner_id: str = "", include_tags: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, name_contains: str | None = None, metadata_filter: dict | None = None, limit: int = 100, ) -> dict[str, int]: """Return tag counts for assets matching the given filters. Uses the same filtering logic as list_references_page but returns {tag_name: count} instead of paginated references. """ # Build a subquery of matching reference IDs ref_sq = ( select(AssetReference.id) .join(Asset, Asset.id == AssetReference.asset_id) .where(build_visible_owner_clause(owner_id)) .where(AssetReference.is_missing == False) # noqa: E712 .where(AssetReference.deleted_at.is_(None)) ) if name_contains: escaped, esc = escape_sql_like_string(name_contains) ref_sq = ref_sq.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc)) ref_sq = apply_tag_filters(ref_sq, include_tags, exclude_tags) ref_sq = apply_metadata_filter(ref_sq, metadata_filter) ref_sq = ref_sq.subquery() # Count tags across those references q = ( select( AssetReferenceTag.tag_name, func.count(AssetReferenceTag.asset_reference_id).label("cnt"), ) .where(AssetReferenceTag.asset_reference_id.in_(select(ref_sq.c.id))) .group_by(AssetReferenceTag.tag_name) .order_by(func.count(AssetReferenceTag.asset_reference_id).desc(), AssetReferenceTag.tag_name.asc()) .limit(limit) ) rows = session.execute(q).all() return {tag_name: int(cnt) for tag_name, cnt in rows} def bulk_insert_tags_and_meta( session: Session, tag_rows: list[dict], meta_rows: list[dict], ) -> None: """Batch insert into asset_reference_tags and asset_reference_meta. Uses ON CONFLICT DO NOTHING. Args: session: Database session tag_rows: Dicts with: asset_reference_id, tag_name, origin, added_at meta_rows: Dicts with: asset_reference_id, key, ordinal, val_* """ if tag_rows: ins_tags = sqlite.insert(AssetReferenceTag).on_conflict_do_nothing( index_elements=[ AssetReferenceTag.asset_reference_id, AssetReferenceTag.tag_name, ] ) for chunk in iter_row_chunks(tag_rows, cols_per_row=4): session.execute(ins_tags, chunk) if meta_rows: ins_meta = sqlite.insert(AssetReferenceMeta).on_conflict_do_nothing( index_elements=[ AssetReferenceMeta.asset_reference_id, AssetReferenceMeta.key, AssetReferenceMeta.ordinal, ] ) for chunk in iter_row_chunks(meta_rows, cols_per_row=7): session.execute(ins_meta, chunk) ================================================ FILE: app/assets/helpers.py ================================================ import os from datetime import datetime, timezone from typing import Sequence def select_best_live_path(states: Sequence) -> str: """ Return the best on-disk path among cache states: 1) Prefer a path that exists with needs_verify == False (already verified). 2) Otherwise, pick the first path that exists. 3) Otherwise return empty string. """ alive = [ s for s in states if getattr(s, "file_path", None) and os.path.isfile(s.file_path) ] if not alive: return "" for s in alive: if not getattr(s, "needs_verify", False): return s.file_path return alive[0].file_path def escape_sql_like_string(s: str, escape: str = "!") -> tuple[str, str]: """Escapes %, _ and the escape char in a LIKE prefix. Returns (escaped_prefix, escape_char). """ s = s.replace(escape, escape + escape) # escape the escape char first s = s.replace("%", escape + "%").replace("_", escape + "_") # escape LIKE wildcards return s, escape def get_utc_now() -> datetime: """Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC.""" return datetime.now(timezone.utc).replace(tzinfo=None) def normalize_tags(tags: list[str] | None) -> list[str]: """ Normalize a list of tags by: - Stripping whitespace and converting to lowercase. - Removing duplicates. """ return list(dict.fromkeys(t.strip().lower() for t in (tags or []) if (t or "").strip())) def validate_blake3_hash(s: str) -> str: """Validate and normalize a blake3 hash string. Returns canonical 'blake3:' or raises ValueError. """ s = s.strip().lower() if not s or ":" not in s: raise ValueError("hash must be 'blake3:'") algo, digest = s.split(":", 1) if ( algo != "blake3" or len(digest) != 64 or any(c for c in digest if c not in "0123456789abcdef") ): raise ValueError("hash must be 'blake3:'") return f"{algo}:{digest}" ================================================ FILE: app/assets/scanner.py ================================================ import logging import os from pathlib import Path from typing import Callable, Literal, TypedDict import folder_paths from app.assets.database.queries import ( add_missing_tag_for_asset_id, bulk_update_enrichment_level, bulk_update_is_missing, bulk_update_needs_verify, delete_orphaned_seed_asset, delete_references_by_ids, ensure_tags_exist, get_asset_by_hash, get_references_for_prefixes, get_unenriched_references, mark_references_missing_outside_prefixes, reassign_asset_references, remove_missing_tag_for_asset_id, set_reference_system_metadata, update_asset_hash_and_mime, ) from app.assets.services.bulk_ingest import ( SeedAssetSpec, batch_insert_seed_assets, ) from app.assets.services.file_utils import ( get_mtime_ns, is_visible, list_files_recursively, verify_file_unchanged, ) from app.assets.services.hashing import HashCheckpoint, compute_blake3_hash from app.assets.services.metadata_extract import extract_file_metadata from app.assets.services.path_utils import ( compute_relative_filename, get_comfy_models_folders, get_name_and_tags_from_asset_path, ) from app.database.db import create_session class _RefInfo(TypedDict): ref_id: str file_path: str exists: bool stat_unchanged: bool needs_verify: bool class _AssetAccumulator(TypedDict): hash: str | None size_db: int refs: list[_RefInfo] RootType = Literal["models", "input", "output"] def get_prefixes_for_root(root: RootType) -> list[str]: if root == "models": bases: list[str] = [] for _bucket, paths in get_comfy_models_folders(): bases.extend(paths) return [os.path.abspath(p) for p in bases] if root == "input": return [os.path.abspath(folder_paths.get_input_directory())] if root == "output": return [os.path.abspath(folder_paths.get_output_directory())] return [] def get_all_known_prefixes() -> list[str]: """Get all known asset prefixes across all root types.""" all_roots: tuple[RootType, ...] = ("models", "input", "output") return [p for root in all_roots for p in get_prefixes_for_root(root)] def collect_models_files() -> list[str]: out: list[str] = [] for folder_name, bases in get_comfy_models_folders(): rel_files = folder_paths.get_filename_list(folder_name) or [] for rel_path in rel_files: if not all(is_visible(part) for part in Path(rel_path).parts): continue abs_path = folder_paths.get_full_path(folder_name, rel_path) if not abs_path: continue abs_path = os.path.abspath(abs_path) allowed = False abs_p = Path(abs_path) for b in bases: if abs_p.is_relative_to(os.path.abspath(b)): allowed = True break if allowed: out.append(abs_path) return out def sync_references_with_filesystem( session, root: RootType, collect_existing_paths: bool = False, update_missing_tags: bool = False, ) -> set[str] | None: """Reconcile asset references with filesystem for a root. - Toggle needs_verify per reference using mtime/size stat check - For hashed assets with at least one stat-unchanged ref: delete stale missing refs - For seed assets with all refs missing: delete Asset and its references - Optionally add/remove 'missing' tags based on stat check in this root - Optionally return surviving absolute paths Args: session: Database session root: Root type to scan collect_existing_paths: If True, return set of surviving file paths update_missing_tags: If True, update 'missing' tags based on file status Returns: Set of surviving absolute paths if collect_existing_paths=True, else None """ prefixes = get_prefixes_for_root(root) if not prefixes: return set() if collect_existing_paths else None rows = get_references_for_prefixes( session, prefixes, include_missing=update_missing_tags ) by_asset: dict[str, _AssetAccumulator] = {} for row in rows: acc = by_asset.get(row.asset_id) if acc is None: acc = {"hash": row.asset_hash, "size_db": row.size_bytes, "refs": []} by_asset[row.asset_id] = acc stat_unchanged = False try: exists = True stat_unchanged = verify_file_unchanged( mtime_db=row.mtime_ns, size_db=acc["size_db"], stat_result=os.stat(row.file_path, follow_symlinks=True), ) except FileNotFoundError: exists = False except PermissionError: exists = True logging.debug("Permission denied accessing %s", row.file_path) except OSError as e: exists = False logging.debug("OSError checking %s: %s", row.file_path, e) acc["refs"].append( { "ref_id": row.reference_id, "file_path": row.file_path, "exists": exists, "stat_unchanged": stat_unchanged, "needs_verify": row.needs_verify, } ) to_set_verify: list[str] = [] to_clear_verify: list[str] = [] stale_ref_ids: list[str] = [] to_mark_missing: list[str] = [] to_clear_missing: list[str] = [] survivors: set[str] = set() for aid, acc in by_asset.items(): a_hash = acc["hash"] refs = acc["refs"] any_unchanged = any(r["stat_unchanged"] for r in refs) all_missing = all(not r["exists"] for r in refs) for r in refs: if not r["exists"]: to_mark_missing.append(r["ref_id"]) continue if r["stat_unchanged"]: to_clear_missing.append(r["ref_id"]) if r["needs_verify"]: to_clear_verify.append(r["ref_id"]) if not r["stat_unchanged"] and not r["needs_verify"]: to_set_verify.append(r["ref_id"]) if a_hash is None: if refs and all_missing: delete_orphaned_seed_asset(session, aid) else: for r in refs: if r["exists"]: survivors.add(os.path.abspath(r["file_path"])) continue if any_unchanged: for r in refs: if not r["exists"]: stale_ref_ids.append(r["ref_id"]) if update_missing_tags: try: remove_missing_tag_for_asset_id(session, asset_id=aid) except Exception as e: logging.warning( "Failed to remove missing tag for asset %s: %s", aid, e ) elif update_missing_tags: try: add_missing_tag_for_asset_id(session, asset_id=aid, origin="automatic") except Exception as e: logging.warning("Failed to add missing tag for asset %s: %s", aid, e) for r in refs: if r["exists"]: survivors.add(os.path.abspath(r["file_path"])) delete_references_by_ids(session, stale_ref_ids) stale_set = set(stale_ref_ids) to_mark_missing = [ref_id for ref_id in to_mark_missing if ref_id not in stale_set] bulk_update_is_missing(session, to_mark_missing, value=True) bulk_update_is_missing(session, to_clear_missing, value=False) bulk_update_needs_verify(session, to_set_verify, value=True) bulk_update_needs_verify(session, to_clear_verify, value=False) return survivors if collect_existing_paths else None def sync_root_safely(root: RootType) -> set[str]: """Sync a single root's references with the filesystem. Returns survivors (existing paths) or empty set on failure. """ try: with create_session() as sess: survivors = sync_references_with_filesystem( sess, root, collect_existing_paths=True, update_missing_tags=True, ) sess.commit() return survivors or set() except Exception as e: logging.exception("fast DB scan failed for %s: %s", root, e) return set() def mark_missing_outside_prefixes_safely(prefixes: list[str]) -> int: """Mark references as missing when outside the given prefixes. This is a non-destructive soft-delete. Returns count marked or 0 on failure. """ try: with create_session() as sess: count = mark_references_missing_outside_prefixes(sess, prefixes) sess.commit() return count except Exception as e: logging.exception("marking missing assets failed: %s", e) return 0 def collect_paths_for_roots(roots: tuple[RootType, ...]) -> list[str]: """Collect all file paths for the given roots.""" paths: list[str] = [] if "models" in roots: paths.extend(collect_models_files()) if "input" in roots: paths.extend(list_files_recursively(folder_paths.get_input_directory())) if "output" in roots: paths.extend(list_files_recursively(folder_paths.get_output_directory())) return paths def build_asset_specs( paths: list[str], existing_paths: set[str], enable_metadata_extraction: bool = True, compute_hashes: bool = False, ) -> tuple[list[SeedAssetSpec], set[str], int]: """Build asset specs from paths, returning (specs, tag_pool, skipped_count). Args: paths: List of file paths to process existing_paths: Set of paths that already exist in the database enable_metadata_extraction: If True, extract tier 1 & 2 metadata compute_hashes: If True, compute blake3 hashes (slow for large files) """ specs: list[SeedAssetSpec] = [] tag_pool: set[str] = set() skipped = 0 for p in paths: abs_p = os.path.abspath(p) if abs_p in existing_paths: skipped += 1 continue try: stat_p = os.stat(abs_p, follow_symlinks=True) except OSError: continue if not stat_p.st_size: continue name, tags = get_name_and_tags_from_asset_path(abs_p) rel_fname = compute_relative_filename(abs_p) # Extract metadata (tier 1: filesystem, tier 2: safetensors header) metadata = None if enable_metadata_extraction: metadata = extract_file_metadata( abs_p, stat_result=stat_p, relative_filename=rel_fname, ) # Compute hash if requested asset_hash: str | None = None if compute_hashes: try: digest, _ = compute_blake3_hash(abs_p) asset_hash = "blake3:" + digest except Exception as e: logging.warning("Failed to hash %s: %s", abs_p, e) mime_type = metadata.content_type if metadata else None specs.append( { "abs_path": abs_p, "size_bytes": stat_p.st_size, "mtime_ns": get_mtime_ns(stat_p), "info_name": name, "tags": tags, "fname": rel_fname, "metadata": metadata, "hash": asset_hash, "mime_type": mime_type, } ) tag_pool.update(tags) return specs, tag_pool, skipped def insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -> int: """Insert asset specs into database, returning count of created refs.""" if not specs: return 0 with create_session() as sess: if tag_pool: ensure_tags_exist(sess, tag_pool, tag_type="user") result = batch_insert_seed_assets(sess, specs=specs, owner_id="") sess.commit() return result.inserted_refs # Enrichment level constants ENRICHMENT_STUB = 0 # Fast scan: path, size, mtime only ENRICHMENT_METADATA = 1 # Metadata extracted (safetensors header, mime type) ENRICHMENT_HASHED = 2 # Hash computed (blake3) def get_unenriched_assets_for_roots( roots: tuple[RootType, ...], max_level: int = ENRICHMENT_STUB, limit: int = 1000, ) -> list: """Get assets that need enrichment for the given roots. Args: roots: Tuple of root types to scan max_level: Maximum enrichment level to include limit: Maximum number of rows to return Returns: List of UnenrichedReferenceRow """ prefixes: list[str] = [] for root in roots: prefixes.extend(get_prefixes_for_root(root)) if not prefixes: return [] with create_session() as sess: return get_unenriched_references( sess, prefixes, max_level=max_level, limit=limit ) def enrich_asset( session, file_path: str, reference_id: str, asset_id: str, extract_metadata: bool = True, compute_hash: bool = False, interrupt_check: Callable[[], bool] | None = None, hash_checkpoints: dict[str, HashCheckpoint] | None = None, ) -> int: """Enrich a single asset with metadata and/or hash. Args: session: Database session (caller manages lifecycle) file_path: Absolute path to the file reference_id: ID of the reference to update asset_id: ID of the asset to update (for mime_type and hash) extract_metadata: If True, extract safetensors header and mime type compute_hash: If True, compute blake3 hash interrupt_check: Optional non-blocking callable that returns True if the operation should be interrupted (e.g. paused or cancelled) hash_checkpoints: Optional dict for saving/restoring hash progress across interruptions, keyed by file path Returns: New enrichment level achieved """ new_level = ENRICHMENT_STUB try: stat_p = os.stat(file_path, follow_symlinks=True) except OSError: return new_level rel_fname = compute_relative_filename(file_path) mime_type: str | None = None metadata = None if extract_metadata: metadata = extract_file_metadata( file_path, stat_result=stat_p, relative_filename=rel_fname, ) if metadata: mime_type = metadata.content_type new_level = ENRICHMENT_METADATA full_hash: str | None = None if compute_hash: try: mtime_before = get_mtime_ns(stat_p) size_before = stat_p.st_size # Restore checkpoint if available and file unchanged checkpoint = None if hash_checkpoints is not None: checkpoint = hash_checkpoints.get(file_path) if checkpoint is not None: cur_stat = os.stat(file_path, follow_symlinks=True) if (checkpoint.mtime_ns != get_mtime_ns(cur_stat) or checkpoint.file_size != cur_stat.st_size): checkpoint = None hash_checkpoints.pop(file_path, None) else: mtime_before = get_mtime_ns(cur_stat) digest, new_checkpoint = compute_blake3_hash( file_path, interrupt_check=interrupt_check, checkpoint=checkpoint, ) if digest is None: # Interrupted — save checkpoint for later resumption if hash_checkpoints is not None and new_checkpoint is not None: new_checkpoint.mtime_ns = mtime_before new_checkpoint.file_size = size_before hash_checkpoints[file_path] = new_checkpoint return new_level # Completed — clear any saved checkpoint if hash_checkpoints is not None: hash_checkpoints.pop(file_path, None) stat_after = os.stat(file_path, follow_symlinks=True) mtime_after = get_mtime_ns(stat_after) if mtime_before != mtime_after: logging.warning("File modified during hashing, discarding hash: %s", file_path) else: full_hash = f"blake3:{digest}" metadata_ok = not extract_metadata or metadata is not None if metadata_ok: new_level = ENRICHMENT_HASHED except Exception as e: logging.warning("Failed to hash %s: %s", file_path, e) if extract_metadata and metadata: system_metadata = metadata.to_user_metadata() set_reference_system_metadata(session, reference_id, system_metadata) if full_hash: existing = get_asset_by_hash(session, full_hash) if existing and existing.id != asset_id: reassign_asset_references(session, asset_id, existing.id, reference_id) delete_orphaned_seed_asset(session, asset_id) if mime_type: update_asset_hash_and_mime(session, existing.id, mime_type=mime_type) else: update_asset_hash_and_mime(session, asset_id, full_hash, mime_type) elif mime_type: update_asset_hash_and_mime(session, asset_id, mime_type=mime_type) bulk_update_enrichment_level(session, [reference_id], new_level) session.commit() return new_level def enrich_assets_batch( rows: list, extract_metadata: bool = True, compute_hash: bool = False, interrupt_check: Callable[[], bool] | None = None, hash_checkpoints: dict[str, HashCheckpoint] | None = None, ) -> tuple[int, list[str]]: """Enrich a batch of assets. Uses a single DB session for the entire batch, committing after each individual asset to avoid long-held transactions while eliminating per-asset session creation overhead. Args: rows: List of UnenrichedReferenceRow from get_unenriched_assets_for_roots extract_metadata: If True, extract metadata for each asset compute_hash: If True, compute hash for each asset interrupt_check: Optional non-blocking callable that returns True if the operation should be interrupted (e.g. paused or cancelled) hash_checkpoints: Optional dict for saving/restoring hash progress across interruptions, keyed by file path Returns: Tuple of (enriched_count, failed_reference_ids) """ enriched = 0 failed_ids: list[str] = [] with create_session() as sess: for row in rows: if interrupt_check is not None and interrupt_check(): break try: new_level = enrich_asset( sess, file_path=row.file_path, reference_id=row.reference_id, asset_id=row.asset_id, extract_metadata=extract_metadata, compute_hash=compute_hash, interrupt_check=interrupt_check, hash_checkpoints=hash_checkpoints, ) if new_level > row.enrichment_level: enriched += 1 else: failed_ids.append(row.reference_id) except Exception as e: logging.warning("Failed to enrich %s: %s", row.file_path, e) sess.rollback() failed_ids.append(row.reference_id) return enriched, failed_ids ================================================ FILE: app/assets/seeder.py ================================================ """Background asset seeder with thread management and cancellation support.""" import logging import os import threading import time from dataclasses import dataclass, field from enum import Enum from typing import Callable from app.assets.scanner import ( ENRICHMENT_METADATA, ENRICHMENT_STUB, RootType, build_asset_specs, collect_paths_for_roots, enrich_assets_batch, get_all_known_prefixes, get_prefixes_for_root, get_unenriched_assets_for_roots, insert_asset_specs, mark_missing_outside_prefixes_safely, sync_root_safely, ) from app.database.db import dependencies_available class ScanInProgressError(Exception): """Raised when an operation cannot proceed because a scan is running.""" class State(Enum): """Seeder state machine states.""" IDLE = "IDLE" RUNNING = "RUNNING" PAUSED = "PAUSED" CANCELLING = "CANCELLING" class ScanPhase(Enum): """Scan phase options.""" FAST = "fast" # Phase 1: filesystem only (stubs) ENRICH = "enrich" # Phase 2: metadata + hash FULL = "full" # Both phases sequentially @dataclass class Progress: """Progress information for a scan operation.""" scanned: int = 0 total: int = 0 created: int = 0 skipped: int = 0 @dataclass class ScanStatus: """Current status of the asset seeder.""" state: State progress: Progress | None errors: list[str] = field(default_factory=list) ProgressCallback = Callable[[Progress], None] class _AssetSeeder: """Background asset scanning manager. Spawns ephemeral daemon threads for scanning. Each scan creates a new thread that exits when complete. Use the module-level ``asset_seeder`` instance. """ def __init__(self) -> None: self._lock = threading.Lock() self._state = State.IDLE self._progress: Progress | None = None self._last_progress: Progress | None = None self._errors: list[str] = [] self._thread: threading.Thread | None = None self._cancel_event = threading.Event() self._run_gate = threading.Event() self._run_gate.set() # Start unpaused (set = running, clear = paused) self._roots: tuple[RootType, ...] = () self._phase: ScanPhase = ScanPhase.FULL self._compute_hashes: bool = False self._prune_first: bool = False self._progress_callback: ProgressCallback | None = None self._disabled: bool = False def disable(self) -> None: """Disable the asset seeder, preventing any scans from starting.""" self._disabled = True logging.info("Asset seeder disabled") def is_disabled(self) -> bool: """Check if the asset seeder is disabled.""" return self._disabled def start( self, roots: tuple[RootType, ...] = ("models", "input", "output"), phase: ScanPhase = ScanPhase.FULL, progress_callback: ProgressCallback | None = None, prune_first: bool = False, compute_hashes: bool = False, ) -> bool: """Start a background scan for the given roots. Args: roots: Tuple of root types to scan (models, input, output) phase: Scan phase to run (FAST, ENRICH, or FULL for both) progress_callback: Optional callback called with progress updates prune_first: If True, prune orphaned assets before scanning compute_hashes: If True, compute blake3 hashes (slow) Returns: True if scan was started, False if already running """ if self._disabled: logging.debug("Asset seeder is disabled, skipping start") return False logging.info("Seeder start (roots=%s, phase=%s)", roots, phase.value) with self._lock: if self._state != State.IDLE: logging.info("Asset seeder already running, skipping start") return False self._state = State.RUNNING self._progress = Progress() self._errors = [] self._roots = roots self._phase = phase self._prune_first = prune_first self._compute_hashes = compute_hashes self._progress_callback = progress_callback self._cancel_event.clear() self._run_gate.set() # Ensure unpaused when starting self._thread = threading.Thread( target=self._run_scan, name="_AssetSeeder", daemon=True, ) self._thread.start() return True def start_fast( self, roots: tuple[RootType, ...] = ("models", "input", "output"), progress_callback: ProgressCallback | None = None, prune_first: bool = False, ) -> bool: """Start a fast scan (phase 1 only) - creates stub records. Args: roots: Tuple of root types to scan progress_callback: Optional callback for progress updates prune_first: If True, prune orphaned assets before scanning Returns: True if scan was started, False if already running """ return self.start( roots=roots, phase=ScanPhase.FAST, progress_callback=progress_callback, prune_first=prune_first, compute_hashes=False, ) def start_enrich( self, roots: tuple[RootType, ...] = ("models", "input", "output"), progress_callback: ProgressCallback | None = None, compute_hashes: bool = False, ) -> bool: """Start an enrichment scan (phase 2 only) - extracts metadata and hashes. Args: roots: Tuple of root types to scan progress_callback: Optional callback for progress updates compute_hashes: If True, compute blake3 hashes Returns: True if scan was started, False if already running """ return self.start( roots=roots, phase=ScanPhase.ENRICH, progress_callback=progress_callback, prune_first=False, compute_hashes=compute_hashes, ) def cancel(self) -> bool: """Request cancellation of the current scan. Returns: True if cancellation was requested, False if not running or paused """ with self._lock: if self._state not in (State.RUNNING, State.PAUSED): return False logging.info("Asset seeder cancelling (was %s)", self._state.value) self._state = State.CANCELLING self._cancel_event.set() self._run_gate.set() # Unblock if paused so thread can exit return True def stop(self) -> bool: """Stop the current scan (alias for cancel). Returns: True if stop was requested, False if not running """ return self.cancel() def pause(self) -> bool: """Pause the current scan. The scan will complete its current batch before pausing. Returns: True if pause was requested, False if not running """ with self._lock: if self._state != State.RUNNING: return False logging.info("Asset seeder pausing") self._state = State.PAUSED self._run_gate.clear() return True def resume(self) -> bool: """Resume a paused scan. This is a noop if the scan is not in the PAUSED state Returns: True if resumed, False if not paused """ with self._lock: if self._state != State.PAUSED: return False logging.info("Asset seeder resuming") self._state = State.RUNNING self._run_gate.set() self._emit_event("assets.seed.resumed", {}) return True def restart( self, roots: tuple[RootType, ...] | None = None, phase: ScanPhase | None = None, progress_callback: ProgressCallback | None = None, prune_first: bool | None = None, compute_hashes: bool | None = None, timeout: float = 5.0, ) -> bool: """Cancel any running scan and start a new one. Args: roots: Roots to scan (defaults to previous roots) phase: Scan phase (defaults to previous phase) progress_callback: Progress callback (defaults to previous) prune_first: Prune before scan (defaults to previous) compute_hashes: Compute hashes (defaults to previous) timeout: Max seconds to wait for current scan to stop Returns: True if new scan was started, False if failed to stop previous """ logging.info("Asset seeder restart requested") with self._lock: prev_roots = self._roots prev_phase = self._phase prev_callback = self._progress_callback prev_prune = self._prune_first prev_hashes = self._compute_hashes self.cancel() if not self.wait(timeout=timeout): return False cb = progress_callback if progress_callback is not None else prev_callback return self.start( roots=roots if roots is not None else prev_roots, phase=phase if phase is not None else prev_phase, progress_callback=cb, prune_first=prune_first if prune_first is not None else prev_prune, compute_hashes=( compute_hashes if compute_hashes is not None else prev_hashes ), ) def wait(self, timeout: float | None = None) -> bool: """Wait for the current scan to complete. Args: timeout: Maximum seconds to wait, or None for no timeout Returns: True if scan completed, False if timeout expired or no scan running """ with self._lock: thread = self._thread if thread is None: return True thread.join(timeout=timeout) return not thread.is_alive() def get_status(self) -> ScanStatus: """Get the current status and progress of the seeder.""" with self._lock: src = self._progress or self._last_progress return ScanStatus( state=self._state, progress=Progress( scanned=src.scanned, total=src.total, created=src.created, skipped=src.skipped, ) if src else None, errors=list(self._errors), ) def shutdown(self, timeout: float = 5.0) -> None: """Gracefully shutdown: cancel any running scan and wait for thread. Args: timeout: Maximum seconds to wait for thread to exit """ self.cancel() self.wait(timeout=timeout) with self._lock: self._thread = None def mark_missing_outside_prefixes(self) -> int: """Mark references as missing when outside all known root prefixes. This is a non-destructive soft-delete operation. Assets and their metadata are preserved, but references are flagged as missing. They can be restored if the file reappears in a future scan. This operation is decoupled from scanning to prevent partial scans from accidentally marking assets belonging to other roots. Should be called explicitly when cleanup is desired, typically after a full scan of all roots or during maintenance. Returns: Number of references marked as missing Raises: ScanInProgressError: If a scan is currently running """ with self._lock: if self._state != State.IDLE: raise ScanInProgressError( "Cannot mark missing assets while scan is running" ) self._state = State.RUNNING try: if not dependencies_available(): logging.warning( "Database dependencies not available, skipping mark missing" ) return 0 all_prefixes = get_all_known_prefixes() marked = mark_missing_outside_prefixes_safely(all_prefixes) if marked > 0: logging.info("Marked %d references as missing", marked) return marked finally: with self._lock: self._last_progress = self._progress self._state = State.IDLE self._progress = None def _is_cancelled(self) -> bool: """Check if cancellation has been requested.""" return self._cancel_event.is_set() def _is_paused_or_cancelled(self) -> bool: """Non-blocking check: True if paused or cancelled. Use as interrupt_check for I/O-bound work (e.g. hashing) so that file handles are released immediately on pause rather than held open while blocked. The caller is responsible for blocking on _check_pause_and_cancel() afterward. """ return not self._run_gate.is_set() or self._cancel_event.is_set() def _check_pause_and_cancel(self) -> bool: """Block while paused, then check if cancelled. Call this at checkpoint locations in scan loops. It will: 1. Block indefinitely while paused (until resume or cancel) 2. Return True if cancelled, False to continue Returns: True if scan should stop, False to continue """ if not self._run_gate.is_set(): self._emit_event("assets.seed.paused", {}) self._run_gate.wait() # Blocks if paused return self._is_cancelled() def _emit_event(self, event_type: str, data: dict) -> None: """Emit a WebSocket event if server is available.""" try: from server import PromptServer if hasattr(PromptServer, "instance") and PromptServer.instance: PromptServer.instance.send_sync(event_type, data) except Exception: pass def _update_progress( self, scanned: int | None = None, total: int | None = None, created: int | None = None, skipped: int | None = None, ) -> None: """Update progress counters (thread-safe).""" callback: ProgressCallback | None = None progress: Progress | None = None with self._lock: if self._progress is None: return if scanned is not None: self._progress.scanned = scanned if total is not None: self._progress.total = total if created is not None: self._progress.created = created if skipped is not None: self._progress.skipped = skipped if self._progress_callback: callback = self._progress_callback progress = Progress( scanned=self._progress.scanned, total=self._progress.total, created=self._progress.created, skipped=self._progress.skipped, ) if callback and progress: try: callback(progress) except Exception: pass _MAX_ERRORS = 200 def _add_error(self, message: str) -> None: """Add an error message (thread-safe), capped at _MAX_ERRORS.""" with self._lock: if len(self._errors) < self._MAX_ERRORS: self._errors.append(message) def _log_scan_config(self, roots: tuple[RootType, ...]) -> None: """Log the directories that will be scanned.""" import folder_paths for root in roots: if root == "models": logging.info( "Asset scan [models] directory: %s", os.path.abspath(folder_paths.models_dir), ) else: prefixes = get_prefixes_for_root(root) if prefixes: logging.info("Asset scan [%s] directories: %s", root, prefixes) def _run_scan(self) -> None: """Main scan loop running in background thread.""" t_start = time.perf_counter() roots = self._roots phase = self._phase cancelled = False total_created = 0 total_enriched = 0 skipped_existing = 0 total_paths = 0 try: if not dependencies_available(): self._add_error("Database dependencies not available") self._emit_event( "assets.seed.error", {"message": "Database dependencies not available"}, ) return if self._prune_first: all_prefixes = get_all_known_prefixes() marked = mark_missing_outside_prefixes_safely(all_prefixes) if marked > 0: logging.info("Marked %d refs as missing before scan", marked) if self._check_pause_and_cancel(): logging.info("Asset scan cancelled after pruning phase") cancelled = True return self._log_scan_config(roots) # Phase 1: Fast scan (stub records) if phase in (ScanPhase.FAST, ScanPhase.FULL): created, skipped, paths = self._run_fast_phase(roots) total_created, skipped_existing, total_paths = created, skipped, paths if self._check_pause_and_cancel(): cancelled = True return self._emit_event( "assets.seed.fast_complete", { "roots": list(roots), "created": total_created, "skipped": skipped_existing, "total": total_paths, }, ) # Phase 2: Enrichment scan (metadata + hashes) if phase in (ScanPhase.ENRICH, ScanPhase.FULL): if self._check_pause_and_cancel(): cancelled = True return enrich_cancelled, total_enriched = self._run_enrich_phase(roots) if enrich_cancelled: cancelled = True return self._emit_event( "assets.seed.enrich_complete", { "roots": list(roots), "enriched": total_enriched, }, ) elapsed = time.perf_counter() - t_start logging.info( "Scan(%s, %s) done %.3fs: created=%d enriched=%d skipped=%d", roots, phase.value, elapsed, total_created, total_enriched, skipped_existing, ) self._emit_event( "assets.seed.completed", { "phase": phase.value, "total": total_paths, "created": total_created, "enriched": total_enriched, "skipped": skipped_existing, "elapsed": round(elapsed, 3), }, ) except Exception as e: self._add_error(f"Scan failed: {e}") logging.exception("Asset scan failed") self._emit_event("assets.seed.error", {"message": str(e)}) finally: if cancelled: self._emit_event( "assets.seed.cancelled", { "scanned": self._progress.scanned if self._progress else 0, "total": total_paths, "created": total_created, }, ) with self._lock: self._last_progress = self._progress self._state = State.IDLE self._progress = None def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, int, int]: """Run phase 1: fast scan to create stub records. Returns: Tuple of (total_created, skipped_existing, total_paths) """ t_fast_start = time.perf_counter() total_created = 0 skipped_existing = 0 existing_paths: set[str] = set() t_sync = time.perf_counter() for r in roots: if self._check_pause_and_cancel(): return total_created, skipped_existing, 0 existing_paths.update(sync_root_safely(r)) logging.debug( "Fast scan: sync_root phase took %.3fs (%d existing paths)", time.perf_counter() - t_sync, len(existing_paths), ) if self._check_pause_and_cancel(): return total_created, skipped_existing, 0 t_collect = time.perf_counter() paths = collect_paths_for_roots(roots) logging.debug( "Fast scan: collect_paths took %.3fs (%d paths found)", time.perf_counter() - t_collect, len(paths), ) total_paths = len(paths) self._update_progress(total=total_paths) self._emit_event( "assets.seed.started", {"roots": list(roots), "total": total_paths, "phase": "fast"}, ) # Use stub specs (no metadata extraction, no hashing) t_specs = time.perf_counter() specs, tag_pool, skipped_existing = build_asset_specs( paths, existing_paths, enable_metadata_extraction=False, compute_hashes=False, ) logging.debug( "Fast scan: build_asset_specs took %.3fs (%d specs, %d skipped)", time.perf_counter() - t_specs, len(specs), skipped_existing, ) self._update_progress(skipped=skipped_existing) if self._check_pause_and_cancel(): return total_created, skipped_existing, total_paths batch_size = 500 last_progress_time = time.perf_counter() progress_interval = 1.0 for i in range(0, len(specs), batch_size): if self._check_pause_and_cancel(): logging.info( "Fast scan cancelled after %d/%d files (created=%d)", i, len(specs), total_created, ) return total_created, skipped_existing, total_paths batch = specs[i : i + batch_size] batch_tags = {t for spec in batch for t in spec["tags"]} try: created = insert_asset_specs(batch, batch_tags) total_created += created except Exception as e: self._add_error(f"Batch insert failed at offset {i}: {e}") logging.exception("Batch insert failed at offset %d", i) scanned = i + len(batch) now = time.perf_counter() self._update_progress(scanned=scanned, created=total_created) if now - last_progress_time >= progress_interval: self._emit_event( "assets.seed.progress", { "phase": "fast", "scanned": scanned, "total": len(specs), "created": total_created, }, ) last_progress_time = now self._update_progress(scanned=len(specs), created=total_created) logging.info( "Fast scan complete: %.3fs total (created=%d, skipped=%d, total_paths=%d)", time.perf_counter() - t_fast_start, total_created, skipped_existing, total_paths, ) return total_created, skipped_existing, total_paths def _run_enrich_phase(self, roots: tuple[RootType, ...]) -> tuple[bool, int]: """Run phase 2: enrich existing records with metadata and hashes. Returns: Tuple of (cancelled, total_enriched) """ total_enriched = 0 batch_size = 100 last_progress_time = time.perf_counter() progress_interval = 1.0 # Get the target enrichment level based on compute_hashes if not self._compute_hashes: target_max_level = ENRICHMENT_STUB else: target_max_level = ENRICHMENT_METADATA self._emit_event( "assets.seed.started", {"roots": list(roots), "phase": "enrich"}, ) skip_ids: set[str] = set() consecutive_empty = 0 max_consecutive_empty = 3 # Hash checkpoints survive across batches so interrupted hashes # can be resumed without re-reading the entire file. hash_checkpoints: dict[str, object] = {} while True: if self._check_pause_and_cancel(): logging.info("Enrich scan cancelled after %d assets", total_enriched) return True, total_enriched # Fetch next batch of unenriched assets unenriched = get_unenriched_assets_for_roots( roots, max_level=target_max_level, limit=batch_size, ) # Filter out previously failed references if skip_ids: unenriched = [r for r in unenriched if r.reference_id not in skip_ids] if not unenriched: break enriched, failed_ids = enrich_assets_batch( unenriched, extract_metadata=True, compute_hash=self._compute_hashes, interrupt_check=self._is_paused_or_cancelled, hash_checkpoints=hash_checkpoints, ) total_enriched += enriched skip_ids.update(failed_ids) if enriched == 0: consecutive_empty += 1 if consecutive_empty >= max_consecutive_empty: logging.warning( "Enrich phase stopping: %d consecutive batches with no progress (%d skipped)", consecutive_empty, len(skip_ids), ) break else: consecutive_empty = 0 now = time.perf_counter() if now - last_progress_time >= progress_interval: self._emit_event( "assets.seed.progress", { "phase": "enrich", "enriched": total_enriched, }, ) last_progress_time = now return False, total_enriched asset_seeder = _AssetSeeder() ================================================ FILE: app/assets/services/__init__.py ================================================ from app.assets.services.asset_management import ( asset_exists, delete_asset_reference, get_asset_by_hash, get_asset_detail, list_assets_page, resolve_asset_for_download, set_asset_preview, update_asset_metadata, ) from app.assets.services.bulk_ingest import ( BulkInsertResult, batch_insert_seed_assets, cleanup_unreferenced_assets, ) from app.assets.services.file_utils import ( get_mtime_ns, get_size_and_mtime_ns, list_files_recursively, verify_file_unchanged, ) from app.assets.services.ingest import ( DependencyMissingError, HashMismatchError, create_from_hash, upload_from_temp_path, ) from app.assets.database.queries import ( AddTagsResult, RemoveTagsResult, ) from app.assets.services.schemas import ( AssetData, AssetDetailResult, AssetSummaryData, DownloadResolutionResult, IngestResult, ListAssetsResult, ReferenceData, RegisterAssetResult, TagUsage, UploadResult, UserMetadata, ) from app.assets.services.tagging import ( apply_tags, list_tags, remove_tags, ) __all__ = [ "AddTagsResult", "AssetData", "AssetDetailResult", "AssetSummaryData", "ReferenceData", "BulkInsertResult", "DependencyMissingError", "DownloadResolutionResult", "HashMismatchError", "IngestResult", "ListAssetsResult", "RegisterAssetResult", "RemoveTagsResult", "TagUsage", "UploadResult", "UserMetadata", "apply_tags", "asset_exists", "batch_insert_seed_assets", "create_from_hash", "delete_asset_reference", "get_asset_by_hash", "get_asset_detail", "get_mtime_ns", "get_size_and_mtime_ns", "list_assets_page", "list_files_recursively", "list_tags", "cleanup_unreferenced_assets", "remove_tags", "resolve_asset_for_download", "set_asset_preview", "update_asset_metadata", "upload_from_temp_path", "verify_file_unchanged", ] ================================================ FILE: app/assets/services/asset_management.py ================================================ import contextlib import mimetypes import os from typing import Sequence from app.assets.database.models import Asset from app.assets.database.queries import ( asset_exists_by_hash, reference_exists_for_asset_id, delete_reference_by_id, fetch_reference_and_asset, soft_delete_reference_by_id, fetch_reference_asset_and_tags, get_asset_by_hash as queries_get_asset_by_hash, get_reference_by_id, get_reference_with_owner_check, list_references_page, list_all_file_paths_by_asset_id, list_references_by_asset_id, set_reference_metadata, set_reference_preview, set_reference_tags, update_asset_hash_and_mime, update_reference_access_time, update_reference_name, update_reference_updated_at, ) from app.assets.helpers import select_best_live_path from app.assets.services.path_utils import compute_relative_filename from app.assets.services.schemas import ( AssetData, AssetDetailResult, AssetSummaryData, DownloadResolutionResult, ListAssetsResult, UserMetadata, extract_asset_data, extract_reference_data, ) from app.database.db import create_session def get_asset_detail( reference_id: str, owner_id: str = "", ) -> AssetDetailResult | None: with create_session() as session: result = fetch_reference_asset_and_tags( session, reference_id=reference_id, owner_id=owner_id, ) if not result: return None ref, asset, tags = result return AssetDetailResult( ref=extract_reference_data(ref), asset=extract_asset_data(asset), tags=tags, ) def update_asset_metadata( reference_id: str, name: str | None = None, tags: Sequence[str] | None = None, user_metadata: UserMetadata = None, tag_origin: str = "manual", owner_id: str = "", mime_type: str | None = None, preview_id: str | None = None, ) -> AssetDetailResult: with create_session() as session: ref = get_reference_with_owner_check(session, reference_id, owner_id) touched = False if name is not None and name != ref.name: update_reference_name(session, reference_id=reference_id, name=name) touched = True computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None new_meta: dict | None = None if user_metadata is not None: new_meta = dict(user_metadata) elif computed_filename: current_meta = ref.user_metadata or {} if current_meta.get("filename") != computed_filename: new_meta = dict(current_meta) if new_meta is not None: if computed_filename: new_meta["filename"] = computed_filename set_reference_metadata( session, reference_id=reference_id, user_metadata=new_meta ) touched = True if tags is not None: set_reference_tags( session, reference_id=reference_id, tags=tags, origin=tag_origin, ) touched = True if mime_type is not None: updated = update_asset_hash_and_mime( session, asset_id=ref.asset_id, mime_type=mime_type ) if updated: touched = True if preview_id is not None: set_reference_preview( session, reference_id=reference_id, preview_reference_id=preview_id, ) touched = True if touched and user_metadata is None: update_reference_updated_at(session, reference_id=reference_id) result = fetch_reference_asset_and_tags( session, reference_id=reference_id, owner_id=owner_id, ) if not result: raise RuntimeError("State changed during update") ref, asset, tag_list = result detail = AssetDetailResult( ref=extract_reference_data(ref), asset=extract_asset_data(asset), tags=tag_list, ) session.commit() return detail def delete_asset_reference( reference_id: str, owner_id: str, delete_content_if_orphan: bool = True, ) -> bool: with create_session() as session: if not delete_content_if_orphan: # Soft delete: mark the reference as deleted but keep everything deleted = soft_delete_reference_by_id( session, reference_id=reference_id, owner_id=owner_id ) session.commit() return deleted ref_row = get_reference_by_id(session, reference_id=reference_id) asset_id = ref_row.asset_id if ref_row else None file_path = ref_row.file_path if ref_row else None deleted = delete_reference_by_id( session, reference_id=reference_id, owner_id=owner_id ) if not deleted: session.commit() return False if not asset_id: session.commit() return True still_exists = reference_exists_for_asset_id(session, asset_id=asset_id) if still_exists: session.commit() return True # Orphaned asset - gather ALL file paths (including # soft-deleted / missing refs) so their on-disk files get cleaned up. file_paths = list_all_file_paths_by_asset_id(session, asset_id=asset_id) # Also include the just-deleted file path if file_path: file_paths.append(file_path) asset_row = session.get(Asset, asset_id) if asset_row is not None: session.delete(asset_row) session.commit() # Delete files after commit for p in file_paths: with contextlib.suppress(Exception): if p and os.path.isfile(p): os.remove(p) return True def set_asset_preview( reference_id: str, preview_reference_id: str | None = None, owner_id: str = "", ) -> AssetDetailResult: with create_session() as session: get_reference_with_owner_check(session, reference_id, owner_id) set_reference_preview( session, reference_id=reference_id, preview_reference_id=preview_reference_id, ) result = fetch_reference_asset_and_tags( session, reference_id=reference_id, owner_id=owner_id ) if not result: raise RuntimeError("State changed during preview update") ref, asset, tags = result detail = AssetDetailResult( ref=extract_reference_data(ref), asset=extract_asset_data(asset), tags=tags, ) session.commit() return detail def asset_exists(asset_hash: str) -> bool: with create_session() as session: return asset_exists_by_hash(session, asset_hash=asset_hash) def get_asset_by_hash(asset_hash: str) -> AssetData | None: with create_session() as session: asset = queries_get_asset_by_hash(session, asset_hash=asset_hash) return extract_asset_data(asset) def list_assets_page( owner_id: str = "", include_tags: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, name_contains: str | None = None, metadata_filter: dict | None = None, limit: int = 20, offset: int = 0, sort: str = "created_at", order: str = "desc", ) -> ListAssetsResult: with create_session() as session: refs, tag_map, total = list_references_page( session, owner_id=owner_id, include_tags=include_tags, exclude_tags=exclude_tags, name_contains=name_contains, metadata_filter=metadata_filter, limit=limit, offset=offset, sort=sort, order=order, ) items: list[AssetSummaryData] = [] for ref in refs: items.append( AssetSummaryData( ref=extract_reference_data(ref), asset=extract_asset_data(ref.asset), tags=tag_map.get(ref.id, []), ) ) return ListAssetsResult(items=items, total=total) def resolve_hash_to_path( asset_hash: str, owner_id: str = "", ) -> DownloadResolutionResult | None: """Resolve a blake3 hash to an on-disk file path. Only references visible to *owner_id* are considered (owner-less references are always visible). Returns a DownloadResolutionResult with abs_path, content_type, and download_name, or None if no asset or live path is found. """ with create_session() as session: asset = queries_get_asset_by_hash(session, asset_hash) if not asset: return None refs = list_references_by_asset_id(session, asset_id=asset.id) visible = [ r for r in refs if r.owner_id == "" or r.owner_id == owner_id ] abs_path = select_best_live_path(visible) if not abs_path: return None display_name = os.path.basename(abs_path) for ref in visible: if ref.file_path == abs_path and ref.name: display_name = ref.name break ctype = ( asset.mime_type or mimetypes.guess_type(display_name)[0] or "application/octet-stream" ) return DownloadResolutionResult( abs_path=abs_path, content_type=ctype, download_name=display_name, ) def resolve_asset_for_download( reference_id: str, owner_id: str = "", ) -> DownloadResolutionResult: with create_session() as session: pair = fetch_reference_and_asset( session, reference_id=reference_id, owner_id=owner_id ) if not pair: raise ValueError(f"AssetReference {reference_id} not found") ref, asset = pair # For references with file_path, use that directly if ref.file_path and os.path.isfile(ref.file_path): abs_path = ref.file_path else: # For API-created refs without file_path, find a path from other refs refs = list_references_by_asset_id(session, asset_id=asset.id) abs_path = select_best_live_path(refs) if not abs_path: raise FileNotFoundError( f"No live path for AssetReference {reference_id} " f"(asset id={asset.id}, name={ref.name})" ) # Capture ORM attributes before commit (commit expires loaded objects) ref_name = ref.name asset_mime = asset.mime_type update_reference_access_time(session, reference_id=reference_id) session.commit() ctype = ( asset_mime or mimetypes.guess_type(ref_name or abs_path)[0] or "application/octet-stream" ) download_name = ref_name or os.path.basename(abs_path) return DownloadResolutionResult( abs_path=abs_path, content_type=ctype, download_name=download_name, ) ================================================ FILE: app/assets/services/bulk_ingest.py ================================================ from __future__ import annotations import os import uuid from dataclasses import dataclass from datetime import datetime from typing import TYPE_CHECKING, Any, TypedDict from sqlalchemy.orm import Session from app.assets.database.queries import ( bulk_insert_assets, bulk_insert_references_ignore_conflicts, bulk_insert_tags_and_meta, delete_assets_by_ids, get_existing_asset_ids, get_reference_ids_by_ids, get_references_by_paths_and_asset_ids, get_unreferenced_unhashed_asset_ids, restore_references_by_paths, ) from app.assets.helpers import get_utc_now if TYPE_CHECKING: from app.assets.services.metadata_extract import ExtractedMetadata class SeedAssetSpec(TypedDict): """Spec for seeding an asset from filesystem.""" abs_path: str size_bytes: int mtime_ns: int info_name: str tags: list[str] fname: str metadata: ExtractedMetadata | None hash: str | None mime_type: str | None class AssetRow(TypedDict): """Row data for inserting an Asset.""" id: str hash: str | None size_bytes: int mime_type: str | None created_at: datetime class ReferenceRow(TypedDict): """Row data for inserting an AssetReference.""" id: str asset_id: str file_path: str mtime_ns: int owner_id: str name: str preview_id: str | None user_metadata: dict[str, Any] | None created_at: datetime updated_at: datetime last_access_time: datetime class TagRow(TypedDict): """Row data for inserting a Tag.""" asset_reference_id: str tag_name: str origin: str added_at: datetime class MetadataRow(TypedDict): """Row data for inserting asset metadata.""" asset_reference_id: str key: str ordinal: int val_str: str | None val_num: float | None val_bool: bool | None val_json: dict[str, Any] | None @dataclass class BulkInsertResult: """Result of bulk asset insertion.""" inserted_refs: int won_paths: int lost_paths: int def batch_insert_seed_assets( session: Session, specs: list[SeedAssetSpec], owner_id: str = "", ) -> BulkInsertResult: """Seed assets from filesystem specs in batch. Each spec is a dict with keys: - abs_path: str - size_bytes: int - mtime_ns: int - info_name: str - tags: list[str] - fname: Optional[str] This function orchestrates: 1. Insert seed Assets (hash=NULL) 2. Claim references with ON CONFLICT DO NOTHING on file_path 3. Query to find winners (paths where our asset_id was inserted) 4. Delete Assets for losers (path already claimed by another asset) 5. Insert tags and metadata for successfully inserted references Returns: BulkInsertResult with inserted_refs, won_paths, lost_paths """ if not specs: return BulkInsertResult(inserted_refs=0, won_paths=0, lost_paths=0) current_time = get_utc_now() asset_rows: list[AssetRow] = [] reference_rows: list[ReferenceRow] = [] path_to_asset_id: dict[str, str] = {} asset_id_to_ref_data: dict[str, dict] = {} absolute_path_list: list[str] = [] for spec in specs: absolute_path = os.path.abspath(spec["abs_path"]) asset_id = str(uuid.uuid4()) reference_id = str(uuid.uuid4()) absolute_path_list.append(absolute_path) path_to_asset_id[absolute_path] = asset_id mime_type = spec.get("mime_type") asset_rows.append( { "id": asset_id, "hash": spec.get("hash"), "size_bytes": spec["size_bytes"], "mime_type": mime_type, "created_at": current_time, } ) # Build user_metadata from extracted metadata or fallback to filename extracted_metadata = spec.get("metadata") if extracted_metadata: user_metadata: dict[str, Any] | None = extracted_metadata.to_user_metadata() elif spec["fname"]: user_metadata = {"filename": spec["fname"]} else: user_metadata = None reference_rows.append( { "id": reference_id, "asset_id": asset_id, "file_path": absolute_path, "mtime_ns": spec["mtime_ns"], "owner_id": owner_id, "name": spec["info_name"], "preview_id": None, "user_metadata": user_metadata, "created_at": current_time, "updated_at": current_time, "last_access_time": current_time, } ) asset_id_to_ref_data[asset_id] = { "reference_id": reference_id, "tags": spec["tags"], "filename": spec["fname"], "extracted_metadata": extracted_metadata, } bulk_insert_assets(session, asset_rows) # Filter reference rows to only those whose assets were actually inserted # (assets with duplicate hashes are silently dropped by ON CONFLICT DO NOTHING) inserted_asset_ids = get_existing_asset_ids( session, [r["asset_id"] for r in reference_rows] ) reference_rows = [r for r in reference_rows if r["asset_id"] in inserted_asset_ids] bulk_insert_references_ignore_conflicts(session, reference_rows) restore_references_by_paths(session, absolute_path_list) winning_paths = get_references_by_paths_and_asset_ids(session, path_to_asset_id) inserted_paths = { path for path in absolute_path_list if path_to_asset_id[path] in inserted_asset_ids } losing_paths = inserted_paths - winning_paths lost_asset_ids = [path_to_asset_id[path] for path in losing_paths] if lost_asset_ids: delete_assets_by_ids(session, lost_asset_ids) if not winning_paths: return BulkInsertResult( inserted_refs=0, won_paths=0, lost_paths=len(losing_paths), ) # Get reference IDs for winners winning_ref_ids = [ asset_id_to_ref_data[path_to_asset_id[path]]["reference_id"] for path in winning_paths ] inserted_ref_ids = get_reference_ids_by_ids(session, winning_ref_ids) tag_rows: list[TagRow] = [] metadata_rows: list[MetadataRow] = [] if inserted_ref_ids: for path in winning_paths: asset_id = path_to_asset_id[path] ref_data = asset_id_to_ref_data[asset_id] ref_id = ref_data["reference_id"] if ref_id not in inserted_ref_ids: continue for tag in ref_data["tags"]: tag_rows.append( { "asset_reference_id": ref_id, "tag_name": tag, "origin": "automatic", "added_at": current_time, } ) # Use extracted metadata for meta rows if available extracted_metadata = ref_data.get("extracted_metadata") if extracted_metadata: metadata_rows.extend(extracted_metadata.to_meta_rows(ref_id)) elif ref_data["filename"]: # Fallback: just store filename metadata_rows.append( { "asset_reference_id": ref_id, "key": "filename", "ordinal": 0, "val_str": ref_data["filename"], "val_num": None, "val_bool": None, "val_json": None, } ) bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=metadata_rows) return BulkInsertResult( inserted_refs=len(inserted_ref_ids), won_paths=len(winning_paths), lost_paths=len(losing_paths), ) def cleanup_unreferenced_assets(session: Session) -> int: """Hard-delete unhashed assets with no active references. This is a destructive operation intended for explicit cleanup. Only deletes assets where hash=None and all references are missing. Returns: Number of assets deleted """ unreferenced_ids = get_unreferenced_unhashed_asset_ids(session) return delete_assets_by_ids(session, unreferenced_ids) ================================================ FILE: app/assets/services/file_utils.py ================================================ import os def get_mtime_ns(stat_result: os.stat_result) -> int: """Extract mtime in nanoseconds from a stat result.""" return getattr( stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000) ) def get_size_and_mtime_ns(path: str, follow_symlinks: bool = True) -> tuple[int, int]: """Get file size in bytes and mtime in nanoseconds.""" st = os.stat(path, follow_symlinks=follow_symlinks) return st.st_size, get_mtime_ns(st) def verify_file_unchanged( mtime_db: int | None, size_db: int | None, stat_result: os.stat_result, ) -> bool: """Check if a file is unchanged based on mtime and size. Returns True if the file's mtime and size match the database values. Returns False if mtime_db is None or values don't match. size_db=None means don't check size; 0 is a valid recorded size. """ if mtime_db is None: return False actual_mtime_ns = get_mtime_ns(stat_result) if int(mtime_db) != int(actual_mtime_ns): return False if size_db is not None: return int(stat_result.st_size) == int(size_db) return True def is_visible(name: str) -> bool: """Return True if a file or directory name is visible (not hidden).""" return not name.startswith(".") def list_files_recursively(base_dir: str) -> list[str]: """Recursively list all files in a directory, following symlinks.""" out: list[str] = [] base_abs = os.path.abspath(base_dir) if not os.path.isdir(base_abs): return out # Track seen real directory identities to prevent circular symlink loops seen_dirs: set[tuple[int, int]] = set() for dirpath, subdirs, filenames in os.walk( base_abs, topdown=True, followlinks=True ): try: st = os.stat(dirpath) dir_id = (st.st_dev, st.st_ino) except OSError: subdirs.clear() continue if dir_id in seen_dirs: subdirs.clear() continue seen_dirs.add(dir_id) subdirs[:] = [d for d in subdirs if is_visible(d)] for name in filenames: if not is_visible(name): continue out.append(os.path.abspath(os.path.join(dirpath, name))) return out ================================================ FILE: app/assets/services/hashing.py ================================================ import io import os from contextlib import contextmanager from dataclasses import dataclass from typing import IO, Any, Callable, Iterator import logging try: from blake3 import blake3 except ModuleNotFoundError: logging.warning("WARNING: blake3 package not installed") DEFAULT_CHUNK = 8 * 1024 * 1024 InterruptCheck = Callable[[], bool] @dataclass class HashCheckpoint: """Saved state for resuming an interrupted hash computation.""" bytes_processed: int hasher: Any # blake3 hasher instance mtime_ns: int = 0 file_size: int = 0 @contextmanager def _open_for_hashing(fp: str | IO[bytes]) -> Iterator[tuple[IO[bytes], bool]]: """Yield (file_object, is_path) with appropriate setup/teardown.""" if hasattr(fp, "read"): seekable = getattr(fp, "seekable", lambda: False)() orig_pos = None if seekable: try: orig_pos = fp.tell() if orig_pos != 0: fp.seek(0) except io.UnsupportedOperation: orig_pos = None try: yield fp, False finally: if orig_pos is not None: fp.seek(orig_pos) else: with open(os.fspath(fp), "rb") as f: yield f, True def compute_blake3_hash( fp: str | IO[bytes], chunk_size: int = DEFAULT_CHUNK, interrupt_check: InterruptCheck | None = None, checkpoint: HashCheckpoint | None = None, ) -> tuple[str | None, HashCheckpoint | None]: """Compute BLAKE3 hash of a file, with optional checkpoint support. Args: fp: File path or file-like object chunk_size: Size of chunks to read at a time interrupt_check: Optional callable that returns True if the operation should be interrupted (e.g. paused or cancelled). Must be non-blocking so file handles are released immediately. Checked between chunk reads. checkpoint: Optional checkpoint to resume from (file paths only) Returns: Tuple of (hex_digest, None) on completion, or (None, checkpoint) on interruption (file paths only), or (None, None) on interruption of a file object """ if chunk_size <= 0: chunk_size = DEFAULT_CHUNK with _open_for_hashing(fp) as (f, is_path): if checkpoint is not None and is_path: f.seek(checkpoint.bytes_processed) h = checkpoint.hasher bytes_processed = checkpoint.bytes_processed else: h = blake3() bytes_processed = 0 while True: if interrupt_check is not None and interrupt_check(): if is_path: return None, HashCheckpoint( bytes_processed=bytes_processed, hasher=h, ) return None, None chunk = f.read(chunk_size) if not chunk: break h.update(chunk) bytes_processed += len(chunk) return h.hexdigest(), None ================================================ FILE: app/assets/services/ingest.py ================================================ import contextlib import logging import mimetypes import os from typing import Any, Sequence from sqlalchemy.orm import Session import app.assets.services.hashing as hashing from app.assets.database.queries import ( add_tags_to_reference, fetch_reference_and_asset, get_asset_by_hash, get_reference_by_file_path, get_reference_tags, get_or_create_reference, reference_exists, remove_missing_tag_for_asset_id, set_reference_metadata, set_reference_tags, update_asset_hash_and_mime, upsert_asset, upsert_reference, validate_tags_exist, ) from app.assets.helpers import normalize_tags from app.assets.services.file_utils import get_size_and_mtime_ns from app.assets.services.path_utils import ( compute_relative_filename, get_name_and_tags_from_asset_path, resolve_destination_from_tags, validate_path_within_base, ) from app.assets.services.schemas import ( IngestResult, RegisterAssetResult, UploadResult, UserMetadata, extract_asset_data, extract_reference_data, ) from app.database.db import create_session def _ingest_file_from_path( abs_path: str, asset_hash: str, size_bytes: int, mtime_ns: int, mime_type: str | None = None, info_name: str | None = None, owner_id: str = "", preview_id: str | None = None, user_metadata: UserMetadata = None, tags: Sequence[str] = (), tag_origin: str = "manual", require_existing_tags: bool = False, ) -> IngestResult: locator = os.path.abspath(abs_path) user_metadata = user_metadata or {} asset_created = False asset_updated = False ref_created = False ref_updated = False reference_id: str | None = None with create_session() as session: if preview_id: if not reference_exists(session, preview_id): preview_id = None asset, asset_created, asset_updated = upsert_asset( session, asset_hash=asset_hash, size_bytes=size_bytes, mime_type=mime_type, ) ref_created, ref_updated = upsert_reference( session, asset_id=asset.id, file_path=locator, name=info_name or os.path.basename(locator), mtime_ns=mtime_ns, owner_id=owner_id, ) # Get the reference we just created/updated ref = get_reference_by_file_path(session, locator) if ref: reference_id = ref.id if preview_id and ref.preview_id != preview_id: ref.preview_id = preview_id norm = normalize_tags(list(tags)) if norm: if require_existing_tags: validate_tags_exist(session, norm) add_tags_to_reference( session, reference_id=reference_id, tags=norm, origin=tag_origin, create_if_missing=not require_existing_tags, ) _update_metadata_with_filename( session, reference_id=reference_id, file_path=ref.file_path, current_metadata=ref.user_metadata, user_metadata=user_metadata, ) try: remove_missing_tag_for_asset_id(session, asset_id=asset.id) except Exception: logging.exception("Failed to clear 'missing' tag for asset %s", asset.id) session.commit() return IngestResult( asset_created=asset_created, asset_updated=asset_updated, ref_created=ref_created, ref_updated=ref_updated, reference_id=reference_id, ) def _register_existing_asset( asset_hash: str, name: str, user_metadata: UserMetadata = None, tags: list[str] | None = None, tag_origin: str = "manual", owner_id: str = "", mime_type: str | None = None, preview_id: str | None = None, ) -> RegisterAssetResult: user_metadata = user_metadata or {} with create_session() as session: asset = get_asset_by_hash(session, asset_hash=asset_hash) if not asset: raise ValueError(f"No asset with hash {asset_hash}") if mime_type and not asset.mime_type: update_asset_hash_and_mime(session, asset_id=asset.id, mime_type=mime_type) if preview_id: if not reference_exists(session, preview_id): preview_id = None ref, ref_created = get_or_create_reference( session, asset_id=asset.id, owner_id=owner_id, name=name, preview_id=preview_id, ) if not ref_created: if preview_id and ref.preview_id != preview_id: ref.preview_id = preview_id tag_names = get_reference_tags(session, reference_id=ref.id) result = RegisterAssetResult( ref=extract_reference_data(ref), asset=extract_asset_data(asset), tags=tag_names, created=False, ) session.commit() return result new_meta = dict(user_metadata) computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None if computed_filename: new_meta["filename"] = computed_filename if new_meta: set_reference_metadata( session, reference_id=ref.id, user_metadata=new_meta, ) if tags is not None: set_reference_tags( session, reference_id=ref.id, tags=tags, origin=tag_origin, ) tag_names = get_reference_tags(session, reference_id=ref.id) session.refresh(ref) result = RegisterAssetResult( ref=extract_reference_data(ref), asset=extract_asset_data(asset), tags=tag_names, created=True, ) session.commit() return result def _update_metadata_with_filename( session: Session, reference_id: str, file_path: str | None, current_metadata: dict | None, user_metadata: dict[str, Any], ) -> None: computed_filename = compute_relative_filename(file_path) if file_path else None current_meta = current_metadata or {} new_meta = dict(current_meta) for k, v in user_metadata.items(): new_meta[k] = v if computed_filename: new_meta["filename"] = computed_filename if new_meta != current_meta: set_reference_metadata( session, reference_id=reference_id, user_metadata=new_meta, ) def _sanitize_filename(name: str | None, fallback: str) -> str: n = os.path.basename((name or "").strip() or fallback) return n if n else fallback class HashMismatchError(Exception): pass class DependencyMissingError(Exception): def __init__(self, message: str): self.message = message super().__init__(message) def upload_from_temp_path( temp_path: str, name: str | None = None, tags: list[str] | None = None, user_metadata: dict | None = None, client_filename: str | None = None, owner_id: str = "", expected_hash: str | None = None, mime_type: str | None = None, preview_id: str | None = None, ) -> UploadResult: try: digest, _ = hashing.compute_blake3_hash(temp_path) except ImportError as e: raise DependencyMissingError(str(e)) except Exception as e: raise RuntimeError(f"failed to hash uploaded file: {e}") asset_hash = "blake3:" + digest if expected_hash and asset_hash != expected_hash.strip().lower(): raise HashMismatchError("Uploaded file hash does not match provided hash.") with create_session() as session: existing = get_asset_by_hash(session, asset_hash=asset_hash) if existing is not None: with contextlib.suppress(Exception): if temp_path and os.path.exists(temp_path): os.remove(temp_path) display_name = _sanitize_filename(name or client_filename, fallback=digest) result = _register_existing_asset( asset_hash=asset_hash, name=display_name, user_metadata=user_metadata or {}, tags=tags or [], tag_origin="manual", owner_id=owner_id, mime_type=mime_type, preview_id=preview_id, ) return UploadResult( ref=result.ref, asset=result.asset, tags=result.tags, created_new=False, ) if not tags: raise ValueError("tags are required for new asset uploads") base_dir, subdirs = resolve_destination_from_tags(tags) dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir os.makedirs(dest_dir, exist_ok=True) src_for_ext = (client_filename or name or "").strip() _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else "" ext = _ext if 0 < len(_ext) <= 16 else "" hashed_basename = f"{digest}{ext}" dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename)) validate_path_within_base(dest_abs, base_dir) content_type = mime_type or ( mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0] or mimetypes.guess_type(hashed_basename, strict=False)[0] or "application/octet-stream" ) try: os.replace(temp_path, dest_abs) except Exception as e: raise RuntimeError(f"failed to move uploaded file into place: {e}") try: size_bytes, mtime_ns = get_size_and_mtime_ns(dest_abs) except OSError as e: raise RuntimeError(f"failed to stat destination file: {e}") ingest_result = _ingest_file_from_path( asset_hash=asset_hash, abs_path=dest_abs, size_bytes=size_bytes, mtime_ns=mtime_ns, mime_type=content_type, info_name=_sanitize_filename(name or client_filename, fallback=digest), owner_id=owner_id, preview_id=preview_id, user_metadata=user_metadata or {}, tags=tags, tag_origin="manual", require_existing_tags=False, ) reference_id = ingest_result.reference_id if not reference_id: raise RuntimeError("failed to create asset reference") with create_session() as session: pair = fetch_reference_and_asset( session, reference_id=reference_id, owner_id=owner_id ) if not pair: raise RuntimeError("inconsistent DB state after ingest") ref, asset = pair tag_names = get_reference_tags(session, reference_id=ref.id) return UploadResult( ref=extract_reference_data(ref), asset=extract_asset_data(asset), tags=tag_names, created_new=ingest_result.asset_created, ) def register_file_in_place( abs_path: str, name: str, tags: list[str], owner_id: str = "", mime_type: str | None = None, ) -> UploadResult: """Register an already-saved file in the asset database without moving it. Tags are derived from the filesystem path (root category + subfolder names), merged with any caller-provided tags, matching the behavior of the scanner. If the path is not under a known root, only the caller-provided tags are used. """ try: _, path_tags = get_name_and_tags_from_asset_path(abs_path) except ValueError: path_tags = [] merged_tags = normalize_tags([*path_tags, *tags]) try: digest, _ = hashing.compute_blake3_hash(abs_path) except ImportError as e: raise DependencyMissingError(str(e)) except Exception as e: raise RuntimeError(f"failed to hash file: {e}") asset_hash = "blake3:" + digest size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path) content_type = mime_type or ( mimetypes.guess_type(abs_path, strict=False)[0] or "application/octet-stream" ) ingest_result = _ingest_file_from_path( abs_path=abs_path, asset_hash=asset_hash, size_bytes=size_bytes, mtime_ns=mtime_ns, mime_type=content_type, info_name=_sanitize_filename(name, fallback=digest), owner_id=owner_id, tags=merged_tags, tag_origin="upload", require_existing_tags=False, ) reference_id = ingest_result.reference_id if not reference_id: raise RuntimeError("failed to create asset reference") with create_session() as session: pair = fetch_reference_and_asset( session, reference_id=reference_id, owner_id=owner_id ) if not pair: raise RuntimeError("inconsistent DB state after ingest") ref, asset = pair tag_names = get_reference_tags(session, reference_id=ref.id) return UploadResult( ref=extract_reference_data(ref), asset=extract_asset_data(asset), tags=tag_names, created_new=ingest_result.asset_created, ) def create_from_hash( hash_str: str, name: str, tags: list[str] | None = None, user_metadata: dict | None = None, owner_id: str = "", mime_type: str | None = None, preview_id: str | None = None, ) -> UploadResult | None: canonical = hash_str.strip().lower() try: result = _register_existing_asset( asset_hash=canonical, name=_sanitize_filename( name, fallback=canonical.split(":", 1)[1] if ":" in canonical else canonical ), user_metadata=user_metadata or {}, tags=tags or [], tag_origin="manual", owner_id=owner_id, mime_type=mime_type, preview_id=preview_id, ) except ValueError: logging.warning("create_from_hash: no asset found for hash %s", canonical) return None return UploadResult( ref=result.ref, asset=result.asset, tags=result.tags, created_new=False, ) ================================================ FILE: app/assets/services/metadata_extract.py ================================================ """Metadata extraction for asset scanning. Tier 1: Filesystem metadata (zero parsing) Tier 2: Safetensors header metadata (fast JSON read only) """ from __future__ import annotations import json import logging import mimetypes import os import struct from dataclasses import dataclass from typing import Any from utils.mime_types import init_mime_types init_mime_types() # Supported safetensors extensions SAFETENSORS_EXTENSIONS = frozenset({".safetensors", ".sft"}) # Maximum safetensors header size to read (8MB) MAX_SAFETENSORS_HEADER_SIZE = 8 * 1024 * 1024 @dataclass class ExtractedMetadata: """Metadata extracted from a file during scanning.""" # Tier 1: Filesystem (always available) filename: str = "" file_path: str = "" # Full absolute path to the file content_length: int = 0 content_type: str | None = None format: str = "" # file extension without dot # Tier 2: Safetensors header (if available) base_model: str | None = None trained_words: list[str] | None = None air: str | None = None # CivitAI AIR identifier has_preview_images: bool = False # Source provenance (populated if embedded in safetensors) source_url: str | None = None source_arn: str | None = None repo_url: str | None = None preview_url: str | None = None source_hash: str | None = None # HuggingFace specific repo_id: str | None = None revision: str | None = None filepath: str | None = None resolve_url: str | None = None def to_user_metadata(self) -> dict[str, Any]: """Convert to user_metadata dict for AssetReference.user_metadata JSON field.""" data: dict[str, Any] = { "filename": self.filename, "content_length": self.content_length, "format": self.format, } if self.file_path: data["file_path"] = self.file_path if self.content_type: data["content_type"] = self.content_type # Tier 2 fields if self.base_model: data["base_model"] = self.base_model if self.trained_words: data["trained_words"] = self.trained_words if self.air: data["air"] = self.air if self.has_preview_images: data["has_preview_images"] = True # Source provenance if self.source_url: data["source_url"] = self.source_url if self.source_arn: data["source_arn"] = self.source_arn if self.repo_url: data["repo_url"] = self.repo_url if self.preview_url: data["preview_url"] = self.preview_url if self.source_hash: data["source_hash"] = self.source_hash # HuggingFace if self.repo_id: data["repo_id"] = self.repo_id if self.revision: data["revision"] = self.revision if self.filepath: data["filepath"] = self.filepath if self.resolve_url: data["resolve_url"] = self.resolve_url return data def to_meta_rows(self, reference_id: str) -> list[dict]: """Convert to asset_reference_meta rows for typed/indexed querying.""" rows: list[dict] = [] def add_str(key: str, val: str | None, ordinal: int = 0) -> None: if val: rows.append({ "asset_reference_id": reference_id, "key": key, "ordinal": ordinal, "val_str": val[:2048] if len(val) > 2048 else val, "val_num": None, "val_bool": None, "val_json": None, }) def add_num(key: str, val: int | float | None) -> None: if val is not None: rows.append({ "asset_reference_id": reference_id, "key": key, "ordinal": 0, "val_str": None, "val_num": val, "val_bool": None, "val_json": None, }) def add_bool(key: str, val: bool | None) -> None: if val is not None: rows.append({ "asset_reference_id": reference_id, "key": key, "ordinal": 0, "val_str": None, "val_num": None, "val_bool": val, "val_json": None, }) # Tier 1 add_str("filename", self.filename) add_num("content_length", self.content_length) add_str("content_type", self.content_type) add_str("format", self.format) # Tier 2 add_str("base_model", self.base_model) add_str("air", self.air) has_previews = self.has_preview_images if self.has_preview_images else None add_bool("has_preview_images", has_previews) # trained_words as multiple rows with ordinals if self.trained_words: for i, word in enumerate(self.trained_words[:100]): # limit to 100 words add_str("trained_words", word, ordinal=i) # Source provenance add_str("source_url", self.source_url) add_str("source_arn", self.source_arn) add_str("repo_url", self.repo_url) add_str("preview_url", self.preview_url) add_str("source_hash", self.source_hash) # HuggingFace add_str("repo_id", self.repo_id) add_str("revision", self.revision) add_str("filepath", self.filepath) add_str("resolve_url", self.resolve_url) return rows def _read_safetensors_header( path: str, max_size: int = MAX_SAFETENSORS_HEADER_SIZE ) -> dict[str, Any] | None: """Read only the JSON header from a safetensors file. This is very fast - reads 8 bytes for header length, then the JSON header. No tensor data is loaded. Args: path: Absolute path to safetensors file max_size: Maximum header size to read (default 8MB) Returns: Parsed header dict or None if failed """ try: with open(path, "rb") as f: header_bytes = f.read(8) if len(header_bytes) < 8: return None length_of_header = struct.unpack(" max_size: return None header_data = f.read(length_of_header) if len(header_data) < length_of_header: return None return json.loads(header_data.decode("utf-8")) except (OSError, json.JSONDecodeError, UnicodeDecodeError, struct.error): return None def _extract_safetensors_metadata( header: dict[str, Any], meta: ExtractedMetadata ) -> None: """Extract metadata from safetensors header __metadata__ section. Modifies meta in-place. """ st_meta = header.get("__metadata__", {}) if not isinstance(st_meta, dict): return # Common model metadata meta.base_model = ( st_meta.get("ss_base_model_version") or st_meta.get("modelspec.base_model") or st_meta.get("base_model") ) # Trained words / trigger words trained_words = st_meta.get("ss_tag_frequency") if trained_words and isinstance(trained_words, str): try: tag_freq = json.loads(trained_words) # Extract unique tags from all datasets all_tags: set[str] = set() for dataset_tags in tag_freq.values(): if isinstance(dataset_tags, dict): all_tags.update(dataset_tags.keys()) if all_tags: meta.trained_words = sorted(all_tags)[:100] except json.JSONDecodeError: pass # Direct trained_words field (some formats) if not meta.trained_words: tw = st_meta.get("trained_words") if isinstance(tw, str): try: parsed = json.loads(tw) if isinstance(parsed, list): meta.trained_words = [str(x) for x in parsed] else: meta.trained_words = [w.strip() for w in tw.split(",") if w.strip()] except json.JSONDecodeError: meta.trained_words = [w.strip() for w in tw.split(",") if w.strip()] elif isinstance(tw, list): meta.trained_words = [str(x) for x in tw] # CivitAI AIR meta.air = st_meta.get("air") or st_meta.get("modelspec.air") # Preview images (ssmd_cover_images) cover_images = st_meta.get("ssmd_cover_images") if cover_images: meta.has_preview_images = True # Source provenance fields meta.source_url = st_meta.get("source_url") meta.source_arn = st_meta.get("source_arn") meta.repo_url = st_meta.get("repo_url") meta.preview_url = st_meta.get("preview_url") meta.source_hash = st_meta.get("source_hash") or st_meta.get("sshs_model_hash") # HuggingFace fields meta.repo_id = st_meta.get("repo_id") or st_meta.get("hf_repo_id") meta.revision = st_meta.get("revision") or st_meta.get("hf_revision") meta.filepath = st_meta.get("filepath") or st_meta.get("hf_filepath") meta.resolve_url = st_meta.get("resolve_url") or st_meta.get("hf_url") def extract_file_metadata( abs_path: str, stat_result: os.stat_result | None = None, relative_filename: str | None = None, ) -> ExtractedMetadata: """Extract metadata from a file using tier 1 and tier 2 methods. Tier 1: Filesystem metadata from path and stat Tier 2: Safetensors header parsing if applicable Args: abs_path: Absolute path to the file stat_result: Optional pre-fetched stat result (saves a syscall) relative_filename: Optional relative filename to use instead of basename (e.g., "flux/123/model.safetensors" for model paths) Returns: ExtractedMetadata with all available fields populated """ meta = ExtractedMetadata() # Tier 1: Filesystem metadata meta.filename = relative_filename or os.path.basename(abs_path) meta.file_path = abs_path _, ext = os.path.splitext(abs_path) meta.format = ext.lstrip(".").lower() if ext else "" mime_type, _ = mimetypes.guess_type(abs_path) meta.content_type = mime_type # Size from stat if stat_result is None: try: stat_result = os.stat(abs_path, follow_symlinks=True) except OSError: pass if stat_result: meta.content_length = stat_result.st_size # Tier 2: Safetensors header (if applicable and enabled) if ext.lower() in SAFETENSORS_EXTENSIONS: header = _read_safetensors_header(abs_path) if header: try: _extract_safetensors_metadata(header, meta) except Exception as e: logging.debug("Safetensors meta extract failed %s: %s", abs_path, e) return meta ================================================ FILE: app/assets/services/path_utils.py ================================================ import os from pathlib import Path from typing import Literal import folder_paths from app.assets.helpers import normalize_tags _NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"}) def get_comfy_models_folders() -> list[tuple[str, list[str]]]: """Build list of (folder_name, base_paths[]) for all model locations. Includes every category registered in folder_names_and_paths, regardless of whether its paths are under the main models_dir, but excludes non-model entries like custom_nodes. """ targets: list[tuple[str, list[str]]] = [] for name, values in folder_paths.folder_names_and_paths.items(): if name in _NON_MODEL_FOLDER_NAMES: continue paths, _exts = values[0], values[1] if paths: targets.append((name, paths)) return targets def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]: """Validates and maps tags -> (base_dir, subdirs_for_fs)""" if not tags: raise ValueError("tags must not be empty") root = tags[0].lower() if root == "models": if len(tags) < 2: raise ValueError("at least two tags required for model asset") try: bases = folder_paths.folder_names_and_paths[tags[1]][0] except KeyError: raise ValueError(f"unknown model category '{tags[1]}'") if not bases: raise ValueError(f"no base path configured for category '{tags[1]}'") base_dir = os.path.abspath(bases[0]) raw_subdirs = tags[2:] elif root == "input": base_dir = os.path.abspath(folder_paths.get_input_directory()) raw_subdirs = tags[1:] elif root == "output": base_dir = os.path.abspath(folder_paths.get_output_directory()) raw_subdirs = tags[1:] else: raise ValueError(f"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'") _sep_chars = frozenset(("/", "\\", os.sep)) for i in raw_subdirs: if i in (".", "..") or _sep_chars & set(i): raise ValueError("invalid path component in tags") return base_dir, raw_subdirs if raw_subdirs else [] def validate_path_within_base(candidate: str, base: str) -> None: cand_abs = Path(os.path.abspath(candidate)) base_abs = Path(os.path.abspath(base)) if not cand_abs.is_relative_to(base_abs): raise ValueError("destination escapes base directory") def compute_relative_filename(file_path: str) -> str | None: """ Return the model's path relative to the last well-known folder (the model category), using forward slashes, eg: /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors" /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors" For non-model paths, returns None. """ try: root_category, rel_path = get_asset_category_and_relative_path(file_path) except ValueError: return None p = Path(rel_path) parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)] if not parts: return None if root_category == "models": # parts[0] is the category ("checkpoints", "vae", etc) – drop it inside = parts[1:] if len(parts) > 1 else [parts[0]] return "/".join(inside) return "/".join(parts) # input/output: keep all parts def get_asset_category_and_relative_path( file_path: str, ) -> tuple[Literal["input", "output", "models"], str]: """Determine which root category a file path belongs to. Categories: - 'input': under folder_paths.get_input_directory() - 'output': under folder_paths.get_output_directory() - 'models': under any base path from get_comfy_models_folders() Returns: (root_category, relative_path_inside_that_root) Raises: ValueError: path does not belong to any known root. """ fp_abs = os.path.abspath(file_path) def _check_is_within(child: str, parent: str) -> bool: return Path(child).is_relative_to(parent) def _compute_relative(child: str, parent: str) -> str: # Normalize relative path, stripping any leading ".." components # by anchoring to root (os.sep) then computing relpath back from it. return os.path.relpath( os.path.join(os.sep, os.path.relpath(child, parent)), os.sep ) # 1) input input_base = os.path.abspath(folder_paths.get_input_directory()) if _check_is_within(fp_abs, input_base): return "input", _compute_relative(fp_abs, input_base) # 2) output output_base = os.path.abspath(folder_paths.get_output_directory()) if _check_is_within(fp_abs, output_base): return "output", _compute_relative(fp_abs, output_base) # 3) models (check deepest matching base to avoid ambiguity) best: tuple[int, str, str] | None = None # (base_len, bucket, rel_inside_bucket) for bucket, bases in get_comfy_models_folders(): for b in bases: base_abs = os.path.abspath(b) if not _check_is_within(fp_abs, base_abs): continue cand = (len(base_abs), bucket, _compute_relative(fp_abs, base_abs)) if best is None or cand[0] > best[0]: best = cand if best is not None: _, bucket, rel_inside = best combined = os.path.join(bucket, rel_inside) return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep) raise ValueError( f"Path is not within input, output, or configured model bases: {file_path}" ) def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]: """Return (name, tags) derived from a filesystem path. - name: base filename with extension - tags: [root_category] + parent folder names in order Raises: ValueError: path does not belong to any known root. """ root_category, some_path = get_asset_category_and_relative_path(file_path) p = Path(some_path) parent_parts = [ part for part in p.parent.parts if part not in (".", "..", p.anchor) ] return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts]))) ================================================ FILE: app/assets/services/schemas.py ================================================ from dataclasses import dataclass from datetime import datetime from typing import Any, NamedTuple from app.assets.database.models import Asset, AssetReference UserMetadata = dict[str, Any] | None @dataclass(frozen=True) class AssetData: hash: str | None size_bytes: int | None mime_type: str | None @dataclass(frozen=True) class ReferenceData: """Data transfer object for AssetReference.""" id: str name: str file_path: str | None user_metadata: UserMetadata preview_id: str | None created_at: datetime updated_at: datetime system_metadata: dict[str, Any] | None = None job_id: str | None = None last_access_time: datetime | None = None @dataclass(frozen=True) class AssetDetailResult: ref: ReferenceData asset: AssetData | None tags: list[str] @dataclass(frozen=True) class RegisterAssetResult: ref: ReferenceData asset: AssetData tags: list[str] created: bool @dataclass(frozen=True) class IngestResult: asset_created: bool asset_updated: bool ref_created: bool ref_updated: bool reference_id: str | None class TagUsage(NamedTuple): name: str tag_type: str count: int @dataclass(frozen=True) class AssetSummaryData: ref: ReferenceData asset: AssetData | None tags: list[str] @dataclass(frozen=True) class ListAssetsResult: items: list[AssetSummaryData] total: int @dataclass(frozen=True) class DownloadResolutionResult: abs_path: str content_type: str download_name: str @dataclass(frozen=True) class UploadResult: ref: ReferenceData asset: AssetData tags: list[str] created_new: bool def extract_reference_data(ref: AssetReference) -> ReferenceData: return ReferenceData( id=ref.id, name=ref.name, file_path=ref.file_path, user_metadata=ref.user_metadata, preview_id=ref.preview_id, system_metadata=ref.system_metadata, job_id=ref.job_id, created_at=ref.created_at, updated_at=ref.updated_at, last_access_time=ref.last_access_time, ) def extract_asset_data(asset: Asset | None) -> AssetData | None: if asset is None: return None return AssetData( hash=asset.hash, size_bytes=asset.size_bytes, mime_type=asset.mime_type, ) ================================================ FILE: app/assets/services/tagging.py ================================================ from typing import Sequence from app.assets.database.queries import ( AddTagsResult, RemoveTagsResult, add_tags_to_reference, get_reference_with_owner_check, list_tags_with_usage, remove_tags_from_reference, ) from app.assets.database.queries.tags import list_tag_counts_for_filtered_assets from app.assets.services.schemas import TagUsage from app.database.db import create_session def apply_tags( reference_id: str, tags: list[str], origin: str = "manual", owner_id: str = "", ) -> AddTagsResult: with create_session() as session: ref_row = get_reference_with_owner_check(session, reference_id, owner_id) result = add_tags_to_reference( session, reference_id=reference_id, tags=tags, origin=origin, create_if_missing=True, reference_row=ref_row, ) session.commit() return result def remove_tags( reference_id: str, tags: list[str], owner_id: str = "", ) -> RemoveTagsResult: with create_session() as session: get_reference_with_owner_check(session, reference_id, owner_id) result = remove_tags_from_reference( session, reference_id=reference_id, tags=tags, ) session.commit() return result def list_tags( prefix: str | None = None, limit: int = 100, offset: int = 0, order: str = "count_desc", include_zero: bool = True, owner_id: str = "", ) -> tuple[list[TagUsage], int]: limit = max(1, min(1000, limit)) offset = max(0, offset) with create_session() as session: rows, total = list_tags_with_usage( session, prefix=prefix, limit=limit, offset=offset, include_zero=include_zero, order=order, owner_id=owner_id, ) return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total def list_tag_histogram( owner_id: str = "", include_tags: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, name_contains: str | None = None, metadata_filter: dict | None = None, limit: int = 100, ) -> dict[str, int]: with create_session() as session: return list_tag_counts_for_filtered_assets( session, owner_id=owner_id, include_tags=include_tags, exclude_tags=exclude_tags, name_contains=name_contains, metadata_filter=metadata_filter, limit=limit, ) ================================================ FILE: app/custom_node_manager.py ================================================ from __future__ import annotations import os import folder_paths import glob from aiohttp import web import json import logging from functools import lru_cache from utils.json_util import merge_json_recursive # Extra locale files to load into main.json EXTRA_LOCALE_FILES = [ "nodeDefs.json", "commands.json", "settings.json", ] def safe_load_json_file(file_path: str) -> dict: if not os.path.exists(file_path): return {} try: with open(file_path, "r", encoding="utf-8") as f: return json.load(f) except json.JSONDecodeError: logging.error(f"Error loading {file_path}") return {} class CustomNodeManager: @lru_cache(maxsize=1) def build_translations(self): """Load all custom nodes translations during initialization. Translations are expected to be loaded from `locales/` folder. The folder structure is expected to be the following: - custom_nodes/ - custom_node_1/ - locales/ - en/ - main.json - commands.json - settings.json returned translations are expected to be in the following format: { "en": { "nodeDefs": {...}, "commands": {...}, "settings": {...}, ...{other main.json keys} } } """ translations = {} for folder in folder_paths.get_folder_paths("custom_nodes"): # Sort glob results for deterministic ordering for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))): locales_dir = os.path.join(custom_node_dir, "locales") if not os.path.exists(locales_dir): continue for lang_dir in glob.glob(os.path.join(locales_dir, "*/")): lang_code = os.path.basename(os.path.dirname(lang_dir)) if lang_code not in translations: translations[lang_code] = {} # Load main.json main_file = os.path.join(lang_dir, "main.json") node_translations = safe_load_json_file(main_file) # Load extra locale files for extra_file in EXTRA_LOCALE_FILES: extra_file_path = os.path.join(lang_dir, extra_file) key = extra_file.split(".")[0] json_data = safe_load_json_file(extra_file_path) if json_data: node_translations[key] = json_data if node_translations: translations[lang_code] = merge_json_recursive( translations[lang_code], node_translations ) return translations def add_routes(self, routes, webapp, loadedModules): example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"] @routes.get("/workflow_templates") async def get_workflow_templates(request): """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted.""" files = [] for folder in folder_paths.get_folder_paths("custom_nodes"): for folder_name in example_workflow_folder_names: pattern = os.path.join(folder, f"*/{folder_name}/*.json") matched_files = glob.glob(pattern) files.extend(matched_files) workflow_templates_dict = ( {} ) # custom_nodes folder name -> example workflow names for file in files: custom_nodes_name = os.path.basename( os.path.dirname(os.path.dirname(file)) ) workflow_name = os.path.splitext(os.path.basename(file))[0] workflow_templates_dict.setdefault(custom_nodes_name, []).append( workflow_name ) return web.json_response(workflow_templates_dict) # Serve workflow templates from custom nodes. for module_name, module_dir in loadedModules: for folder_name in example_workflow_folder_names: workflows_dir = os.path.join(module_dir, folder_name) if os.path.exists(workflows_dir): if folder_name != "example_workflows": logging.debug( "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", folder_name, module_name) webapp.add_routes( [ web.static( "/api/workflow_templates/" + module_name, workflows_dir ) ] ) @routes.get("/i18n") async def get_i18n(request): """Returns translations from all custom nodes' locales folders.""" return web.json_response(self.build_translations()) ================================================ FILE: app/database/db.py ================================================ import logging import os import shutil from app.logger import log_startup_warning from utils.install_util import get_missing_requirements_message from filelock import FileLock, Timeout from comfy.cli_args import args _DB_AVAILABLE = False Session = None try: from alembic import command from alembic.config import Config from alembic.runtime.migration import MigrationContext from alembic.script import ScriptDirectory from sqlalchemy import create_engine, event from sqlalchemy.orm import sessionmaker from sqlalchemy.pool import StaticPool from app.database.models import Base import app.assets.database.models # noqa: F401 — register models with Base.metadata _DB_AVAILABLE = True except ImportError as e: log_startup_warning( f""" ------------------------------------------------------------------------ Error importing dependencies: {e} {get_missing_requirements_message()} This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------ """.strip() ) def dependencies_available(): """ Temporary function to check if the dependencies are available """ return _DB_AVAILABLE def can_create_session(): """ Temporary function to check if the database is available to create a session During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created """ return dependencies_available() and Session is not None def get_alembic_config(): root_path = os.path.join(os.path.dirname(__file__), "../..") config_path = os.path.abspath(os.path.join(root_path, "alembic.ini")) scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db")) config = Config(config_path) config.set_main_option("script_location", scripts_path) config.set_main_option("sqlalchemy.url", args.database_url) return config def get_db_path(): url = args.database_url if url.startswith("sqlite:///"): return url.split("///")[1] else: raise ValueError(f"Unsupported database URL '{url}'.") _db_lock = None def _acquire_file_lock(db_path): """Acquire an OS-level file lock to prevent multi-process access. Uses filelock for cross-platform support (macOS, Linux, Windows). The OS automatically releases the lock when the process exits, even on crashes. """ global _db_lock lock_path = db_path + ".lock" _db_lock = FileLock(lock_path) try: _db_lock.acquire(timeout=0) except Timeout: raise RuntimeError( f"Could not acquire lock on database '{db_path}'. " "Another ComfyUI process may already be using it. " "Use --database-url to specify a separate database file." ) def _is_memory_db(db_url): """Check if the database URL refers to an in-memory SQLite database.""" return db_url in ("sqlite:///:memory:", "sqlite://") def init_db(): db_url = args.database_url logging.debug(f"Database URL: {db_url}") if _is_memory_db(db_url): _init_memory_db(db_url) else: _init_file_db(db_url) def _init_memory_db(db_url): """Initialize an in-memory SQLite database using metadata.create_all. Alembic migrations don't work with in-memory SQLite because each connection gets its own separate database — tables created by Alembic's internal connection are lost immediately. """ engine = create_engine( db_url, poolclass=StaticPool, connect_args={"check_same_thread": False}, ) @event.listens_for(engine, "connect") def set_sqlite_pragma(dbapi_connection, connection_record): cursor = dbapi_connection.cursor() cursor.execute("PRAGMA foreign_keys=ON") cursor.close() Base.metadata.create_all(engine) global Session Session = sessionmaker(bind=engine) def _init_file_db(db_url): """Initialize a file-backed SQLite database using Alembic migrations.""" db_path = get_db_path() db_exists = os.path.exists(db_path) config = get_alembic_config() # Check if we need to upgrade engine = create_engine(db_url) # Enable foreign key enforcement for SQLite @event.listens_for(engine, "connect") def set_sqlite_pragma(dbapi_connection, connection_record): cursor = dbapi_connection.cursor() cursor.execute("PRAGMA foreign_keys=ON") cursor.close() conn = engine.connect() context = MigrationContext.configure(conn) current_rev = context.get_current_revision() script = ScriptDirectory.from_config(config) target_rev = script.get_current_head() if target_rev is None: logging.warning("No target revision found.") elif current_rev != target_rev: # Backup the database pre upgrade backup_path = db_path + ".bkp" if db_exists: shutil.copy(db_path, backup_path) else: backup_path = None try: command.upgrade(config, target_rev) logging.info(f"Database upgraded from {current_rev} to {target_rev}") except Exception as e: if backup_path: # Restore the database from backup if upgrade fails shutil.copy(backup_path, db_path) os.remove(backup_path) logging.exception("Error upgrading database: ") raise e # Acquire an OS-level file lock after migrations are complete. # Alembic uses its own connection, so we must wait until it's done # before locking — otherwise our own lock blocks the migration. conn.close() _acquire_file_lock(db_path) global Session Session = sessionmaker(bind=engine) def create_session(): return Session() ================================================ FILE: app/database/models.py ================================================ from typing import Any from datetime import datetime from sqlalchemy import MetaData from sqlalchemy.orm import DeclarativeBase NAMING_CONVENTION = { "ix": "ix_%(table_name)s_%(column_0_N_name)s", "uq": "uq_%(table_name)s_%(column_0_N_name)s", "ck": "ck_%(table_name)s_%(constraint_name)s", "fk": "fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s", "pk": "pk_%(table_name)s", } class Base(DeclarativeBase): metadata = MetaData(naming_convention=NAMING_CONVENTION) def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]: fields = obj.__table__.columns.keys() out: dict[str, Any] = {} for field in fields: val = getattr(obj, field) if val is None and not include_none: continue if isinstance(val, datetime): out[field] = val.isoformat() else: out[field] = val return out # TODO: Define models here ================================================ FILE: app/frontend_management.py ================================================ from __future__ import annotations import argparse import logging import os import re import sys import tempfile import zipfile import importlib from dataclasses import dataclass from functools import cached_property from pathlib import Path from typing import Dict, TypedDict, Optional from aiohttp import web from importlib.metadata import version import requests from typing_extensions import NotRequired from utils.install_util import get_missing_requirements_message, get_required_packages_versions from comfy.cli_args import DEFAULT_VERSION_STRING import app.logger def frontend_install_warning_message(): return f""" {get_missing_requirements_message()} This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead. """.strip() def parse_version(version: str) -> tuple[int, int, int]: return tuple(map(int, version.split("."))) def is_valid_version(version: str) -> bool: """Validate if a string is a valid semantic version (X.Y.Z format).""" pattern = r"^(\d+)\.(\d+)\.(\d+)$" return bool(re.match(pattern, version)) def get_installed_frontend_version(): """Get the currently installed frontend package version.""" frontend_version_str = version("comfyui-frontend-package") return frontend_version_str def get_required_frontend_version(): return get_required_packages_versions().get("comfyui-frontend-package", None) def check_frontend_version(): """Check if the frontend version is up to date.""" try: frontend_version_str = get_installed_frontend_version() frontend_version = parse_version(frontend_version_str) required_frontend_str = get_required_frontend_version() required_frontend = parse_version(required_frontend_str) if frontend_version < required_frontend: app.logger.log_startup_warning( f""" ________________________________________________________________________ WARNING WARNING WARNING WARNING WARNING Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}. {frontend_install_warning_message()} ________________________________________________________________________ """.strip() ) else: logging.info("ComfyUI frontend version: {}".format(frontend_version_str)) except Exception as e: logging.error(f"Failed to check frontend version: {e}") REQUEST_TIMEOUT = 10 # seconds class Asset(TypedDict): url: str class Release(TypedDict): id: int tag_name: str name: str prerelease: bool created_at: str published_at: str body: str assets: NotRequired[list[Asset]] @dataclass class FrontEndProvider: owner: str repo: str @property def folder_name(self) -> str: return f"{self.owner}_{self.repo}" @property def release_url(self) -> str: return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases" @cached_property def all_releases(self) -> list[Release]: releases = [] api_url = self.release_url while api_url: response = requests.get(api_url, timeout=REQUEST_TIMEOUT) response.raise_for_status() # Raises an HTTPError if the response was an error releases.extend(response.json()) # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly. if "next" in response.links: api_url = response.links["next"]["url"] else: api_url = None return releases @cached_property def latest_release(self) -> Release: latest_release_url = f"{self.release_url}/latest" response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT) response.raise_for_status() # Raises an HTTPError if the response was an error return response.json() @cached_property def latest_prerelease(self) -> Release: """Get the latest pre-release version - even if it's older than the latest release""" release = [release for release in self.all_releases if release["prerelease"]] if not release: raise ValueError("No pre-releases found") # GitHub returns releases in reverse chronological order, so first is latest return release[0] def get_release(self, version: str) -> Release: if version == "latest": return self.latest_release elif version == "prerelease": return self.latest_prerelease else: for release in self.all_releases: if release["tag_name"] in [version, f"v{version}"]: return release raise ValueError(f"Version {version} not found in releases") def download_release_asset_zip(release: Release, destination_path: str) -> None: """Download dist.zip from github release.""" asset_url = None for asset in release.get("assets", []): if asset["name"] == "dist.zip": asset_url = asset["url"] break if not asset_url: raise ValueError("dist.zip not found in the release assets") # Use a temporary file to download the zip content with tempfile.TemporaryFile() as tmp_file: headers = {"Accept": "application/octet-stream"} response = requests.get( asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT ) response.raise_for_status() # Ensure we got a successful response # Write the content to the temporary file tmp_file.write(response.content) # Go back to the beginning of the temporary file tmp_file.seek(0) # Extract the zip file content to the destination path with zipfile.ZipFile(tmp_file, "r") as zip_ref: zip_ref.extractall(destination_path) class FrontendManager: CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions") @classmethod def get_required_frontend_version(cls) -> str: """Get the required frontend package version.""" return get_required_frontend_version() @classmethod def get_installed_templates_version(cls) -> str: """Get the currently installed workflow templates package version.""" try: templates_version_str = version("comfyui-workflow-templates") return templates_version_str except Exception: return None @classmethod def get_required_templates_version(cls) -> str: return get_required_packages_versions().get("comfyui-workflow-templates", None) @classmethod def default_frontend_path(cls) -> str: try: import comfyui_frontend_package return str(importlib.resources.files(comfyui_frontend_package) / "static") except ImportError: logging.error( f""" ********** ERROR *********** comfyui-frontend-package is not installed. {frontend_install_warning_message()} ********** ERROR *********** """.strip() ) sys.exit(-1) @classmethod def template_asset_map(cls) -> Optional[Dict[str, str]]: """Return a mapping of template asset names to their absolute paths.""" try: from comfyui_workflow_templates import ( get_asset_path, iter_templates, ) except ImportError: logging.error( f""" ********** ERROR *********** comfyui-workflow-templates is not installed. {frontend_install_warning_message()} ********** ERROR *********** """.strip() ) return None try: template_entries = list(iter_templates()) except Exception as exc: logging.error(f"Failed to enumerate workflow templates: {exc}") return None asset_map: Dict[str, str] = {} try: for entry in template_entries: for asset in entry.assets: asset_map[asset.filename] = get_asset_path( entry.template_id, asset.filename ) except Exception as exc: logging.error(f"Failed to resolve template asset paths: {exc}") return None if not asset_map: logging.error("No workflow template assets found. Did the packages install correctly?") return None return asset_map @classmethod def legacy_templates_path(cls) -> Optional[str]: """Return the legacy templates directory shipped inside the meta package.""" try: import comfyui_workflow_templates return str( importlib.resources.files(comfyui_workflow_templates) / "templates" ) except ImportError: logging.error( f""" ********** ERROR *********** comfyui-workflow-templates is not installed. {frontend_install_warning_message()} ********** ERROR *********** """.strip() ) return None @classmethod def embedded_docs_path(cls) -> str: """Get the path to embedded documentation""" try: import comfyui_embedded_docs return str( importlib.resources.files(comfyui_embedded_docs) / "docs" ) except ImportError: logging.info("comfyui-embedded-docs package not found") return None @classmethod def parse_version_string(cls, value: str) -> tuple[str, str, str]: """ Args: value (str): The version string to parse. Returns: tuple[str, str]: A tuple containing provider name and version. Raises: argparse.ArgumentTypeError: If the version string is invalid. """ VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$" match_result = re.match(VERSION_PATTERN, value) if match_result is None: raise argparse.ArgumentTypeError(f"Invalid version string: {value}") return match_result.group(1), match_result.group(2), match_result.group(3) @classmethod def init_frontend_unsafe( cls, version_string: str, provider: Optional[FrontEndProvider] = None ) -> str: """ Initializes the frontend for the specified version. Args: version_string (str): The version string. provider (FrontEndProvider, optional): The provider to use. Defaults to None. Returns: str: The path to the initialized frontend. Raises: Exception: If there is an error during the initialization process. main error source might be request timeout or invalid URL. """ if version_string == DEFAULT_VERSION_STRING: check_frontend_version() return cls.default_frontend_path() repo_owner, repo_name, version = cls.parse_version_string(version_string) if version.startswith("v"): expected_path = str( Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v") ) if os.path.exists(expected_path): logging.info( f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}" ) return expected_path logging.info( f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..." ) provider = provider or FrontEndProvider(repo_owner, repo_name) release = provider.get_release(version) semantic_version = release["tag_name"].lstrip("v") web_root = str( Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version ) if not os.path.exists(web_root): try: os.makedirs(web_root, exist_ok=True) logging.info( "Downloading frontend(%s) version(%s) to (%s)", provider.folder_name, semantic_version, web_root, ) logging.debug(release) download_release_asset_zip(release, destination_path=web_root) finally: # Clean up the directory if it is empty, i.e. the download failed if not os.listdir(web_root): os.rmdir(web_root) return web_root @classmethod def init_frontend(cls, version_string: str) -> str: """ Initializes the frontend with the specified version string. Args: version_string (str): The version string to initialize the frontend with. Returns: str: The path of the initialized frontend. """ try: return cls.init_frontend_unsafe(version_string) except Exception as e: logging.error("Failed to initialize frontend: %s", e) logging.info("Falling back to the default frontend.") check_frontend_version() return cls.default_frontend_path() @classmethod def template_asset_handler(cls): assets = cls.template_asset_map() if not assets: return None async def serve_template(request: web.Request) -> web.StreamResponse: rel_path = request.match_info.get("path", "") target = assets.get(rel_path) if target is None: raise web.HTTPNotFound() return web.FileResponse(target) return serve_template ================================================ FILE: app/logger.py ================================================ from collections import deque from datetime import datetime import io import logging import sys import threading logs = None stdout_interceptor = None stderr_interceptor = None class LogInterceptor(io.TextIOWrapper): def __init__(self, stream, *args, **kwargs): buffer = stream.buffer encoding = stream.encoding super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering) self._lock = threading.Lock() self._flush_callbacks = [] self._logs_since_flush = [] def write(self, data): entry = {"t": datetime.now().isoformat(), "m": data} with self._lock: self._logs_since_flush.append(entry) # Simple handling for cr to overwrite the last output if it isnt a full line # else logs just get full of progress messages if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"): logs.pop() logs.append(entry) super().write(data) def flush(self): super().flush() for cb in self._flush_callbacks: cb(self._logs_since_flush) self._logs_since_flush = [] def on_flush(self, callback): self._flush_callbacks.append(callback) def get_logs(): return logs def on_flush(callback): if stdout_interceptor is not None: stdout_interceptor.on_flush(callback) if stderr_interceptor is not None: stderr_interceptor.on_flush(callback) def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False): global logs if logs: return # Override output streams and log to buffer logs = deque(maxlen=capacity) global stdout_interceptor global stderr_interceptor stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout) stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr) # Setup default global logger logger = logging.getLogger() logger.setLevel(log_level) stream_handler = logging.StreamHandler() stream_handler.setFormatter(logging.Formatter("%(message)s")) if use_stdout: # Only errors and critical to stderr stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR) # Lesser to stdout stdout_handler = logging.StreamHandler(sys.stdout) stdout_handler.setFormatter(logging.Formatter("%(message)s")) stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR) logger.addHandler(stdout_handler) logger.addHandler(stream_handler) STARTUP_WARNINGS = [] def log_startup_warning(msg): logging.warning(msg) STARTUP_WARNINGS.append(msg) def print_startup_warnings(): for s in STARTUP_WARNINGS: logging.warning(s) STARTUP_WARNINGS.clear() ================================================ FILE: app/model_manager.py ================================================ from __future__ import annotations import os import base64 import json import time import logging import folder_paths import glob import comfy.utils from aiohttp import web from PIL import Image from io import BytesIO from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types class ModelFileManager: def __init__(self) -> None: self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {} def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None: return self.cache.get(key, default) def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]): self.cache[key] = value def clear_cache(self): self.cache.clear() def add_routes(self, routes): # NOTE: This is an experiment to replace `/models` @routes.get("/experiment/models") async def get_model_folders(request): model_types = list(folder_paths.folder_names_and_paths.keys()) folder_black_list = ["configs", "custom_nodes"] output_folders: list[dict] = [] for folder in model_types: if folder in folder_black_list: continue output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)}) return web.json_response(output_folders) # NOTE: This is an experiment to replace `/models/{folder}` @routes.get("/experiment/models/{folder}") async def get_all_models(request): folder = request.match_info.get("folder", None) if folder not in folder_paths.folder_names_and_paths: return web.Response(status=404) files = self.get_model_file_list(folder) return web.json_response(files) @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}") async def get_model_preview(request): folder_name = request.match_info.get("folder", None) path_index = int(request.match_info.get("path_index", None)) filename = request.match_info.get("filename", None) if folder_name not in folder_paths.folder_names_and_paths: return web.Response(status=404) folders = folder_paths.folder_names_and_paths[folder_name] folder = folders[0][path_index] full_filename = os.path.join(folder, filename) previews = self.get_model_previews(full_filename) default_preview = previews[0] if len(previews) > 0 else None if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)): return web.Response(status=404) try: with Image.open(default_preview) as img: img_bytes = BytesIO() img.save(img_bytes, format="WEBP") img_bytes.seek(0) return web.Response(body=img_bytes.getvalue(), content_type="image/webp") except: return web.Response(status=404) def get_model_file_list(self, folder_name: str): folder_name = map_legacy(folder_name) folders = folder_paths.folder_names_and_paths[folder_name] output_list: list[dict] = [] for index, folder in enumerate(folders[0]): if not os.path.isdir(folder): continue out = self.cache_model_file_list_(folder) if out is None: out = self.recursive_search_models_(folder, index) self.set_cache(folder, out) output_list.extend(out[0]) return output_list def cache_model_file_list_(self, folder: str): model_file_list_cache = self.get_cache(folder) if model_file_list_cache is None: return None if not os.path.isdir(folder): return None if os.path.getmtime(folder) != model_file_list_cache[1]: return None for x in model_file_list_cache[1]: time_modified = model_file_list_cache[1][x] folder = x if os.path.getmtime(folder) != time_modified: return None return model_file_list_cache def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]: if not os.path.isdir(directory): return [], {}, time.perf_counter() excluded_dir_names = [".git"] # TODO use settings include_hidden_files = False result: list[str] = [] dirs: dict[str, float] = {} for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] if not include_hidden_files: subdirs[:] = [d for d in subdirs if not d.startswith(".")] filenames = [f for f in filenames if not f.startswith(".")] filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions) for file_name in filenames: try: full_path = os.path.join(dirpath, file_name) relative_path = os.path.relpath(full_path, directory) # Get file metadata file_info = { "name": relative_path, "pathIndex": pathIndex, "modified": os.path.getmtime(full_path), # Add modification time "created": os.path.getctime(full_path), # Add creation time "size": os.path.getsize(full_path) # Add file size } result.append(file_info) except Exception as e: logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.") continue for d in subdirs: path: str = os.path.join(dirpath, d) try: dirs[path] = os.path.getmtime(path) except FileNotFoundError: logging.warning(f"Warning: Unable to access {path}. Skipping this path.") continue return result, dirs, time.perf_counter() def get_model_previews(self, filepath: str) -> list[str | BytesIO]: dirname = os.path.dirname(filepath) if not os.path.exists(dirname): return [] basename = os.path.splitext(filepath)[0] match_files = glob.glob(f"{basename}.*", recursive=False) image_files = filter_files_content_types(match_files, "image") safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None) safetensors_metadata = {} result: list[str | BytesIO] = [] for filename in image_files: _basename = os.path.splitext(filename)[0] if _basename == basename: result.append(filename) if _basename == f"{basename}.preview": result.append(filename) if safetensors_file: safetensors_filepath = os.path.join(dirname, safetensors_file) header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024) if header: safetensors_metadata = json.loads(header) safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None) if safetensors_images: safetensors_images = json.loads(safetensors_images) for image in safetensors_images: result.append(BytesIO(base64.b64decode(image))) return result def __exit__(self, exc_type, exc_value, traceback): self.clear_cache() ================================================ FILE: app/node_replace_manager.py ================================================ from __future__ import annotations from aiohttp import web from typing import TYPE_CHECKING, TypedDict if TYPE_CHECKING: from comfy_api.latest._io_public import NodeReplace from comfy_execution.graph_utils import is_link import nodes class NodeStruct(TypedDict): inputs: dict[str, str | int | float | bool | tuple[str, int]] class_type: str _meta: dict[str, str] def copy_node_struct(node_struct: NodeStruct, empty_inputs: bool = False) -> NodeStruct: new_node_struct = node_struct.copy() if empty_inputs: new_node_struct["inputs"] = {} else: new_node_struct["inputs"] = node_struct["inputs"].copy() new_node_struct["_meta"] = node_struct["_meta"].copy() return new_node_struct class NodeReplaceManager: """Manages node replacement registrations.""" def __init__(self): self._replacements: dict[str, list[NodeReplace]] = {} def register(self, node_replace: NodeReplace): """Register a node replacement mapping.""" self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace) def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None: """Get replacements for an old node ID.""" return self._replacements.get(old_node_id) def has_replacement(self, old_node_id: str) -> bool: """Check if a replacement exists for an old node ID.""" return old_node_id in self._replacements def apply_replacements(self, prompt: dict[str, NodeStruct]): connections: dict[str, list[tuple[str, str, int]]] = {} need_replacement: set[str] = set() for node_number, node_struct in prompt.items(): if "class_type" not in node_struct or "inputs" not in node_struct: continue class_type = node_struct["class_type"] # need replacement if not in NODE_CLASS_MAPPINGS and has replacement if class_type not in nodes.NODE_CLASS_MAPPINGS.keys() and self.has_replacement(class_type): need_replacement.add(node_number) # keep track of connections for input_id, input_value in node_struct["inputs"].items(): if is_link(input_value): conn_number = input_value[0] connections.setdefault(conn_number, []).append((node_number, input_id, input_value[1])) for node_number in need_replacement: node_struct = prompt[node_number] class_type = node_struct["class_type"] replacements = self.get_replacement(class_type) if replacements is None: continue # just use the first replacement replacement = replacements[0] new_node_id = replacement.new_node_id # if replacement is not a valid node, skip trying to replace it as will only cause confusion if new_node_id not in nodes.NODE_CLASS_MAPPINGS.keys(): continue # first, replace node id (class_type) new_node_struct = copy_node_struct(node_struct, empty_inputs=True) new_node_struct["class_type"] = new_node_id # TODO: consider replacing display_name in _meta as well for error reporting purposes; would need to query node schema # second, replace inputs if replacement.input_mapping is not None: for input_map in replacement.input_mapping: if "set_value" in input_map: new_node_struct["inputs"][input_map["new_id"]] = input_map["set_value"] elif "old_id" in input_map: new_node_struct["inputs"][input_map["new_id"]] = node_struct["inputs"][input_map["old_id"]] # finalize input replacement prompt[node_number] = new_node_struct # third, replace outputs if replacement.output_mapping is not None: # re-mapping outputs requires changing the input values of nodes that receive connections from this one if node_number in connections: for conns in connections[node_number]: conn_node_number, conn_input_id, old_output_idx = conns for output_map in replacement.output_mapping: if output_map["old_idx"] == old_output_idx: new_output_idx = output_map["new_idx"] previous_input = prompt[conn_node_number]["inputs"][conn_input_id] previous_input[1] = new_output_idx def as_dict(self): """Serialize all replacements to dict.""" return { k: [v.as_dict() for v in v_list] for k, v_list in self._replacements.items() } def add_routes(self, routes): @routes.get("/node_replacements") async def get_node_replacements(request): return web.json_response(self.as_dict()) ================================================ FILE: app/subgraph_manager.py ================================================ from __future__ import annotations from typing import TypedDict import os import folder_paths import glob from aiohttp import web import hashlib class Source: custom_node = "custom_node" templates = "templates" class SubgraphEntry(TypedDict): source: str """ Source of subgraph - custom_nodes vs templates. """ path: str """ Relative path of the subgraph file. For custom nodes, will be the relative directory like /subgraphs/.json """ name: str """ Name of subgraph file. """ info: CustomNodeSubgraphEntryInfo """ Additional info about subgraph; in the case of custom_nodes, will contain nodepack name """ data: str class CustomNodeSubgraphEntryInfo(TypedDict): node_pack: str """Node pack name.""" class SubgraphManager: def __init__(self): self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None self.cached_blueprint_subgraphs: dict[SubgraphEntry] | None = None def _create_entry(self, file: str, source: str, node_pack: str) -> tuple[str, SubgraphEntry]: """Create a subgraph entry from a file path. Expects normalized path (forward slashes).""" entry_id = hashlib.sha256(f"{source}{file}".encode()).hexdigest() entry: SubgraphEntry = { "source": source, "name": os.path.splitext(os.path.basename(file))[0], "path": file, "info": {"node_pack": node_pack}, } return entry_id, entry async def load_entry_data(self, entry: SubgraphEntry): with open(entry['path'], 'r', encoding='utf-8') as f: entry['data'] = f.read() return entry async def sanitize_entry(self, entry: SubgraphEntry | None, remove_data=False) -> SubgraphEntry | None: if entry is None: return None entry = entry.copy() entry.pop('path', None) if remove_data: entry.pop('data', None) return entry async def sanitize_entries(self, entries: dict[str, SubgraphEntry], remove_data=False) -> dict[str, SubgraphEntry]: entries = entries.copy() for key in list(entries.keys()): entries[key] = await self.sanitize_entry(entries[key], remove_data) return entries async def get_custom_node_subgraphs(self, loadedModules, force_reload=False): """Load subgraphs from custom nodes.""" if not force_reload and self.cached_custom_node_subgraphs is not None: return self.cached_custom_node_subgraphs subgraphs_dict: dict[SubgraphEntry] = {} for folder in folder_paths.get_folder_paths("custom_nodes"): pattern = os.path.join(folder, "*/subgraphs/*.json") for file in glob.glob(pattern): file = file.replace('\\', '/') node_pack = "custom_nodes." + file.split('/')[-3] entry_id, entry = self._create_entry(file, Source.custom_node, node_pack) subgraphs_dict[entry_id] = entry self.cached_custom_node_subgraphs = subgraphs_dict return subgraphs_dict async def get_blueprint_subgraphs(self, force_reload=False): """Load subgraphs from the blueprints directory.""" if not force_reload and self.cached_blueprint_subgraphs is not None: return self.cached_blueprint_subgraphs subgraphs_dict: dict[SubgraphEntry] = {} blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints') if os.path.exists(blueprints_dir): for file in glob.glob(os.path.join(blueprints_dir, "*.json")): file = file.replace('\\', '/') entry_id, entry = self._create_entry(file, Source.templates, "comfyui") subgraphs_dict[entry_id] = entry self.cached_blueprint_subgraphs = subgraphs_dict return subgraphs_dict async def get_all_subgraphs(self, loadedModules, force_reload=False): """Get all subgraphs from all sources (custom nodes and blueprints).""" custom_node_subgraphs = await self.get_custom_node_subgraphs(loadedModules, force_reload) blueprint_subgraphs = await self.get_blueprint_subgraphs(force_reload) return {**custom_node_subgraphs, **blueprint_subgraphs} async def get_subgraph(self, id: str, loadedModules): """Get a specific subgraph by ID from any source.""" entry = (await self.get_all_subgraphs(loadedModules)).get(id) if entry is not None and entry.get('data') is None: await self.load_entry_data(entry) return entry def add_routes(self, routes, loadedModules): @routes.get("/global_subgraphs") async def get_global_subgraphs(request): subgraphs_dict = await self.get_all_subgraphs(loadedModules) return web.json_response(await self.sanitize_entries(subgraphs_dict, remove_data=True)) @routes.get("/global_subgraphs/{id}") async def get_global_subgraph(request): id = request.match_info.get("id", None) subgraph = await self.get_subgraph(id, loadedModules) return web.json_response(await self.sanitize_entry(subgraph)) ================================================ FILE: app/user_manager.py ================================================ from __future__ import annotations import json import os import re import uuid import glob import shutil import logging import tempfile from aiohttp import web from urllib import parse from comfy.cli_args import args import folder_paths from .app_settings import AppSettings from typing import TypedDict default_user = "default" class FileInfo(TypedDict): path: str size: int modified: int created: int def get_file_info(path: str, relative_to: str) -> FileInfo: return { "path": os.path.relpath(path, relative_to).replace(os.sep, '/'), "size": os.path.getsize(path), "modified": os.path.getmtime(path), "created": os.path.getctime(path) } class UserManager(): def __init__(self): user_directory = folder_paths.get_user_directory() self.settings = AppSettings(self) if not os.path.exists(user_directory): os.makedirs(user_directory, exist_ok=True) if not args.multi_user: logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******") logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******") if args.multi_user: if os.path.isfile(self.get_users_file()): with open(self.get_users_file()) as f: self.users = json.load(f) else: self.users = {} else: self.users = {"default": "default"} def get_users_file(self): return os.path.join(folder_paths.get_user_directory(), "users.json") def get_request_user_id(self, request): user = "default" if args.multi_user and "comfy-user" in request.headers: user = request.headers["comfy-user"] # Block System Users (use same error message to prevent probing) if user.startswith(folder_paths.SYSTEM_USER_PREFIX): raise KeyError("Unknown user: " + user) if user not in self.users: raise KeyError("Unknown user: " + user) return user def get_request_user_filepath(self, request, file, type="userdata", create_dir=True): if type == "userdata": root_dir = folder_paths.get_user_directory() else: raise KeyError("Unknown filepath type:" + type) user = self.get_request_user_id(request) user_root = folder_paths.get_public_user_directory(user) if user_root is None: return None path = user_root # prevent leaving /{type} if os.path.commonpath((root_dir, user_root)) != root_dir: return None if file is not None: # Check if filename is url encoded if "%" in file: file = parse.unquote(file) # prevent leaving /{type}/{user} path = os.path.abspath(os.path.join(user_root, file)) if os.path.commonpath((user_root, path)) != user_root: return None parent = os.path.split(path)[0] if create_dir and not os.path.exists(parent): os.makedirs(parent, exist_ok=True) return path def add_user(self, name): name = name.strip() if not name: raise ValueError("username not provided") if name.startswith(folder_paths.SYSTEM_USER_PREFIX): raise ValueError("System User prefix not allowed") user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name) if user_id.startswith(folder_paths.SYSTEM_USER_PREFIX): raise ValueError("System User prefix not allowed") user_id = user_id + "_" + str(uuid.uuid4()) self.users[user_id] = name with open(self.get_users_file(), "w") as f: json.dump(self.users, f) return user_id def add_routes(self, routes): self.settings.add_routes(routes) @routes.get("/users") async def get_users(request): if args.multi_user: return web.json_response({"storage": "server", "users": self.users}) else: user_dir = self.get_request_user_filepath(request, None, create_dir=False) return web.json_response({ "storage": "server", "migrated": os.path.exists(user_dir) }) @routes.post("/users") async def post_users(request): body = await request.json() username = body["username"] if username in self.users.values(): return web.json_response({"error": "Duplicate username."}, status=400) try: user_id = self.add_user(username) except ValueError as e: return web.json_response({"error": str(e)}, status=400) return web.json_response(user_id) @routes.get("/userdata") async def listuserdata(request): """ List user data files in a specified directory. This endpoint allows listing files in a user's data directory, with options for recursion, full file information, and path splitting. Query Parameters: - dir (required): The directory to list files from. - recurse (optional): If "true", recursively list files in subdirectories. - full_info (optional): If "true", return detailed file information (path, size, modified time). - split (optional): If "true", split file paths into components (only applies when full_info is false). Returns: - 400: If 'dir' parameter is missing. - 403: If the requested path is not allowed. - 404: If the requested directory does not exist. - 200: JSON response with the list of files or file information. The response format depends on the query parameters: - Default: List of relative file paths. - full_info=true: List of dictionaries with file details. - split=true (and full_info=false): List of lists, each containing path components. """ directory = request.rel_url.query.get('dir', '') if not directory: return web.Response(status=400, text="Directory not provided") path = self.get_request_user_filepath(request, directory) if not path: return web.Response(status=403, text="Invalid directory") if not os.path.exists(path): return web.Response(status=404, text="Directory not found") recurse = request.rel_url.query.get('recurse', '').lower() == "true" full_info = request.rel_url.query.get('full_info', '').lower() == "true" split_path = request.rel_url.query.get('split', '').lower() == "true" # Use different patterns based on whether we're recursing or not if recurse: pattern = os.path.join(glob.escape(path), '**', '*') else: pattern = os.path.join(glob.escape(path), '*') def process_full_path(full_path: str) -> FileInfo | str | list[str]: if full_info: return get_file_info(full_path, path) rel_path = os.path.relpath(full_path, path).replace(os.sep, '/') if split_path: return [rel_path] + rel_path.split('/') return rel_path results = [ process_full_path(full_path) for full_path in glob.glob(pattern, recursive=recurse) if os.path.isfile(full_path) ] return web.json_response(results) @routes.get("/v2/userdata") async def list_userdata_v2(request): """ List files and directories in a user's data directory. This endpoint provides a structured listing of contents within a specified subdirectory of the user's data storage. Query Parameters: - path (optional): The relative path within the user's data directory to list. Defaults to the root (''). Returns: - 400: If the requested path is invalid, outside the user's data directory, or is not a directory. - 404: If the requested path does not exist. - 403: If the user is invalid. - 500: If there is an error reading the directory contents. - 200: JSON response containing a list of file and directory objects. Each object includes: - name: The name of the file or directory. - type: 'file' or 'directory'. - path: The relative path from the user's data root. - size (for files): The size in bytes. - modified (for files): The last modified timestamp (Unix epoch). """ requested_rel_path = request.rel_url.query.get('path', '') # URL-decode the path parameter try: requested_rel_path = parse.unquote(requested_rel_path) except Exception as e: logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}") return web.Response(status=400, text="Invalid characters in path parameter") # Check user validity and get the absolute path for the requested directory try: base_user_path = self.get_request_user_filepath(request, None, create_dir=False) if requested_rel_path: target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False) else: target_abs_path = base_user_path except KeyError as e: # Invalid user detected by get_request_user_id inside get_request_user_filepath logging.warning(f"Access denied for user: {e}") return web.Response(status=403, text="Invalid user specified in request") if not target_abs_path: # Path traversal or other issue detected by get_request_user_filepath return web.Response(status=400, text="Invalid path requested") # Handle cases where the user directory or target path doesn't exist if not os.path.exists(target_abs_path): # Check if it's the base user directory that's missing (new user case) if target_abs_path == base_user_path: # It's okay if the base user directory doesn't exist yet, return empty list return web.json_response([]) else: # A specific subdirectory was requested but doesn't exist return web.Response(status=404, text="Requested path not found") if not os.path.isdir(target_abs_path): return web.Response(status=400, text="Requested path is not a directory") results = [] try: for root, dirs, files in os.walk(target_abs_path, topdown=True): # Process directories for dir_name in dirs: dir_path = os.path.join(root, dir_name) rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/') results.append({ "name": dir_name, "path": rel_path, "type": "directory" }) # Process files for file_name in files: file_path = os.path.join(root, file_name) rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/') entry_info = { "name": file_name, "path": rel_path, "type": "file" } try: stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk entry_info["size"] = stats.st_size entry_info["modified"] = stats.st_mtime except OSError as stat_error: logging.warning(f"Could not stat file {file_path}: {stat_error}") pass # Include file with available info results.append(entry_info) except OSError as e: logging.error(f"Error listing directory {target_abs_path}: {e}") return web.Response(status=500, text="Error reading directory contents") # Sort results alphabetically, directories first then files results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower())) return web.json_response(results) def get_user_data_path(request, check_exists = False, param = "file"): file = request.match_info.get(param, None) if not file: return web.Response(status=400) path = self.get_request_user_filepath(request, file) if not path: return web.Response(status=403) if check_exists and not os.path.exists(path): return web.Response(status=404) return path @routes.get("/userdata/{file}") async def getuserdata(request): path = get_user_data_path(request, check_exists=True) if not isinstance(path, str): return path return web.FileResponse(path) @routes.post("/userdata/{file}") async def post_userdata(request): """ Upload or update a user data file. This endpoint handles file uploads to a user's data directory, with options for controlling overwrite behavior and response format. Query Parameters: - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". - full_info (optional): If "true", returns detailed file information (path, size, modified time). If "false", returns only the relative file path. Path Parameters: - file: The target file path (URL encoded if necessary). Returns: - 400: If 'file' parameter is missing. - 403: If the requested path is not allowed. - 409: If overwrite=false and the file already exists. - 200: JSON response with either: - Full file information (if full_info=true) - Relative file path (if full_info=false) The request body should contain the raw file content to be written. """ path = get_user_data_path(request) if not isinstance(path, str): return path overwrite = request.query.get("overwrite", 'true') != "false" full_info = request.query.get('full_info', 'false').lower() == "true" if not overwrite and os.path.exists(path): return web.Response(status=409, text="File already exists") try: body = await request.read() dir_name = os.path.dirname(path) fd, tmp_path = tempfile.mkstemp(dir=dir_name) try: with os.fdopen(fd, "wb") as f: f.write(body) os.replace(tmp_path, path) except: os.unlink(tmp_path) raise except OSError as e: logging.warning(f"Error saving file '{path}': {e}") return web.Response( status=400, reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|" ) user_path = self.get_request_user_filepath(request, None) if full_info: resp = get_file_info(path, user_path) else: resp = os.path.relpath(path, user_path) return web.json_response(resp) @routes.delete("/userdata/{file}") async def delete_userdata(request): path = get_user_data_path(request, check_exists=True) if not isinstance(path, str): return path os.remove(path) return web.Response(status=204) @routes.post("/userdata/{file}/move/{dest}") async def move_userdata(request): """ Move or rename a user data file. This endpoint handles moving or renaming files within a user's data directory, with options for controlling overwrite behavior and response format. Path Parameters: - file: The source file path (URL encoded if necessary) - dest: The destination file path (URL encoded if necessary) Query Parameters: - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". - full_info (optional): If "true", returns detailed file information (path, size, modified time). If "false", returns only the relative file path. Returns: - 400: If either 'file' or 'dest' parameter is missing - 403: If either requested path is not allowed - 404: If the source file does not exist - 409: If overwrite=false and the destination file already exists - 200: JSON response with either: - Full file information (if full_info=true) - Relative file path (if full_info=false) """ source = get_user_data_path(request, check_exists=True) if not isinstance(source, str): return source dest = get_user_data_path(request, check_exists=False, param="dest") if not isinstance(dest, str): return dest overwrite = request.query.get("overwrite", 'true') != "false" full_info = request.query.get('full_info', 'false').lower() == "true" if not overwrite and os.path.exists(dest): return web.Response(status=409, text="File already exists") logging.info(f"moving '{source}' -> '{dest}'") shutil.move(source, dest) user_path = self.get_request_user_filepath(request, None) if full_info: resp = get_file_info(dest, user_path) else: resp = os.path.relpath(dest, user_path) return web.json_response(resp) ================================================ FILE: blueprints/.glsl/Brightness_and_Contrast_1.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform float u_float0; // Brightness slider -100..100 uniform float u_float1; // Contrast slider -100..100 in vec2 v_texCoord; out vec4 fragColor; const float MID_GRAY = 0.18; // 18% reflectance // sRGB gamma 2.2 approximation vec3 srgbToLinear(vec3 c) { return pow(max(c, 0.0), vec3(2.2)); } vec3 linearToSrgb(vec3 c) { return pow(max(c, 0.0), vec3(1.0/2.2)); } float mapBrightness(float b) { return clamp(b / 100.0, -1.0, 1.0); } float mapContrast(float c) { return clamp(c / 100.0 + 1.0, 0.0, 2.0); } void main() { vec4 orig = texture(u_image0, v_texCoord); float brightness = mapBrightness(u_float0); float contrast = mapContrast(u_float1); vec3 lin = srgbToLinear(orig.rgb); lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY; // Convert back to sRGB vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0)); fragColor = vec4(result, orig.a); } ================================================ FILE: blueprints/.glsl/Chromatic_Aberration_16.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform vec2 u_resolution; uniform int u_int0; // Mode uniform float u_float0; // Amount (0 to 100) in vec2 v_texCoord; out vec4 fragColor; const int MODE_LINEAR = 0; const int MODE_RADIAL = 1; const int MODE_BARREL = 2; const int MODE_SWIRL = 3; const int MODE_DIAGONAL = 4; const float AMOUNT_SCALE = 0.0005; const float RADIAL_MULT = 4.0; const float BARREL_MULT = 8.0; const float INV_SQRT2 = 0.70710678118; void main() { vec2 uv = v_texCoord; vec4 original = texture(u_image0, uv); float amount = u_float0 * AMOUNT_SCALE; if (amount < 0.000001) { fragColor = original; return; } // Aspect-corrected coordinates for circular effects float aspect = u_resolution.x / u_resolution.y; vec2 centered = uv - 0.5; vec2 corrected = vec2(centered.x * aspect, centered.y); float r = length(corrected); vec2 dir = r > 0.0001 ? corrected / r : vec2(0.0); vec2 offset = vec2(0.0); if (u_int0 == MODE_LINEAR) { // Horizontal shift (no aspect correction needed) offset = vec2(amount, 0.0); } else if (u_int0 == MODE_RADIAL) { // Outward from center, stronger at edges offset = dir * r * amount * RADIAL_MULT; offset.x /= aspect; // Convert back to UV space } else if (u_int0 == MODE_BARREL) { // Lens distortion simulation (r² falloff) offset = dir * r * r * amount * BARREL_MULT; offset.x /= aspect; // Convert back to UV space } else if (u_int0 == MODE_SWIRL) { // Perpendicular to radial (rotational aberration) vec2 perp = vec2(-dir.y, dir.x); offset = perp * r * amount * RADIAL_MULT; offset.x /= aspect; // Convert back to UV space } else if (u_int0 == MODE_DIAGONAL) { // 45° offset (no aspect correction needed) offset = vec2(amount, amount) * INV_SQRT2; } float red = texture(u_image0, uv + offset).r; float green = original.g; float blue = texture(u_image0, uv - offset).b; fragColor = vec4(red, green, blue, original.a); } ================================================ FILE: blueprints/.glsl/Color_Adjustment_15.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform float u_float0; // temperature (-100 to 100) uniform float u_float1; // tint (-100 to 100) uniform float u_float2; // vibrance (-100 to 100) uniform float u_float3; // saturation (-100 to 100) in vec2 v_texCoord; out vec4 fragColor; const float INPUT_SCALE = 0.01; const float TEMP_TINT_PRIMARY = 0.3; const float TEMP_TINT_SECONDARY = 0.15; const float VIBRANCE_BOOST = 2.0; const float SATURATION_BOOST = 2.0; const float SKIN_PROTECTION = 0.5; const float EPSILON = 0.001; const vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114); void main() { vec4 tex = texture(u_image0, v_texCoord); vec3 color = tex.rgb; // Scale inputs: -100/100 → -1/1 float temperature = u_float0 * INPUT_SCALE; float tint = u_float1 * INPUT_SCALE; float vibrance = u_float2 * INPUT_SCALE; float saturation = u_float3 * INPUT_SCALE; // Temperature (warm/cool): positive = warm, negative = cool color.r += temperature * TEMP_TINT_PRIMARY; color.b -= temperature * TEMP_TINT_PRIMARY; // Tint (green/magenta): positive = green, negative = magenta color.g += tint * TEMP_TINT_PRIMARY; color.r -= tint * TEMP_TINT_SECONDARY; color.b -= tint * TEMP_TINT_SECONDARY; // Single clamp after temperature/tint color = clamp(color, 0.0, 1.0); // Vibrance with skin protection if (vibrance != 0.0) { float maxC = max(color.r, max(color.g, color.b)); float minC = min(color.r, min(color.g, color.b)); float sat = maxC - minC; float gray = dot(color, LUMA_WEIGHTS); if (vibrance < 0.0) { // Desaturate: -100 → gray color = mix(vec3(gray), color, 1.0 + vibrance); } else { // Boost less saturated colors more float vibranceAmt = vibrance * (1.0 - sat); // Branchless skin tone protection float isWarmTone = step(color.b, color.g) * step(color.g, color.r); float warmth = (color.r - color.b) / max(maxC, EPSILON); float skinTone = isWarmTone * warmth * sat * (1.0 - sat); vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION); color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST); } } // Saturation if (saturation != 0.0) { float gray = dot(color, LUMA_WEIGHTS); float satMix = saturation < 0.0 ? 1.0 + saturation // -100 → gray : 1.0 + saturation * SATURATION_BOOST; // +100 → 3x boost color = mix(vec3(gray), color, satMix); } fragColor = vec4(clamp(color, 0.0, 1.0), tex.a); } ================================================ FILE: blueprints/.glsl/Edge-Preserving_Blur_128.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform float u_float0; // Blur radius (0–20, default ~5) uniform float u_float1; // Edge threshold (0–100, default ~30) uniform int u_int0; // Step size (0/1 = every pixel, 2+ = skip pixels) in vec2 v_texCoord; out vec4 fragColor; const int MAX_RADIUS = 20; const float EPSILON = 0.0001; // Perceptual luminance float getLuminance(vec3 rgb) { return dot(rgb, vec3(0.299, 0.587, 0.114)); } vec4 bilateralFilter(vec2 uv, vec2 texelSize, int radius, float sigmaSpatial, float sigmaColor) { vec4 center = texture(u_image0, uv); vec3 centerRGB = center.rgb; float invSpatial2 = -0.5 / (sigmaSpatial * sigmaSpatial); float invColor2 = -0.5 / (sigmaColor * sigmaColor + EPSILON); vec3 sumRGB = vec3(0.0); float sumWeight = 0.0; int step = max(u_int0, 1); float radius2 = float(radius * radius); for (int dy = -MAX_RADIUS; dy <= MAX_RADIUS; dy++) { if (dy < -radius || dy > radius) continue; if (abs(dy) % step != 0) continue; for (int dx = -MAX_RADIUS; dx <= MAX_RADIUS; dx++) { if (dx < -radius || dx > radius) continue; if (abs(dx) % step != 0) continue; vec2 offset = vec2(float(dx), float(dy)); float dist2 = dot(offset, offset); if (dist2 > radius2) continue; vec3 sampleRGB = texture(u_image0, uv + offset * texelSize).rgb; // Spatial Gaussian float spatialWeight = exp(dist2 * invSpatial2); // Perceptual color distance (weighted RGB) vec3 diff = sampleRGB - centerRGB; float colorDist = dot(diff * diff, vec3(0.299, 0.587, 0.114)); float colorWeight = exp(colorDist * invColor2); float w = spatialWeight * colorWeight; sumRGB += sampleRGB * w; sumWeight += w; } } vec3 resultRGB = sumRGB / max(sumWeight, EPSILON); return vec4(resultRGB, center.a); // preserve center alpha } void main() { vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0)); float radiusF = clamp(u_float0, 0.0, float(MAX_RADIUS)); int radius = int(radiusF + 0.5); if (radius == 0) { fragColor = texture(u_image0, v_texCoord); return; } // Edge threshold → color sigma // Squared curve for better low-end control float t = clamp(u_float1, 0.0, 100.0) / 100.0; t *= t; float sigmaColor = mix(0.01, 0.5, t); // Spatial sigma tied to radius float sigmaSpatial = max(radiusF * 0.75, 0.5); fragColor = bilateralFilter( v_texCoord, texelSize, radius, sigmaSpatial, sigmaColor ); } ================================================ FILE: blueprints/.glsl/Film_Grain_15.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform vec2 u_resolution; uniform float u_float0; // grain amount [0.0 – 1.0] typical: 0.2–0.8 uniform float u_float1; // grain size [0.3 – 3.0] lower = finer grain uniform float u_float2; // color amount [0.0 – 1.0] 0 = monochrome, 1 = RGB grain uniform float u_float3; // luminance bias [0.0 – 1.0] 0 = uniform, 1 = shadows only uniform int u_int0; // noise mode [0 or 1] 0 = smooth, 1 = grainy in vec2 v_texCoord; layout(location = 0) out vec4 fragColor0; // High-quality integer hash (pcg-like) uint pcg(uint v) { uint state = v * 747796405u + 2891336453u; uint word = ((state >> ((state >> 28u) + 4u)) ^ state) * 277803737u; return (word >> 22u) ^ word; } // 2D -> 1D hash input uint hash2d(uvec2 p) { return pcg(p.x + pcg(p.y)); } // Hash to float [0, 1] float hashf(uvec2 p) { return float(hash2d(p)) / float(0xffffffffu); } // Hash to float with offset (for RGB channels) float hashf(uvec2 p, uint offset) { return float(pcg(hash2d(p) + offset)) / float(0xffffffffu); } // Convert uniform [0,1] to roughly Gaussian distribution // Using simple approximation: average of multiple samples float toGaussian(uvec2 p) { float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u); return (sum - 2.0) * 0.7; // Centered, scaled } float toGaussian(uvec2 p, uint offset) { float sum = hashf(p, offset) + hashf(p, offset + 1u) + hashf(p, offset + 2u) + hashf(p, offset + 3u); return (sum - 2.0) * 0.7; } // Smooth noise with better interpolation float smoothNoise(vec2 p) { vec2 i = floor(p); vec2 f = fract(p); // Quintic interpolation (less banding than cubic) f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0); uvec2 ui = uvec2(i); float a = toGaussian(ui); float b = toGaussian(ui + uvec2(1u, 0u)); float c = toGaussian(ui + uvec2(0u, 1u)); float d = toGaussian(ui + uvec2(1u, 1u)); return mix(mix(a, b, f.x), mix(c, d, f.x), f.y); } float smoothNoise(vec2 p, uint offset) { vec2 i = floor(p); vec2 f = fract(p); f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0); uvec2 ui = uvec2(i); float a = toGaussian(ui, offset); float b = toGaussian(ui + uvec2(1u, 0u), offset); float c = toGaussian(ui + uvec2(0u, 1u), offset); float d = toGaussian(ui + uvec2(1u, 1u), offset); return mix(mix(a, b, f.x), mix(c, d, f.x), f.y); } void main() { vec4 color = texture(u_image0, v_texCoord); // Luminance (Rec.709) float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722)); // Grain UV (resolution-independent) vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01); uvec2 grainPixel = uvec2(grainUV); float g; vec3 grainRGB; if (u_int0 == 1) { // Grainy mode: pure hash noise (no interpolation = no banding) g = toGaussian(grainPixel); grainRGB = vec3( toGaussian(grainPixel, 100u), toGaussian(grainPixel, 200u), toGaussian(grainPixel, 300u) ); } else { // Smooth mode: interpolated with quintic curve g = smoothNoise(grainUV); grainRGB = vec3( smoothNoise(grainUV, 100u), smoothNoise(grainUV, 200u), smoothNoise(grainUV, 300u) ); } // Luminance weighting (less grain in highlights) float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0)); // Strength float strength = u_float0 * 0.15; // Color vs monochrome grain vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0)); color.rgb += grainColor * strength * lumWeight; fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a); } ================================================ FILE: blueprints/.glsl/Glow_30.frag ================================================ #version 300 es precision mediump float; uniform sampler2D u_image0; uniform vec2 u_resolution; uniform int u_int0; // Blend mode uniform int u_int1; // Color tint uniform float u_float0; // Intensity uniform float u_float1; // Radius uniform float u_float2; // Threshold in vec2 v_texCoord; out vec4 fragColor; const int BLEND_ADD = 0; const int BLEND_SCREEN = 1; const int BLEND_SOFT = 2; const int BLEND_OVERLAY = 3; const int BLEND_LIGHTEN = 4; const float GOLDEN_ANGLE = 2.39996323; const int MAX_SAMPLES = 48; const vec3 LUMA = vec3(0.299, 0.587, 0.114); float hash(vec2 p) { p = fract(p * vec2(123.34, 456.21)); p += dot(p, p + 45.32); return fract(p.x * p.y); } vec3 hexToRgb(int h) { return vec3( float((h >> 16) & 255), float((h >> 8) & 255), float(h & 255) ) * (1.0 / 255.0); } vec3 blend(vec3 base, vec3 glow, int mode) { if (mode == BLEND_SCREEN) { return 1.0 - (1.0 - base) * (1.0 - glow); } if (mode == BLEND_SOFT) { return mix( base - (1.0 - 2.0 * glow) * base * (1.0 - base), base + (2.0 * glow - 1.0) * (sqrt(base) - base), step(0.5, glow) ); } if (mode == BLEND_OVERLAY) { return mix( 2.0 * base * glow, 1.0 - 2.0 * (1.0 - base) * (1.0 - glow), step(0.5, base) ); } if (mode == BLEND_LIGHTEN) { return max(base, glow); } return base + glow; } void main() { vec4 original = texture(u_image0, v_texCoord); float intensity = u_float0 * 0.05; float radius = u_float1 * u_float1 * 0.012; if (intensity < 0.001 || radius < 0.1) { fragColor = original; return; } float threshold = 1.0 - u_float2 * 0.01; float t0 = threshold - 0.15; float t1 = threshold + 0.15; vec2 texelSize = 1.0 / u_resolution; float radius2 = radius * radius; float sampleScale = clamp(radius * 0.75, 0.35, 1.0); int samples = int(float(MAX_SAMPLES) * sampleScale); float noise = hash(gl_FragCoord.xy); float angleOffset = noise * GOLDEN_ANGLE; float radiusJitter = 0.85 + noise * 0.3; float ca = cos(GOLDEN_ANGLE); float sa = sin(GOLDEN_ANGLE); vec2 dir = vec2(cos(angleOffset), sin(angleOffset)); vec3 glow = vec3(0.0); float totalWeight = 0.0; // Center tap float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA)); glow += original.rgb * centerMask * 2.0; totalWeight += 2.0; for (int i = 1; i < MAX_SAMPLES; i++) { if (i >= samples) break; float fi = float(i); float dist = sqrt(fi / float(samples)) * radius * radiusJitter; vec2 offset = dir * dist * texelSize; vec3 c = texture(u_image0, v_texCoord + offset).rgb; float mask = smoothstep(t0, t1, dot(c, LUMA)); float w = 1.0 - (dist * dist) / (radius2 * 1.5); w = max(w, 0.0); w *= w; glow += c * mask * w; totalWeight += w; dir = vec2( dir.x * ca - dir.y * sa, dir.x * sa + dir.y * ca ); } glow *= intensity / max(totalWeight, 0.001); if (u_int1 > 0) { glow *= hexToRgb(u_int1); } vec3 result = blend(original.rgb, glow, u_int0); result += (noise - 0.5) * (1.0 / 255.0); fragColor = vec4(clamp(result, 0.0, 1.0), original.a); } ================================================ FILE: blueprints/.glsl/Hue_and_Saturation_1.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform int u_int0; // Mode: 0=Master, 1=Reds, 2=Yellows, 3=Greens, 4=Cyans, 5=Blues, 6=Magentas, 7=Colorize uniform int u_int1; // Color Space: 0=HSL, 1=HSB/HSV uniform float u_float0; // Hue (-180 to 180) uniform float u_float1; // Saturation (-100 to 100) uniform float u_float2; // Lightness/Brightness (-100 to 100) uniform float u_float3; // Overlap (0 to 100) - feathering between adjacent color ranges in vec2 v_texCoord; out vec4 fragColor; // Color range modes const int MODE_MASTER = 0; const int MODE_RED = 1; const int MODE_YELLOW = 2; const int MODE_GREEN = 3; const int MODE_CYAN = 4; const int MODE_BLUE = 5; const int MODE_MAGENTA = 6; const int MODE_COLORIZE = 7; // Color space modes const int COLORSPACE_HSL = 0; const int COLORSPACE_HSB = 1; const float EPSILON = 0.0001; //============================================================================= // RGB <-> HSL Conversions //============================================================================= vec3 rgb2hsl(vec3 c) { float maxC = max(max(c.r, c.g), c.b); float minC = min(min(c.r, c.g), c.b); float delta = maxC - minC; float h = 0.0; float s = 0.0; float l = (maxC + minC) * 0.5; if (delta > EPSILON) { s = l < 0.5 ? delta / (maxC + minC) : delta / (2.0 - maxC - minC); if (maxC == c.r) { h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0); } else if (maxC == c.g) { h = (c.b - c.r) / delta + 2.0; } else { h = (c.r - c.g) / delta + 4.0; } h /= 6.0; } return vec3(h, s, l); } float hue2rgb(float p, float q, float t) { t = fract(t); if (t < 1.0/6.0) return p + (q - p) * 6.0 * t; if (t < 0.5) return q; if (t < 2.0/3.0) return p + (q - p) * (2.0/3.0 - t) * 6.0; return p; } vec3 hsl2rgb(vec3 hsl) { if (hsl.y < EPSILON) return vec3(hsl.z); float q = hsl.z < 0.5 ? hsl.z * (1.0 + hsl.y) : hsl.z + hsl.y - hsl.z * hsl.y; float p = 2.0 * hsl.z - q; return vec3( hue2rgb(p, q, hsl.x + 1.0/3.0), hue2rgb(p, q, hsl.x), hue2rgb(p, q, hsl.x - 1.0/3.0) ); } vec3 rgb2hsb(vec3 c) { float maxC = max(max(c.r, c.g), c.b); float minC = min(min(c.r, c.g), c.b); float delta = maxC - minC; float h = 0.0; float s = (maxC > EPSILON) ? delta / maxC : 0.0; float b = maxC; if (delta > EPSILON) { if (maxC == c.r) { h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0); } else if (maxC == c.g) { h = (c.b - c.r) / delta + 2.0; } else { h = (c.r - c.g) / delta + 4.0; } h /= 6.0; } return vec3(h, s, b); } vec3 hsb2rgb(vec3 hsb) { vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0); return hsb.z * mix(vec3(1.0), rgb, hsb.y); } //============================================================================= // Color Range Weight Calculation //============================================================================= float hueDistance(float a, float b) { float d = abs(a - b); return min(d, 1.0 - d); } float getHueWeight(float hue, float center, float overlap) { float baseWidth = 1.0 / 6.0; float feather = baseWidth * overlap; float d = hueDistance(hue, center); float inner = baseWidth * 0.5; float outer = inner + feather; return 1.0 - smoothstep(inner, outer, d); } float getModeWeight(float hue, int mode, float overlap) { if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0; if (mode == MODE_RED) { return max( getHueWeight(hue, 0.0, overlap), getHueWeight(hue, 1.0, overlap) ); } float center = float(mode - 1) / 6.0; return getHueWeight(hue, center, overlap); } //============================================================================= // Adjustment Functions //============================================================================= float adjustLightness(float l, float amount) { return amount > 0.0 ? l + (1.0 - l) * amount : l + l * amount; } float adjustBrightness(float b, float amount) { return clamp(b + amount, 0.0, 1.0); } float adjustSaturation(float s, float amount) { return amount > 0.0 ? s + (1.0 - s) * amount : s + s * amount; } vec3 colorize(vec3 rgb, float hue, float sat, float light) { float lum = dot(rgb, vec3(0.299, 0.587, 0.114)); float l = adjustLightness(lum, light); vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0)); return hsl2rgb(hsl); } //============================================================================= // Main //============================================================================= void main() { vec4 original = texture(u_image0, v_texCoord); float hueShift = u_float0 / 360.0; // -180..180 -> -0.5..0.5 float satAmount = u_float1 / 100.0; // -100..100 -> -1..1 float lightAmount= u_float2 / 100.0; // -100..100 -> -1..1 float overlap = u_float3 / 100.0; // 0..100 -> 0..1 vec3 result; if (u_int0 == MODE_COLORIZE) { result = colorize(original.rgb, hueShift, satAmount, lightAmount); fragColor = vec4(result, original.a); return; } vec3 hsx = (u_int1 == COLORSPACE_HSL) ? rgb2hsl(original.rgb) : rgb2hsb(original.rgb); float weight = getModeWeight(hsx.x, u_int0, overlap); if (u_int0 != MODE_MASTER && hsx.y < EPSILON) { weight = 0.0; } if (weight > EPSILON) { float h = fract(hsx.x + hueShift * weight); float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0); float v = (u_int1 == COLORSPACE_HSL) ? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0) : clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0); vec3 adjusted = vec3(h, s, v); result = (u_int1 == COLORSPACE_HSL) ? hsl2rgb(adjusted) : hsb2rgb(adjusted); } else { result = original.rgb; } fragColor = vec4(result, original.a); } ================================================ FILE: blueprints/.glsl/Image_Blur_1.frag ================================================ #version 300 es #pragma passes 2 precision highp float; // Blur type constants const int BLUR_GAUSSIAN = 0; const int BLUR_BOX = 1; const int BLUR_RADIAL = 2; // Radial blur config const int RADIAL_SAMPLES = 12; const float RADIAL_STRENGTH = 0.0003; uniform sampler2D u_image0; uniform vec2 u_resolution; uniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL) uniform float u_float0; // Blur radius/amount uniform int u_pass; // Pass index (0 = horizontal, 1 = vertical) in vec2 v_texCoord; layout(location = 0) out vec4 fragColor0; float gaussian(float x, float sigma) { return exp(-(x * x) / (2.0 * sigma * sigma)); } void main() { vec2 texelSize = 1.0 / u_resolution; float radius = max(u_float0, 0.0); // Radial (angular) blur - single pass, doesn't use separable if (u_int0 == BLUR_RADIAL) { // Only execute on first pass if (u_pass > 0) { fragColor0 = texture(u_image0, v_texCoord); return; } vec2 center = vec2(0.5); vec2 dir = v_texCoord - center; float dist = length(dir); if (dist < 1e-4) { fragColor0 = texture(u_image0, v_texCoord); return; } vec4 sum = vec4(0.0); float totalWeight = 0.0; float angleStep = radius * RADIAL_STRENGTH; dir /= dist; float cosStep = cos(angleStep); float sinStep = sin(angleStep); float negAngle = -float(RADIAL_SAMPLES) * angleStep; vec2 rotDir = vec2( dir.x * cos(negAngle) - dir.y * sin(negAngle), dir.x * sin(negAngle) + dir.y * cos(negAngle) ); for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) { vec2 uv = center + rotDir * dist; float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES); sum += texture(u_image0, uv) * w; totalWeight += w; rotDir = vec2( rotDir.x * cosStep - rotDir.y * sinStep, rotDir.x * sinStep + rotDir.y * cosStep ); } fragColor0 = sum / max(totalWeight, 0.001); return; } // Separable Gaussian / Box blur int samples = int(ceil(radius)); if (samples == 0) { fragColor0 = texture(u_image0, v_texCoord); return; } // Direction: pass 0 = horizontal, pass 1 = vertical vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0); vec4 color = vec4(0.0); float totalWeight = 0.0; float sigma = radius / 2.0; for (int i = -samples; i <= samples; i++) { vec2 offset = dir * float(i) * texelSize; vec4 sample_color = texture(u_image0, v_texCoord + offset); float weight; if (u_int0 == BLUR_GAUSSIAN) { weight = gaussian(float(i), sigma); } else { // BLUR_BOX weight = 1.0; } color += sample_color * weight; totalWeight += weight; } fragColor0 = color / totalWeight; } ================================================ FILE: blueprints/.glsl/Image_Channels_23.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; in vec2 v_texCoord; layout(location = 0) out vec4 fragColor0; layout(location = 1) out vec4 fragColor1; layout(location = 2) out vec4 fragColor2; layout(location = 3) out vec4 fragColor3; void main() { vec4 color = texture(u_image0, v_texCoord); // Output each channel as grayscale to separate render targets fragColor0 = vec4(vec3(color.r), 1.0); // Red channel fragColor1 = vec4(vec3(color.g), 1.0); // Green channel fragColor2 = vec4(vec3(color.b), 1.0); // Blue channel fragColor3 = vec4(vec3(color.a), 1.0); // Alpha channel } ================================================ FILE: blueprints/.glsl/Image_Levels_1.frag ================================================ #version 300 es precision highp float; // Levels Adjustment // u_int0: channel (0=RGB, 1=R, 2=G, 3=B) default: 0 // u_float0: input black (0-255) default: 0 // u_float1: input white (0-255) default: 255 // u_float2: gamma (0.01-9.99) default: 1.0 // u_float3: output black (0-255) default: 0 // u_float4: output white (0-255) default: 255 uniform sampler2D u_image0; uniform int u_int0; uniform float u_float0; uniform float u_float1; uniform float u_float2; uniform float u_float3; uniform float u_float4; in vec2 v_texCoord; out vec4 fragColor; vec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) { float inRange = max(inWhite - inBlack, 0.0001); vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0); result = pow(result, vec3(1.0 / gamma)); result = mix(vec3(outBlack), vec3(outWhite), result); return result; } float applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) { float inRange = max(inWhite - inBlack, 0.0001); float result = clamp((value - inBlack) / inRange, 0.0, 1.0); result = pow(result, 1.0 / gamma); result = mix(outBlack, outWhite, result); return result; } void main() { vec4 texColor = texture(u_image0, v_texCoord); vec3 color = texColor.rgb; float inBlack = u_float0 / 255.0; float inWhite = u_float1 / 255.0; float gamma = u_float2; float outBlack = u_float3 / 255.0; float outWhite = u_float4 / 255.0; vec3 result; if (u_int0 == 0) { result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite); } else if (u_int0 == 1) { result = color; result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite); } else if (u_int0 == 2) { result = color; result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite); } else if (u_int0 == 3) { result = color; result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite); } else { result = color; } fragColor = vec4(result, texColor.a); } ================================================ FILE: blueprints/.glsl/README.md ================================================ # GLSL Shader Sources This folder contains the GLSL fragment shaders extracted from blueprint JSON files for easier editing and version control. ## File Naming Convention `{Blueprint_Name}_{node_id}.frag` - **Blueprint_Name**: The JSON filename with spaces/special chars replaced by underscores - **node_id**: The GLSLShader node ID within the subgraph ## Usage ```bash # Extract shaders from blueprint JSONs to this folder python update_blueprints.py extract # Patch edited shaders back into blueprint JSONs python update_blueprints.py patch ``` ## Workflow 1. Run `extract` to pull current shaders from JSONs 2. Edit `.frag` files 3. Run `patch` to update the blueprint JSONs 4. Test 5. Commit both `.frag` files and updated JSONs ================================================ FILE: blueprints/.glsl/Sharpen_23.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform vec2 u_resolution; uniform float u_float0; // strength [0.0 – 2.0] typical: 0.3–1.0 in vec2 v_texCoord; layout(location = 0) out vec4 fragColor0; void main() { vec2 texel = 1.0 / u_resolution; // Sample center and neighbors vec4 center = texture(u_image0, v_texCoord); vec4 top = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y)); vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0, texel.y)); vec4 left = texture(u_image0, v_texCoord + vec2(-texel.x, 0.0)); vec4 right = texture(u_image0, v_texCoord + vec2( texel.x, 0.0)); // Edge enhancement (Laplacian) vec4 edges = center * 4.0 - top - bottom - left - right; // Add edges back scaled by strength vec4 sharpened = center + edges * u_float0; fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a); } ================================================ FILE: blueprints/.glsl/Unsharp_Mask_26.frag ================================================ #version 300 es precision highp float; uniform sampler2D u_image0; uniform vec2 u_resolution; uniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5 uniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels uniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen in vec2 v_texCoord; layout(location = 0) out vec4 fragColor0; float gaussian(float x, float sigma) { return exp(-(x * x) / (2.0 * sigma * sigma)); } float getLuminance(vec3 color) { return dot(color, vec3(0.2126, 0.7152, 0.0722)); } void main() { vec2 texel = 1.0 / u_resolution; float radius = max(u_float1, 0.5); float amount = u_float0; float threshold = u_float2; vec4 original = texture(u_image0, v_texCoord); // Gaussian blur for the "unsharp" mask int samples = int(ceil(radius)); float sigma = radius / 2.0; vec4 blurred = vec4(0.0); float totalWeight = 0.0; for (int x = -samples; x <= samples; x++) { for (int y = -samples; y <= samples; y++) { vec2 offset = vec2(float(x), float(y)) * texel; vec4 sample_color = texture(u_image0, v_texCoord + offset); float dist = length(vec2(float(x), float(y))); float weight = gaussian(dist, sigma); blurred += sample_color * weight; totalWeight += weight; } } blurred /= totalWeight; // Unsharp mask = original - blurred vec3 mask = original.rgb - blurred.rgb; // Luminance-based threshold with smooth falloff float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb)); float thresholdScale = smoothstep(0.0, threshold, lumaDelta); mask *= thresholdScale; // Sharpen: original + mask * amount vec3 sharpened = original.rgb + mask * amount; fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a); } ================================================ FILE: blueprints/.glsl/update_blueprints.py ================================================ #!/usr/bin/env python3 """ Shader Blueprint Updater Syncs GLSL shader files between this folder and blueprint JSON files. File naming convention: {Blueprint Name}_{node_id}.frag Usage: python update_blueprints.py extract # Extract shaders from JSONs to here python update_blueprints.py patch # Patch shaders back into JSONs python update_blueprints.py # Same as patch (default) """ import json import logging import sys import re from pathlib import Path logging.basicConfig(level=logging.INFO, format='%(message)s') logger = logging.getLogger(__name__) GLSL_DIR = Path(__file__).parent BLUEPRINTS_DIR = GLSL_DIR.parent def get_blueprint_files(): """Get all blueprint JSON files.""" return sorted(BLUEPRINTS_DIR.glob("*.json")) def sanitize_filename(name): """Convert blueprint name to safe filename.""" return re.sub(r'[^\w\-]', '_', name) def extract_shaders(): """Extract all shaders from blueprint JSONs to this folder.""" extracted = 0 for json_path in get_blueprint_files(): blueprint_name = json_path.stem try: with open(json_path, 'r') as f: data = json.load(f) except (json.JSONDecodeError, IOError) as e: logger.warning("Skipping %s: %s", json_path.name, e) continue # Find GLSLShader nodes in subgraphs for subgraph in data.get('definitions', {}).get('subgraphs', []): for node in subgraph.get('nodes', []): if node.get('type') == 'GLSLShader': node_id = node.get('id') widgets = node.get('widgets_values', []) # Find shader code (first string that looks like GLSL) for widget in widgets: if isinstance(widget, str) and widget.startswith('#version'): safe_name = sanitize_filename(blueprint_name) frag_name = f"{safe_name}_{node_id}.frag" frag_path = GLSL_DIR / frag_name with open(frag_path, 'w') as f: f.write(widget) logger.info(" Extracted: %s", frag_name) extracted += 1 break logger.info("\nExtracted %d shader(s)", extracted) def patch_shaders(): """Patch shaders from this folder back into blueprint JSONs.""" # Build lookup: blueprint_name -> [(node_id, shader_code), ...] shader_updates = {} for frag_path in sorted(GLSL_DIR.glob("*.frag")): # Parse filename: {blueprint_name}_{node_id}.frag parts = frag_path.stem.rsplit('_', 1) if len(parts) != 2: logger.warning("Skipping %s: invalid filename format", frag_path.name) continue blueprint_name, node_id_str = parts try: node_id = int(node_id_str) except ValueError: logger.warning("Skipping %s: invalid node_id", frag_path.name) continue with open(frag_path, 'r') as f: shader_code = f.read() if blueprint_name not in shader_updates: shader_updates[blueprint_name] = [] shader_updates[blueprint_name].append((node_id, shader_code)) # Apply updates to JSON files patched = 0 for json_path in get_blueprint_files(): blueprint_name = sanitize_filename(json_path.stem) if blueprint_name not in shader_updates: continue try: with open(json_path, 'r') as f: data = json.load(f) except (json.JSONDecodeError, IOError) as e: logger.error("Error reading %s: %s", json_path.name, e) continue modified = False for node_id, shader_code in shader_updates[blueprint_name]: # Find the node and update for subgraph in data.get('definitions', {}).get('subgraphs', []): for node in subgraph.get('nodes', []): if node.get('id') == node_id and node.get('type') == 'GLSLShader': widgets = node.get('widgets_values', []) if len(widgets) > 0 and widgets[0] != shader_code: widgets[0] = shader_code modified = True logger.info(" Patched: %s (node %d)", json_path.name, node_id) patched += 1 if modified: with open(json_path, 'w') as f: json.dump(data, f) if patched == 0: logger.info("No changes to apply.") else: logger.info("\nPatched %d shader(s)", patched) def main(): if len(sys.argv) < 2: command = "patch" else: command = sys.argv[1].lower() if command == "extract": logger.info("Extracting shaders from blueprints...") extract_shaders() elif command in ("patch", "update", "apply"): logger.info("Patching shaders into blueprints...") patch_shaders() else: logger.info(__doc__) sys.exit(1) if __name__ == "__main__": main() ================================================ FILE: blueprints/Brightness and Contrast.json ================================================ {"revision": 0, "last_node_id": 140, "last_link_id": 0, "nodes": [{"id": 140, "type": "916dff42-6166-4d45-b028-04eaf69fbb35", "pos": [500, 1440], "size": [250, 178], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}], "properties": {"proxyWidgets": [["4", "value"], ["5", "value"]]}, "widgets_values": [], "title": "Brightness and Contrast"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "916dff42-6166-4d45-b028-04eaf69fbb35", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 143, "lastLinkId": 118, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Brightness and Contrast", "inputNode": {"id": -10, "bounding": [360, -176, 120, 60]}, "outputNode": {"id": -20, "bounding": [1410, -176, 120, 60]}, "inputs": [{"id": "a5aae7ea-b511-4045-b5da-94101e269cd7", "name": "images.image0", "type": "IMAGE", "linkIds": [117], "localized_name": "images.image0", "label": "image", "pos": [460, -156]}], "outputs": [{"id": "30b72604-69b3-4944-b253-a9099bbd73a9", "name": "IMAGE0", "type": "IMAGE", "linkIds": [118], "localized_name": "IMAGE0", "label": "IMAGE", "pos": [1430, -156]}], "widgets": [], "nodes": [{"id": 4, "type": "PrimitiveFloat", "pos": [540, -280], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "brightness", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [115]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [50]}, {"id": 5, "type": "PrimitiveFloat", "pos": [540, -170], "size": [270, 58], "flags": {}, "order": 1, "mode": 0, "inputs": [{"label": "contrast", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [116]}], "properties": {"Node name for S&R": "PrimitiveFloat", "min": 0, "max": 100, "precision": 1, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [136, 136, 136]}, {"offset": 0.4, "color": [68, 68, 68]}, {"offset": 0.6, "color": [187, 187, 187]}, {"offset": 0.8, "color": [0, 0, 0]}, {"offset": 1, "color": [255, 255, 255]}]}, "widgets_values": [0]}, {"id": 143, "type": "GLSLShader", "pos": [840, -280], "size": [400, 212], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 117}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 115}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 116}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [118]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..100\nuniform float u_float1; // Contrast slider -100..100\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float MID_GRAY = 0.18; // 18% reflectance\n\n// sRGB gamma 2.2 approximation\nvec3 srgbToLinear(vec3 c) {\n return pow(max(c, 0.0), vec3(2.2));\n}\n\nvec3 linearToSrgb(vec3 c) {\n return pow(max(c, 0.0), vec3(1.0/2.2));\n}\n\nfloat mapBrightness(float b) {\n return clamp(b / 100.0, -1.0, 1.0);\n}\n\nfloat mapContrast(float c) {\n return clamp(c / 100.0 + 1.0, 0.0, 2.0);\n}\n\nvoid main() {\n vec4 orig = texture(u_image0, v_texCoord);\n\n float brightness = mapBrightness(u_float0);\n float contrast = mapContrast(u_float1);\n\n vec3 lin = srgbToLinear(orig.rgb);\n\n lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\n\n // Convert back to sRGB\n vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\n\n fragColor = vec4(result, orig.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 115, "origin_id": 4, "origin_slot": 0, "target_id": 143, "target_slot": 2, "type": "FLOAT"}, {"id": 116, "origin_id": 5, "origin_slot": 0, "target_id": 143, "target_slot": 3, "type": "FLOAT"}, {"id": 117, "origin_id": -10, "origin_slot": 0, "target_id": 143, "target_slot": 0, "type": "IMAGE"}, {"id": 118, "origin_id": 143, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}, "extra": {}} ================================================ FILE: blueprints/Canny to Image (Z-Image-Turbo).json ================================================ {"id": 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toGaussian(uvec2 p) {\n float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u);\n return (sum - 2.0) * 0.7; // Centered, scaled\n}\n\nfloat toGaussian(uvec2 p, uint offset) {\n float sum = hashf(p, offset) + hashf(p, offset + 1u) \n + hashf(p, offset + 2u) + hashf(p, offset + 3u);\n return (sum - 2.0) * 0.7;\n}\n\n// Smooth noise with better interpolation\nfloat smoothNoise(vec2 p) {\n vec2 i = floor(p);\n vec2 f = fract(p);\n \n // Quintic interpolation (less banding than cubic)\n f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n \n uvec2 ui = uvec2(i);\n float a = toGaussian(ui);\n float b = toGaussian(ui + uvec2(1u, 0u));\n float c = toGaussian(ui + uvec2(0u, 1u));\n float d = toGaussian(ui + uvec2(1u, 1u));\n \n return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nfloat smoothNoise(vec2 p, uint offset) {\n vec2 i = floor(p);\n vec2 f = fract(p);\n \n f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n \n uvec2 ui = uvec2(i);\n float a = toGaussian(ui, offset);\n float 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{\n float maxC = max(max(c.r, c.g), c.b);\n float minC = min(min(c.r, c.g), c.b);\n float delta = maxC - minC;\n\n float h = 0.0;\n float s = (maxC > EPSILON) ? delta / maxC : 0.0;\n float b = maxC;\n\n if (delta > EPSILON) {\n if (maxC == c.r) {\n h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n } else if (maxC == c.g) {\n h = (c.b - c.r) / delta + 2.0;\n } else {\n h = (c.r - c.g) / delta + 4.0;\n }\n h /= 6.0;\n }\n\n return vec3(h, s, b);\n}\n\nvec3 hsb2rgb(vec3 hsb) {\n vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);\n return hsb.z * mix(vec3(1.0), rgb, hsb.y);\n}\n\n//=============================================================================\n// Color Range Weight Calculation\n//=============================================================================\n\nfloat hueDistance(float a, float b) {\n float d = abs(a - b);\n return min(d, 1.0 - d);\n}\n\nfloat getHueWeight(float hue, float center, float overlap) {\n float baseWidth = 1.0 / 6.0;\n float feather = baseWidth * overlap;\n\n float d = hueDistance(hue, center);\n\n float inner = baseWidth * 0.5;\n float outer = inner + feather;\n\n return 1.0 - smoothstep(inner, outer, d);\n}\n\nfloat getModeWeight(float hue, int mode, float overlap) {\n if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;\n\n if (mode == MODE_RED) {\n return max(\n getHueWeight(hue, 0.0, overlap),\n getHueWeight(hue, 1.0, overlap)\n );\n }\n\n float center = float(mode - 1) / 6.0;\n return getHueWeight(hue, center, overlap);\n}\n\n//=============================================================================\n// Adjustment Functions\n//=============================================================================\n\nfloat adjustLightness(float l, float amount) {\n return amount > 0.0\n ? l + (1.0 - l) * amount\n : l + l * amount;\n}\n\nfloat adjustBrightness(float b, float amount) {\n return clamp(b + amount, 0.0, 1.0);\n}\n\nfloat adjustSaturation(float s, float amount) {\n 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-float(RADIAL_SAMPLES) * angleStep;\n vec2 rotDir = vec2(\n dir.x * cos(negAngle) - dir.y * sin(negAngle),\n dir.x * sin(negAngle) + dir.y * cos(negAngle)\n );\n\n for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n vec2 uv = center + rotDir * dist;\n float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n sum += texture(u_image0, uv) * w;\n totalWeight += w;\n\n rotDir = vec2(\n rotDir.x * cosStep - rotDir.y * sinStep,\n rotDir.x * sinStep + rotDir.y * cosStep\n );\n }\n\n fragColor0 = sum / max(totalWeight, 0.001);\n return;\n }\n\n // Separable Gaussian / Box blur\n int samples = int(ceil(radius));\n\n if (samples == 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n // Direction: pass 0 = horizontal, pass 1 = vertical\n vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n vec4 color = vec4(0.0);\n float totalWeight = 0.0;\n float sigma = radius / 2.0;\n\n for (int i = -samples; i <= samples; i++) {\n vec2 offset = dir * float(i) * texelSize;\n 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This is essential for tasks like content creation, design inspiration, and artistic exploration.\n- Profile: As an AI Image Analysis and Description Specialist, you possess extensive knowledge in computer vision, image processing, and natural language generation. You are adept at interpreting visual data and translating it into descriptive text that can guide the creation of new images.\n- Skills: Proficiency in image recognition, feature extraction, descriptive language generation, and understanding of artistic elements such as composition, color, and texture.\n- Goals: To analyze the provided image, generate a comprehensive and detailed description that captures the key visual elements, and ensure this description can effectively serve as a drawing prompt for creating similar images.\n- Constrains: The description must be clear, concise, and specific enough to guide the creation of a similar image. It should avoid ambiguity and focus on the most salient features of the image. The output should only contain the drawing prompt.\n- OutputFormat: A detailed text description of the image, highlighting key visual elements such as objects, colors, composition, and any unique features.\n- Workflow:\n 1. Analyze the image to identify key visual elements including objects, colors, and composition.\n 2. Generate a detailed description that captures the essence of the image, ensuring it is specific and actionable.\n 3. 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[], "outputs": [], "properties": {}, "widgets_values": ["Set cfg to 1.0 for a speed boost at the cost of consistency. Samplers like res_multistep work pretty well at cfg 1.0\n\nThe official number of steps is 50 but I think that's too much. Even just 10 steps seems to work."], "color": "#432", "bgcolor": "#653"}, {"id": 76, "type": "VAEEncode", "pos": [430, 830], "size": [140, 46], "flags": {"collapsed": true}, "order": 11, "mode": 0, "inputs": [{"localized_name": "pixels", "name": "pixels", "type": "IMAGE", "link": 396}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 144}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "links": [208]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.51", "Node name for S&R": "VAEEncode"}, "widgets_values": []}, {"id": 122, "type": "SetLatentNoiseMask", "pos": [430, 890], "size": [230, 50], "flags": {"collapsed": true}, "order": 15, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 208}, {"localized_name": "mask", "name": "mask", "type": "MASK", "link": 219}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "links": [210]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.59", "Node name for S&R": "SetLatentNoiseMask"}, "widgets_values": []}, {"id": 223, "type": "MarkdownNote", "pos": [860, 670], "size": [300, 160], "flags": {}, "order": 2, "mode": 0, "inputs": [], "outputs": [], "title": "Note: KSampler settings", "properties": {}, "widgets_values": ["You can test and find the best setting by yourself. 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If you have enough VRAM, you can change the size"], "color": "#222", "bgcolor": "#000"}, {"id": 105, "type": "MarkdownNote", "pos": [-469.9999795985529, 279.9998197772136], "size": [480, 170.65104166666669], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [], "title": "VRAM Usage", "properties": {"ue_properties": {"version": "7.1", "widget_ue_connectable": {}, "input_ue_unconnectable": {}}}, "widgets_values": ["## GPU:RTX4090D 24GB\n\n| Model | Size |VRAM Usage | 1st Generation | 2nd Generation |\n|---------------------|-------|-----------|---------------|-----------------|\n| fp8_scaled |640*640| 84% | ≈ 536s | ≈ 513s |\n| fp8_scaled + 4steps LoRA | 640*640 | 83% | ≈ 97s | ≈ 71s |"], "color": "#222", "bgcolor": "#000"}, {"id": 66, "type": "MarkdownNote", "pos": [-469.9999795985529, -320.00012452364496], "size": [480, 572.1354166666667], "flags": {}, "order": 6, "mode": 0, "inputs": [], "outputs": [], "title": "Model Links", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ │ └─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 115, "type": "Note", "pos": [29.999978639114225, -470.00010361843204], "size": [360, 88], "flags": {}, "order": 7, "mode": 0, "inputs": [], "outputs": [], "title": "About 4 Steps LoRA", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. This template provides two workflows, and you can enable one as needed."], "color": "#222", "bgcolor": "#000"}, {"id": 117, "type": "CreateVideo", "pos": [1030, 650], "size": [270, 78], "flags": {}, "order": 18, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 220}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [221]}], "properties": {"cnr_id": "comfy-core", "ver": "0.11.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [16]}, {"id": 87, "type": "VAEDecode", "pos": [1020, 540], "size": [210, 46], "flags": {}, "order": 15, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 175}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 176}], "outputs": 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"CreateVideo"}, "widgets_values": [16]}, {"id": 112, "type": "Note", "pos": [30.33320002485607, -428.6666237736725], "size": [359.9869791666667, 52], "flags": {}, "order": 4, "mode": 0, "inputs": [], "outputs": [], "title": "About 4 Steps LoRA", "properties": {}, "widgets_values": ["Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. This template provides two workflows, and you can enable one as needed."], "color": "#222", "bgcolor": "#000"}, {"id": 62, "type": "MarkdownNote", "pos": [-489.666771800538, -278.666700527147], "size": [479.9869791666667, 542.1354166666667], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [], "title": "Model Links", "properties": {}, "widgets_values": ["[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model** \n- [wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors)\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├───wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors\n│ │ └───wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 87, "type": "VAEDecode", "pos": [1020.3331497597994, 471.3333837135574], "size": [210, 46], "flags": {"collapsed": false}, "order": 14, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 157}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 158}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [195]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "VAEDecode"}, "widgets_values": []}, {"id": 72, "type": "CLIPTextEncode", "pos": [440.3333139376125, 331.3333305479798], "size": [500, 170], "flags": {}, "order": 6, "mode": 0, "inputs": 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"floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 41}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 42}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 43}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [40]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5\nuniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels\nuniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n vec2 texel = 1.0 / u_resolution;\n float radius = max(u_float1, 0.5);\n float amount = u_float0;\n float threshold = u_float2;\n\n vec4 original = texture(u_image0, v_texCoord);\n\n // Gaussian blur for the \"unsharp\" mask\n int samples = int(ceil(radius));\n float sigma = radius / 2.0;\n\n vec4 blurred = vec4(0.0);\n float totalWeight = 0.0;\n\n for (int x = -samples; x <= samples; x++) {\n for (int y = -samples; y <= samples; y++) {\n vec2 offset = vec2(float(x), float(y)) * texel;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float dist = length(vec2(float(x), float(y)));\n float weight = gaussian(dist, sigma);\n blurred += sample_color * weight;\n totalWeight += weight;\n }\n }\n blurred /= totalWeight;\n\n // Unsharp mask = original - blurred\n vec3 mask = original.rgb - blurred.rgb;\n\n // Luminance-based threshold with smooth falloff\n float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n mask *= thresholdScale;\n\n // Sharpen: original + mask * amount\n vec3 sharpened = original.rgb + mask * amount;\n\n fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n", "from_input"]}], "groups": [], "links": [{"id": 41, "origin_id": 27, "origin_slot": 0, "target_id": 26, "target_slot": 2, "type": "FLOAT"}, {"id": 42, "origin_id": 28, "origin_slot": 0, "target_id": 26, "target_slot": 3, "type": "FLOAT"}, {"id": 43, "origin_id": 29, "origin_slot": 0, "target_id": 26, "target_slot": 4, "type": "FLOAT"}, {"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 26, "target_slot": 0, "type": "IMAGE"}, {"id": 40, "origin_id": 26, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Sharpen"}]}} ================================================ FILE: blueprints/Video Captioning (Gemini).json ================================================ {"revision": 0, "last_node_id": 233, "last_link_id": 0, "nodes": [{"id": 233, "type": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", "pos": [0, 1140], "size": [400, 260], 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"d8cbd7eb-636a-4d7b-8ff6-b22f1755e26c", "name": "prompt", "type": "STRING", "linkIds": [15], "pos": [-6770, 2550]}, {"id": "b034e26a-d114-4604-aec2-32783e86aa6b", "name": "model", "type": "COMBO", "linkIds": [16], "pos": [-6770, 2570]}, {"id": "f7363f60-a106-4e06-90af-df5f53355b98", "name": "video", "type": "VIDEO", "linkIds": [17], "pos": [-6770, 2590]}], "outputs": [{"id": "e12c6e80-5210-4328-a581-bc8924c53070", "name": "STRING", "type": "STRING", "linkIds": [6], "localized_name": "STRING", "pos": [-6220, 2550]}], "widgets": [], "nodes": [{"id": 1, "type": "GeminiNode", "pos": [-6690, 2360], "size": [390, 430], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "shape": 7, "type": "IMAGE", "link": null}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "video", "name": "video", "shape": 7, "type": "VIDEO", "link": 17}, {"localized_name": "files", "name": "files", "shape": 7, "type": "GEMINI_INPUT_FILES", "link": null}, {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": 15}, {"localized_name": "model", "name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": 16}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "system_prompt", "name": "system_prompt", "shape": 7, "type": "STRING", "widget": {"name": "system_prompt"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": [6]}], "properties": {"cnr_id": "comfy-core", "ver": "0.5.1", "Node name for S&R": "GeminiNode"}, "widgets_values": ["Describe this video", "gemini-2.5-pro", 511865409297955, "randomize", "- Role: AI Video Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze videos (including frame sequences, dynamic movements, audio-visual elements) and generate detailed, structured descriptions. These descriptions must be directly usable as video generation prompts to create similar videos, serving core tasks such as video content creation, creative inspiration extraction, and artistic style exploration.\n- Profile: As an AI Video Analysis and Description Specialist, you possess expertise in computer vision, video temporal sequence processing, motion analysis, and multi-modal natural language generation. You excel at interpreting dynamic visual data (frame-by-frame features + continuous motion) and translating it into precise descriptive text that fully guides the creation of new videos with matching style, rhythm, and content.\n- Skills: Proficiency in video frame feature extraction, motion trajectory recognition, temporal rhythm analysis, scene/shot segmentation, color grading detection, camera movement identification (pan/tilt/zoom/dolly), audio-visual element correlation analysis, and descriptive language generation that captures both static visual features and dynamic temporal characteristics. Mastery of artistic elements in video: composition (per frame + dynamic framing), color palette (consistent + transitional), texture (surface details + motion blur), pacing (frame rate, shot duration), and sound style (background music, ambient sound cues).\n- Goals: To analyze the provided video comprehensively, generate a detailed, structured description that captures all key video elements (static visual features + dynamic motion/temporal characteristics + audio-visual style), and ensure this description can directly serve as a high-quality prompt for creating similar videos.\n- Constraints: \n 1. The description must be clear, structured, and specific enough to guide end-to-end video creation (including frame rate, shot duration, camera movement, motion speed, color transitions).\n 2. Avoid ambiguity; focus on the most salient static (per-frame) and dynamic (temporal) features of the video.\n 3. Prioritize video-specific elements: motion trajectory, shot types (close-up/wide shot/etc.), camera movement, frame rate, scene transitions, rhythm/pacing, and temporal color changes.\n 4. The output must only contain the video generation prompt (no extra explanations).\n- OutputFormat: A detailed, hierarchical text description of the video, structured as follows:\n 1. Core Content & Narrative: Brief overview of the video's subject and temporal progression\n 2. Visual Style (Static): Per-frame key elements (objects, colors, composition, lighting, texture)\n 3. Dynamic Elements (Temporal): Motion details (speed, trajectory, direction), camera movement (type, speed, direction), shot duration/frame rate, scene transitions\n 4. Audio-Visual Style: Color grading (consistent/transitional), rhythm/pacing, and implied audio style (if discernible)\n- Workflow:\n 1. Analyze the video to segment shots/scenes, identify frame-by-frame static visual elements (objects, colors, composition) and cross-frame dynamic elements (motion, camera movement, temporal changes).\n 2. Extract video-specific technical features: frame rate, shot duration, scene transition types, motion speed/rhythm.\n 3. Generate a structured, detailed description that captures the essence of the video (static + dynamic + temporal characteristics), ensuring specificity and actionability for video generation.\n 4. Refine the description for clarity, conciseness, and alignment with video generation prompt norms (e.g., including frame rate, camera movement terms, motion speed descriptors)."], "color": "#432", "bgcolor": "#653"}], "groups": [], "links": [{"id": 6, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "*"}, {"id": 15, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "STRING"}, {"id": 16, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 5, "type": "COMBO"}, {"id": 17, "origin_id": -10, "origin_slot": 2, "target_id": 1, "target_slot": 2, "type": "VIDEO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Text generation/Video Captioning"}]}} ================================================ FILE: blueprints/Video Inpaint(Wan2.1 VACE).json ================================================ {"id": "2f429c60-2e03-4117-908b-31e1fab04bba", "revision": 0, "last_node_id": 229, "last_link_id": 366, "nodes": [{"id": 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However, to avoid requiring users to install additional custom nodes, our templates only use core nodes. You can refer to this implementation idea to achieve video inpainting.\n\nYou can use KJNode’s Points Editor and Sam2Segmentation to create some dynamic mask functions.\n\nCustom node links:\n- [ComfyUI-KJNodes](https://github.com/kijai/ComfyUI-KJNodes)\n- [ComfyUI-segment-anything-2](https://github.com/kijai/ComfyUI-segment-anything-2)"], "color": "#432", "bgcolor": "#000"}, {"id": 7, "type": "CLIPTextEncode", "pos": [-80, 390], "size": [425.27801513671875, 180.6060791015625], "flags": {}, "order": 8, "mode": 0, "inputs": [{"localized_name": "clip", "name": "clip", "type": "CLIP", "link": 75}, {"localized_name": "text", "name": "text", "type": "STRING", "widget": {"name": "text"}, "link": null}], "outputs": [{"localized_name": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "slot_index": 0, "links": [97]}], "title": "CLIP Text Encode (Negative Prompt)", "properties": {"cnr_id": "comfy-core", "ver": "0.3.34", "Node name for S&R": "CLIPTextEncode", "enableTabs": false, 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{"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "GetVideoComponents"}}, {"id": 1, "type": "UpscaleModelLoader", "pos": [750, 450], "size": [280, 60], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "model_name", "name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": 19}], "outputs": [{"localized_name": "UPSCALE_MODEL", "name": "UPSCALE_MODEL", "type": "UPSCALE_MODEL", "links": [1]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "UpscaleModelLoader", "models": [{"name": "RealESRGAN_x4plus.safetensors", "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors", "directory": "upscale_models"}]}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "groups": [], "links": [{"id": 1, "origin_id": 1, "origin_slot": 0, "target_id": 2, "target_slot": 0, "type": "UPSCALE_MODEL"}, {"id": 14, "origin_id": 10, "origin_slot": 0, "target_id": 2, "target_slot": 1, "type": "IMAGE"}, {"id": 13, "origin_id": 2, "origin_slot": 0, "target_id": 11, "target_slot": 0, "type": "IMAGE"}, {"id": 16, "origin_id": 10, "origin_slot": 1, "target_id": 11, "target_slot": 1, "type": "AUDIO"}, {"id": 12, "origin_id": 10, "origin_slot": 2, "target_id": 11, "target_slot": 2, "type": "FLOAT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 10, "target_slot": 0, "type": "VIDEO"}, {"id": 15, "origin_id": 11, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "VIDEO"}, {"id": 19, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 0, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Video generation and editing/Enhance video"}]}, "extra": {}} ================================================ FILE: blueprints/put_blueprints_here ================================================ ================================================ FILE: comfy/audio_encoders/audio_encoders.py ================================================ from .wav2vec2 import Wav2Vec2Model from .whisper import WhisperLargeV3 import comfy.model_management import comfy.ops import comfy.utils import logging import torchaudio class AudioEncoderModel(): def __init__(self, config): self.load_device = comfy.model_management.text_encoder_device() offload_device = comfy.model_management.text_encoder_offload_device() self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) model_type = config.pop("model_type") model_config = dict(config) model_config.update({ "dtype": self.dtype, "device": offload_device, "operations": comfy.ops.manual_cast }) if model_type == "wav2vec2": self.model = Wav2Vec2Model(**model_config) elif model_type == "whisper3": self.model = WhisperLargeV3(**model_config) self.model.eval() self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) self.model_sample_rate = 16000 comfy.model_management.archive_model_dtypes(self.model) def load_sd(self, sd): return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic()) def get_sd(self): return self.model.state_dict() def encode_audio(self, audio, sample_rate): comfy.model_management.load_model_gpu(self.patcher) audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate) out, all_layers = self.model(audio.to(self.load_device)) outputs = {} outputs["encoded_audio"] = out outputs["encoded_audio_all_layers"] = all_layers outputs["audio_samples"] = audio.shape[2] return outputs def load_audio_encoder_from_sd(sd, prefix=""): sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""}) if "encoder.layer_norm.bias" in sd: #wav2vec2 embed_dim = sd["encoder.layer_norm.bias"].shape[0] if embed_dim == 1024:# large config = { "model_type": "wav2vec2", "embed_dim": 1024, "num_heads": 16, "num_layers": 24, "conv_norm": True, "conv_bias": True, "do_normalize": True, "do_stable_layer_norm": True } elif embed_dim == 768: # base config = { "model_type": "wav2vec2", "embed_dim": 768, "num_heads": 12, "num_layers": 12, "conv_norm": False, "conv_bias": False, "do_normalize": False, # chinese-wav2vec2-base has this False "do_stable_layer_norm": False } else: raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim)) elif "model.encoder.embed_positions.weight" in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"model.": ""}) config = { "model_type": "whisper3", } else: raise RuntimeError("ERROR: audio encoder not supported.") audio_encoder = AudioEncoderModel(config) m, u = audio_encoder.load_sd(sd) if len(m) > 0: logging.warning("missing audio encoder: {}".format(m)) if len(u) > 0: logging.warning("unexpected audio encoder: {}".format(u)) return audio_encoder ================================================ FILE: comfy/audio_encoders/wav2vec2.py ================================================ import torch import torch.nn as nn from comfy.ldm.modules.attention import optimized_attention_masked class LayerNormConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): super().__init__() self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype) self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype) def forward(self, x): x = self.conv(x) return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1)) class LayerGroupNormConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): super().__init__() self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype) self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype) def forward(self, x): x = self.conv(x) return torch.nn.functional.gelu(self.layer_norm(x)) class ConvNoNorm(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): super().__init__() self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype) def forward(self, x): x = self.conv(x) return torch.nn.functional.gelu(x) class ConvFeatureEncoder(nn.Module): def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None): super().__init__() if conv_norm: self.conv_layers = nn.ModuleList([ LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations), LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), ]) else: self.conv_layers = nn.ModuleList([ LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations), ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), ]) def forward(self, x): x = x.unsqueeze(1) for conv in self.conv_layers: x = conv(x) return x.transpose(1, 2) class FeatureProjection(nn.Module): def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None): super().__init__() self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype) self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype) def forward(self, x): x = self.layer_norm(x) x = self.projection(x) return x class PositionalConvEmbedding(nn.Module): def __init__(self, embed_dim=768, kernel_size=128, groups=16): super().__init__() self.conv = nn.Conv1d( embed_dim, embed_dim, kernel_size=kernel_size, padding=kernel_size // 2, groups=groups, ) self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2) self.activation = nn.GELU() def forward(self, x): x = x.transpose(1, 2) x = self.conv(x)[:, :, :-1] x = self.activation(x) x = x.transpose(1, 2) return x class TransformerEncoder(nn.Module): def __init__( self, embed_dim=768, num_heads=12, num_layers=12, mlp_ratio=4.0, do_stable_layer_norm=True, dtype=None, device=None, operations=None ): super().__init__() self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim) self.layers = nn.ModuleList([ TransformerEncoderLayer( embed_dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, do_stable_layer_norm=do_stable_layer_norm, device=device, dtype=dtype, operations=operations ) for _ in range(num_layers) ]) self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype) self.do_stable_layer_norm = do_stable_layer_norm def forward(self, x, mask=None): x = x + self.pos_conv_embed(x) all_x = () if not self.do_stable_layer_norm: x = self.layer_norm(x) for layer in self.layers: all_x += (x,) x = layer(x, mask) if self.do_stable_layer_norm: x = self.layer_norm(x) all_x += (x,) return x, all_x class Attention(nn.Module): def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.head_dim = embed_dim // num_heads self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) def forward(self, x, mask=None): assert (mask is None) # TODO? q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) out = optimized_attention_masked(q, k, v, self.num_heads) return self.out_proj(out) class FeedForward(nn.Module): def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None): super().__init__() self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype) self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype) def forward(self, x): x = self.intermediate_dense(x) x = torch.nn.functional.gelu(x) x = self.output_dense(x) return x class TransformerEncoderLayer(nn.Module): def __init__( self, embed_dim=768, num_heads=12, mlp_ratio=4.0, do_stable_layer_norm=True, dtype=None, device=None, operations=None ): super().__init__() self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations) self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype) self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations) self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype) self.do_stable_layer_norm = do_stable_layer_norm def forward(self, x, mask=None): residual = x if self.do_stable_layer_norm: x = self.layer_norm(x) x = self.attention(x, mask=mask) x = residual + x if not self.do_stable_layer_norm: x = self.layer_norm(x) return self.final_layer_norm(x + self.feed_forward(x)) else: return x + self.feed_forward(self.final_layer_norm(x)) class Wav2Vec2Model(nn.Module): """Complete Wav2Vec 2.0 model.""" def __init__( self, embed_dim=1024, final_dim=256, num_heads=16, num_layers=24, conv_norm=True, conv_bias=True, do_normalize=True, do_stable_layer_norm=True, dtype=None, device=None, operations=None ): super().__init__() conv_dim = 512 self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations) self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations) self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype)) self.do_normalize = do_normalize self.encoder = TransformerEncoder( embed_dim=embed_dim, num_heads=num_heads, num_layers=num_layers, do_stable_layer_norm=do_stable_layer_norm, device=device, dtype=dtype, operations=operations ) def forward(self, x, mask_time_indices=None, return_dict=False): x = torch.mean(x, dim=1) if self.do_normalize: x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7) features = self.feature_extractor(x) features = self.feature_projection(features) batch_size, seq_len, _ = features.shape x, all_x = self.encoder(features) return x, all_x ================================================ FILE: comfy/audio_encoders/whisper.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from typing import Optional from comfy.ldm.modules.attention import optimized_attention_masked import comfy.ops class WhisperFeatureExtractor(nn.Module): def __init__(self, n_mels=128, device=None): super().__init__() self.sample_rate = 16000 self.n_fft = 400 self.hop_length = 160 self.n_mels = n_mels self.chunk_length = 30 self.n_samples = 480000 self.mel_spectrogram = torchaudio.transforms.MelSpectrogram( sample_rate=self.sample_rate, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels, f_min=0, f_max=8000, norm="slaney", mel_scale="slaney", ).to(device) def __call__(self, audio): audio = torch.mean(audio, dim=1) batch_size = audio.shape[0] processed_audio = [] for i in range(batch_size): aud = audio[i] if aud.shape[0] > self.n_samples: aud = aud[:self.n_samples] elif aud.shape[0] < self.n_samples: aud = F.pad(aud, (0, self.n_samples - aud.shape[0])) processed_audio.append(aud) audio = torch.stack(processed_audio) mel_spec = self.mel_spectrogram(audio.to(self.mel_spectrogram.spectrogram.window.device))[:, :, :-1].to(audio.device) log_mel_spec = torch.clamp(mel_spec, min=1e-10).log10() log_mel_spec = torch.maximum(log_mel_spec, log_mel_spec.max() - 8.0) log_mel_spec = (log_mel_spec + 4.0) / 4.0 return log_mel_spec class MultiHeadAttention(nn.Module): def __init__(self, d_model: int, n_heads: int, dtype=None, device=None, operations=None): super().__init__() assert d_model % n_heads == 0 self.d_model = d_model self.n_heads = n_heads self.d_k = d_model // n_heads self.q_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) self.k_proj = operations.Linear(d_model, d_model, bias=False, dtype=dtype, device=device) self.v_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) self.out_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) def forward( self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: batch_size, seq_len, _ = query.shape q = self.q_proj(query) k = self.k_proj(key) v = self.v_proj(value) attn_output = optimized_attention_masked(q, k, v, self.n_heads, mask) attn_output = self.out_proj(attn_output) return attn_output class EncoderLayer(nn.Module): def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, device=None, operations=None): super().__init__() self.self_attn = MultiHeadAttention(d_model, n_heads, dtype=dtype, device=device, operations=operations) self.self_attn_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device) self.fc1 = operations.Linear(d_model, d_ff, dtype=dtype, device=device) self.fc2 = operations.Linear(d_ff, d_model, dtype=dtype, device=device) self.final_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device) def forward( self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None ) -> torch.Tensor: residual = x x = self.self_attn_layer_norm(x) x = self.self_attn(x, x, x, attention_mask) x = residual + x residual = x x = self.final_layer_norm(x) x = self.fc1(x) x = F.gelu(x) x = self.fc2(x) x = residual + x return x class AudioEncoder(nn.Module): def __init__( self, n_mels: int = 128, n_ctx: int = 1500, n_state: int = 1280, n_head: int = 20, n_layer: int = 32, dtype=None, device=None, operations=None ): super().__init__() self.conv1 = operations.Conv1d(n_mels, n_state, kernel_size=3, padding=1, dtype=dtype, device=device) self.conv2 = operations.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1, dtype=dtype, device=device) self.embed_positions = operations.Embedding(n_ctx, n_state, dtype=dtype, device=device) self.layers = nn.ModuleList([ EncoderLayer(n_state, n_head, n_state * 4, dtype=dtype, device=device, operations=operations) for _ in range(n_layer) ]) self.layer_norm = operations.LayerNorm(n_state, dtype=dtype, device=device) def forward(self, x: torch.Tensor) -> torch.Tensor: x = F.gelu(self.conv1(x)) x = F.gelu(self.conv2(x)) x = x.transpose(1, 2) x = x + comfy.ops.cast_to_input(self.embed_positions.weight[:, :x.shape[1]], x) all_x = () for layer in self.layers: all_x += (x,) x = layer(x) x = self.layer_norm(x) all_x += (x,) return x, all_x class WhisperLargeV3(nn.Module): def __init__( self, n_mels: int = 128, n_audio_ctx: int = 1500, n_audio_state: int = 1280, n_audio_head: int = 20, n_audio_layer: int = 32, dtype=None, device=None, operations=None ): super().__init__() self.feature_extractor = WhisperFeatureExtractor(n_mels=n_mels, device=device) self.encoder = AudioEncoder( n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer, dtype=dtype, device=device, operations=operations ) def forward(self, audio): mel = self.feature_extractor(audio) x, all_x = self.encoder(mel) return x, all_x ================================================ FILE: comfy/cldm/cldm.py ================================================ #taken from: https://github.com/lllyasviel/ControlNet #and modified import torch import torch.nn as nn from ..ldm.modules.diffusionmodules.util import ( timestep_embedding, ) from ..ldm.modules.attention import SpatialTransformer from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample from ..ldm.util import exists from .control_types import UNION_CONTROLNET_TYPES from collections import OrderedDict import comfy.ops from comfy.ldm.modules.attention import optimized_attention class OptimizedAttention(nn.Module): def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.heads = nhead self.c = c self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) def forward(self, x): x = self.in_proj(x) q, k, v = x.split(self.c, dim=2) out = optimized_attention(q, k, v, self.heads) return self.out_proj(out) class QuickGELU(nn.Module): def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class ResBlockUnionControlnet(nn.Module): def __init__(self, dim, nhead, dtype=None, device=None, operations=None): super().__init__() self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations) self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device) self.mlp = nn.Sequential( OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()), ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))])) self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device) def attention(self, x: torch.Tensor): return self.attn(x) def forward(self, x: torch.Tensor): x = x + self.attention(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class ControlledUnetModel(UNetModel): #implemented in the ldm unet pass class ControlNet(nn.Module): def __init__( self, image_size, in_channels, model_channels, hint_channels, num_res_blocks, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, dtype=torch.float32, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, attn_precision=None, union_controlnet_num_control_type=None, device=None, operations=comfy.ops.disable_weight_init, **kwargs, ): super().__init__() assert use_spatial_transformer == True, "use_spatial_transformer has to be true" if use_spatial_transformer: assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' # from omegaconf.listconfig import ListConfig # if type(context_dim) == ListConfig: # context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.dims = dims self.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) transformer_depth = transformer_depth[:] self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = dtype self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim) elif self.num_classes == "continuous": self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) self.input_hint_block = TimestepEmbedSequential( operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations, ) ] ch = mult * model_channels num_transformers = transformer_depth.pop(0) if num_transformers > 0: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append( SpatialTransformer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations ) ) ) ch = out_ch input_block_chans.append(ch) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels mid_block = [ ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] if transformer_depth_middle >= 0: mid_block += [SpatialTransformer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = TimestepEmbedSequential(*mid_block) self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) self._feature_size += ch if union_controlnet_num_control_type is not None: self.num_control_type = union_controlnet_num_control_type num_trans_channel = 320 num_trans_head = 8 num_trans_layer = 1 num_proj_channel = 320 # task_scale_factor = num_trans_channel ** 0.5 self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device)) self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)]) self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device) #----------------------------------------------------------------------------------------------------- control_add_embed_dim = 256 class ControlAddEmbedding(nn.Module): def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None): super().__init__() self.num_control_type = num_control_type self.in_dim = in_dim self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device) self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device) def forward(self, control_type, dtype, device): c_type = torch.zeros((self.num_control_type,), device=device) c_type[control_type] = 1.0 c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim)) return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type))) self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations) else: self.task_embedding = None self.control_add_embedding = None def union_controlnet_merge(self, hint, control_type, emb, context): # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main inputs = [] condition_list = [] for idx in range(min(1, len(control_type))): controlnet_cond = self.input_hint_block(hint[idx], emb, context) feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) if idx < len(control_type): feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device) inputs.append(feat_seq.unsqueeze(1)) condition_list.append(controlnet_cond) x = torch.cat(inputs, dim=1) x = self.transformer_layes(x) controlnet_cond_fuser = None for idx in range(len(control_type)): alpha = self.spatial_ch_projs(x[:, idx]) alpha = alpha.unsqueeze(-1).unsqueeze(-1) o = condition_list[idx] + alpha if controlnet_cond_fuser is None: controlnet_cond_fuser = o else: controlnet_cond_fuser += o return controlnet_cond_fuser def make_zero_conv(self, channels, operations=None, dtype=None, device=None): return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) def forward(self, x, hint, timesteps, context, y=None, **kwargs): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) guided_hint = None if self.control_add_embedding is not None: #Union Controlnet control_type = kwargs.get("control_type", []) if any([c >= self.num_control_type for c in control_type]): max_type = max(control_type) max_type_name = { v: k for k, v in UNION_CONTROLNET_TYPES.items() }[max_type] raise ValueError( f"Control type {max_type_name}({max_type}) is out of range for the number of control types" + f"({self.num_control_type}) supported.\n" + "Please consider using the ProMax ControlNet Union model.\n" + "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main" ) emb += self.control_add_embedding(control_type, emb.dtype, emb.device) if len(control_type) > 0: if len(hint.shape) < 5: hint = hint.unsqueeze(dim=0) guided_hint = self.union_controlnet_merge(hint, control_type, emb, context) if guided_hint is None: guided_hint = self.input_hint_block(hint, emb, context) out_output = [] out_middle = [] if self.num_classes is not None: if y is None: raise ValueError("y is None, did you try using a controlnet for SDXL on SD1?") emb = emb + self.label_emb(y) h = x for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: h = module(h, emb, context) h += guided_hint guided_hint = None else: h = module(h, emb, context) out_output.append(zero_conv(h, emb, context)) h = self.middle_block(h, emb, context) out_middle.append(self.middle_block_out(h, emb, context)) return {"middle": out_middle, "output": out_output} ================================================ FILE: comfy/cldm/control_types.py ================================================ UNION_CONTROLNET_TYPES = { "openpose": 0, "depth": 1, "hed/pidi/scribble/ted": 2, "canny/lineart/anime_lineart/mlsd": 3, "normal": 4, "segment": 5, "tile": 6, "repaint": 7, } ================================================ FILE: comfy/cldm/dit_embedder.py ================================================ import math from typing import List, Optional, Tuple import torch import torch.nn as nn from torch import Tensor from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch class ControlNetEmbedder(nn.Module): def __init__( self, img_size: int, patch_size: int, in_chans: int, attention_head_dim: int, num_attention_heads: int, adm_in_channels: int, num_layers: int, main_model_double: int, double_y_emb: bool, device: torch.device, dtype: torch.dtype, pos_embed_max_size: Optional[int] = None, operations = None, ): super().__init__() self.main_model_double = main_model_double self.dtype = dtype self.hidden_size = num_attention_heads * attention_head_dim self.patch_size = patch_size self.x_embedder = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=self.hidden_size, strict_img_size=pos_embed_max_size is None, device=device, dtype=dtype, operations=operations, ) self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) self.double_y_emb = double_y_emb if self.double_y_emb: self.orig_y_embedder = VectorEmbedder( adm_in_channels, self.hidden_size, dtype, device, operations=operations ) self.y_embedder = VectorEmbedder( self.hidden_size, self.hidden_size, dtype, device, operations=operations ) else: self.y_embedder = VectorEmbedder( adm_in_channels, self.hidden_size, dtype, device, operations=operations ) self.transformer_blocks = nn.ModuleList( DismantledBlock( hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True, dtype=dtype, device=device, operations=operations ) for _ in range(num_layers) ) # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features # TODO double check this logic when 8b self.use_y_embedder = True self.controlnet_blocks = nn.ModuleList([]) for _ in range(len(self.transformer_blocks)): controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) self.controlnet_blocks.append(controlnet_block) self.pos_embed_input = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=self.hidden_size, strict_img_size=False, device=device, dtype=dtype, operations=operations, ) def forward( self, x: torch.Tensor, timesteps: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, hint = None, ) -> Tuple[Tensor, List[Tensor]]: x_shape = list(x.shape) x = self.x_embedder(x) if not self.double_y_emb: h = (x_shape[-2] + 1) // self.patch_size w = (x_shape[-1] + 1) // self.patch_size x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device) c = self.t_embedder(timesteps, dtype=x.dtype) if y is not None and self.y_embedder is not None: if self.double_y_emb: y = self.orig_y_embedder(y) y = self.y_embedder(y) c = c + y x = x + self.pos_embed_input(hint) block_out = () repeat = math.ceil(self.main_model_double / len(self.transformer_blocks)) for i in range(len(self.transformer_blocks)): out = self.transformer_blocks[i](x, c) if not self.double_y_emb: x = out block_out += (self.controlnet_blocks[i](out),) * repeat return {"output": block_out} ================================================ FILE: comfy/cldm/mmdit.py ================================================ import torch from typing import Optional import comfy.ldm.modules.diffusionmodules.mmdit class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): def __init__( self, num_blocks = None, control_latent_channels = None, dtype = None, device = None, operations = None, **kwargs, ): super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs) # controlnet_blocks self.controlnet_blocks = torch.nn.ModuleList([]) for _ in range(len(self.joint_blocks)): self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype)) if control_latent_channels is None: control_latent_channels = self.in_channels self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed( None, self.patch_size, control_latent_channels, self.hidden_size, bias=True, strict_img_size=False, dtype=dtype, device=device, operations=operations ) def forward( self, x: torch.Tensor, timesteps: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, hint = None, ) -> torch.Tensor: #weird sd3 controlnet specific stuff y = torch.zeros_like(y) if self.context_processor is not None: context = self.context_processor(context) hw = x.shape[-2:] x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device) x += self.pos_embed_input(hint) c = self.t_embedder(timesteps, dtype=x.dtype) if y is not None and self.y_embedder is not None: y = self.y_embedder(y) c = c + y if context is not None: context = self.context_embedder(context) output = [] blocks = len(self.joint_blocks) for i in range(blocks): context, x = self.joint_blocks[i]( context, x, c=c, use_checkpoint=self.use_checkpoint, ) out = self.controlnet_blocks[i](x) count = self.depth // blocks if i == blocks - 1: count -= 1 for j in range(count): output.append(out) return {"output": output} ================================================ FILE: comfy/cli_args.py ================================================ import argparse import enum import os import comfy.options class EnumAction(argparse.Action): """ Argparse action for handling Enums """ def __init__(self, **kwargs): # Pop off the type value enum_type = kwargs.pop("type", None) # Ensure an Enum subclass is provided if enum_type is None: raise ValueError("type must be assigned an Enum when using EnumAction") if not issubclass(enum_type, enum.Enum): raise TypeError("type must be an Enum when using EnumAction") # Generate choices from the Enum choices = tuple(e.value for e in enum_type) kwargs.setdefault("choices", choices) kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") super(EnumAction, self).__init__(**kwargs) self._enum = enum_type def __call__(self, parser, namespace, values, option_string=None): # Convert value back into an Enum value = self._enum(values) setattr(namespace, self.dest, value) parser = argparse.ArgumentParser() parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)") parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function") parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function") parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.") parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.") parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.") parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.") parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.") parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use. All other devices will not be visible.") parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.") cm_group = parser.add_mutually_exclusive_group() cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") fp_group = parser.add_mutually_exclusive_group() fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") fpunet_group = parser.add_mutually_exclusive_group() fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.") fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.") fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.") fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16") fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.") fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.") fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Store unet weights in fp8_e8m0fnu.") fpvae_group = parser.add_mutually_exclusive_group() fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.") fpte_group = parser.add_mutually_exclusive_group() fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).") fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).") fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.") parser.add_argument("--fp16-intermediates", action="store_true", help="Experimental: Use fp16 for intermediate tensors between nodes instead of fp32.") parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.") parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.") parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.") class LatentPreviewMethod(enum.Enum): NoPreviews = "none" Auto = "auto" Latent2RGB = "latent2rgb" TAESD = "taesd" @classmethod def from_string(cls, value: str): for member in cls: if member.value == value: return member return None parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction) parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.") cache_group = parser.add_mutually_exclusive_group() cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.") cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.") cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.") cache_group.add_argument("--cache-ram", nargs='?', const=4.0, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB") attn_group = parser.add_mutually_exclusive_group() attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.") attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.") parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") upcast = parser.add_mutually_exclusive_group() upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.") upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.") parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.") manager_group = parser.add_mutually_exclusive_group() manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.") manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager") vram_group = parser.add_mutually_exclusive_group() vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.") parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.") parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.") parser.add_argument("--disable-dynamic-vram", action="store_true", help="Disable dynamic VRAM and use estimate based model loading.") parser.add_argument("--enable-dynamic-vram", action="store_true", help="Enable dynamic VRAM on systems where it's not enabled by default.") parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.") parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.") parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.") class PerformanceFeature(enum.Enum): Fp16Accumulation = "fp16_accumulation" Fp8MatrixMultiplication = "fp8_matrix_mult" CublasOps = "cublas_ops" AutoTune = "autotune" parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature)))) parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.") parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.") parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.") parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.") parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes. Also prevents the frontend from communicating with the internet.") parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level') parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).") # The default built-in provider hosted under web/ DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest" parser.add_argument( "--front-end-version", type=str, default=DEFAULT_VERSION_STRING, help=""" Specifies the version of the frontend to be used. This command needs internet connectivity to query and download available frontend implementations from GitHub releases. The version string should be in the format of: [repoOwner]/[repoName]@[version] where version is one of: "latest" or a valid version number (e.g. "1.0.0") """, ) def is_valid_directory(path: str) -> str: """Validate if the given path is a directory, and check permissions.""" if not os.path.exists(path): raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.") if not os.path.isdir(path): raise argparse.ArgumentTypeError(f"'{path}' is not a directory.") if not os.access(path, os.R_OK): raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.") return path parser.add_argument( "--front-end-root", type=is_valid_directory, default=None, help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.", ) parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.") parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.") parser.add_argument( "--comfy-api-base", type=str, default="https://api.comfy.org", help="Set the base URL for the ComfyUI API. (default: https://api.comfy.org)", ) database_default_path = os.path.abspath( os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db") ) parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.") parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).") if comfy.options.args_parsing: args = parser.parse_args() else: args = parser.parse_args([]) if args.windows_standalone_build: args.auto_launch = True if args.disable_auto_launch: args.auto_launch = False if args.force_fp16: args.fp16_unet = True # '--fast' is not provided, use an empty set if args.fast is None: args.fast = set() # '--fast' is provided with an empty list, enable all optimizations elif args.fast == []: args.fast = set(PerformanceFeature) # '--fast' is provided with a list of performance features, use that list else: args.fast = set(args.fast) def enables_dynamic_vram(): if args.enable_dynamic_vram: return True return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu ================================================ FILE: comfy/clip_config_bigg.json ================================================ { "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 49407, "hidden_act": "gelu", "hidden_size": 1280, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 5120, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 20, "num_hidden_layers": 32, "pad_token_id": 1, "projection_dim": 1280, "torch_dtype": "float32", "vocab_size": 49408 } ================================================ FILE: comfy/clip_model.py ================================================ import torch from comfy.ldm.modules.attention import optimized_attention_for_device import comfy.ops import math def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True): image = image[:, :, :, :3] if image.shape[3] > 3 else image mean = torch.tensor(mean, device=image.device, dtype=image.dtype) std = torch.tensor(std, device=image.device, dtype=image.dtype) image = image.movedim(-1, 1) if not (image.shape[2] == size and image.shape[3] == size): if crop: scale = (size / min(image.shape[2], image.shape[3])) scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3])) else: scale_size = (size, size) image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True) h = (image.shape[2] - size)//2 w = (image.shape[3] - size)//2 image = image[:,:,h:h+size,w:w+size] image = torch.clip((255. * image), 0, 255).round() / 255.0 return (image - mean.view([3,1,1])) / std.view([3,1,1]) def siglip2_flex_calc_resolution(oh, ow, patch_size, max_num_patches, eps=1e-5): def scale_dim(size, scale): scaled = math.ceil(size * scale / patch_size) * patch_size return max(patch_size, int(scaled)) # Binary search for optimal scale lo, hi = eps / 10, 100.0 while hi - lo >= eps: mid = (lo + hi) / 2 h, w = scale_dim(oh, mid), scale_dim(ow, mid) if (h // patch_size) * (w // patch_size) <= max_num_patches: lo = mid else: hi = mid return scale_dim(oh, lo), scale_dim(ow, lo) def siglip2_preprocess(image, size, patch_size, num_patches, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True): if size > 0: return clip_preprocess(image, size=size, mean=mean, std=std, crop=crop) image = image[:, :, :, :3] if image.shape[3] > 3 else image mean = torch.tensor(mean, device=image.device, dtype=image.dtype) std = torch.tensor(std, device=image.device, dtype=image.dtype) image = image.movedim(-1, 1) b, c, h, w = image.shape h, w = siglip2_flex_calc_resolution(h, w, patch_size, num_patches) image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear", antialias=True) image = torch.clip((255. * image), 0, 255).round() / 255.0 return (image - mean.view([3, 1, 1])) / std.view([3, 1, 1]) class CLIPAttention(torch.nn.Module): def __init__(self, embed_dim, heads, dtype, device, operations): super().__init__() self.heads = heads self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x, mask=None, optimized_attention=None): q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) out = optimized_attention(q, k, v, self.heads, mask) return self.out_proj(out) ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), "gelu": torch.nn.functional.gelu, "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), } class CLIPMLP(torch.nn.Module): def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): super().__init__() self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) self.activation = ACTIVATIONS[activation] self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x): x = self.fc1(x) x = self.activation(x) x = self.fc2(x) return x class CLIPLayer(torch.nn.Module): def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) def forward(self, x, mask=None, optimized_attention=None): x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) x += self.mlp(self.layer_norm2(x)) return x class CLIPEncoder(torch.nn.Module): def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) def forward(self, x, mask=None, intermediate_output=None): optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) all_intermediate = None if intermediate_output is not None: if intermediate_output == "all": all_intermediate = [] intermediate_output = None elif intermediate_output < 0: intermediate_output = len(self.layers) + intermediate_output intermediate = None for i, l in enumerate(self.layers): x = l(x, mask, optimized_attention) if i == intermediate_output: intermediate = x.clone() if all_intermediate is not None: all_intermediate.append(x.unsqueeze(1).clone()) if all_intermediate is not None: intermediate = torch.cat(all_intermediate, dim=1) return x, intermediate class CLIPEmbeddings(torch.nn.Module): def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None): super().__init__() self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device) def forward(self, input_tokens, dtype=torch.float32): return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device) class CLIPTextModel_(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] num_positions = config_dict["max_position_embeddings"] self.eos_token_id = config_dict["eos_token_id"] super().__init__() self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32, embeds_info=[]): if embeds is not None: x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device) else: x = self.embeddings(input_tokens, dtype=dtype) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1) if mask is not None: mask += causal_mask else: mask = causal_mask x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) x = self.final_layer_norm(x) if i is not None and final_layer_norm_intermediate: i = self.final_layer_norm(i) if num_tokens is not None: pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))] else: pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] return x, i, pooled_output class CLIPTextModel(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.num_layers = config_dict["num_hidden_layers"] self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) embed_dim = config_dict["hidden_size"] self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) self.dtype = dtype def get_input_embeddings(self): return self.text_model.embeddings.token_embedding def set_input_embeddings(self, embeddings): self.text_model.embeddings.token_embedding = embeddings def forward(self, *args, **kwargs): x = self.text_model(*args, **kwargs) out = self.text_projection(x[2]) return (x[0], x[1], out, x[2]) def siglip2_pos_embed(embed_weight, embeds, orig_shape): embed_weight_len = round(embed_weight.shape[0] ** 0.5) embed_weight = comfy.ops.cast_to_input(embed_weight, embeds).movedim(1, 0).reshape(1, -1, embed_weight_len, embed_weight_len) embed_weight = torch.nn.functional.interpolate(embed_weight, size=orig_shape, mode="bilinear", align_corners=False, antialias=True) embed_weight = embed_weight.reshape(-1, embed_weight.shape[-2] * embed_weight.shape[-1]).movedim(0, 1) return embeds + embed_weight class Siglip2Embeddings(torch.nn.Module): def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", num_patches=None, dtype=None, device=None, operations=None): super().__init__() self.patch_embedding = operations.Linear(num_channels * patch_size * patch_size, embed_dim, dtype=dtype, device=device) self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device) self.patch_size = patch_size def forward(self, pixel_values): b, c, h, w = pixel_values.shape img = pixel_values.movedim(1, -1).reshape(b, h // self.patch_size, self.patch_size, w // self.patch_size, self.patch_size, c) img = img.permute(0, 1, 3, 2, 4, 5) img = img.reshape(b, img.shape[1] * img.shape[2], -1) img = self.patch_embedding(img) return siglip2_pos_embed(self.position_embedding.weight, img, (h // self.patch_size, w // self.patch_size)) class CLIPVisionEmbeddings(torch.nn.Module): def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None): super().__init__() num_patches = (image_size // patch_size) ** 2 if model_type == "siglip_vision_model": self.class_embedding = None patch_bias = True else: num_patches = num_patches + 1 self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) patch_bias = False self.patch_embedding = operations.Conv2d( in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=patch_bias, dtype=dtype, device=device ) self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device) def forward(self, pixel_values): embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) if self.class_embedding is not None: embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds) class CLIPVision(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] model_type = config_dict["model_type"] if model_type in ["siglip2_vision_model"]: self.embeddings = Siglip2Embeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, num_patches=config_dict.get("num_patches", None), dtype=dtype, device=device, operations=operations) else: self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations) if model_type in ["siglip_vision_model", "siglip2_vision_model"]: self.pre_layrnorm = lambda a: a self.output_layernorm = True else: self.pre_layrnorm = operations.LayerNorm(embed_dim) self.output_layernorm = False self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.post_layernorm = operations.LayerNorm(embed_dim) def forward(self, pixel_values, attention_mask=None, intermediate_output=None): x = self.embeddings(pixel_values) x = self.pre_layrnorm(x) #TODO: attention_mask? x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) if self.output_layernorm: x = self.post_layernorm(x) pooled_output = x else: pooled_output = self.post_layernorm(x[:, 0, :]) return x, i, pooled_output class LlavaProjector(torch.nn.Module): def __init__(self, in_dim, out_dim, dtype, device, operations): super().__init__() self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype) self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype) def forward(self, x): return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:]))) class CLIPVisionModelProjection(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.vision_model = CLIPVision(config_dict, dtype, device, operations) if "projection_dim" in config_dict: self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) else: self.visual_projection = lambda a: a if "llava3" == config_dict.get("projector_type", None): self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations) else: self.multi_modal_projector = None def forward(self, *args, **kwargs): x = self.vision_model(*args, **kwargs) out = self.visual_projection(x[2]) projected = None if self.multi_modal_projector is not None: projected = self.multi_modal_projector(x[1]) return (x[0], x[1], out, projected) ================================================ FILE: comfy/clip_vision.py ================================================ from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace import os import json import logging import comfy.ops import comfy.model_patcher import comfy.model_management import comfy.utils import comfy.clip_model import comfy.image_encoders.dino2 class Output: def __getitem__(self, key): return getattr(self, key) def __setitem__(self, key, item): setattr(self, key, item) clip_preprocess = comfy.clip_model.clip_preprocess # Prevent some stuff from breaking, TODO: remove eventually IMAGE_ENCODERS = { "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection, "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection, "siglip2_vision_model": comfy.clip_model.CLIPVisionModelProjection, "dinov2": comfy.image_encoders.dino2.Dinov2Model, } class ClipVisionModel(): def __init__(self, json_config): with open(json_config) as f: config = json.load(f) self.image_size = config.get("image_size", 224) self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073]) self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711]) self.model_type = config.get("model_type", "clip_vision_model") self.config = config.copy() model_class = IMAGE_ENCODERS.get(self.model_type) if self.model_type == "siglip_vision_model": self.return_all_hidden_states = True else: self.return_all_hidden_states = False self.load_device = comfy.model_management.text_encoder_device() offload_device = comfy.model_management.text_encoder_offload_device() self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast) self.model.eval() self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) def load_sd(self, sd): return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic()) def get_sd(self): return self.model.state_dict() def encode_image(self, image, crop=True): comfy.model_management.load_model_gpu(self.patcher) if self.model_type == "siglip2_vision_model": pixel_values = comfy.clip_model.siglip2_preprocess(image.to(self.load_device), size=self.image_size, patch_size=self.config.get("patch_size", 16), num_patches=self.config.get("num_patches", 256), mean=self.image_mean, std=self.image_std, crop=crop).float() else: pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float() out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2) outputs = Output() outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) outputs["image_sizes"] = [pixel_values.shape[1:]] * pixel_values.shape[0] if self.return_all_hidden_states: all_hs = out[1].to(comfy.model_management.intermediate_device()) outputs["penultimate_hidden_states"] = all_hs[:, -2] outputs["all_hidden_states"] = all_hs else: outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) outputs["mm_projected"] = out[3] return outputs def convert_to_transformers(sd, prefix): sd_k = sd.keys() if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: keys_to_replace = { "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", } for x in keys_to_replace: if x in sd_k: sd[keys_to_replace[x]] = sd.pop(x) if "{}proj".format(prefix) in sd_k: sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) sd = transformers_convert(sd, prefix, "vision_model.", 48) else: replace_prefix = {prefix: ""} sd = state_dict_prefix_replace(sd, replace_prefix) return sd def load_clipvision_from_sd(sd, prefix="", convert_keys=False): if convert_keys: sd = convert_to_transformers(sd, prefix) if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0] if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152: patch_embedding_shape = sd["vision_model.embeddings.patch_embedding.weight"].shape if len(patch_embedding_shape) == 2: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip2_base_naflex.json") else: if embed_shape == 729: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json") elif embed_shape == 1024: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json") elif embed_shape == 577: if "multi_modal_projector.linear_1.bias" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json") else: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") else: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") # Dinov2 elif 'encoder.layer.39.layer_scale2.lambda1' in sd: json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json") elif 'encoder.layer.23.layer_scale2.lambda1' in sd: json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json") else: return None clip = ClipVisionModel(json_config) m, u = clip.load_sd(sd) if len(m) > 0: logging.warning("missing clip vision: {}".format(m)) u = set(u) keys = list(sd.keys()) for k in keys: if k not in u: sd.pop(k) return clip def load(ckpt_path): sd = load_torch_file(ckpt_path) if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) else: return load_clipvision_from_sd(sd) ================================================ FILE: comfy/clip_vision_config_g.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "gelu", "hidden_size": 1664, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 8192, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 48, "patch_size": 14, "projection_dim": 1280, "torch_dtype": "float32" } ================================================ FILE: comfy/clip_vision_config_h.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "gelu", "hidden_size": 1280, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 5120, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 32, "patch_size": 14, "projection_dim": 1024, "torch_dtype": "float32" } ================================================ FILE: comfy/clip_vision_config_vitl.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "quick_gelu", "hidden_size": 1024, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "torch_dtype": "float32" } ================================================ FILE: comfy/clip_vision_config_vitl_336.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "quick_gelu", "hidden_size": 1024, "image_size": 336, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-5, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "torch_dtype": "float32" } ================================================ FILE: comfy/clip_vision_config_vitl_336_llava.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "quick_gelu", "hidden_size": 1024, "image_size": 336, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-5, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "projector_type": "llava3", "torch_dtype": "float32" } ================================================ FILE: comfy/clip_vision_siglip2_base_naflex.json ================================================ { "num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": -1, "intermediate_size": 4304, "model_type": "siglip2_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 16, "num_patches": 256, "image_mean": [0.5, 0.5, 0.5], "image_std": [0.5, 0.5, 0.5] } ================================================ FILE: comfy/clip_vision_siglip_384.json ================================================ { "num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 384, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14, "image_mean": [0.5, 0.5, 0.5], "image_std": [0.5, 0.5, 0.5] } ================================================ FILE: comfy/clip_vision_siglip_512.json ================================================ { "num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 512, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 16, "image_mean": [0.5, 0.5, 0.5], "image_std": [0.5, 0.5, 0.5] } ================================================ FILE: comfy/comfy_types/README.md ================================================ # Comfy Typing ## Type hinting for ComfyUI Node development This module provides type hinting and concrete convenience types for node developers. If cloned to the custom_nodes directory of ComfyUI, types can be imported using: ```python from comfy.comfy_types import IO, ComfyNodeABC, CheckLazyMixin class ExampleNode(ComfyNodeABC): @classmethod def INPUT_TYPES(s) -> InputTypeDict: return {"required": {}} ``` Full example is in [examples/example_nodes.py](examples/example_nodes.py). # Types A few primary types are documented below. More complete information is available via the docstrings on each type. ## `IO` A string enum of built-in and a few custom data types. Includes the following special types and their requisite plumbing: - `ANY`: `"*"` - `NUMBER`: `"FLOAT,INT"` - `PRIMITIVE`: `"STRING,FLOAT,INT,BOOLEAN"` ## `ComfyNodeABC` An abstract base class for nodes, offering type-hinting / autocomplete, and somewhat-alright docstrings. ### Type hinting for `INPUT_TYPES` ![INPUT_TYPES auto-completion in Visual Studio Code](examples/input_types.png) ### `INPUT_TYPES` return dict ![INPUT_TYPES return value type hinting in Visual Studio Code](examples/required_hint.png) ### Options for individual inputs ![INPUT_TYPES return value option auto-completion in Visual Studio Code](examples/input_options.png) ================================================ FILE: comfy/comfy_types/__init__.py ================================================ import torch from typing import Callable, Protocol, TypedDict, Optional, List from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator class UnetApplyFunction(Protocol): """Function signature protocol on comfy.model_base.BaseModel.apply_model""" def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor: pass class UnetApplyConds(TypedDict): """Optional conditions for unet apply function.""" c_concat: Optional[torch.Tensor] c_crossattn: Optional[torch.Tensor] control: Optional[torch.Tensor] transformer_options: Optional[dict] class UnetParams(TypedDict): # Tensor of shape [B, C, H, W] input: torch.Tensor # Tensor of shape [B] timestep: torch.Tensor c: UnetApplyConds # List of [0, 1], [0], [1], ... # 0 means conditional, 1 means conditional unconditional cond_or_uncond: List[int] UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor] __all__ = [ "UnetWrapperFunction", UnetApplyConds.__name__, UnetParams.__name__, UnetApplyFunction.__name__, IO.__name__, InputTypeDict.__name__, ComfyNodeABC.__name__, CheckLazyMixin.__name__, FileLocator.__name__, ] ================================================ FILE: comfy/comfy_types/examples/example_nodes.py ================================================ from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict from inspect import cleandoc class ExampleNode(ComfyNodeABC): """An example node that just adds 1 to an input integer. * Requires a modern IDE to provide any benefit (detail: an IDE configured with analysis paths etc). * This node is intended as an example for developers only. """ DESCRIPTION = cleandoc(__doc__) CATEGORY = "examples" @classmethod def INPUT_TYPES(s) -> InputTypeDict: return { "required": { "input_int": (IO.INT, {"defaultInput": True}), } } RETURN_TYPES = (IO.INT,) RETURN_NAMES = ("input_plus_one",) FUNCTION = "execute" def execute(self, input_int: int): return (input_int + 1,) ================================================ FILE: comfy/comfy_types/node_typing.py ================================================ """Comfy-specific type hinting""" from __future__ import annotations from typing import Literal, TypedDict, Optional from typing_extensions import NotRequired from abc import ABC, abstractmethod from enum import Enum class StrEnum(str, Enum): """Base class for string enums. Python's StrEnum is not available until 3.11.""" def __str__(self) -> str: return self.value class IO(StrEnum): """Node input/output data types. Includes functionality for ``"*"`` (`ANY`) and ``"MULTI,TYPES"``. """ STRING = "STRING" IMAGE = "IMAGE" MASK = "MASK" LATENT = "LATENT" BOOLEAN = "BOOLEAN" INT = "INT" FLOAT = "FLOAT" COMBO = "COMBO" CONDITIONING = "CONDITIONING" SAMPLER = "SAMPLER" SIGMAS = "SIGMAS" GUIDER = "GUIDER" NOISE = "NOISE" CLIP = "CLIP" CONTROL_NET = "CONTROL_NET" VAE = "VAE" MODEL = "MODEL" LORA_MODEL = "LORA_MODEL" LOSS_MAP = "LOSS_MAP" CLIP_VISION = "CLIP_VISION" CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT" STYLE_MODEL = "STYLE_MODEL" GLIGEN = "GLIGEN" UPSCALE_MODEL = "UPSCALE_MODEL" AUDIO = "AUDIO" WEBCAM = "WEBCAM" POINT = "POINT" FACE_ANALYSIS = "FACE_ANALYSIS" BBOX = "BBOX" SEGS = "SEGS" VIDEO = "VIDEO" ANY = "*" """Always matches any type, but at a price. Causes some functionality issues (e.g. reroutes, link types), and should be avoided whenever possible. """ NUMBER = "FLOAT,INT" """A float or an int - could be either""" PRIMITIVE = "STRING,FLOAT,INT,BOOLEAN" """Could be any of: string, float, int, or bool""" def __ne__(self, value: object) -> bool: if self == "*" or value == "*": return False if not isinstance(value, str): return True a = frozenset(self.split(",")) b = frozenset(value.split(",")) return not (b.issubset(a) or a.issubset(b)) class RemoteInputOptions(TypedDict): route: str """The route to the remote source.""" refresh_button: bool """Specifies whether to show a refresh button in the UI below the widget.""" control_after_refresh: Literal["first", "last"] """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on.""" timeout: int """The maximum amount of time to wait for a response from the remote source in milliseconds.""" max_retries: int """The maximum number of retries before aborting the request.""" refresh: int """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed.""" class MultiSelectOptions(TypedDict): placeholder: NotRequired[str] """The placeholder text to display in the multi-select widget when no items are selected.""" chip: NotRequired[bool] """Specifies whether to use chips instead of comma separated values for the multi-select widget.""" class InputTypeOptions(TypedDict): """Provides type hinting for the return type of the INPUT_TYPES node function. Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`). Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes """ default: NotRequired[bool | str | float | int | list | tuple] """The default value of the widget""" defaultInput: NotRequired[bool] """@deprecated in v1.16 frontend. v1.16 frontend allows input socket and widget to co-exist. - defaultInput on required inputs should be dropped. - defaultInput on optional inputs should be replaced with forceInput. Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3364 """ forceInput: NotRequired[bool] """Forces the input to be an input slot rather than a widget even a widget is available for the input type.""" lazy: NotRequired[bool] """Declares that this input uses lazy evaluation""" rawLink: NotRequired[bool] """When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. `["nodeId", ]`). Designed for node expansion.""" tooltip: NotRequired[str] """Tooltip for the input (or widget), shown on pointer hover""" socketless: NotRequired[bool] """All inputs (including widgets) have an input socket to connect links. When ``true``, if there is a widget for this input, no socket will be created. Available from frontend v1.17.5 Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548 """ widgetType: NotRequired[str] """Specifies a type to be used for widget initialization if different from the input type. Available from frontend v1.18.0 https://github.com/Comfy-Org/ComfyUI_frontend/pull/3550""" # class InputTypeNumber(InputTypeOptions): # default: float | int min: NotRequired[float] """The minimum value of a number (``FLOAT`` | ``INT``)""" max: NotRequired[float] """The maximum value of a number (``FLOAT`` | ``INT``)""" step: NotRequired[float] """The amount to increment or decrement a widget by when stepping up/down (``FLOAT`` | ``INT``)""" round: NotRequired[float] """Floats are rounded by this value (``FLOAT``)""" # class InputTypeBoolean(InputTypeOptions): # default: bool label_on: NotRequired[str] """The label to use in the UI when the bool is True (``BOOLEAN``)""" label_off: NotRequired[str] """The label to use in the UI when the bool is False (``BOOLEAN``)""" # class InputTypeString(InputTypeOptions): # default: str multiline: NotRequired[bool] """Use a multiline text box (``STRING``)""" placeholder: NotRequired[str] """Placeholder text to display in the UI when empty (``STRING``)""" # Deprecated: # defaultVal: str dynamicPrompts: NotRequired[bool] """Causes the front-end to evaluate dynamic prompts (``STRING``)""" # class InputTypeCombo(InputTypeOptions): image_upload: NotRequired[bool] """Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`.""" image_folder: NotRequired[Literal["input", "output", "temp"]] """Specifies which folder to get preview images from if the input has the ``image_upload`` flag. """ remote: NotRequired[RemoteInputOptions] """Specifies the configuration for a remote input. Available after ComfyUI frontend v1.9.7 https://github.com/Comfy-Org/ComfyUI_frontend/pull/2422""" control_after_generate: NotRequired[bool] """Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types.""" options: NotRequired[list[str | int | float]] """COMBO type only. Specifies the selectable options for the combo widget. Prefer: ["COMBO", {"options": ["Option 1", "Option 2", "Option 3"]}] Over: [["Option 1", "Option 2", "Option 3"]] """ multi_select: NotRequired[MultiSelectOptions] """COMBO type only. Specifies the configuration for a multi-select widget. Available after ComfyUI frontend v1.13.4 https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987""" gradient_stops: NotRequired[list[dict]] """Gradient color stops for gradientslider display mode. Each stop is {"offset": float, "color": [r, g, b]}.""" class HiddenInputTypeDict(TypedDict): """Provides type hinting for the hidden entry of node INPUT_TYPES.""" node_id: NotRequired[Literal["UNIQUE_ID"]] """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" unique_id: NotRequired[Literal["UNIQUE_ID"]] """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" prompt: NotRequired[Literal["PROMPT"]] """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" extra_pnginfo: NotRequired[Literal["EXTRA_PNGINFO"]] """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" dynprompt: NotRequired[Literal["DYNPROMPT"]] """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" class InputTypeDict(TypedDict): """Provides type hinting for node INPUT_TYPES. Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs """ required: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] """Describes all inputs that must be connected for the node to execute.""" optional: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] """Describes inputs which do not need to be connected.""" hidden: NotRequired[HiddenInputTypeDict] """Offers advanced functionality and server-client communication. Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs """ class ComfyNodeABC(ABC): """Abstract base class for Comfy nodes. Includes the names and expected types of attributes. Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview """ DESCRIPTION: str """Node description, shown as a tooltip when hovering over the node. Usage:: # Explicitly define the description DESCRIPTION = "Example description here." # Use the docstring of the node class. DESCRIPTION = cleandoc(__doc__) """ CATEGORY: str """The category of the node, as per the "Add Node" menu. Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category """ EXPERIMENTAL: bool """Flags a node as experimental, informing users that it may change or not work as expected.""" DEPRECATED: bool """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" DEV_ONLY: bool """Flags a node as dev-only, hiding it from search/menus unless dev mode is enabled.""" API_NODE: Optional[bool] """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.""" @classmethod @abstractmethod def INPUT_TYPES(s) -> InputTypeDict: """Defines node inputs. * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute. * The ``optional`` key can be added to describe inputs which do not need to be connected. * The ``hidden`` key offers some advanced functionality. More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types """ return {"required": {}} OUTPUT_NODE: bool """Flags this node as an output node, causing any inputs it requires to be executed. If a node is not connected to any output nodes, that node will not be executed. Usage:: OUTPUT_NODE = True From the docs: By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is. Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node """ INPUT_IS_LIST: bool """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes. All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in. This also affects ``check_lazy_status``. From the docs: A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``. Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing """ OUTPUT_IS_LIST: tuple[bool, ...] """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items. Connected nodes that do not implement `INPUT_IS_LIST` will be executed once for every item in the list. A ``tuple[bool]``, where the items match those in `RETURN_TYPES`:: RETURN_TYPES = (IO.INT, IO.INT, IO.STRING) OUTPUT_IS_LIST = (True, True, False) # The string output will be handled normally From the docs: In order to tell Comfy that the list being returned should not be wrapped, but treated as a series of data for sequential processing, the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`, specifying which outputs which should be so treated. Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing """ RETURN_TYPES: tuple[IO, ...] """A tuple representing the outputs of this node. Usage:: RETURN_TYPES = (IO.INT, "INT", "CUSTOM_TYPE") Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types """ RETURN_NAMES: tuple[str, ...] """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")`` Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names """ OUTPUT_TOOLTIPS: tuple[str, ...] """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`.""" FUNCTION: str """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"` Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function """ class CheckLazyMixin: """Provides a basic check_lazy_status implementation and type hinting for nodes that use lazy inputs.""" def check_lazy_status(self, **kwargs) -> list[str]: """Returns a list of input names that should be evaluated. This basic mixin impl. requires all inputs. :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \ When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``. Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status """ need = [name for name in kwargs if kwargs[name] is None] return need class FileLocator(TypedDict): """Provides type hinting for the file location""" filename: str """The filename of the file.""" subfolder: str """The subfolder of the file.""" type: Literal["input", "output", "temp"] """The root folder of the file.""" ================================================ FILE: comfy/conds.py ================================================ import torch import math import comfy.utils import logging def is_equal(x, y): if torch.is_tensor(x) and torch.is_tensor(y): return torch.equal(x, y) elif isinstance(x, dict) and isinstance(y, dict): if x.keys() != y.keys(): return False return all(is_equal(x[k], y[k]) for k in x) elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)): if type(x) is not type(y) or len(x) != len(y): return False return all(is_equal(a, b) for a, b in zip(x, y)) else: try: return x == y except Exception: logging.warning("comparison issue with COND") return False class CONDRegular: def __init__(self, cond): self.cond = cond def _copy_with(self, cond): return self.__class__(cond) def process_cond(self, batch_size, **kwargs): return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size)) def can_concat(self, other): if self.cond.shape != other.cond.shape: return False if self.cond.device != other.cond.device: logging.warning("WARNING: conds not on same device, skipping concat.") return False return True def concat(self, others): conds = [self.cond] for x in others: conds.append(x.cond) return torch.cat(conds) def size(self): return list(self.cond.size()) class CONDNoiseShape(CONDRegular): def process_cond(self, batch_size, area, **kwargs): data = self.cond if area is not None: dims = len(area) // 2 for i in range(dims): data = data.narrow(i + 2, area[i + dims], area[i]) return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size)) class CONDCrossAttn(CONDRegular): def can_concat(self, other): s1 = self.cond.shape s2 = other.cond.shape if s1 != s2: if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen return False mult_min = math.lcm(s1[1], s2[1]) diff = mult_min // min(s1[1], s2[1]) if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much return False if self.cond.device != other.cond.device: logging.warning("WARNING: conds not on same device: skipping concat.") return False return True def concat(self, others): conds = [self.cond] crossattn_max_len = self.cond.shape[1] for x in others: c = x.cond crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1]) conds.append(c) out = [] for c in conds: if c.shape[1] < crossattn_max_len: c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result out.append(c) return torch.cat(out) class CONDConstant(CONDRegular): def __init__(self, cond): self.cond = cond def process_cond(self, batch_size, **kwargs): return self._copy_with(self.cond) def can_concat(self, other): if not is_equal(self.cond, other.cond): return False return True def concat(self, others): return self.cond def size(self): return [1] class CONDList(CONDRegular): def __init__(self, cond): self.cond = cond def process_cond(self, batch_size, **kwargs): out = [] for c in self.cond: out.append(comfy.utils.repeat_to_batch_size(c, batch_size)) return self._copy_with(out) def can_concat(self, other): if len(self.cond) != len(other.cond): return False for i in range(len(self.cond)): if self.cond[i].shape != other.cond[i].shape: return False return True def concat(self, others): out = [] for i in range(len(self.cond)): o = [self.cond[i]] for x in others: o.append(x.cond[i]) out.append(torch.cat(o)) return out def size(self): # hackish implementation to make the mem estimation work o = 0 c = 1 for c in self.cond: size = c.size() o += math.prod(size) if len(size) > 1: c = size[1] return [1, c, o // c] ================================================ FILE: comfy/context_windows.py ================================================ from __future__ import annotations from typing import TYPE_CHECKING, Callable import torch import numpy as np import collections from dataclasses import dataclass from abc import ABC, abstractmethod import logging import comfy.model_management import comfy.patcher_extension if TYPE_CHECKING: from comfy.model_base import BaseModel from comfy.model_patcher import ModelPatcher from comfy.controlnet import ControlBase class ContextWindowABC(ABC): def __init__(self): ... @abstractmethod def get_tensor(self, full: torch.Tensor) -> torch.Tensor: """ Get torch.Tensor applicable to current window. """ raise NotImplementedError("Not implemented.") @abstractmethod def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torch.Tensor: """ Apply torch.Tensor of window to the full tensor, in place. Returns reference to updated full tensor, not a copy. """ raise NotImplementedError("Not implemented.") class ContextHandlerABC(ABC): def __init__(self): ... @abstractmethod def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool: raise NotImplementedError("Not implemented.") @abstractmethod def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: ContextWindowABC, device=None) -> list: raise NotImplementedError("Not implemented.") @abstractmethod def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): raise NotImplementedError("Not implemented.") class IndexListContextWindow(ContextWindowABC): def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0): self.index_list = index_list self.context_length = len(index_list) self.dim = dim self.total_frames = total_frames self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames) def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor: if dim is None: dim = self.dim if dim == 0 and full.shape[dim] == 1: return full idx = tuple([slice(None)] * dim + [self.index_list]) window = full[idx] if retain_index_list: idx = tuple([slice(None)] * dim + [retain_index_list]) window[idx] = full[idx] return window.to(device) def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor: if dim is None: dim = self.dim idx = tuple([slice(None)] * dim + [self.index_list]) full[idx] += to_add return full def get_region_index(self, num_regions: int) -> int: region_idx = int(self.center_ratio * num_regions) return min(max(region_idx, 0), num_regions - 1) class IndexListCallbacks: EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows" COMBINE_CONTEXT_WINDOW_RESULTS = "combine_context_window_results" EXECUTE_START = "execute_start" EXECUTE_CLEANUP = "execute_cleanup" RESIZE_COND_ITEM = "resize_cond_item" def init_callbacks(self): return {} def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, device, temporal_dim: int, temporal_scale: int=1, temporal_offset: int=0, retain_index_list: list[int]=[]): if not (hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor)): return None cond_tensor = cond_value.cond if temporal_dim >= cond_tensor.ndim: return None cond_size = cond_tensor.size(temporal_dim) if temporal_scale == 1: expected_size = x_in.size(window.dim) - temporal_offset if cond_size != expected_size: return None if temporal_offset == 0 and temporal_scale == 1: sliced = window.get_tensor(cond_tensor, device, dim=temporal_dim, retain_index_list=retain_index_list) return cond_value._copy_with(sliced) # skip leading latent positions that have no corresponding conditioning (e.g. reference frames) if temporal_offset > 0: indices = [i - temporal_offset for i in window.index_list[temporal_offset:]] indices = [i for i in indices if 0 <= i] else: indices = list(window.index_list) if not indices: return None if temporal_scale > 1: scaled = [] for i in indices: for k in range(temporal_scale): si = i * temporal_scale + k if si < cond_size: scaled.append(si) indices = scaled if not indices: return None idx = tuple([slice(None)] * temporal_dim + [indices]) sliced = cond_tensor[idx].to(device) return cond_value._copy_with(sliced) @dataclass class ContextSchedule: name: str func: Callable @dataclass class ContextFuseMethod: name: str func: Callable ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window']) class IndexListContextHandler(ContextHandlerABC): def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1, closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False): self.context_schedule = context_schedule self.fuse_method = fuse_method self.context_length = context_length self.context_overlap = context_overlap self.context_stride = context_stride self.closed_loop = closed_loop self.dim = dim self._step = 0 self.freenoise = freenoise self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else [] self.split_conds_to_windows = split_conds_to_windows self.callbacks = {} def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool: # for now, assume first dim is batch - should have stored on BaseModel in actual implementation if x_in.size(self.dim) > self.context_length: logging.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.") if self.cond_retain_index_list: logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}") return True return False def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase: if control.previous_controlnet is not None: self.prepare_control_objects(control.previous_controlnet, device) return control def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: IndexListContextWindow, device=None) -> list: if cond_in is None: return None # reuse or resize cond items to match context requirements resized_cond = [] # if multiple conds, split based on primary region if self.split_conds_to_windows and len(cond_in) > 1: region = window.get_region_index(len(cond_in)) logging.info(f"Splitting conds to windows; using region {region} for window {window.index_list[0]}-{window.index_list[-1]} with center ratio {window.center_ratio:.3f}") cond_in = [cond_in[region]] # cond object is a list containing a dict - outer list is irrelevant, so just loop through it for actual_cond in cond_in: resized_actual_cond = actual_cond.copy() # now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary for key in actual_cond: try: cond_item = actual_cond[key] if isinstance(cond_item, torch.Tensor): # check that tensor is the expected length - x.size(0) if self.dim < cond_item.ndim and cond_item.size(self.dim) == x_in.size(self.dim): # if so, it's subsetting time - tell controls the expected indeces so they can handle them actual_cond_item = window.get_tensor(cond_item) resized_actual_cond[key] = actual_cond_item.to(device) else: resized_actual_cond[key] = cond_item.to(device) # look for control elif key == "control": resized_actual_cond[key] = self.prepare_control_objects(cond_item, device) elif isinstance(cond_item, dict): new_cond_item = cond_item.copy() # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor) for cond_key, cond_value in new_cond_item.items(): # Allow callbacks to handle custom conditioning items handled = False for callback in comfy.patcher_extension.get_all_callbacks( IndexListCallbacks.RESIZE_COND_ITEM, self.callbacks ): result = callback(cond_key, cond_value, window, x_in, device, new_cond_item) if result is not None: new_cond_item[cond_key] = result handled = True break if not handled and self._model is not None: result = self._model.resize_cond_for_context_window( cond_key, cond_value, window, x_in, device, retain_index_list=self.cond_retain_index_list) if result is not None: new_cond_item[cond_key] = result handled = True if handled: continue if isinstance(cond_value, torch.Tensor): if (self.dim < cond_value.ndim and cond_value.size(self.dim) == x_in.size(self.dim)) or \ (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)): new_cond_item[cond_key] = window.get_tensor(cond_value, device) # Handle audio_embed (temporal dim is 1) elif cond_key == "audio_embed" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): audio_cond = cond_value.cond if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim): new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1)) # Handle vace_context (temporal dim is 3) elif cond_key == "vace_context" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): vace_cond = cond_value.cond if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim): sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list) new_cond_item[cond_key] = cond_value._copy_with(sliced_vace) # if has cond that is a Tensor, check if needs to be subset elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): if (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \ (cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim)): new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device, retain_index_list=self.cond_retain_index_list)) elif cond_key == "num_video_frames": # for SVD new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond) new_cond_item[cond_key].cond = window.context_length resized_actual_cond[key] = new_cond_item else: resized_actual_cond[key] = cond_item finally: del cond_item # just in case to prevent VRAM issues resized_cond.append(resized_actual_cond) return resized_cond def set_step(self, timestep: torch.Tensor, model_options: dict[str]): mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001) matches = torch.nonzero(mask) if torch.numel(matches) == 0: return # substep from multi-step sampler: keep self._step from the last full step self._step = int(matches[0].item()) def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]: full_length = x_in.size(self.dim) # TODO: choose dim based on model context_windows = self.context_schedule.func(full_length, self, model_options) context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows] return context_windows def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): self._model = model self.set_step(timestep, model_options) context_windows = self.get_context_windows(model, x_in, model_options) enumerated_context_windows = list(enumerate(context_windows)) conds_final = [torch.zeros_like(x_in) for _ in conds] if self.fuse_method.name == ContextFuseMethods.RELATIVE: counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] else: counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds] for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks): callback(self, model, x_in, conds, timestep, model_options) for enum_window in enumerated_context_windows: results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options) for result in results: self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep, conds_final, counts_final, biases_final) try: # finalize conds if self.fuse_method.name == ContextFuseMethods.RELATIVE: # relative is already normalized, so return as is del counts_final return conds_final else: # normalize conds via division by context usage counts for i in range(len(conds_final)): conds_final[i] /= counts_final[i] del counts_final return conds_final finally: for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks): callback(self, model, x_in, conds, timestep, model_options) def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]], model_options, device=None, first_device=None): results: list[ContextResults] = [] for window_idx, window in enumerated_context_windows: # allow processing to end between context window executions for faster Cancel comfy.model_management.throw_exception_if_processing_interrupted() for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks): callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device) # update exposed params model_options["transformer_options"]["context_window"] = window # get subsections of x, timestep, conds sub_x = window.get_tensor(x_in, device) sub_timestep = window.get_tensor(timestep, device, dim=0) sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds] sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options) if device is not None: for i in range(len(sub_conds_out)): sub_conds_out[i] = sub_conds_out[i].to(x_in.device) results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window)) return results def combine_context_window_results(self, x_in: torch.Tensor, sub_conds_out, sub_conds, window: IndexListContextWindow, window_idx: int, total_windows: int, timestep: torch.Tensor, conds_final: list[torch.Tensor], counts_final: list[torch.Tensor], biases_final: list[torch.Tensor]): if self.fuse_method.name == ContextFuseMethods.RELATIVE: for pos, idx in enumerate(window.index_list): # bias is the influence of a specific index in relation to the whole context window bias = 1 - abs(idx - (window.index_list[0] + window.index_list[-1]) / 2) / ((window.index_list[-1] - window.index_list[0] + 1e-2) / 2) bias = max(1e-2, bias) # take weighted average relative to total bias of current idx for i in range(len(sub_conds_out)): bias_total = biases_final[i][idx] prev_weight = (bias_total / (bias_total + bias)) new_weight = (bias / (bias_total + bias)) # account for dims of tensors idx_window = tuple([slice(None)] * self.dim + [idx]) pos_window = tuple([slice(None)] * self.dim + [pos]) # apply new values conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight biases_final[i][idx] = bias_total + bias else: # add conds and counts based on weights of fuse method weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep) weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device) for i in range(len(sub_conds_out)): window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor) window.add_window(counts_final[i], weights_tensor) for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.COMBINE_CONTEXT_WINDOW_RESULTS, self.callbacks): callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final) def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs): # limit noise_shape length to context_length for more accurate vram use estimation model_options = kwargs.get("model_options", None) if model_options is None: raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.") handler: IndexListContextHandler = model_options.get("context_handler", None) if handler is not None: noise_shape = list(noise_shape) noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length) return executor(model, noise_shape, *args, **kwargs) def create_prepare_sampling_wrapper(model: ModelPatcher): model.add_wrapper_with_key( comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, "ContextWindows_prepare_sampling", _prepare_sampling_wrapper ) def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, noise, *args, **kwargs): model_options = extra_args.get("model_options", None) if model_options is None: raise Exception("model_options not found in sampler_sample_wrapper; this should never happen, something went wrong.") handler: IndexListContextHandler = model_options.get("context_handler", None) if handler is None: raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.") if not handler.freenoise: return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs) noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args["seed"]) return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs) def create_sampler_sample_wrapper(model: ModelPatcher): model.add_wrapper_with_key( comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, "ContextWindows_sampler_sample", _sampler_sample_wrapper ) def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor: total_dims = len(x_in.shape) weights_tensor = torch.Tensor(weights).to(device=device) for _ in range(dim): weights_tensor = weights_tensor.unsqueeze(0) for _ in range(total_dims - dim - 1): weights_tensor = weights_tensor.unsqueeze(-1) return weights_tensor def get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]: total_dims = len(x_in.shape) shape = [] for _ in range(dim): shape.append(1) shape.append(x_in.shape[dim]) for _ in range(total_dims - dim - 1): shape.append(1) return shape class ContextSchedules: UNIFORM_LOOPED = "looped_uniform" UNIFORM_STANDARD = "standard_uniform" STATIC_STANDARD = "standard_static" BATCHED = "batched" # from https://github.com/neggles/animatediff-cli/blob/main/src/animatediff/pipelines/context.py def create_windows_uniform_looped(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): windows = [] if num_frames < handler.context_length: windows.append(list(range(num_frames))) return windows context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1) # obtain uniform windows as normal, looping and all for context_step in 1 << np.arange(context_stride): pad = int(round(num_frames * ordered_halving(handler._step))) for j in range( int(ordered_halving(handler._step) * context_step) + pad, num_frames + pad + (0 if handler.closed_loop else -handler.context_overlap), (handler.context_length * context_step - handler.context_overlap), ): windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)]) return windows def create_windows_uniform_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): # unlike looped, uniform_straight does NOT allow windows that loop back to the beginning; # instead, they get shifted to the corresponding end of the frames. # in the case that a window (shifted or not) is identical to the previous one, it gets skipped. windows = [] if num_frames <= handler.context_length: windows.append(list(range(num_frames))) return windows context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1) # first, obtain uniform windows as normal, looping and all for context_step in 1 << np.arange(context_stride): pad = int(round(num_frames * ordered_halving(handler._step))) for j in range( int(ordered_halving(handler._step) * context_step) + pad, num_frames + pad + (-handler.context_overlap), (handler.context_length * context_step - handler.context_overlap), ): windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)]) # now that windows are created, shift any windows that loop, and delete duplicate windows delete_idxs = [] win_i = 0 while win_i < len(windows): # if window is rolls over itself, need to shift it is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames) if is_roll: roll_val = windows[win_i][roll_idx] # roll_val might not be 0 for windows of higher strides shift_window_to_end(windows[win_i], num_frames=num_frames) # check if next window (cyclical) is missing roll_val if roll_val not in windows[(win_i+1) % len(windows)]: # need to insert new window here - just insert window starting at roll_val windows.insert(win_i+1, list(range(roll_val, roll_val + handler.context_length))) # delete window if it's not unique for pre_i in range(0, win_i): if windows[win_i] == windows[pre_i]: delete_idxs.append(win_i) break win_i += 1 # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation delete_idxs.reverse() for i in delete_idxs: windows.pop(i) return windows def create_windows_static_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): windows = [] if num_frames <= handler.context_length: windows.append(list(range(num_frames))) return windows # always return the same set of windows delta = handler.context_length - handler.context_overlap for start_idx in range(0, num_frames, delta): # if past the end of frames, move start_idx back to allow same context_length ending = start_idx + handler.context_length if ending >= num_frames: final_delta = ending - num_frames final_start_idx = start_idx - final_delta windows.append(list(range(final_start_idx, final_start_idx + handler.context_length))) break windows.append(list(range(start_idx, start_idx + handler.context_length))) return windows def create_windows_batched(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): windows = [] if num_frames <= handler.context_length: windows.append(list(range(num_frames))) return windows # always return the same set of windows; # no overlap, just cut up based on context_length; # last window size will be different if num_frames % opts.context_length != 0 for start_idx in range(0, num_frames, handler.context_length): windows.append(list(range(start_idx, min(start_idx + handler.context_length, num_frames)))) return windows def create_windows_default(num_frames: int, handler: IndexListContextHandler): return [list(range(num_frames))] CONTEXT_MAPPING = { ContextSchedules.UNIFORM_LOOPED: create_windows_uniform_looped, ContextSchedules.UNIFORM_STANDARD: create_windows_uniform_standard, ContextSchedules.STATIC_STANDARD: create_windows_static_standard, ContextSchedules.BATCHED: create_windows_batched, } def get_matching_context_schedule(context_schedule: str) -> ContextSchedule: func = CONTEXT_MAPPING.get(context_schedule, None) if func is None: raise ValueError(f"Unknown context_schedule '{context_schedule}'.") return ContextSchedule(context_schedule, func) def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None): return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs) def create_weights_flat(length: int, **kwargs) -> list[float]: # weight is the same for all return [1.0] * length def create_weights_pyramid(length: int, **kwargs) -> list[float]: # weight is based on the distance away from the edge of the context window; # based on weighted average concept in FreeNoise paper if length % 2 == 0: max_weight = length // 2 weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1)) else: max_weight = (length + 1) // 2 weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1)) return weight_sequence def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs): # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302 # only expected overlap is given different weights weights_torch = torch.ones((length)) # blend left-side on all except first window if min(idxs) > 0: ramp_up = torch.linspace(1e-37, 1, handler.context_overlap) weights_torch[:handler.context_overlap] = ramp_up # blend right-side on all except last window if max(idxs) < full_length-1: ramp_down = torch.linspace(1, 1e-37, handler.context_overlap) weights_torch[-handler.context_overlap:] = ramp_down return weights_torch class ContextFuseMethods: FLAT = "flat" PYRAMID = "pyramid" RELATIVE = "relative" OVERLAP_LINEAR = "overlap-linear" LIST = [PYRAMID, FLAT, OVERLAP_LINEAR] LIST_STATIC = [PYRAMID, RELATIVE, FLAT, OVERLAP_LINEAR] FUSE_MAPPING = { ContextFuseMethods.FLAT: create_weights_flat, ContextFuseMethods.PYRAMID: create_weights_pyramid, ContextFuseMethods.RELATIVE: create_weights_pyramid, ContextFuseMethods.OVERLAP_LINEAR: create_weights_overlap_linear, } def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod: func = FUSE_MAPPING.get(fuse_method, None) if func is None: raise ValueError(f"Unknown fuse_method '{fuse_method}'.") return ContextFuseMethod(fuse_method, func) # Returns fraction that has denominator that is a power of 2 def ordered_halving(val): # get binary value, padded with 0s for 64 bits bin_str = f"{val:064b}" # flip binary value, padding included bin_flip = bin_str[::-1] # convert binary to int as_int = int(bin_flip, 2) # divide by 1 << 64, equivalent to 2**64, or 18446744073709551616, # or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's) return as_int / (1 << 64) def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]: all_indexes = list(range(num_frames)) for w in windows: for val in w: try: all_indexes.remove(val) except ValueError: pass return all_indexes def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]: prev_val = -1 for i, val in enumerate(window): val = val % num_frames if val < prev_val: return True, i prev_val = val return False, -1 def shift_window_to_start(window: list[int], num_frames: int): start_val = window[0] for i in range(len(window)): # 1) subtract each element by start_val to move vals relative to the start of all frames # 2) add num_frames and take modulus to get adjusted vals window[i] = ((window[i] - start_val) + num_frames) % num_frames def shift_window_to_end(window: list[int], num_frames: int): # 1) shift window to start shift_window_to_start(window, num_frames) end_val = window[-1] end_delta = num_frames - end_val - 1 for i in range(len(window)): # 2) add end_delta to each val to slide windows to end window[i] = window[i] + end_delta # https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/blob/90fb1331201a4b29488089e4fbffc0d82cc6d0a9/animatediff/sample_settings.py#L465 def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, context_overlap: int, seed: int): logging.info("Context windows: Applying FreeNoise") generator = torch.Generator(device='cpu').manual_seed(seed) latent_video_length = noise.shape[dim] delta = context_length - context_overlap for start_idx in range(0, latent_video_length - context_length, delta): place_idx = start_idx + context_length actual_delta = min(delta, latent_video_length - place_idx) if actual_delta <= 0: break list_idx = torch.randperm(actual_delta, generator=generator, device='cpu') + start_idx source_slice = [slice(None)] * noise.ndim source_slice[dim] = list_idx target_slice = [slice(None)] * noise.ndim target_slice[dim] = slice(place_idx, place_idx + actual_delta) noise[tuple(target_slice)] = noise[tuple(source_slice)] return noise ================================================ FILE: comfy/controlnet.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Comfy This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch from enum import Enum import math import os import logging import comfy.utils import comfy.model_management import comfy.model_detection import comfy.model_patcher import comfy.ops import comfy.latent_formats import comfy.model_base import comfy.cldm.cldm import comfy.t2i_adapter.adapter import comfy.ldm.cascade.controlnet import comfy.cldm.mmdit import comfy.ldm.hydit.controlnet import comfy.ldm.flux.controlnet import comfy.ldm.qwen_image.controlnet import comfy.cldm.dit_embedder from typing import TYPE_CHECKING if TYPE_CHECKING: from comfy.hooks import HookGroup def broadcast_image_to(tensor, target_batch_size, batched_number): current_batch_size = tensor.shape[0] if current_batch_size == 1: return tensor per_batch = target_batch_size // batched_number tensor = tensor[:per_batch] if per_batch > tensor.shape[0]: tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) current_batch_size = tensor.shape[0] if current_batch_size == target_batch_size: return tensor else: return torch.cat([tensor] * batched_number, dim=0) class StrengthType(Enum): CONSTANT = 1 LINEAR_UP = 2 class ControlBase: def __init__(self): self.cond_hint_original = None self.cond_hint = None self.strength = 1.0 self.timestep_percent_range = (0.0, 1.0) self.latent_format = None self.vae = None self.global_average_pooling = False self.timestep_range = None self.compression_ratio = 8 self.upscale_algorithm = 'nearest-exact' self.extra_args = {} self.previous_controlnet = None self.extra_conds = [] self.strength_type = StrengthType.CONSTANT self.concat_mask = False self.extra_concat_orig = [] self.extra_concat = None self.extra_hooks: HookGroup = None self.preprocess_image = lambda a: a def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]): self.cond_hint_original = cond_hint self.strength = strength self.timestep_percent_range = timestep_percent_range if self.latent_format is not None: if vae is None: logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.") self.vae = vae self.extra_concat_orig = extra_concat.copy() if self.concat_mask and len(self.extra_concat_orig) == 0: self.extra_concat_orig.append(torch.tensor([[[[1.0]]]])) return self def pre_run(self, model, percent_to_timestep_function): self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) if self.previous_controlnet is not None: self.previous_controlnet.pre_run(model, percent_to_timestep_function) def set_previous_controlnet(self, controlnet): self.previous_controlnet = controlnet return self def cleanup(self): if self.previous_controlnet is not None: self.previous_controlnet.cleanup() self.cond_hint = None self.extra_concat = None self.timestep_range = None def get_models(self): out = [] if self.previous_controlnet is not None: out += self.previous_controlnet.get_models() return out def get_extra_hooks(self): out = [] if self.extra_hooks is not None: out.append(self.extra_hooks) if self.previous_controlnet is not None: out += self.previous_controlnet.get_extra_hooks() return out def copy_to(self, c): c.cond_hint_original = self.cond_hint_original c.strength = self.strength c.timestep_percent_range = self.timestep_percent_range c.global_average_pooling = self.global_average_pooling c.compression_ratio = self.compression_ratio c.upscale_algorithm = self.upscale_algorithm c.latent_format = self.latent_format c.extra_args = self.extra_args.copy() c.vae = self.vae c.extra_conds = self.extra_conds.copy() c.strength_type = self.strength_type c.concat_mask = self.concat_mask c.extra_concat_orig = self.extra_concat_orig.copy() c.extra_hooks = self.extra_hooks.clone() if self.extra_hooks else None c.preprocess_image = self.preprocess_image def inference_memory_requirements(self, dtype): if self.previous_controlnet is not None: return self.previous_controlnet.inference_memory_requirements(dtype) return 0 def control_merge(self, control, control_prev, output_dtype): out = {'input':[], 'middle':[], 'output': []} for key in control: control_output = control[key] applied_to = set() for i in range(len(control_output)): x = control_output[i] if x is not None: if self.global_average_pooling: x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once applied_to.add(x) if self.strength_type == StrengthType.CONSTANT: x *= self.strength elif self.strength_type == StrengthType.LINEAR_UP: x *= (self.strength ** float(len(control_output) - i)) if output_dtype is not None and x.dtype != output_dtype: x = x.to(output_dtype) out[key].append(x) if control_prev is not None: for x in ['input', 'middle', 'output']: o = out[x] for i in range(len(control_prev[x])): prev_val = control_prev[x][i] if i >= len(o): o.append(prev_val) elif prev_val is not None: if o[i] is None: o[i] = prev_val else: if o[i].shape[0] < prev_val.shape[0]: o[i] = prev_val + o[i] else: o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue return out def set_extra_arg(self, argument, value=None): self.extra_args[argument] = value class ControlNet(ControlBase): def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False, preprocess_image=lambda a: a): super().__init__() self.control_model = control_model self.load_device = load_device if control_model is not None: self.control_model_wrapped = comfy.model_patcher.CoreModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) self.compression_ratio = compression_ratio self.global_average_pooling = global_average_pooling self.model_sampling_current = None self.manual_cast_dtype = manual_cast_dtype self.latent_format = latent_format self.extra_conds += extra_conds self.strength_type = strength_type self.concat_mask = concat_mask self.preprocess_image = preprocess_image def get_control(self, x_noisy, t, cond, batched_number, transformer_options): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options) if self.timestep_range is not None: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if control_prev is not None: return control_prev else: return None dtype = self.control_model.dtype if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.cond_hint = None compression_ratio = self.compression_ratio if self.vae is not None: compression_ratio *= self.vae.spacial_compression_encode() else: if self.latent_format is not None: raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.") self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, "center") self.cond_hint = self.preprocess_image(self.cond_hint) if self.vae is not None: loaded_models = comfy.model_management.loaded_models(only_currently_used=True) self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1)) comfy.model_management.load_models_gpu(loaded_models) if self.latent_format is not None: self.cond_hint = self.latent_format.process_in(self.cond_hint) if len(self.extra_concat_orig) > 0: to_concat = [] for c in self.extra_concat_orig: c = c.to(self.cond_hint.device) c = comfy.utils.common_upscale(c, self.cond_hint.shape[-1], self.cond_hint.shape[-2], self.upscale_algorithm, "center") if c.ndim < self.cond_hint.ndim: c = c.unsqueeze(2) c = comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[2], dim=2) to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0])) self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1) self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) context = cond.get('crossattn_controlnet', cond['c_crossattn']) extra = self.extra_args.copy() for c in self.extra_conds: temp = cond.get(c, None) if temp is not None: extra[c] = comfy.model_base.convert_tensor(temp, dtype, x_noisy.device) timestep = self.model_sampling_current.timestep(t) x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=comfy.model_management.cast_to_device(context, x_noisy.device, dtype), **extra) return self.control_merge(control, control_prev, output_dtype=None) def copy(self): c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) c.control_model = self.control_model c.control_model_wrapped = self.control_model_wrapped self.copy_to(c) return c def get_models(self): out = super().get_models() out.append(self.control_model_wrapped) return out def pre_run(self, model, percent_to_timestep_function): super().pre_run(model, percent_to_timestep_function) self.model_sampling_current = model.model_sampling def cleanup(self): self.model_sampling_current = None super().cleanup() class QwenFunControlNet(ControlNet): def get_control(self, x_noisy, t, cond, batched_number, transformer_options): # Fun checkpoints are more sensitive to high strengths in the generic # ControlNet merge path. Use a soft response curve so strength=1.0 stays # unchanged while >1 grows more gently. original_strength = self.strength self.strength = math.sqrt(max(self.strength, 0.0)) try: return super().get_control(x_noisy, t, cond, batched_number, transformer_options) finally: self.strength = original_strength def pre_run(self, model, percent_to_timestep_function): super().pre_run(model, percent_to_timestep_function) self.set_extra_arg("base_model", model.diffusion_model) def copy(self): c = QwenFunControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) c.control_model = self.control_model c.control_model_wrapped = self.control_model_wrapped self.copy_to(c) return c class ControlLoraOps: class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: super().__init__() self.in_features = in_features self.out_features = out_features self.weight = None self.up = None self.down = None self.bias = None def forward(self, input): weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True) if self.up is not None: x = torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) else: x = torch.nn.functional.linear(input, weight, bias) comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream) return x class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.dilation = dilation self.transposed = False self.output_padding = 0 self.groups = groups self.padding_mode = padding_mode self.weight = None self.bias = None self.up = None self.down = None def forward(self, input): weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True) if self.up is not None: x = torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) else: x = torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream) return x class ControlLora(ControlNet): def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options ControlBase.__init__(self) self.control_weights = control_weights self.global_average_pooling = global_average_pooling self.extra_conds += ["y"] def pre_run(self, model, percent_to_timestep_function): super().pre_run(model, percent_to_timestep_function) controlnet_config = model.model_config.unet_config.copy() controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] self.manual_cast_dtype = model.manual_cast_dtype dtype = model.get_dtype() if self.manual_cast_dtype is None: class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init): pass else: class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast): pass dtype = self.manual_cast_dtype controlnet_config["operations"] = control_lora_ops controlnet_config["dtype"] = dtype self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) self.control_model.to(comfy.model_management.get_torch_device()) diffusion_model = model.diffusion_model sd = diffusion_model.state_dict() for k in sd: weight = sd[k] try: comfy.utils.set_attr_param(self.control_model, k, weight) except: pass for k in self.control_weights: if (k not in {"lora_controlnet"}): if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k): comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) def copy(self): c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) self.copy_to(c) return c def cleanup(self): del self.control_model self.control_model = None super().cleanup() def get_models(self): out = ControlBase.get_models(self) return out def inference_memory_requirements(self, dtype): return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) def controlnet_config(sd, model_options={}): model_config = comfy.model_detection.model_config_from_unet(sd, "", True) unet_dtype = model_options.get("dtype", None) if unet_dtype is None: weight_dtype = comfy.utils.weight_dtype(sd) supported_inference_dtypes = list(model_config.supported_inference_dtypes) unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) load_device = comfy.model_management.get_torch_device() manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) operations = model_options.get("custom_operations", None) if operations is None: operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) offload_device = comfy.model_management.unet_offload_device() return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device def controlnet_load_state_dict(control_model, sd): missing, unexpected = control_model.load_state_dict(sd, strict=False) if len(missing) > 0: logging.warning("missing controlnet keys: {}".format(missing)) if len(unexpected) > 0: logging.debug("unexpected controlnet keys: {}".format(unexpected)) return control_model def load_controlnet_mmdit(sd, model_options={}): new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.') for k in sd: new_sd[k] = sd[k] concat_mask = False control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1] if control_latent_channels == 17: #inpaint controlnet concat_mask = True control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) control_model = controlnet_load_state_dict(control_model, new_sd) latent_format = comfy.latent_formats.SD3() latent_format.shift_factor = 0 #SD3 controlnet weirdness control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype) return control class ControlNetSD35(ControlNet): def pre_run(self, model, percent_to_timestep_function): if self.control_model.double_y_emb: missing, unexpected = self.control_model.orig_y_embedder.load_state_dict(model.diffusion_model.y_embedder.state_dict(), strict=False) else: missing, unexpected = self.control_model.x_embedder.load_state_dict(model.diffusion_model.x_embedder.state_dict(), strict=False) super().pre_run(model, percent_to_timestep_function) def copy(self): c = ControlNetSD35(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) c.control_model = self.control_model c.control_model_wrapped = self.control_model_wrapped self.copy_to(c) return c def load_controlnet_sd35(sd, model_options={}): control_type = -1 if "control_type" in sd: control_type = round(sd.pop("control_type").item()) # blur_cnet = control_type == 0 canny_cnet = control_type == 1 depth_cnet = control_type == 2 new_sd = {} for k in comfy.utils.MMDIT_MAP_BASIC: if k[1] in sd: new_sd[k[0]] = sd.pop(k[1]) for k in sd: new_sd[k] = sd[k] sd = new_sd y_emb_shape = sd["y_embedder.mlp.0.weight"].shape depth = y_emb_shape[0] // 64 hidden_size = 64 * depth num_heads = depth head_dim = hidden_size // num_heads num_blocks = comfy.model_detection.count_blocks(new_sd, 'transformer_blocks.{}.') load_device = comfy.model_management.get_torch_device() offload_device = comfy.model_management.unet_offload_device() unet_dtype = comfy.model_management.unet_dtype(model_params=-1) manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) operations = model_options.get("custom_operations", None) if operations is None: operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) control_model = comfy.cldm.dit_embedder.ControlNetEmbedder(img_size=None, patch_size=2, in_chans=16, num_layers=num_blocks, main_model_double=depth, double_y_emb=y_emb_shape[0] == y_emb_shape[1], attention_head_dim=head_dim, num_attention_heads=num_heads, adm_in_channels=2048, device=offload_device, dtype=unet_dtype, operations=operations) control_model = controlnet_load_state_dict(control_model, sd) latent_format = comfy.latent_formats.SD3() preprocess_image = lambda a: a if canny_cnet: preprocess_image = lambda a: (a * 255 * 0.5 + 0.5) elif depth_cnet: preprocess_image = lambda a: 1.0 - a control = ControlNetSD35(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, preprocess_image=preprocess_image) return control def load_controlnet_hunyuandit(controlnet_data, model_options={}): model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options) control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype) control_model = controlnet_load_state_dict(control_model, controlnet_data) latent_format = comfy.latent_formats.SDXL() extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img'] control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT) return control def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}): model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options) control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) sd = model_config.process_unet_state_dict(sd) control_model = controlnet_load_state_dict(control_model, sd) extra_conds = ['y', 'guidance'] control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) return control def load_controlnet_flux_instantx(sd, model_options={}): new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) for k in sd: new_sd[k] = sd[k] num_union_modes = 0 union_cnet = "controlnet_mode_embedder.weight" if union_cnet in new_sd: num_union_modes = new_sd[union_cnet].shape[0] control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4 concat_mask = False if control_latent_channels == 17: concat_mask = True control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) control_model = controlnet_load_state_dict(control_model, new_sd) latent_format = comfy.latent_formats.Flux() extra_conds = ['y', 'guidance'] control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) return control def load_controlnet_qwen_instantx(sd, model_options={}): model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options) control_latent_channels = sd.get("controlnet_x_embedder.weight").shape[1] extra_condition_channels = 0 concat_mask = False if control_latent_channels == 68: #inpaint controlnet extra_condition_channels = control_latent_channels - 64 concat_mask = True control_model = comfy.ldm.qwen_image.controlnet.QwenImageControlNetModel(extra_condition_channels=extra_condition_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) control_model = controlnet_load_state_dict(control_model, sd) latent_format = comfy.latent_formats.Wan21() extra_conds = [] control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) return control def load_controlnet_qwen_fun(sd, model_options={}): load_device = comfy.model_management.get_torch_device() weight_dtype = comfy.utils.weight_dtype(sd) unet_dtype = model_options.get("dtype", weight_dtype) manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) operations = model_options.get("custom_operations", None) if operations is None: operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) in_features = sd["control_img_in.weight"].shape[1] inner_dim = sd["control_img_in.weight"].shape[0] block_weight = sd["control_blocks.0.attn.to_q.weight"] attention_head_dim = sd["control_blocks.0.attn.norm_q.weight"].shape[0] num_attention_heads = max(1, block_weight.shape[0] // max(1, attention_head_dim)) model = comfy.ldm.qwen_image.controlnet.QwenImageFunControlNetModel( control_in_features=in_features, inner_dim=inner_dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, num_control_blocks=5, main_model_double=60, injection_layers=(0, 12, 24, 36, 48), operations=operations, device=comfy.model_management.unet_offload_device(), dtype=unet_dtype, ) model = controlnet_load_state_dict(model, sd) latent_format = comfy.latent_formats.Wan21() control = QwenFunControlNet( model, compression_ratio=1, latent_format=latent_format, # Fun checkpoints already expect their own 33-channel context handling. # Enabling generic concat_mask injects an extra mask channel at apply-time # and breaks the intended fallback packing path. concat_mask=False, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=[], ) return control def convert_mistoline(sd): return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."}) def load_controlnet_state_dict(state_dict, model=None, model_options={}): controlnet_data = state_dict if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT return load_controlnet_hunyuandit(controlnet_data, model_options=model_options) if "lora_controlnet" in controlnet_data: return ControlLora(controlnet_data, model_options=model_options) controlnet_config = None supported_inference_dtypes = None if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data) diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" count = 0 loop = True while loop: suffix = [".weight", ".bias"] for s in suffix: k_in = "controlnet_down_blocks.{}{}".format(count, s) k_out = "zero_convs.{}.0{}".format(count, s) if k_in not in controlnet_data: loop = False break diffusers_keys[k_in] = k_out count += 1 count = 0 loop = True while loop: suffix = [".weight", ".bias"] for s in suffix: if count == 0: k_in = "controlnet_cond_embedding.conv_in{}".format(s) else: k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) k_out = "input_hint_block.{}{}".format(count * 2, s) if k_in not in controlnet_data: k_in = "controlnet_cond_embedding.conv_out{}".format(s) loop = False diffusers_keys[k_in] = k_out count += 1 new_sd = {} for k in diffusers_keys: if k in controlnet_data: new_sd[diffusers_keys[k]] = controlnet_data.pop(k) if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0] for k in list(controlnet_data.keys()): new_k = k.replace('.attn.in_proj_', '.attn.in_proj.') new_sd[new_k] = controlnet_data.pop(k) leftover_keys = controlnet_data.keys() if len(leftover_keys) > 0: logging.warning("leftover keys: {}".format(leftover_keys)) controlnet_data = new_sd elif "controlnet_blocks.0.weight" in controlnet_data: if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data: return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options) elif "pos_embed_input.proj.weight" in controlnet_data: if "transformer_blocks.0.adaLN_modulation.1.bias" in controlnet_data: return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format else: return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet elif "transformer_blocks.0.img_mlp.net.0.proj.weight" in controlnet_data: return load_controlnet_qwen_instantx(controlnet_data, model_options=model_options) elif "controlnet_x_embedder.weight" in controlnet_data: return load_controlnet_flux_instantx(controlnet_data, model_options=model_options) elif "control_blocks.0.after_proj.weight" in controlnet_data and "control_img_in.weight" in controlnet_data: return load_controlnet_qwen_fun(controlnet_data, model_options=model_options) elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options) pth_key = 'control_model.zero_convs.0.0.weight' pth = False key = 'zero_convs.0.0.weight' if pth_key in controlnet_data: pth = True key = pth_key prefix = "control_model." elif key in controlnet_data: prefix = "" else: net = load_t2i_adapter(controlnet_data, model_options=model_options) if net is None: logging.error("error could not detect control model type.") return net if controlnet_config is None: model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True) supported_inference_dtypes = list(model_config.supported_inference_dtypes) controlnet_config = model_config.unet_config unet_dtype = model_options.get("dtype", None) if unet_dtype is None: weight_dtype = comfy.utils.weight_dtype(controlnet_data) if supported_inference_dtypes is None: supported_inference_dtypes = [comfy.model_management.unet_dtype()] unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) load_device = comfy.model_management.get_torch_device() manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) operations = model_options.get("custom_operations", None) if operations is None: operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype) controlnet_config["operations"] = operations controlnet_config["dtype"] = unet_dtype controlnet_config["device"] = comfy.model_management.unet_offload_device() controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) if pth: if 'difference' in controlnet_data: if model is not None: comfy.model_management.load_models_gpu([model]) model_sd = model.model_state_dict() for x in controlnet_data: c_m = "control_model." if x.startswith(c_m): sd_key = "diffusion_model.{}".format(x[len(c_m):]) if sd_key in model_sd: cd = controlnet_data[x] cd += model_sd[sd_key].type(cd.dtype).to(cd.device) else: logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") class WeightsLoader(torch.nn.Module): pass w = WeightsLoader() w.control_model = control_model missing, unexpected = w.load_state_dict(controlnet_data, strict=False) else: missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) if len(missing) > 0: logging.warning("missing controlnet keys: {}".format(missing)) if len(unexpected) > 0: logging.debug("unexpected controlnet keys: {}".format(unexpected)) global_average_pooling = model_options.get("global_average_pooling", False) control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) return control def load_controlnet(ckpt_path, model=None, model_options={}): model_options = model_options.copy() if "global_average_pooling" not in model_options: filename = os.path.splitext(ckpt_path)[0] if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling model_options["global_average_pooling"] = True cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options) if cnet is None: logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path)) return cnet class T2IAdapter(ControlBase): def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None): super().__init__() self.t2i_model = t2i_model self.channels_in = channels_in self.control_input = None self.compression_ratio = compression_ratio self.upscale_algorithm = upscale_algorithm if device is None: device = comfy.model_management.get_torch_device() self.device = device def scale_image_to(self, width, height): unshuffle_amount = self.t2i_model.unshuffle_amount width = math.ceil(width / unshuffle_amount) * unshuffle_amount height = math.ceil(height / unshuffle_amount) * unshuffle_amount return width, height def get_control(self, x_noisy, t, cond, batched_number, transformer_options): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options) if self.timestep_range is not None: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if control_prev is not None: return control_prev else: return None if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.control_input = None self.cond_hint = None width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio) self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device) if self.channels_in == 1 and self.cond_hint.shape[1] > 1: self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) if self.control_input is None: self.t2i_model.to(x_noisy.dtype) self.t2i_model.to(self.device) self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) self.t2i_model.cpu() control_input = {} for k in self.control_input: control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k])) return self.control_merge(control_input, control_prev, x_noisy.dtype) def copy(self): c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm) self.copy_to(c) return c def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options compression_ratio = 8 upscale_algorithm = 'nearest-exact' if 'adapter' in t2i_data: t2i_data = t2i_data['adapter'] if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format prefix_replace = {} for i in range(4): for j in range(2): prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) prefix_replace["adapter.body.{}.".format(i, )] = "body.{}.".format(i * 2) prefix_replace["adapter."] = "" t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace) keys = t2i_data.keys() if "body.0.in_conv.weight" in keys: cin = t2i_data['body.0.in_conv.weight'].shape[1] model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) elif 'conv_in.weight' in keys: cin = t2i_data['conv_in.weight'].shape[1] channel = t2i_data['conv_in.weight'].shape[0] ksize = t2i_data['body.0.block2.weight'].shape[2] use_conv = False down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) if len(down_opts) > 0: use_conv = True xl = False if cin == 256 or cin == 768: xl = True model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) elif "backbone.0.0.weight" in keys: model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) compression_ratio = 32 upscale_algorithm = 'bilinear' elif "backbone.10.blocks.0.weight" in keys: model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) compression_ratio = 1 upscale_algorithm = 'nearest-exact' else: return None missing, unexpected = model_ad.load_state_dict(t2i_data) if len(missing) > 0: logging.warning("t2i missing {}".format(missing)) if len(unexpected) > 0: logging.debug("t2i unexpected {}".format(unexpected)) return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm) ================================================ FILE: comfy/diffusers_convert.py ================================================ import re import torch import logging # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py # ================# # VAE Conversion # # ================# vae_conversion_map = [ # (stable-diffusion, HF Diffusers) ("nin_shortcut", "conv_shortcut"), ("norm_out", "conv_norm_out"), ("mid.attn_1.", "mid_block.attentions.0."), ] for i in range(4): # down_blocks have two resnets for j in range(2): hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." sd_down_prefix = f"encoder.down.{i}.block.{j}." vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) if i < 3: hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." sd_downsample_prefix = f"down.{i}.downsample." vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"up.{3 - i}.upsample." vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) # up_blocks have three resnets # also, up blocks in hf are numbered in reverse from sd for j in range(3): hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) # this part accounts for mid blocks in both the encoder and the decoder for i in range(2): hf_mid_res_prefix = f"mid_block.resnets.{i}." sd_mid_res_prefix = f"mid.block_{i + 1}." vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) vae_conversion_map_attn = [ # (stable-diffusion, HF Diffusers) ("norm.", "group_norm."), ("q.", "query."), ("k.", "key."), ("v.", "value."), ("q.", "to_q."), ("k.", "to_k."), ("v.", "to_v."), ("proj_out.", "to_out.0."), ("proj_out.", "proj_attn."), ] def reshape_weight_for_sd(w, conv3d=False): # convert HF linear weights to SD conv2d weights if conv3d: return w.reshape(*w.shape, 1, 1, 1) else: return w.reshape(*w.shape, 1, 1) def convert_vae_state_dict(vae_state_dict): mapping = {k: k for k in vae_state_dict.keys()} conv3d = False for k, v in mapping.items(): for sd_part, hf_part in vae_conversion_map: v = v.replace(hf_part, sd_part) if v.endswith(".conv.weight"): if not conv3d and vae_state_dict[k].ndim == 5: conv3d = True mapping[k] = v for k, v in mapping.items(): if "attentions" in k: for sd_part, hf_part in vae_conversion_map_attn: v = v.replace(hf_part, sd_part) mapping[k] = v new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} weights_to_convert = ["q", "k", "v", "proj_out"] for k, v in new_state_dict.items(): for weight_name in weights_to_convert: if f"mid.attn_1.{weight_name}.weight" in k: logging.debug(f"Reshaping {k} for SD format") new_state_dict[k] = reshape_weight_for_sd(v, conv3d=conv3d) return new_state_dict # =========================# # Text Encoder Conversion # # =========================# textenc_conversion_lst = [ # (stable-diffusion, HF Diffusers) ("resblocks.", "text_model.encoder.layers."), ("ln_1", "layer_norm1"), ("ln_2", "layer_norm2"), (".c_fc.", ".fc1."), (".c_proj.", ".fc2."), (".attn", ".self_attn"), ("ln_final.", "transformer.text_model.final_layer_norm."), ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), ] protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} textenc_pattern = re.compile("|".join(protected.keys())) # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp code2idx = {"q": 0, "k": 1, "v": 2} # This function exists because at the time of writing torch.cat can't do fp8 with cuda def cat_tensors(tensors): x = 0 for t in tensors: x += t.shape[0] shape = [x] + list(tensors[0].shape)[1:] out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype) x = 0 for t in tensors: out[x:x + t.shape[0]] = t x += t.shape[0] return out def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): new_state_dict = {} capture_qkv_weight = {} capture_qkv_bias = {} for k, v in text_enc_dict.items(): if not k.startswith(prefix): continue if ( k.endswith(".self_attn.q_proj.weight") or k.endswith(".self_attn.k_proj.weight") or k.endswith(".self_attn.v_proj.weight") ): k_pre = k[: -len(".q_proj.weight")] k_code = k[-len("q_proj.weight")] if k_pre not in capture_qkv_weight: capture_qkv_weight[k_pre] = [None, None, None] capture_qkv_weight[k_pre][code2idx[k_code]] = v continue if ( k.endswith(".self_attn.q_proj.bias") or k.endswith(".self_attn.k_proj.bias") or k.endswith(".self_attn.v_proj.bias") ): k_pre = k[: -len(".q_proj.bias")] k_code = k[-len("q_proj.bias")] if k_pre not in capture_qkv_bias: capture_qkv_bias[k_pre] = [None, None, None] capture_qkv_bias[k_pre][code2idx[k_code]] = v continue text_proj = "transformer.text_projection.weight" if k.endswith(text_proj): new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous() else: relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) new_state_dict[relabelled_key] = v for k_pre, tensors in capture_qkv_weight.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors) for k_pre, tensors in capture_qkv_bias.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors) return new_state_dict def convert_text_enc_state_dict(text_enc_dict): return text_enc_dict ================================================ FILE: comfy/diffusers_load.py ================================================ import os import comfy.sd def first_file(path, filenames): for f in filenames: p = os.path.join(path, f) if os.path.exists(p): return p return None def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) text_encoder_paths = [text_encoder1_path] if text_encoder2_path is not None: text_encoder_paths.append(text_encoder2_path) unet = comfy.sd.load_diffusion_model(unet_path) clip = None if output_clip: clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) vae = None if output_vae: sd = comfy.utils.load_torch_file(vae_path) vae = comfy.sd.VAE(sd=sd) return (unet, clip, vae) ================================================ FILE: comfy/extra_samplers/uni_pc.py ================================================ #code taken from: https://github.com/wl-zhao/UniPC and modified import torch import math import logging from tqdm.auto import trange class NoiseScheduleVP: def __init__( self, schedule='discrete', betas=None, alphas_cumprod=None, continuous_beta_0=0.1, continuous_beta_1=20., ): r"""Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. *** The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: log_alpha_t = self.marginal_log_mean_coeff(t) sigma_t = self.marginal_std(t) lambda_t = self.marginal_lambda(t) Moreover, as lambda(t) is an invertible function, we also support its inverse function: t = self.inverse_lambda(lambda_t) =============================================================== We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). 1. For discrete-time DPMs: For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: t_i = (i + 1) / N e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. Args: betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. **Important**: Please pay special attention for the args for `alphas_cumprod`: The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have alpha_{t_n} = \sqrt{\hat{alpha_n}}, and log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). 2. For continuous-time DPMs: We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise schedule are the default settings in DDPM and improved-DDPM: Args: beta_min: A `float` number. The smallest beta for the linear schedule. beta_max: A `float` number. The largest beta for the linear schedule. cosine_s: A `float` number. The hyperparameter in the cosine schedule. cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. T: A `float` number. The ending time of the forward process. =============================================================== Args: schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, 'linear' or 'cosine' for continuous-time DPMs. Returns: A wrapper object of the forward SDE (VP type). =============================================================== Example: # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): >>> ns = NoiseScheduleVP('discrete', betas=betas) # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) # For continuous-time DPMs (VPSDE), linear schedule: >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) """ if schedule not in ['discrete', 'linear', 'cosine']: raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) self.schedule = schedule if schedule == 'discrete': if betas is not None: log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) else: assert alphas_cumprod is not None log_alphas = 0.5 * torch.log(alphas_cumprod) self.total_N = len(log_alphas) self.T = 1. self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) self.log_alpha_array = log_alphas.reshape((1, -1,)) else: self.total_N = 1000 self.beta_0 = continuous_beta_0 self.beta_1 = continuous_beta_1 self.cosine_s = 0.008 self.cosine_beta_max = 999. self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) self.schedule = schedule if schedule == 'cosine': # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. self.T = 0.9946 else: self.T = 1. def marginal_log_mean_coeff(self, t): """ Compute log(alpha_t) of a given continuous-time label t in [0, T]. """ if self.schedule == 'discrete': return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) elif self.schedule == 'linear': return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 elif self.schedule == 'cosine': log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 return log_alpha_t def marginal_alpha(self, t): """ Compute alpha_t of a given continuous-time label t in [0, T]. """ return torch.exp(self.marginal_log_mean_coeff(t)) def marginal_std(self, t): """ Compute sigma_t of a given continuous-time label t in [0, T]. """ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) def marginal_lambda(self, t): """ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. """ log_mean_coeff = self.marginal_log_mean_coeff(t) log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) return log_mean_coeff - log_std def inverse_lambda(self, lamb): """ Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. """ if self.schedule == 'linear': tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) Delta = self.beta_0**2 + tmp return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) elif self.schedule == 'discrete': log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) return t.reshape((-1,)) else: log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s t = t_fn(log_alpha) return t def model_wrapper( model, noise_schedule, model_type="noise", model_kwargs={}, guidance_type="uncond", condition=None, unconditional_condition=None, guidance_scale=1., classifier_fn=None, classifier_kwargs={}, ): """Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. We support four types of the diffusion model by setting `model_type`: 1. "noise": noise prediction model. (Trained by predicting noise). 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). 3. "v": velocity prediction model. (Trained by predicting the velocity). The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." arXiv preprint arXiv:2202.00512 (2022). [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." arXiv preprint arXiv:2210.02303 (2022). 4. "score": marginal score function. (Trained by denoising score matching). Note that the score function and the noise prediction model follows a simple relationship: ``` noise(x_t, t) = -sigma_t * score(x_t, t) ``` We support three types of guided sampling by DPMs by setting `guidance_type`: 1. "uncond": unconditional sampling by DPMs. The input `model` has the following format: `` model(x, t_input, **model_kwargs) -> noise | x_start | v | score `` 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. The input `model` has the following format: `` model(x, t_input, **model_kwargs) -> noise | x_start | v | score `` The input `classifier_fn` has the following format: `` classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) `` [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. The input `model` has the following format: `` model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score `` And if cond == `unconditional_condition`, the model output is the unconditional DPM output. [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." arXiv preprint arXiv:2207.12598 (2022). The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) or continuous-time labels (i.e. epsilon to T). We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: `` def model_fn(x, t_continuous) -> noise: t_input = get_model_input_time(t_continuous) return noise_pred(model, x, t_input, **model_kwargs) `` where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. =============================================================== Args: model: A diffusion model with the corresponding format described above. noise_schedule: A noise schedule object, such as NoiseScheduleVP. model_type: A `str`. The parameterization type of the diffusion model. "noise" or "x_start" or "v" or "score". model_kwargs: A `dict`. A dict for the other inputs of the model function. guidance_type: A `str`. The type of the guidance for sampling. "uncond" or "classifier" or "classifier-free". condition: A pytorch tensor. The condition for the guided sampling. Only used for "classifier" or "classifier-free" guidance type. unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. Only used for "classifier-free" guidance type. guidance_scale: A `float`. The scale for the guided sampling. classifier_fn: A classifier function. Only used for the classifier guidance. classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. Returns: A noise prediction model that accepts the noised data and the continuous time as the inputs. """ def get_model_input_time(t_continuous): """ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. For continuous-time DPMs, we just use `t_continuous`. """ if noise_schedule.schedule == 'discrete': return (t_continuous - 1. / noise_schedule.total_N) * 1000. else: return t_continuous def noise_pred_fn(x, t_continuous, cond=None): if t_continuous.reshape((-1,)).shape[0] == 1: t_continuous = t_continuous.expand((x.shape[0])) t_input = get_model_input_time(t_continuous) output = model(x, t_input, **model_kwargs) if model_type == "noise": return output elif model_type == "x_start": alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) dims = x.dim() return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) elif model_type == "v": alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) dims = x.dim() return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x elif model_type == "score": sigma_t = noise_schedule.marginal_std(t_continuous) dims = x.dim() return -expand_dims(sigma_t, dims) * output def cond_grad_fn(x, t_input): """ Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). """ with torch.enable_grad(): x_in = x.detach().requires_grad_(True) log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) return torch.autograd.grad(log_prob.sum(), x_in)[0] def model_fn(x, t_continuous): """ The noise predicition model function that is used for DPM-Solver. """ if t_continuous.reshape((-1,)).shape[0] == 1: t_continuous = t_continuous.expand((x.shape[0])) if guidance_type == "uncond": return noise_pred_fn(x, t_continuous) elif guidance_type == "classifier": assert classifier_fn is not None t_input = get_model_input_time(t_continuous) cond_grad = cond_grad_fn(x, t_input) sigma_t = noise_schedule.marginal_std(t_continuous) noise = noise_pred_fn(x, t_continuous) return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad elif guidance_type == "classifier-free": if guidance_scale == 1. or unconditional_condition is None: return noise_pred_fn(x, t_continuous, cond=condition) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t_continuous] * 2) c_in = torch.cat([unconditional_condition, condition]) noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) return noise_uncond + guidance_scale * (noise - noise_uncond) assert model_type in ["noise", "x_start", "v"] assert guidance_type in ["uncond", "classifier", "classifier-free"] return model_fn class UniPC: def __init__( self, model_fn, noise_schedule, predict_x0=True, thresholding=False, max_val=1., variant='bh1', ): """Construct a UniPC. We support both data_prediction and noise_prediction. """ self.model = model_fn self.noise_schedule = noise_schedule self.variant = variant self.predict_x0 = predict_x0 self.thresholding = thresholding self.max_val = max_val def dynamic_thresholding_fn(self, x0, t=None): """ The dynamic thresholding method. """ dims = x0.dim() p = self.dynamic_thresholding_ratio s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) x0 = torch.clamp(x0, -s, s) / s return x0 def noise_prediction_fn(self, x, t): """ Return the noise prediction model. """ return self.model(x, t) def data_prediction_fn(self, x, t): """ Return the data prediction model (with thresholding). """ noise = self.noise_prediction_fn(x, t) dims = x.dim() alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) if self.thresholding: p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) x0 = torch.clamp(x0, -s, s) / s return x0 def model_fn(self, x, t): """ Convert the model to the noise prediction model or the data prediction model. """ if self.predict_x0: return self.data_prediction_fn(x, t) else: return self.noise_prediction_fn(x, t) def get_time_steps(self, skip_type, t_T, t_0, N, device): """Compute the intermediate time steps for sampling. """ if skip_type == 'logSNR': lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) return self.noise_schedule.inverse_lambda(logSNR_steps) elif skip_type == 'time_uniform': return torch.linspace(t_T, t_0, N + 1).to(device) elif skip_type == 'time_quadratic': t_order = 2 t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) return t else: raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): """ Get the order of each step for sampling by the singlestep DPM-Solver. """ if order == 3: K = steps // 3 + 1 if steps % 3 == 0: orders = [3,] * (K - 2) + [2, 1] elif steps % 3 == 1: orders = [3,] * (K - 1) + [1] else: orders = [3,] * (K - 1) + [2] elif order == 2: if steps % 2 == 0: K = steps // 2 orders = [2,] * K else: K = steps // 2 + 1 orders = [2,] * (K - 1) + [1] elif order == 1: K = steps orders = [1,] * steps else: raise ValueError("'order' must be '1' or '2' or '3'.") if skip_type == 'logSNR': # To reproduce the results in DPM-Solver paper timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) else: timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] return timesteps_outer, orders def denoise_to_zero_fn(self, x, s): """ Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. """ return self.data_prediction_fn(x, s) def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): if len(t.shape) == 0: t = t.view(-1) if 'bh' in self.variant: return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) else: assert self.variant == 'vary_coeff' return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): logging.info(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') ns = self.noise_schedule assert order <= len(model_prev_list) # first compute rks t_prev_0 = t_prev_list[-1] lambda_prev_0 = ns.marginal_lambda(t_prev_0) lambda_t = ns.marginal_lambda(t) model_prev_0 = model_prev_list[-1] sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) log_alpha_t = ns.marginal_log_mean_coeff(t) alpha_t = torch.exp(log_alpha_t) h = lambda_t - lambda_prev_0 rks = [] D1s = [] for i in range(1, order): t_prev_i = t_prev_list[-(i + 1)] model_prev_i = model_prev_list[-(i + 1)] lambda_prev_i = ns.marginal_lambda(t_prev_i) rk = (lambda_prev_i - lambda_prev_0) / h rks.append(rk) D1s.append((model_prev_i - model_prev_0) / rk) rks.append(1.) rks = torch.tensor(rks, device=x.device) K = len(rks) # build C matrix C = [] col = torch.ones_like(rks) for k in range(1, K + 1): C.append(col) col = col * rks / (k + 1) C = torch.stack(C, dim=1) if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) # (B, K) C_inv_p = torch.linalg.inv(C[:-1, :-1]) A_p = C_inv_p if use_corrector: C_inv = torch.linalg.inv(C) A_c = C_inv hh = -h if self.predict_x0 else h h_phi_1 = torch.expm1(hh) h_phi_ks = [] factorial_k = 1 h_phi_k = h_phi_1 for k in range(1, K + 2): h_phi_ks.append(h_phi_k) h_phi_k = h_phi_k / hh - 1 / factorial_k factorial_k *= (k + 1) model_t = None if self.predict_x0: x_t_ = ( sigma_t / sigma_prev_0 * x - alpha_t * h_phi_1 * model_prev_0 ) # now predictor x_t = x_t_ if len(D1s) > 0: # compute the residuals for predictor for k in range(K - 1): x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) # now corrector if use_corrector: model_t = self.model_fn(x_t, t) D1_t = (model_t - model_prev_0) x_t = x_t_ k = 0 for k in range(K - 1): x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) else: log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) x_t_ = ( (torch.exp(log_alpha_t - log_alpha_prev_0)) * x - (sigma_t * h_phi_1) * model_prev_0 ) # now predictor x_t = x_t_ if len(D1s) > 0: # compute the residuals for predictor for k in range(K - 1): x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) # now corrector if use_corrector: model_t = self.model_fn(x_t, t) D1_t = (model_t - model_prev_0) x_t = x_t_ k = 0 for k in range(K - 1): x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) return x_t, model_t def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') ns = self.noise_schedule assert order <= len(model_prev_list) dims = x.dim() # first compute rks t_prev_0 = t_prev_list[-1] lambda_prev_0 = ns.marginal_lambda(t_prev_0) lambda_t = ns.marginal_lambda(t) model_prev_0 = model_prev_list[-1] sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) alpha_t = torch.exp(log_alpha_t) h = lambda_t - lambda_prev_0 rks = [] D1s = [] for i in range(1, order): t_prev_i = t_prev_list[-(i + 1)] model_prev_i = model_prev_list[-(i + 1)] lambda_prev_i = ns.marginal_lambda(t_prev_i) rk = ((lambda_prev_i - lambda_prev_0) / h)[0] rks.append(rk) D1s.append((model_prev_i - model_prev_0) / rk) rks.append(1.) rks = torch.tensor(rks, device=x.device) R = [] b = [] hh = -h[0] if self.predict_x0 else h[0] h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 h_phi_k = h_phi_1 / hh - 1 factorial_i = 1 if self.variant == 'bh1': B_h = hh elif self.variant == 'bh2': B_h = torch.expm1(hh) else: raise NotImplementedError() for i in range(1, order + 1): R.append(torch.pow(rks, i - 1)) b.append(h_phi_k * factorial_i / B_h) factorial_i *= (i + 1) h_phi_k = h_phi_k / hh - 1 / factorial_i R = torch.stack(R) b = torch.tensor(b, device=x.device) # now predictor use_predictor = len(D1s) > 0 and x_t is None if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) # (B, K) if x_t is None: # for order 2, we use a simplified version if order == 2: rhos_p = torch.tensor([0.5], device=b.device) else: rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) else: D1s = None if use_corrector: # print('using corrector') # for order 1, we use a simplified version if order == 1: rhos_c = torch.tensor([0.5], device=b.device) else: rhos_c = torch.linalg.solve(R, b) model_t = None if self.predict_x0: x_t_ = ( expand_dims(sigma_t / sigma_prev_0, dims) * x - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 ) if x_t is None: if use_predictor: pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res if use_corrector: model_t = self.model_fn(x_t, t) if D1s is not None: corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) else: corr_res = 0 D1_t = (model_t - model_prev_0) x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) else: x_t_ = ( expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 ) if x_t is None: if use_predictor: pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res if use_corrector: model_t = self.model_fn(x_t, t) if D1s is not None: corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) else: corr_res = 0 D1_t = (model_t - model_prev_0) x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) return x_t, model_t def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False ): # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end # t_T = self.noise_schedule.T if t_start is None else t_start steps = len(timesteps) - 1 if method == 'multistep': assert steps >= order # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) assert timesteps.shape[0] - 1 == steps # with torch.no_grad(): for step_index in trange(steps, disable=disable_pbar): if step_index == 0: vec_t = timesteps[0].expand((x.shape[0])) model_prev_list = [self.model_fn(x, vec_t)] t_prev_list = [vec_t] elif step_index < order: init_order = step_index # Init the first `order` values by lower order multistep DPM-Solver. # for init_order in range(1, order): vec_t = timesteps[init_order].expand(x.shape[0]) x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) if model_x is None: model_x = self.model_fn(x, vec_t) model_prev_list.append(model_x) t_prev_list.append(vec_t) else: extra_final_step = 0 if step_index == (steps - 1): extra_final_step = 1 for step in range(step_index, step_index + 1 + extra_final_step): vec_t = timesteps[step].expand(x.shape[0]) if lower_order_final: step_order = min(order, steps + 1 - step) else: step_order = order # print('this step order:', step_order) if step == steps: # print('do not run corrector at the last step') use_corrector = False else: use_corrector = True x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) for i in range(order - 1): t_prev_list[i] = t_prev_list[i + 1] model_prev_list[i] = model_prev_list[i + 1] t_prev_list[-1] = vec_t # We do not need to evaluate the final model value. if step < steps: if model_x is None: model_x = self.model_fn(x, vec_t) model_prev_list[-1] = model_x if callback is not None: callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]}) else: raise NotImplementedError() # if denoise_to_zero: # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) return x ############################################################# # other utility functions ############################################################# def interpolate_fn(x, xp, yp): """ A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a differentiable way (i.e. applicable for autograd). The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) Args: x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. yp: PyTorch tensor with shape [C, K]. Returns: The function values f(x), with shape [N, C]. """ N, K = x.shape[0], xp.shape[1] all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) sorted_all_x, x_indices = torch.sort(all_x, dim=2) x_idx = torch.argmin(x_indices, dim=2) cand_start_idx = x_idx - 1 start_idx = torch.where( torch.eq(x_idx, 0), torch.tensor(1, device=x.device), torch.where( torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, ), ) end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) start_idx2 = torch.where( torch.eq(x_idx, 0), torch.tensor(0, device=x.device), torch.where( torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, ), ) y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) return cand def expand_dims(v, dims): """ Expand the tensor `v` to the dim `dims`. Args: `v`: a PyTorch tensor with shape [N]. `dim`: a `int`. Returns: a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. """ return v[(...,) + (None,)*(dims - 1)] class SigmaConvert: schedule = "" def marginal_log_mean_coeff(self, sigma): return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) def marginal_alpha(self, t): return torch.exp(self.marginal_log_mean_coeff(t)) def marginal_std(self, t): return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) def marginal_lambda(self, t): """ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. """ log_mean_coeff = self.marginal_log_mean_coeff(t) log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) return log_mean_coeff - log_std def predict_eps_sigma(model, input, sigma_in, **kwargs): sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) input = input * ((sigma ** 2 + 1.0) ** 0.5) return (input - model(input, sigma_in, **kwargs)) / sigma def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'): timesteps = sigmas.clone() if sigmas[-1] == 0: timesteps = sigmas[:] timesteps[-1] = 0.001 else: timesteps = sigmas.clone() ns = SigmaConvert() noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0) model_type = "noise" model_fn = model_wrapper( lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), ns, model_type=model_type, guidance_type="uncond", model_kwargs=extra_args, ) order = min(3, len(timesteps) - 2) uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant) x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) x /= ns.marginal_alpha(timesteps[-1]) return x def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False): return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2') ================================================ FILE: comfy/float.py ================================================ import torch def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None): mantissa_scaled = torch.where( normal_mask, (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS), (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS))) ) mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator) return mantissa_scaled.floor() / (2**MANTISSA_BITS) #Not 100% sure about this def manual_stochastic_round_to_float8(x, dtype, generator=None): if dtype == torch.float8_e4m3fn: EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 4, 3, 7 elif dtype == torch.float8_e5m2: EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 5, 2, 15 else: raise ValueError("Unsupported dtype") x = x.half() sign = torch.sign(x) abs_x = x.abs() sign = torch.where(abs_x == 0, 0, sign) # Combine exponent calculation and clamping exponent = torch.clamp( torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS, 0, 2**EXPONENT_BITS - 1 ) # Combine mantissa calculation and rounding normal_mask = ~(exponent == 0) abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator) sign *= torch.where( normal_mask, (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x), (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x ) inf = torch.finfo(dtype) torch.clamp(sign, min=inf.min, max=inf.max, out=sign) return sign def stochastic_rounding(value, dtype, seed=0): if dtype == torch.float32: return value.to(dtype=torch.float32) if dtype == torch.float16: return value.to(dtype=torch.float16) if dtype == torch.bfloat16: return value.to(dtype=torch.bfloat16) if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2: generator = torch.Generator(device=value.device) generator.manual_seed(seed) output = torch.empty_like(value, dtype=dtype) num_slices = max(1, (value.numel() / (4096 * 4096))) slice_size = max(1, round(value.shape[0] / num_slices)) for i in range(0, value.shape[0], slice_size): output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator)) return output return value.to(dtype=dtype) # TODO: improve this? def stochastic_float_to_fp4_e2m1(x, generator): orig_shape = x.shape sign = torch.signbit(x).to(torch.uint8) exp = torch.floor(torch.log2(x.abs()) + 1.0).clamp(0, 3) x += (torch.rand(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) - 0.5) * (2 ** (exp - 2.0)) * 1.25 x = x.abs() exp = torch.floor(torch.log2(x) + 1.1925).clamp(0, 3) mantissa = torch.where( exp > 0, (x / (2.0 ** (exp - 1)) - 1.0) * 2.0, (x * 2.0), out=x ).round().to(torch.uint8) del x exp = exp.to(torch.uint8) fp4 = (sign << 3) | (exp << 1) | mantissa del sign, exp, mantissa fp4_flat = fp4.view(-1) packed = (fp4_flat[0::2] << 4) | fp4_flat[1::2] return packed.reshape(list(orig_shape)[:-1] + [-1]) def to_blocked(input_matrix, flatten: bool = True) -> torch.Tensor: """ Rearrange a large matrix by breaking it into blocks and applying the rearrangement pattern. See: https://docs.nvidia.com/cuda/cublas/index.html#d-block-scaling-factors-layout Args: input_matrix: Input tensor of shape (H, W) Returns: Rearranged tensor of shape (32*ceil_div(H,128), 16*ceil_div(W,4)) """ def ceil_div(a, b): return (a + b - 1) // b rows, cols = input_matrix.shape n_row_blocks = ceil_div(rows, 128) n_col_blocks = ceil_div(cols, 4) # Calculate the padded shape padded_rows = n_row_blocks * 128 padded_cols = n_col_blocks * 4 padded = input_matrix if (rows, cols) != (padded_rows, padded_cols): padded = torch.zeros( (padded_rows, padded_cols), device=input_matrix.device, dtype=input_matrix.dtype, ) padded[:rows, :cols] = input_matrix # Rearrange the blocks blocks = padded.view(n_row_blocks, 128, n_col_blocks, 4).permute(0, 2, 1, 3) rearranged = blocks.reshape(-1, 4, 32, 4).transpose(1, 2).reshape(-1, 32, 16) if flatten: return rearranged.flatten() return rearranged.reshape(padded_rows, padded_cols) def stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator): F4_E2M1_MAX = 6.0 F8_E4M3_MAX = 448.0 orig_shape = x.shape block_size = 16 x = x.reshape(orig_shape[0], -1, block_size) scaled_block_scales_fp8 = torch.clamp(((torch.amax(torch.abs(x), dim=-1)) / F4_E2M1_MAX) / per_tensor_scale.to(x.dtype), max=F8_E4M3_MAX).to(torch.float8_e4m3fn) x = x / (per_tensor_scale.to(x.dtype) * scaled_block_scales_fp8.to(x.dtype)).unsqueeze(-1) x = x.view(orig_shape).nan_to_num() data_lp = stochastic_float_to_fp4_e2m1(x, generator=generator) return data_lp, scaled_block_scales_fp8 def stochastic_round_quantize_nvfp4(x, per_tensor_scale, pad_16x, seed=0): def roundup(x: int, multiple: int) -> int: """Round up x to the nearest multiple.""" return ((x + multiple - 1) // multiple) * multiple generator = torch.Generator(device=x.device) generator.manual_seed(seed) # Handle padding if pad_16x: rows, cols = x.shape padded_rows = roundup(rows, 16) padded_cols = roundup(cols, 16) if padded_rows != rows or padded_cols != cols: x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows)) x, blocked_scaled = stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator) return x, to_blocked(blocked_scaled, flatten=False) def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16x, seed=0, block_size=4096 * 4096): def roundup(x: int, multiple: int) -> int: """Round up x to the nearest multiple.""" return ((x + multiple - 1) // multiple) * multiple orig_shape = x.shape # Handle padding if pad_16x: rows, cols = x.shape padded_rows = roundup(rows, 16) padded_cols = roundup(cols, 16) if padded_rows != rows or padded_cols != cols: x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows)) # Note: We update orig_shape because the output tensor logic below assumes x.shape matches # what we want to produce. If we pad here, we want the padded output. orig_shape = x.shape orig_shape = list(orig_shape) output_fp4 = torch.empty(orig_shape[:-1] + [orig_shape[-1] // 2], dtype=torch.uint8, device=x.device) output_block = torch.empty(orig_shape[:-1] + [orig_shape[-1] // 16], dtype=torch.float8_e4m3fn, device=x.device) generator = torch.Generator(device=x.device) generator.manual_seed(seed) num_slices = max(1, (x.numel() / block_size)) slice_size = max(1, (round(x.shape[0] / num_slices))) for i in range(0, x.shape[0], slice_size): fp4, block = stochastic_round_quantize_nvfp4_block(x[i: i + slice_size], per_tensor_scale, generator=generator) output_fp4[i:i + slice_size].copy_(fp4) output_block[i:i + slice_size].copy_(block) return output_fp4, to_blocked(output_block, flatten=False) def stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0): def roundup(x_val, multiple): return ((x_val + multiple - 1) // multiple) * multiple if pad_32x: rows, cols = x.shape padded_rows = roundup(rows, 32) padded_cols = roundup(cols, 32) if padded_rows != rows or padded_cols != cols: x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows)) F8_E4M3_MAX = 448.0 E8M0_BIAS = 127 BLOCK_SIZE = 32 rows, cols = x.shape x_blocked = x.reshape(rows, -1, BLOCK_SIZE) max_abs = torch.amax(torch.abs(x_blocked), dim=-1) # E8M0 block scales (power-of-2 exponents) scale_needed = torch.clamp(max_abs.float() / F8_E4M3_MAX, min=2**(-127)) exp_biased = torch.clamp(torch.ceil(torch.log2(scale_needed)).to(torch.int32) + E8M0_BIAS, 0, 254) block_scales_e8m0 = exp_biased.to(torch.uint8) zero_mask = (max_abs == 0) block_scales_f32 = (block_scales_e8m0.to(torch.int32) << 23).view(torch.float32) block_scales_f32 = torch.where(zero_mask, torch.ones_like(block_scales_f32), block_scales_f32) # Scale per-block then stochastic round data_scaled = (x_blocked.float() / block_scales_f32.unsqueeze(-1)).reshape(rows, cols) output_fp8 = stochastic_rounding(data_scaled, torch.float8_e4m3fn, seed=seed) block_scales_e8m0 = torch.where(zero_mask, torch.zeros_like(block_scales_e8m0), block_scales_e8m0) return output_fp8, to_blocked(block_scales_e8m0, flatten=False).view(torch.float8_e8m0fnu) ================================================ FILE: comfy/gligen.py ================================================ import math import torch from torch import nn from .ldm.modules.attention import CrossAttention, FeedForward import comfy.ops ops = comfy.ops.manual_cast class GatedCrossAttentionDense(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() self.attn = CrossAttention( query_dim=query_dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, operations=ops) self.ff = FeedForward(query_dim, glu=True) self.norm1 = ops.LayerNorm(query_dim) self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) # this can be useful: we can externally change magnitude of tanh(alpha) # for example, when it is set to 0, then the entire model is same as # original one self.scale = 1 def forward(self, x, objs): x = x + self.scale * \ torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class GatedSelfAttentionDense(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() # we need a linear projection since we need cat visual feature and obj # feature self.linear = ops.Linear(context_dim, query_dim) self.attn = CrossAttention( query_dim=query_dim, context_dim=query_dim, heads=n_heads, dim_head=d_head, operations=ops) self.ff = FeedForward(query_dim, glu=True) self.norm1 = ops.LayerNorm(query_dim) self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) # this can be useful: we can externally change magnitude of tanh(alpha) # for example, when it is set to 0, then the entire model is same as # original one self.scale = 1 def forward(self, x, objs): N_visual = x.shape[1] objs = self.linear(objs) x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class GatedSelfAttentionDense2(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() # we need a linear projection since we need cat visual feature and obj # feature self.linear = ops.Linear(context_dim, query_dim) self.attn = CrossAttention( query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops) self.ff = FeedForward(query_dim, glu=True) self.norm1 = ops.LayerNorm(query_dim) self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) # this can be useful: we can externally change magnitude of tanh(alpha) # for example, when it is set to 0, then the entire model is same as # original one self.scale = 1 def forward(self, x, objs): B, N_visual, _ = x.shape B, N_ground, _ = objs.shape objs = self.linear(objs) # sanity check size_v = math.sqrt(N_visual) size_g = math.sqrt(N_ground) assert int(size_v) == size_v, "Visual tokens must be square rootable" assert int(size_g) == size_g, "Grounding tokens must be square rootable" size_v = int(size_v) size_g = int(size_g) # select grounding token and resize it to visual token size as residual out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ :, N_visual:, :] out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) out = torch.nn.functional.interpolate( out, (size_v, size_v), mode='bicubic') residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) # add residual to visual feature x = x + self.scale * torch.tanh(self.alpha_attn) * residual x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class FourierEmbedder(): def __init__(self, num_freqs=64, temperature=100): self.num_freqs = num_freqs self.temperature = temperature self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) @torch.no_grad() def __call__(self, x, cat_dim=-1): "x: arbitrary shape of tensor. dim: cat dim" out = [] for freq in self.freq_bands: out.append(torch.sin(freq * x)) out.append(torch.cos(freq * x)) return torch.cat(out, cat_dim) class PositionNet(nn.Module): def __init__(self, in_dim, out_dim, fourier_freqs=8): super().__init__() self.in_dim = in_dim self.out_dim = out_dim self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy self.linears = nn.Sequential( ops.Linear(self.in_dim + self.position_dim, 512), nn.SiLU(), ops.Linear(512, 512), nn.SiLU(), ops.Linear(512, out_dim), ) self.null_positive_feature = torch.nn.Parameter( torch.zeros([self.in_dim])) self.null_position_feature = torch.nn.Parameter( torch.zeros([self.position_dim])) def forward(self, boxes, masks, positive_embeddings): B, N, _ = boxes.shape masks = masks.unsqueeze(-1) positive_embeddings = positive_embeddings # embedding position (it may includes padding as placeholder) xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C # learnable null embedding positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) # replace padding with learnable null embedding positive_embeddings = positive_embeddings * \ masks + (1 - masks) * positive_null xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null objs = self.linears( torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) assert objs.shape == torch.Size([B, N, self.out_dim]) return objs class Gligen(nn.Module): def __init__(self, modules, position_net, key_dim): super().__init__() self.module_list = nn.ModuleList(modules) self.position_net = position_net self.key_dim = key_dim self.max_objs = 30 self.current_device = torch.device("cpu") def _set_position(self, boxes, masks, positive_embeddings): objs = self.position_net(boxes, masks, positive_embeddings) def func(x, extra_options): key = extra_options["transformer_index"] module = self.module_list[key] return module(x, objs.to(device=x.device, dtype=x.dtype)) return func def set_position(self, latent_image_shape, position_params, device): batch, c, h, w = latent_image_shape masks = torch.zeros([self.max_objs], device="cpu") boxes = [] positive_embeddings = [] for p in position_params: x1 = (p[4]) / w y1 = (p[3]) / h x2 = (p[4] + p[2]) / w y2 = (p[3] + p[1]) / h masks[len(boxes)] = 1.0 boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] positive_embeddings += [p[0]] append_boxes = [] append_conds = [] if len(boxes) < self.max_objs: append_boxes = [torch.zeros( [self.max_objs - len(boxes), 4], device="cpu")] append_conds = [torch.zeros( [self.max_objs - len(boxes), self.key_dim], device="cpu")] box_out = torch.cat( boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) masks = masks.unsqueeze(0).repeat(batch, 1) conds = torch.cat(positive_embeddings + append_conds).unsqueeze(0).repeat(batch, 1, 1) return self._set_position( box_out.to(device), masks.to(device), conds.to(device)) def set_empty(self, latent_image_shape, device): batch, c, h, w = latent_image_shape masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) box_out = torch.zeros([self.max_objs, 4], device="cpu").repeat(batch, 1, 1) conds = torch.zeros([self.max_objs, self.key_dim], device="cpu").repeat(batch, 1, 1) return self._set_position( box_out.to(device), masks.to(device), conds.to(device)) def load_gligen(sd): sd_k = sd.keys() output_list = [] key_dim = 768 for a in ["input_blocks", "middle_block", "output_blocks"]: for b in range(20): k_temp = filter(lambda k: "{}.{}.".format(a, b) in k and ".fuser." in k, sd_k) k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) n_sd = {} for k in k_temp: n_sd[k[1]] = sd[k[0]] if len(n_sd) > 0: query_dim = n_sd["linear.weight"].shape[0] key_dim = n_sd["linear.weight"].shape[1] if key_dim == 768: # SD1.x n_heads = 8 d_head = query_dim // n_heads else: d_head = 64 n_heads = query_dim // d_head gated = GatedSelfAttentionDense( query_dim, key_dim, n_heads, d_head) gated.load_state_dict(n_sd, strict=False) output_list.append(gated) if "position_net.null_positive_feature" in sd_k: in_dim = sd["position_net.null_positive_feature"].shape[0] out_dim = sd["position_net.linears.4.weight"].shape[0] class WeightsLoader(torch.nn.Module): pass w = WeightsLoader() w.position_net = PositionNet(in_dim, out_dim) w.load_state_dict(sd, strict=False) gligen = Gligen(output_list, w.position_net, key_dim) return gligen ================================================ FILE: comfy/hooks.py ================================================ from __future__ import annotations from typing import TYPE_CHECKING, Callable import enum import math import torch import numpy as np import itertools import logging if TYPE_CHECKING: from comfy.model_patcher import ModelPatcher, PatcherInjection from comfy.model_base import BaseModel from comfy.sd import CLIP import comfy.lora import comfy.model_management import comfy.patcher_extension from node_helpers import conditioning_set_values # ####################################################################################################### # Hooks explanation # ------------------- # The purpose of hooks is to allow conds to influence sampling without the need for ComfyUI core code to # make explicit special cases like it does for ControlNet and GLIGEN. # # This is necessary for nodes/features that are intended for use with masked or scheduled conds, or those # that should run special code when a 'marked' cond is used in sampling. # ####################################################################################################### class EnumHookMode(enum.Enum): ''' Priority of hook memory optimization vs. speed, mostly related to WeightHooks. MinVram: No caching will occur for any operations related to hooks. MaxSpeed: Excess VRAM (and RAM, once VRAM is sufficiently depleted) will be used to cache hook weights when switching hook groups. ''' MinVram = "minvram" MaxSpeed = "maxspeed" class EnumHookType(enum.Enum): ''' Hook types, each of which has different expected behavior. ''' Weight = "weight" ObjectPatch = "object_patch" AdditionalModels = "add_models" TransformerOptions = "transformer_options" Injections = "add_injections" class EnumWeightTarget(enum.Enum): Model = "model" Clip = "clip" class EnumHookScope(enum.Enum): ''' Determines if hook should be limited in its influence over sampling. AllConditioning: hook will affect all conds used in sampling. HookedOnly: hook will only affect the conds it was attached to. ''' AllConditioning = "all_conditioning" HookedOnly = "hooked_only" class _HookRef: pass def default_should_register(hook: Hook, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): '''Example for how custom_should_register function can look like.''' return True def create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[str]: '''Creates base dictionary for use with Hooks' target param.''' d = {} if target is not None: d['target'] = target d.update(kwargs) return d class Hook: def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=None, hook_id: str=None, hook_keyframe: HookKeyframeGroup=None, hook_scope=EnumHookScope.AllConditioning): self.hook_type = hook_type '''Enum identifying the general class of this hook.''' self.hook_ref = hook_ref if hook_ref else _HookRef() '''Reference shared between hook clones that have the same value. Should NOT be modified.''' self.hook_id = hook_id '''Optional string ID to identify hook; useful if need to consolidate duplicates at registration time.''' self.hook_keyframe = hook_keyframe if hook_keyframe else HookKeyframeGroup() '''Keyframe storage that can be referenced to get strength for current sampling step.''' self.hook_scope = hook_scope '''Scope of where this hook should apply in terms of the conds used in sampling run.''' self.custom_should_register = default_should_register '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register''' @property def strength(self): return self.hook_keyframe.strength def initialize_timesteps(self, model: BaseModel): self.reset() self.hook_keyframe.initialize_timesteps(model) def reset(self): self.hook_keyframe.reset() def clone(self): c: Hook = self.__class__() c.hook_type = self.hook_type c.hook_ref = self.hook_ref c.hook_id = self.hook_id c.hook_keyframe = self.hook_keyframe c.hook_scope = self.hook_scope c.custom_should_register = self.custom_should_register return c def should_register(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): return self.custom_should_register(self, model, model_options, target_dict, registered) def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): raise NotImplementedError("add_hook_patches should be defined for Hook subclasses") def __eq__(self, other: Hook): return self.__class__ == other.__class__ and self.hook_ref == other.hook_ref def __hash__(self): return hash(self.hook_ref) class WeightHook(Hook): ''' Hook responsible for tracking weights to be applied to some model/clip. Note, value of hook_scope is ignored and is treated as HookedOnly. ''' def __init__(self, strength_model=1.0, strength_clip=1.0): super().__init__(hook_type=EnumHookType.Weight, hook_scope=EnumHookScope.HookedOnly) self.weights: dict = None self.weights_clip: dict = None self.need_weight_init = True self._strength_model = strength_model self._strength_clip = strength_clip self.hook_scope = EnumHookScope.HookedOnly # this value does not matter for WeightHooks, just for docs @property def strength_model(self): return self._strength_model * self.strength @property def strength_clip(self): return self._strength_clip * self.strength def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): if not self.should_register(model, model_options, target_dict, registered): return False weights = None target = target_dict.get('target', None) if target == EnumWeightTarget.Clip: strength = self._strength_clip else: strength = self._strength_model if self.need_weight_init: key_map = {} if target == EnumWeightTarget.Clip: key_map = comfy.lora.model_lora_keys_clip(model.model, key_map) else: key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) weights = comfy.lora.load_lora(self.weights, key_map, log_missing=False) else: if target == EnumWeightTarget.Clip: weights = self.weights_clip else: weights = self.weights model.add_hook_patches(hook=self, patches=weights, strength_patch=strength) registered.add(self) return True # TODO: add logs about any keys that were not applied def clone(self): c: WeightHook = super().clone() c.weights = self.weights c.weights_clip = self.weights_clip c.need_weight_init = self.need_weight_init c._strength_model = self._strength_model c._strength_clip = self._strength_clip return c class ObjectPatchHook(Hook): def __init__(self, object_patches: dict[str]=None, hook_scope=EnumHookScope.AllConditioning): super().__init__(hook_type=EnumHookType.ObjectPatch) self.object_patches = object_patches self.hook_scope = hook_scope def clone(self): c: ObjectPatchHook = super().clone() c.object_patches = self.object_patches return c def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): raise NotImplementedError("ObjectPatchHook is not supported yet in ComfyUI.") class AdditionalModelsHook(Hook): ''' Hook responsible for telling model management any additional models that should be loaded. Note, value of hook_scope is ignored and is treated as AllConditioning. ''' def __init__(self, models: list[ModelPatcher]=None, key: str=None): super().__init__(hook_type=EnumHookType.AdditionalModels) self.models = models self.key = key def clone(self): c: AdditionalModelsHook = super().clone() c.models = self.models.copy() if self.models else self.models c.key = self.key return c def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): if not self.should_register(model, model_options, target_dict, registered): return False registered.add(self) return True class TransformerOptionsHook(Hook): ''' Hook responsible for adding wrappers, callbacks, patches, or anything else related to transformer_options. ''' def __init__(self, transformers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None, hook_scope=EnumHookScope.AllConditioning): super().__init__(hook_type=EnumHookType.TransformerOptions) self.transformers_dict = transformers_dict self.hook_scope = hook_scope self._skip_adding = False '''Internal value used to avoid double load of transformer_options when hook_scope is AllConditioning.''' def clone(self): c: TransformerOptionsHook = super().clone() c.transformers_dict = self.transformers_dict c._skip_adding = self._skip_adding return c def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): if not self.should_register(model, model_options, target_dict, registered): return False # NOTE: to_load_options will be used to manually load patches/wrappers/callbacks from hooks self._skip_adding = False if self.hook_scope == EnumHookScope.AllConditioning: add_model_options = {"transformer_options": self.transformers_dict, "to_load_options": self.transformers_dict} # skip_adding if included in AllConditioning to avoid double loading self._skip_adding = True else: add_model_options = {"to_load_options": self.transformers_dict} registered.add(self) comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False) return True def on_apply_hooks(self, model: ModelPatcher, transformer_options: dict[str]): if not self._skip_adding: comfy.patcher_extension.merge_nested_dicts(transformer_options, self.transformers_dict, copy_dict1=False) WrapperHook = TransformerOptionsHook '''Only here for backwards compatibility, WrapperHook is identical to TransformerOptionsHook.''' class InjectionsHook(Hook): def __init__(self, key: str=None, injections: list[PatcherInjection]=None, hook_scope=EnumHookScope.AllConditioning): super().__init__(hook_type=EnumHookType.Injections) self.key = key self.injections = injections self.hook_scope = hook_scope def clone(self): c: InjectionsHook = super().clone() c.key = self.key c.injections = self.injections.copy() if self.injections else self.injections return c def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): raise NotImplementedError("InjectionsHook is not supported yet in ComfyUI.") class HookGroup: ''' Stores groups of hooks, and allows them to be queried by type. To prevent breaking their functionality, never modify the underlying self.hooks or self._hook_dict vars directly; always use the provided functions on HookGroup. ''' def __init__(self): self.hooks: list[Hook] = [] self._hook_dict: dict[EnumHookType, list[Hook]] = {} def __len__(self): return len(self.hooks) def add(self, hook: Hook): if hook not in self.hooks: self.hooks.append(hook) self._hook_dict.setdefault(hook.hook_type, []).append(hook) def remove(self, hook: Hook): if hook in self.hooks: self.hooks.remove(hook) self._hook_dict[hook.hook_type].remove(hook) def get_type(self, hook_type: EnumHookType): return self._hook_dict.get(hook_type, []) def contains(self, hook: Hook): return hook in self.hooks def is_subset_of(self, other: HookGroup): self_hooks = set(self.hooks) other_hooks = set(other.hooks) return self_hooks.issubset(other_hooks) def new_with_common_hooks(self, other: HookGroup): c = HookGroup() for hook in self.hooks: if other.contains(hook): c.add(hook.clone()) return c def clone(self): c = HookGroup() for hook in self.hooks: c.add(hook.clone()) return c def clone_and_combine(self, other: HookGroup): c = self.clone() if other is not None: for hook in other.hooks: c.add(hook.clone()) return c def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup): if hook_kf is None: hook_kf = HookKeyframeGroup() else: hook_kf = hook_kf.clone() for hook in self.hooks: hook.hook_keyframe = hook_kf def get_hooks_for_clip_schedule(self): scheduled_hooks: dict[WeightHook, list[tuple[tuple[float,float], HookKeyframe]]] = {} # only care about WeightHooks, for now for hook in self.get_type(EnumHookType.Weight): hook: WeightHook hook_schedule = [] # if no hook keyframes, assign default value if len(hook.hook_keyframe.keyframes) == 0: hook_schedule.append(((0.0, 1.0), None)) scheduled_hooks[hook] = hook_schedule continue # find ranges of values prev_keyframe = hook.hook_keyframe.keyframes[0] for keyframe in hook.hook_keyframe.keyframes: if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength): hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe)) prev_keyframe = keyframe elif keyframe.start_percent == prev_keyframe.start_percent: prev_keyframe = keyframe # create final range, assuming last start_percent was not 1.0 if not math.isclose(prev_keyframe.start_percent, 1.0): hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe)) scheduled_hooks[hook] = hook_schedule # hooks should not have their schedules in a list of tuples all_ranges: list[tuple[float, float]] = [] for range_kfs in scheduled_hooks.values(): for t_range, keyframe in range_kfs: all_ranges.append(t_range) # turn list of ranges into boundaries boundaries_set = set(itertools.chain.from_iterable(all_ranges)) boundaries_set.add(0.0) boundaries = sorted(boundaries_set) real_ranges = [(boundaries[i], boundaries[i + 1]) for i in range(len(boundaries) - 1)] # with real ranges defined, give appropriate hooks w/ keyframes for each range scheduled_keyframes: list[tuple[tuple[float,float], list[tuple[WeightHook, HookKeyframe]]]] = [] for t_range in real_ranges: hooks_schedule = [] for hook, val in scheduled_hooks.items(): keyframe = None # check if is a keyframe that works for the current t_range for stored_range, stored_kf in val: # if stored start is less than current end, then fits - give it assigned keyframe if stored_range[0] < t_range[1] and stored_range[1] > t_range[0]: keyframe = stored_kf break hooks_schedule.append((hook, keyframe)) scheduled_keyframes.append((t_range, hooks_schedule)) return scheduled_keyframes def reset(self): for hook in self.hooks: hook.reset() @staticmethod def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) -> HookGroup: actual: list[HookGroup] = [] for group in hooks_list: if group is not None: actual.append(group) if len(actual) < require_count: raise Exception(f"Need at least {require_count} hooks to combine, but only had {len(actual)}.") # if no hooks, then return None if len(actual) == 0: return None # if only 1 hook, just return itself without cloning elif len(actual) == 1: return actual[0] final_hook: HookGroup = None for hook in actual: if final_hook is None: final_hook = hook.clone() else: final_hook = final_hook.clone_and_combine(hook) return final_hook class HookKeyframe: def __init__(self, strength: float, start_percent=0.0, guarantee_steps=1): self.strength = strength # scheduling self.start_percent = float(start_percent) self.start_t = 999999999.9 self.guarantee_steps = guarantee_steps def get_effective_guarantee_steps(self, max_sigma: torch.Tensor): '''If keyframe starts before current sampling range (max_sigma), treat as 0.''' if self.start_t > max_sigma: return 0 return self.guarantee_steps def clone(self): c = HookKeyframe(strength=self.strength, start_percent=self.start_percent, guarantee_steps=self.guarantee_steps) c.start_t = self.start_t return c class HookKeyframeGroup: def __init__(self): self.keyframes: list[HookKeyframe] = [] self._current_keyframe: HookKeyframe = None self._current_used_steps = 0 self._current_index = 0 self._current_strength = None self._curr_t = -1. # properties shadow those of HookWeightsKeyframe @property def strength(self): if self._current_keyframe is not None: return self._current_keyframe.strength return 1.0 def reset(self): self._current_keyframe = None self._current_used_steps = 0 self._current_index = 0 self._current_strength = None self.curr_t = -1. self._set_first_as_current() def add(self, keyframe: HookKeyframe): # add to end of list, then sort self.keyframes.append(keyframe) self.keyframes = get_sorted_list_via_attr(self.keyframes, "start_percent") self._set_first_as_current() def _set_first_as_current(self): if len(self.keyframes) > 0: self._current_keyframe = self.keyframes[0] else: self._current_keyframe = None def has_guarantee_steps(self): for kf in self.keyframes: if kf.guarantee_steps > 0: return True return False def has_index(self, index: int): return index >= 0 and index < len(self.keyframes) def is_empty(self): return len(self.keyframes) == 0 def clone(self): c = HookKeyframeGroup() for keyframe in self.keyframes: c.keyframes.append(keyframe.clone()) c._set_first_as_current() return c def initialize_timesteps(self, model: BaseModel): for keyframe in self.keyframes: keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent) def prepare_current_keyframe(self, curr_t: float, transformer_options: dict[str, torch.Tensor]) -> bool: if self.is_empty(): return False if curr_t == self._curr_t: return False max_sigma = torch.max(transformer_options["sample_sigmas"]) prev_index = self._current_index prev_strength = self._current_strength # if met guaranteed steps, look for next keyframe in case need to switch if self._current_used_steps >= self._current_keyframe.get_effective_guarantee_steps(max_sigma): # if has next index, loop through and see if need to switch if self.has_index(self._current_index+1): for i in range(self._current_index+1, len(self.keyframes)): eval_c = self.keyframes[i] # check if start_t is greater or equal to curr_t # NOTE: t is in terms of sigmas, not percent, so bigger number = earlier step in sampling if eval_c.start_t >= curr_t: self._current_index = i self._current_strength = eval_c.strength self._current_keyframe = eval_c self._current_used_steps = 0 # if guarantee_steps greater than zero, stop searching for other keyframes if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0: break # if eval_c is outside the percent range, stop looking further else: break # update steps current context is used self._current_used_steps += 1 # update current timestep this was performed on self._curr_t = curr_t # return True if keyframe changed, False if no change return prev_index != self._current_index and prev_strength != self._current_strength class InterpolationMethod: LINEAR = "linear" EASE_IN = "ease_in" EASE_OUT = "ease_out" EASE_IN_OUT = "ease_in_out" _LIST = [LINEAR, EASE_IN, EASE_OUT, EASE_IN_OUT] @classmethod def get_weights(cls, num_from: float, num_to: float, length: int, method: str, reverse=False): diff = num_to - num_from if method == cls.LINEAR: weights = torch.linspace(num_from, num_to, length) elif method == cls.EASE_IN: index = torch.linspace(0, 1, length) weights = diff * np.power(index, 2) + num_from elif method == cls.EASE_OUT: index = torch.linspace(0, 1, length) weights = diff * (1 - np.power(1 - index, 2)) + num_from elif method == cls.EASE_IN_OUT: index = torch.linspace(0, 1, length) weights = diff * ((1 - np.cos(index * np.pi)) / 2) + num_from else: raise ValueError(f"Unrecognized interpolation method '{method}'.") if reverse: weights = weights.flip(dims=(0,)) return weights def get_sorted_list_via_attr(objects: list, attr: str) -> list: if not objects: return objects elif len(objects) <= 1: return [x for x in objects] # now that we know we have to sort, do it following these rules: # a) if objects have same value of attribute, maintain their relative order # b) perform sorting of the groups of objects with same attributes unique_attrs = {} for o in objects: val_attr = getattr(o, attr) attr_list: list = unique_attrs.get(val_attr, list()) attr_list.append(o) if val_attr not in unique_attrs: unique_attrs[val_attr] = attr_list # now that we have the unique attr values grouped together in relative order, sort them by key sorted_attrs = dict(sorted(unique_attrs.items())) # now flatten out the dict into a list to return sorted_list = [] for object_list in sorted_attrs.values(): sorted_list.extend(object_list) return sorted_list def create_transformer_options_from_hooks(model: ModelPatcher, hooks: HookGroup, transformer_options: dict[str]=None): # if no hooks or is not a ModelPatcher for sampling, return empty dict if hooks is None or model.is_clip: return {} if transformer_options is None: transformer_options = {} for hook in hooks.get_type(EnumHookType.TransformerOptions): hook: TransformerOptionsHook hook.on_apply_hooks(model, transformer_options) return transformer_options def create_hook_lora(lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float): hook_group = HookGroup() hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip) hook_group.add(hook) hook.weights = lora return hook_group def create_hook_model_as_lora(weights_model, weights_clip, strength_model: float, strength_clip: float): hook_group = HookGroup() hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip) hook_group.add(hook) patches_model = None patches_clip = None if weights_model is not None: patches_model = {} for key in weights_model: patches_model[key] = ("model_as_lora", (weights_model[key],)) if weights_clip is not None: patches_clip = {} for key in weights_clip: patches_clip[key] = ("model_as_lora", (weights_clip[key],)) hook.weights = patches_model hook.weights_clip = patches_clip hook.need_weight_init = False return hook_group def get_patch_weights_from_model(model: ModelPatcher, discard_model_sampling=True): if model is None: return None patches_model: dict[str, torch.Tensor] = model.model.state_dict() if discard_model_sampling: # do not include ANY model_sampling components of the model that should act as a patch for key in list(patches_model.keys()): if key.startswith("model_sampling"): patches_model.pop(key, None) return patches_model # NOTE: this function shows how to register weight hooks directly on the ModelPatchers def load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float): key_map = {} if model is not None: key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) if clip is not None: key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) hook_group = HookGroup() hook = WeightHook() hook_group.add(hook) loaded: dict[str] = comfy.lora.load_lora(lora, key_map) if model is not None: new_modelpatcher = model.clone() k = new_modelpatcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=strength_model) else: k = () new_modelpatcher = None if clip is not None: new_clip = clip.clone() k1 = new_clip.patcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=strength_clip) else: k1 = () new_clip = None k = set(k) k1 = set(k1) for x in loaded: if (x not in k) and (x not in k1): logging.warning(f"NOT LOADED {x}") return (new_modelpatcher, new_clip, hook_group) def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, HookGroup], cache: dict[tuple[HookGroup, HookGroup], HookGroup]): hooks_key = 'hooks' # if hooks only exist in one dict, do what's needed so that it ends up in c_dict if hooks_key not in values: return if hooks_key not in c_dict: hooks_value = values.get(hooks_key, None) if hooks_value is not None: c_dict[hooks_key] = hooks_value return # otherwise, need to combine with minimum duplication via cache hooks_tuple = (c_dict[hooks_key], values[hooks_key]) cached_hooks = cache.get(hooks_tuple, None) if cached_hooks is None: new_hooks = hooks_tuple[0].clone_and_combine(hooks_tuple[1]) cache[hooks_tuple] = new_hooks c_dict[hooks_key] = new_hooks else: c_dict[hooks_key] = cache[hooks_tuple] def conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True, cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): c = [] if cache is None: cache = {} for t in conditioning: n = [t[0], t[1].copy()] for k in values: if append_hooks and k == 'hooks': _combine_hooks_from_values(n[1], values, cache) else: n[1][k] = values[k] c.append(n) return c def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True, cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): if hooks is None: return cond return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks, cache=cache) def set_timesteps_for_conditioning(cond, timestep_range: tuple[float,float]): if timestep_range is None: return cond return conditioning_set_values(cond, {"start_percent": timestep_range[0], "end_percent": timestep_range[1]}) def set_mask_for_conditioning(cond, mask: torch.Tensor, set_cond_area: str, strength: float): if mask is None: return cond set_area_to_bounds = False if set_cond_area != 'default': set_area_to_bounds = True if len(mask.shape) < 3: mask = mask.unsqueeze(0) return conditioning_set_values(cond, {'mask': mask, 'set_area_to_bounds': set_area_to_bounds, 'mask_strength': strength}) def combine_conditioning(conds: list): combined_conds = [] for cond in conds: combined_conds.extend(cond) return combined_conds def combine_with_new_conds(conds: list, new_conds: list): combined_conds = [] for c, new_c in zip(conds, new_conds): combined_conds.append(combine_conditioning([c, new_c])) return combined_conds def set_conds_props(conds: list, strength: float, set_cond_area: str, mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): final_conds = [] cache = {} for c in conds: # first, apply lora_hook to conditioning, if provided c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks, cache=cache) # next, apply mask to conditioning c = set_mask_for_conditioning(cond=c, mask=mask, strength=strength, set_cond_area=set_cond_area) # apply timesteps, if present c = set_timesteps_for_conditioning(cond=c, timestep_range=timesteps_range) # finally, apply mask to conditioning and store final_conds.append(c) return final_conds def set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1.0, set_cond_area: str="default", mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): combined_conds = [] cache = {} for c, masked_c in zip(conds, new_conds): # first, apply lora_hook to new conditioning, if provided masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks, cache=cache) # next, apply mask to new conditioning, if provided masked_c = set_mask_for_conditioning(cond=masked_c, mask=mask, set_cond_area=set_cond_area, strength=strength) # apply timesteps, if present masked_c = set_timesteps_for_conditioning(cond=masked_c, timestep_range=timesteps_range) # finally, combine with existing conditioning and store combined_conds.append(combine_conditioning([c, masked_c])) return combined_conds def set_default_conds_and_combine(conds: list, new_conds: list, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): combined_conds = [] cache = {} for c, new_c in zip(conds, new_conds): # first, apply lora_hook to new conditioning, if provided new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks, cache=cache) # next, add default_cond key to cond so that during sampling, it can be identified new_c = conditioning_set_values(new_c, {'default': True}) # apply timesteps, if present new_c = set_timesteps_for_conditioning(cond=new_c, timestep_range=timesteps_range) # finally, combine with existing conditioning and store combined_conds.append(combine_conditioning([c, new_c])) return combined_conds ================================================ FILE: comfy/image_encoders/dino2.py ================================================ import torch from comfy.text_encoders.bert import BertAttention import comfy.model_management from comfy.ldm.modules.attention import optimized_attention_for_device class Dino2AttentionOutput(torch.nn.Module): def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): super().__init__() self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) def forward(self, x): return self.dense(x) class Dino2AttentionBlock(torch.nn.Module): def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): super().__init__() self.attention = BertAttention(embed_dim, heads, dtype, device, operations) self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) def forward(self, x, mask, optimized_attention): return self.output(self.attention(x, mask, optimized_attention)) class LayerScale(torch.nn.Module): def __init__(self, dim, dtype, device, operations): super().__init__() self.lambda1 = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) def forward(self, x): return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype) class Dinov2MLP(torch.nn.Module): def __init__(self, hidden_size: int, dtype, device, operations): super().__init__() mlp_ratio = 4 hidden_features = int(hidden_size * mlp_ratio) self.fc1 = operations.Linear(hidden_size, hidden_features, bias = True, device=device, dtype=dtype) self.fc2 = operations.Linear(hidden_features, hidden_size, bias = True, device=device, dtype=dtype) def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: hidden_state = self.fc1(hidden_state) hidden_state = torch.nn.functional.gelu(hidden_state) hidden_state = self.fc2(hidden_state) return hidden_state class SwiGLUFFN(torch.nn.Module): def __init__(self, dim, dtype, device, operations): super().__init__() in_features = out_features = dim hidden_features = int(dim * 4) hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 self.weights_in = operations.Linear(in_features, 2 * hidden_features, bias=True, device=device, dtype=dtype) self.weights_out = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype) def forward(self, x): x = self.weights_in(x) x1, x2 = x.chunk(2, dim=-1) x = torch.nn.functional.silu(x1) * x2 return self.weights_out(x) class Dino2Block(torch.nn.Module): def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn): super().__init__() self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations) self.layer_scale1 = LayerScale(dim, dtype, device, operations) self.layer_scale2 = LayerScale(dim, dtype, device, operations) if use_swiglu_ffn: self.mlp = SwiGLUFFN(dim, dtype, device, operations) else: self.mlp = Dinov2MLP(dim, dtype, device, operations) self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) def forward(self, x, optimized_attention): x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention)) x = x + self.layer_scale2(self.mlp(self.norm2(x))) return x class Dino2Encoder(torch.nn.Module): def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn): super().__init__() self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn) for _ in range(num_layers)]) def forward(self, x, intermediate_output=None): optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) if intermediate_output is not None: if intermediate_output < 0: intermediate_output = len(self.layer) + intermediate_output intermediate = None for i, layer in enumerate(self.layer): x = layer(x, optimized_attention) if i == intermediate_output: intermediate = x.clone() return x, intermediate class Dino2PatchEmbeddings(torch.nn.Module): def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None): super().__init__() self.projection = operations.Conv2d( in_channels=num_channels, out_channels=dim, kernel_size=patch_size, stride=patch_size, bias=True, dtype=dtype, device=device ) def forward(self, pixel_values): return self.projection(pixel_values).flatten(2).transpose(1, 2) class Dino2Embeddings(torch.nn.Module): def __init__(self, dim, dtype, device, operations): super().__init__() patch_size = 14 image_size = 518 self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations) self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device)) self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device)) def forward(self, pixel_values): x = self.patch_embeddings(pixel_values) # TODO: mask_token? x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1) x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype) return x class Dinov2Model(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() num_layers = config_dict["num_hidden_layers"] dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] layer_norm_eps = config_dict["layer_norm_eps"] use_swiglu_ffn = config_dict["use_swiglu_ffn"] self.embeddings = Dino2Embeddings(dim, dtype, device, operations) self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn) self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) def forward(self, pixel_values, attention_mask=None, intermediate_output=None): x = self.embeddings(pixel_values) x, i = self.encoder(x, intermediate_output=intermediate_output) x = self.layernorm(x) pooled_output = x[:, 0, :] return x, i, pooled_output, None ================================================ FILE: comfy/image_encoders/dino2_giant.json ================================================ { "attention_probs_dropout_prob": 0.0, "drop_path_rate": 0.0, "hidden_act": "gelu", "hidden_dropout_prob": 0.0, "hidden_size": 1536, "image_size": 518, "initializer_range": 0.02, "layer_norm_eps": 1e-06, "layerscale_value": 1.0, "mlp_ratio": 4, "model_type": "dinov2", "num_attention_heads": 24, "num_channels": 3, "num_hidden_layers": 40, "patch_size": 14, "qkv_bias": true, "use_swiglu_ffn": true, "image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225] } ================================================ FILE: comfy/image_encoders/dino2_large.json ================================================ { "hidden_size": 1024, "use_mask_token": true, "patch_size": 14, "image_size": 518, "num_channels": 3, "num_attention_heads": 16, "initializer_range": 0.02, "attention_probs_dropout_prob": 0.0, "hidden_dropout_prob": 0.0, "hidden_act": "gelu", "mlp_ratio": 4, "model_type": "dinov2", "num_hidden_layers": 24, "layer_norm_eps": 1e-6, "qkv_bias": true, "use_swiglu_ffn": false, "layerscale_value": 1.0, "drop_path_rate": 0.0, "image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225] } ================================================ FILE: comfy/k_diffusion/deis.py ================================================ #Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py #under Apache 2 license import torch import numpy as np # A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis). ############################# ### Utils for DEIS solver ### ############################# #---------------------------------------------------------------------------- # Transfer from the input time (sigma) used in EDM to that (t) used in DEIS. def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80): vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1) vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu()) return t_steps, vp_beta_min, vp_beta_d + vp_beta_min #---------------------------------------------------------------------------- def cal_poly(prev_t, j, taus): poly = 1 for k in range(prev_t.shape[0]): if k == j: continue poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k]) return poly #---------------------------------------------------------------------------- # Transfer from t to alpha_t. def t2alpha_fn(beta_0, beta_1, t): return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0) #---------------------------------------------------------------------------- def cal_intergrand(beta_0, beta_1, taus): with torch.inference_mode(mode=False): taus = taus.clone() beta_0 = beta_0.clone() beta_1 = beta_1.clone() with torch.enable_grad(): taus.requires_grad_(True) alpha = t2alpha_fn(beta_0, beta_1, taus) log_alpha = alpha.log() log_alpha.sum().backward() d_log_alpha_dtau = taus.grad integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha)) return integrand #---------------------------------------------------------------------------- def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'): """ Get the coefficient list for DEIS sampling. Args: t_steps: A pytorch tensor. The time steps for sampling. max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4 N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'. deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS. Returns: A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True. """ if deis_mode == 'tab': t_steps, beta_0, beta_1 = edm2t(t_steps) C = [] for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): order = min(i+1, max_order) if order == 1: C.append([]) else: taus = torch.linspace(t_cur, t_next, N) # split the interval for integral appximation dtau = (t_next - t_cur) / N prev_t = t_steps[[i - k for k in range(order)]] coeff_temp = [] integrand = cal_intergrand(beta_0, beta_1, taus) for j in range(order): poly = cal_poly(prev_t, j, taus) coeff_temp.append(torch.sum(integrand * poly) * dtau) C.append(coeff_temp) elif deis_mode == 'rhoab': # Analytical solution, second order def get_def_intergral_2(a, b, start, end, c): coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b return coeff / ((c - a) * (c - b)) # Analytical solution, third order def get_def_intergral_3(a, b, c, start, end, d): coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \ + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c return coeff / ((d - a) * (d - b) * (d - c)) C = [] for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): order = min(i, max_order) if order == 0: C.append([]) else: prev_t = t_steps[[i - k for k in range(order+1)]] if order == 1: coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1])) coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur)) coeff_temp = [coeff_cur, coeff_prev1] elif order == 2: coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur) coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1]) coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2]) coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2] elif order == 3: coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur) coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1]) coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2]) coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3]) coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3] C.append(coeff_temp) return C ================================================ FILE: comfy/k_diffusion/sa_solver.py ================================================ # SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019) # Conference: https://proceedings.neurips.cc/paper_files/paper/2023/file/f4a6806490d31216a3ba667eb240c897-Paper-Conference.pdf # Codebase ref: https://github.com/scxue/SA-Solver import math from typing import Union, Callable import torch def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_order: int, tau_t: float) -> torch.Tensor: """Compute (1 + tau^2) * integral of exp((1 + tau^2) * x) * x^p dx from s to t with exp((1 + tau^2) * t) factored out, using integration by parts. Integral of exp((1 + tau^2) * x) * x^p dx = product_terms[p] - (p / (1 + tau^2)) * integral of exp((1 + tau^2) * x) * x^(p-1) dx, with base case p=0 where integral equals product_terms[0]. where product_terms[p] = x^p * exp((1 + tau^2) * x) / (1 + tau^2). Construct a recursive coefficient matrix following the above recursive relation to compute all integral terms up to p = (solver_order - 1). Return coefficients used by the SA-Solver in data prediction mode. Args: s: Start time s. t: End time t. solver_order: Current order of the solver. tau_t: Stochastic strength parameter in the SDE. Returns: Exponential coefficients used in data prediction, with exp((1 + tau^2) * t) factored out, ordered from p=0 to p=solver_order−1, shape (solver_order,). """ tau_mul = 1 + tau_t ** 2 h = t - s p = torch.arange(solver_order, dtype=s.dtype, device=s.device) # product_terms after factoring out exp((1 + tau^2) * t) # Includes (1 + tau^2) factor from outside the integral product_terms_factored = (t ** p - s ** p * (-tau_mul * h).exp()) # Lower triangular recursive coefficient matrix # Accumulates recursive coefficients based on p / (1 + tau^2) recursive_depth_mat = p.unsqueeze(1) - p.unsqueeze(0) log_factorial = (p + 1).lgamma() recursive_coeff_mat = log_factorial.unsqueeze(1) - log_factorial.unsqueeze(0) if tau_t > 0: recursive_coeff_mat = recursive_coeff_mat - (recursive_depth_mat * math.log(tau_mul)) signs = torch.where(recursive_depth_mat % 2 == 0, 1.0, -1.0) recursive_coeff_mat = (recursive_coeff_mat.exp() * signs).tril() return recursive_coeff_mat @ product_terms_factored def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, is_corrector_step: bool = False) -> torch.Tensor: """Compute simple order-2 b coefficients from SA-Solver paper (Appendix D. Implementation Details).""" tau_mul = 1 + tau_t ** 2 h = lambda_t - lambda_s alpha_t = sigma_next * lambda_t.exp() if is_corrector_step: # Simplified 1-step (order-2) corrector b_1 = alpha_t * (0.5 * tau_mul * h) b_2 = alpha_t * (-h * tau_mul).expm1().neg() - b_1 else: # Simplified 2-step predictor b_2 = alpha_t * (0.5 * tau_mul * h ** 2) / (curr_lambdas[-2] - lambda_s) b_1 = alpha_t * (-h * tau_mul).expm1().neg() - b_2 return torch.stack([b_2, b_1]) def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, simple_order_2: bool = False, is_corrector_step: bool = False) -> torch.Tensor: """Compute b_i coefficients for the SA-Solver (see eqs. 15 and 18). The solver order corresponds to the number of input lambdas (half-logSNR points). Args: sigma_next: Sigma at end time t. curr_lambdas: Lambda time points used to construct the Lagrange basis, shape (N,). lambda_s: Lambda at start time s. lambda_t: Lambda at end time t. tau_t: Stochastic strength parameter in the SDE. simple_order_2: Whether to enable the simple order-2 scheme. is_corrector_step: Flag for corrector step in simple order-2 mode. Returns: b_i coefficients for the SA-Solver, shape (N,), where N is the solver order. """ num_timesteps = curr_lambdas.shape[0] if simple_order_2 and num_timesteps == 2: return compute_simple_stochastic_adams_b_coeffs(sigma_next, curr_lambdas, lambda_s, lambda_t, tau_t, is_corrector_step) # Compute coefficients by solving a linear system from Lagrange basis interpolation exp_integral_coeffs = compute_exponential_coeffs(lambda_s, lambda_t, num_timesteps, tau_t) vandermonde_matrix_T = torch.vander(curr_lambdas, num_timesteps, increasing=True).T lagrange_integrals = torch.linalg.solve(vandermonde_matrix_T, exp_integral_coeffs) # (sigma_t * exp(-tau^2 * lambda_t)) * exp((1 + tau^2) * lambda_t) # = sigma_t * exp(lambda_t) = alpha_t # exp((1 + tau^2) * lambda_t) is extracted from the integral alpha_t = sigma_next * lambda_t.exp() return alpha_t * lagrange_integrals def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: float = 1.0) -> Callable[[Union[torch.Tensor, float]], float]: """Return a function that controls the stochasticity of SA-Solver. When eta = 0, SA-Solver runs as ODE. The official approach uses time t to determine the SDE interval, while here we use sigma instead. See: https://github.com/scxue/SA-Solver/blob/main/README.md """ def tau_func(sigma: Union[torch.Tensor, float]) -> float: if eta <= 0: return 0.0 # ODE if isinstance(sigma, torch.Tensor): sigma = sigma.item() return eta if start_sigma >= sigma >= end_sigma else 0.0 return tau_func ================================================ FILE: comfy/k_diffusion/sampling.py ================================================ import math from functools import partial from scipy import integrate import torch from torch import nn import torchsde from tqdm.auto import tqdm from . import utils from . import deis from . import sa_solver import comfy.model_patcher import comfy.model_sampling import comfy.memory_management from comfy.utils import model_trange as trange def append_zero(x): return torch.cat([x, x.new_zeros([1])]) def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): """Constructs the noise schedule of Karras et al. (2022).""" ramp = torch.linspace(0, 1, n, device=device) min_inv_rho = sigma_min ** (1 / rho) max_inv_rho = sigma_max ** (1 / rho) sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho return append_zero(sigmas).to(device) def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): """Constructs an exponential noise schedule.""" sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() return append_zero(sigmas) def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): """Constructs an polynomial in log sigma noise schedule.""" ramp = torch.linspace(1, 0, n, device=device) ** rho sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) return append_zero(sigmas) def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): """Constructs a continuous VP noise schedule.""" t = torch.linspace(1, eps_s, n, device=device) sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t)) return append_zero(sigmas) def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'): """Constructs the noise schedule proposed by Tiankai et al. (2024). """ epsilon = 1e-5 # avoid log(0) x = torch.linspace(0, 1, n, device=device) clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max) lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon) sigmas = clamp(torch.exp(lmb)) return sigmas def to_d(x, sigma, denoised): """Converts a denoiser output to a Karras ODE derivative.""" return (x - denoised) / utils.append_dims(sigma, x.ndim) def get_ancestral_step(sigma_from, sigma_to, eta=1.): """Calculates the noise level (sigma_down) to step down to and the amount of noise to add (sigma_up) when doing an ancestral sampling step.""" if not eta: return sigma_to, 0. sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 return sigma_down, sigma_up def default_noise_sampler(x, seed=None): if seed is not None: if x.device == torch.device("cpu"): seed += 1 generator = torch.Generator(device=x.device) generator.manual_seed(seed) else: generator = None return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) class BatchedBrownianTree: """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" def __init__(self, x, t0, t1, seed=None, **kwargs): self.cpu_tree = kwargs.pop("cpu", True) t0, t1, self.sign = self.sort(t0, t1) w0 = kwargs.pop('w0', None) if w0 is None: w0 = torch.zeros_like(x) self.batched = False if seed is None: seed = (torch.randint(0, 2 ** 63 - 1, ()).item(),) elif isinstance(seed, (tuple, list)): if len(seed) != x.shape[0]: raise ValueError("Passing a list or tuple of seeds to BatchedBrownianTree requires a length matching the batch size.") self.batched = True w0 = w0[0] else: seed = (seed,) if self.cpu_tree: t0, w0, t1 = t0.detach().cpu(), w0.detach().cpu(), t1.detach().cpu() self.trees = tuple(torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed) @staticmethod def sort(a, b): return (a, b, 1) if a < b else (b, a, -1) def __call__(self, t0, t1): t0, t1, sign = self.sort(t0, t1) device, dtype = t0.device, t0.dtype if self.cpu_tree: t0, t1 = t0.detach().cpu().float(), t1.detach().cpu().float() w = torch.stack([tree(t0, t1) for tree in self.trees]).to(device=device, dtype=dtype) * (self.sign * sign) return w if self.batched else w[0] class BrownianTreeNoiseSampler: """A noise sampler backed by a torchsde.BrownianTree. Args: x (Tensor): The tensor whose shape, device and dtype to use to generate random samples. sigma_min (float): The low end of the valid interval. sigma_max (float): The high end of the valid interval. seed (int or List[int]): The random seed. If a list of seeds is supplied instead of a single integer, then the noise sampler will use one BrownianTree per batch item, each with its own seed. transform (callable): A function that maps sigma to the sampler's internal timestep. """ def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): self.transform = transform t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) def __call__(self, sigma, sigma_next): t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) return self.tree(t0, t1) / (t1 - t0).abs().sqrt() def sigma_to_half_log_snr(sigma, model_sampling): """Convert sigma to half-logSNR log(alpha_t / sigma_t).""" if isinstance(model_sampling, comfy.model_sampling.CONST): # log((1 - t) / t) = log((1 - sigma) / sigma) return sigma.logit().neg() return sigma.log().neg() def half_log_snr_to_sigma(half_log_snr, model_sampling): """Convert half-logSNR log(alpha_t / sigma_t) to sigma.""" if isinstance(model_sampling, comfy.model_sampling.CONST): # 1 / (1 + exp(half_log_snr)) return half_log_snr.neg().sigmoid() return half_log_snr.neg().exp() def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4): """Adjust the first sigma to avoid invalid logSNR.""" if len(sigmas) <= 1: return sigmas if isinstance(model_sampling, comfy.model_sampling.CONST): if sigmas[0] >= 1: sigmas = sigmas.clone() sigmas[0] = model_sampling.percent_to_sigma(percent_offset) return sigmas def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor: """Compute the result of h*phi_1(h) in exponential integrator methods.""" return torch.expm1(h) def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor: """Compute the result of h*phi_2(h) in exponential integrator methods.""" return (torch.expm1(h) - h) / h @torch.no_grad() def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): if s_churn > 0: gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) else: gamma = 0 sigma_hat = sigmas[i] if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat # Euler method x = x + d * dt return x @torch.no_grad() def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) """Ancestral sampling with Euler method steps.""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigma_down == 0: x = denoised else: d = to_d(x, sigmas[i], denoised) # Euler method dt = sigma_down - sigmas[i] x = x + d * dt + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None): """Ancestral sampling with Euler method steps.""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: x = denoised else: downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta sigma_down = sigmas[i + 1] * downstep_ratio alpha_ip1 = 1 - sigmas[i + 1] alpha_down = 1 - sigma_down renoise_coeff = (sigmas[i + 1]**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5 # Euler method sigma_down_i_ratio = sigma_down / sigmas[i] x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised if eta > 0: x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff return x @torch.no_grad() def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): if s_churn > 0: gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) else: gamma = 0 sigma_hat = sigmas[i] sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat if sigmas[i + 1] == 0: # Euler method x = x + d * dt else: # Heun's method x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) d_prime = (d + d_2) / 2 x = x + d_prime * dt return x @torch.no_grad() def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): if s_churn > 0: gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) else: gamma = 0 sigma_hat = sigmas[i] if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) if sigmas[i + 1] == 0: # Euler method dt = sigmas[i + 1] - sigma_hat x = x + d * dt else: # DPM-Solver-2 sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() dt_1 = sigma_mid - sigma_hat dt_2 = sigmas[i + 1] - sigma_hat x_2 = x + d * dt_1 denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) d_2 = to_d(x_2, sigma_mid, denoised_2) x = x + d_2 * dt_2 return x @torch.no_grad() def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) """Ancestral sampling with DPM-Solver second-order steps.""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d = to_d(x, sigmas[i], denoised) if sigma_down == 0: # Euler method dt = sigma_down - sigmas[i] x = x + d * dt else: # DPM-Solver-2 sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() dt_1 = sigma_mid - sigmas[i] dt_2 = sigma_down - sigmas[i] x_2 = x + d * dt_1 denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) d_2 = to_d(x_2, sigma_mid, denoised_2) x = x + d_2 * dt_2 x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver second-order steps.""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta sigma_down = sigmas[i+1] * downstep_ratio alpha_ip1 = 1 - sigmas[i+1] alpha_down = 1 - sigma_down renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d = to_d(x, sigmas[i], denoised) if sigma_down == 0: # Euler method dt = sigma_down - sigmas[i] x = x + d * dt else: # DPM-Solver-2 sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() dt_1 = sigma_mid - sigmas[i] dt_2 = sigma_down - sigmas[i] x_2 = x + d * dt_1 denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) d_2 = to_d(x_2, sigma_mid, denoised_2) x = x + d_2 * dt_2 x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff return x def linear_multistep_coeff(order, t, i, j): if order - 1 > i: raise ValueError(f'Order {order} too high for step {i}') def fn(tau): prod = 1. for k in range(order): if j == k: continue prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) return prod return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] @torch.no_grad() def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigmas_cpu = sigmas.detach().cpu().numpy() ds = [] for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) d = to_d(x, sigmas[i], denoised) ds.append(d) if len(ds) > order: ds.pop(0) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Denoising step x = denoised else: cur_order = min(i + 1, order) coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) return x class PIDStepSizeController: """A PID controller for ODE adaptive step size control.""" def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): self.h = h self.b1 = (pcoeff + icoeff + dcoeff) / order self.b2 = -(pcoeff + 2 * dcoeff) / order self.b3 = dcoeff / order self.accept_safety = accept_safety self.eps = eps self.errs = [] def limiter(self, x): return 1 + math.atan(x - 1) def propose_step(self, error): inv_error = 1 / (float(error) + self.eps) if not self.errs: self.errs = [inv_error, inv_error, inv_error] self.errs[0] = inv_error factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 factor = self.limiter(factor) accept = factor >= self.accept_safety if accept: self.errs[2] = self.errs[1] self.errs[1] = self.errs[0] self.h *= factor return accept class DPMSolver(nn.Module): """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): super().__init__() self.model = model self.extra_args = {} if extra_args is None else extra_args self.eps_callback = eps_callback self.info_callback = info_callback def t(self, sigma): return -sigma.log() def sigma(self, t): return t.neg().exp() def eps(self, eps_cache, key, x, t, *args, **kwargs): if key in eps_cache: return eps_cache[key], eps_cache sigma = self.sigma(t) * x.new_ones([x.shape[0]]) eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) if self.eps_callback is not None: self.eps_callback() return eps, {key: eps, **eps_cache} def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) x_1 = x - self.sigma(t_next) * h.expm1() * eps return x_1, eps_cache def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) s1 = t + r1 * h u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) return x_2, eps_cache def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) s1 = t + r1 * h s2 = t + r2 * h u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) return x_3, eps_cache def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler if not t_end > t_start and eta: raise ValueError('eta must be 0 for reverse sampling') m = math.floor(nfe / 3) + 1 ts = torch.linspace(t_start, t_end, m + 1, device=x.device) if nfe % 3 == 0: orders = [3] * (m - 2) + [2, 1] else: orders = [3] * (m - 1) + [nfe % 3] for i in range(len(orders)): eps_cache = {} t, t_next = ts[i], ts[i + 1] if eta: sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) t_next_ = torch.minimum(t_end, self.t(sd)) su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 else: t_next_, su = t_next, 0. eps, eps_cache = self.eps(eps_cache, 'eps', x, t) denoised = x - self.sigma(t) * eps if self.info_callback is not None: self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) if orders[i] == 1: x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) elif orders[i] == 2: x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) else: x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) return x def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler if order not in {2, 3}: raise ValueError('order should be 2 or 3') forward = t_end > t_start if not forward and eta: raise ValueError('eta must be 0 for reverse sampling') h_init = abs(h_init) * (1 if forward else -1) atol = torch.tensor(atol) rtol = torch.tensor(rtol) s = t_start x_prev = x accept = True pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} while s < t_end - 1e-5 if forward else s > t_end + 1e-5: eps_cache = {} t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) if eta: sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) t_ = torch.minimum(t_end, self.t(sd)) su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 else: t_, su = t, 0. eps, eps_cache = self.eps(eps_cache, 'eps', x, s) denoised = x - self.sigma(s) * eps if order == 2: x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) else: x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 accept = pid.propose_step(error) if accept: x_prev = x_low x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) s = t info['n_accept'] += 1 else: info['n_reject'] += 1 info['nfe'] += order info['steps'] += 1 if self.info_callback is not None: self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) return x, info @torch.no_grad() def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" if sigma_min <= 0 or sigma_max <= 0: raise ValueError('sigma_min and sigma_max must not be 0') with tqdm(total=n, disable=disable) as pbar: dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) if callback is not None: dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) @torch.no_grad() def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" if sigma_min <= 0 or sigma_max <= 0: raise ValueError('sigma_min and sigma_max must not be 0') with tqdm(disable=disable) as pbar: dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) if callback is not None: dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) if return_info: return x, info return x @torch.no_grad() def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigma_down == 0: # Euler method d = to_d(x, sigmas[i], denoised) dt = sigma_down - sigmas[i] x = x + d * dt else: # DPM-Solver++(2S) t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) r = 1 / 2 h = t_next - t s = t + r * h x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 # Noise addition if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1 lambda_fn = lambda sigma: ((1-sigma)/sigma).log() # logged_x = x.unsqueeze(0) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta sigma_down = sigmas[i+1] * downstep_ratio alpha_ip1 = 1 - sigmas[i+1] alpha_down = 1 - sigma_down renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Euler method d = to_d(x, sigmas[i], denoised) dt = sigma_down - sigmas[i] x = x + d * dt else: # DPM-Solver++(2S) if sigmas[i] == 1.0: sigma_s = 0.9999 else: t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down) r = 1 / 2 h = t_down - t_i s = t_i + r * h sigma_s = sigma_fn(s) # sigma_s = sigmas[i+1] sigma_s_i_ratio = sigma_s / sigmas[i] u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised D_i = model(u, sigma_s * s_in, **extra_args) sigma_down_i_ratio = sigma_down / sigmas[i] x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff) # Noise addition if sigmas[i + 1] > 0 and eta > 0: x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0) return x @torch.no_grad() def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): """DPM-Solver++ (stochastic).""" if len(sigmas) <= 1: return x extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() seed = extra_args.get("seed", None) noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Denoising step x = denoised else: # DPM-Solver++ lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) h = lambda_t - lambda_s lambda_s_1 = lambda_s + r * h fac = 1 / (2 * r) sigma_s_1 = sigma_fn(lambda_s_1) alpha_s = sigmas[i] * lambda_s.exp() alpha_s_1 = sigma_s_1 * lambda_s_1.exp() alpha_t = sigmas[i + 1] * lambda_t.exp() # Step 1 sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta) lambda_s_1_ = sd.log().neg() h_ = lambda_s_1_ - lambda_s x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised if eta > 0 and s_noise > 0: x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) # Step 2 sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta) lambda_t_ = sd.log().neg() h_ = lambda_t_ - lambda_s denoised_d = (1 - fac) * denoised + fac * denoised_2 x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d if eta > 0 and s_noise > 0: x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su return x @torch.no_grad() def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): """DPM-Solver++(2M).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() old_denoised = None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) h = t_next - t if old_denoised is None or sigmas[i + 1] == 0: x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised else: h_last = t - t_fn(sigmas[i - 1]) r = h_last / h denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d old_denoised = denoised return x @torch.no_grad() def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): """DPM-Solver++(2M) SDE.""" if len(sigmas) <= 1: return x if solver_type not in {'heun', 'midpoint'}: raise ValueError('solver_type must be \'heun\' or \'midpoint\'') extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) old_denoised = None h, h_last = None, None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Denoising step x = denoised else: # DPM-Solver++(2M) SDE lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) h = lambda_t - lambda_s h_eta = h * (eta + 1) alpha_t = sigmas[i + 1] * lambda_t.exp() x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised if old_denoised is not None: r = h_last / h if solver_type == 'heun': x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised) elif solver_type == 'midpoint': x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised) if eta > 0 and s_noise > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise old_denoised = denoised h_last = h return x @torch.no_grad() def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'): return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) @torch.no_grad() def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """DPM-Solver++(3M) SDE.""" if len(sigmas) <= 1: return x extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) denoised_1, denoised_2 = None, None h, h_1, h_2 = None, None, None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Denoising step x = denoised else: lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) h = lambda_t - lambda_s h_eta = h * (eta + 1) alpha_t = sigmas[i + 1] * lambda_t.exp() x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised if h_2 is not None: # DPM-Solver++(3M) SDE r0 = h_1 / h r1 = h_2 / h d1_0 = (denoised - denoised_1) / r0 d1_1 = (denoised_1 - denoised_2) / r1 d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) d2 = (d1_0 - d1_1) / (r0 + r1) phi_2 = h_eta.neg().expm1() / h_eta + 1 phi_3 = phi_2 / h_eta - 0.5 x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2 elif h_1 is not None: # DPM-Solver++(2M) SDE r = h_1 / h d = (denoised - denoised_1) / r phi_2 = h_eta.neg().expm1() / h_eta + 1 x = x + (alpha_t * phi_2) * d if eta > 0 and s_noise > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise denoised_1, denoised_2 = denoised, denoised_1 h_1, h_2 = h, h_1 return x @torch.no_grad() def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): if len(sigmas) <= 1: return x extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) @torch.no_grad() def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'): if len(sigmas) <= 1: return x extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) @torch.no_grad() def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): if len(sigmas) <= 1: return x extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) @torch.no_grad() def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): if len(sigmas) <= 1: return x extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): alpha_cumprod = 1 / ((sigma * sigma) + 1) alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) alpha = (alpha_cumprod / alpha_cumprod_prev) mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) if sigma_prev > 0: mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) return mu def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) if sigmas[i + 1] != 0: x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) return x @torch.no_grad() def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) @torch.no_grad() def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = denoised if sigmas[i + 1] > 0: x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) return x @torch.no_grad() def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) s_end = sigmas[-1] for i in trange(len(sigmas) - 1, disable=disable): gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. eps = torch.randn_like(x) * s_noise sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat if sigmas[i + 1] == s_end: # Euler method x = x + d * dt elif sigmas[i + 2] == s_end: # Heun's method x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) w = 2 * sigmas[0] w2 = sigmas[i+1]/w w1 = 1 - w2 d_prime = d * w1 + d_2 * w2 x = x + d_prime * dt else: # Heun++ x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) dt_2 = sigmas[i + 2] - sigmas[i + 1] x_3 = x_2 + d_2 * dt_2 denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) d_3 = to_d(x_3, sigmas[i + 2], denoised_3) w = 3 * sigmas[0] w2 = sigmas[i + 1] / w w3 = sigmas[i + 2] / w w1 = 1 - w2 - w3 d_prime = w1 * d + w2 * d_2 + w3 * d_3 x = x + d_prime * dt return x #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py #under Apache 2 license def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) x_next = x buffer_model = [] for i in trange(len(sigmas) - 1, disable=disable): t_cur = sigmas[i] t_next = sigmas[i + 1] x_cur = x_next denoised = model(x_cur, t_cur * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d_cur = (x_cur - denoised) / t_cur order = min(max_order, i+1) if t_next == 0: # Denoising step x_next = denoised elif order == 1: # First Euler step. x_next = x_cur + (t_next - t_cur) * d_cur elif order == 2: # Use one history point. x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 elif order == 3: # Use two history points. x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12 elif order == 4: # Use three history points. x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24 if len(buffer_model) == max_order - 1: for k in range(max_order - 2): buffer_model[k] = buffer_model[k+1] buffer_model[-1] = d_cur else: buffer_model.append(d_cur) return x_next #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py #under Apache 2 license def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) x_next = x t_steps = sigmas buffer_model = [] for i in trange(len(sigmas) - 1, disable=disable): t_cur = sigmas[i] t_next = sigmas[i + 1] x_cur = x_next denoised = model(x_cur, t_cur * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d_cur = (x_cur - denoised) / t_cur order = min(max_order, i+1) if t_next == 0: # Denoising step x_next = denoised elif order == 1: # First Euler step. x_next = x_cur + (t_next - t_cur) * d_cur elif order == 2: # Use one history point. h_n = (t_next - t_cur) h_n_1 = (t_cur - t_steps[i-1]) coeff1 = (2 + (h_n / h_n_1)) / 2 coeff2 = -(h_n / h_n_1) / 2 x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1]) elif order == 3: # Use two history points. h_n = (t_next - t_cur) h_n_1 = (t_cur - t_steps[i-1]) h_n_2 = (t_steps[i-1] - t_steps[i-2]) temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 coeff1 = (2 + (h_n / h_n_1)) / 2 + temp coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp coeff3 = temp * h_n_1 / h_n_2 x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2]) elif order == 4: # Use three history points. h_n = (t_next - t_cur) h_n_1 = (t_cur - t_steps[i-1]) h_n_2 = (t_steps[i-1] - t_steps[i-2]) h_n_3 = (t_steps[i-2] - t_steps[i-3]) temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \ * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3)) coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2 coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2 coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2 coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2 x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3]) if len(buffer_model) == max_order - 1: for k in range(max_order - 2): buffer_model[k] = buffer_model[k+1] buffer_model[-1] = d_cur.detach() else: buffer_model.append(d_cur.detach()) return x_next #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py #under Apache 2 license @torch.no_grad() def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) x_next = x t_steps = sigmas coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode) buffer_model = [] for i in trange(len(sigmas) - 1, disable=disable): t_cur = sigmas[i] t_next = sigmas[i + 1] x_cur = x_next denoised = model(x_cur, t_cur * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d_cur = (x_cur - denoised) / t_cur order = min(max_order, i+1) if t_next <= 0: order = 1 if order == 1: # First Euler step. x_next = x_cur + (t_next - t_cur) * d_cur elif order == 2: # Use one history point. coeff_cur, coeff_prev1 = coeff_list[i] x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] elif order == 3: # Use two history points. coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i] x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] elif order == 4: # Use three history points. coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i] x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3] if len(buffer_model) == max_order - 1: for k in range(max_order - 2): buffer_model[k] = buffer_model[k+1] buffer_model[-1] = d_cur.detach() else: buffer_model.append(d_cur.detach()) return x_next @torch.no_grad() def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with Euler method steps (CFG++).""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) uncond_denoised = None def post_cfg_function(args): nonlocal uncond_denoised uncond_denoised = args["uncond_denoised"] return args["denoised"] model_options = extra_args.get("model_options", {}).copy() extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Denoising step x = denoised else: alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp() alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp() d = to_d(x, sigmas[i], alpha_s * uncond_denoised) # to noise # DDIM stochastic sampling sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta) sigma_down = alpha_t * sigma_down # Euler method x = alpha_t * denoised + sigma_down * d if eta > 0 and s_noise > 0: x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): """Euler method steps (CFG++).""" return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None) @torch.no_grad() def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler temp = [0] def post_cfg_function(args): temp[0] = args["uncond_denoised"] return args["denoised"] model_options = extra_args.get("model_options", {}).copy() extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigma_down == 0: # Euler method d = to_d(x, sigmas[i], temp[0]) x = denoised + d * sigma_down else: # DPM-Solver++(2S) t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird r = 1 / 2 h = t_next - t s = t + r * h x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2 # Noise addition if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): """DPM-Solver++(2M).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) t_fn = lambda sigma: sigma.log().neg() old_uncond_denoised = None uncond_denoised = None def post_cfg_function(args): nonlocal uncond_denoised uncond_denoised = args["uncond_denoised"] return args["denoised"] model_options = extra_args.get("model_options", {}).copy() extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) h = t_next - t if old_uncond_denoised is None or sigmas[i + 1] == 0: denoised_mix = -torch.exp(-h) * uncond_denoised else: h_last = t - t_fn(sigmas[i - 1]) r = h_last / h denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised) x = denoised + denoised_mix + torch.exp(-h) * x old_uncond_denoised = uncond_denoised return x @torch.no_grad() def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False): extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() phi1_fn = lambda t: torch.expm1(t) / t phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t old_sigma_down = None old_denoised = None uncond_denoised = None def post_cfg_function(args): nonlocal uncond_denoised uncond_denoised = args["uncond_denoised"] return args["denoised"] if cfg_pp: model_options = extra_args.get("model_options", {}).copy() extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) if sigma_down == 0 or old_denoised is None: # Euler method if cfg_pp: d = to_d(x, sigmas[i], uncond_denoised) x = denoised + d * sigma_down else: d = to_d(x, sigmas[i], denoised) dt = sigma_down - sigmas[i] x = x + d * dt else: # Second order multistep method in https://arxiv.org/pdf/2308.02157 t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1]) h = t_next - t c2 = (t_prev - t_old) / h phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h) b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0) b2 = torch.nan_to_num(phi2_val / c2, nan=0.0) if cfg_pp: x = x + (denoised - uncond_denoised) x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised) else: x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised) # Noise addition if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up if cfg_pp: old_denoised = uncond_denoised else: old_denoised = denoised old_sigma_down = sigma_down return x @torch.no_grad() def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False) @torch.no_grad() def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True) @torch.no_grad() def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False) @torch.no_grad() def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True) @torch.no_grad() def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False): """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) old_d = None uncond_denoised = None def post_cfg_function(args): nonlocal uncond_denoised uncond_denoised = args["uncond_denoised"] return args["denoised"] if cfg_pp: model_options = extra_args.get("model_options", {}).copy() extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if cfg_pp: d = to_d(x, sigmas[i], uncond_denoised) else: d = to_d(x, sigmas[i], denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) dt = sigmas[i + 1] - sigmas[i] if sigmas[i + 1] == 0: # Denoising step x = denoised else: # Euler method if cfg_pp: x = denoised + d * sigmas[i + 1] else: x = x + d * dt if i >= 1: # Gradient estimation d_bar = (ge_gamma - 1) * (d - old_d) x = x + d_bar * dt old_d = d return x @torch.no_grad() def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.): return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True) @torch.no_grad() def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3): """Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169. Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py. """ extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) def default_er_sde_noise_scaler(x): return x * ((x ** 0.3).exp() + 10.0) noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler num_integration_points = 200.0 point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device) model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling) er_lambdas = half_log_snrs.neg().exp() # er_lambda_t = sigma_t / alpha_t old_denoised = None old_denoised_d = None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) stage_used = min(max_stage, i + 1) if sigmas[i + 1] == 0: x = denoised else: er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1] alpha_s = sigmas[i] / er_lambda_s alpha_t = sigmas[i + 1] / er_lambda_t r_alpha = alpha_t / alpha_s r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s) # Stage 1 Euler x = r_alpha * r * x + alpha_t * (1 - r) * denoised if stage_used >= 2: dt = er_lambda_t - er_lambda_s lambda_step_size = -dt / num_integration_points lambda_pos = er_lambda_t + point_indice * lambda_step_size scaled_pos = noise_scaler(lambda_pos) # Stage 2 s = torch.sum(1 / scaled_pos) * lambda_step_size denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1]) x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d if stage_used >= 3: # Stage 3 s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2) x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u old_denoised_d = denoised_d if s_noise > 0: x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0) old_denoised = denoised return x @torch.no_grad() def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5, solver_type="phi_1"): """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2. arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023) """ if solver_type not in {"phi_1", "phi_2"}: raise ValueError("solver_type must be 'phi_1' or 'phi_2'") extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) inject_noise = eta > 0 and s_noise > 0 model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) fac = 1 / (2 * r) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: x = denoised continue lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) h = lambda_t - lambda_s h_eta = h * (eta + 1) lambda_s_1 = torch.lerp(lambda_s, lambda_t, r) sigma_s_1 = sigma_fn(lambda_s_1) alpha_s_1 = sigma_s_1 * lambda_s_1.exp() alpha_t = sigmas[i + 1] * lambda_t.exp() # Step 1 x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r * h_eta) * denoised if inject_noise: sde_noise = (-2 * r * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1) x_2 = x_2 + sde_noise * sigma_s_1 * s_noise denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) # Step 2 if solver_type == "phi_1": denoised_d = torch.lerp(denoised, denoised_2, fac) x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d elif solver_type == "phi_2": b2 = ei_h_phi_2(-h_eta) / r b1 = ei_h_phi_1(-h_eta) - b2 x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b2 * denoised_2) if inject_noise: segment_factor = (r - 1) * h * eta sde_noise = sde_noise * segment_factor.exp() sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigmas[i + 1]) x = x + sde_noise * sigmas[i + 1] * s_noise return x @torch.no_grad() def sample_exp_heun_2_x0(model, x, sigmas, extra_args=None, callback=None, disable=None, solver_type="phi_2"): """Deterministic exponential Heun second order method in data prediction (x0) and logSNR time.""" return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None, r=1.0, solver_type=solver_type) @torch.no_grad() def sample_exp_heun_2_x0_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type="phi_2"): """Stochastic exponential Heun second order method in data prediction (x0) and logSNR time.""" return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=1.0, solver_type=solver_type) @torch.no_grad() def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3): """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3. arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023) """ extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) inject_noise = eta > 0 and s_noise > 0 model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: x = denoised continue lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) h = lambda_t - lambda_s h_eta = h * (eta + 1) lambda_s_1 = torch.lerp(lambda_s, lambda_t, r_1) lambda_s_2 = torch.lerp(lambda_s, lambda_t, r_2) sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2) alpha_s_1 = sigma_s_1 * lambda_s_1.exp() alpha_s_2 = sigma_s_2 * lambda_s_2.exp() alpha_t = sigmas[i + 1] * lambda_t.exp() # Step 1 x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r_1 * h_eta) * denoised if inject_noise: sde_noise = (-2 * r_1 * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1) x_2 = x_2 + sde_noise * sigma_s_1 * s_noise denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) # Step 2 a3_2 = r_2 / r_1 * ei_h_phi_2(-r_2 * h_eta) a3_1 = ei_h_phi_1(-r_2 * h_eta) - a3_2 x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * (a3_1 * denoised + a3_2 * denoised_2) if inject_noise: segment_factor = (r_1 - r_2) * h * eta sde_noise = sde_noise * segment_factor.exp() sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigma_s_2) x_3 = x_3 + sde_noise * sigma_s_2 * s_noise denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args) # Step 3 b3 = ei_h_phi_2(-h_eta) / r_2 b1 = ei_h_phi_1(-h_eta) - b3 x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b3 * denoised_3) if inject_noise: segment_factor = (r_2 - 1) * h * eta sde_noise = sde_noise * segment_factor.exp() sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_2, sigmas[i + 1]) x = x + sde_noise * sigmas[i + 1] * s_noise return x @torch.no_grad() def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False): """Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023).""" if len(sigmas) <= 1: return x extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling) if tau_func is None: # Use default interval for stochastic sampling start_sigma = model_sampling.percent_to_sigma(0.2) end_sigma = model_sampling.percent_to_sigma(0.8) tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0) max_used_order = max(predictor_order, corrector_order) x_pred = x # x: current state, x_pred: predicted next state h = 0.0 tau_t = 0.0 noise = 0.0 pred_list = [] # Lower order near the end to improve stability lower_order_to_end = sigmas[-1].item() == 0 for i in trange(len(sigmas) - 1, disable=disable): # Evaluation denoised = model(x_pred, sigmas[i] * s_in, **extra_args) if callback is not None: callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) pred_list.append(denoised) pred_list = pred_list[-max_used_order:] predictor_order_used = min(predictor_order, len(pred_list)) if i == 0 or (sigmas[i + 1] == 0 and not use_pece): corrector_order_used = 0 else: corrector_order_used = min(corrector_order, len(pred_list)) if lower_order_to_end: predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i) corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i) # Corrector if corrector_order_used == 0: # Update by the predicted state x = x_pred else: curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1] b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( sigmas[i], curr_lambdas, lambdas[i - 1], lambdas[i], tau_t, simple_order_2, is_corrector_step=True, ) pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1) # (B, K, ...) corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res if tau_t > 0 and s_noise > 0: # The noise from the previous predictor step x = x + noise if use_pece: # Evaluate the corrected state denoised = model(x, sigmas[i] * s_in, **extra_args) pred_list[-1] = denoised # Predictor if sigmas[i + 1] == 0: # Denoising step x_pred = denoised else: tau_t = tau_func(sigmas[i + 1]) curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1] b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( sigmas[i + 1], curr_lambdas, lambdas[i], lambdas[i + 1], tau_t, simple_order_2, is_corrector_step=False, ) pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1) # (B, K, ...) pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) h = lambdas[i + 1] - lambdas[i] x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res if tau_t > 0 and s_noise > 0: noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise x_pred = x_pred + noise return x_pred @torch.no_grad() def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False): """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023).""" return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2) ================================================ FILE: comfy/k_diffusion/utils.py ================================================ from contextlib import contextmanager import hashlib import math from pathlib import Path import shutil import urllib import warnings from PIL import Image import torch from torch import nn, optim from torch.utils import data def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): """Apply passed in transforms for HuggingFace Datasets.""" images = [transform(image.convert(mode)) for image in examples[image_key]] return {image_key: images} def append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') expanded = x[(...,) + (None,) * dims_to_append] # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. # https://github.com/pytorch/pytorch/issues/84364 return expanded.detach().clone() if expanded.device.type == 'mps' else expanded def n_params(module): """Returns the number of trainable parameters in a module.""" return sum(p.numel() for p in module.parameters()) def download_file(path, url, digest=None): """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" path = Path(path) path.parent.mkdir(parents=True, exist_ok=True) if not path.exists(): with urllib.request.urlopen(url) as response, open(path, 'wb') as f: shutil.copyfileobj(response, f) if digest is not None: file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() if digest != file_digest: raise OSError(f'hash of {path} (url: {url}) failed to validate') return path @contextmanager def train_mode(model, mode=True): """A context manager that places a model into training mode and restores the previous mode on exit.""" modes = [module.training for module in model.modules()] try: yield model.train(mode) finally: for i, module in enumerate(model.modules()): module.training = modes[i] def eval_mode(model): """A context manager that places a model into evaluation mode and restores the previous mode on exit.""" return train_mode(model, False) @torch.no_grad() def ema_update(model, averaged_model, decay): """Incorporates updated model parameters into an exponential moving averaged version of a model. It should be called after each optimizer step.""" model_params = dict(model.named_parameters()) averaged_params = dict(averaged_model.named_parameters()) assert model_params.keys() == averaged_params.keys() for name, param in model_params.items(): averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) model_buffers = dict(model.named_buffers()) averaged_buffers = dict(averaged_model.named_buffers()) assert model_buffers.keys() == averaged_buffers.keys() for name, buf in model_buffers.items(): averaged_buffers[name].copy_(buf) class EMAWarmup: """Implements an EMA warmup using an inverse decay schedule. If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are good values for models you plan to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at 215.4k steps). Args: inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. power (float): Exponential factor of EMA warmup. Default: 1. min_value (float): The minimum EMA decay rate. Default: 0. max_value (float): The maximum EMA decay rate. Default: 1. start_at (int): The epoch to start averaging at. Default: 0. last_epoch (int): The index of last epoch. Default: 0. """ def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, last_epoch=0): self.inv_gamma = inv_gamma self.power = power self.min_value = min_value self.max_value = max_value self.start_at = start_at self.last_epoch = last_epoch def state_dict(self): """Returns the state of the class as a :class:`dict`.""" return dict(self.__dict__.items()) def load_state_dict(self, state_dict): """Loads the class's state. Args: state_dict (dict): scaler state. Should be an object returned from a call to :meth:`state_dict`. """ self.__dict__.update(state_dict) def get_value(self): """Gets the current EMA decay rate.""" epoch = max(0, self.last_epoch - self.start_at) value = 1 - (1 + epoch / self.inv_gamma) ** -self.power return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) def step(self): """Updates the step count.""" self.last_epoch += 1 class InverseLR(optim.lr_scheduler._LRScheduler): """Implements an inverse decay learning rate schedule with an optional exponential warmup. When last_epoch=-1, sets initial lr as lr. inv_gamma is the number of steps/epochs required for the learning rate to decay to (1 / 2)**power of its original value. Args: optimizer (Optimizer): Wrapped optimizer. inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. power (float): Exponential factor of learning rate decay. Default: 1. warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) Default: 0. min_lr (float): The minimum learning rate. Default: 0. last_epoch (int): The index of last epoch. Default: -1. verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False``. """ def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., last_epoch=-1, verbose=False): self.inv_gamma = inv_gamma self.power = power if not 0. <= warmup < 1: raise ValueError('Invalid value for warmup') self.warmup = warmup self.min_lr = min_lr super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.") return self._get_closed_form_lr() def _get_closed_form_lr(self): warmup = 1 - self.warmup ** (self.last_epoch + 1) lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power return [warmup * max(self.min_lr, base_lr * lr_mult) for base_lr in self.base_lrs] class ExponentialLR(optim.lr_scheduler._LRScheduler): """Implements an exponential learning rate schedule with an optional exponential warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate continuously by decay (default 0.5) every num_steps steps. Args: optimizer (Optimizer): Wrapped optimizer. num_steps (float): The number of steps to decay the learning rate by decay in. decay (float): The factor by which to decay the learning rate every num_steps steps. Default: 0.5. warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) Default: 0. min_lr (float): The minimum learning rate. Default: 0. last_epoch (int): The index of last epoch. Default: -1. verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False``. """ def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., last_epoch=-1, verbose=False): self.num_steps = num_steps self.decay = decay if not 0. <= warmup < 1: raise ValueError('Invalid value for warmup') self.warmup = warmup self.min_lr = min_lr super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.") return self._get_closed_form_lr() def _get_closed_form_lr(self): warmup = 1 - self.warmup ** (self.last_epoch + 1) lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch return [warmup * max(self.min_lr, base_lr * lr_mult) for base_lr in self.base_lrs] def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): """Draws samples from an lognormal distribution.""" return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): """Draws samples from an optionally truncated log-logistic distribution.""" min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) min_cdf = min_value.log().sub(loc).div(scale).sigmoid() max_cdf = max_value.log().sub(loc).div(scale).sigmoid() u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf return u.logit().mul(scale).add(loc).exp().to(dtype) def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): """Draws samples from an log-uniform distribution.""" min_value = math.log(min_value) max_value = math.log(max_value) return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): """Draws samples from a truncated v-diffusion training timestep distribution.""" min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf return torch.tan(u * math.pi / 2) * sigma_data def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): """Draws samples from a split lognormal distribution.""" n = torch.randn(shape, device=device, dtype=dtype).abs() u = torch.rand(shape, device=device, dtype=dtype) n_left = n * -scale_1 + loc n_right = n * scale_2 + loc ratio = scale_1 / (scale_1 + scale_2) return torch.where(u < ratio, n_left, n_right).exp() class FolderOfImages(data.Dataset): """Recursively finds all images in a directory. It does not support classes/targets.""" IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} def __init__(self, root, transform=None): super().__init__() self.root = Path(root) self.transform = nn.Identity() if transform is None else transform self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) def __repr__(self): return f'FolderOfImages(root="{self.root}", len: {len(self)})' def __len__(self): return len(self.paths) def __getitem__(self, key): path = self.paths[key] with open(path, 'rb') as f: image = Image.open(f).convert('RGB') image = self.transform(image) return image, class CSVLogger: def __init__(self, filename, columns): self.filename = Path(filename) self.columns = columns if self.filename.exists(): self.file = open(self.filename, 'a') else: self.file = open(self.filename, 'w') self.write(*self.columns) def write(self, *args): print(*args, sep=',', file=self.file, flush=True) @contextmanager def tf32_mode(cudnn=None, matmul=None): """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" cudnn_old = torch.backends.cudnn.allow_tf32 matmul_old = torch.backends.cuda.matmul.allow_tf32 try: if cudnn is not None: torch.backends.cudnn.allow_tf32 = cudnn if matmul is not None: torch.backends.cuda.matmul.allow_tf32 = matmul yield finally: if cudnn is not None: torch.backends.cudnn.allow_tf32 = cudnn_old if matmul is not None: torch.backends.cuda.matmul.allow_tf32 = matmul_old ================================================ FILE: comfy/latent_formats.py ================================================ import torch class LatentFormat: scale_factor = 1.0 latent_channels = 4 latent_dimensions = 2 latent_rgb_factors = None latent_rgb_factors_bias = None latent_rgb_factors_reshape = None taesd_decoder_name = None spacial_downscale_ratio = 8 def process_in(self, latent): return latent * self.scale_factor def process_out(self, latent): return latent / self.scale_factor class SD15(LatentFormat): def __init__(self, scale_factor=0.18215): self.scale_factor = scale_factor self.latent_rgb_factors = [ # R G B [ 0.3512, 0.2297, 0.3227], [ 0.3250, 0.4974, 0.2350], [-0.2829, 0.1762, 0.2721], [-0.2120, -0.2616, -0.7177] ] self.taesd_decoder_name = "taesd_decoder" class SDXL(LatentFormat): scale_factor = 0.13025 def __init__(self): self.latent_rgb_factors = [ # R G B [ 0.3651, 0.4232, 0.4341], [-0.2533, -0.0042, 0.1068], [ 0.1076, 0.1111, -0.0362], [-0.3165, -0.2492, -0.2188] ] self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011] self.taesd_decoder_name = "taesdxl_decoder" class SDXL_Playground_2_5(LatentFormat): def __init__(self): self.scale_factor = 0.5 self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) self.latent_rgb_factors = [ # R G B [ 0.3920, 0.4054, 0.4549], [-0.2634, -0.0196, 0.0653], [ 0.0568, 0.1687, -0.0755], [-0.3112, -0.2359, -0.2076] ] self.taesd_decoder_name = "taesdxl_decoder" def process_in(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return (latent - latents_mean) * self.scale_factor / latents_std def process_out(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return latent * latents_std / self.scale_factor + latents_mean class SD_X4(LatentFormat): def __init__(self): self.scale_factor = 0.08333 self.latent_rgb_factors = [ [-0.2340, -0.3863, -0.3257], [ 0.0994, 0.0885, -0.0908], [-0.2833, -0.2349, -0.3741], [ 0.2523, -0.0055, -0.1651] ] class SC_Prior(LatentFormat): latent_channels = 16 spacial_downscale_ratio = 42 def __init__(self): self.scale_factor = 1.0 self.latent_rgb_factors = [ [-0.0326, -0.0204, -0.0127], [-0.1592, -0.0427, 0.0216], [ 0.0873, 0.0638, -0.0020], [-0.0602, 0.0442, 0.1304], [ 0.0800, -0.0313, -0.1796], [-0.0810, -0.0638, -0.1581], [ 0.1791, 0.1180, 0.0967], [ 0.0740, 0.1416, 0.0432], [-0.1745, -0.1888, -0.1373], [ 0.2412, 0.1577, 0.0928], [ 0.1908, 0.0998, 0.0682], [ 0.0209, 0.0365, -0.0092], [ 0.0448, -0.0650, -0.1728], [-0.1658, -0.1045, -0.1308], [ 0.0542, 0.1545, 0.1325], [-0.0352, -0.1672, -0.2541] ] class SC_B(LatentFormat): spacial_downscale_ratio = 4 def __init__(self): self.scale_factor = 1.0 / 0.43 self.latent_rgb_factors = [ [ 0.1121, 0.2006, 0.1023], [-0.2093, -0.0222, -0.0195], [-0.3087, -0.1535, 0.0366], [ 0.0290, -0.1574, -0.4078] ] class SD3(LatentFormat): latent_channels = 16 def __init__(self): self.scale_factor = 1.5305 self.shift_factor = 0.0609 self.latent_rgb_factors = [ [-0.0922, -0.0175, 0.0749], [ 0.0311, 0.0633, 0.0954], [ 0.1994, 0.0927, 0.0458], [ 0.0856, 0.0339, 0.0902], [ 0.0587, 0.0272, -0.0496], [-0.0006, 0.1104, 0.0309], [ 0.0978, 0.0306, 0.0427], [-0.0042, 0.1038, 0.1358], [-0.0194, 0.0020, 0.0669], [-0.0488, 0.0130, -0.0268], [ 0.0922, 0.0988, 0.0951], [-0.0278, 0.0524, -0.0542], [ 0.0332, 0.0456, 0.0895], [-0.0069, -0.0030, -0.0810], [-0.0596, -0.0465, -0.0293], [-0.1448, -0.1463, -0.1189] ] self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925] self.taesd_decoder_name = "taesd3_decoder" def process_in(self, latent): return (latent - self.shift_factor) * self.scale_factor def process_out(self, latent): return (latent / self.scale_factor) + self.shift_factor class StableAudio1(LatentFormat): latent_channels = 64 latent_dimensions = 1 class Flux(SD3): latent_channels = 16 def __init__(self): self.scale_factor = 0.3611 self.shift_factor = 0.1159 self.latent_rgb_factors =[ [-0.0346, 0.0244, 0.0681], [ 0.0034, 0.0210, 0.0687], [ 0.0275, -0.0668, -0.0433], [-0.0174, 0.0160, 0.0617], [ 0.0859, 0.0721, 0.0329], [ 0.0004, 0.0383, 0.0115], [ 0.0405, 0.0861, 0.0915], [-0.0236, -0.0185, -0.0259], [-0.0245, 0.0250, 0.1180], [ 0.1008, 0.0755, -0.0421], [-0.0515, 0.0201, 0.0011], [ 0.0428, -0.0012, -0.0036], [ 0.0817, 0.0765, 0.0749], [-0.1264, -0.0522, -0.1103], [-0.0280, -0.0881, -0.0499], [-0.1262, -0.0982, -0.0778] ] self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] self.taesd_decoder_name = "taef1_decoder" def process_in(self, latent): return (latent - self.shift_factor) * self.scale_factor def process_out(self, latent): return (latent / self.scale_factor) + self.shift_factor class Flux2(LatentFormat): latent_channels = 128 spacial_downscale_ratio = 16 def __init__(self): self.latent_rgb_factors =[ [0.0058, 0.0113, 0.0073], [0.0495, 0.0443, 0.0836], [-0.0099, 0.0096, 0.0644], [0.2144, 0.3009, 0.3652], [0.0166, -0.0039, -0.0054], [0.0157, 0.0103, -0.0160], [-0.0398, 0.0902, -0.0235], [-0.0052, 0.0095, 0.0109], [-0.3527, -0.2712, -0.1666], [-0.0301, -0.0356, -0.0180], [-0.0107, 0.0078, 0.0013], [0.0746, 0.0090, -0.0941], [0.0156, 0.0169, 0.0070], [-0.0034, -0.0040, -0.0114], [0.0032, 0.0181, 0.0080], [-0.0939, -0.0008, 0.0186], [0.0018, 0.0043, 0.0104], [0.0284, 0.0056, -0.0127], [-0.0024, -0.0022, -0.0030], [0.1207, -0.0026, 0.0065], [0.0128, 0.0101, 0.0142], [0.0137, -0.0072, -0.0007], [0.0095, 0.0092, -0.0059], [0.0000, -0.0077, -0.0049], [-0.0465, -0.0204, -0.0312], [0.0095, 0.0012, -0.0066], [0.0290, -0.0034, 0.0025], [0.0220, 0.0169, -0.0048], [-0.0332, -0.0457, -0.0468], [-0.0085, 0.0389, 0.0609], [-0.0076, 0.0003, -0.0043], [-0.0111, -0.0460, -0.0614], ] self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2) def process_in(self, latent): return latent def process_out(self, latent): return latent class Mochi(LatentFormat): latent_channels = 12 latent_dimensions = 3 def __init__(self): self.scale_factor = 1.0 self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141, -0.05565264470995561, 0.012767231469026969, -0.04703542746246419, 0.043896967884726704, -0.09346305707025976, -0.09918314763016893, -0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1) self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617, 0.959253732819592, 0.8244560132752793, 0.917259975397747, 0.9294154431013696, 1.3720942357788521, 0.881393668867029, 0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1) self.latent_rgb_factors =[ [-0.0069, -0.0045, 0.0018], [ 0.0154, -0.0692, -0.0274], [ 0.0333, 0.0019, 0.0206], [-0.1390, 0.0628, 0.1678], [-0.0725, 0.0134, -0.1898], [ 0.0074, -0.0270, -0.0209], [-0.0176, -0.0277, -0.0221], [ 0.5294, 0.5204, 0.3852], [-0.0326, -0.0446, -0.0143], [-0.0659, 0.0153, -0.0153], [ 0.0185, -0.0217, 0.0014], [-0.0396, -0.0495, -0.0281] ] self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453] self.taesd_decoder_name = None #TODO def process_in(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return (latent - latents_mean) * self.scale_factor / latents_std def process_out(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return latent * latents_std / self.scale_factor + latents_mean class LTXV(LatentFormat): latent_channels = 128 latent_dimensions = 3 spacial_downscale_ratio = 32 def __init__(self): self.latent_rgb_factors = [ [ 1.1202e-02, -6.3815e-04, -1.0021e-02], [ 8.6031e-02, 6.5813e-02, 9.5409e-04], [-1.2576e-02, -7.5734e-03, -4.0528e-03], [ 9.4063e-03, -2.1688e-03, 2.6093e-03], [ 3.7636e-03, 1.2765e-02, 9.1548e-03], [ 2.1024e-02, -5.2973e-03, 3.4373e-03], [-8.8896e-03, -1.9703e-02, -1.8761e-02], [-1.3160e-02, -1.0523e-02, 1.9709e-03], [-1.5152e-03, -6.9891e-03, -7.5810e-03], [-1.7247e-03, 4.6560e-04, -3.3839e-03], [ 1.3617e-02, 4.7077e-03, -2.0045e-03], [ 1.0256e-02, 7.7318e-03, 1.3948e-02], [-1.6108e-02, -6.2151e-03, 1.1561e-03], [ 7.3407e-03, 1.5628e-02, 4.4865e-04], [ 9.5357e-04, -2.9518e-03, -1.4760e-02], [ 1.9143e-02, 1.0868e-02, 1.2264e-02], [ 4.4575e-03, 3.6682e-05, -6.8508e-03], [-4.5681e-04, 3.2570e-03, 7.7929e-03], [ 3.3902e-02, 3.3405e-02, 3.7454e-02], [-2.3001e-02, -2.4877e-03, -3.1033e-03], [ 5.0265e-02, 3.8841e-02, 3.3539e-02], [-4.1018e-03, -1.1095e-03, 1.5859e-03], [-1.2689e-01, -1.3107e-01, -2.1005e-01], [ 2.6276e-02, 1.4189e-02, -3.5963e-03], [-4.8679e-03, 8.8486e-03, 7.8029e-03], [-1.6610e-03, -4.8597e-03, -5.2060e-03], [-2.1010e-03, 2.3610e-03, 9.3796e-03], [-2.2482e-02, -2.1305e-02, -1.5087e-02], [-1.5753e-02, -1.0646e-02, -6.5083e-03], [-4.6975e-03, 5.0288e-03, -6.7390e-03], [ 1.1951e-02, 2.0712e-02, 1.6191e-02], [-6.3704e-03, -8.4827e-03, -9.5483e-03], [ 7.2610e-03, -9.9326e-03, -2.2978e-02], [-9.1904e-04, 6.2882e-03, 9.5720e-03], [-3.7178e-02, -3.7123e-02, -5.6713e-02], [-1.3373e-01, -1.0720e-01, -5.3801e-02], [-5.3702e-03, 8.1256e-03, 8.8397e-03], [-1.5247e-01, -2.1437e-01, -2.1843e-01], [ 3.1441e-02, 7.0335e-03, -9.7541e-03], [ 2.1528e-03, -8.9817e-03, -2.1023e-02], [ 3.8461e-03, -5.8957e-03, -1.5014e-02], [-4.3470e-03, -1.2940e-02, -1.5972e-02], [-5.4781e-03, -1.0842e-02, -3.0204e-03], [-6.5347e-03, 3.0806e-03, -1.0163e-02], [-5.0414e-03, -7.1503e-03, -8.9686e-04], [-8.5851e-03, -2.4351e-03, 1.0674e-03], [-9.0016e-03, -9.6493e-03, 1.5692e-03], [ 5.0914e-03, 1.2099e-02, 1.9968e-02], [ 1.3758e-02, 1.1669e-02, 8.1958e-03], [-1.0518e-02, -1.1575e-02, -4.1307e-03], [-2.8410e-02, -3.1266e-02, -2.2149e-02], [ 2.9336e-03, 3.6511e-02, 1.8717e-02], [-1.6703e-02, -1.6696e-02, -4.4529e-03], [ 4.8818e-02, 4.0063e-02, 8.7410e-03], [-1.5066e-02, -5.7328e-04, 2.9785e-03], [-1.7613e-02, -8.1034e-03, 1.3086e-02], [-9.2633e-03, 1.0803e-02, -6.3489e-03], [ 3.0851e-03, 4.7750e-04, 1.2347e-02], [-2.2785e-02, -2.3043e-02, -2.6005e-02], [-2.4787e-02, -1.5389e-02, -2.2104e-02], [-2.3572e-02, 1.0544e-03, 1.2361e-02], [-7.8915e-03, -1.2271e-03, -6.0968e-03], [-1.1478e-02, -1.2543e-03, 6.2679e-03], [-5.4229e-02, 2.6644e-02, 6.3394e-03], [ 4.4216e-03, -7.3338e-03, -1.0464e-02], [-4.5013e-03, 1.6082e-03, 1.4420e-02], [ 1.3673e-02, 8.8877e-03, 4.1253e-03], [-1.0145e-02, 9.0072e-03, 1.5695e-02], [-5.6234e-03, 1.1847e-03, 8.1261e-03], [-3.7171e-03, -5.3538e-03, 1.2590e-03], [ 2.9476e-02, 2.1424e-02, 3.0424e-02], [-3.4925e-02, -2.4340e-02, -2.5316e-02], [-3.4127e-02, -2.2406e-02, -1.0589e-02], [-1.7342e-02, -1.3249e-02, -1.0719e-02], [-2.1478e-03, -8.6051e-03, -2.9878e-03], [ 1.2089e-03, -4.2391e-03, -6.8569e-03], [ 9.0411e-04, -6.6886e-03, -6.7547e-05], [ 1.6048e-02, -1.0057e-02, -2.8929e-02], [ 1.2290e-03, 1.0163e-02, 1.8861e-02], [ 1.7264e-02, 2.7257e-04, 1.3785e-02], [-1.3482e-02, -3.6427e-03, 6.7481e-04], [ 4.6782e-03, -5.2423e-03, 2.4467e-03], [-5.9113e-03, -6.2244e-03, -1.8162e-03], [ 1.5496e-02, 1.4582e-02, 1.9514e-03], [ 7.4958e-03, 1.5886e-03, -8.2305e-03], [ 1.9086e-02, 1.6360e-03, -3.9674e-03], [-5.7021e-03, -2.7307e-03, -4.1066e-03], [ 1.7450e-03, 1.4602e-02, 2.5794e-02], [-8.2788e-04, 2.2902e-03, 4.5161e-03], [ 1.1632e-02, 8.9193e-03, -7.2813e-03], [ 7.5721e-03, 2.6784e-03, 1.1393e-02], [ 5.1939e-03, 3.6903e-03, 1.4049e-02], [-1.8383e-02, -2.2529e-02, -2.4477e-02], [ 5.8842e-04, -5.7874e-03, -1.4770e-02], [-1.6125e-02, -8.6101e-03, -1.4533e-02], [ 2.0540e-02, 2.0729e-02, 6.4338e-03], [ 3.3587e-03, -1.1226e-02, -1.6444e-02], [-1.4742e-03, -1.0489e-02, 1.7097e-03], [ 2.8130e-02, 2.3546e-02, 3.2791e-02], [-1.8532e-02, -1.2842e-02, -8.7756e-03], [-8.0533e-03, -1.0771e-02, -1.7536e-02], [-3.9009e-03, 1.6150e-02, 3.3359e-02], [-7.4554e-03, -1.4154e-02, -6.1910e-03], [ 3.4734e-03, -1.1370e-02, -1.0581e-02], [ 1.1476e-02, 3.9281e-03, 2.8231e-03], [ 7.1639e-03, -1.4741e-03, -3.8066e-03], [ 2.2250e-03, -8.7552e-03, -9.5719e-03], [ 2.4146e-02, 2.1696e-02, 2.8056e-02], [-5.4365e-03, -2.4291e-02, -1.7802e-02], [ 7.4263e-03, 1.0510e-02, 1.2705e-02], [ 6.2669e-03, 6.2658e-03, 1.9211e-02], [ 1.6378e-02, 9.4933e-03, 6.6971e-03], [ 1.7173e-02, 2.3601e-02, 2.3296e-02], [-1.4568e-02, -9.8279e-03, -1.1556e-02], [ 1.4431e-02, 1.4430e-02, 6.6362e-03], [-6.8230e-03, 1.8863e-02, 1.4555e-02], [ 6.1156e-03, 3.4700e-03, -2.6662e-03], [-2.6983e-03, -5.9402e-03, -9.2276e-03], [ 1.0235e-02, 7.4173e-03, -7.6243e-03], [-1.3255e-02, 1.9322e-02, -9.2153e-04], [ 2.4222e-03, -4.8039e-03, -1.5759e-02], [ 2.6244e-02, 2.5951e-02, 2.0249e-02], [ 1.5711e-02, 1.8498e-02, 2.7407e-03], [-2.1714e-03, 4.7214e-03, -2.2443e-02], [-7.4747e-03, 7.4166e-03, 1.4430e-02], [-8.3906e-03, -7.9776e-03, 9.7927e-03], [ 3.8321e-02, 9.6622e-03, -1.9268e-02], [-1.4605e-02, -6.7032e-03, 3.9675e-03] ] self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512] class LTXAV(LTXV): def __init__(self): self.latent_rgb_factors = None self.latent_rgb_factors_bias = None class HunyuanVideo(LatentFormat): latent_channels = 16 latent_dimensions = 3 scale_factor = 0.476986 latent_rgb_factors = [ [-0.0395, -0.0331, 0.0445], [ 0.0696, 0.0795, 0.0518], [ 0.0135, -0.0945, -0.0282], [ 0.0108, -0.0250, -0.0765], [-0.0209, 0.0032, 0.0224], [-0.0804, -0.0254, -0.0639], [-0.0991, 0.0271, -0.0669], [-0.0646, -0.0422, -0.0400], [-0.0696, -0.0595, -0.0894], [-0.0799, -0.0208, -0.0375], [ 0.1166, 0.1627, 0.0962], [ 0.1165, 0.0432, 0.0407], [-0.2315, -0.1920, -0.1355], [-0.0270, 0.0401, -0.0821], [-0.0616, -0.0997, -0.0727], [ 0.0249, -0.0469, -0.1703] ] latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761] taesd_decoder_name = "taehv" class Cosmos1CV8x8x8(LatentFormat): latent_channels = 16 latent_dimensions = 3 latent_rgb_factors = [ [ 0.1817, 0.2284, 0.2423], [-0.0586, -0.0862, -0.3108], [-0.4703, -0.4255, -0.3995], [ 0.0803, 0.1963, 0.1001], [-0.0820, -0.1050, 0.0400], [ 0.2511, 0.3098, 0.2787], [-0.1830, -0.2117, -0.0040], [-0.0621, -0.2187, -0.0939], [ 0.3619, 0.1082, 0.1455], [ 0.3164, 0.3922, 0.2575], [ 0.1152, 0.0231, -0.0462], [-0.1434, -0.3609, -0.3665], [ 0.0635, 0.1471, 0.1680], [-0.3635, -0.1963, -0.3248], [-0.1865, 0.0365, 0.2346], [ 0.0447, 0.0994, 0.0881] ] latent_rgb_factors_bias = [-0.1223, -0.1889, -0.1976] class Wan21(LatentFormat): latent_channels = 16 latent_dimensions = 3 latent_rgb_factors = [ [-0.1299, -0.1692, 0.2932], [ 0.0671, 0.0406, 0.0442], [ 0.3568, 0.2548, 0.1747], [ 0.0372, 0.2344, 0.1420], [ 0.0313, 0.0189, -0.0328], [ 0.0296, -0.0956, -0.0665], [-0.3477, -0.4059, -0.2925], [ 0.0166, 0.1902, 0.1975], [-0.0412, 0.0267, -0.1364], [-0.1293, 0.0740, 0.1636], [ 0.0680, 0.3019, 0.1128], [ 0.0032, 0.0581, 0.0639], [-0.1251, 0.0927, 0.1699], [ 0.0060, -0.0633, 0.0005], [ 0.3477, 0.2275, 0.2950], [ 0.1984, 0.0913, 0.1861] ] latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360] def __init__(self): self.scale_factor = 1.0 self.latents_mean = torch.tensor([ -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 ]).view(1, self.latent_channels, 1, 1, 1) self.latents_std = torch.tensor([ 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 ]).view(1, self.latent_channels, 1, 1, 1) self.taesd_decoder_name = "lighttaew2_1" def process_in(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return (latent - latents_mean) * self.scale_factor / latents_std def process_out(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return latent * latents_std / self.scale_factor + latents_mean class Wan22(Wan21): latent_channels = 48 latent_dimensions = 3 spacial_downscale_ratio = 16 latent_rgb_factors = [ [ 0.0119, 0.0103, 0.0046], [-0.1062, -0.0504, 0.0165], [ 0.0140, 0.0409, 0.0491], [-0.0813, -0.0677, 0.0607], [ 0.0656, 0.0851, 0.0808], [ 0.0264, 0.0463, 0.0912], [ 0.0295, 0.0326, 0.0590], [-0.0244, -0.0270, 0.0025], [ 0.0443, -0.0102, 0.0288], [-0.0465, -0.0090, -0.0205], [ 0.0359, 0.0236, 0.0082], [-0.0776, 0.0854, 0.1048], [ 0.0564, 0.0264, 0.0561], [ 0.0006, 0.0594, 0.0418], [-0.0319, -0.0542, -0.0637], [-0.0268, 0.0024, 0.0260], [ 0.0539, 0.0265, 0.0358], [-0.0359, -0.0312, -0.0287], [-0.0285, -0.1032, -0.1237], [ 0.1041, 0.0537, 0.0622], [-0.0086, -0.0374, -0.0051], [ 0.0390, 0.0670, 0.2863], [ 0.0069, 0.0144, 0.0082], [ 0.0006, -0.0167, 0.0079], [ 0.0313, -0.0574, -0.0232], [-0.1454, -0.0902, -0.0481], [ 0.0714, 0.0827, 0.0447], [-0.0304, -0.0574, -0.0196], [ 0.0401, 0.0384, 0.0204], [-0.0758, -0.0297, -0.0014], [ 0.0568, 0.1307, 0.1372], [-0.0055, -0.0310, -0.0380], [ 0.0239, -0.0305, 0.0325], [-0.0663, -0.0673, -0.0140], [-0.0416, -0.0047, -0.0023], [ 0.0166, 0.0112, -0.0093], [-0.0211, 0.0011, 0.0331], [ 0.1833, 0.1466, 0.2250], [-0.0368, 0.0370, 0.0295], [-0.3441, -0.3543, -0.2008], [-0.0479, -0.0489, -0.0420], [-0.0660, -0.0153, 0.0800], [-0.0101, 0.0068, 0.0156], [-0.0690, -0.0452, -0.0927], [-0.0145, 0.0041, 0.0015], [ 0.0421, 0.0451, 0.0373], [ 0.0504, -0.0483, -0.0356], [-0.0837, 0.0168, 0.0055] ] latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388] def __init__(self): self.scale_factor = 1.0 self.taesd_decoder_name = "lighttaew2_2" self.latents_mean = torch.tensor([ -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825, -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230, -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748, 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667, ]).view(1, self.latent_channels, 1, 1, 1) self.latents_std = torch.tensor([ 0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013, 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, 0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744 ]).view(1, self.latent_channels, 1, 1, 1) class HunyuanImage21(LatentFormat): latent_channels = 64 latent_dimensions = 2 spacial_downscale_ratio = 32 scale_factor = 0.75289 latent_rgb_factors = [ [-0.0154, -0.0397, -0.0521], [ 0.0005, 0.0093, 0.0006], [-0.0805, -0.0773, -0.0586], [-0.0494, -0.0487, -0.0498], [-0.0212, -0.0076, -0.0261], [-0.0179, -0.0417, -0.0505], [ 0.0158, 0.0310, 0.0239], [ 0.0409, 0.0516, 0.0201], [ 0.0350, 0.0553, 0.0036], [-0.0447, -0.0327, -0.0479], [-0.0038, -0.0221, -0.0365], [-0.0423, -0.0718, -0.0654], [ 0.0039, 0.0368, 0.0104], [ 0.0655, 0.0217, 0.0122], [ 0.0490, 0.1638, 0.2053], [ 0.0932, 0.0829, 0.0650], [-0.0186, -0.0209, -0.0135], [-0.0080, -0.0076, -0.0148], [-0.0284, -0.0201, 0.0011], [-0.0642, -0.0294, -0.0777], [-0.0035, 0.0076, -0.0140], [ 0.0519, 0.0731, 0.0887], [-0.0102, 0.0095, 0.0704], [ 0.0068, 0.0218, -0.0023], [-0.0726, -0.0486, -0.0519], [ 0.0260, 0.0295, 0.0263], [ 0.0250, 0.0333, 0.0341], [ 0.0168, -0.0120, -0.0174], [ 0.0226, 0.1037, 0.0114], [ 0.2577, 0.1906, 0.1604], [-0.0646, -0.0137, -0.0018], [-0.0112, 0.0309, 0.0358], [-0.0347, 0.0146, -0.0481], [ 0.0234, 0.0179, 0.0201], [ 0.0157, 0.0313, 0.0225], [ 0.0423, 0.0675, 0.0524], [-0.0031, 0.0027, -0.0255], [ 0.0447, 0.0555, 0.0330], [-0.0152, 0.0103, 0.0299], [-0.0755, -0.0489, -0.0635], [ 0.0853, 0.0788, 0.1017], [-0.0272, -0.0294, -0.0471], [ 0.0440, 0.0400, -0.0137], [ 0.0335, 0.0317, -0.0036], [-0.0344, -0.0621, -0.0984], [-0.0127, -0.0630, -0.0620], [-0.0648, 0.0360, 0.0924], [-0.0781, -0.0801, -0.0409], [ 0.0363, 0.0613, 0.0499], [ 0.0238, 0.0034, 0.0041], [-0.0135, 0.0258, 0.0310], [ 0.0614, 0.1086, 0.0589], [ 0.0428, 0.0350, 0.0205], [ 0.0153, 0.0173, -0.0018], [-0.0288, -0.0455, -0.0091], [ 0.0344, 0.0109, -0.0157], [-0.0205, -0.0247, -0.0187], [ 0.0487, 0.0126, 0.0064], [-0.0220, -0.0013, 0.0074], [-0.0203, -0.0094, -0.0048], [-0.0719, 0.0429, -0.0442], [ 0.1042, 0.0497, 0.0356], [-0.0659, -0.0578, -0.0280], [-0.0060, -0.0322, -0.0234]] latent_rgb_factors_bias = [0.0007, -0.0256, -0.0206] class HunyuanImage21Refiner(LatentFormat): latent_channels = 64 latent_dimensions = 3 scale_factor = 1.03682 def process_in(self, latent): out = latent * self.scale_factor out = torch.cat((out[:, :, :1], out), dim=2) out = out.permute(0, 2, 1, 3, 4) b, f_times_2, c, h, w = out.shape out = out.reshape(b, f_times_2 // 2, 2 * c, h, w) out = out.permute(0, 2, 1, 3, 4).contiguous() return out def process_out(self, latent): z = latent / self.scale_factor z = z.permute(0, 2, 1, 3, 4) b, f, c, h, w = z.shape z = z.reshape(b, f, 2, c // 2, h, w) z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w) z = z.permute(0, 2, 1, 3, 4) z = z[:, :, 1:] return z class HunyuanVideo15(LatentFormat): latent_rgb_factors = [ [ 0.0568, -0.0521, -0.0131], [ 0.0014, 0.0735, 0.0326], [ 0.0186, 0.0531, -0.0138], [-0.0031, 0.0051, 0.0288], [ 0.0110, 0.0556, 0.0432], [-0.0041, -0.0023, -0.0485], [ 0.0530, 0.0413, 0.0253], [ 0.0283, 0.0251, 0.0339], [ 0.0277, -0.0372, -0.0093], [ 0.0393, 0.0944, 0.1131], [ 0.0020, 0.0251, 0.0037], [-0.0017, 0.0012, 0.0234], [ 0.0468, 0.0436, 0.0203], [ 0.0354, 0.0439, -0.0233], [ 0.0090, 0.0123, 0.0346], [ 0.0382, 0.0029, 0.0217], [ 0.0261, -0.0300, 0.0030], [-0.0088, -0.0220, -0.0283], [-0.0272, -0.0121, -0.0363], [-0.0664, -0.0622, 0.0144], [ 0.0414, 0.0479, 0.0529], [ 0.0355, 0.0612, -0.0247], [ 0.0147, 0.0264, 0.0174], [ 0.0438, 0.0038, 0.0542], [ 0.0431, -0.0573, -0.0033], [-0.0162, -0.0211, -0.0406], [-0.0487, -0.0295, -0.0393], [ 0.0005, -0.0109, 0.0253], [ 0.0296, 0.0591, 0.0353], [ 0.0119, 0.0181, -0.0306], [-0.0085, -0.0362, 0.0229], [ 0.0005, -0.0106, 0.0242] ] latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644] latent_channels = 32 latent_dimensions = 3 spacial_downscale_ratio = 16 scale_factor = 1.03682 taesd_decoder_name = "lighttaehy1_5" class Hunyuan3Dv2(LatentFormat): latent_channels = 64 latent_dimensions = 1 scale_factor = 0.9990943042622529 class Hunyuan3Dv2_1(LatentFormat): scale_factor = 1.0039506158752403 latent_channels = 64 latent_dimensions = 1 class Hunyuan3Dv2mini(LatentFormat): latent_channels = 64 latent_dimensions = 1 scale_factor = 1.0188137142395404 class ACEAudio(LatentFormat): latent_channels = 8 latent_dimensions = 2 class ACEAudio15(LatentFormat): latent_channels = 64 latent_dimensions = 1 class ChromaRadiance(LatentFormat): latent_channels = 3 spacial_downscale_ratio = 1 def __init__(self): self.latent_rgb_factors = [ # R G B [ 1.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 1.0 ] ] def process_in(self, latent): return latent def process_out(self, latent): return latent class ZImagePixelSpace(ChromaRadiance): """Pixel-space latent format for ZImage DCT variant. No VAE encoding/decoding — the model operates directly on RGB pixels. """ pass ================================================ FILE: comfy/ldm/ace/ace_step15.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F import itertools from comfy.ldm.modules.attention import optimized_attention import comfy.model_management from comfy.ldm.flux.layers import timestep_embedding def get_silence_latent(length, device): head = torch.tensor([[[ 0.5707, 0.0982, 0.6909, -0.5658, 0.6266, 0.6996, -0.1365, -0.1291, -0.0776, -0.1171, -0.2743, -0.8422, -0.1168, 1.5539, -4.6936, 0.7436, -1.1846, -0.2637, 0.6933, -6.7266, 0.0966, -0.1187, -0.3501, -1.1736, 0.0587, -2.0517, -1.3651, 0.7508, -0.2490, -1.3548, -0.1290, -0.7261, 1.1132, -0.3249, 0.2337, 0.3004, 0.6605, -0.0298, -0.1989, -0.4041, 0.2843, -1.0963, -0.5519, 0.2639, -1.0436, -0.1183, 0.0640, 0.4460, -1.1001, -0.6172, -1.3241, 1.1379, 0.5623, -0.1507, -0.1963, -0.4742, -2.4697, 0.5302, 0.5381, 0.4636, -0.1782, -0.0687, 1.0333, 0.4202], [ 0.3040, -0.1367, 0.6200, 0.0665, -0.0642, 0.4655, -0.1187, -0.0440, 0.2941, -0.2753, 0.0173, -0.2421, -0.0147, 1.5603, -2.7025, 0.7907, -0.9736, -0.0682, 0.1294, -5.0707, -0.2167, 0.3302, -0.1513, -0.8100, -0.3894, -0.2884, -0.3149, 0.8660, -0.3817, -1.7061, 0.5824, -0.4840, 0.6938, 0.1859, 0.1753, 0.3081, 0.0195, 0.1403, -0.0754, -0.2091, 0.1251, -0.1578, -0.4968, -0.1052, -0.4554, -0.0320, 0.1284, 0.4974, -1.1889, -0.0344, -0.8313, 0.2953, 0.5445, -0.6249, -0.1595, -0.0682, -3.1412, 0.0484, 0.4153, 0.8260, -0.1526, -0.0625, 0.5366, 0.8473], [ 5.3524e-02, -1.7534e-01, 5.4443e-01, -4.3501e-01, -2.1317e-03, 3.7200e-01, -4.0143e-03, -1.5516e-01, -1.2968e-01, -1.5375e-01, -7.7107e-02, -2.0593e-01, -3.2780e-01, 1.5142e+00, -2.6101e+00, 5.8698e-01, -1.2716e+00, -2.4773e-01, -2.7933e-02, -5.0799e+00, 1.1601e-01, 4.0987e-01, -2.2030e-02, -6.6495e-01, -2.0995e-01, -6.3474e-01, -1.5893e-01, 8.2745e-01, -2.2992e-01, -1.6816e+00, 5.4440e-01, -4.9579e-01, 5.5128e-01, 3.0477e-01, 8.3052e-02, -6.1782e-02, 5.9036e-03, 2.9553e-01, -8.0645e-02, -1.0060e-01, 1.9144e-01, -3.8124e-01, -7.2949e-01, 2.4520e-02, -5.0814e-01, 2.3977e-01, 9.2943e-02, 3.9256e-01, -1.1993e+00, -3.2752e-01, -7.2707e-01, 2.9476e-01, 4.3542e-01, -8.8597e-01, -4.1686e-01, -8.5390e-02, -2.9018e+00, 6.4988e-02, 5.3945e-01, 9.1988e-01, 5.8762e-02, -7.0098e-02, 6.4772e-01, 8.9118e-01], [-3.2225e-02, -1.3195e-01, 5.6411e-01, -5.4766e-01, -5.2170e-03, 3.1425e-01, -5.4367e-02, -1.9419e-01, -1.3059e-01, -1.3660e-01, -9.0984e-02, -1.9540e-01, -2.5590e-01, 1.5440e+00, -2.6349e+00, 6.8273e-01, -1.2532e+00, -1.9810e-01, -2.2793e-02, -5.0506e+00, 1.8818e-01, 5.0109e-01, 7.3546e-03, -6.8771e-01, -3.0676e-01, -7.3257e-01, -1.6687e-01, 9.2232e-01, -1.8987e-01, -1.7267e+00, 5.3355e-01, -5.3179e-01, 4.4953e-01, 2.8820e-01, 1.3012e-01, -2.0943e-01, -1.1348e-01, 3.3929e-01, -1.5069e-01, -1.2919e-01, 1.8929e-01, -3.6166e-01, -8.0756e-01, 6.6387e-02, -5.8867e-01, 1.6978e-01, 1.0134e-01, 3.3877e-01, -1.2133e+00, -3.2492e-01, -8.1237e-01, 3.8101e-01, 4.3765e-01, -8.0596e-01, -4.4531e-01, -4.7513e-02, -2.9266e+00, 1.1741e-03, 4.5123e-01, 9.3075e-01, 5.3688e-02, -1.9621e-01, 6.4530e-01, 9.3870e-01]]], device=device).movedim(-1, 1) silence_latent = torch.tensor([[[-1.3672e-01, -1.5820e-01, 5.8594e-01, -5.7422e-01, 3.0273e-02, 2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01, -2.7710e-02, -1.8066e-01, -2.9688e-01, 1.6016e+00, -2.6719e+00, 7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00, 2.4316e-01, 4.7266e-01, 4.6387e-02, -6.6406e-01, -2.1973e-01, -6.7578e-01, -1.5723e-01, 9.5312e-01, -2.0020e-01, -1.7109e+00, 5.8984e-01, -5.7422e-01, 5.1562e-01, 2.8320e-01, 1.4551e-01, -1.8750e-01, -5.9814e-02, 3.6719e-01, -1.0059e-01, -1.5723e-01, 2.0605e-01, -4.3359e-01, -8.2812e-01, 4.5654e-02, -6.6016e-01, 1.4844e-01, 9.4727e-02, 3.8477e-01, -1.2578e+00, -3.3203e-01, -8.5547e-01, 4.3359e-01, 4.2383e-01, -8.9453e-01, -5.0391e-01, -5.6152e-02, -2.9219e+00, -2.4658e-02, 5.0391e-01, 9.8438e-01, 7.2754e-02, -2.1582e-01, 6.3672e-01, 1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, length) silence_latent[:, :, :head.shape[-1]] = head return silence_latent def get_layer_class(operations, layer_name): if operations is not None and hasattr(operations, layer_name): return getattr(operations, layer_name) return getattr(nn, layer_name) class RotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=32768, base=1000000.0, dtype=None, device=None, operations=None): super().__init__() self.dim = dim self.base = base self.max_position_embeddings = max_position_embeddings inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.float32, device=device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._set_cos_sin_cache(max_position_embeddings, device=device, dtype=torch.get_default_dtype() if dtype is None else dtype) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32) freqs = torch.outer(t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len, x.device, x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype, device=x.device), self.sin_cached[:seq_len].to(dtype=x.dtype, device=x.device), ) def rotate_half(x): x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin): cos = cos.unsqueeze(0).unsqueeze(0) sin = sin.unsqueeze(0).unsqueeze(0) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class MLP(nn.Module): def __init__(self, hidden_size, intermediate_size, dtype=None, device=None, operations=None): super().__init__() Linear = get_layer_class(operations, "Linear") self.gate_proj = Linear(hidden_size, intermediate_size, bias=False, dtype=dtype, device=device) self.up_proj = Linear(hidden_size, intermediate_size, bias=False, dtype=dtype, device=device) self.down_proj = Linear(intermediate_size, hidden_size, bias=False, dtype=dtype, device=device) self.act_fn = nn.SiLU() def forward(self, x): return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) class TimestepEmbedding(nn.Module): def __init__(self, in_channels: int, time_embed_dim: int, scale: float = 1000, dtype=None, device=None, operations=None): super().__init__() Linear = get_layer_class(operations, "Linear") self.linear_1 = Linear(in_channels, time_embed_dim, bias=True, dtype=dtype, device=device) self.act1 = nn.SiLU() self.linear_2 = Linear(time_embed_dim, time_embed_dim, bias=True, dtype=dtype, device=device) self.in_channels = in_channels self.act2 = nn.SiLU() self.time_proj = Linear(time_embed_dim, time_embed_dim * 6, dtype=dtype, device=device) self.scale = scale def forward(self, t, dtype=None): t_freq = timestep_embedding(t, self.in_channels, time_factor=self.scale) temb = self.linear_1(t_freq.to(dtype=dtype)) temb = self.act1(temb) temb = self.linear_2(temb) timestep_proj = self.time_proj(self.act2(temb)).view(-1, 6, temb.shape[-1]) return temb, timestep_proj class AceStepAttention(nn.Module): def __init__( self, hidden_size, num_heads, num_kv_heads, head_dim, rms_norm_eps=1e-6, is_cross_attention=False, sliding_window=None, dtype=None, device=None, operations=None ): super().__init__() self.hidden_size = hidden_size self.num_heads = num_heads self.num_kv_heads = num_kv_heads self.head_dim = head_dim self.is_cross_attention = is_cross_attention self.sliding_window = sliding_window Linear = get_layer_class(operations, "Linear") self.q_proj = Linear(hidden_size, num_heads * head_dim, bias=False, dtype=dtype, device=device) self.k_proj = Linear(hidden_size, num_kv_heads * head_dim, bias=False, dtype=dtype, device=device) self.v_proj = Linear(hidden_size, num_kv_heads * head_dim, bias=False, dtype=dtype, device=device) self.o_proj = Linear(num_heads * head_dim, hidden_size, bias=False, dtype=dtype, device=device) self.q_norm = operations.RMSNorm(head_dim, eps=rms_norm_eps, dtype=dtype, device=device) self.k_norm = operations.RMSNorm(head_dim, eps=rms_norm_eps, dtype=dtype, device=device) def forward( self, hidden_states, encoder_hidden_states=None, attention_mask=None, position_embeddings=None, ): bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim) query_states = self.q_norm(query_states) query_states = query_states.transpose(1, 2) if self.is_cross_attention and encoder_hidden_states is not None: bsz_enc, kv_len, _ = encoder_hidden_states.size() key_states = self.k_proj(encoder_hidden_states) value_states = self.v_proj(encoder_hidden_states) key_states = key_states.view(bsz_enc, kv_len, self.num_kv_heads, self.head_dim) key_states = self.k_norm(key_states) value_states = value_states.view(bsz_enc, kv_len, self.num_kv_heads, self.head_dim) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) else: kv_len = q_len key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) key_states = key_states.view(bsz, q_len, self.num_kv_heads, self.head_dim) key_states = self.k_norm(key_states) value_states = value_states.view(bsz, q_len, self.num_kv_heads, self.head_dim) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if position_embeddings is not None: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) n_rep = self.num_heads // self.num_kv_heads if n_rep > 1: key_states = key_states.repeat_interleave(n_rep, dim=1) value_states = value_states.repeat_interleave(n_rep, dim=1) attn_bias = None if self.sliding_window is not None and not self.is_cross_attention: indices = torch.arange(q_len, device=query_states.device) diff = indices.unsqueeze(1) - indices.unsqueeze(0) in_window = torch.abs(diff) <= self.sliding_window window_bias = torch.zeros((q_len, kv_len), device=query_states.device, dtype=query_states.dtype) min_value = torch.finfo(query_states.dtype).min window_bias.masked_fill_(~in_window, min_value) window_bias = window_bias.unsqueeze(0).unsqueeze(0) if attn_bias is not None: if attn_bias.dtype == torch.bool: base_bias = torch.zeros_like(window_bias) base_bias.masked_fill_(~attn_bias, min_value) attn_bias = base_bias + window_bias else: attn_bias = attn_bias + window_bias else: attn_bias = window_bias attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False) attn_output = self.o_proj(attn_output) return attn_output class AceStepDiTLayer(nn.Module): def __init__( self, hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps=1e-6, layer_type="full_attention", sliding_window=128, dtype=None, device=None, operations=None ): super().__init__() self_attn_window = sliding_window if layer_type == "sliding_attention" else None self.self_attn_norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.self_attn = AceStepAttention( hidden_size, num_heads, num_kv_heads, head_dim, rms_norm_eps, is_cross_attention=False, sliding_window=self_attn_window, dtype=dtype, device=device, operations=operations ) self.cross_attn_norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.cross_attn = AceStepAttention( hidden_size, num_heads, num_kv_heads, head_dim, rms_norm_eps, is_cross_attention=True, dtype=dtype, device=device, operations=operations ) self.mlp_norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.mlp = MLP(hidden_size, intermediate_size, dtype=dtype, device=device, operations=operations) self.scale_shift_table = nn.Parameter(torch.empty(1, 6, hidden_size, dtype=dtype, device=device)) def forward( self, hidden_states, temb, encoder_hidden_states, position_embeddings, attention_mask=None, encoder_attention_mask=None ): modulation = comfy.model_management.cast_to(self.scale_shift_table, dtype=temb.dtype, device=temb.device) + temb shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = modulation.chunk(6, dim=1) norm_hidden = self.self_attn_norm(hidden_states) norm_hidden = norm_hidden * (1 + scale_msa) + shift_msa attn_out = self.self_attn( norm_hidden, position_embeddings=position_embeddings, attention_mask=attention_mask ) hidden_states = hidden_states + attn_out * gate_msa norm_hidden = self.cross_attn_norm(hidden_states) attn_out = self.cross_attn( norm_hidden, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask ) hidden_states = hidden_states + attn_out norm_hidden = self.mlp_norm(hidden_states) norm_hidden = norm_hidden * (1 + c_scale_msa) + c_shift_msa mlp_out = self.mlp(norm_hidden) hidden_states = hidden_states + mlp_out * c_gate_msa return hidden_states class AceStepEncoderLayer(nn.Module): def __init__( self, hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps=1e-6, dtype=None, device=None, operations=None ): super().__init__() self.self_attn = AceStepAttention( hidden_size, num_heads, num_kv_heads, head_dim, rms_norm_eps, is_cross_attention=False, dtype=dtype, device=device, operations=operations ) self.input_layernorm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.post_attention_layernorm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.mlp = MLP(hidden_size, intermediate_size, dtype=dtype, device=device, operations=operations) def forward(self, hidden_states, position_embeddings, attention_mask=None): residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn( hidden_states=hidden_states, position_embeddings=position_embeddings, attention_mask=attention_mask ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class AceStepLyricEncoder(nn.Module): def __init__( self, text_hidden_dim, hidden_size, num_layers, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps=1e-6, dtype=None, device=None, operations=None ): super().__init__() Linear = get_layer_class(operations, "Linear") self.embed_tokens = Linear(text_hidden_dim, hidden_size, dtype=dtype, device=device) self.norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.rotary_emb = RotaryEmbedding( head_dim, base=1000000.0, dtype=dtype, device=device, operations=operations ) self.layers = nn.ModuleList([ AceStepEncoderLayer( hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps, dtype=dtype, device=device, operations=operations ) for _ in range(num_layers) ]) def forward(self, inputs_embeds, attention_mask=None): hidden_states = self.embed_tokens(inputs_embeds) seq_len = hidden_states.shape[1] cos, sin = self.rotary_emb(hidden_states, seq_len=seq_len) position_embeddings = (cos, sin) for layer in self.layers: hidden_states = layer( hidden_states, position_embeddings=position_embeddings, attention_mask=attention_mask ) hidden_states = self.norm(hidden_states) return hidden_states class AceStepTimbreEncoder(nn.Module): def __init__( self, timbre_hidden_dim, hidden_size, num_layers, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps=1e-6, dtype=None, device=None, operations=None ): super().__init__() Linear = get_layer_class(operations, "Linear") self.embed_tokens = Linear(timbre_hidden_dim, hidden_size, dtype=dtype, device=device) self.norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.rotary_emb = RotaryEmbedding( head_dim, base=1000000.0, dtype=dtype, device=device, operations=operations ) self.layers = nn.ModuleList([ AceStepEncoderLayer( hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps, dtype=dtype, device=device, operations=operations ) for _ in range(num_layers) ]) self.special_token = nn.Parameter(torch.empty(1, 1, hidden_size, device=device, dtype=dtype)) def unpack_timbre_embeddings(self, timbre_embs_packed, refer_audio_order_mask): N, d = timbre_embs_packed.shape device = timbre_embs_packed.device B = N counts = torch.bincount(refer_audio_order_mask, minlength=B) max_count = counts.max().item() sorted_indices = torch.argsort( refer_audio_order_mask * N + torch.arange(N, device=device), stable=True ) sorted_batch_ids = refer_audio_order_mask[sorted_indices] positions = torch.arange(N, device=device) batch_starts = torch.cat([torch.tensor([0], device=device), torch.cumsum(counts, dim=0)[:-1]]) positions_in_sorted = positions - batch_starts[sorted_batch_ids] inverse_indices = torch.empty_like(sorted_indices) inverse_indices[sorted_indices] = torch.arange(N, device=device) positions_in_batch = positions_in_sorted[inverse_indices] indices_2d = refer_audio_order_mask * max_count + positions_in_batch one_hot = F.one_hot(indices_2d, num_classes=B * max_count).to(timbre_embs_packed.dtype) timbre_embs_flat = one_hot.t() @ timbre_embs_packed timbre_embs_unpack = timbre_embs_flat.view(B, max_count, d) mask_flat = (one_hot.sum(dim=0) > 0).long() new_mask = mask_flat.view(B, max_count) return timbre_embs_unpack, new_mask def forward(self, refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask, attention_mask=None): hidden_states = self.embed_tokens(refer_audio_acoustic_hidden_states_packed) if hidden_states.dim() == 2: hidden_states = hidden_states.unsqueeze(0) seq_len = hidden_states.shape[1] cos, sin = self.rotary_emb(hidden_states, seq_len=seq_len) for layer in self.layers: hidden_states = layer( hidden_states, position_embeddings=(cos, sin), attention_mask=attention_mask ) hidden_states = self.norm(hidden_states) flat_states = hidden_states[:, 0, :] unpacked_embs, unpacked_mask = self.unpack_timbre_embeddings(flat_states, refer_audio_order_mask) return unpacked_embs, unpacked_mask def pack_sequences(hidden1, hidden2, mask1, mask2): hidden_cat = torch.cat([hidden1, hidden2], dim=1) B, L, D = hidden_cat.shape if mask1 is not None and mask2 is not None: mask_cat = torch.cat([mask1, mask2], dim=1) sort_idx = mask_cat.argsort(dim=1, descending=True, stable=True) gather_idx = sort_idx.unsqueeze(-1).expand(B, L, D) hidden_sorted = torch.gather(hidden_cat, 1, gather_idx) lengths = mask_cat.sum(dim=1) new_mask = (torch.arange(L, device=hidden_cat.device).unsqueeze(0) < lengths.unsqueeze(1)) else: new_mask = None hidden_sorted = hidden_cat return hidden_sorted, new_mask class AceStepConditionEncoder(nn.Module): def __init__( self, text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps=1e-6, dtype=None, device=None, operations=None ): super().__init__() Linear = get_layer_class(operations, "Linear") self.text_projector = Linear(text_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device) self.lyric_encoder = AceStepLyricEncoder( text_hidden_dim=text_hidden_dim, hidden_size=hidden_size, num_layers=num_lyric_layers, num_heads=num_heads, num_kv_heads=num_kv_heads, head_dim=head_dim, intermediate_size=intermediate_size, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.timbre_encoder = AceStepTimbreEncoder( timbre_hidden_dim=timbre_hidden_dim, hidden_size=hidden_size, num_layers=num_timbre_layers, num_heads=num_heads, num_kv_heads=num_kv_heads, head_dim=head_dim, intermediate_size=intermediate_size, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) def forward( self, text_hidden_states=None, text_attention_mask=None, lyric_hidden_states=None, lyric_attention_mask=None, refer_audio_acoustic_hidden_states_packed=None, refer_audio_order_mask=None ): text_emb = self.text_projector(text_hidden_states) lyric_emb = self.lyric_encoder( inputs_embeds=lyric_hidden_states, attention_mask=lyric_attention_mask ) timbre_emb, timbre_mask = self.timbre_encoder( refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask ) merged_emb, merged_mask = pack_sequences(lyric_emb, timbre_emb, lyric_attention_mask, timbre_mask) final_emb, final_mask = pack_sequences(merged_emb, text_emb, merged_mask, text_attention_mask) return final_emb, final_mask # -------------------------------------------------------------------------------- # Main Diffusion Model (DiT) # -------------------------------------------------------------------------------- class AceStepDiTModel(nn.Module): def __init__( self, in_channels, hidden_size, num_layers, num_heads, num_kv_heads, head_dim, intermediate_size, patch_size, audio_acoustic_hidden_dim, layer_types=None, sliding_window=128, rms_norm_eps=1e-6, dtype=None, device=None, operations=None ): super().__init__() self.patch_size = patch_size self.rotary_emb = RotaryEmbedding( head_dim, base=1000000.0, dtype=dtype, device=device, operations=operations ) Conv1d = get_layer_class(operations, "Conv1d") ConvTranspose1d = get_layer_class(operations, "ConvTranspose1d") Linear = get_layer_class(operations, "Linear") self.proj_in = nn.Sequential( nn.Identity(), Conv1d( in_channels, hidden_size, kernel_size=patch_size, stride=patch_size, dtype=dtype, device=device)) self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations) self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations) self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device) if layer_types is None: layer_types = ["full_attention"] * num_layers if len(layer_types) < num_layers: layer_types = list(itertools.islice(itertools.cycle(layer_types), num_layers)) self.layers = nn.ModuleList([ AceStepDiTLayer( hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps, layer_type=layer_types[i], sliding_window=sliding_window, dtype=dtype, device=device, operations=operations ) for i in range(num_layers) ]) self.norm_out = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.proj_out = nn.Sequential( nn.Identity(), ConvTranspose1d(hidden_size, audio_acoustic_hidden_dim, kernel_size=patch_size, stride=patch_size, dtype=dtype, device=device) ) self.scale_shift_table = nn.Parameter(torch.empty(1, 2, hidden_size, dtype=dtype, device=device)) def forward( self, hidden_states, timestep, timestep_r, attention_mask, encoder_hidden_states, encoder_attention_mask, context_latents ): temb_t, proj_t = self.time_embed(timestep, dtype=hidden_states.dtype) temb_r, proj_r = self.time_embed_r(timestep - timestep_r, dtype=hidden_states.dtype) temb = temb_t + temb_r timestep_proj = proj_t + proj_r x = torch.cat([context_latents, hidden_states], dim=-1) original_seq_len = x.shape[1] pad_length = 0 if x.shape[1] % self.patch_size != 0: pad_length = self.patch_size - (x.shape[1] % self.patch_size) x = F.pad(x, (0, 0, 0, pad_length), mode='constant', value=0) x = x.transpose(1, 2) x = self.proj_in(x) x = x.transpose(1, 2) encoder_hidden_states = self.condition_embedder(encoder_hidden_states) seq_len = x.shape[1] cos, sin = self.rotary_emb(x, seq_len=seq_len) for layer in self.layers: x = layer( hidden_states=x, temb=timestep_proj, encoder_hidden_states=encoder_hidden_states, position_embeddings=(cos, sin), attention_mask=None, encoder_attention_mask=None ) shift, scale = (comfy.model_management.cast_to(self.scale_shift_table, dtype=temb.dtype, device=temb.device) + temb.unsqueeze(1)).chunk(2, dim=1) x = self.norm_out(x) * (1 + scale) + shift x = x.transpose(1, 2) x = self.proj_out(x) x = x.transpose(1, 2) x = x[:, :original_seq_len, :] return x class AttentionPooler(nn.Module): def __init__(self, hidden_size, num_layers, head_dim, rms_norm_eps, dtype=None, device=None, operations=None): super().__init__() Linear = get_layer_class(operations, "Linear") self.embed_tokens = Linear(hidden_size, hidden_size, dtype=dtype, device=device) self.norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device) self.rotary_emb = RotaryEmbedding(head_dim, dtype=dtype, device=device, operations=operations) self.special_token = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device)) self.layers = nn.ModuleList([ AceStepEncoderLayer( hidden_size, 16, 8, head_dim, hidden_size * 3, rms_norm_eps, dtype=dtype, device=device, operations=operations ) for _ in range(num_layers) ]) def forward(self, x): B, T, P, D = x.shape x = self.embed_tokens(x) special = comfy.model_management.cast_to(self.special_token, device=x.device, dtype=x.dtype).expand(B, T, 1, -1) x = torch.cat([special, x], dim=2) x = x.view(B * T, P + 1, D) cos, sin = self.rotary_emb(x, seq_len=P + 1) for layer in self.layers: x = layer(x, (cos, sin)) x = self.norm(x) return x[:, 0, :].view(B, T, D) class FSQ(nn.Module): def __init__( self, levels, dim=None, device=None, dtype=None, operations=None ): super().__init__() _levels = torch.tensor(levels, dtype=torch.int32, device=device) self.register_buffer('_levels', _levels, persistent=False) _basis = torch.cumprod(torch.tensor([1] + levels[:-1], dtype=torch.int32, device=device), dim=0) self.register_buffer('_basis', _basis, persistent=False) self.codebook_dim = len(levels) self.dim = dim if dim is not None else self.codebook_dim requires_projection = self.dim != self.codebook_dim if requires_projection: self.project_in = operations.Linear(self.dim, self.codebook_dim, device=device, dtype=dtype) self.project_out = operations.Linear(self.codebook_dim, self.dim, device=device, dtype=dtype) else: self.project_in = nn.Identity() self.project_out = nn.Identity() self.codebook_size = self._levels.prod().item() indices = torch.arange(self.codebook_size, device=device) implicit_codebook = self._indices_to_codes(indices) if dtype is not None: implicit_codebook = implicit_codebook.to(dtype) self.register_buffer('implicit_codebook', implicit_codebook, persistent=False) def bound(self, z): levels_minus_1 = (comfy.model_management.cast_to(self._levels, device=z.device, dtype=z.dtype) - 1) scale = 2. / levels_minus_1 bracket = (levels_minus_1 * (torch.tanh(z) + 1) / 2.) + 0.5 zhat = bracket.floor() bracket_ste = bracket + (zhat - bracket).detach() return scale * bracket_ste - 1. def _indices_to_codes(self, indices): indices = indices.unsqueeze(-1) codes_non_centered = (indices // self._basis) % self._levels return codes_non_centered.float() * (2. / (self._levels.float() - 1)) - 1. def codes_to_indices(self, zhat): zhat_normalized = (zhat + 1.) / (2. / (comfy.model_management.cast_to(self._levels, device=zhat.device, dtype=zhat.dtype) - 1)) return (zhat_normalized * comfy.model_management.cast_to(self._basis, device=zhat.device, dtype=zhat.dtype)).sum(dim=-1).round().to(torch.int32) def forward(self, z): orig_dtype = z.dtype z = self.project_in(z) codes = self.bound(z) indices = self.codes_to_indices(codes) out = self.project_out(codes) return out.to(orig_dtype), indices class ResidualFSQ(nn.Module): def __init__( self, levels, num_quantizers, dim=None, bound_hard_clamp=True, device=None, dtype=None, operations=None, **kwargs ): super().__init__() codebook_dim = len(levels) dim = dim if dim is not None else codebook_dim requires_projection = codebook_dim != dim if requires_projection: self.project_in = operations.Linear(dim, codebook_dim, device=device, dtype=dtype) self.project_out = operations.Linear(codebook_dim, dim, device=device, dtype=dtype) else: self.project_in = nn.Identity() self.project_out = nn.Identity() self.layers = nn.ModuleList() levels_tensor = torch.tensor(levels, device=device) scales = [] for ind in range(num_quantizers): scale_val = levels_tensor.float() ** -ind scales.append(scale_val) self.layers.append(FSQ( levels=levels, dim=codebook_dim, device=device, dtype=dtype, operations=operations )) scales_tensor = torch.stack(scales) if dtype is not None: scales_tensor = scales_tensor.to(dtype) self.register_buffer('scales', scales_tensor, persistent=False) if bound_hard_clamp: val = 1 + (1 / (levels_tensor.float() - 1)) if dtype is not None: val = val.to(dtype) self.register_buffer('soft_clamp_input_value', val, persistent=False) def get_output_from_indices(self, indices, dtype=torch.float32): if indices.dim() == 2: indices = indices.unsqueeze(-1) all_codes = [] for i, layer in enumerate(self.layers): idx = indices[..., i].long() codes = F.embedding(idx, comfy.model_management.cast_to(layer.implicit_codebook, device=idx.device, dtype=dtype)) all_codes.append(codes * comfy.model_management.cast_to(self.scales[i], device=idx.device, dtype=dtype)) codes_summed = torch.stack(all_codes).sum(dim=0) return self.project_out(codes_summed) def forward(self, x): x = self.project_in(x) if hasattr(self, 'soft_clamp_input_value'): sc_val = comfy.model_management.cast_to(self.soft_clamp_input_value, device=x.device, dtype=x.dtype) x = (x / sc_val).tanh() * sc_val quantized_out = torch.tensor(0., device=x.device, dtype=x.dtype) residual = x all_indices = [] for layer, scale in zip(self.layers, self.scales): scale = comfy.model_management.cast_to(scale, device=x.device, dtype=x.dtype) quantized, indices = layer(residual / scale) quantized = quantized * scale residual = residual - quantized.detach() quantized_out = quantized_out + quantized all_indices.append(indices) quantized_out = self.project_out(quantized_out) all_indices = torch.stack(all_indices, dim=-1) return quantized_out, all_indices class AceStepAudioTokenizer(nn.Module): def __init__( self, audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim, fsq_levels, fsq_input_num_quantizers, num_layers, head_dim, rms_norm_eps, dtype=None, device=None, operations=None ): super().__init__() Linear = get_layer_class(operations, "Linear") self.audio_acoustic_proj = Linear(audio_acoustic_hidden_dim, hidden_size, dtype=dtype, device=device) self.attention_pooler = AttentionPooler( hidden_size, num_layers, head_dim, rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.pool_window_size = pool_window_size self.fsq_dim = fsq_dim self.quantizer = ResidualFSQ( dim=fsq_dim, levels=fsq_levels, num_quantizers=fsq_input_num_quantizers, bound_hard_clamp=True, dtype=dtype, device=device, operations=operations ) def forward(self, hidden_states): hidden_states = self.audio_acoustic_proj(hidden_states) hidden_states = self.attention_pooler(hidden_states) quantized, indices = self.quantizer(hidden_states) return quantized, indices def tokenize(self, x): B, T, D = x.shape P = self.pool_window_size if T % P != 0: pad = P - (T % P) x = F.pad(x, (0, 0, 0, pad)) T = x.shape[1] T_patch = T // P x = x.view(B, T_patch, P, D) quantized, indices = self.forward(x) return quantized, indices class AudioTokenDetokenizer(nn.Module): def __init__( self, hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers, head_dim, dtype=None, device=None, operations=None ): super().__init__() Linear = get_layer_class(operations, "Linear") self.pool_window_size = pool_window_size self.embed_tokens = Linear(hidden_size, hidden_size, dtype=dtype, device=device) self.special_tokens = nn.Parameter(torch.empty(1, pool_window_size, hidden_size, dtype=dtype, device=device)) self.rotary_emb = RotaryEmbedding(head_dim, dtype=dtype, device=device, operations=operations) self.layers = nn.ModuleList([ AceStepEncoderLayer( hidden_size, 16, 8, head_dim, hidden_size * 3, 1e-6, dtype=dtype, device=device, operations=operations ) for _ in range(num_layers) ]) self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device) self.proj_out = Linear(hidden_size, audio_acoustic_hidden_dim, dtype=dtype, device=device) def forward(self, x): B, T, D = x.shape x = self.embed_tokens(x) x = x.unsqueeze(2).repeat(1, 1, self.pool_window_size, 1) x = x + comfy.model_management.cast_to(self.special_tokens.expand(B, T, -1, -1), device=x.device, dtype=x.dtype) x = x.view(B * T, self.pool_window_size, D) cos, sin = self.rotary_emb(x, seq_len=self.pool_window_size) for layer in self.layers: x = layer(x, (cos, sin)) x = self.norm(x) x = self.proj_out(x) return x.view(B, T * self.pool_window_size, -1) class AceStepConditionGenerationModel(nn.Module): def __init__( self, in_channels=192, hidden_size=2048, text_hidden_dim=1024, timbre_hidden_dim=64, audio_acoustic_hidden_dim=64, num_dit_layers=24, num_lyric_layers=8, num_timbre_layers=4, num_tokenizer_layers=2, num_heads=16, num_kv_heads=8, head_dim=128, intermediate_size=6144, patch_size=2, pool_window_size=5, rms_norm_eps=1e-06, timestep_mu=-0.4, timestep_sigma=1.0, data_proportion=0.5, sliding_window=128, layer_types=None, fsq_dim=2048, fsq_levels=[8, 8, 8, 5, 5, 5], fsq_input_num_quantizers=1, audio_model=None, dtype=None, device=None, operations=None ): super().__init__() self.dtype = dtype self.timestep_mu = timestep_mu self.timestep_sigma = timestep_sigma self.data_proportion = data_proportion self.pool_window_size = pool_window_size if layer_types is None: layer_types = [] for i in range(num_dit_layers): layer_types.append("sliding_attention" if i % 2 == 0 else "full_attention") self.decoder = AceStepDiTModel( in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim, intermediate_size, patch_size, audio_acoustic_hidden_dim, layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.encoder = AceStepConditionEncoder( text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.tokenizer = AceStepAudioTokenizer( audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.detokenizer = AudioTokenDetokenizer( hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim, dtype=dtype, device=device, operations=operations ) self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device)) def prepare_condition( self, text_hidden_states, text_attention_mask, lyric_hidden_states, lyric_attention_mask, refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask, src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=None, audio_codes=None ): encoder_hidden, encoder_mask = self.encoder( text_hidden_states, text_attention_mask, lyric_hidden_states, lyric_attention_mask, refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask ) if precomputed_lm_hints_25Hz is not None: lm_hints = precomputed_lm_hints_25Hz else: if audio_codes is not None: if audio_codes.shape[1] * 5 < src_latents.shape[1]: audio_codes = torch.nn.functional.pad(audio_codes, (0, math.ceil(src_latents.shape[1] / 5) - audio_codes.shape[1]), "constant", 35847) lm_hints_5Hz = self.tokenizer.quantizer.get_output_from_indices(audio_codes, dtype=text_hidden_states.dtype) else: lm_hints_5Hz, indices = self.tokenizer.tokenize(refer_audio_acoustic_hidden_states_packed) lm_hints = self.detokenizer(lm_hints_5Hz) lm_hints = lm_hints[:, :src_latents.shape[1], :] if is_covers is None or is_covers is True: src_latents = lm_hints elif is_covers is False: src_latents = refer_audio_acoustic_hidden_states_packed context_latents = torch.cat([src_latents, chunk_masks.to(src_latents.dtype)], dim=-1) return encoder_hidden, encoder_mask, context_latents def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **kwargs): text_attention_mask = None lyric_attention_mask = None refer_audio_order_mask = None attention_mask = None chunk_masks = None src_latents = None precomputed_lm_hints_25Hz = None lyric_hidden_states = lyric_embed text_hidden_states = context refer_audio_acoustic_hidden_states_packed = refer_audio.movedim(-1, -2) x = x.movedim(-1, -2) if refer_audio_order_mask is None: refer_audio_order_mask = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long) if src_latents is None: src_latents = x if chunk_masks is None: chunk_masks = torch.ones_like(x) enc_hidden, enc_mask, context_latents = self.prepare_condition( text_hidden_states, text_attention_mask, lyric_hidden_states, lyric_attention_mask, refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask, src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=precomputed_lm_hints_25Hz, audio_codes=audio_codes ) if replace_with_null_embeds: enc_hidden[:] = self.null_condition_emb.to(enc_hidden) out = self.decoder(hidden_states=x, timestep=timestep, timestep_r=timestep, attention_mask=attention_mask, encoder_hidden_states=enc_hidden, encoder_attention_mask=enc_mask, context_latents=context_latents ) return out.movedim(-1, -2) ================================================ FILE: comfy/ldm/ace/attention.py ================================================ # Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py # Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Tuple, Union, Optional import torch import torch.nn.functional as F from torch import nn import comfy.model_management from comfy.ldm.modules.attention import optimized_attention class Attention(nn.Module): def __init__( self, query_dim: int, cross_attention_dim: Optional[int] = None, heads: int = 8, kv_heads: Optional[int] = None, dim_head: int = 64, dropout: float = 0.0, bias: bool = False, qk_norm: Optional[str] = None, added_kv_proj_dim: Optional[int] = None, added_proj_bias: Optional[bool] = True, out_bias: bool = True, scale_qk: bool = True, only_cross_attention: bool = False, eps: float = 1e-5, rescale_output_factor: float = 1.0, residual_connection: bool = False, processor=None, out_dim: int = None, out_context_dim: int = None, context_pre_only=None, pre_only=False, elementwise_affine: bool = True, is_causal: bool = False, dtype=None, device=None, operations=None ): super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads self.query_dim = query_dim self.use_bias = bias self.is_cross_attention = cross_attention_dim is not None self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim self.rescale_output_factor = rescale_output_factor self.residual_connection = residual_connection self.dropout = dropout self.fused_projections = False self.out_dim = out_dim if out_dim is not None else query_dim self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim self.context_pre_only = context_pre_only self.pre_only = pre_only self.is_causal = is_causal self.scale_qk = scale_qk self.scale = dim_head**-0.5 if self.scale_qk else 1.0 self.heads = out_dim // dim_head if out_dim is not None else heads # for slice_size > 0 the attention score computation # is split across the batch axis to save memory # You can set slice_size with `set_attention_slice` self.sliceable_head_dim = heads self.added_kv_proj_dim = added_kv_proj_dim self.only_cross_attention = only_cross_attention if self.added_kv_proj_dim is None and self.only_cross_attention: raise ValueError( "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." ) self.group_norm = None self.spatial_norm = None self.norm_q = None self.norm_k = None self.norm_cross = None self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) if not self.only_cross_attention: # only relevant for the `AddedKVProcessor` classes self.to_k = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) self.to_v = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) else: self.to_k = None self.to_v = None self.added_proj_bias = added_proj_bias if self.added_kv_proj_dim is not None: self.add_k_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) self.add_v_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) if self.context_pre_only is not None: self.add_q_proj = operations.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias, dtype=dtype, device=device) else: self.add_q_proj = None self.add_k_proj = None self.add_v_proj = None if not self.pre_only: self.to_out = nn.ModuleList([]) self.to_out.append(operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device)) self.to_out.append(nn.Dropout(dropout)) else: self.to_out = None if self.context_pre_only is not None and not self.context_pre_only: self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) else: self.to_add_out = None self.norm_added_q = None self.norm_added_k = None self.processor = processor def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, transformer_options={}, **cross_attention_kwargs, ) -> torch.Tensor: return self.processor( self, hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, transformer_options=transformer_options, **cross_attention_kwargs, ) class CustomLiteLAProcessor2_0: """Attention processor used typically in processing the SD3-like self-attention projections. add rms norm for query and key and apply RoPE""" def __init__(self): self.kernel_func = nn.ReLU(inplace=False) self.eps = 1e-15 self.pad_val = 1.0 def apply_rotary_emb( self, x: torch.Tensor, freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], ) -> Tuple[torch.Tensor, torch.Tensor]: """ Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are returned as real tensors. Args: x (`torch.Tensor`): Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) Returns: Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. """ cos, sin = freqs_cis # [S, D] cos = cos[None, None] sin = sin[None, None] cos, sin = cos.to(x.device), sin.to(x.device) x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) return out def __call__( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_states: torch.FloatTensor = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, *args, **kwargs, ) -> torch.FloatTensor: hidden_states_len = hidden_states.shape[1] input_ndim = hidden_states.ndim if input_ndim == 4: batch_size, channel, height, width = hidden_states.shape hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) if encoder_hidden_states is not None: context_input_ndim = encoder_hidden_states.ndim if context_input_ndim == 4: batch_size, channel, height, width = encoder_hidden_states.shape encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2) batch_size = hidden_states.shape[0] # `sample` projections. dtype = hidden_states.dtype query = attn.to_q(hidden_states) key = attn.to_k(hidden_states) value = attn.to_v(hidden_states) # `context` projections. has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") if encoder_hidden_states is not None and has_encoder_hidden_state_proj: encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states) encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) # attention if not attn.is_cross_attention: query = torch.cat([query, encoder_hidden_states_query_proj], dim=1) key = torch.cat([key, encoder_hidden_states_key_proj], dim=1) value = torch.cat([value, encoder_hidden_states_value_proj], dim=1) else: query = hidden_states key = encoder_hidden_states value = encoder_hidden_states inner_dim = key.shape[-1] head_dim = inner_dim // attn.heads query = query.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) key = key.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1).transpose(-1, -2) value = value.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) # RoPE需要 [B, H, S, D] 输入 # 此时 query是 [B, H, D, S], 需要转成 [B, H, S, D] 才能应用RoPE query = query.permute(0, 1, 3, 2) # [B, H, S, D] (从 [B, H, D, S]) # Apply query and key normalization if needed if attn.norm_q is not None: query = attn.norm_q(query) if attn.norm_k is not None: key = attn.norm_k(key) # Apply RoPE if needed if rotary_freqs_cis is not None: query = self.apply_rotary_emb(query, rotary_freqs_cis) if not attn.is_cross_attention: key = self.apply_rotary_emb(key, rotary_freqs_cis) elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) # 此时 query是 [B, H, S, D]，需要还原成 [B, H, D, S] query = query.permute(0, 1, 3, 2) # [B, H, D, S] if attention_mask is not None: # attention_mask: [B, S] -> [B, 1, S, 1] attention_mask = attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S, 1] query = query * attention_mask.permute(0, 1, 3, 2) # [B, H, S, D] * [B, 1, S, 1] if not attn.is_cross_attention: key = key * attention_mask # key: [B, h, S, D] 与 mask [B, 1, S, 1] 相乘 value = value * attention_mask.permute(0, 1, 3, 2) # 如果 value 是 [B, h, D, S]，那么需调整mask以匹配S维度 if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: encoder_attention_mask = encoder_attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S_enc, 1] # 此时 key: [B, h, S_enc, D], value: [B, h, D, S_enc] key = key * encoder_attention_mask # [B, h, S_enc, D] * [B, 1, S_enc, 1] value = value * encoder_attention_mask.permute(0, 1, 3, 2) # [B, h, D, S_enc] * [B, 1, 1, S_enc] query = self.kernel_func(query) key = self.kernel_func(key) query, key, value = query.float(), key.float(), value.float() value = F.pad(value, (0, 0, 0, 1), mode="constant", value=self.pad_val) vk = torch.matmul(value, key) hidden_states = torch.matmul(vk, query) if hidden_states.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.float() hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) hidden_states = hidden_states.view(batch_size, attn.heads * head_dim, -1).permute(0, 2, 1) hidden_states = hidden_states.to(dtype) if encoder_hidden_states is not None: encoder_hidden_states = encoder_hidden_states.to(dtype) # Split the attention outputs. if encoder_hidden_states is not None and not attn.is_cross_attention and has_encoder_hidden_state_proj: hidden_states, encoder_hidden_states = ( hidden_states[:, : hidden_states_len], hidden_states[:, hidden_states_len:], ) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) if encoder_hidden_states is not None and not attn.context_pre_only and not attn.is_cross_attention and hasattr(attn, "to_add_out"): encoder_hidden_states = attn.to_add_out(encoder_hidden_states) if input_ndim == 4: hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) if encoder_hidden_states is not None and context_input_ndim == 4: encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) if torch.get_autocast_gpu_dtype() == torch.float16: hidden_states = hidden_states.clip(-65504, 65504) if encoder_hidden_states is not None: encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504) return hidden_states, encoder_hidden_states class CustomerAttnProcessor2_0: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). """ def apply_rotary_emb( self, x: torch.Tensor, freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], ) -> Tuple[torch.Tensor, torch.Tensor]: """ Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are returned as real tensors. Args: x (`torch.Tensor`): Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) Returns: Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. """ cos, sin = freqs_cis # [S, D] cos = cos[None, None] sin = sin[None, None] cos, sin = cos.to(x.device), sin.to(x.device) x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) return out def __call__( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_states: torch.FloatTensor = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, transformer_options={}, *args, **kwargs, ) -> torch.Tensor: residual = hidden_states input_ndim = hidden_states.ndim if input_ndim == 4: batch_size, channel, height, width = hidden_states.shape hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) batch_size, sequence_length, _ = ( hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape ) has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") if attn.group_norm is not None: hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states) if encoder_hidden_states is None: encoder_hidden_states = hidden_states elif attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) inner_dim = key.shape[-1] head_dim = inner_dim // attn.heads query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) if attn.norm_q is not None: query = attn.norm_q(query) if attn.norm_k is not None: key = attn.norm_k(key) # Apply RoPE if needed if rotary_freqs_cis is not None: query = self.apply_rotary_emb(query, rotary_freqs_cis) if not attn.is_cross_attention: key = self.apply_rotary_emb(key, rotary_freqs_cis) elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: # attention_mask: N x S1 # encoder_attention_mask: N x S2 # cross attention 整合attention_mask和encoder_attention_mask combined_mask = attention_mask[:, :, None] * encoder_attention_mask[:, None, :] attention_mask = torch.where(combined_mask == 1, 0.0, -torch.inf) attention_mask = attention_mask[:, None, :, :].expand(-1, attn.heads, -1, -1).to(query.dtype) elif not attn.is_cross_attention and attention_mask is not None: attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) # the output of sdp = (batch, num_heads, seq_len, head_dim) hidden_states = optimized_attention( query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options, ).to(query.dtype) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) if input_ndim == 4: hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) if attn.residual_connection: hidden_states = hidden_states + residual hidden_states = hidden_states / attn.rescale_output_factor return hidden_states def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore """Repeat `val` for `repeat_time` times and return the list or val if list/tuple.""" if isinstance(x, (list, tuple)): return list(x) return [x for _ in range(repeat_time)] def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore """Return tuple with min_len by repeating element at idx_repeat.""" # convert to list first x = val2list(x) # repeat elements if necessary if len(x) > 0: x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))] return tuple(x) def t2i_modulate(x, shift, scale): return x * (1 + scale) + shift def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]: if isinstance(kernel_size, tuple): return tuple([get_same_padding(ks) for ks in kernel_size]) else: assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number" return kernel_size // 2 class ConvLayer(nn.Module): def __init__( self, in_dim: int, out_dim: int, kernel_size=3, stride=1, dilation=1, groups=1, padding: Union[int, None] = None, use_bias=False, norm=None, act=None, dtype=None, device=None, operations=None ): super().__init__() if padding is None: padding = get_same_padding(kernel_size) padding *= dilation self.in_dim = in_dim self.out_dim = out_dim self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.groups = groups self.padding = padding self.use_bias = use_bias self.conv = operations.Conv1d( in_dim, out_dim, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=use_bias, device=device, dtype=dtype ) if norm is not None: self.norm = operations.RMSNorm(out_dim, elementwise_affine=False, dtype=dtype, device=device) else: self.norm = None if act is not None: self.act = nn.SiLU(inplace=True) else: self.act = None def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.conv(x) if self.norm: x = self.norm(x) if self.act: x = self.act(x) return x class GLUMBConv(nn.Module): def __init__( self, in_features: int, hidden_features: int, out_feature=None, kernel_size=3, stride=1, padding: Union[int, None] = None, use_bias=False, norm=(None, None, None), act=("silu", "silu", None), dilation=1, dtype=None, device=None, operations=None ): out_feature = out_feature or in_features super().__init__() use_bias = val2tuple(use_bias, 3) norm = val2tuple(norm, 3) act = val2tuple(act, 3) self.glu_act = nn.SiLU(inplace=False) self.inverted_conv = ConvLayer( in_features, hidden_features * 2, 1, use_bias=use_bias[0], norm=norm[0], act=act[0], dtype=dtype, device=device, operations=operations, ) self.depth_conv = ConvLayer( hidden_features * 2, hidden_features * 2, kernel_size, stride=stride, groups=hidden_features * 2, padding=padding, use_bias=use_bias[1], norm=norm[1], act=None, dilation=dilation, dtype=dtype, device=device, operations=operations, ) self.point_conv = ConvLayer( hidden_features, out_feature, 1, use_bias=use_bias[2], norm=norm[2], act=act[2], dtype=dtype, device=device, operations=operations, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.transpose(1, 2) x = self.inverted_conv(x) x = self.depth_conv(x) x, gate = torch.chunk(x, 2, dim=1) gate = self.glu_act(gate) x = x * gate x = self.point_conv(x) x = x.transpose(1, 2) return x class LinearTransformerBlock(nn.Module): """ A Sana block with global shared adaptive layer norm (adaLN-single) conditioning. """ def __init__( self, dim, num_attention_heads, attention_head_dim, use_adaln_single=True, cross_attention_dim=None, added_kv_proj_dim=None, context_pre_only=False, mlp_ratio=4.0, add_cross_attention=False, add_cross_attention_dim=None, qk_norm=None, dtype=None, device=None, operations=None ): super().__init__() self.norm1 = operations.RMSNorm(dim, elementwise_affine=False, eps=1e-6) self.attn = Attention( query_dim=dim, cross_attention_dim=cross_attention_dim, added_kv_proj_dim=added_kv_proj_dim, dim_head=attention_head_dim, heads=num_attention_heads, out_dim=dim, bias=True, qk_norm=qk_norm, processor=CustomLiteLAProcessor2_0(), dtype=dtype, device=device, operations=operations, ) self.add_cross_attention = add_cross_attention self.context_pre_only = context_pre_only if add_cross_attention and add_cross_attention_dim is not None: self.cross_attn = Attention( query_dim=dim, cross_attention_dim=add_cross_attention_dim, added_kv_proj_dim=add_cross_attention_dim, dim_head=attention_head_dim, heads=num_attention_heads, out_dim=dim, context_pre_only=context_pre_only, bias=True, qk_norm=qk_norm, processor=CustomerAttnProcessor2_0(), dtype=dtype, device=device, operations=operations, ) self.norm2 = operations.RMSNorm(dim, 1e-06, elementwise_affine=False) self.ff = GLUMBConv( in_features=dim, hidden_features=int(dim * mlp_ratio), use_bias=(True, True, False), norm=(None, None, None), act=("silu", "silu", None), dtype=dtype, device=device, operations=operations, ) self.use_adaln_single = use_adaln_single if use_adaln_single: self.scale_shift_table = nn.Parameter(torch.empty(6, dim, dtype=dtype, device=device)) def forward( self, hidden_states: torch.FloatTensor, encoder_hidden_states: torch.FloatTensor = None, attention_mask: torch.FloatTensor = None, encoder_attention_mask: torch.FloatTensor = None, rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, temb: torch.FloatTensor = None, transformer_options={}, ): N = hidden_states.shape[0] # step 1: AdaLN single if self.use_adaln_single: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( comfy.model_management.cast_to(self.scale_shift_table[None], dtype=temb.dtype, device=temb.device) + temb.reshape(N, 6, -1) ).chunk(6, dim=1) norm_hidden_states = self.norm1(hidden_states) if self.use_adaln_single: norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa # step 2: attention if not self.add_cross_attention: attn_output, encoder_hidden_states = self.attn( hidden_states=norm_hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=rotary_freqs_cis_cross, transformer_options=transformer_options, ) else: attn_output, _ = self.attn( hidden_states=norm_hidden_states, attention_mask=attention_mask, encoder_hidden_states=None, encoder_attention_mask=None, rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=None, transformer_options=transformer_options, ) if self.use_adaln_single: attn_output = gate_msa * attn_output hidden_states = attn_output + hidden_states if self.add_cross_attention: attn_output = self.cross_attn( hidden_states=hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=rotary_freqs_cis_cross, transformer_options=transformer_options, ) hidden_states = attn_output + hidden_states # step 3: add norm norm_hidden_states = self.norm2(hidden_states) if self.use_adaln_single: norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp # step 4: feed forward ff_output = self.ff(norm_hidden_states) if self.use_adaln_single: ff_output = gate_mlp * ff_output hidden_states = hidden_states + ff_output return hidden_states ================================================ FILE: comfy/ldm/ace/lyric_encoder.py ================================================ # Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py from typing import Optional, Tuple, Union import math import torch from torch import nn import comfy.model_management class ConvolutionModule(nn.Module): """ConvolutionModule in Conformer model.""" def __init__(self, channels: int, kernel_size: int = 15, activation: nn.Module = nn.ReLU(), norm: str = "batch_norm", causal: bool = False, bias: bool = True, dtype=None, device=None, operations=None): """Construct an ConvolutionModule object. Args: channels (int): The number of channels of conv layers. kernel_size (int): Kernel size of conv layers. causal (int): Whether use causal convolution or not """ super().__init__() self.pointwise_conv1 = operations.Conv1d( channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device ) # self.lorder is used to distinguish if it's a causal convolution, # if self.lorder > 0: it's a causal convolution, the input will be # padded with self.lorder frames on the left in forward. # else: it's a symmetrical convolution if causal: padding = 0 self.lorder = kernel_size - 1 else: # kernel_size should be an odd number for none causal convolution assert (kernel_size - 1) % 2 == 0 padding = (kernel_size - 1) // 2 self.lorder = 0 self.depthwise_conv = operations.Conv1d( channels, channels, kernel_size, stride=1, padding=padding, groups=channels, bias=bias, dtype=dtype, device=device ) assert norm in ['batch_norm', 'layer_norm'] if norm == "batch_norm": self.use_layer_norm = False self.norm = nn.BatchNorm1d(channels) else: self.use_layer_norm = True self.norm = operations.LayerNorm(channels, dtype=dtype, device=device) self.pointwise_conv2 = operations.Conv1d( channels, channels, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device ) self.activation = activation def forward( self, x: torch.Tensor, mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), cache: torch.Tensor = torch.zeros((0, 0, 0)), ) -> Tuple[torch.Tensor, torch.Tensor]: """Compute convolution module. Args: x (torch.Tensor): Input tensor (#batch, time, channels). mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), (0, 0, 0) means fake mask. cache (torch.Tensor): left context cache, it is only used in causal convolution (#batch, channels, cache_t), (0, 0, 0) meas fake cache. Returns: torch.Tensor: Output tensor (#batch, time, channels). """ # exchange the temporal dimension and the feature dimension x = x.transpose(1, 2) # (#batch, channels, time) # mask batch padding if mask_pad.size(2) > 0: # time > 0 x.masked_fill_(~mask_pad, 0.0) if self.lorder > 0: if cache.size(2) == 0: # cache_t == 0 x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0) else: assert cache.size(0) == x.size(0) # equal batch assert cache.size(1) == x.size(1) # equal channel x = torch.cat((cache, x), dim=2) assert (x.size(2) > self.lorder) new_cache = x[:, :, -self.lorder:] else: # It's better we just return None if no cache is required, # However, for JIT export, here we just fake one tensor instead of # None. new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) # GLU mechanism x = self.pointwise_conv1(x) # (batch, 2*channel, dim) x = nn.functional.glu(x, dim=1) # (batch, channel, dim) # 1D Depthwise Conv x = self.depthwise_conv(x) if self.use_layer_norm: x = x.transpose(1, 2) x = self.activation(self.norm(x)) if self.use_layer_norm: x = x.transpose(1, 2) x = self.pointwise_conv2(x) # mask batch padding if mask_pad.size(2) > 0: # time > 0 x.masked_fill_(~mask_pad, 0.0) return x.transpose(1, 2), new_cache class PositionwiseFeedForward(torch.nn.Module): """Positionwise feed forward layer. FeedForward are appied on each position of the sequence. The output dim is same with the input dim. Args: idim (int): Input dimenstion. hidden_units (int): The number of hidden units. dropout_rate (float): Dropout rate. activation (torch.nn.Module): Activation function """ def __init__( self, idim: int, hidden_units: int, dropout_rate: float, activation: torch.nn.Module = torch.nn.ReLU(), dtype=None, device=None, operations=None ): """Construct a PositionwiseFeedForward object.""" super(PositionwiseFeedForward, self).__init__() self.w_1 = operations.Linear(idim, hidden_units, dtype=dtype, device=device) self.activation = activation self.dropout = torch.nn.Dropout(dropout_rate) self.w_2 = operations.Linear(hidden_units, idim, dtype=dtype, device=device) def forward(self, xs: torch.Tensor) -> torch.Tensor: """Forward function. Args: xs: input tensor (B, L, D) Returns: output tensor, (B, L, D) """ return self.w_2(self.dropout(self.activation(self.w_1(xs)))) class Swish(torch.nn.Module): """Construct an Swish object.""" def forward(self, x: torch.Tensor) -> torch.Tensor: """Return Swish activation function.""" return x * torch.sigmoid(x) class MultiHeadedAttention(nn.Module): """Multi-Head Attention layer. Args: n_head (int): The number of heads. n_feat (int): The number of features. dropout_rate (float): Dropout rate. """ def __init__(self, n_head: int, n_feat: int, dropout_rate: float, key_bias: bool = True, dtype=None, device=None, operations=None): """Construct an MultiHeadedAttention object.""" super().__init__() assert n_feat % n_head == 0 # We assume d_v always equals d_k self.d_k = n_feat // n_head self.h = n_head self.linear_q = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) self.linear_k = operations.Linear(n_feat, n_feat, bias=key_bias, dtype=dtype, device=device) self.linear_v = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) self.linear_out = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) self.dropout = nn.Dropout(p=dropout_rate) def forward_qkv( self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Transform query, key and value. Args: query (torch.Tensor): Query tensor (#batch, time1, size). key (torch.Tensor): Key tensor (#batch, time2, size). value (torch.Tensor): Value tensor (#batch, time2, size). Returns: torch.Tensor: Transformed query tensor, size (#batch, n_head, time1, d_k). torch.Tensor: Transformed key tensor, size (#batch, n_head, time2, d_k). torch.Tensor: Transformed value tensor, size (#batch, n_head, time2, d_k). """ n_batch = query.size(0) q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) q = q.transpose(1, 2) # (batch, head, time1, d_k) k = k.transpose(1, 2) # (batch, head, time2, d_k) v = v.transpose(1, 2) # (batch, head, time2, d_k) return q, k, v def forward_attention( self, value: torch.Tensor, scores: torch.Tensor, mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) ) -> torch.Tensor: """Compute attention context vector. Args: value (torch.Tensor): Transformed value, size (#batch, n_head, time2, d_k). scores (torch.Tensor): Attention score, size (#batch, n_head, time1, time2). mask (torch.Tensor): Mask, size (#batch, 1, time2) or (#batch, time1, time2), (0, 0, 0) means fake mask. Returns: torch.Tensor: Transformed value (#batch, time1, d_model) weighted by the attention score (#batch, time1, time2). """ n_batch = value.size(0) if mask is not None and mask.size(2) > 0: # time2 > 0 mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) # For last chunk, time2 might be larger than scores.size(-1) mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) scores = scores.masked_fill(mask, -float('inf')) attn = torch.softmax(scores, dim=-1).masked_fill( mask, 0.0) # (batch, head, time1, time2) else: attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) p_attn = self.dropout(attn) x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) x = (x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) ) # (batch, time1, d_model) return self.linear_out(x) # (batch, time1, d_model) def forward( self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), pos_emb: torch.Tensor = torch.empty(0), cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) ) -> Tuple[torch.Tensor, torch.Tensor]: """Compute scaled dot product attention. Args: query (torch.Tensor): Query tensor (#batch, time1, size). key (torch.Tensor): Key tensor (#batch, time2, size). value (torch.Tensor): Value tensor (#batch, time2, size). mask (torch.Tensor): Mask tensor (#batch, 1, time2) or (#batch, time1, time2). 1.When applying cross attention between decoder and encoder, the batch padding mask for input is in (#batch, 1, T) shape. 2.When applying self attention of encoder, the mask is in (#batch, T, T) shape. 3.When applying self attention of decoder, the mask is in (#batch, L, L) shape. 4.If the different position in decoder see different block of the encoder, such as Mocha, the passed in mask could be in (#batch, L, T) shape. But there is no such case in current CosyVoice. cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), where `cache_t == chunk_size * num_decoding_left_chunks` and `head * d_k == size` Returns: torch.Tensor: Output tensor (#batch, time1, d_model). torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) where `cache_t == chunk_size * num_decoding_left_chunks` and `head * d_k == size` """ q, k, v = self.forward_qkv(query, key, value) if cache.size(0) > 0: key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) k = torch.cat([key_cache, k], dim=2) v = torch.cat([value_cache, v], dim=2) new_cache = torch.cat((k, v), dim=-1) scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) return self.forward_attention(v, scores, mask), new_cache class RelPositionMultiHeadedAttention(MultiHeadedAttention): """Multi-Head Attention layer with relative position encoding. Paper: https://arxiv.org/abs/1901.02860 Args: n_head (int): The number of heads. n_feat (int): The number of features. dropout_rate (float): Dropout rate. """ def __init__(self, n_head: int, n_feat: int, dropout_rate: float, key_bias: bool = True, dtype=None, device=None, operations=None): """Construct an RelPositionMultiHeadedAttention object.""" super().__init__(n_head, n_feat, dropout_rate, key_bias, dtype=dtype, device=device, operations=operations) # linear transformation for positional encoding self.linear_pos = operations.Linear(n_feat, n_feat, bias=False, dtype=dtype, device=device) # these two learnable bias are used in matrix c and matrix d # as described in https://arxiv.org/abs/1901.02860 Section 3.3 self.pos_bias_u = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) self.pos_bias_v = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) # torch.nn.init.xavier_uniform_(self.pos_bias_u) # torch.nn.init.xavier_uniform_(self.pos_bias_v) def rel_shift(self, x: torch.Tensor) -> torch.Tensor: """Compute relative positional encoding. Args: x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). time1 means the length of query vector. Returns: torch.Tensor: Output tensor. """ zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype) x_padded = torch.cat([zero_pad, x], dim=-1) x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2)) x = x_padded[:, :, 1:].view_as(x)[ :, :, :, : x.size(-1) // 2 + 1 ] # only keep the positions from 0 to time2 return x def forward( self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), pos_emb: torch.Tensor = torch.empty(0), cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) ) -> Tuple[torch.Tensor, torch.Tensor]: """Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: query (torch.Tensor): Query tensor (#batch, time1, size). key (torch.Tensor): Key tensor (#batch, time2, size). value (torch.Tensor): Value tensor (#batch, time2, size). mask (torch.Tensor): Mask tensor (#batch, 1, time2) or (#batch, time1, time2), (0, 0, 0) means fake mask. pos_emb (torch.Tensor): Positional embedding tensor (#batch, time2, size). cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), where `cache_t == chunk_size * num_decoding_left_chunks` and `head * d_k == size` Returns: torch.Tensor: Output tensor (#batch, time1, d_model). torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) where `cache_t == chunk_size * num_decoding_left_chunks` and `head * d_k == size` """ q, k, v = self.forward_qkv(query, key, value) q = q.transpose(1, 2) # (batch, time1, head, d_k) if cache.size(0) > 0: key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) k = torch.cat([key_cache, k], dim=2) v = torch.cat([value_cache, v], dim=2) # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's # non-trivial to calculate `next_cache_start` here. new_cache = torch.cat((k, v), dim=-1) n_batch_pos = pos_emb.size(0) p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) p = p.transpose(1, 2) # (batch, head, time1, d_k) # (batch, head, time1, d_k) q_with_bias_u = (q + comfy.model_management.cast_to(self.pos_bias_u, dtype=q.dtype, device=q.device)).transpose(1, 2) # (batch, head, time1, d_k) q_with_bias_v = (q + comfy.model_management.cast_to(self.pos_bias_v, dtype=q.dtype, device=q.device)).transpose(1, 2) # compute attention score # first compute matrix a and matrix c # as described in https://arxiv.org/abs/1901.02860 Section 3.3 # (batch, head, time1, time2) matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) # compute matrix b and matrix d # (batch, head, time1, time2) matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used if matrix_ac.shape != matrix_bd.shape: matrix_bd = self.rel_shift(matrix_bd) scores = (matrix_ac + matrix_bd) / math.sqrt( self.d_k) # (batch, head, time1, time2) return self.forward_attention(v, scores, mask), new_cache def subsequent_mask( size: int, device: torch.device = torch.device("cpu"), ) -> torch.Tensor: """Create mask for subsequent steps (size, size). This mask is used only in decoder which works in an auto-regressive mode. This means the current step could only do attention with its left steps. In encoder, fully attention is used when streaming is not necessary and the sequence is not long. In this case, no attention mask is needed. When streaming is need, chunk-based attention is used in encoder. See subsequent_chunk_mask for the chunk-based attention mask. Args: size (int): size of mask str device (str): "cpu" or "cuda" or torch.Tensor.device dtype (torch.device): result dtype Returns: torch.Tensor: mask Examples: >>> subsequent_mask(3) [[1, 0, 0], [1, 1, 0], [1, 1, 1]] """ arange = torch.arange(size, device=device) mask = arange.expand(size, size) arange = arange.unsqueeze(-1) mask = mask <= arange return mask def subsequent_chunk_mask( size: int, chunk_size: int, num_left_chunks: int = -1, device: torch.device = torch.device("cpu"), ) -> torch.Tensor: """Create mask for subsequent steps (size, size) with chunk size, this is for streaming encoder Args: size (int): size of mask chunk_size (int): size of chunk num_left_chunks (int): number of left chunks <0: use full chunk >=0: use num_left_chunks device (torch.device): "cpu" or "cuda" or torch.Tensor.device Returns: torch.Tensor: mask Examples: >>> subsequent_chunk_mask(4, 2) [[1, 1, 0, 0], [1, 1, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]] """ ret = torch.zeros(size, size, device=device, dtype=torch.bool) for i in range(size): if num_left_chunks < 0: start = 0 else: start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) ending = min((i // chunk_size + 1) * chunk_size, size) ret[i, start:ending] = True return ret def add_optional_chunk_mask(xs: torch.Tensor, masks: torch.Tensor, use_dynamic_chunk: bool, use_dynamic_left_chunk: bool, decoding_chunk_size: int, static_chunk_size: int, num_decoding_left_chunks: int, enable_full_context: bool = True): """ Apply optional mask for encoder. Args: xs (torch.Tensor): padded input, (B, L, D), L for max length mask (torch.Tensor): mask for xs, (B, 1, L) use_dynamic_chunk (bool): whether to use dynamic chunk or not use_dynamic_left_chunk (bool): whether to use dynamic left chunk for training. decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's 0: default for training, use random dynamic chunk. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. static_chunk_size (int): chunk size for static chunk training/decoding if it's greater than 0, if use_dynamic_chunk is true, this parameter will be ignored num_decoding_left_chunks: number of left chunks, this is for decoding, the chunk size is decoding_chunk_size. >=0: use num_decoding_left_chunks <0: use all left chunks enable_full_context (bool): True: chunk size is either [1, 25] or full context(max_len) False: chunk size ~ U[1, 25] Returns: torch.Tensor: chunk mask of the input xs. """ # Whether to use chunk mask or not if use_dynamic_chunk: max_len = xs.size(1) if decoding_chunk_size < 0: chunk_size = max_len num_left_chunks = -1 elif decoding_chunk_size > 0: chunk_size = decoding_chunk_size num_left_chunks = num_decoding_left_chunks else: # chunk size is either [1, 25] or full context(max_len). # Since we use 4 times subsampling and allow up to 1s(100 frames) # delay, the maximum frame is 100 / 4 = 25. chunk_size = torch.randint(1, max_len, (1, )).item() num_left_chunks = -1 if chunk_size > max_len // 2 and enable_full_context: chunk_size = max_len else: chunk_size = chunk_size % 25 + 1 if use_dynamic_left_chunk: max_left_chunks = (max_len - 1) // chunk_size num_left_chunks = torch.randint(0, max_left_chunks, (1, )).item() chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size, num_left_chunks, xs.device) # (L, L) chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) chunk_masks = masks & chunk_masks # (B, L, L) elif static_chunk_size > 0: num_left_chunks = num_decoding_left_chunks chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size, num_left_chunks, xs.device) # (L, L) chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) chunk_masks = masks & chunk_masks # (B, L, L) else: chunk_masks = masks return chunk_masks class ConformerEncoderLayer(nn.Module): """Encoder layer module. Args: size (int): Input dimension. self_attn (torch.nn.Module): Self-attention module instance. `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance can be used as the argument. feed_forward (torch.nn.Module): Feed-forward module instance. `PositionwiseFeedForward` instance can be used as the argument. feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance. `PositionwiseFeedForward` instance can be used as the argument. conv_module (torch.nn.Module): Convolution module instance. `ConvlutionModule` instance can be used as the argument. dropout_rate (float): Dropout rate. normalize_before (bool): True: use layer_norm before each sub-block. False: use layer_norm after each sub-block. """ def __init__( self, size: int, self_attn: torch.nn.Module, feed_forward: Optional[nn.Module] = None, feed_forward_macaron: Optional[nn.Module] = None, conv_module: Optional[nn.Module] = None, dropout_rate: float = 0.1, normalize_before: bool = True, dtype=None, device=None, operations=None ): """Construct an EncoderLayer object.""" super().__init__() self.self_attn = self_attn self.feed_forward = feed_forward self.feed_forward_macaron = feed_forward_macaron self.conv_module = conv_module self.norm_ff = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the FNN module self.norm_mha = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the MHA module if feed_forward_macaron is not None: self.norm_ff_macaron = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) self.ff_scale = 0.5 else: self.ff_scale = 1.0 if self.conv_module is not None: self.norm_conv = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the CNN module self.norm_final = operations.LayerNorm( size, eps=1e-5, dtype=dtype, device=device) # for the final output of the block self.dropout = nn.Dropout(dropout_rate) self.size = size self.normalize_before = normalize_before def forward( self, x: torch.Tensor, mask: torch.Tensor, pos_emb: torch.Tensor, mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """Compute encoded features. Args: x (torch.Tensor): (#batch, time, size) mask (torch.Tensor): Mask tensor for the input (#batch, time，time), (0, 0, 0) means fake mask. pos_emb (torch.Tensor): positional encoding, must not be None for ConformerEncoderLayer. mask_pad (torch.Tensor): batch padding mask used for conv module. (#batch, 1，time), (0, 0, 0) means fake mask. att_cache (torch.Tensor): Cache tensor of the KEY & VALUE (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. cnn_cache (torch.Tensor): Convolution cache in conformer layer (#batch=1, size, cache_t2) Returns: torch.Tensor: Output tensor (#batch, time, size). torch.Tensor: Mask tensor (#batch, time, time). torch.Tensor: att_cache tensor, (#batch=1, head, cache_t1 + time, d_k * 2). torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). """ # whether to use macaron style if self.feed_forward_macaron is not None: residual = x if self.normalize_before: x = self.norm_ff_macaron(x) x = residual + self.ff_scale * self.dropout( self.feed_forward_macaron(x)) if not self.normalize_before: x = self.norm_ff_macaron(x) # multi-headed self-attention module residual = x if self.normalize_before: x = self.norm_mha(x) x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) x = residual + self.dropout(x_att) if not self.normalize_before: x = self.norm_mha(x) # convolution module # Fake new cnn cache here, and then change it in conv_module new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) if self.conv_module is not None: residual = x if self.normalize_before: x = self.norm_conv(x) x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) x = residual + self.dropout(x) if not self.normalize_before: x = self.norm_conv(x) # feed forward module residual = x if self.normalize_before: x = self.norm_ff(x) x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) if not self.normalize_before: x = self.norm_ff(x) if self.conv_module is not None: x = self.norm_final(x) return x, mask, new_att_cache, new_cnn_cache class EspnetRelPositionalEncoding(torch.nn.Module): """Relative positional encoding module (new implementation). Details can be found in https://github.com/espnet/espnet/pull/2816. See : Appendix B in https://arxiv.org/abs/1901.02860 Args: d_model (int): Embedding dimension. dropout_rate (float): Dropout rate. max_len (int): Maximum input length. """ def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): """Construct an PositionalEncoding object.""" super(EspnetRelPositionalEncoding, self).__init__() self.d_model = d_model self.xscale = math.sqrt(self.d_model) self.dropout = torch.nn.Dropout(p=dropout_rate) self.pe = None self.extend_pe(torch.tensor(0.0).expand(1, max_len)) def extend_pe(self, x: torch.Tensor): """Reset the positional encodings.""" if self.pe is not None: # self.pe contains both positive and negative parts # the length of self.pe is 2 * input_len - 1 if self.pe.size(1) >= x.size(1) * 2 - 1: if self.pe.dtype != x.dtype or self.pe.device != x.device: self.pe = self.pe.to(dtype=x.dtype, device=x.device) return # Suppose `i` means to the position of query vecotr and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: """Add positional encoding. Args: x (torch.Tensor): Input tensor (batch, time, `*`). Returns: torch.Tensor: Encoded tensor (batch, time, `*`). """ self.extend_pe(x) x = x * self.xscale pos_emb = self.position_encoding(size=x.size(1), offset=offset) return self.dropout(x), self.dropout(pos_emb) def position_encoding(self, offset: Union[int, torch.Tensor], size: int) -> torch.Tensor: """ For getting encoding in a streaming fashion Attention!!!!! we apply dropout only once at the whole utterance level in a none streaming way, but will call this function several times with increasing input size in a streaming scenario, so the dropout will be applied several times. Args: offset (int or torch.tensor): start offset size (int): required size of position encoding Returns: torch.Tensor: Corresponding encoding """ pos_emb = self.pe[ :, self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size, ] return pos_emb class LinearEmbed(torch.nn.Module): """Linear transform the input without subsampling Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. """ def __init__(self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module, dtype=None, device=None, operations=None): """Construct an linear object.""" super().__init__() self.out = torch.nn.Sequential( operations.Linear(idim, odim, dtype=dtype, device=device), operations.LayerNorm(odim, eps=1e-5, dtype=dtype, device=device), torch.nn.Dropout(dropout_rate), ) self.pos_enc = pos_enc_class #rel_pos_espnet def position_encoding(self, offset: Union[int, torch.Tensor], size: int) -> torch.Tensor: return self.pos_enc.position_encoding(offset, size) def forward( self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Input x. Args: x (torch.Tensor): Input tensor (#batch, time, idim). x_mask (torch.Tensor): Input mask (#batch, 1, time). Returns: torch.Tensor: linear input tensor (#batch, time', odim), where time' = time . torch.Tensor: linear input mask (#batch, 1, time'), where time' = time . """ x = self.out(x) x, pos_emb = self.pos_enc(x, offset) return x, pos_emb ATTENTION_CLASSES = { "selfattn": MultiHeadedAttention, "rel_selfattn": RelPositionMultiHeadedAttention, } ACTIVATION_CLASSES = { "hardtanh": torch.nn.Hardtanh, "tanh": torch.nn.Tanh, "relu": torch.nn.ReLU, "selu": torch.nn.SELU, "swish": getattr(torch.nn, "SiLU", Swish), "gelu": torch.nn.GELU, } def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: """Make mask tensor containing indices of padded part. See description of make_non_pad_mask. Args: lengths (torch.Tensor): Batch of lengths (B,). Returns: torch.Tensor: Mask tensor containing indices of padded part. Examples: >>> lengths = [5, 3, 2] >>> make_pad_mask(lengths) masks = [[0, 0, 0, 0 ,0], [0, 0, 0, 1, 1], [0, 0, 1, 1, 1]] """ batch_size = lengths.size(0) max_len = max_len if max_len > 0 else lengths.max().item() seq_range = torch.arange(0, max_len, dtype=torch.int64, device=lengths.device) seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) seq_length_expand = lengths.unsqueeze(-1) mask = seq_range_expand >= seq_length_expand return mask #https://github.com/FunAudioLLM/CosyVoice/blob/main/examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml class ConformerEncoder(torch.nn.Module): """Conformer encoder module.""" def __init__( self, input_size: int, output_size: int = 1024, attention_heads: int = 16, linear_units: int = 4096, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = 'linear', pos_enc_layer_type: str = 'rel_pos_espnet', normalize_before: bool = True, static_chunk_size: int = 1, # 1: causal_mask; 0: full_mask use_dynamic_chunk: bool = False, use_dynamic_left_chunk: bool = False, positionwise_conv_kernel_size: int = 1, macaron_style: bool =False, selfattention_layer_type: str = "rel_selfattn", activation_type: str = "swish", use_cnn_module: bool = False, cnn_module_kernel: int = 15, causal: bool = False, cnn_module_norm: str = "batch_norm", key_bias: bool = True, dtype=None, device=None, operations=None ): """Construct ConformerEncoder Args: input_size to use_dynamic_chunk, see in BaseEncoder positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer. macaron_style (bool): Whether to use macaron style for positionwise layer. selfattention_layer_type (str): Encoder attention layer type, the parameter has no effect now, it's just for configure compatibility. #'rel_selfattn' activation_type (str): Encoder activation function type. use_cnn_module (bool): Whether to use convolution module. cnn_module_kernel (int): Kernel size of convolution module. causal (bool): whether to use causal convolution or not. key_bias: whether use bias in attention.linear_k, False for whisper models. """ super().__init__() self.output_size = output_size self.embed = LinearEmbed(input_size, output_size, dropout_rate, EspnetRelPositionalEncoding(output_size, positional_dropout_rate), dtype=dtype, device=device, operations=operations) self.normalize_before = normalize_before self.after_norm = operations.LayerNorm(output_size, eps=1e-5, dtype=dtype, device=device) self.use_dynamic_chunk = use_dynamic_chunk self.static_chunk_size = static_chunk_size self.use_dynamic_chunk = use_dynamic_chunk self.use_dynamic_left_chunk = use_dynamic_left_chunk activation = ACTIVATION_CLASSES[activation_type]() # self-attention module definition encoder_selfattn_layer_args = ( attention_heads, output_size, attention_dropout_rate, key_bias, ) # feed-forward module definition positionwise_layer_args = ( output_size, linear_units, dropout_rate, activation, ) # convolution module definition convolution_layer_args = (output_size, cnn_module_kernel, activation, cnn_module_norm, causal) self.encoders = torch.nn.ModuleList([ ConformerEncoderLayer( output_size, RelPositionMultiHeadedAttention( *encoder_selfattn_layer_args, dtype=dtype, device=device, operations=operations), PositionwiseFeedForward(*positionwise_layer_args, dtype=dtype, device=device, operations=operations), PositionwiseFeedForward( *positionwise_layer_args, dtype=dtype, device=device, operations=operations) if macaron_style else None, ConvolutionModule( *convolution_layer_args, dtype=dtype, device=device, operations=operations) if use_cnn_module else None, dropout_rate, normalize_before, dtype=dtype, device=device, operations=operations ) for _ in range(num_blocks) ]) def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, pos_emb: torch.Tensor, mask_pad: torch.Tensor) -> torch.Tensor: for layer in self.encoders: xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) return xs def forward( self, xs: torch.Tensor, pad_mask: torch.Tensor, decoding_chunk_size: int = 0, num_decoding_left_chunks: int = -1, ) -> Tuple[torch.Tensor, torch.Tensor]: """Embed positions in tensor. Args: xs: padded input tensor (B, T, D) xs_lens: input length (B) decoding_chunk_size: decoding chunk size for dynamic chunk 0: default for training, use random dynamic chunk. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. num_decoding_left_chunks: number of left chunks, this is for decoding, the chunk size is decoding_chunk_size. >=0: use num_decoding_left_chunks <0: use all left chunks Returns: encoder output tensor xs, and subsampled masks xs: padded output tensor (B, T' ~= T/subsample_rate, D) masks: torch.Tensor batch padding mask after subsample (B, 1, T' ~= T/subsample_rate) NOTE(xcsong): We pass the `__call__` method of the modules instead of `forward` to the checkpointing API because `__call__` attaches all the hooks of the module. https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 """ masks = None if pad_mask is not None: masks = pad_mask.to(torch.bool).unsqueeze(1) # (B, 1, T) xs, pos_emb = self.embed(xs) mask_pad = masks # (B, 1, T/subsample_rate) chunk_masks = add_optional_chunk_mask(xs, masks, self.use_dynamic_chunk, self.use_dynamic_left_chunk, decoding_chunk_size, self.static_chunk_size, num_decoding_left_chunks) xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) if self.normalize_before: xs = self.after_norm(xs) # Here we assume the mask is not changed in encoder layers, so just # return the masks before encoder layers, and the masks will be used # for cross attention with decoder later return xs, masks ================================================ FILE: comfy/ldm/ace/model.py ================================================ # Original from: https://github.com/ace-step/ACE-Step/blob/main/models/ace_step_transformer.py # Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, List, Union import torch from torch import nn import comfy.model_management import comfy.patcher_extension from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps from .attention import LinearTransformerBlock, t2i_modulate from .lyric_encoder import ConformerEncoder as LyricEncoder def cross_norm(hidden_states, controlnet_input): # input N x T x c mean_hidden_states, std_hidden_states = hidden_states.mean(dim=(1,2), keepdim=True), hidden_states.std(dim=(1,2), keepdim=True) mean_controlnet_input, std_controlnet_input = controlnet_input.mean(dim=(1,2), keepdim=True), controlnet_input.std(dim=(1,2), keepdim=True) controlnet_input = (controlnet_input - mean_controlnet_input) * (std_hidden_states / (std_controlnet_input + 1e-12)) + mean_hidden_states return controlnet_input # Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2 class Qwen2RotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, dtype=None, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=device).float() / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32 ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) class T2IFinalLayer(nn.Module): """ The final layer of Sana. """ def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, patch_size[0] * patch_size[1] * out_channels, bias=True, dtype=dtype, device=device) self.scale_shift_table = nn.Parameter(torch.empty(2, hidden_size, dtype=dtype, device=device)) self.out_channels = out_channels self.patch_size = patch_size def unpatchfy( self, hidden_states: torch.Tensor, width: int, ): # 4 unpatchify new_height, new_width = 1, hidden_states.size(1) hidden_states = hidden_states.reshape( shape=(hidden_states.shape[0], new_height, new_width, self.patch_size[0], self.patch_size[1], self.out_channels) ).contiguous() hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(hidden_states.shape[0], self.out_channels, new_height * self.patch_size[0], new_width * self.patch_size[1]) ).contiguous() if width > new_width: output = torch.nn.functional.pad(output, (0, width - new_width, 0, 0), 'constant', 0) elif width < new_width: output = output[:, :, :, :width] return output def forward(self, x, t, output_length): shift, scale = (comfy.model_management.cast_to(self.scale_shift_table[None], device=t.device, dtype=t.dtype) + t[:, None]).chunk(2, dim=1) x = t2i_modulate(self.norm_final(x), shift, scale) x = self.linear(x) # unpatchify output = self.unpatchfy(x, output_length) return output class PatchEmbed(nn.Module): """2D Image to Patch Embedding""" def __init__( self, height=16, width=4096, patch_size=(16, 1), in_channels=8, embed_dim=1152, bias=True, dtype=None, device=None, operations=None ): super().__init__() patch_size_h, patch_size_w = patch_size self.early_conv_layers = nn.Sequential( operations.Conv2d(in_channels, in_channels*256, kernel_size=patch_size, stride=patch_size, padding=0, bias=bias, dtype=dtype, device=device), operations.GroupNorm(num_groups=32, num_channels=in_channels*256, eps=1e-6, affine=True, dtype=dtype, device=device), operations.Conv2d(in_channels*256, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device) ) self.patch_size = patch_size self.height, self.width = height // patch_size_h, width // patch_size_w self.base_size = self.width def forward(self, latent): # early convolutions, N x C x H x W -> N x 256 * sqrt(patch_size) x H/patch_size x W/patch_size latent = self.early_conv_layers(latent) latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC return latent class ACEStepTransformer2DModel(nn.Module): # _supports_gradient_checkpointing = True def __init__( self, in_channels: Optional[int] = 8, num_layers: int = 28, inner_dim: int = 1536, attention_head_dim: int = 64, num_attention_heads: int = 24, mlp_ratio: float = 4.0, out_channels: int = 8, max_position: int = 32768, rope_theta: float = 1000000.0, speaker_embedding_dim: int = 512, text_embedding_dim: int = 768, ssl_encoder_depths: List[int] = [9, 9], ssl_names: List[str] = ["mert", "m-hubert"], ssl_latent_dims: List[int] = [1024, 768], lyric_encoder_vocab_size: int = 6681, lyric_hidden_size: int = 1024, patch_size: List[int] = [16, 1], max_height: int = 16, max_width: int = 4096, audio_model=None, dtype=None, device=None, operations=None ): super().__init__() self.dtype = dtype self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.inner_dim = inner_dim self.out_channels = out_channels self.max_position = max_position self.patch_size = patch_size self.rope_theta = rope_theta self.rotary_emb = Qwen2RotaryEmbedding( dim=self.attention_head_dim, max_position_embeddings=self.max_position, base=self.rope_theta, dtype=dtype, device=device, ) # 2. Define input layers self.in_channels = in_channels self.num_layers = num_layers # 3. Define transformers blocks self.transformer_blocks = nn.ModuleList( [ LinearTransformerBlock( dim=self.inner_dim, num_attention_heads=self.num_attention_heads, attention_head_dim=attention_head_dim, mlp_ratio=mlp_ratio, add_cross_attention=True, add_cross_attention_dim=self.inner_dim, dtype=dtype, device=device, operations=operations, ) for i in range(self.num_layers) ] ) self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=self.inner_dim, dtype=dtype, device=device, operations=operations) self.t_block = nn.Sequential(nn.SiLU(), operations.Linear(self.inner_dim, 6 * self.inner_dim, bias=True, dtype=dtype, device=device)) # speaker self.speaker_embedder = operations.Linear(speaker_embedding_dim, self.inner_dim, dtype=dtype, device=device) # genre self.genre_embedder = operations.Linear(text_embedding_dim, self.inner_dim, dtype=dtype, device=device) # lyric self.lyric_embs = operations.Embedding(lyric_encoder_vocab_size, lyric_hidden_size, dtype=dtype, device=device) self.lyric_encoder = LyricEncoder(input_size=lyric_hidden_size, static_chunk_size=0, dtype=dtype, device=device, operations=operations) self.lyric_proj = operations.Linear(lyric_hidden_size, self.inner_dim, dtype=dtype, device=device) projector_dim = 2 * self.inner_dim self.projectors = nn.ModuleList([ nn.Sequential( operations.Linear(self.inner_dim, projector_dim, dtype=dtype, device=device), nn.SiLU(), operations.Linear(projector_dim, projector_dim, dtype=dtype, device=device), nn.SiLU(), operations.Linear(projector_dim, ssl_dim, dtype=dtype, device=device), ) for ssl_dim in ssl_latent_dims ]) self.proj_in = PatchEmbed( height=max_height, width=max_width, patch_size=patch_size, embed_dim=self.inner_dim, bias=True, dtype=dtype, device=device, operations=operations, ) self.final_layer = T2IFinalLayer(self.inner_dim, patch_size=patch_size, out_channels=out_channels, dtype=dtype, device=device, operations=operations) def forward_lyric_encoder( self, lyric_token_idx: Optional[torch.LongTensor] = None, lyric_mask: Optional[torch.LongTensor] = None, out_dtype=None, ): # N x T x D lyric_embs = self.lyric_embs(lyric_token_idx, out_dtype=out_dtype) prompt_prenet_out, _mask = self.lyric_encoder(lyric_embs, lyric_mask, decoding_chunk_size=1, num_decoding_left_chunks=-1) prompt_prenet_out = self.lyric_proj(prompt_prenet_out) return prompt_prenet_out def encode( self, encoder_text_hidden_states: Optional[torch.Tensor] = None, text_attention_mask: Optional[torch.LongTensor] = None, speaker_embeds: Optional[torch.FloatTensor] = None, lyric_token_idx: Optional[torch.LongTensor] = None, lyric_mask: Optional[torch.LongTensor] = None, lyrics_strength=1.0, ): bs = encoder_text_hidden_states.shape[0] device = encoder_text_hidden_states.device # speaker embedding encoder_spk_hidden_states = self.speaker_embedder(speaker_embeds).unsqueeze(1) # genre embedding encoder_text_hidden_states = self.genre_embedder(encoder_text_hidden_states) # lyric encoder_lyric_hidden_states = self.forward_lyric_encoder( lyric_token_idx=lyric_token_idx, lyric_mask=lyric_mask, out_dtype=encoder_text_hidden_states.dtype, ) encoder_lyric_hidden_states *= lyrics_strength encoder_hidden_states = torch.cat([encoder_spk_hidden_states, encoder_text_hidden_states, encoder_lyric_hidden_states], dim=1) encoder_hidden_mask = None if text_attention_mask is not None: speaker_mask = torch.ones(bs, 1, device=device) encoder_hidden_mask = torch.cat([speaker_mask, text_attention_mask, lyric_mask], dim=1) return encoder_hidden_states, encoder_hidden_mask def decode( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_hidden_mask: torch.Tensor, timestep: Optional[torch.Tensor], output_length: int = 0, block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, controlnet_scale: Union[float, torch.Tensor] = 1.0, transformer_options={}, ): embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype)) temb = self.t_block(embedded_timestep) hidden_states = self.proj_in(hidden_states) # controlnet logic if block_controlnet_hidden_states is not None: control_condi = cross_norm(hidden_states, block_controlnet_hidden_states) hidden_states = hidden_states + control_condi * controlnet_scale # inner_hidden_states = [] rotary_freqs_cis = self.rotary_emb(hidden_states, seq_len=hidden_states.shape[1]) encoder_rotary_freqs_cis = self.rotary_emb(encoder_hidden_states, seq_len=encoder_hidden_states.shape[1]) for index_block, block in enumerate(self.transformer_blocks): hidden_states = block( hidden_states=hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_hidden_mask, rotary_freqs_cis=rotary_freqs_cis, rotary_freqs_cis_cross=encoder_rotary_freqs_cis, temb=temb, transformer_options=transformer_options, ) output = self.final_layer(hidden_states, embedded_timestep, output_length) return output def forward(self, x, timestep, attention_mask=None, context: Optional[torch.Tensor] = None, text_attention_mask: Optional[torch.LongTensor] = None, speaker_embeds: Optional[torch.FloatTensor] = None, lyric_token_idx: Optional[torch.LongTensor] = None, lyric_mask: Optional[torch.LongTensor] = None, block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, controlnet_scale: Union[float, torch.Tensor] = 1.0, lyrics_strength=1.0, **kwargs ): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) ).execute(x, timestep, attention_mask, context, text_attention_mask, speaker_embeds, lyric_token_idx, lyric_mask, block_controlnet_hidden_states, controlnet_scale, lyrics_strength, **kwargs) def _forward( self, x, timestep, attention_mask=None, context: Optional[torch.Tensor] = None, text_attention_mask: Optional[torch.LongTensor] = None, speaker_embeds: Optional[torch.FloatTensor] = None, lyric_token_idx: Optional[torch.LongTensor] = None, lyric_mask: Optional[torch.LongTensor] = None, block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, controlnet_scale: Union[float, torch.Tensor] = 1.0, lyrics_strength=1.0, **kwargs ): hidden_states = x encoder_text_hidden_states = context encoder_hidden_states, encoder_hidden_mask = self.encode( encoder_text_hidden_states=encoder_text_hidden_states, text_attention_mask=text_attention_mask, speaker_embeds=speaker_embeds, lyric_token_idx=lyric_token_idx, lyric_mask=lyric_mask, lyrics_strength=lyrics_strength, ) output_length = hidden_states.shape[-1] transformer_options = kwargs.get("transformer_options", {}) output = self.decode( hidden_states=hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_hidden_mask=encoder_hidden_mask, timestep=timestep, output_length=output_length, block_controlnet_hidden_states=block_controlnet_hidden_states, controlnet_scale=controlnet_scale, transformer_options=transformer_options, ) return output ================================================ FILE: comfy/ldm/ace/vae/autoencoder_dc.py ================================================ # Rewritten from diffusers import torch import torch.nn as nn import torch.nn.functional as F from typing import Tuple, Union import comfy.model_management import comfy.ops ops = comfy.ops.disable_weight_init class RMSNorm(ops.RMSNorm): def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False): super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine) if elementwise_affine: self.bias = nn.Parameter(torch.empty(dim)) if bias else None def forward(self, x): x = super().forward(x) if self.elementwise_affine: if self.bias is not None: x = x + comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device) return x def get_normalization(norm_type, num_features, num_groups=32, eps=1e-5): if norm_type == "batch_norm": return nn.BatchNorm2d(num_features) elif norm_type == "group_norm": return ops.GroupNorm(num_groups, num_features) elif norm_type == "layer_norm": return ops.LayerNorm(num_features) elif norm_type == "rms_norm": return RMSNorm(num_features, eps=eps, elementwise_affine=True, bias=True) else: raise ValueError(f"Unknown normalization type: {norm_type}") def get_activation(activation_type): if activation_type == "relu": return nn.ReLU() elif activation_type == "relu6": return nn.ReLU6() elif activation_type == "silu": return nn.SiLU() elif activation_type == "leaky_relu": return nn.LeakyReLU(0.2) else: raise ValueError(f"Unknown activation type: {activation_type}") class ResBlock(nn.Module): def __init__( self, in_channels: int, out_channels: int, norm_type: str = "batch_norm", act_fn: str = "relu6", ) -> None: super().__init__() self.norm_type = norm_type self.nonlinearity = get_activation(act_fn) if act_fn is not None else nn.Identity() self.conv1 = ops.Conv2d(in_channels, in_channels, 3, 1, 1) self.conv2 = ops.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False) self.norm = get_normalization(norm_type, out_channels) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: residual = hidden_states hidden_states = self.conv1(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv2(hidden_states) if self.norm_type == "rms_norm": # move channel to the last dimension so we apply RMSnorm across channel dimension hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) else: hidden_states = self.norm(hidden_states) return hidden_states + residual class SanaMultiscaleAttentionProjection(nn.Module): def __init__( self, in_channels: int, num_attention_heads: int, kernel_size: int, ) -> None: super().__init__() channels = 3 * in_channels self.proj_in = ops.Conv2d( channels, channels, kernel_size, padding=kernel_size // 2, groups=channels, bias=False, ) self.proj_out = ops.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.proj_in(hidden_states) hidden_states = self.proj_out(hidden_states) return hidden_states class SanaMultiscaleLinearAttention(nn.Module): def __init__( self, in_channels: int, out_channels: int, num_attention_heads: int = None, attention_head_dim: int = 8, mult: float = 1.0, norm_type: str = "batch_norm", kernel_sizes: tuple = (5,), eps: float = 1e-15, residual_connection: bool = False, ): super().__init__() self.eps = eps self.attention_head_dim = attention_head_dim self.norm_type = norm_type self.residual_connection = residual_connection num_attention_heads = ( int(in_channels // attention_head_dim * mult) if num_attention_heads is None else num_attention_heads ) inner_dim = num_attention_heads * attention_head_dim self.to_q = ops.Linear(in_channels, inner_dim, bias=False) self.to_k = ops.Linear(in_channels, inner_dim, bias=False) self.to_v = ops.Linear(in_channels, inner_dim, bias=False) self.to_qkv_multiscale = nn.ModuleList() for kernel_size in kernel_sizes: self.to_qkv_multiscale.append( SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size) ) self.nonlinearity = nn.ReLU() self.to_out = ops.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False) self.norm_out = get_normalization(norm_type, out_channels) def apply_linear_attention(self, query, key, value): value = F.pad(value, (0, 0, 0, 1), mode="constant", value=1) scores = torch.matmul(value, key.transpose(-1, -2)) hidden_states = torch.matmul(scores, query) hidden_states = hidden_states.to(dtype=torch.float32) hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) return hidden_states def apply_quadratic_attention(self, query, key, value): scores = torch.matmul(key.transpose(-1, -2), query) scores = scores.to(dtype=torch.float32) scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps) hidden_states = torch.matmul(value, scores.to(value.dtype)) return hidden_states def forward(self, hidden_states): height, width = hidden_states.shape[-2:] if height * width > self.attention_head_dim: use_linear_attention = True else: use_linear_attention = False residual = hidden_states batch_size, _, height, width = list(hidden_states.size()) original_dtype = hidden_states.dtype hidden_states = hidden_states.movedim(1, -1) query = self.to_q(hidden_states) key = self.to_k(hidden_states) value = self.to_v(hidden_states) hidden_states = torch.cat([query, key, value], dim=3) hidden_states = hidden_states.movedim(-1, 1) multi_scale_qkv = [hidden_states] for block in self.to_qkv_multiscale: multi_scale_qkv.append(block(hidden_states)) hidden_states = torch.cat(multi_scale_qkv, dim=1) if use_linear_attention: # for linear attention upcast hidden_states to float32 hidden_states = hidden_states.to(dtype=torch.float32) hidden_states = hidden_states.reshape(batch_size, -1, 3 * self.attention_head_dim, height * width) query, key, value = hidden_states.chunk(3, dim=2) query = self.nonlinearity(query) key = self.nonlinearity(key) if use_linear_attention: hidden_states = self.apply_linear_attention(query, key, value) hidden_states = hidden_states.to(dtype=original_dtype) else: hidden_states = self.apply_quadratic_attention(query, key, value) hidden_states = torch.reshape(hidden_states, (batch_size, -1, height, width)) hidden_states = self.to_out(hidden_states.movedim(1, -1)).movedim(-1, 1) if self.norm_type == "rms_norm": hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) else: hidden_states = self.norm_out(hidden_states) if self.residual_connection: hidden_states = hidden_states + residual return hidden_states class EfficientViTBlock(nn.Module): def __init__( self, in_channels: int, mult: float = 1.0, attention_head_dim: int = 32, qkv_multiscales: tuple = (5,), norm_type: str = "batch_norm", ) -> None: super().__init__() self.attn = SanaMultiscaleLinearAttention( in_channels=in_channels, out_channels=in_channels, mult=mult, attention_head_dim=attention_head_dim, norm_type=norm_type, kernel_sizes=qkv_multiscales, residual_connection=True, ) self.conv_out = GLUMBConv( in_channels=in_channels, out_channels=in_channels, norm_type="rms_norm", ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.attn(x) x = self.conv_out(x) return x class GLUMBConv(nn.Module): def __init__( self, in_channels: int, out_channels: int, expand_ratio: float = 4, norm_type: str = None, residual_connection: bool = True, ) -> None: super().__init__() hidden_channels = int(expand_ratio * in_channels) self.norm_type = norm_type self.residual_connection = residual_connection self.nonlinearity = nn.SiLU() self.conv_inverted = ops.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0) self.conv_depth = ops.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2) self.conv_point = ops.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False) self.norm = None if norm_type == "rms_norm": self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: if self.residual_connection: residual = hidden_states hidden_states = self.conv_inverted(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv_depth(hidden_states) hidden_states, gate = torch.chunk(hidden_states, 2, dim=1) hidden_states = hidden_states * self.nonlinearity(gate) hidden_states = self.conv_point(hidden_states) if self.norm_type == "rms_norm": # move channel to the last dimension so we apply RMSnorm across channel dimension hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) if self.residual_connection: hidden_states = hidden_states + residual return hidden_states def get_block( block_type: str, in_channels: int, out_channels: int, attention_head_dim: int, norm_type: str, act_fn: str, qkv_mutliscales: tuple = (), ): if block_type == "ResBlock": block = ResBlock(in_channels, out_channels, norm_type, act_fn) elif block_type == "EfficientViTBlock": block = EfficientViTBlock( in_channels, attention_head_dim=attention_head_dim, norm_type=norm_type, qkv_multiscales=qkv_mutliscales ) else: raise ValueError(f"Block with {block_type=} is not supported.") return block class DCDownBlock2d(nn.Module): def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None: super().__init__() self.downsample = downsample self.factor = 2 self.stride = 1 if downsample else 2 self.group_size = in_channels * self.factor**2 // out_channels self.shortcut = shortcut out_ratio = self.factor**2 if downsample: assert out_channels % out_ratio == 0 out_channels = out_channels // out_ratio self.conv = ops.Conv2d( in_channels, out_channels, kernel_size=3, stride=self.stride, padding=1, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: x = self.conv(hidden_states) if self.downsample: x = F.pixel_unshuffle(x, self.factor) if self.shortcut: y = F.pixel_unshuffle(hidden_states, self.factor) y = y.unflatten(1, (-1, self.group_size)) y = y.mean(dim=2) hidden_states = x + y else: hidden_states = x return hidden_states class DCUpBlock2d(nn.Module): def __init__( self, in_channels: int, out_channels: int, interpolate: bool = False, shortcut: bool = True, interpolation_mode: str = "nearest", ) -> None: super().__init__() self.interpolate = interpolate self.interpolation_mode = interpolation_mode self.shortcut = shortcut self.factor = 2 self.repeats = out_channels * self.factor**2 // in_channels out_ratio = self.factor**2 if not interpolate: out_channels = out_channels * out_ratio self.conv = ops.Conv2d(in_channels, out_channels, 3, 1, 1) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: if self.interpolate: x = F.interpolate(hidden_states, scale_factor=self.factor, mode=self.interpolation_mode) x = self.conv(x) else: x = self.conv(hidden_states) x = F.pixel_shuffle(x, self.factor) if self.shortcut: y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats) y = F.pixel_shuffle(y, self.factor) hidden_states = x + y else: hidden_states = x return hidden_states class Encoder(nn.Module): def __init__( self, in_channels: int, latent_channels: int, attention_head_dim: int = 32, block_type: str or tuple = "ResBlock", block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), layers_per_block: tuple = (2, 2, 2, 2, 2, 2), qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), downsample_block_type: str = "pixel_unshuffle", out_shortcut: bool = True, ): super().__init__() num_blocks = len(block_out_channels) if isinstance(block_type, str): block_type = (block_type,) * num_blocks if layers_per_block[0] > 0: self.conv_in = ops.Conv2d( in_channels, block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], kernel_size=3, stride=1, padding=1, ) else: self.conv_in = DCDownBlock2d( in_channels=in_channels, out_channels=block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], downsample=downsample_block_type == "pixel_unshuffle", shortcut=False, ) down_blocks = [] for i, (out_channel, num_layers) in enumerate(zip(block_out_channels, layers_per_block)): down_block_list = [] for _ in range(num_layers): block = get_block( block_type[i], out_channel, out_channel, attention_head_dim=attention_head_dim, norm_type="rms_norm", act_fn="silu", qkv_mutliscales=qkv_multiscales[i], ) down_block_list.append(block) if i < num_blocks - 1 and num_layers > 0: downsample_block = DCDownBlock2d( in_channels=out_channel, out_channels=block_out_channels[i + 1], downsample=downsample_block_type == "pixel_unshuffle", shortcut=True, ) down_block_list.append(downsample_block) down_blocks.append(nn.Sequential(*down_block_list)) self.down_blocks = nn.ModuleList(down_blocks) self.conv_out = ops.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1) self.out_shortcut = out_shortcut if out_shortcut: self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.conv_in(hidden_states) for down_block in self.down_blocks: hidden_states = down_block(hidden_states) if self.out_shortcut: x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size)) x = x.mean(dim=2) hidden_states = self.conv_out(hidden_states) + x else: hidden_states = self.conv_out(hidden_states) return hidden_states class Decoder(nn.Module): def __init__( self, in_channels: int, latent_channels: int, attention_head_dim: int = 32, block_type: str or tuple = "ResBlock", block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), layers_per_block: tuple = (2, 2, 2, 2, 2, 2), qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), norm_type: str or tuple = "rms_norm", act_fn: str or tuple = "silu", upsample_block_type: str = "pixel_shuffle", in_shortcut: bool = True, ): super().__init__() num_blocks = len(block_out_channels) if isinstance(block_type, str): block_type = (block_type,) * num_blocks if isinstance(norm_type, str): norm_type = (norm_type,) * num_blocks if isinstance(act_fn, str): act_fn = (act_fn,) * num_blocks self.conv_in = ops.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1) self.in_shortcut = in_shortcut if in_shortcut: self.in_shortcut_repeats = block_out_channels[-1] // latent_channels up_blocks = [] for i, (out_channel, num_layers) in reversed(list(enumerate(zip(block_out_channels, layers_per_block)))): up_block_list = [] if i < num_blocks - 1 and num_layers > 0: upsample_block = DCUpBlock2d( block_out_channels[i + 1], out_channel, interpolate=upsample_block_type == "interpolate", shortcut=True, ) up_block_list.append(upsample_block) for _ in range(num_layers): block = get_block( block_type[i], out_channel, out_channel, attention_head_dim=attention_head_dim, norm_type=norm_type[i], act_fn=act_fn[i], qkv_mutliscales=qkv_multiscales[i], ) up_block_list.append(block) up_blocks.insert(0, nn.Sequential(*up_block_list)) self.up_blocks = nn.ModuleList(up_blocks) channels = block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1] self.norm_out = RMSNorm(channels, 1e-5, elementwise_affine=True, bias=True) self.conv_act = nn.ReLU() self.conv_out = None if layers_per_block[0] > 0: self.conv_out = ops.Conv2d(channels, in_channels, 3, 1, 1) else: self.conv_out = DCUpBlock2d( channels, in_channels, interpolate=upsample_block_type == "interpolate", shortcut=False ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: if self.in_shortcut: x = hidden_states.repeat_interleave( self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats ) hidden_states = self.conv_in(hidden_states) + x else: hidden_states = self.conv_in(hidden_states) for up_block in reversed(self.up_blocks): hidden_states = up_block(hidden_states) hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states class AutoencoderDC(nn.Module): def __init__( self, in_channels: int = 2, latent_channels: int = 8, attention_head_dim: int = 32, encoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], decoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), encoder_layers_per_block: Tuple[int] = (2, 2, 3, 3), decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3), encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), upsample_block_type: str = "interpolate", downsample_block_type: str = "Conv", decoder_norm_types: Union[str, Tuple[str]] = "rms_norm", decoder_act_fns: Union[str, Tuple[str]] = "silu", scaling_factor: float = 0.41407, ) -> None: super().__init__() self.encoder = Encoder( in_channels=in_channels, latent_channels=latent_channels, attention_head_dim=attention_head_dim, block_type=encoder_block_types, block_out_channels=encoder_block_out_channels, layers_per_block=encoder_layers_per_block, qkv_multiscales=encoder_qkv_multiscales, downsample_block_type=downsample_block_type, ) self.decoder = Decoder( in_channels=in_channels, latent_channels=latent_channels, attention_head_dim=attention_head_dim, block_type=decoder_block_types, block_out_channels=decoder_block_out_channels, layers_per_block=decoder_layers_per_block, qkv_multiscales=decoder_qkv_multiscales, norm_type=decoder_norm_types, act_fn=decoder_act_fns, upsample_block_type=upsample_block_type, ) self.scaling_factor = scaling_factor self.spatial_compression_ratio = 2 ** (len(encoder_block_out_channels) - 1) def encode(self, x: torch.Tensor) -> torch.Tensor: """Internal encoding function.""" encoded = self.encoder(x) return encoded * self.scaling_factor def decode(self, z: torch.Tensor) -> torch.Tensor: # Scale the latents back z = z / self.scaling_factor decoded = self.decoder(z) return decoded def forward(self, x: torch.Tensor) -> torch.Tensor: z = self.encode(x) return self.decode(z) ================================================ FILE: comfy/ldm/ace/vae/music_dcae_pipeline.py ================================================ # Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py import torch from .autoencoder_dc import AutoencoderDC import logging try: import torchaudio except: logging.warning("torchaudio missing, ACE model will be broken") import torchvision.transforms as transforms from .music_vocoder import ADaMoSHiFiGANV1 class MusicDCAE(torch.nn.Module): def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_config={}): super(MusicDCAE, self).__init__() self.dcae = AutoencoderDC(**dcae_config) self.vocoder = ADaMoSHiFiGANV1(**vocoder_config) if source_sample_rate is None: self.source_sample_rate = 48000 else: self.source_sample_rate = source_sample_rate self.transform = transforms.Compose([ transforms.Normalize(0.5, 0.5), ]) self.min_mel_value = -11.0 self.max_mel_value = 3.0 self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000))) self.mel_chunk_size = 1024 self.time_dimention_multiple = 8 self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple self.scale_factor = 0.1786 self.shift_factor = -1.9091 def forward_mel(self, audios): mels = [] for i in range(len(audios)): image = self.vocoder.mel_transform(audios[i]) mels.append(image) mels = torch.stack(mels) return mels @torch.no_grad() def encode(self, audios, audio_lengths=None, sr=None): if audio_lengths is None: audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0]) audio_lengths = audio_lengths.to(audios.device) if sr is None: sr = self.source_sample_rate if sr != 44100: audios = torchaudio.functional.resample(audios, sr, 44100) max_audio_len = audios.shape[-1] if max_audio_len % (8 * 512) != 0: audios = torch.nn.functional.pad(audios, (0, 8 * 512 - max_audio_len % (8 * 512))) mels = self.forward_mel(audios) mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value) mels = self.transform(mels) latents = [] for mel in mels: latent = self.dcae.encoder(mel.unsqueeze(0)) latents.append(latent) latents = torch.cat(latents, dim=0) latents = (latents - self.shift_factor) * self.scale_factor return latents @torch.no_grad() def decode(self, latents, audio_lengths=None, sr=None): latents = latents / self.scale_factor + self.shift_factor pred_wavs = [] for latent in latents: mels = self.dcae.decoder(latent.unsqueeze(0)) mels = mels * 0.5 + 0.5 mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value wav = self.vocoder.decode(mels[0]).squeeze(1) if sr is not None: wav = torchaudio.functional.resample(wav, 44100, sr) else: sr = 44100 pred_wavs.append(wav) if audio_lengths is not None: pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)] return torch.stack(pred_wavs) def forward(self, audios, audio_lengths=None, sr=None): latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr) sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr) return sr, pred_wavs, latents, latent_lengths ================================================ FILE: comfy/ldm/ace/vae/music_log_mel.py ================================================ # Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_log_mel.py import torch import torch.nn as nn from torch import Tensor import logging try: from torchaudio.transforms import MelScale except: logging.warning("torchaudio missing, ACE model will be broken") import comfy.model_management class LinearSpectrogram(nn.Module): def __init__( self, n_fft=2048, win_length=2048, hop_length=512, center=False, mode="pow2_sqrt", ): super().__init__() self.n_fft = n_fft self.win_length = win_length self.hop_length = hop_length self.center = center self.mode = mode self.register_buffer("window", torch.hann_window(win_length)) def forward(self, y: Tensor) -> Tensor: if y.ndim == 3: y = y.squeeze(1) y = torch.nn.functional.pad( y.unsqueeze(1), ( (self.win_length - self.hop_length) // 2, (self.win_length - self.hop_length + 1) // 2, ), mode="reflect", ).squeeze(1) dtype = y.dtype spec = torch.stft( y.float(), self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window=comfy.model_management.cast_to(self.window, dtype=torch.float32, device=y.device), center=self.center, pad_mode="reflect", normalized=False, onesided=True, return_complex=True, ) spec = torch.view_as_real(spec) if self.mode == "pow2_sqrt": spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) spec = spec.to(dtype) return spec class LogMelSpectrogram(nn.Module): def __init__( self, sample_rate=44100, n_fft=2048, win_length=2048, hop_length=512, n_mels=128, center=False, f_min=0.0, f_max=None, ): super().__init__() self.sample_rate = sample_rate self.n_fft = n_fft self.win_length = win_length self.hop_length = hop_length self.center = center self.n_mels = n_mels self.f_min = f_min self.f_max = f_max or sample_rate // 2 self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center) self.mel_scale = MelScale( self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1, "slaney", "slaney", ) def compress(self, x: Tensor) -> Tensor: return torch.log(torch.clamp(x, min=1e-5)) def decompress(self, x: Tensor) -> Tensor: return torch.exp(x) def forward(self, x: Tensor, return_linear: bool = False) -> Tensor: linear = self.spectrogram(x) x = self.mel_scale(linear) x = self.compress(x) # print(x.shape) if return_linear: return x, self.compress(linear) return x ================================================ FILE: comfy/ldm/ace/vae/music_vocoder.py ================================================ # Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_vocoder.py import torch from torch import nn from functools import partial from math import prod from typing import Callable, Tuple, List import numpy as np import torch.nn.functional as F from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm from .music_log_mel import LogMelSpectrogram import comfy.model_management import comfy.ops ops = comfy.ops.disable_weight_init def drop_path( x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True ): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ # noqa: E501 if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * ( x.ndim - 1 ) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" # noqa: E501 def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): super(DropPath, self).__init__() self.drop_prob = drop_prob self.scale_by_keep = scale_by_keep def forward(self, x): return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) def extra_repr(self): return f"drop_prob={round(self.drop_prob,3):0.3f}" class LayerNorm(nn.Module): r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). """ # noqa: E501 def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): super().__init__() self.weight = nn.Parameter(torch.ones(normalized_shape)) self.bias = nn.Parameter(torch.zeros(normalized_shape)) self.eps = eps self.data_format = data_format if self.data_format not in ["channels_last", "channels_first"]: raise NotImplementedError self.normalized_shape = (normalized_shape,) def forward(self, x): if self.data_format == "channels_last": return F.layer_norm( x, self.normalized_shape, comfy.model_management.cast_to(self.weight, dtype=x.dtype, device=x.device), comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device), self.eps ) elif self.data_format == "channels_first": u = x.mean(1, keepdim=True) s = (x - u).pow(2).mean(1, keepdim=True) x = (x - u) / torch.sqrt(s + self.eps) x = comfy.model_management.cast_to(self.weight[:, None], dtype=x.dtype, device=x.device) * x + comfy.model_management.cast_to(self.bias[:, None], dtype=x.dtype, device=x.device) return x class ConvNeXtBlock(nn.Module): r"""ConvNeXt Block. There are two equivalent implementations: (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back We use (2) as we find it slightly faster in PyTorch Args: dim (int): Number of input channels. drop_path (float): Stochastic depth rate. Default: 0.0 layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. kernel_size (int): Kernel size for depthwise conv. Default: 7. dilation (int): Dilation for depthwise conv. Default: 1. """ # noqa: E501 def __init__( self, dim: int, drop_path: float = 0.0, layer_scale_init_value: float = 1e-6, mlp_ratio: float = 4.0, kernel_size: int = 7, dilation: int = 1, ): super().__init__() self.dwconv = ops.Conv1d( dim, dim, kernel_size=kernel_size, padding=int(dilation * (kernel_size - 1) / 2), groups=dim, ) # depthwise conv self.norm = LayerNorm(dim, eps=1e-6) self.pwconv1 = ops.Linear( dim, int(mlp_ratio * dim) ) # pointwise/1x1 convs, implemented with linear layers self.act = nn.GELU() self.pwconv2 = ops.Linear(int(mlp_ratio * dim), dim) self.gamma = ( nn.Parameter(torch.empty((dim)), requires_grad=False) if layer_scale_init_value > 0 else None ) self.drop_path = DropPath( drop_path) if drop_path > 0.0 else nn.Identity() def forward(self, x, apply_residual: bool = True): input = x x = self.dwconv(x) x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C) x = self.norm(x) x = self.pwconv1(x) x = self.act(x) x = self.pwconv2(x) if self.gamma is not None: x = comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) * x x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L) x = self.drop_path(x) if apply_residual: x = input + x return x class ParallelConvNeXtBlock(nn.Module): def __init__(self, kernel_sizes: List[int], *args, **kwargs): super().__init__() self.blocks = nn.ModuleList( [ ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs) for kernel_size in kernel_sizes ] ) def forward(self, x: torch.Tensor) -> torch.Tensor: return torch.stack( [block(x, apply_residual=False) for block in self.blocks] + [x], dim=1, ).sum(dim=1) class ConvNeXtEncoder(nn.Module): def __init__( self, input_channels=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.0, layer_scale_init_value=1e-6, kernel_sizes: Tuple[int] = (7,), ): super().__init__() assert len(depths) == len(dims) self.channel_layers = nn.ModuleList() stem = nn.Sequential( ops.Conv1d( input_channels, dims[0], kernel_size=7, padding=3, padding_mode="replicate", ), LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), ) self.channel_layers.append(stem) for i in range(len(depths) - 1): mid_layer = nn.Sequential( LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), ops.Conv1d(dims[i], dims[i + 1], kernel_size=1), ) self.channel_layers.append(mid_layer) block_fn = ( partial(ConvNeXtBlock, kernel_size=kernel_sizes[0]) if len(kernel_sizes) == 1 else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes) ) self.stages = nn.ModuleList() drop_path_rates = [ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) ] cur = 0 for i in range(len(depths)): stage = nn.Sequential( *[ block_fn( dim=dims[i], drop_path=drop_path_rates[cur + j], layer_scale_init_value=layer_scale_init_value, ) for j in range(depths[i]) ] ) self.stages.append(stage) cur += depths[i] self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first") def forward( self, x: torch.Tensor, ) -> torch.Tensor: for channel_layer, stage in zip(self.channel_layers, self.stages): x = channel_layer(x) x = stage(x) return self.norm(x) def get_padding(kernel_size, dilation=1): return (kernel_size * dilation - dilation) // 2 class ResBlock1(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): super().__init__() self.convs1 = nn.ModuleList( [ torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]), ) ), torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]), ) ), torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]), ) ), ] ) self.convs2 = nn.ModuleList( [ torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ) ), torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ) ), torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ) ), ] ) def forward(self, x): for c1, c2 in zip(self.convs1, self.convs2): xt = F.silu(x) xt = c1(xt) xt = F.silu(xt) xt = c2(xt) x = xt + x return x def remove_weight_norm(self): for conv in self.convs1: remove_weight_norm(conv) for conv in self.convs2: remove_weight_norm(conv) class HiFiGANGenerator(nn.Module): def __init__( self, *, hop_length: int = 512, upsample_rates: Tuple[int] = (8, 8, 2, 2, 2), upsample_kernel_sizes: Tuple[int] = (16, 16, 8, 2, 2), resblock_kernel_sizes: Tuple[int] = (3, 7, 11), resblock_dilation_sizes: Tuple[Tuple[int]] = ( (1, 3, 5), (1, 3, 5), (1, 3, 5)), num_mels: int = 128, upsample_initial_channel: int = 512, use_template: bool = True, pre_conv_kernel_size: int = 7, post_conv_kernel_size: int = 7, post_activation: Callable = partial(nn.SiLU, inplace=True), ): super().__init__() assert ( prod(upsample_rates) == hop_length ), f"hop_length must be {prod(upsample_rates)}" self.conv_pre = torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( num_mels, upsample_initial_channel, pre_conv_kernel_size, 1, padding=get_padding(pre_conv_kernel_size), ) ) self.num_upsamples = len(upsample_rates) self.num_kernels = len(resblock_kernel_sizes) self.noise_convs = nn.ModuleList() self.use_template = use_template self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): c_cur = upsample_initial_channel // (2 ** (i + 1)) self.ups.append( torch.nn.utils.parametrizations.weight_norm( ops.ConvTranspose1d( upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2, ) ) ) if not use_template: continue if i + 1 < len(upsample_rates): stride_f0 = np.prod(upsample_rates[i + 1:]) self.noise_convs.append( ops.Conv1d( 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2, ) ) else: self.noise_convs.append(ops.Conv1d(1, c_cur, kernel_size=1)) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = upsample_initial_channel // (2 ** (i + 1)) for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): self.resblocks.append(ResBlock1(ch, k, d)) self.activation_post = post_activation() self.conv_post = torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( ch, 1, post_conv_kernel_size, 1, padding=get_padding(post_conv_kernel_size), ) ) def forward(self, x, template=None): x = self.conv_pre(x) for i in range(self.num_upsamples): x = F.silu(x, inplace=True) x = self.ups[i](x) if self.use_template: x = x + self.noise_convs[i](template) xs = None for j in range(self.num_kernels): if xs is None: xs = self.resblocks[i * self.num_kernels + j](x) else: xs += self.resblocks[i * self.num_kernels + j](x) x = xs / self.num_kernels x = self.activation_post(x) x = self.conv_post(x) x = torch.tanh(x) return x def remove_weight_norm(self): for up in self.ups: remove_weight_norm(up) for block in self.resblocks: block.remove_weight_norm() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post) class ADaMoSHiFiGANV1(nn.Module): def __init__( self, input_channels: int = 128, depths: List[int] = [3, 3, 9, 3], dims: List[int] = [128, 256, 384, 512], drop_path_rate: float = 0.0, kernel_sizes: Tuple[int] = (7,), upsample_rates: Tuple[int] = (4, 4, 2, 2, 2, 2, 2), upsample_kernel_sizes: Tuple[int] = (8, 8, 4, 4, 4, 4, 4), resblock_kernel_sizes: Tuple[int] = (3, 7, 11, 13), resblock_dilation_sizes: Tuple[Tuple[int]] = ( (1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5)), num_mels: int = 512, upsample_initial_channel: int = 1024, use_template: bool = False, pre_conv_kernel_size: int = 13, post_conv_kernel_size: int = 13, sampling_rate: int = 44100, n_fft: int = 2048, win_length: int = 2048, hop_length: int = 512, f_min: int = 40, f_max: int = 16000, n_mels: int = 128, ): super().__init__() self.backbone = ConvNeXtEncoder( input_channels=input_channels, depths=depths, dims=dims, drop_path_rate=drop_path_rate, kernel_sizes=kernel_sizes, ) self.head = HiFiGANGenerator( hop_length=hop_length, upsample_rates=upsample_rates, upsample_kernel_sizes=upsample_kernel_sizes, resblock_kernel_sizes=resblock_kernel_sizes, resblock_dilation_sizes=resblock_dilation_sizes, num_mels=num_mels, upsample_initial_channel=upsample_initial_channel, use_template=use_template, pre_conv_kernel_size=pre_conv_kernel_size, post_conv_kernel_size=post_conv_kernel_size, ) self.sampling_rate = sampling_rate self.mel_transform = LogMelSpectrogram( sample_rate=sampling_rate, n_fft=n_fft, win_length=win_length, hop_length=hop_length, f_min=f_min, f_max=f_max, n_mels=n_mels, ) self.eval() @torch.no_grad() def decode(self, mel): y = self.backbone(mel) y = self.head(y) return y @torch.no_grad() def encode(self, x): return self.mel_transform(x) def forward(self, mel): y = self.backbone(mel) y = self.head(y) return y ================================================ FILE: comfy/ldm/anima/model.py ================================================ from comfy.ldm.cosmos.predict2 import MiniTrainDIT import torch from torch import nn import torch.nn.functional as F def rotate_half(x): x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1): cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) x_embed = (x * cos) + (rotate_half(x) * sin) return x_embed class RotaryEmbedding(nn.Module): def __init__(self, head_dim): super().__init__() self.rope_theta = 10000 inv_freq = 1.0 / (self.rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.int64).to(dtype=torch.float) / head_dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) @torch.no_grad() def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() sin = emb.sin() return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) class Attention(nn.Module): def __init__(self, query_dim, context_dim, n_heads, head_dim, device=None, dtype=None, operations=None): super().__init__() inner_dim = head_dim * n_heads self.n_heads = n_heads self.head_dim = head_dim self.query_dim = query_dim self.context_dim = context_dim self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype) self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) self.o_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype) def forward(self, x, mask=None, context=None, position_embeddings=None, position_embeddings_context=None): context = x if context is None else context input_shape = x.shape[:-1] q_shape = (*input_shape, self.n_heads, self.head_dim) context_shape = context.shape[:-1] kv_shape = (*context_shape, self.n_heads, self.head_dim) query_states = self.q_norm(self.q_proj(x).view(q_shape)).transpose(1, 2) key_states = self.k_norm(self.k_proj(context).view(kv_shape)).transpose(1, 2) value_states = self.v_proj(context).view(kv_shape).transpose(1, 2) if position_embeddings is not None: assert position_embeddings_context is not None cos, sin = position_embeddings query_states = apply_rotary_pos_emb(query_states, cos, sin) cos, sin = position_embeddings_context key_states = apply_rotary_pos_emb(key_states, cos, sin) attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=mask) attn_output = attn_output.transpose(1, 2).reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output def init_weights(self): torch.nn.init.zeros_(self.o_proj.weight) class TransformerBlock(nn.Module): def __init__(self, source_dim, model_dim, num_heads=16, mlp_ratio=4.0, use_self_attn=False, layer_norm=False, device=None, dtype=None, operations=None): super().__init__() self.use_self_attn = use_self_attn if self.use_self_attn: self.norm_self_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype) self.self_attn = Attention( query_dim=model_dim, context_dim=model_dim, n_heads=num_heads, head_dim=model_dim//num_heads, device=device, dtype=dtype, operations=operations, ) self.norm_cross_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype) self.cross_attn = Attention( query_dim=model_dim, context_dim=source_dim, n_heads=num_heads, head_dim=model_dim//num_heads, device=device, dtype=dtype, operations=operations, ) self.norm_mlp = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype) self.mlp = nn.Sequential( operations.Linear(model_dim, int(model_dim * mlp_ratio), device=device, dtype=dtype), nn.GELU(), operations.Linear(int(model_dim * mlp_ratio), model_dim, device=device, dtype=dtype) ) def forward(self, x, context, target_attention_mask=None, source_attention_mask=None, position_embeddings=None, position_embeddings_context=None): if self.use_self_attn: normed = self.norm_self_attn(x) attn_out = self.self_attn(normed, mask=target_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings) x = x + attn_out normed = self.norm_cross_attn(x) attn_out = self.cross_attn(normed, mask=source_attention_mask, context=context, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context) x = x + attn_out x = x + self.mlp(self.norm_mlp(x)) return x def init_weights(self): torch.nn.init.zeros_(self.mlp[2].weight) self.cross_attn.init_weights() class LLMAdapter(nn.Module): def __init__( self, source_dim=1024, target_dim=1024, model_dim=1024, num_layers=6, num_heads=16, use_self_attn=True, layer_norm=False, device=None, dtype=None, operations=None, ): super().__init__() self.embed = operations.Embedding(32128, target_dim, device=device, dtype=dtype) if model_dim != target_dim: self.in_proj = operations.Linear(target_dim, model_dim, device=device, dtype=dtype) else: self.in_proj = nn.Identity() self.rotary_emb = RotaryEmbedding(model_dim//num_heads) self.blocks = nn.ModuleList([ TransformerBlock(source_dim, model_dim, num_heads=num_heads, use_self_attn=use_self_attn, layer_norm=layer_norm, device=device, dtype=dtype, operations=operations) for _ in range(num_layers) ]) self.out_proj = operations.Linear(model_dim, target_dim, device=device, dtype=dtype) self.norm = operations.RMSNorm(target_dim, eps=1e-6, device=device, dtype=dtype) def forward(self, source_hidden_states, target_input_ids, target_attention_mask=None, source_attention_mask=None): if target_attention_mask is not None: target_attention_mask = target_attention_mask.to(torch.bool) if target_attention_mask.ndim == 2: target_attention_mask = target_attention_mask.unsqueeze(1).unsqueeze(1) if source_attention_mask is not None: source_attention_mask = source_attention_mask.to(torch.bool) if source_attention_mask.ndim == 2: source_attention_mask = source_attention_mask.unsqueeze(1).unsqueeze(1) context = source_hidden_states x = self.in_proj(self.embed(target_input_ids, out_dtype=context.dtype)) position_ids = torch.arange(x.shape[1], device=x.device).unsqueeze(0) position_ids_context = torch.arange(context.shape[1], device=x.device).unsqueeze(0) position_embeddings = self.rotary_emb(x, position_ids) position_embeddings_context = self.rotary_emb(x, position_ids_context) for block in self.blocks: x = block(x, context, target_attention_mask=target_attention_mask, source_attention_mask=source_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context) return self.norm(self.out_proj(x)) class Anima(MiniTrainDIT): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.llm_adapter = LLMAdapter(device=kwargs.get("device"), dtype=kwargs.get("dtype"), operations=kwargs.get("operations")) def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=None): if text_ids is not None: out = self.llm_adapter(text_embeds, text_ids) if t5xxl_weights is not None: out = out * t5xxl_weights if out.shape[1] < 512: out = torch.nn.functional.pad(out, (0, 0, 0, 512 - out.shape[1])) return out else: return text_embeds def forward(self, x, timesteps, context, **kwargs): t5xxl_ids = kwargs.pop("t5xxl_ids", None) if t5xxl_ids is not None: context = self.preprocess_text_embeds(context, t5xxl_ids, t5xxl_weights=kwargs.pop("t5xxl_weights", None)) return super().forward(x, timesteps, context, **kwargs) ================================================ FILE: comfy/ldm/audio/autoencoder.py ================================================ # code adapted from: https://github.com/Stability-AI/stable-audio-tools import torch from torch import nn from typing import Literal import math import comfy.ops ops = comfy.ops.disable_weight_init def vae_sample(mean, scale): stdev = nn.functional.softplus(scale) + 1e-4 var = stdev * stdev logvar = torch.log(var) latents = torch.randn_like(mean) * stdev + mean kl = (mean * mean + var - logvar - 1).sum(1).mean() return latents, kl class VAEBottleneck(nn.Module): def __init__(self): super().__init__() self.is_discrete = False def encode(self, x, return_info=False, **kwargs): info = {} mean, scale = x.chunk(2, dim=1) x, kl = vae_sample(mean, scale) info["kl"] = kl if return_info: return x, info else: return x def decode(self, x): return x def snake_beta(x, alpha, beta): return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2) # Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license class SnakeBeta(nn.Module): def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): super(SnakeBeta, self).__init__() self.in_features = in_features # initialize alpha self.alpha_logscale = alpha_logscale if self.alpha_logscale: # log scale alphas initialized to zeros self.alpha = nn.Parameter(torch.zeros(in_features) * alpha) self.beta = nn.Parameter(torch.zeros(in_features) * alpha) else: # linear scale alphas initialized to ones self.alpha = nn.Parameter(torch.ones(in_features) * alpha) self.beta = nn.Parameter(torch.ones(in_features) * alpha) # self.alpha.requires_grad = alpha_trainable # self.beta.requires_grad = alpha_trainable self.no_div_by_zero = 0.000000001 def forward(self, x): alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T] beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device) if self.alpha_logscale: alpha = torch.exp(alpha) beta = torch.exp(beta) x = snake_beta(x, alpha, beta) return x def WNConv1d(*args, **kwargs): return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) def WNConvTranspose1d(*args, **kwargs): return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: if activation == "elu": act = torch.nn.ELU() elif activation == "snake": act = SnakeBeta(channels) elif activation == "none": act = torch.nn.Identity() else: raise ValueError(f"Unknown activation {activation}") if antialias: act = Activation1d(act) # noqa: F821 Activation1d is not defined return act class ResidualUnit(nn.Module): def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False): super().__init__() self.dilation = dilation padding = (dilation * (7-1)) // 2 self.layers = nn.Sequential( get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), WNConv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=7, dilation=dilation, padding=padding), get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), WNConv1d(in_channels=out_channels, out_channels=out_channels, kernel_size=1) ) def forward(self, x): res = x #x = checkpoint(self.layers, x) x = self.layers(x) return x + res class EncoderBlock(nn.Module): def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False): super().__init__() self.layers = nn.Sequential( ResidualUnit(in_channels=in_channels, out_channels=in_channels, dilation=1, use_snake=use_snake), ResidualUnit(in_channels=in_channels, out_channels=in_channels, dilation=3, use_snake=use_snake), ResidualUnit(in_channels=in_channels, out_channels=in_channels, dilation=9, use_snake=use_snake), get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), WNConv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)), ) def forward(self, x): return self.layers(x) class DecoderBlock(nn.Module): def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False): super().__init__() if use_nearest_upsample: upsample_layer = nn.Sequential( nn.Upsample(scale_factor=stride, mode="nearest"), WNConv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=2*stride, stride=1, bias=False, padding='same') ) else: upsample_layer = WNConvTranspose1d(in_channels=in_channels, out_channels=out_channels, kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)) self.layers = nn.Sequential( get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), upsample_layer, ResidualUnit(in_channels=out_channels, out_channels=out_channels, dilation=1, use_snake=use_snake), ResidualUnit(in_channels=out_channels, out_channels=out_channels, dilation=3, use_snake=use_snake), ResidualUnit(in_channels=out_channels, out_channels=out_channels, dilation=9, use_snake=use_snake), ) def forward(self, x): return self.layers(x) class OobleckEncoder(nn.Module): def __init__(self, in_channels=2, channels=128, latent_dim=32, c_mults = [1, 2, 4, 8], strides = [2, 4, 8, 8], use_snake=False, antialias_activation=False ): super().__init__() c_mults = [1] + c_mults self.depth = len(c_mults) layers = [ WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3) ] for i in range(self.depth-1): layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)] layers += [ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels), WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1) ] self.layers = nn.Sequential(*layers) def forward(self, x): return self.layers(x) class OobleckDecoder(nn.Module): def __init__(self, out_channels=2, channels=128, latent_dim=32, c_mults = [1, 2, 4, 8], strides = [2, 4, 8, 8], use_snake=False, antialias_activation=False, use_nearest_upsample=False, final_tanh=True): super().__init__() c_mults = [1] + c_mults self.depth = len(c_mults) layers = [ WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3), ] for i in range(self.depth-1, 0, -1): layers += [DecoderBlock( in_channels=c_mults[i]*channels, out_channels=c_mults[i-1]*channels, stride=strides[i-1], use_snake=use_snake, antialias_activation=antialias_activation, use_nearest_upsample=use_nearest_upsample ) ] layers += [ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels), WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False), nn.Tanh() if final_tanh else nn.Identity() ] self.layers = nn.Sequential(*layers) def forward(self, x): return self.layers(x) class AudioOobleckVAE(nn.Module): def __init__(self, in_channels=2, channels=128, latent_dim=64, c_mults = [1, 2, 4, 8, 16], strides = [2, 4, 4, 8, 8], use_snake=True, antialias_activation=False, use_nearest_upsample=False, final_tanh=False): super().__init__() self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation) self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation, use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh) self.bottleneck = VAEBottleneck() def encode(self, x): return self.bottleneck.encode(self.encoder(x)) def decode(self, x): return self.decoder(self.bottleneck.decode(x)) ================================================ FILE: comfy/ldm/audio/dit.py ================================================ # code adapted from: https://github.com/Stability-AI/stable-audio-tools from comfy.ldm.modules.attention import optimized_attention import typing as tp import torch from einops import rearrange from torch import nn from torch.nn import functional as F import math import comfy.ops class FourierFeatures(nn.Module): def __init__(self, in_features, out_features, std=1., dtype=None, device=None): super().__init__() assert out_features % 2 == 0 self.weight = nn.Parameter(torch.empty( [out_features // 2, in_features], dtype=dtype, device=device)) def forward(self, input): f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input) return torch.cat([f.cos(), f.sin()], dim=-1) # norms class LayerNorm(nn.Module): def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None): """ bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less """ super().__init__() self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) if bias: self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) else: self.beta = None def forward(self, x): beta = self.beta if beta is not None: beta = comfy.ops.cast_to_input(beta, x) return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta) class GLU(nn.Module): def __init__( self, dim_in, dim_out, activation, use_conv = False, conv_kernel_size = 3, dtype=None, device=None, operations=None, ): super().__init__() self.act = activation self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device) self.use_conv = use_conv def forward(self, x): if self.use_conv: x = rearrange(x, 'b n d -> b d n') x = self.proj(x) x = rearrange(x, 'b d n -> b n d') else: x = self.proj(x) x, gate = x.chunk(2, dim = -1) return x * self.act(gate) class AbsolutePositionalEmbedding(nn.Module): def __init__(self, dim, max_seq_len): super().__init__() self.scale = dim ** -0.5 self.max_seq_len = max_seq_len self.emb = nn.Embedding(max_seq_len, dim) def forward(self, x, pos = None, seq_start_pos = None): seq_len, device = x.shape[1], x.device assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}' if pos is None: pos = torch.arange(seq_len, device = device) if seq_start_pos is not None: pos = (pos - seq_start_pos[..., None]).clamp(min = 0) pos_emb = self.emb(pos) pos_emb = pos_emb * self.scale return pos_emb class ScaledSinusoidalEmbedding(nn.Module): def __init__(self, dim, theta = 10000): super().__init__() assert (dim % 2) == 0, 'dimension must be divisible by 2' self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5) half_dim = dim // 2 freq_seq = torch.arange(half_dim).float() / half_dim inv_freq = theta ** -freq_seq self.register_buffer('inv_freq', inv_freq, persistent = False) def forward(self, x, pos = None, seq_start_pos = None): seq_len, device = x.shape[1], x.device if pos is None: pos = torch.arange(seq_len, device = device) if seq_start_pos is not None: pos = pos - seq_start_pos[..., None] emb = torch.einsum('i, j -> i j', pos, self.inv_freq) emb = torch.cat((emb.sin(), emb.cos()), dim = -1) return emb * self.scale class RotaryEmbedding(nn.Module): def __init__( self, dim, use_xpos = False, scale_base = 512, interpolation_factor = 1., base = 10000, base_rescale_factor = 1., dtype=None, device=None, ): super().__init__() # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning # has some connection to NTK literature # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ base *= base_rescale_factor ** (dim / (dim - 2)) # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype)) assert interpolation_factor >= 1. self.interpolation_factor = interpolation_factor if not use_xpos: self.register_buffer('scale', None) return scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) self.scale_base = scale_base self.register_buffer('scale', scale) def forward_from_seq_len(self, seq_len, device, dtype): # device = self.inv_freq.device t = torch.arange(seq_len, device=device, dtype=dtype) return self.forward(t) def forward(self, t): # device = self.inv_freq.device device = t.device # t = t.to(torch.float32) t = t / self.interpolation_factor freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t)) freqs = torch.cat((freqs, freqs), dim = -1) if self.scale is None: return freqs, 1. power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base # noqa: F821 seq_len is not defined scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1') scale = torch.cat((scale, scale), dim = -1) return freqs, scale def rotate_half(x): x = rearrange(x, '... (j d) -> ... j d', j = 2) x1, x2 = x.unbind(dim = -2) return torch.cat((-x2, x1), dim = -1) def apply_rotary_pos_emb(t, freqs, scale = 1): out_dtype = t.dtype # cast to float32 if necessary for numerical stability dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32)) rot_dim, seq_len = freqs.shape[-1], t.shape[-2] freqs, t = freqs.to(dtype), t.to(dtype) freqs = freqs[-seq_len:, :] if t.ndim == 4 and freqs.ndim == 3: freqs = rearrange(freqs, 'b n d -> b 1 n d') # partial rotary embeddings, Wang et al. GPT-J t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:] t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype) return torch.cat((t, t_unrotated), dim = -1) class FeedForward(nn.Module): def __init__( self, dim, dim_out = None, mult = 4, no_bias = False, glu = True, use_conv = False, conv_kernel_size = 3, zero_init_output = True, dtype=None, device=None, operations=None, ): super().__init__() inner_dim = int(dim * mult) # Default to SwiGLU activation = nn.SiLU() dim_out = dim if dim_out is None else dim_out if glu: linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations) else: linear_in = nn.Sequential( rearrange('b n d -> b d n') if use_conv else nn.Identity(), operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device), rearrange('b n d -> b d n') if use_conv else nn.Identity(), activation ) linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device) # # init last linear layer to 0 # if zero_init_output: # nn.init.zeros_(linear_out.weight) # if not no_bias: # nn.init.zeros_(linear_out.bias) self.ff = nn.Sequential( linear_in, rearrange('b d n -> b n d') if use_conv else nn.Identity(), linear_out, rearrange('b n d -> b d n') if use_conv else nn.Identity(), ) def forward(self, x): return self.ff(x) class Attention(nn.Module): def __init__( self, dim, dim_heads = 64, dim_context = None, causal = False, zero_init_output=True, qk_norm = False, natten_kernel_size = None, dtype=None, device=None, operations=None, ): super().__init__() self.dim = dim self.dim_heads = dim_heads self.causal = causal dim_kv = dim_context if dim_context is not None else dim self.num_heads = dim // dim_heads self.kv_heads = dim_kv // dim_heads if dim_context is not None: self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device) else: self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device) self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) # if zero_init_output: # nn.init.zeros_(self.to_out.weight) self.qk_norm = qk_norm def forward( self, x, context = None, mask = None, context_mask = None, rotary_pos_emb = None, causal = None, transformer_options={}, ): h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None kv_input = context if has_context else x if hasattr(self, 'to_q'): # Use separate linear projections for q and k/v q = self.to_q(x) q = rearrange(q, 'b n (h d) -> b h n d', h = h) k, v = self.to_kv(kv_input).chunk(2, dim=-1) k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v)) else: # Use fused linear projection q, k, v = self.to_qkv(x).chunk(3, dim=-1) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) # Normalize q and k for cosine sim attention if self.qk_norm: q = F.normalize(q, dim=-1) k = F.normalize(k, dim=-1) if rotary_pos_emb is not None and not has_context: freqs, _ = rotary_pos_emb q_dtype = q.dtype k_dtype = k.dtype q = q.to(torch.float32) k = k.to(torch.float32) freqs = freqs.to(torch.float32) q = apply_rotary_pos_emb(q, freqs) k = apply_rotary_pos_emb(k, freqs) q = q.to(q_dtype) k = k.to(k_dtype) input_mask = context_mask if input_mask is None and not has_context: input_mask = mask # determine masking masks = [] if input_mask is not None: input_mask = rearrange(input_mask, 'b j -> b 1 1 j') masks.append(~input_mask) # Other masks will be added here later n = q.shape[-2] causal = self.causal if causal is None else causal if n == 1 and causal: causal = False if h != kv_h: # Repeat interleave kv_heads to match q_heads heads_per_kv_head = h // kv_h k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options) out = self.to_out(out) if mask is not None: mask = rearrange(mask, 'b n -> b n 1') out = out.masked_fill(~mask, 0.) return out class ConformerModule(nn.Module): def __init__( self, dim, norm_kwargs = {}, ): super().__init__() self.dim = dim self.in_norm = LayerNorm(dim, **norm_kwargs) self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False) self.glu = GLU(dim, dim, nn.SiLU()) self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False) self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm self.swish = nn.SiLU() self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False) def forward(self, x): x = self.in_norm(x) x = rearrange(x, 'b n d -> b d n') x = self.pointwise_conv(x) x = rearrange(x, 'b d n -> b n d') x = self.glu(x) x = rearrange(x, 'b n d -> b d n') x = self.depthwise_conv(x) x = rearrange(x, 'b d n -> b n d') x = self.mid_norm(x) x = self.swish(x) x = rearrange(x, 'b n d -> b d n') x = self.pointwise_conv_2(x) x = rearrange(x, 'b d n -> b n d') return x class TransformerBlock(nn.Module): def __init__( self, dim, dim_heads = 64, cross_attend = False, dim_context = None, global_cond_dim = None, causal = False, zero_init_branch_outputs = True, conformer = False, layer_ix = -1, remove_norms = False, attn_kwargs = {}, ff_kwargs = {}, norm_kwargs = {}, dtype=None, device=None, operations=None, ): super().__init__() self.dim = dim self.dim_heads = dim_heads self.cross_attend = cross_attend self.dim_context = dim_context self.causal = causal self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() self.self_attn = Attention( dim, dim_heads = dim_heads, causal = causal, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations, **attn_kwargs ) if cross_attend: self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() self.cross_attn = Attention( dim, dim_heads = dim_heads, dim_context=dim_context, causal = causal, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations, **attn_kwargs ) self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs) self.layer_ix = layer_ix self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None self.global_cond_dim = global_cond_dim if global_cond_dim is not None: self.to_scale_shift_gate = nn.Sequential( nn.SiLU(), nn.Linear(global_cond_dim, dim * 6, bias=False) ) nn.init.zeros_(self.to_scale_shift_gate[1].weight) #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias) def forward( self, x, context = None, global_cond=None, mask = None, context_mask = None, rotary_pos_emb = None, transformer_options={} ): if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1) # self-attention with adaLN residual = x x = self.pre_norm(x) x = x * (1 + scale_self) + shift_self x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options) x = x * torch.sigmoid(1 - gate_self) x = x + residual if context is not None: x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options) if self.conformer is not None: x = x + self.conformer(x) # feedforward with adaLN residual = x x = self.ff_norm(x) x = x * (1 + scale_ff) + shift_ff x = self.ff(x) x = x * torch.sigmoid(1 - gate_ff) x = x + residual else: x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options) if context is not None: x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options) if self.conformer is not None: x = x + self.conformer(x) x = x + self.ff(self.ff_norm(x)) return x class ContinuousTransformer(nn.Module): def __init__( self, dim, depth, *, dim_in = None, dim_out = None, dim_heads = 64, cross_attend=False, cond_token_dim=None, global_cond_dim=None, causal=False, rotary_pos_emb=True, zero_init_branch_outputs=True, conformer=False, use_sinusoidal_emb=False, use_abs_pos_emb=False, abs_pos_emb_max_length=10000, dtype=None, device=None, operations=None, **kwargs ): super().__init__() self.dim = dim self.depth = depth self.causal = causal self.layers = nn.ModuleList([]) self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity() self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity() if rotary_pos_emb: self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype) else: self.rotary_pos_emb = None self.use_sinusoidal_emb = use_sinusoidal_emb if use_sinusoidal_emb: self.pos_emb = ScaledSinusoidalEmbedding(dim) self.use_abs_pos_emb = use_abs_pos_emb if use_abs_pos_emb: self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length) for i in range(depth): self.layers.append( TransformerBlock( dim, dim_heads = dim_heads, cross_attend = cross_attend, dim_context = cond_token_dim, global_cond_dim = global_cond_dim, causal = causal, zero_init_branch_outputs = zero_init_branch_outputs, conformer=conformer, layer_ix=i, dtype=dtype, device=device, operations=operations, **kwargs ) ) def forward( self, x, mask = None, prepend_embeds = None, prepend_mask = None, global_cond = None, return_info = False, **kwargs ): transformer_options = kwargs.get("transformer_options", {}) patches_replace = transformer_options.get("patches_replace", {}) batch, seq, device = *x.shape[:2], x.device context = kwargs["context"] info = { "hidden_states": [], } x = self.project_in(x) if prepend_embeds is not None: prepend_length, prepend_dim = prepend_embeds.shape[1:] assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension' x = torch.cat((prepend_embeds, x), dim = -2) if prepend_mask is not None or mask is not None: mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool) prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool) mask = torch.cat((prepend_mask, mask), dim = -1) # Attention layers if self.rotary_pos_emb is not None: rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device) else: rotary_pos_emb = None if self.use_sinusoidal_emb or self.use_abs_pos_emb: x = x + self.pos_emb(x) blocks_replace = patches_replace.get("dit", {}) # Iterate over the transformer layers for i, layer in enumerate(self.layers): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"], transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options) # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) if return_info: info["hidden_states"].append(x) x = self.project_out(x) if return_info: return x, info return x class AudioDiffusionTransformer(nn.Module): def __init__(self, io_channels=64, patch_size=1, embed_dim=1536, cond_token_dim=768, project_cond_tokens=False, global_cond_dim=1536, project_global_cond=True, input_concat_dim=0, prepend_cond_dim=0, depth=24, num_heads=24, transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer", global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend", audio_model="", dtype=None, device=None, operations=None, **kwargs): super().__init__() self.dtype = dtype self.cond_token_dim = cond_token_dim # Timestep embeddings timestep_features_dim = 256 self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device) self.to_timestep_embed = nn.Sequential( operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device), ) if cond_token_dim > 0: # Conditioning tokens cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim self.to_cond_embed = nn.Sequential( operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device), nn.SiLU(), operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device) ) else: cond_embed_dim = 0 if global_cond_dim > 0: # Global conditioning global_embed_dim = global_cond_dim if not project_global_cond else embed_dim self.to_global_embed = nn.Sequential( operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device), nn.SiLU(), operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device) ) if prepend_cond_dim > 0: # Prepend conditioning self.to_prepend_embed = nn.Sequential( operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device), nn.SiLU(), operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) ) self.input_concat_dim = input_concat_dim dim_in = io_channels + self.input_concat_dim self.patch_size = patch_size # Transformer self.transformer_type = transformer_type self.global_cond_type = global_cond_type if self.transformer_type == "continuous_transformer": global_dim = None if self.global_cond_type == "adaLN": # The global conditioning is projected to the embed_dim already at this point global_dim = embed_dim self.transformer = ContinuousTransformer( dim=embed_dim, depth=depth, dim_heads=embed_dim // num_heads, dim_in=dim_in * patch_size, dim_out=io_channels * patch_size, cross_attend = cond_token_dim > 0, cond_token_dim = cond_embed_dim, global_cond_dim=global_dim, dtype=dtype, device=device, operations=operations, **kwargs ) else: raise ValueError(f"Unknown transformer type: {self.transformer_type}") self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device) self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device) def _forward( self, x, t, mask=None, cross_attn_cond=None, cross_attn_cond_mask=None, input_concat_cond=None, global_embed=None, prepend_cond=None, prepend_cond_mask=None, return_info=False, **kwargs): if cross_attn_cond is not None: cross_attn_cond = self.to_cond_embed(cross_attn_cond) if global_embed is not None: # Project the global conditioning to the embedding dimension global_embed = self.to_global_embed(global_embed) prepend_inputs = None prepend_mask = None prepend_length = 0 if prepend_cond is not None: # Project the prepend conditioning to the embedding dimension prepend_cond = self.to_prepend_embed(prepend_cond) prepend_inputs = prepend_cond if prepend_cond_mask is not None: prepend_mask = prepend_cond_mask if input_concat_cond is not None: # Interpolate input_concat_cond to the same length as x if input_concat_cond.shape[2] != x.shape[2]: input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest') x = torch.cat([x, input_concat_cond], dim=1) # Get the batch of timestep embeddings timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim) # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists if global_embed is not None: global_embed = global_embed + timestep_embed else: global_embed = timestep_embed # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer if self.global_cond_type == "prepend": if prepend_inputs is None: # Prepend inputs are just the global embed, and the mask is all ones prepend_inputs = global_embed.unsqueeze(1) prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool) else: # Prepend inputs are the prepend conditioning + the global embed prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1) prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1) prepend_length = prepend_inputs.shape[1] x = self.preprocess_conv(x) + x x = rearrange(x, "b c t -> b t c") extra_args = {} if self.global_cond_type == "adaLN": extra_args["global_cond"] = global_embed if self.patch_size > 1: x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size) if self.transformer_type == "x-transformers": output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs) elif self.transformer_type == "continuous_transformer": output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs) if return_info: output, info = output elif self.transformer_type == "mm_transformer": output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs) output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:] if self.patch_size > 1: output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size) output = self.postprocess_conv(output) + output if return_info: return output, info return output def forward( self, x, timestep, context=None, context_mask=None, input_concat_cond=None, global_embed=None, negative_global_embed=None, prepend_cond=None, prepend_cond_mask=None, mask=None, return_info=False, control=None, **kwargs): return self._forward( x, timestep, cross_attn_cond=context, cross_attn_cond_mask=context_mask, input_concat_cond=input_concat_cond, global_embed=global_embed, prepend_cond=prepend_cond, prepend_cond_mask=prepend_cond_mask, mask=mask, return_info=return_info, **kwargs ) ================================================ FILE: comfy/ldm/audio/embedders.py ================================================ # code adapted from: https://github.com/Stability-AI/stable-audio-tools import torch import torch.nn as nn from torch import Tensor from typing import List, Union from einops import rearrange import math import comfy.ops class LearnedPositionalEmbedding(nn.Module): """Used for continuous time""" def __init__(self, dim: int): super().__init__() assert (dim % 2) == 0 half_dim = dim // 2 self.weights = nn.Parameter(torch.empty(half_dim)) def forward(self, x: Tensor) -> Tensor: x = rearrange(x, "b -> b 1") freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) fouriered = torch.cat((x, fouriered), dim=-1) return fouriered def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module: return nn.Sequential( LearnedPositionalEmbedding(dim), comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features), ) class NumberEmbedder(nn.Module): def __init__( self, features: int, dim: int = 256, ): super().__init__() self.features = features self.embedding = TimePositionalEmbedding(dim=dim, out_features=features) def forward(self, x: Union[List[float], Tensor]) -> Tensor: if not torch.is_tensor(x): device = next(self.embedding.parameters()).device x = torch.tensor(x, device=device) assert isinstance(x, Tensor) shape = x.shape x = rearrange(x, "... -> (...)") embedding = self.embedding(x) x = embedding.view(*shape, self.features) return x # type: ignore class Conditioner(nn.Module): def __init__( self, dim: int, output_dim: int, project_out: bool = False ): super().__init__() self.dim = dim self.output_dim = output_dim self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity() def forward(self, x): raise NotImplementedError() class NumberConditioner(Conditioner): ''' Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings ''' def __init__(self, output_dim: int, min_val: float=0, max_val: float=1 ): super().__init__(output_dim, output_dim) self.min_val = min_val self.max_val = max_val self.embedder = NumberEmbedder(features=output_dim) def forward(self, floats, device=None): # Cast the inputs to floats floats = [float(x) for x in floats] if device is None: device = next(self.embedder.parameters()).device floats = torch.tensor(floats).to(device) floats = floats.clamp(self.min_val, self.max_val) normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val) # Cast floats to same type as embedder embedder_dtype = next(self.embedder.parameters()).dtype normalized_floats = normalized_floats.to(embedder_dtype) float_embeds = self.embedder(normalized_floats).unsqueeze(1) return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)] ================================================ FILE: comfy/ldm/aura/mmdit.py ================================================ #AuraFlow MMDiT #Originally written by the AuraFlow Authors import math import torch import torch.nn as nn import torch.nn.functional as F from comfy.ldm.modules.attention import optimized_attention import comfy.ops import comfy.patcher_extension import comfy.ldm.common_dit def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) def find_multiple(n: int, k: int) -> int: if n % k == 0: return n return n + k - (n % k) class MLP(nn.Module): def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None: super().__init__() if hidden_dim is None: hidden_dim = 4 * dim n_hidden = int(2 * hidden_dim / 3) n_hidden = find_multiple(n_hidden, 256) self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device) def forward(self, x: torch.Tensor) -> torch.Tensor: x = F.silu(self.c_fc1(x)) * self.c_fc2(x) x = self.c_proj(x) return x class MultiHeadLayerNorm(nn.Module): def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None): # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78 super().__init__() self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) mean = hidden_states.mean(-1, keepdim=True) variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True) hidden_states = (hidden_states - mean) * torch.rsqrt( variance + self.variance_epsilon ) hidden_states = self.weight.to(torch.float32) * hidden_states return hidden_states.to(input_dtype) class SingleAttention(nn.Module): def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): super().__init__() self.n_heads = n_heads self.head_dim = dim // n_heads # this is for cond self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.q_norm1 = ( MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) if mh_qknorm else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) ) self.k_norm1 = ( MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) if mh_qknorm else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) ) #@torch.compile() def forward(self, c, transformer_options={}): bsz, seqlen1, _ = c.shape q, k, v = self.w1q(c), self.w1k(c), self.w1v(c) q = q.view(bsz, seqlen1, self.n_heads, self.head_dim) k = k.view(bsz, seqlen1, self.n_heads, self.head_dim) v = v.view(bsz, seqlen1, self.n_heads, self.head_dim) q, k = self.q_norm1(q), self.k_norm1(k) output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options) c = self.w1o(output) return c class DoubleAttention(nn.Module): def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): super().__init__() self.n_heads = n_heads self.head_dim = dim // n_heads # this is for cond self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) # this is for x self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.q_norm1 = ( MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) if mh_qknorm else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) ) self.k_norm1 = ( MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) if mh_qknorm else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) ) self.q_norm2 = ( MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) if mh_qknorm else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) ) self.k_norm2 = ( MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) if mh_qknorm else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) ) #@torch.compile() def forward(self, c, x, transformer_options={}): bsz, seqlen1, _ = c.shape bsz, seqlen2, _ = x.shape cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c) cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim) ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim) cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim) cq, ck = self.q_norm1(cq), self.k_norm1(ck) xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x) xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim) xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim) xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim) xq, xk = self.q_norm2(xq), self.k_norm2(xk) # concat all q, k, v = ( torch.cat([cq, xq], dim=1), torch.cat([ck, xk], dim=1), torch.cat([cv, xv], dim=1), ) output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options) c, x = output.split([seqlen1, seqlen2], dim=1) c = self.w1o(c) x = self.w2o(x) return c, x class MMDiTBlock(nn.Module): def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None): super().__init__() self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) if not is_last: self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) self.modC = nn.Sequential( nn.SiLU(), operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), ) else: self.modC = nn.Sequential( nn.SiLU(), operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), ) self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) self.modX = nn.Sequential( nn.SiLU(), operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), ) self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations) self.is_last = is_last #@torch.compile() def forward(self, c, x, global_cond, transformer_options={}, **kwargs): cres, xres = c, x cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = ( self.modC(global_cond).chunk(6, dim=1) ) c = modulate(self.normC1(c), cshift_msa, cscale_msa) # xpath xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = ( self.modX(global_cond).chunk(6, dim=1) ) x = modulate(self.normX1(x), xshift_msa, xscale_msa) # attention c, x = self.attn(c, x, transformer_options=transformer_options) c = self.normC2(cres + cgate_msa.unsqueeze(1) * c) c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp)) c = cres + c x = self.normX2(xres + xgate_msa.unsqueeze(1) * x) x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp)) x = xres + x return c, x class DiTBlock(nn.Module): # like MMDiTBlock, but it only has X def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None): super().__init__() self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) self.modCX = nn.Sequential( nn.SiLU(), operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), ) self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations) self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) #@torch.compile() def forward(self, cx, global_cond, transformer_options={}, **kwargs): cxres = cx shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX( global_cond ).chunk(6, dim=1) cx = modulate(self.norm1(cx), shift_msa, scale_msa) cx = self.attn(cx, transformer_options=transformer_options) cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx) mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp)) cx = gate_mlp.unsqueeze(1) * mlpout cx = cxres + cx return cx class TimestepEmbedder(nn.Module): def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): super().__init__() self.mlp = nn.Sequential( operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size @staticmethod def timestep_embedding(t, dim, max_period=10000): half = dim // 2 freqs = 1000 * torch.exp( -math.log(max_period) * torch.arange(start=0, end=half) / half ).to(t.device) args = t[:, None] * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat( [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 ) return embedding #@torch.compile() def forward(self, t, dtype): t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) t_emb = self.mlp(t_freq) return t_emb class MMDiT(nn.Module): def __init__( self, in_channels=4, out_channels=4, patch_size=2, dim=3072, n_layers=36, n_double_layers=4, n_heads=12, global_conddim=3072, cond_seq_dim=2048, max_seq=32 * 32, device=None, dtype=None, operations=None, ): super().__init__() self.dtype = dtype self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations) self.cond_seq_linear = operations.Linear( cond_seq_dim, dim, bias=False, dtype=dtype, device=device ) # linear for something like text sequence. self.init_x_linear = operations.Linear( patch_size * patch_size * in_channels, dim, dtype=dtype, device=device ) # init linear for patchified image. self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device)) self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device)) self.double_layers = nn.ModuleList([]) self.single_layers = nn.ModuleList([]) for idx in range(n_double_layers): self.double_layers.append( MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations) ) for idx in range(n_double_layers, n_layers): self.single_layers.append( DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations) ) self.final_linear = operations.Linear( dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device ) self.modF = nn.Sequential( nn.SiLU(), operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), ) self.out_channels = out_channels self.patch_size = patch_size self.n_double_layers = n_double_layers self.n_layers = n_layers self.h_max = round(max_seq**0.5) self.w_max = round(max_seq**0.5) @torch.no_grad() def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)): # extend pe pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]] pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1) # now we need to extend this to target_dim. for this we will use interpolation. # we will use torch.nn.functional.interpolate pe_as_2d = F.interpolate( pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear" ) pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1) self.positional_encoding.data = pe_new.unsqueeze(0).contiguous() self.h_max, self.w_max = target_dim def pe_selection_index_based_on_dim(self, h, w): h_p, w_p = h // self.patch_size, w // self.patch_size original_pe_indexes = torch.arange(self.positional_encoding.shape[1]) original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max) starth = self.h_max // 2 - h_p // 2 endh =starth + h_p startw = self.w_max // 2 - w_p // 2 endw = startw + w_p original_pe_indexes = original_pe_indexes[ starth:endh, startw:endw ] return original_pe_indexes.flatten() def unpatchify(self, x, h, w): c = self.out_channels p = self.patch_size x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) x = torch.einsum("nhwpqc->nchpwq", x) imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) return imgs def patchify(self, x): B, C, H, W = x.size() x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) x = x.view( B, C, (H + 1) // self.patch_size, self.patch_size, (W + 1) // self.patch_size, self.patch_size, ) x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2) return x def apply_pos_embeds(self, x, h, w): h = (h + 1) // self.patch_size w = (w + 1) // self.patch_size max_dim = max(h, w) cur_dim = self.h_max pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x) if max_dim > cur_dim: pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1) cur_dim = max_dim from_h = (cur_dim - h) // 2 from_w = (cur_dim - w) // 2 pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w] return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1]) def forward(self, x, timestep, context, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, transformer_options, **kwargs) def _forward(self, x, timestep, context, transformer_options={}, **kwargs): patches_replace = transformer_options.get("patches_replace", {}) # patchify x, add PE b, c, h, w = x.shape # pe_indexes = self.pe_selection_index_based_on_dim(h, w) # print(pe_indexes, pe_indexes.shape) x = self.init_x_linear(self.patchify(x)) # B, T_x, D x = self.apply_pos_embeds(x, h, w) # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype) # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype) # process conditions for MMDiT Blocks c_seq = context # B, T_c, D_c t = timestep c = self.cond_seq_linear(c_seq) # B, T_c, D c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1) global_cond = self.t_embedder(t, x.dtype) # B, D blocks_replace = patches_replace.get("dit", {}) if len(self.double_layers) > 0: for i, layer in enumerate(self.double_layers): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["txt"], out["img"] = layer(args["txt"], args["img"], args["vec"], transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap}) c = out["txt"] x = out["img"] else: c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs) if len(self.single_layers) > 0: c_len = c.size(1) cx = torch.cat([c, x], dim=1) for i, layer in enumerate(self.single_layers): if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"]) return out out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap}) cx = out["img"] else: cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs) x = cx[:, c_len:] fshift, fscale = self.modF(global_cond).chunk(2, dim=1) x = modulate(x, fshift, fscale) x = self.final_linear(x) x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w] return x ================================================ FILE: comfy/ldm/cascade/common.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch import torch.nn as nn from comfy.ldm.modules.attention import optimized_attention import comfy.ops class OptimizedAttention(nn.Module): def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.heads = nhead self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device) self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device) self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) def forward(self, q, k, v, transformer_options={}): q = self.to_q(q) k = self.to_k(k) v = self.to_v(v) out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options) return self.out_proj(out) class Attention2D(nn.Module): def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device) def forward(self, x, kv, self_attn=False, transformer_options={}): orig_shape = x.shape x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4 if self_attn: kv = torch.cat([x, kv], dim=1) # x = self.attn(x, kv, kv, need_weights=False)[0] x = self.attn(x, kv, kv, transformer_options=transformer_options) x = x.permute(0, 2, 1).view(*orig_shape) return x def LayerNorm2d_op(operations): class LayerNorm2d(operations.LayerNorm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self, x): return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return LayerNorm2d class GlobalResponseNorm(nn.Module): "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105" def __init__(self, dim, dtype=None, device=None): super().__init__() self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) def forward(self, x): Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x class ResBlock(nn.Module): def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2): super().__init__() self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device) # self.depthwise = SAMBlock(c, num_heads, expansion) self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.channelwise = nn.Sequential( operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device), nn.GELU(), GlobalResponseNorm(c * 4, dtype=dtype, device=device), nn.Dropout(dropout), operations.Linear(c * 4, c, dtype=dtype, device=device) ) def forward(self, x, x_skip=None): x_res = x x = self.norm(self.depthwise(x)) if x_skip is not None: x = torch.cat([x, x_skip], dim=1) x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return x + x_res class AttnBlock(nn.Module): def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.self_attn = self_attn self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations) self.kv_mapper = nn.Sequential( nn.SiLU(), operations.Linear(c_cond, c, dtype=dtype, device=device) ) def forward(self, x, kv, transformer_options={}): kv = self.kv_mapper(kv) x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options) return x class FeedForwardBlock(nn.Module): def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.channelwise = nn.Sequential( operations.Linear(c, c * 4, dtype=dtype, device=device), nn.GELU(), GlobalResponseNorm(c * 4, dtype=dtype, device=device), nn.Dropout(dropout), operations.Linear(c * 4, c, dtype=dtype, device=device) ) def forward(self, x): x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return x class TimestepBlock(nn.Module): def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None): super().__init__() self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device) self.conds = conds for cname in conds: setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)) def forward(self, x, t): t = t.chunk(len(self.conds) + 1, dim=1) a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1) for i, c in enumerate(self.conds): ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1) a, b = a + ac, b + bc return x * (1 + a) + b ================================================ FILE: comfy/ldm/cascade/controlnet.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torchvision from torch import nn from .common import LayerNorm2d_op class CNetResBlock(nn.Module): def __init__(self, c, dtype=None, device=None, operations=None): super().__init__() self.blocks = nn.Sequential( LayerNorm2d_op(operations)(c, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c, c, kernel_size=3, padding=1), LayerNorm2d_op(operations)(c, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c, c, kernel_size=3, padding=1), ) def forward(self, x): return x + self.blocks(x) class ControlNet(nn.Module): def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn): super().__init__() if bottleneck_mode is None: bottleneck_mode = 'effnet' self.proj_blocks = proj_blocks if bottleneck_mode == 'effnet': embd_channels = 1280 self.backbone = torchvision.models.efficientnet_v2_s().features.eval() if c_in != 3: in_weights = self.backbone[0][0].weight.data self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device) if c_in > 3: # nn.init.constant_(self.backbone[0][0].weight, 0) self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone() else: self.backbone[0][0].weight.data = in_weights[:, :c_in].clone() elif bottleneck_mode == 'simple': embd_channels = c_in self.backbone = nn.Sequential( operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device), nn.LeakyReLU(0.2, inplace=True), operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device), ) elif bottleneck_mode == 'large': self.backbone = nn.Sequential( operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), nn.LeakyReLU(0.2, inplace=True), operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)], operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), ) embd_channels = 1280 else: raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}') self.projections = nn.ModuleList() for _ in range(len(proj_blocks)): self.projections.append(nn.Sequential( operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device), nn.LeakyReLU(0.2, inplace=True), operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device), )) # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection self.xl = False self.input_channels = c_in self.unshuffle_amount = 8 def forward(self, x): x = self.backbone(x) proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] for i, idx in enumerate(self.proj_blocks): proj_outputs[idx] = self.projections[i](x) return {"input": proj_outputs[::-1]} ================================================ FILE: comfy/ldm/cascade/stage_a.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch from torch import nn from torch.autograd import Function import comfy.ops ops = comfy.ops.disable_weight_init class vector_quantize(Function): @staticmethod def forward(ctx, x, codebook): with torch.no_grad(): codebook_sqr = torch.sum(codebook ** 2, dim=1) x_sqr = torch.sum(x ** 2, dim=1, keepdim=True) dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0) _, indices = dist.min(dim=1) ctx.save_for_backward(indices, codebook) ctx.mark_non_differentiable(indices) nn = torch.index_select(codebook, 0, indices) return nn, indices @staticmethod def backward(ctx, grad_output, grad_indices): grad_inputs, grad_codebook = None, None if ctx.needs_input_grad[0]: grad_inputs = grad_output.clone() if ctx.needs_input_grad[1]: # Gradient wrt. the codebook indices, codebook = ctx.saved_tensors grad_codebook = torch.zeros_like(codebook) grad_codebook.index_add_(0, indices, grad_output) return (grad_inputs, grad_codebook) class VectorQuantize(nn.Module): def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False): """ Takes an input of variable size (as long as the last dimension matches the embedding size). Returns one tensor containing the nearest neigbour embeddings to each of the inputs, with the same size as the input, vq and commitment components for the loss as a touple in the second output and the indices of the quantized vectors in the third: quantized, (vq_loss, commit_loss), indices """ super(VectorQuantize, self).__init__() self.codebook = nn.Embedding(k, embedding_size) self.codebook.weight.data.uniform_(-1./k, 1./k) self.vq = vector_quantize.apply self.ema_decay = ema_decay self.ema_loss = ema_loss if ema_loss: self.register_buffer('ema_element_count', torch.ones(k)) self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight)) def _laplace_smoothing(self, x, epsilon): n = torch.sum(x) return ((x + epsilon) / (n + x.size(0) * epsilon) * n) def _updateEMA(self, z_e_x, indices): mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float() elem_count = mask.sum(dim=0) weight_sum = torch.mm(mask.t(), z_e_x) self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count) self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5) self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum) self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1) def idx2vq(self, idx, dim=-1): q_idx = self.codebook(idx) if dim != -1: q_idx = q_idx.movedim(-1, dim) return q_idx def forward(self, x, get_losses=True, dim=-1): if dim != -1: x = x.movedim(dim, -1) z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach()) vq_loss, commit_loss = None, None if self.ema_loss and self.training: self._updateEMA(z_e_x.detach(), indices.detach()) # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices) if get_losses: vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean() commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean() z_q_x = z_q_x.view(x.shape) if dim != -1: z_q_x = z_q_x.movedim(-1, dim) return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1]) class ResBlock(nn.Module): def __init__(self, c, c_hidden): super().__init__() # depthwise/attention self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) self.depthwise = nn.Sequential( nn.ReplicationPad2d(1), ops.Conv2d(c, c, kernel_size=3, groups=c) ) # channelwise self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) self.channelwise = nn.Sequential( ops.Linear(c, c_hidden), nn.GELU(), ops.Linear(c_hidden, c), ) self.gammas = nn.Parameter(torch.zeros(6), requires_grad=False) def _norm(self, x, norm): return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) def forward(self, x): mods = self.gammas x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1] try: x = x + self.depthwise(x_temp) * mods[2] except: #operation not implemented for bf16 x_temp = self.depthwise[0](x_temp.float()).to(x.dtype) x = x + self.depthwise[1](x_temp) * mods[2] x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4] x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5] return x class StageA(nn.Module): def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192): super().__init__() self.c_latent = c_latent c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))] # Encoder blocks self.in_block = nn.Sequential( nn.PixelUnshuffle(2), ops.Conv2d(3 * 4, c_levels[0], kernel_size=1) ) down_blocks = [] for i in range(levels): if i > 0: down_blocks.append(ops.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) block = ResBlock(c_levels[i], c_levels[i] * 4) down_blocks.append(block) down_blocks.append(nn.Sequential( ops.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1 )) self.down_blocks = nn.Sequential(*down_blocks) self.down_blocks[0] self.codebook_size = codebook_size self.vquantizer = VectorQuantize(c_latent, k=codebook_size) # Decoder blocks up_blocks = [nn.Sequential( ops.Conv2d(c_latent, c_levels[-1], kernel_size=1) )] for i in range(levels): for j in range(bottleneck_blocks if i == 0 else 1): block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4) up_blocks.append(block) if i < levels - 1: up_blocks.append( ops.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, padding=1)) self.up_blocks = nn.Sequential(*up_blocks) self.out_block = nn.Sequential( ops.Conv2d(c_levels[0], 3 * 4, kernel_size=1), nn.PixelShuffle(2), ) def encode(self, x, quantize=False): x = self.in_block(x) x = self.down_blocks(x) if quantize: qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1) return qe, x, indices, vq_loss + commit_loss * 0.25 else: return x def decode(self, x): x = self.up_blocks(x) x = self.out_block(x) return x def forward(self, x, quantize=False): qe, x, _, vq_loss = self.encode(x, quantize) x = self.decode(qe) return x, vq_loss class Discriminator(nn.Module): def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6): super().__init__() d = max(depth - 3, 3) layers = [ nn.utils.spectral_norm(ops.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)), nn.LeakyReLU(0.2), ] for i in range(depth - 1): c_in = c_hidden // (2 ** max((d - i), 0)) c_out = c_hidden // (2 ** max((d - 1 - i), 0)) layers.append(nn.utils.spectral_norm(ops.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) layers.append(nn.InstanceNorm2d(c_out)) layers.append(nn.LeakyReLU(0.2)) self.encoder = nn.Sequential(*layers) self.shuffle = ops.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) self.logits = nn.Sigmoid() def forward(self, x, cond=None): x = self.encoder(x) if cond is not None: cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1)) x = torch.cat([x, cond], dim=1) x = self.shuffle(x) x = self.logits(x) return x ================================================ FILE: comfy/ldm/cascade/stage_b.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import math import torch from torch import nn from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock class StageB(nn.Module): def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280], nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]], block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280, c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True, t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None): super().__init__() self.dtype = dtype self.c_r = c_r self.t_conds = t_conds self.c_clip_seq = c_clip_seq if not isinstance(dropout, list): dropout = [dropout] * len(c_hidden) if not isinstance(self_attn, list): self_attn = [self_attn] * len(c_hidden) # CONDITIONING self.effnet_mapper = nn.Sequential( operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) ) self.pixels_mapper = nn.Sequential( operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) ) self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device) self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.embedding = nn.Sequential( nn.PixelUnshuffle(patch_size), operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) ) def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): if block_type == 'C': return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'A': return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'F': return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'T': return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) else: raise Exception(f'Block type {block_type} not supported') # BLOCKS # -- down blocks self.down_blocks = nn.ModuleList() self.down_downscalers = nn.ModuleList() self.down_repeat_mappers = nn.ModuleList() for i in range(len(c_hidden)): if i > 0: self.down_downscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device), )) else: self.down_downscalers.append(nn.Identity()) down_block = nn.ModuleList() for _ in range(blocks[0][i]): for block_type in level_config[i]: block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) down_block.append(block) self.down_blocks.append(down_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[0][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.down_repeat_mappers.append(block_repeat_mappers) # -- up blocks self.up_blocks = nn.ModuleList() self.up_upscalers = nn.ModuleList() self.up_repeat_mappers = nn.ModuleList() for i in reversed(range(len(c_hidden))): if i > 0: self.up_upscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device), )) else: self.up_upscalers.append(nn.Identity()) up_block = nn.ModuleList() for j in range(blocks[1][::-1][i]): for k, block_type in enumerate(level_config[i]): c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], self_attn=self_attn[i]) up_block.append(block) self.up_blocks.append(up_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[1][::-1][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.up_repeat_mappers.append(block_repeat_mappers) # OUTPUT self.clf = nn.Sequential( LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), nn.PixelShuffle(patch_size), ) # --- WEIGHT INIT --- # self.apply(self._init_weights) # General init # nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings # nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings # nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings # nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings # nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs # nn.init.constant_(self.clf[1].weight, 0) # outputs # # # blocks # for level_block in self.down_blocks + self.up_blocks: # for block in level_block: # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) # elif isinstance(block, TimestepBlock): # for layer in block.modules(): # if isinstance(layer, nn.Linear): # nn.init.constant_(layer.weight, 0) # # def _init_weights(self, m): # if isinstance(m, (nn.Conv2d, nn.Linear)): # torch.nn.init.xavier_uniform_(m.weight) # if m.bias is not None: # nn.init.constant_(m.bias, 0) def gen_r_embedding(self, r, max_positions=10000): r = r * max_positions half_dim = self.c_r // 2 emb = math.log(max_positions) / (half_dim - 1) emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() emb = r[:, None] * emb[None, :] emb = torch.cat([emb.sin(), emb.cos()], dim=1) if self.c_r % 2 == 1: # zero pad emb = nn.functional.pad(emb, (0, 1), mode='constant') return emb def gen_c_embeddings(self, clip): if len(clip.shape) == 2: clip = clip.unsqueeze(1) clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1) clip = self.clip_norm(clip) return clip def _down_encode(self, x, r_embed, clip, transformer_options={}): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: x = downscaler(x) for i in range(len(repmap) + 1): for block in down_block: if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): x = block(x) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if i < len(repmap): x = repmap[i](x) level_outputs.insert(0, x) return level_outputs def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): for j in range(len(repmap) + 1): for k, block in enumerate(up_block): if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): skip = level_outputs[i] if k == 0 and i > 0 else None if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', align_corners=True) x = block(x, skip) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if j < len(repmap): x = repmap[j](x) x = upscaler(x) return x def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs): if pixels is None: pixels = x.new_zeros(x.size(0), 3, 8, 8) # Process the conditioning embeddings r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) for c in self.t_conds: t_cond = kwargs.get(c, torch.zeros_like(r)) r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) clip = self.gen_c_embeddings(clip) # Model Blocks x = self.embedding(x) x = x + self.effnet_mapper( nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', align_corners=True) level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options) x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): for self_params, src_params in zip(self.parameters(), src_model.parameters()): self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) ================================================ FILE: comfy/ldm/cascade/stage_c.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch from torch import nn import math from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock # from .controlnet import ControlNetDeliverer class UpDownBlock2d(nn.Module): def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None): super().__init__() assert mode in ['up', 'down'] interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear', align_corners=True) if enabled else nn.Identity() mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device) self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation]) def forward(self, x): for block in self.blocks: x = block(x) return x class StageC(nn.Module): def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32], blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'], c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3, dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, dtype=None, device=None, operations=None): super().__init__() self.dtype = dtype self.c_r = c_r self.t_conds = t_conds self.c_clip_seq = c_clip_seq if not isinstance(dropout, list): dropout = [dropout] * len(c_hidden) if not isinstance(self_attn, list): self_attn = [self_attn] * len(c_hidden) # CONDITIONING self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device) self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device) self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device) self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.embedding = nn.Sequential( nn.PixelUnshuffle(patch_size), operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6) ) def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): if block_type == 'C': return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'A': return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'F': return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'T': return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) else: raise Exception(f'Block type {block_type} not supported') # BLOCKS # -- down blocks self.down_blocks = nn.ModuleList() self.down_downscalers = nn.ModuleList() self.down_repeat_mappers = nn.ModuleList() for i in range(len(c_hidden)): if i > 0: self.down_downscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6), UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) )) else: self.down_downscalers.append(nn.Identity()) down_block = nn.ModuleList() for _ in range(blocks[0][i]): for block_type in level_config[i]: block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) down_block.append(block) self.down_blocks.append(down_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[0][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.down_repeat_mappers.append(block_repeat_mappers) # -- up blocks self.up_blocks = nn.ModuleList() self.up_upscalers = nn.ModuleList() self.up_repeat_mappers = nn.ModuleList() for i in reversed(range(len(c_hidden))): if i > 0: self.up_upscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6), UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) )) else: self.up_upscalers.append(nn.Identity()) up_block = nn.ModuleList() for j in range(blocks[1][::-1][i]): for k, block_type in enumerate(level_config[i]): c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], self_attn=self_attn[i]) up_block.append(block) self.up_blocks.append(up_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[1][::-1][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.up_repeat_mappers.append(block_repeat_mappers) # OUTPUT self.clf = nn.Sequential( LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), nn.PixelShuffle(patch_size), ) # --- WEIGHT INIT --- # self.apply(self._init_weights) # General init # nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) # conditionings # nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02) # conditionings # nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs # nn.init.constant_(self.clf[1].weight, 0) # outputs # # # blocks # for level_block in self.down_blocks + self.up_blocks: # for block in level_block: # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) # elif isinstance(block, TimestepBlock): # for layer in block.modules(): # if isinstance(layer, nn.Linear): # nn.init.constant_(layer.weight, 0) # # def _init_weights(self, m): # if isinstance(m, (nn.Conv2d, nn.Linear)): # torch.nn.init.xavier_uniform_(m.weight) # if m.bias is not None: # nn.init.constant_(m.bias, 0) def gen_r_embedding(self, r, max_positions=10000): r = r * max_positions half_dim = self.c_r // 2 emb = math.log(max_positions) / (half_dim - 1) emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() emb = r[:, None] * emb[None, :] emb = torch.cat([emb.sin(), emb.cos()], dim=1) if self.c_r % 2 == 1: # zero pad emb = nn.functional.pad(emb, (0, 1), mode='constant') return emb def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img): clip_txt = self.clip_txt_mapper(clip_txt) if len(clip_txt_pooled.shape) == 2: clip_txt_pooled = clip_txt_pooled.unsqueeze(1) if len(clip_img.shape) == 2: clip_img = clip_img.unsqueeze(1) clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1) clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1) clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1) clip = self.clip_norm(clip) return clip def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: x = downscaler(x) for i in range(len(repmap) + 1): for block in down_block: if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): if cnet is not None: next_cnet = cnet.pop() if next_cnet is not None: x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', align_corners=True).to(x.dtype) x = block(x) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if i < len(repmap): x = repmap[i](x) level_outputs.insert(0, x) return level_outputs def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): for j in range(len(repmap) + 1): for k, block in enumerate(up_block): if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): skip = level_outputs[i] if k == 0 and i > 0 else None if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', align_corners=True) if cnet is not None: next_cnet = cnet.pop() if next_cnet is not None: x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', align_corners=True).to(x.dtype) x = block(x, skip) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if j < len(repmap): x = repmap[j](x) x = upscaler(x) return x def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs): # Process the conditioning embeddings r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) for c in self.t_conds: t_cond = kwargs.get(c, torch.zeros_like(r)) r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img) if control is not None: cnet = control.get("input") else: cnet = None # Model Blocks x = self.embedding(x) level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options) x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): for self_params, src_params in zip(self.parameters(), src_model.parameters()): self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) ================================================ FILE: comfy/ldm/cascade/stage_c_coder.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch import torchvision from torch import nn import comfy.ops ops = comfy.ops.disable_weight_init # EfficientNet class EfficientNetEncoder(nn.Module): def __init__(self, c_latent=16): super().__init__() self.backbone = torchvision.models.efficientnet_v2_s().features.eval() self.mapper = nn.Sequential( ops.Conv2d(1280, c_latent, kernel_size=1, bias=False), nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1 ) self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406])) self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225])) def forward(self, x): x = x * 0.5 + 0.5 x = (x - self.mean.view([3,1,1]).to(device=x.device, dtype=x.dtype)) / self.std.view([3,1,1]).to(device=x.device, dtype=x.dtype) o = self.mapper(self.backbone(x)) return o # Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192 class Previewer(nn.Module): def __init__(self, c_in=16, c_hidden=512, c_out=3): super().__init__() self.blocks = nn.Sequential( ops.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels nn.GELU(), nn.BatchNorm2d(c_hidden), ops.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden), ops.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32 nn.GELU(), nn.BatchNorm2d(c_hidden // 2), ops.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden // 2), ops.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64 nn.GELU(), nn.BatchNorm2d(c_hidden // 4), ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden // 4), ops.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128 nn.GELU(), nn.BatchNorm2d(c_hidden // 4), ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden // 4), ops.Conv2d(c_hidden // 4, c_out, kernel_size=1), ) def forward(self, x): return (self.blocks(x) - 0.5) * 2.0 class StageC_coder(nn.Module): def __init__(self): super().__init__() self.previewer = Previewer() self.encoder = EfficientNetEncoder() def encode(self, x): return self.encoder(x) def decode(self, x): return self.previewer(x) ================================================ FILE: comfy/ldm/chroma/layers.py ================================================ import torch from torch import Tensor, nn from comfy.ldm.flux.layers import ( MLPEmbedder, ModulationOut, ) # TODO: remove this in a few months SingleStreamBlock = None DoubleStreamBlock = None class ChromaModulationOut(ModulationOut): @classmethod def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut: return cls( shift=tensor[:, offset : offset + 1, :], scale=tensor[:, offset + 1 : offset + 2, :], gate=tensor[:, offset + 2 : offset + 3, :], ) class Approximator(nn.Module): def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None): super().__init__() self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) self.norms = nn.ModuleList([operations.RMSNorm(hidden_dim, dtype=dtype, device=device) for x in range( n_layers)]) self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) @property def device(self): # Get the device of the module (assumes all parameters are on the same device) return next(self.parameters()).device def forward(self, x: Tensor) -> Tensor: x = self.in_proj(x) for layer, norms in zip(self.layers, self.norms): x = x + layer(norms(x)) x = self.out_proj(x) return x class LastLayer(nn.Module): def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device) def forward(self, x: Tensor, vec: Tensor) -> Tensor: shift, scale = vec shift = shift.squeeze(1) scale = scale.squeeze(1) x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x)) x = self.linear(x) return x ================================================ FILE: comfy/ldm/chroma/model.py ================================================ #Original code can be found on: https://github.com/black-forest-labs/flux from dataclasses import dataclass import torch from torch import Tensor, nn from einops import rearrange, repeat import comfy.patcher_extension import comfy.ldm.common_dit from comfy.ldm.flux.layers import ( EmbedND, timestep_embedding, DoubleStreamBlock, SingleStreamBlock, ) from .layers import ( LastLayer, Approximator, ChromaModulationOut, ) @dataclass class ChromaParams: in_channels: int out_channels: int context_in_dim: int hidden_size: int mlp_ratio: float num_heads: int depth: int depth_single_blocks: int axes_dim: list theta: int patch_size: int qkv_bias: bool in_dim: int out_dim: int hidden_dim: int n_layers: int txt_ids_dims: list vec_in_dim: int class Chroma(nn.Module): """ Transformer model for flow matching on sequences. """ def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): super().__init__() self.dtype = dtype params = ChromaParams(**kwargs) self.params = params self.patch_size = params.patch_size self.in_channels = params.in_channels self.out_channels = params.out_channels if params.hidden_size % params.num_heads != 0: raise ValueError( f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" ) pe_dim = params.hidden_size // params.num_heads if sum(params.axes_dim) != pe_dim: raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") self.hidden_size = params.hidden_size self.num_heads = params.num_heads self.in_dim = params.in_dim self.out_dim = params.out_dim self.hidden_dim = params.hidden_dim self.n_layers = params.n_layers self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) # set as nn identity for now, will overwrite it later. self.distilled_guidance_layer = Approximator( in_dim=self.in_dim, hidden_dim=self.hidden_dim, out_dim=self.out_dim, n_layers=self.n_layers, dtype=dtype, device=device, operations=operations ) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, modulation=False, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=False, dtype=dtype, device=device, operations=operations) for _ in range(params.depth_single_blocks) ] ) if final_layer: self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) self.skip_mmdit = [] self.skip_dit = [] self.lite = False def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0): # This function slices up the modulations tensor which has the following layout: # single : num_single_blocks * 3 elements # double_img : num_double_blocks * 6 elements # double_txt : num_double_blocks * 6 elements # final : 2 elements if block_type == "final": return (tensor[:, -2:-1, :], tensor[:, -1:, :]) single_block_count = self.params.depth_single_blocks double_block_count = self.params.depth offset = 3 * idx if block_type == "single": return ChromaModulationOut.from_offset(tensor, offset) # Double block modulations are 6 elements so we double 3 * idx. offset *= 2 if block_type in {"double_img", "double_txt"}: # Advance past the single block modulations. offset += 3 * single_block_count if block_type == "double_txt": # Advance past the double block img modulations. offset += 6 * double_block_count return ( ChromaModulationOut.from_offset(tensor, offset), ChromaModulationOut.from_offset(tensor, offset + 3), ) raise ValueError("Bad block_type") def forward_orig( self, img: Tensor, img_ids: Tensor, txt: Tensor, txt_ids: Tensor, timesteps: Tensor, guidance: Tensor = None, control = None, transformer_options={}, attn_mask: Tensor = None, ) -> Tensor: transformer_options = transformer_options.copy() patches_replace = transformer_options.get("patches_replace", {}) # running on sequences img img = self.img_in(img) # distilled vector guidance mod_index_length = 344 distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype) # guidance = guidance * distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) # get all modulation index modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype) # we need to broadcast the modulation index here so each batch has all of the index modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) # and we need to broadcast timestep and guidance along too timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype) # then and only then we could concatenate it together input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) mod_vectors = self.distilled_guidance_layer(input_vec) txt = self.txt_in(txt) ids = torch.cat((txt_ids, img_ids), dim=1) pe = self.pe_embedder(ids) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.double_blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.double_blocks): transformer_options["block_index"] = i if i not in self.skip_mmdit: double_mod = ( self.get_modulations(mod_vectors, "double_img", idx=i), self.get_modulations(mod_vectors, "double_txt", idx=i), ) if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args.get("transformer_options")) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": double_mod, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] else: img, txt = block(img=img, txt=txt, vec=double_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") if i < len(control_i): add = control_i[i] if add is not None: img += add img = torch.cat((txt, img), 1) transformer_options["total_blocks"] = len(self.single_blocks) transformer_options["block_type"] = "single" transformer_options["img_slice"] = [txt.shape[1], img.shape[1]] for i, block in enumerate(self.single_blocks): transformer_options["block_index"] = i if i not in self.skip_dit: single_mod = self.get_modulations(mod_vectors, "single", idx=i) if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args.get("transformer_options")) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": single_mod, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") if i < len(control_o): add = control_o[i] if add is not None: img[:, txt.shape[1] :, ...] += add img = img[:, txt.shape[1] :, ...] if hasattr(self, "final_layer"): final_mod = self.get_modulations(mod_vectors, "final") img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels) return img def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs) def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): bs, c, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size) if img.ndim != 3 or context.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") h_len = ((h + (self.patch_size // 2)) // self.patch_size) w_len = ((w + (self.patch_size // 2)) // self.patch_size) img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w] ================================================ FILE: comfy/ldm/chroma_radiance/layers.py ================================================ # Adapted from https://github.com/lodestone-rock/flow from functools import lru_cache import torch from torch import nn class NerfEmbedder(nn.Module): """ An embedder module that combines input features with a 2D positional encoding that mimics the Discrete Cosine Transform (DCT). This module takes an input tensor of shape (B, P^2, C), where P is the patch size, and enriches it with positional information before projecting it to a new hidden size. """ def __init__( self, in_channels: int, hidden_size_input: int, max_freqs: int, dtype=None, device=None, operations=None, ): """ Initializes the NerfEmbedder. Args: in_channels (int): The number of channels in the input tensor. hidden_size_input (int): The desired dimension of the output embedding. max_freqs (int): The number of frequency components to use for both the x and y dimensions of the positional encoding. The total number of positional features will be max_freqs^2. """ super().__init__() self.dtype = dtype self.max_freqs = max_freqs self.hidden_size_input = hidden_size_input # A linear layer to project the concatenated input features and # positional encodings to the final output dimension. self.embedder = nn.Sequential( operations.Linear(in_channels + max_freqs**2, hidden_size_input, dtype=dtype, device=device) ) @lru_cache(maxsize=4) def fetch_pos(self, patch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor: """ Generates and caches 2D DCT-like positional embeddings for a given patch size. The LRU cache is a performance optimization that avoids recomputing the same positional grid on every forward pass. Args: patch_size (int): The side length of the square input patch. device: The torch device to create the tensors on. dtype: The torch dtype for the tensors. Returns: A tensor of shape (1, patch_size^2, max_freqs^2) containing the positional embeddings. """ # Create normalized 1D coordinate grids from 0 to 1. pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype) pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype) # Create a 2D meshgrid of coordinates. pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing="ij") # Reshape positions to be broadcastable with frequencies. # Shape becomes (patch_size^2, 1, 1). pos_x = pos_x.reshape(-1, 1, 1) pos_y = pos_y.reshape(-1, 1, 1) # Create a 1D tensor of frequency values from 0 to max_freqs-1. freqs = torch.linspace(0, self.max_freqs - 1, self.max_freqs, dtype=dtype, device=device) # Reshape frequencies to be broadcastable for creating 2D basis functions. # freqs_x shape: (1, max_freqs, 1) # freqs_y shape: (1, 1, max_freqs) freqs_x = freqs[None, :, None] freqs_y = freqs[None, None, :] # A custom weighting coefficient, not part of standard DCT. # This seems to down-weight the contribution of higher-frequency interactions. coeffs = (1 + freqs_x * freqs_y) ** -1 # Calculate the 1D cosine basis functions for x and y coordinates. # This is the core of the DCT formulation. dct_x = torch.cos(pos_x * freqs_x * torch.pi) dct_y = torch.cos(pos_y * freqs_y * torch.pi) # Combine the 1D basis functions to create 2D basis functions by element-wise # multiplication, and apply the custom coefficients. Broadcasting handles the # combination of all (pos_x, freqs_x) with all (pos_y, freqs_y). # The result is flattened into a feature vector for each position. dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2) return dct def forward(self, inputs: torch.Tensor) -> torch.Tensor: """ Forward pass for the embedder. Args: inputs (Tensor): The input tensor of shape (B, P^2, C). Returns: Tensor: The output tensor of shape (B, P^2, hidden_size_input). """ # Get the batch size, number of pixels, and number of channels. B, P2, C = inputs.shape # Infer the patch side length from the number of pixels (P^2). patch_size = int(P2 ** 0.5) input_dtype = inputs.dtype inputs = inputs.to(dtype=self.dtype) # Fetch the pre-computed or cached positional embeddings. dct = self.fetch_pos(patch_size, inputs.device, self.dtype) # Repeat the positional embeddings for each item in the batch. dct = dct.repeat(B, 1, 1) # Concatenate the original input features with the positional embeddings # along the feature dimension. inputs = torch.cat((inputs, dct), dim=-1) # Project the combined tensor to the target hidden size. return self.embedder(inputs).to(dtype=input_dtype) class NerfGLUBlock(nn.Module): """ A NerfBlock using a Gated Linear Unit (GLU) like MLP. """ def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, dtype=None, device=None, operations=None): super().__init__() # The total number of parameters for the MLP is increased to accommodate # the gate, value, and output projection matrices. # We now need to generate parameters for 3 matrices. total_params = 3 * hidden_size_x**2 * mlp_ratio self.param_generator = operations.Linear(hidden_size_s, total_params, dtype=dtype, device=device) self.norm = operations.RMSNorm(hidden_size_x, dtype=dtype, device=device) self.mlp_ratio = mlp_ratio def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor: batch_size, num_x, hidden_size_x = x.shape mlp_params = self.param_generator(s) # Split the generated parameters into three parts for the gate, value, and output projection. fc1_gate_params, fc1_value_params, fc2_params = mlp_params.chunk(3, dim=-1) # Reshape the parameters into matrices for batch matrix multiplication. fc1_gate = fc1_gate_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio) fc1_value = fc1_value_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio) fc2 = fc2_params.view(batch_size, hidden_size_x * self.mlp_ratio, hidden_size_x) # Normalize the generated weight matrices as in the original implementation. fc1_gate = torch.nn.functional.normalize(fc1_gate, dim=-2) fc1_value = torch.nn.functional.normalize(fc1_value, dim=-2) fc2 = torch.nn.functional.normalize(fc2, dim=-2) res_x = x x = self.norm(x) # Apply the final output projection. x = torch.bmm(torch.nn.functional.silu(torch.bmm(x, fc1_gate)) * torch.bmm(x, fc1_value), fc2) return x + res_x class NerfFinalLayer(nn.Module): def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None): super().__init__() self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, out_channels, dtype=dtype, device=device) def forward(self, x: torch.Tensor) -> torch.Tensor: # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1. # So we temporarily move the channel dimension to the end for the norm operation. return self.linear(self.norm(x.movedim(1, -1))).movedim(-1, 1) class NerfFinalLayerConv(nn.Module): def __init__(self, hidden_size: int, out_channels: int, dtype=None, device=None, operations=None): super().__init__() self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device) self.conv = operations.Conv2d( in_channels=hidden_size, out_channels=out_channels, kernel_size=3, padding=1, dtype=dtype, device=device, ) def forward(self, x: torch.Tensor) -> torch.Tensor: # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1. # So we temporarily move the channel dimension to the end for the norm operation. return self.conv(self.norm(x.movedim(1, -1)).movedim(-1, 1)) ================================================ FILE: comfy/ldm/chroma_radiance/model.py ================================================ # Credits: # Original Flux code can be found on: https://github.com/black-forest-labs/flux # Chroma Radiance adaption referenced from https://github.com/lodestone-rock/flow from dataclasses import dataclass from typing import Optional import torch from torch import Tensor, nn from einops import repeat import comfy.ldm.common_dit from comfy.ldm.flux.layers import EmbedND, DoubleStreamBlock, SingleStreamBlock from comfy.ldm.chroma.model import Chroma, ChromaParams from comfy.ldm.chroma.layers import ( Approximator, ) from .layers import ( NerfEmbedder, NerfGLUBlock, NerfFinalLayer, NerfFinalLayerConv, ) @dataclass class ChromaRadianceParams(ChromaParams): patch_size: int nerf_hidden_size: int nerf_mlp_ratio: int nerf_depth: int nerf_max_freqs: int # Setting nerf_tile_size to 0 disables tiling. nerf_tile_size: int # Currently one of linear (legacy) or conv. nerf_final_head_type: str # None means use the same dtype as the model. nerf_embedder_dtype: Optional[torch.dtype] use_x0: bool class ChromaRadiance(Chroma): """ Transformer model for flow matching on sequences. """ def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): if operations is None: raise RuntimeError("Attempt to create ChromaRadiance object without setting operations") nn.Module.__init__(self) self.dtype = dtype params = ChromaRadianceParams(**kwargs) self.params = params self.patch_size = params.patch_size self.in_channels = params.in_channels self.out_channels = params.out_channels if params.hidden_size % params.num_heads != 0: raise ValueError( f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" ) pe_dim = params.hidden_size // params.num_heads if sum(params.axes_dim) != pe_dim: raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") self.hidden_size = params.hidden_size self.num_heads = params.num_heads self.in_dim = params.in_dim self.out_dim = params.out_dim self.hidden_dim = params.hidden_dim self.n_layers = params.n_layers self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) self.img_in_patch = operations.Conv2d( params.in_channels, params.hidden_size, kernel_size=params.patch_size, stride=params.patch_size, bias=True, dtype=dtype, device=device, ) self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) # set as nn identity for now, will overwrite it later. self.distilled_guidance_layer = Approximator( in_dim=self.in_dim, hidden_dim=self.hidden_dim, out_dim=self.out_dim, n_layers=self.n_layers, dtype=dtype, device=device, operations=operations ) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, modulation=False, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=False, dtype=dtype, device=device, operations=operations, ) for _ in range(params.depth_single_blocks) ] ) # pixel channel concat with DCT self.nerf_image_embedder = NerfEmbedder( in_channels=params.in_channels, hidden_size_input=params.nerf_hidden_size, max_freqs=params.nerf_max_freqs, dtype=params.nerf_embedder_dtype or dtype, device=device, operations=operations, ) self.nerf_blocks = nn.ModuleList([ NerfGLUBlock( hidden_size_s=params.hidden_size, hidden_size_x=params.nerf_hidden_size, mlp_ratio=params.nerf_mlp_ratio, dtype=dtype, device=device, operations=operations, ) for _ in range(params.nerf_depth) ]) if params.nerf_final_head_type == "linear": self.nerf_final_layer = NerfFinalLayer( params.nerf_hidden_size, out_channels=params.in_channels, dtype=dtype, device=device, operations=operations, ) elif params.nerf_final_head_type == "conv": self.nerf_final_layer_conv = NerfFinalLayerConv( params.nerf_hidden_size, out_channels=params.in_channels, dtype=dtype, device=device, operations=operations, ) else: errstr = f"Unsupported nerf_final_head_type {params.nerf_final_head_type}" raise ValueError(errstr) self.skip_mmdit = [] self.skip_dit = [] self.lite = False if params.use_x0: self.register_buffer("__x0__", torch.tensor([])) @property def _nerf_final_layer(self) -> nn.Module: if self.params.nerf_final_head_type == "linear": return self.nerf_final_layer if self.params.nerf_final_head_type == "conv": return self.nerf_final_layer_conv # Impossible to get here as we raise an error on unexpected types on initialization. raise NotImplementedError def img_in(self, img: Tensor) -> Tensor: img = self.img_in_patch(img) # -> [B, Hidden, H/P, W/P] # flatten into a sequence for the transformer. return img.flatten(2).transpose(1, 2) # -> [B, NumPatches, Hidden] def forward_nerf( self, img_orig: Tensor, img_out: Tensor, params: ChromaRadianceParams, ) -> Tensor: B, C, H, W = img_orig.shape num_patches = img_out.shape[1] patch_size = params.patch_size # Store the raw pixel values of each patch for the NeRF head later. # unfold creates patches: [B, C * P * P, NumPatches] nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size) nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P] # Reshape for per-patch processing nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size) nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2) if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size: # Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than # the tile size. img_dct = self.forward_tiled_nerf(nerf_hidden, nerf_pixels, B, C, num_patches, patch_size, params) else: # Get DCT-encoded pixel embeddings [pixel-dct] img_dct = self.nerf_image_embedder(nerf_pixels) # Pass through the dynamic MLP blocks (the NeRF) for block in self.nerf_blocks: img_dct = block(img_dct, nerf_hidden) # Reassemble the patches into the final image. img_dct = img_dct.transpose(1, 2) # -> [B*NumPatches, C, P*P] # Reshape to combine with batch dimension for fold img_dct = img_dct.reshape(B, num_patches, -1) # -> [B, NumPatches, C*P*P] img_dct = img_dct.transpose(1, 2) # -> [B, C*P*P, NumPatches] img_dct = nn.functional.fold( img_dct, output_size=(H, W), kernel_size=patch_size, stride=patch_size, ) return self._nerf_final_layer(img_dct) def forward_tiled_nerf( self, nerf_hidden: Tensor, nerf_pixels: Tensor, batch: int, channels: int, num_patches: int, patch_size: int, params: ChromaRadianceParams, ) -> Tensor: """ Processes the NeRF head in tiles to save memory. nerf_hidden has shape [B, L, D] nerf_pixels has shape [B, L, C * P * P] """ tile_size = params.nerf_tile_size output_tiles = [] # Iterate over the patches in tiles. The dimension L (num_patches) is at index 1. for i in range(0, num_patches, tile_size): end = min(i + tile_size, num_patches) # Slice the current tile from the input tensors nerf_hidden_tile = nerf_hidden[i * batch:end * batch] nerf_pixels_tile = nerf_pixels[i * batch:end * batch] # get DCT-encoded pixel embeddings [pixel-dct] img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile) # pass through the dynamic MLP blocks (the NeRF) for block in self.nerf_blocks: img_dct_tile = block(img_dct_tile, nerf_hidden_tile) output_tiles.append(img_dct_tile) # Concatenate the processed tiles along the patch dimension return torch.cat(output_tiles, dim=0) def radiance_get_override_params(self, overrides: dict) -> ChromaRadianceParams: params = self.params if not overrides: return params params_dict = {k: getattr(params, k) for k in params.__dataclass_fields__} nullable_keys = frozenset(("nerf_embedder_dtype",)) bad_keys = tuple(k for k in overrides if k not in params_dict) if bad_keys: e = f"Unknown key(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}" raise ValueError(e) bad_keys = tuple( k for k, v in overrides.items() if not isinstance(v, type(getattr(params, k))) and (v is not None or k not in nullable_keys) ) if bad_keys: e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}" raise ValueError(e) # At this point it's all valid keys and values so we can merge with the existing params. params_dict |= overrides return params.__class__(**params_dict) def _apply_x0_residual(self, predicted, noisy, timesteps): # non zero during training to prevent 0 div eps = 0.0 return (noisy - predicted) / (timesteps.view(-1,1,1,1) + eps) def _forward( self, x: Tensor, timestep: Tensor, context: Tensor, guidance: Optional[Tensor], control: Optional[dict]=None, transformer_options: dict={}, **kwargs: dict, ) -> Tensor: bs, c, h, w = x.shape img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) if img.ndim != 4: raise ValueError("Input img tensor must be in [B, C, H, W] format.") if context.ndim != 3: raise ValueError("Input txt tensors must have 3 dimensions.") params = self.radiance_get_override_params(transformer_options.get("chroma_radiance_options", {})) h_len = (img.shape[-2] // self.patch_size) w_len = (img.shape[-1] // self.patch_size) img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) img_out = self.forward_orig( img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None), ) out = self.forward_nerf(img, img_out, params)[:, :, :h, :w] # If x0 variant → v-pred, just return this instead if hasattr(self, "__x0__"): out = self._apply_x0_residual(out, img, timestep) return out ================================================ FILE: comfy/ldm/common_dit.py ================================================ import torch import comfy.rmsnorm def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"): if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()): padding_mode = "reflect" pad = () for i in range(img.ndim - 2): pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad return torch.nn.functional.pad(img, pad, mode=padding_mode) rms_norm = comfy.rmsnorm.rms_norm ================================================ FILE: comfy/ldm/cosmos/blocks.py ================================================ # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import Optional import logging import numpy as np import torch from einops import rearrange, repeat from einops.layers.torch import Rearrange from torch import nn from comfy.ldm.modules.attention import optimized_attention def get_normalization(name: str, channels: int, weight_args={}, operations=None): if name == "I": return nn.Identity() elif name == "R": return operations.RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args) else: raise ValueError(f"Normalization {name} not found") class BaseAttentionOp(nn.Module): def __init__(self): super().__init__() class Attention(nn.Module): """ Generalized attention impl. Allowing for both self-attention and cross-attention configurations depending on whether a `context_dim` is provided. If `context_dim` is None, self-attention is assumed. Parameters: query_dim (int): Dimension of each query vector. context_dim (int, optional): Dimension of each context vector. If None, self-attention is assumed. heads (int, optional): Number of attention heads. Defaults to 8. dim_head (int, optional): Dimension of each head. Defaults to 64. dropout (float, optional): Dropout rate applied to the output of the attention block. Defaults to 0.0. attn_op (BaseAttentionOp, optional): Custom attention operation to be used instead of the default. qkv_bias (bool, optional): If True, adds a learnable bias to query, key, and value projections. Defaults to False. out_bias (bool, optional): If True, adds a learnable bias to the output projection. Defaults to False. qkv_norm (str, optional): A string representing normalization strategies for query, key, and value projections. Defaults to "SSI". qkv_norm_mode (str, optional): A string representing normalization mode for query, key, and value projections. Defaults to 'per_head'. Only support 'per_head'. Examples: >>> attn = Attention(query_dim=128, context_dim=256, heads=4, dim_head=32, dropout=0.1) >>> query = torch.randn(10, 128) # Batch size of 10 >>> context = torch.randn(10, 256) # Batch size of 10 >>> output = attn(query, context) # Perform the attention operation Note: https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 """ def __init__( self, query_dim: int, context_dim=None, heads=8, dim_head=64, dropout=0.0, attn_op: Optional[BaseAttentionOp] = None, qkv_bias: bool = False, out_bias: bool = False, qkv_norm: str = "SSI", qkv_norm_mode: str = "per_head", backend: str = "transformer_engine", qkv_format: str = "bshd", weight_args={}, operations=None, ) -> None: super().__init__() self.is_selfattn = context_dim is None # self attention inner_dim = dim_head * heads context_dim = query_dim if context_dim is None else context_dim self.heads = heads self.dim_head = dim_head self.qkv_norm_mode = qkv_norm_mode self.qkv_format = qkv_format if self.qkv_norm_mode == "per_head": norm_dim = dim_head else: raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") self.backend = backend self.to_q = nn.Sequential( operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args), get_normalization(qkv_norm[0], norm_dim, weight_args=weight_args, operations=operations), ) self.to_k = nn.Sequential( operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), get_normalization(qkv_norm[1], norm_dim, weight_args=weight_args, operations=operations), ) self.to_v = nn.Sequential( operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), get_normalization(qkv_norm[2], norm_dim, weight_args=weight_args, operations=operations), ) self.to_out = nn.Sequential( operations.Linear(inner_dim, query_dim, bias=out_bias, **weight_args), nn.Dropout(dropout), ) def cal_qkv( self, x, context=None, mask=None, rope_emb=None, **kwargs ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: del kwargs """ self.to_q, self.to_k, self.to_v are nn.Sequential with projection + normalization layers. Before 07/24/2024, these modules normalize across all heads. After 07/24/2024, to support tensor parallelism and follow the common practice in the community, we support to normalize per head. To keep the checkpoint copatibility with the previous code, we keep the nn.Sequential but call the projection and the normalization layers separately. We use a flag `self.qkv_norm_mode` to control the normalization behavior. The default value of `self.qkv_norm_mode` is "per_head", which means we normalize per head. """ if self.qkv_norm_mode == "per_head": q = self.to_q[0](x) context = x if context is None else context k = self.to_k[0](context) v = self.to_v[0](context) q, k, v = map( lambda t: rearrange(t, "s b (n c) -> b n s c", n=self.heads, c=self.dim_head), (q, k, v), ) else: raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") q = self.to_q[1](q) k = self.to_k[1](k) v = self.to_v[1](v) if self.is_selfattn and rope_emb is not None: # only apply to self-attention! # apply_rotary_pos_emb inlined q_shape = q.shape q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1] q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype) # apply_rotary_pos_emb inlined k_shape = k.shape k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1] k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype) return q, k, v def forward( self, x, context=None, mask=None, rope_emb=None, transformer_options={}, **kwargs, ): """ Args: x (Tensor): The query tensor of shape [B, Mq, K] context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None """ q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs) out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options) del q, k, v out = rearrange(out, " b n s c -> s b (n c)") return self.to_out(out) class FeedForward(nn.Module): """ Transformer FFN with optional gating Parameters: d_model (int): Dimensionality of input features. d_ff (int): Dimensionality of the hidden layer. dropout (float, optional): Dropout rate applied after the activation function. Defaults to 0.1. activation (callable, optional): The activation function applied after the first linear layer. Defaults to nn.ReLU(). is_gated (bool, optional): If set to True, incorporates gating mechanism to the feed-forward layer. Defaults to False. bias (bool, optional): If set to True, adds a bias to the linear layers. Defaults to True. Example: >>> ff = FeedForward(d_model=512, d_ff=2048) >>> x = torch.randn(64, 10, 512) # Example input tensor >>> output = ff(x) >>> print(output.shape) # Expected shape: (64, 10, 512) """ def __init__( self, d_model: int, d_ff: int, dropout: float = 0.1, activation=nn.ReLU(), is_gated: bool = False, bias: bool = False, weight_args={}, operations=None, ) -> None: super().__init__() self.layer1 = operations.Linear(d_model, d_ff, bias=bias, **weight_args) self.layer2 = operations.Linear(d_ff, d_model, bias=bias, **weight_args) self.dropout = nn.Dropout(dropout) self.activation = activation self.is_gated = is_gated if is_gated: self.linear_gate = operations.Linear(d_model, d_ff, bias=False, **weight_args) def forward(self, x: torch.Tensor): g = self.activation(self.layer1(x)) if self.is_gated: x = g * self.linear_gate(x) else: x = g assert self.dropout.p == 0.0, "we skip dropout" return self.layer2(x) class GPT2FeedForward(FeedForward): def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias: bool = False, weight_args={}, operations=None): super().__init__( d_model=d_model, d_ff=d_ff, dropout=dropout, activation=nn.GELU(), is_gated=False, bias=bias, weight_args=weight_args, operations=operations, ) def forward(self, x: torch.Tensor): assert self.dropout.p == 0.0, "we skip dropout" x = self.layer1(x) x = self.activation(x) x = self.layer2(x) return x def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) class Timesteps(nn.Module): def __init__(self, num_channels): super().__init__() self.num_channels = num_channels def forward(self, timesteps): half_dim = self.num_channels // 2 exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) exponent = exponent / (half_dim - 0.0) emb = torch.exp(exponent) emb = timesteps[:, None].float() * emb[None, :] sin_emb = torch.sin(emb) cos_emb = torch.cos(emb) emb = torch.cat([cos_emb, sin_emb], dim=-1) return emb class TimestepEmbedding(nn.Module): def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, weight_args={}, operations=None): super().__init__() logging.debug( f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." ) self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, **weight_args) self.activation = nn.SiLU() self.use_adaln_lora = use_adaln_lora if use_adaln_lora: self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, **weight_args) else: self.linear_2 = operations.Linear(out_features, out_features, bias=True, **weight_args) def forward(self, sample: torch.Tensor) -> torch.Tensor: emb = self.linear_1(sample) emb = self.activation(emb) emb = self.linear_2(emb) if self.use_adaln_lora: adaln_lora_B_3D = emb emb_B_D = sample else: emb_B_D = emb adaln_lora_B_3D = None return emb_B_D, adaln_lora_B_3D class FourierFeatures(nn.Module): """ Implements a layer that generates Fourier features from input tensors, based on randomly sampled frequencies and phases. This can help in learning high-frequency functions in low-dimensional problems. [B] -> [B, D] Parameters: num_channels (int): The number of Fourier features to generate. bandwidth (float, optional): The scaling factor for the frequency of the Fourier features. Defaults to 1. normalize (bool, optional): If set to True, the outputs are scaled by sqrt(2), usually to normalize the variance of the features. Defaults to False. Example: >>> layer = FourierFeatures(num_channels=256, bandwidth=0.5, normalize=True) >>> x = torch.randn(10, 256) # Example input tensor >>> output = layer(x) >>> print(output.shape) # Expected shape: (10, 256) """ def __init__(self, num_channels, bandwidth=1, normalize=False): super().__init__() self.register_buffer("freqs", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True) self.register_buffer("phases", 2 * np.pi * torch.rand(num_channels), persistent=True) self.gain = np.sqrt(2) if normalize else 1 def forward(self, x, gain: float = 1.0): """ Apply the Fourier feature transformation to the input tensor. Args: x (torch.Tensor): The input tensor. gain (float, optional): An additional gain factor applied during the forward pass. Defaults to 1. Returns: torch.Tensor: The transformed tensor, with Fourier features applied. """ in_dtype = x.dtype x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32)) x = x.cos().mul(self.gain * gain).to(in_dtype) return x class PatchEmbed(nn.Module): """ PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, making it suitable for video and image processing tasks. It supports dividing the input into patches and embedding each patch into a vector of size `out_channels`. Parameters: - spatial_patch_size (int): The size of each spatial patch. - temporal_patch_size (int): The size of each temporal patch. - in_channels (int): Number of input channels. Default: 3. - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. """ def __init__( self, spatial_patch_size, temporal_patch_size, in_channels=3, out_channels=768, bias=True, weight_args={}, operations=None, ): super().__init__() self.spatial_patch_size = spatial_patch_size self.temporal_patch_size = temporal_patch_size self.proj = nn.Sequential( Rearrange( "b c (t r) (h m) (w n) -> b t h w (c r m n)", r=temporal_patch_size, m=spatial_patch_size, n=spatial_patch_size, ), operations.Linear( in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=bias, **weight_args ), ) self.out = nn.Identity() def forward(self, x): """ Forward pass of the PatchEmbed module. Parameters: - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where B is the batch size, C is the number of channels, T is the temporal dimension, H is the height, and W is the width of the input. Returns: - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. """ assert x.dim() == 5 _, _, T, H, W = x.shape assert H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 assert T % self.temporal_patch_size == 0 x = self.proj(x) return self.out(x) class FinalLayer(nn.Module): """ The final layer of video DiT. """ def __init__( self, hidden_size, spatial_patch_size, temporal_patch_size, out_channels, use_adaln_lora: bool = False, adaln_lora_dim: int = 256, weight_args={}, operations=None, ): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **weight_args) self.linear = operations.Linear( hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, **weight_args ) self.hidden_size = hidden_size self.n_adaln_chunks = 2 self.use_adaln_lora = use_adaln_lora if use_adaln_lora: self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, adaln_lora_dim, bias=False, **weight_args), operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, **weight_args), ) else: self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, **weight_args) ) def forward( self, x_BT_HW_D, emb_B_D, adaln_lora_B_3D: Optional[torch.Tensor] = None, ): if self.use_adaln_lora: assert adaln_lora_B_3D is not None shift_B_D, scale_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D[:, : 2 * self.hidden_size]).chunk( 2, dim=1 ) else: shift_B_D, scale_B_D = self.adaLN_modulation(emb_B_D).chunk(2, dim=1) B = emb_B_D.shape[0] T = x_BT_HW_D.shape[0] // B shift_BT_D, scale_BT_D = repeat(shift_B_D, "b d -> (b t) d", t=T), repeat(scale_B_D, "b d -> (b t) d", t=T) x_BT_HW_D = modulate(self.norm_final(x_BT_HW_D), shift_BT_D, scale_BT_D) x_BT_HW_D = self.linear(x_BT_HW_D) return x_BT_HW_D class VideoAttn(nn.Module): """ Implements video attention with optional cross-attention capabilities. This module processes video features while maintaining their spatio-temporal structure. It can perform self-attention within the video features or cross-attention with external context features. Parameters: x_dim (int): Dimension of input feature vectors context_dim (Optional[int]): Dimension of context features for cross-attention. None for self-attention num_heads (int): Number of attention heads bias (bool): Whether to include bias in attention projections. Default: False qkv_norm_mode (str): Normalization mode for query/key/value projections. Must be "per_head". Default: "per_head" x_format (str): Format of input tensor. Must be "BTHWD". Default: "BTHWD" Input shape: - x: (T, H, W, B, D) video features - context (optional): (M, B, D) context features for cross-attention where: T: temporal dimension H: height W: width B: batch size D: feature dimension M: context sequence length """ def __init__( self, x_dim: int, context_dim: Optional[int], num_heads: int, bias: bool = False, qkv_norm_mode: str = "per_head", x_format: str = "BTHWD", weight_args={}, operations=None, ) -> None: super().__init__() self.x_format = x_format self.attn = Attention( x_dim, context_dim, num_heads, x_dim // num_heads, qkv_bias=bias, qkv_norm="RRI", out_bias=bias, qkv_norm_mode=qkv_norm_mode, qkv_format="sbhd", weight_args=weight_args, operations=operations, ) def forward( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, crossattn_mask: Optional[torch.Tensor] = None, rope_emb_L_1_1_D: Optional[torch.Tensor] = None, transformer_options: Optional[dict] = {}, ) -> torch.Tensor: """ Forward pass for video attention. Args: x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D) representing batches of video data. context (Tensor): Context tensor of shape (B, M, D) or (M, B, D), where M is the sequence length of the context. crossattn_mask (Optional[Tensor]): An optional mask for cross-attention mechanisms. rope_emb_L_1_1_D (Optional[Tensor]): Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. Returns: Tensor: The output tensor with applied attention, maintaining the input shape. """ x_T_H_W_B_D = x context_M_B_D = context T, H, W, B, D = x_T_H_W_B_D.shape x_THW_B_D = rearrange(x_T_H_W_B_D, "t h w b d -> (t h w) b d") x_THW_B_D = self.attn( x_THW_B_D, context_M_B_D, crossattn_mask, rope_emb=rope_emb_L_1_1_D, transformer_options=transformer_options, ) x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W) return x_T_H_W_B_D def adaln_norm_state(norm_state, x, scale, shift): normalized = norm_state(x) return normalized * (1 + scale) + shift class DITBuildingBlock(nn.Module): """ A building block for the DiT (Diffusion Transformer) architecture that supports different types of attention and MLP operations with adaptive layer normalization. Parameters: block_type (str): Type of block - one of: - "cross_attn"/"ca": Cross-attention - "full_attn"/"fa": Full self-attention - "mlp"/"ff": MLP/feedforward block x_dim (int): Dimension of input features context_dim (Optional[int]): Dimension of context features for cross-attention num_heads (int): Number of attention heads mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 bias (bool): Whether to use bias in layers. Default: False mlp_dropout (float): Dropout rate for MLP. Default: 0.0 qkv_norm_mode (str): QKV normalization mode. Default: "per_head" x_format (str): Input tensor format. Default: "BTHWD" use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 """ def __init__( self, block_type: str, x_dim: int, context_dim: Optional[int], num_heads: int, mlp_ratio: float = 4.0, bias: bool = False, mlp_dropout: float = 0.0, qkv_norm_mode: str = "per_head", x_format: str = "BTHWD", use_adaln_lora: bool = False, adaln_lora_dim: int = 256, weight_args={}, operations=None ) -> None: block_type = block_type.lower() super().__init__() self.x_format = x_format if block_type in ["cross_attn", "ca"]: self.block = VideoAttn( x_dim, context_dim, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations, ) elif block_type in ["full_attn", "fa"]: self.block = VideoAttn( x_dim, None, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations ) elif block_type in ["mlp", "ff"]: self.block = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), dropout=mlp_dropout, bias=bias, weight_args=weight_args, operations=operations) else: raise ValueError(f"Unknown block type: {block_type}") self.block_type = block_type self.use_adaln_lora = use_adaln_lora self.norm_state = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6) self.n_adaln_chunks = 3 if use_adaln_lora: self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(x_dim, adaln_lora_dim, bias=False, **weight_args), operations.Linear(adaln_lora_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args), ) else: self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args)) def forward( self, x: torch.Tensor, emb_B_D: torch.Tensor, crossattn_emb: torch.Tensor, crossattn_mask: Optional[torch.Tensor] = None, rope_emb_L_1_1_D: Optional[torch.Tensor] = None, adaln_lora_B_3D: Optional[torch.Tensor] = None, transformer_options: Optional[dict] = {}, ) -> torch.Tensor: """ Forward pass for dynamically configured blocks with adaptive normalization. Args: x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D). emb_B_D (Tensor): Embedding tensor for adaptive layer normalization modulation. crossattn_emb (Tensor): Tensor for cross-attention blocks. crossattn_mask (Optional[Tensor]): Optional mask for cross-attention. rope_emb_L_1_1_D (Optional[Tensor]): Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. Returns: Tensor: The output tensor after processing through the configured block and adaptive normalization. """ if self.use_adaln_lora: shift_B_D, scale_B_D, gate_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D).chunk( self.n_adaln_chunks, dim=1 ) else: shift_B_D, scale_B_D, gate_B_D = self.adaLN_modulation(emb_B_D).chunk(self.n_adaln_chunks, dim=1) shift_1_1_1_B_D, scale_1_1_1_B_D, gate_1_1_1_B_D = ( shift_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), scale_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), gate_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), ) if self.block_type in ["mlp", "ff"]: x = x + gate_1_1_1_B_D * self.block( adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), ) elif self.block_type in ["full_attn", "fa"]: x = x + gate_1_1_1_B_D * self.block( adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), context=None, rope_emb_L_1_1_D=rope_emb_L_1_1_D, transformer_options=transformer_options, ) elif self.block_type in ["cross_attn", "ca"]: x = x + gate_1_1_1_B_D * self.block( adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), context=crossattn_emb, crossattn_mask=crossattn_mask, rope_emb_L_1_1_D=rope_emb_L_1_1_D, transformer_options=transformer_options, ) else: raise ValueError(f"Unknown block type: {self.block_type}") return x class GeneralDITTransformerBlock(nn.Module): """ A wrapper module that manages a sequence of DITBuildingBlocks to form a complete transformer layer. Each block in the sequence is specified by a block configuration string. Parameters: x_dim (int): Dimension of input features context_dim (int): Dimension of context features for cross-attention blocks num_heads (int): Number of attention heads block_config (str): String specifying block sequence (e.g. "ca-fa-mlp" for cross-attention, full-attention, then MLP) mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 x_format (str): Input tensor format. Default: "BTHWD" use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 The block_config string uses "-" to separate block types: - "ca"/"cross_attn": Cross-attention block - "fa"/"full_attn": Full self-attention block - "mlp"/"ff": MLP/feedforward block Example: block_config = "ca-fa-mlp" creates a sequence of: 1. Cross-attention block 2. Full self-attention block 3. MLP block """ def __init__( self, x_dim: int, context_dim: int, num_heads: int, block_config: str, mlp_ratio: float = 4.0, x_format: str = "BTHWD", use_adaln_lora: bool = False, adaln_lora_dim: int = 256, weight_args={}, operations=None ): super().__init__() self.blocks = nn.ModuleList() self.x_format = x_format for block_type in block_config.split("-"): self.blocks.append( DITBuildingBlock( block_type, x_dim, context_dim, num_heads, mlp_ratio, x_format=self.x_format, use_adaln_lora=use_adaln_lora, adaln_lora_dim=adaln_lora_dim, weight_args=weight_args, operations=operations, ) ) def forward( self, x: torch.Tensor, emb_B_D: torch.Tensor, crossattn_emb: torch.Tensor, crossattn_mask: Optional[torch.Tensor] = None, rope_emb_L_1_1_D: Optional[torch.Tensor] = None, adaln_lora_B_3D: Optional[torch.Tensor] = None, transformer_options: Optional[dict] = {}, ) -> torch.Tensor: for block in self.blocks: x = block( x, emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D=rope_emb_L_1_1_D, adaln_lora_B_3D=adaln_lora_B_3D, transformer_options=transformer_options, ) return x ================================================ FILE: comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py ================================================ # SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """The model definition for 3D layers Adapted from: https://github.com/lucidrains/magvit2-pytorch/blob/ 9f49074179c912736e617d61b32be367eb5f993a/magvit2_pytorch/magvit2_pytorch.py#L889 [MIT License Copyright (c) 2023 Phil Wang] https://github.com/lucidrains/magvit2-pytorch/blob/ 9f49074179c912736e617d61b32be367eb5f993a/LICENSE """ import math from typing import Tuple, Union import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import logging from comfy.ldm.modules.diffusionmodules.model import vae_attention from .patching import ( Patcher, Patcher3D, UnPatcher, UnPatcher3D, ) from .utils import ( CausalNormalize, batch2space, batch2time, cast_tuple, is_odd, nonlinearity, replication_pad, space2batch, time2batch, ) import comfy.ops ops = comfy.ops.disable_weight_init _LEGACY_NUM_GROUPS = 32 class CausalConv3d(nn.Module): def __init__( self, chan_in: int = 1, chan_out: int = 1, kernel_size: Union[int, Tuple[int, int, int]] = 3, pad_mode: str = "constant", **kwargs, ): super().__init__() kernel_size = cast_tuple(kernel_size, 3) time_kernel_size, height_kernel_size, width_kernel_size = kernel_size assert is_odd(height_kernel_size) and is_odd(width_kernel_size) dilation = kwargs.pop("dilation", 1) stride = kwargs.pop("stride", 1) time_stride = kwargs.pop("time_stride", 1) time_dilation = kwargs.pop("time_dilation", 1) padding = kwargs.pop("padding", 1) self.pad_mode = pad_mode time_pad = time_dilation * (time_kernel_size - 1) + (1 - time_stride) self.time_pad = time_pad self.spatial_pad = (padding, padding, padding, padding) stride = (time_stride, stride, stride) dilation = (time_dilation, dilation, dilation) self.conv3d = ops.Conv3d( chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs, ) def _replication_pad(self, x: torch.Tensor) -> torch.Tensor: x_prev = x[:, :, :1, ...].repeat(1, 1, self.time_pad, 1, 1) x = torch.cat([x_prev, x], dim=2) padding = self.spatial_pad + (0, 0) return F.pad(x, padding, mode=self.pad_mode, value=0.0) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self._replication_pad(x) return self.conv3d(x) class CausalUpsample3d(nn.Module): def __init__(self, in_channels: int) -> None: super().__init__() self.conv = CausalConv3d( in_channels, in_channels, kernel_size=3, stride=1, padding=1 ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) time_factor = 1.0 + 1.0 * (x.shape[2] > 1) if isinstance(time_factor, torch.Tensor): time_factor = time_factor.item() x = x.repeat_interleave(int(time_factor), dim=2) # TODO(freda): Check if this causes temporal inconsistency. # Shoule reverse the order of the following two ops, # better perf and better temporal smoothness. x = self.conv(x) return x[..., int(time_factor - 1) :, :, :] class CausalDownsample3d(nn.Module): def __init__(self, in_channels: int) -> None: super().__init__() self.conv = CausalConv3d( in_channels, in_channels, kernel_size=3, stride=2, time_stride=2, padding=0, ) def forward(self, x: torch.Tensor) -> torch.Tensor: pad = (0, 1, 0, 1, 0, 0) x = F.pad(x, pad, mode="constant", value=0) x = replication_pad(x) x = self.conv(x) return x class CausalHybridUpsample3d(nn.Module): def __init__( self, in_channels: int, spatial_up: bool = True, temporal_up: bool = True, **kwargs, ) -> None: super().__init__() self.spatial_up = spatial_up self.temporal_up = temporal_up if not self.spatial_up and not self.temporal_up: return self.conv1 = CausalConv3d( in_channels, in_channels, kernel_size=(3, 1, 1), stride=1, time_stride=1, padding=0, ) self.conv2 = CausalConv3d( in_channels, in_channels, kernel_size=(1, 3, 3), stride=1, time_stride=1, padding=1, ) self.conv3 = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, time_stride=1, padding=0, ) def forward(self, x: torch.Tensor) -> torch.Tensor: if not self.spatial_up and not self.temporal_up: return x # hybrid upsample temporally. if self.temporal_up: time_factor = 1.0 + 1.0 * (x.shape[2] > 1) if isinstance(time_factor, torch.Tensor): time_factor = time_factor.item() x = x.repeat_interleave(int(time_factor), dim=2) x = x[..., int(time_factor - 1) :, :, :] x = self.conv1(x) + x # hybrid upsample spatially. if self.spatial_up: x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) x = self.conv2(x) + x # final 1x1x1 conv. x = self.conv3(x) return x class CausalHybridDownsample3d(nn.Module): def __init__( self, in_channels: int, spatial_down: bool = True, temporal_down: bool = True, **kwargs, ) -> None: super().__init__() self.spatial_down = spatial_down self.temporal_down = temporal_down if not self.spatial_down and not self.temporal_down: return self.conv1 = CausalConv3d( in_channels, in_channels, kernel_size=(1, 3, 3), stride=2, time_stride=1, padding=0, ) self.conv2 = CausalConv3d( in_channels, in_channels, kernel_size=(3, 1, 1), stride=1, time_stride=2, padding=0, ) self.conv3 = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, time_stride=1, padding=0, ) def forward(self, x: torch.Tensor) -> torch.Tensor: if not self.spatial_down and not self.temporal_down: return x # hybrid downsample spatially. if self.spatial_down: pad = (0, 1, 0, 1, 0, 0) x = F.pad(x, pad, mode="constant", value=0) x1 = self.conv1(x) x2 = F.avg_pool3d(x, kernel_size=(1, 2, 2), stride=(1, 2, 2)) x = x1 + x2 # hybrid downsample temporally. if self.temporal_down: x = replication_pad(x) x1 = self.conv2(x) x2 = F.avg_pool3d(x, kernel_size=(2, 1, 1), stride=(2, 1, 1)) x = x1 + x2 # final 1x1x1 conv. x = self.conv3(x) return x class CausalResnetBlock3d(nn.Module): def __init__( self, *, in_channels: int, out_channels: int = None, dropout: float, num_groups: int, ) -> None: super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.norm1 = CausalNormalize(in_channels, num_groups=num_groups) self.conv1 = CausalConv3d( in_channels, out_channels, kernel_size=3, stride=1, padding=1 ) self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) self.dropout = torch.nn.Dropout(dropout) self.conv2 = CausalConv3d( out_channels, out_channels, kernel_size=3, stride=1, padding=1 ) self.nin_shortcut = ( CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) if in_channels != out_channels else nn.Identity() ) def forward(self, x: torch.Tensor) -> torch.Tensor: h = x h = self.norm1(h) h = nonlinearity(h) h = self.conv1(h) h = self.norm2(h) h = nonlinearity(h) h = self.dropout(h) h = self.conv2(h) x = self.nin_shortcut(x) return x + h class CausalResnetBlockFactorized3d(nn.Module): def __init__( self, *, in_channels: int, out_channels: int = None, dropout: float, num_groups: int, ) -> None: super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.norm1 = CausalNormalize(in_channels, num_groups=1) self.conv1 = nn.Sequential( CausalConv3d( in_channels, out_channels, kernel_size=(1, 3, 3), stride=1, padding=1, ), CausalConv3d( out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=0, ), ) self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) self.dropout = torch.nn.Dropout(dropout) self.conv2 = nn.Sequential( CausalConv3d( out_channels, out_channels, kernel_size=(1, 3, 3), stride=1, padding=1, ), CausalConv3d( out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=0, ), ) self.nin_shortcut = ( CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) if in_channels != out_channels else nn.Identity() ) def forward(self, x: torch.Tensor) -> torch.Tensor: h = x h = self.norm1(h) h = nonlinearity(h) h = self.conv1(h) h = self.norm2(h) h = nonlinearity(h) h = self.dropout(h) h = self.conv2(h) x = self.nin_shortcut(x) return x + h class CausalAttnBlock(nn.Module): def __init__(self, in_channels: int, num_groups: int) -> None: super().__init__() self.norm = CausalNormalize(in_channels, num_groups=num_groups) self.q = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.k = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.v = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.proj_out = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.optimized_attention = vae_attention() def forward(self, x: torch.Tensor) -> torch.Tensor: h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention q, batch_size = time2batch(q) k, batch_size = time2batch(k) v, batch_size = time2batch(v) b, c, h, w = q.shape h_ = self.optimized_attention(q, k, v) h_ = batch2time(h_, batch_size) h_ = self.proj_out(h_) return x + h_ class CausalTemporalAttnBlock(nn.Module): def __init__(self, in_channels: int, num_groups: int) -> None: super().__init__() self.norm = CausalNormalize(in_channels, num_groups=num_groups) self.q = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.k = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.v = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.proj_out = CausalConv3d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) def forward(self, x: torch.Tensor) -> torch.Tensor: h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention q, batch_size, height = space2batch(q) k, _, _ = space2batch(k) v, _, _ = space2batch(v) bhw, c, t = q.shape q = q.permute(0, 2, 1) # (bhw, t, c) k = k.permute(0, 2, 1) # (bhw, t, c) v = v.permute(0, 2, 1) # (bhw, t, c) w_ = torch.bmm(q, k.permute(0, 2, 1)) # (bhw, t, t) w_ = w_ * (int(c) ** (-0.5)) # Apply causal mask mask = torch.tril(torch.ones_like(w_)) w_ = w_.masked_fill(mask == 0, float("-inf")) w_ = F.softmax(w_, dim=2) # attend to values h_ = torch.bmm(w_, v) # (bhw, t, c) h_ = h_.permute(0, 2, 1).reshape(bhw, c, t) # (bhw, c, t) h_ = batch2space(h_, batch_size, height) h_ = self.proj_out(h_) return x + h_ class EncoderBase(nn.Module): def __init__( self, in_channels: int, channels: int, channels_mult: list[int], num_res_blocks: int, attn_resolutions: list[int], dropout: float, resolution: int, z_channels: int, **ignore_kwargs, ) -> None: super().__init__() self.num_resolutions = len(channels_mult) self.num_res_blocks = num_res_blocks # Patcher. patch_size = ignore_kwargs.get("patch_size", 1) self.patcher = Patcher( patch_size, ignore_kwargs.get("patch_method", "rearrange") ) in_channels = in_channels * patch_size * patch_size # downsampling self.conv_in = CausalConv3d( in_channels, channels, kernel_size=3, stride=1, padding=1 ) # num of groups for GroupNorm, num_groups=1 for LayerNorm. num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) curr_res = resolution // patch_size in_ch_mult = (1,) + tuple(channels_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = channels * in_ch_mult[i_level] block_out = channels * channels_mult[i_level] for _ in range(self.num_res_blocks): block.append( CausalResnetBlock3d( in_channels=block_in, out_channels=block_out, dropout=dropout, num_groups=num_groups, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: down.downsample = CausalDownsample3d(block_in) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = CausalResnetBlock3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=num_groups, ) self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) self.mid.block_2 = CausalResnetBlock3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=num_groups, ) # end self.norm_out = CausalNormalize(block_in, num_groups=num_groups) self.conv_out = CausalConv3d( block_in, z_channels, kernel_size=3, stride=1, padding=1 ) def patcher3d(self, x: torch.Tensor) -> torch.Tensor: x, batch_size = time2batch(x) x = self.patcher(x) x = batch2time(x, batch_size) return x def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.patcher3d(x) # downsampling hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1]) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions - 1: hs.append(self.down[i_level].downsample(hs[-1])) else: # temporal downsample (last level) time_factor = 1 + 1 * (hs[-1].shape[2] > 1) if isinstance(time_factor, torch.Tensor): time_factor = time_factor.item() hs[-1] = replication_pad(hs[-1]) hs.append( F.avg_pool3d( hs[-1], kernel_size=[time_factor, 1, 1], stride=[2, 1, 1], ) ) # middle h = hs[-1] h = self.mid.block_1(h) h = self.mid.attn_1(h) h = self.mid.block_2(h) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class DecoderBase(nn.Module): def __init__( self, out_channels: int, channels: int, channels_mult: list[int], num_res_blocks: int, attn_resolutions: list[int], dropout: float, resolution: int, z_channels: int, **ignore_kwargs, ): super().__init__() self.num_resolutions = len(channels_mult) self.num_res_blocks = num_res_blocks # UnPatcher. patch_size = ignore_kwargs.get("patch_size", 1) self.unpatcher = UnPatcher( patch_size, ignore_kwargs.get("patch_method", "rearrange") ) out_ch = out_channels * patch_size * patch_size block_in = channels * channels_mult[self.num_resolutions - 1] curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) self.z_shape = (1, z_channels, curr_res, curr_res) logging.debug( "Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape) ) ) # z to block_in self.conv_in = CausalConv3d( z_channels, block_in, kernel_size=3, stride=1, padding=1 ) # num of groups for GroupNorm, num_groups=1 for LayerNorm. num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) # middle self.mid = nn.Module() self.mid.block_1 = CausalResnetBlock3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=num_groups, ) self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) self.mid.block_2 = CausalResnetBlock3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=num_groups, ) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = channels * channels_mult[i_level] for _ in range(self.num_res_blocks + 1): block.append( CausalResnetBlock3d( in_channels=block_in, out_channels=block_out, dropout=dropout, num_groups=num_groups, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = CausalUpsample3d(block_in) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = CausalNormalize(block_in, num_groups=num_groups) self.conv_out = CausalConv3d( block_in, out_ch, kernel_size=3, stride=1, padding=1 ) def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor: x, batch_size = time2batch(x) x = self.unpatcher(x) x = batch2time(x, batch_size) return x def forward(self, z): h = self.conv_in(z) # middle block. h = self.mid.block_1(h) h = self.mid.attn_1(h) h = self.mid.block_2(h) # decoder blocks. for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.up[i_level].block[i_block](h) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) else: # temporal upsample (last level) time_factor = 1.0 + 1.0 * (h.shape[2] > 1) if isinstance(time_factor, torch.Tensor): time_factor = time_factor.item() h = h.repeat_interleave(int(time_factor), dim=2) h = h[..., int(time_factor - 1) :, :, :] h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) h = self.unpatcher3d(h) return h class EncoderFactorized(nn.Module): def __init__( self, in_channels: int, channels: int, channels_mult: list[int], num_res_blocks: int, attn_resolutions: list[int], dropout: float, resolution: int, z_channels: int, spatial_compression: int = 8, temporal_compression: int = 8, **ignore_kwargs, ) -> None: super().__init__() self.num_resolutions = len(channels_mult) self.num_res_blocks = num_res_blocks # Patcher. patch_size = ignore_kwargs.get("patch_size", 1) self.patcher3d = Patcher3D( patch_size, ignore_kwargs.get("patch_method", "haar") ) in_channels = in_channels * patch_size * patch_size * patch_size # calculate the number of downsample operations self.num_spatial_downs = int(math.log2(spatial_compression)) - int( math.log2(patch_size) ) assert ( self.num_spatial_downs <= self.num_resolutions ), f"Spatially downsample {self.num_resolutions} times at most" self.num_temporal_downs = int(math.log2(temporal_compression)) - int( math.log2(patch_size) ) assert ( self.num_temporal_downs <= self.num_resolutions ), f"Temporally downsample {self.num_resolutions} times at most" # downsampling self.conv_in = nn.Sequential( CausalConv3d( in_channels, channels, kernel_size=(1, 3, 3), stride=1, padding=1, ), CausalConv3d( channels, channels, kernel_size=(3, 1, 1), stride=1, padding=0 ), ) curr_res = resolution // patch_size in_ch_mult = (1,) + tuple(channels_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = channels * in_ch_mult[i_level] block_out = channels * channels_mult[i_level] for _ in range(self.num_res_blocks): block.append( CausalResnetBlockFactorized3d( in_channels=block_in, out_channels=block_out, dropout=dropout, num_groups=1, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append( nn.Sequential( CausalAttnBlock(block_in, num_groups=1), CausalTemporalAttnBlock(block_in, num_groups=1), ) ) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: spatial_down = i_level < self.num_spatial_downs temporal_down = i_level < self.num_temporal_downs down.downsample = CausalHybridDownsample3d( block_in, spatial_down=spatial_down, temporal_down=temporal_down, ) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = CausalResnetBlockFactorized3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=1, ) self.mid.attn_1 = nn.Sequential( CausalAttnBlock(block_in, num_groups=1), CausalTemporalAttnBlock(block_in, num_groups=1), ) self.mid.block_2 = CausalResnetBlockFactorized3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=1, ) # end self.norm_out = CausalNormalize(block_in, num_groups=1) self.conv_out = nn.Sequential( CausalConv3d( block_in, z_channels, kernel_size=(1, 3, 3), stride=1, padding=1 ), CausalConv3d( z_channels, z_channels, kernel_size=(3, 1, 1), stride=1, padding=0, ), ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.patcher3d(x) # downsampling h = self.conv_in(x) for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](h) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) if i_level != self.num_resolutions - 1: h = self.down[i_level].downsample(h) # middle h = self.mid.block_1(h) h = self.mid.attn_1(h) h = self.mid.block_2(h) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class DecoderFactorized(nn.Module): def __init__( self, out_channels: int, channels: int, channels_mult: list[int], num_res_blocks: int, attn_resolutions: list[int], dropout: float, resolution: int, z_channels: int, spatial_compression: int = 8, temporal_compression: int = 8, **ignore_kwargs, ): super().__init__() self.num_resolutions = len(channels_mult) self.num_res_blocks = num_res_blocks # UnPatcher. patch_size = ignore_kwargs.get("patch_size", 1) self.unpatcher3d = UnPatcher3D( patch_size, ignore_kwargs.get("patch_method", "haar") ) out_ch = out_channels * patch_size * patch_size * patch_size # calculate the number of upsample operations self.num_spatial_ups = int(math.log2(spatial_compression)) - int( math.log2(patch_size) ) assert ( self.num_spatial_ups <= self.num_resolutions ), f"Spatially upsample {self.num_resolutions} times at most" self.num_temporal_ups = int(math.log2(temporal_compression)) - int( math.log2(patch_size) ) assert ( self.num_temporal_ups <= self.num_resolutions ), f"Temporally upsample {self.num_resolutions} times at most" block_in = channels * channels_mult[self.num_resolutions - 1] curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) self.z_shape = (1, z_channels, curr_res, curr_res) logging.debug( "Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape) ) ) # z to block_in self.conv_in = nn.Sequential( CausalConv3d( z_channels, block_in, kernel_size=(1, 3, 3), stride=1, padding=1 ), CausalConv3d( block_in, block_in, kernel_size=(3, 1, 1), stride=1, padding=0 ), ) # middle self.mid = nn.Module() self.mid.block_1 = CausalResnetBlockFactorized3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=1, ) self.mid.attn_1 = nn.Sequential( CausalAttnBlock(block_in, num_groups=1), CausalTemporalAttnBlock(block_in, num_groups=1), ) self.mid.block_2 = CausalResnetBlockFactorized3d( in_channels=block_in, out_channels=block_in, dropout=dropout, num_groups=1, ) legacy_mode = ignore_kwargs.get("legacy_mode", False) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = channels * channels_mult[i_level] for _ in range(self.num_res_blocks + 1): block.append( CausalResnetBlockFactorized3d( in_channels=block_in, out_channels=block_out, dropout=dropout, num_groups=1, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append( nn.Sequential( CausalAttnBlock(block_in, num_groups=1), CausalTemporalAttnBlock(block_in, num_groups=1), ) ) up = nn.Module() up.block = block up.attn = attn if i_level != 0: # The layer index for temporal/spatial downsampling performed # in the encoder should correspond to the layer index in # reverse order where upsampling is performed in the decoder. # If you've a pre-trained model, you can simply finetune. i_level_reverse = self.num_resolutions - i_level - 1 if legacy_mode: temporal_up = i_level_reverse < self.num_temporal_ups else: temporal_up = 0 < i_level_reverse < self.num_temporal_ups + 1 spatial_up = temporal_up or ( i_level_reverse < self.num_spatial_ups and self.num_spatial_ups > self.num_temporal_ups ) up.upsample = CausalHybridUpsample3d( block_in, spatial_up=spatial_up, temporal_up=temporal_up ) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = CausalNormalize(block_in, num_groups=1) self.conv_out = nn.Sequential( CausalConv3d(block_in, out_ch, kernel_size=(1, 3, 3), stride=1, padding=1), CausalConv3d(out_ch, out_ch, kernel_size=(3, 1, 1), stride=1, padding=0), ) def forward(self, z): h = self.conv_in(z) # middle block. h = self.mid.block_1(h) h = self.mid.attn_1(h) h = self.mid.block_2(h) # decoder blocks. for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.up[i_level].block[i_block](h) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) h = self.unpatcher3d(h) return h ================================================ FILE: comfy/ldm/cosmos/cosmos_tokenizer/patching.py ================================================ # SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """The patcher and unpatcher implementation for 2D and 3D data. The idea of Haar wavelet is to compute LL, LH, HL, HH component as two 1D convolutions. One on the rows and one on the columns. For example, in 1D signal, we have [a, b], then the low-freq compoenent is [a + b] / 2 and high-freq is [a - b] / 2. We can use a 1D convolution with kernel [1, 1] and stride 2 to represent the L component. For H component, we can use a 1D convolution with kernel [1, -1] and stride 2. Although in principle, we typically only do additional Haar wavelet over the LL component. But here we do it for all as we need to support downsampling for more than 2x. For example, 4x downsampling can be done by 2x Haar and additional 2x Haar, and the shape would be. [3, 256, 256] -> [12, 128, 128] -> [48, 64, 64] """ import torch import torch.nn.functional as F from einops import rearrange _WAVELETS = { "haar": torch.tensor([0.7071067811865476, 0.7071067811865476]), "rearrange": torch.tensor([1.0, 1.0]), } _PERSISTENT = False class Patcher(torch.nn.Module): """A module to convert image tensors into patches using torch operations. The main difference from `class Patching` is that this module implements all operations using torch, rather than python or numpy, for efficiency purpose. It's bit-wise identical to the Patching module outputs, with the added benefit of being torch.jit scriptable. """ def __init__(self, patch_size=1, patch_method="haar"): super().__init__() self.patch_size = patch_size self.patch_method = patch_method self.register_buffer( "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT ) self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) self.register_buffer( "_arange", torch.arange(_WAVELETS[patch_method].shape[0]), persistent=_PERSISTENT, ) for param in self.parameters(): param.requires_grad = False def forward(self, x): if self.patch_method == "haar": return self._haar(x) elif self.patch_method == "rearrange": return self._arrange(x) else: raise ValueError("Unknown patch method: " + self.patch_method) def _dwt(self, x, mode="reflect", rescale=False): dtype = x.dtype h = self.wavelets.to(device=x.device) n = h.shape[0] g = x.shape[1] hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) hh = hh.to(dtype=dtype) hl = hl.to(dtype=dtype) x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype) xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2)) xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2)) xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1)) xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1)) xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1)) xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1)) out = torch.cat([xll, xlh, xhl, xhh], dim=1) if rescale: out = out / 2 return out def _haar(self, x): for _ in self.range: x = self._dwt(x, rescale=True) return x def _arrange(self, x): x = rearrange( x, "b c (h p1) (w p2) -> b (c p1 p2) h w", p1=self.patch_size, p2=self.patch_size, ).contiguous() return x class Patcher3D(Patcher): """A 3D discrete wavelet transform for video data, expects 5D tensor, i.e. a batch of videos.""" def __init__(self, patch_size=1, patch_method="haar"): super().__init__(patch_method=patch_method, patch_size=patch_size) self.register_buffer( "patch_size_buffer", patch_size * torch.ones([1], dtype=torch.int32), persistent=_PERSISTENT, ) def _dwt(self, x, wavelet, mode="reflect", rescale=False): dtype = x.dtype h = self.wavelets.to(device=x.device) n = h.shape[0] g = x.shape[1] hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) hh = hh.to(dtype=dtype) hl = hl.to(dtype=dtype) # Handles temporal axis. x = F.pad( x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode ).to(dtype) xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) # Handles spatial axes. xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1) if rescale: out = out / (2 * torch.sqrt(torch.tensor(2.0))) return out def _haar(self, x): xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) for _ in self.range: x = self._dwt(x, "haar", rescale=True) return x def _arrange(self, x): xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) x = rearrange( x, "b c (t p1) (h p2) (w p3) -> b (c p1 p2 p3) t h w", p1=self.patch_size, p2=self.patch_size, p3=self.patch_size, ).contiguous() return x class UnPatcher(torch.nn.Module): """A module to convert patches into image tensorsusing torch operations. The main difference from `class Unpatching` is that this module implements all operations using torch, rather than python or numpy, for efficiency purpose. It's bit-wise identical to the Unpatching module outputs, with the added benefit of being torch.jit scriptable. """ def __init__(self, patch_size=1, patch_method="haar"): super().__init__() self.patch_size = patch_size self.patch_method = patch_method self.register_buffer( "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT ) self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) self.register_buffer( "_arange", torch.arange(_WAVELETS[patch_method].shape[0]), persistent=_PERSISTENT, ) for param in self.parameters(): param.requires_grad = False def forward(self, x): if self.patch_method == "haar": return self._ihaar(x) elif self.patch_method == "rearrange": return self._iarrange(x) else: raise ValueError("Unknown patch method: " + self.patch_method) def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): dtype = x.dtype h = self.wavelets.to(device=x.device) n = h.shape[0] g = x.shape[1] // 4 hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) hh = hh.to(dtype=dtype) hl = hl.to(dtype=dtype) xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1) # Inverse transform. yl = torch.nn.functional.conv_transpose2d( xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) ) yl += torch.nn.functional.conv_transpose2d( xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) ) yh = torch.nn.functional.conv_transpose2d( xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) ) yh += torch.nn.functional.conv_transpose2d( xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) ) y = torch.nn.functional.conv_transpose2d( yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) ) y += torch.nn.functional.conv_transpose2d( yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) ) if rescale: y = y * 2 return y def _ihaar(self, x): for _ in self.range: x = self._idwt(x, "haar", rescale=True) return x def _iarrange(self, x): x = rearrange( x, "b (c p1 p2) h w -> b c (h p1) (w p2)", p1=self.patch_size, p2=self.patch_size, ) return x class UnPatcher3D(UnPatcher): """A 3D inverse discrete wavelet transform for video wavelet decompositions.""" def __init__(self, patch_size=1, patch_method="haar"): super().__init__(patch_method=patch_method, patch_size=patch_size) def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): dtype = x.dtype h = self.wavelets.to(device=x.device) g = x.shape[1] // 8 # split into 8 spatio-temporal filtered tesnors. hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) hl = hl.to(dtype=dtype) hh = hh.to(dtype=dtype) xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1) del x # Height height transposed convolutions. xll = F.conv_transpose3d( xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xlll xll += F.conv_transpose3d( xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xllh xlh = F.conv_transpose3d( xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xlhl xlh += F.conv_transpose3d( xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xlhh xhl = F.conv_transpose3d( xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xhll xhl += F.conv_transpose3d( xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xhlh xhh = F.conv_transpose3d( xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xhhl xhh += F.conv_transpose3d( xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) ) del xhhh # Handles width transposed convolutions. xl = F.conv_transpose3d( xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) ) del xll xl += F.conv_transpose3d( xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) ) del xlh xh = F.conv_transpose3d( xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) ) del xhl xh += F.conv_transpose3d( xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) ) del xhh # Handles time axis transposed convolutions. x = F.conv_transpose3d( xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) ) del xl x += F.conv_transpose3d( xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) ) if rescale: x = x * (2 * torch.sqrt(torch.tensor(2.0))) return x def _ihaar(self, x): for _ in self.range: x = self._idwt(x, "haar", rescale=True) x = x[:, :, self.patch_size - 1 :, ...] return x def _iarrange(self, x): x = rearrange( x, "b (c p1 p2 p3) t h w -> b c (t p1) (h p2) (w p3)", p1=self.patch_size, p2=self.patch_size, p3=self.patch_size, ) x = x[:, :, self.patch_size - 1 :, ...] return x ================================================ FILE: comfy/ldm/cosmos/cosmos_tokenizer/utils.py ================================================ # SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Shared utilities for the networks module.""" from typing import Any import torch from einops import rearrange import comfy.ops ops = comfy.ops.disable_weight_init def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: batch_size = x.shape[0] return rearrange(x, "b c t h w -> (b t) c h w"), batch_size def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor: return rearrange(x, "(b t) c h w -> b c t h w", b=batch_size) def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: batch_size, height = x.shape[0], x.shape[-2] return rearrange(x, "b c t h w -> (b h w) c t"), batch_size, height def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch.Tensor: return rearrange(x, "(b h w) c t -> b c t h w", b=batch_size, h=height) def cast_tuple(t: Any, length: int = 1) -> Any: return t if isinstance(t, tuple) else ((t,) * length) def replication_pad(x): return torch.cat([x[:, :, :1, ...], x], dim=2) def divisible_by(num: int, den: int) -> bool: return (num % den) == 0 def is_odd(n: int) -> bool: return not divisible_by(n, 2) def nonlinearity(x): # x * sigmoid(x) return torch.nn.functional.silu(x) def Normalize(in_channels, num_groups=32): return ops.GroupNorm( num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True ) class CausalNormalize(torch.nn.Module): def __init__(self, in_channels, num_groups=1): super().__init__() self.norm = ops.GroupNorm( num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True, ) self.num_groups = num_groups def forward(self, x): # if num_groups !=1, we apply a spatio-temporal groupnorm for backward compatibility purpose. # All new models should use num_groups=1, otherwise causality is not guaranteed. if self.num_groups == 1: x, batch_size = time2batch(x) return batch2time(self.norm(x), batch_size) return self.norm(x) def exists(v): return v is not None def default(*args): for arg in args: if exists(arg): return arg return None def round_ste(z: torch.Tensor) -> torch.Tensor: """Round with straight through gradients.""" zhat = z.round() return z + (zhat - z).detach() def log(t, eps=1e-5): return t.clamp(min=eps).log() def entropy(prob): return (-prob * log(prob)).sum(dim=-1) ================================================ FILE: comfy/ldm/cosmos/model.py ================================================ # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. """ from typing import Optional, Tuple import torch from einops import rearrange from torch import nn from torchvision import transforms from enum import Enum import logging import comfy.patcher_extension from .blocks import ( FinalLayer, GeneralDITTransformerBlock, PatchEmbed, TimestepEmbedding, Timesteps, ) from .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb class DataType(Enum): IMAGE = "image" VIDEO = "video" class GeneralDIT(nn.Module): """ A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. Args: max_img_h (int): Maximum height of the input images. max_img_w (int): Maximum width of the input images. max_frames (int): Maximum number of frames in the video sequence. in_channels (int): Number of input channels (e.g., RGB channels for color images). out_channels (int): Number of output channels. patch_spatial (tuple): Spatial resolution of patches for input processing. patch_temporal (int): Temporal resolution of patches for input processing. concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. block_config (str): Configuration of the transformer block. See Notes for supported block types. model_channels (int): Base number of channels used throughout the model. num_blocks (int): Number of transformer blocks. num_heads (int): Number of heads in the multi-head attention layers. mlp_ratio (float): Expansion ratio for MLP blocks. block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD'). crossattn_emb_channels (int): Number of embedding channels for cross-attention. use_cross_attn_mask (bool): Whether to use mask in cross-attention. pos_emb_cls (str): Type of positional embeddings. pos_emb_learnable (bool): Whether positional embeddings are learnable. pos_emb_interpolation (str): Method for interpolating positional embeddings. affline_emb_norm (bool): Whether to normalize affine embeddings. use_adaln_lora (bool): Whether to use AdaLN-LoRA. adaln_lora_dim (int): Dimension for AdaLN-LoRA. rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings. extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. Notes: Supported block types in block_config: * cross_attn, ca: Cross attention * full_attn: Full attention on all flattened tokens * mlp, ff: Feed forward block """ def __init__( self, max_img_h: int, max_img_w: int, max_frames: int, in_channels: int, out_channels: int, patch_spatial: tuple, patch_temporal: int, concat_padding_mask: bool = True, # attention settings block_config: str = "FA-CA-MLP", model_channels: int = 768, num_blocks: int = 10, num_heads: int = 16, mlp_ratio: float = 4.0, block_x_format: str = "BTHWD", # cross attention settings crossattn_emb_channels: int = 1024, use_cross_attn_mask: bool = False, # positional embedding settings pos_emb_cls: str = "sincos", pos_emb_learnable: bool = False, pos_emb_interpolation: str = "crop", affline_emb_norm: bool = False, # whether or not to normalize the affine embedding use_adaln_lora: bool = False, adaln_lora_dim: int = 256, rope_h_extrapolation_ratio: float = 1.0, rope_w_extrapolation_ratio: float = 1.0, rope_t_extrapolation_ratio: float = 1.0, extra_per_block_abs_pos_emb: bool = False, extra_per_block_abs_pos_emb_type: str = "sincos", extra_h_extrapolation_ratio: float = 1.0, extra_w_extrapolation_ratio: float = 1.0, extra_t_extrapolation_ratio: float = 1.0, image_model=None, device=None, dtype=None, operations=None, ) -> None: super().__init__() self.max_img_h = max_img_h self.max_img_w = max_img_w self.max_frames = max_frames self.in_channels = in_channels self.out_channels = out_channels self.patch_spatial = patch_spatial self.patch_temporal = patch_temporal self.num_heads = num_heads self.num_blocks = num_blocks self.model_channels = model_channels self.use_cross_attn_mask = use_cross_attn_mask self.concat_padding_mask = concat_padding_mask # positional embedding settings self.pos_emb_cls = pos_emb_cls self.pos_emb_learnable = pos_emb_learnable self.pos_emb_interpolation = pos_emb_interpolation self.affline_emb_norm = affline_emb_norm self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower() self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio self.dtype = dtype weight_args = {"device": device, "dtype": dtype} in_channels = in_channels + 1 if concat_padding_mask else in_channels self.x_embedder = PatchEmbed( spatial_patch_size=patch_spatial, temporal_patch_size=patch_temporal, in_channels=in_channels, out_channels=model_channels, bias=False, weight_args=weight_args, operations=operations, ) self.build_pos_embed(device=device, dtype=dtype) self.block_x_format = block_x_format self.use_adaln_lora = use_adaln_lora self.adaln_lora_dim = adaln_lora_dim self.t_embedder = nn.ModuleList( [Timesteps(model_channels), TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),] ) self.blocks = nn.ModuleDict() for idx in range(num_blocks): self.blocks[f"block{idx}"] = GeneralDITTransformerBlock( x_dim=model_channels, context_dim=crossattn_emb_channels, num_heads=num_heads, block_config=block_config, mlp_ratio=mlp_ratio, x_format=self.block_x_format, use_adaln_lora=use_adaln_lora, adaln_lora_dim=adaln_lora_dim, weight_args=weight_args, operations=operations, ) if self.affline_emb_norm: logging.debug("Building affine embedding normalization layer") self.affline_norm = operations.RMSNorm(model_channels, elementwise_affine=True, eps=1e-6, device=device, dtype=dtype) else: self.affline_norm = nn.Identity() self.final_layer = FinalLayer( hidden_size=self.model_channels, spatial_patch_size=self.patch_spatial, temporal_patch_size=self.patch_temporal, out_channels=self.out_channels, use_adaln_lora=self.use_adaln_lora, adaln_lora_dim=self.adaln_lora_dim, weight_args=weight_args, operations=operations, ) def build_pos_embed(self, device=None, dtype=None): if self.pos_emb_cls == "rope3d": cls_type = VideoRopePosition3DEmb else: raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") kwargs = dict( model_channels=self.model_channels, len_h=self.max_img_h // self.patch_spatial, len_w=self.max_img_w // self.patch_spatial, len_t=self.max_frames // self.patch_temporal, is_learnable=self.pos_emb_learnable, interpolation=self.pos_emb_interpolation, head_dim=self.model_channels // self.num_heads, h_extrapolation_ratio=self.rope_h_extrapolation_ratio, w_extrapolation_ratio=self.rope_w_extrapolation_ratio, t_extrapolation_ratio=self.rope_t_extrapolation_ratio, device=device, ) self.pos_embedder = cls_type( **kwargs, ) if self.extra_per_block_abs_pos_emb: assert self.extra_per_block_abs_pos_emb_type in [ "learnable", ], f"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}" kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio kwargs["device"] = device kwargs["dtype"] = dtype self.extra_pos_embedder = LearnablePosEmbAxis( **kwargs, ) def prepare_embedded_sequence( self, x_B_C_T_H_W: torch.Tensor, fps: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, latent_condition: Optional[torch.Tensor] = None, latent_condition_sigma: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """ Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. Args: x_B_C_T_H_W (torch.Tensor): video fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. If None, a default value (`self.base_fps`) will be used. padding_mask (Optional[torch.Tensor]): current it is not used Returns: Tuple[torch.Tensor, Optional[torch.Tensor]]: - A tensor of shape (B, T, H, W, D) with the embedded sequence. - An optional positional embedding tensor, returned only if the positional embedding class (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. Notes: - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using the `self.pos_embedder` with the shape [T, H, W]. - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the `self.pos_embedder` with the fps tensor. - Otherwise, the positional embeddings are generated without considering fps. """ if self.concat_padding_mask: if padding_mask is not None: padding_mask = transforms.functional.resize( padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST ) else: padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) x_B_C_T_H_W = torch.cat( [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 ) x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) if self.extra_per_block_abs_pos_emb: extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) else: extra_pos_emb = None if "rope" in self.pos_emb_cls.lower(): return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb if "fps_aware" in self.pos_emb_cls: x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device) # [B, T, H, W, D] else: x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] return x_B_T_H_W_D, None, extra_pos_emb def decoder_head( self, x_B_T_H_W_D: torch.Tensor, emb_B_D: torch.Tensor, crossattn_emb: torch.Tensor, origin_shape: Tuple[int, int, int, int, int], # [B, C, T, H, W] crossattn_mask: Optional[torch.Tensor] = None, adaln_lora_B_3D: Optional[torch.Tensor] = None, ) -> torch.Tensor: del crossattn_emb, crossattn_mask B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape x_BT_HW_D = rearrange(x_B_T_H_W_D, "B T H W D -> (B T) (H W) D") x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D) # This is to ensure x_BT_HW_D has the correct shape because # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D). x_BT_HW_D = x_BT_HW_D.view( B * T_before_patchify // self.patch_temporal, H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial, -1, ) x_B_D_T_H_W = rearrange( x_BT_HW_D, "(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)", p1=self.patch_spatial, p2=self.patch_spatial, H=H_before_patchify // self.patch_spatial, W=W_before_patchify // self.patch_spatial, t=self.patch_temporal, B=B, ) return x_B_D_T_H_W def forward_before_blocks( self, x: torch.Tensor, timesteps: torch.Tensor, crossattn_emb: torch.Tensor, crossattn_mask: Optional[torch.Tensor] = None, fps: Optional[torch.Tensor] = None, image_size: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, scalar_feature: Optional[torch.Tensor] = None, data_type: Optional[DataType] = DataType.VIDEO, latent_condition: Optional[torch.Tensor] = None, latent_condition_sigma: Optional[torch.Tensor] = None, **kwargs, ) -> torch.Tensor: """ Args: x: (B, C, T, H, W) tensor of spatial-temp inputs timesteps: (B, ) tensor of timesteps crossattn_emb: (B, N, D) tensor of cross-attention embeddings crossattn_mask: (B, N) tensor of cross-attention masks """ del kwargs assert isinstance( data_type, DataType ), f"Expected DataType, got {type(data_type)}. We need discuss this flag later." original_shape = x.shape x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( x, fps=fps, padding_mask=padding_mask, latent_condition=latent_condition, latent_condition_sigma=latent_condition_sigma, ) # logging affline scale information affline_scale_log_info = {} timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype)) affline_emb_B_D = timesteps_B_D affline_scale_log_info["timesteps_B_D"] = timesteps_B_D.detach() if scalar_feature is not None: raise NotImplementedError("Scalar feature is not implemented yet.") affline_scale_log_info["affline_emb_B_D"] = affline_emb_B_D.detach() affline_emb_B_D = self.affline_norm(affline_emb_B_D) if self.use_cross_attn_mask: if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask): crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max crossattn_mask = crossattn_mask[:, None, None, :] # .to(dtype=torch.bool) # [B, 1, 1, length] else: crossattn_mask = None if self.blocks["block0"].x_format == "THWBD": x = rearrange(x_B_T_H_W_D, "B T H W D -> T H W B D") if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange( extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "B T H W D -> T H W B D" ) crossattn_emb = rearrange(crossattn_emb, "B M D -> M B D") if crossattn_mask: crossattn_mask = rearrange(crossattn_mask, "B M -> M B") elif self.blocks["block0"].x_format == "BTHWD": x = x_B_T_H_W_D else: raise ValueError(f"Unknown x_format {self.blocks[0].x_format}") output = { "x": x, "affline_emb_B_D": affline_emb_B_D, "crossattn_emb": crossattn_emb, "crossattn_mask": crossattn_mask, "rope_emb_L_1_1_D": rope_emb_L_1_1_D, "adaln_lora_B_3D": adaln_lora_B_3D, "original_shape": original_shape, "extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, } return output def forward( self, x: torch.Tensor, timesteps: torch.Tensor, context: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, # crossattn_emb: torch.Tensor, # crossattn_mask: Optional[torch.Tensor] = None, fps: Optional[torch.Tensor] = None, image_size: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, scalar_feature: Optional[torch.Tensor] = None, data_type: Optional[DataType] = DataType.VIDEO, latent_condition: Optional[torch.Tensor] = None, latent_condition_sigma: Optional[torch.Tensor] = None, condition_video_augment_sigma: Optional[torch.Tensor] = None, **kwargs, ): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) ).execute(x, timesteps, context, attention_mask, fps, image_size, padding_mask, scalar_feature, data_type, latent_condition, latent_condition_sigma, condition_video_augment_sigma, **kwargs) def _forward( self, x: torch.Tensor, timesteps: torch.Tensor, context: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, # crossattn_emb: torch.Tensor, # crossattn_mask: Optional[torch.Tensor] = None, fps: Optional[torch.Tensor] = None, image_size: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, scalar_feature: Optional[torch.Tensor] = None, data_type: Optional[DataType] = DataType.VIDEO, latent_condition: Optional[torch.Tensor] = None, latent_condition_sigma: Optional[torch.Tensor] = None, condition_video_augment_sigma: Optional[torch.Tensor] = None, **kwargs, ): """ Args: x: (B, C, T, H, W) tensor of spatial-temp inputs timesteps: (B, ) tensor of timesteps crossattn_emb: (B, N, D) tensor of cross-attention embeddings crossattn_mask: (B, N) tensor of cross-attention masks condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to augment condition input, the lvg model will condition on the condition_video_augment_sigma value; we need forward_before_blocks pass to the forward_before_blocks function. """ crossattn_emb = context crossattn_mask = attention_mask inputs = self.forward_before_blocks( x=x, timesteps=timesteps, crossattn_emb=crossattn_emb, crossattn_mask=crossattn_mask, fps=fps, image_size=image_size, padding_mask=padding_mask, scalar_feature=scalar_feature, data_type=data_type, latent_condition=latent_condition, latent_condition_sigma=latent_condition_sigma, condition_video_augment_sigma=condition_video_augment_sigma, **kwargs, ) x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = ( inputs["x"], inputs["affline_emb_B_D"], inputs["crossattn_emb"], inputs["crossattn_mask"], inputs["rope_emb_L_1_1_D"], inputs["adaln_lora_B_3D"], inputs["original_shape"], ) extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype) del inputs if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: assert ( x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}" transformer_options = kwargs.get("transformer_options", {}) for _, block in self.blocks.items(): assert ( self.blocks["block0"].x_format == block.x_format ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}" if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D x = block( x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D=rope_emb_L_1_1_D, adaln_lora_B_3D=adaln_lora_B_3D, transformer_options=transformer_options, ) x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D") x_B_D_T_H_W = self.decoder_head( x_B_T_H_W_D=x_B_T_H_W_D, emb_B_D=affline_emb_B_D, crossattn_emb=None, origin_shape=original_shape, crossattn_mask=None, adaln_lora_B_3D=adaln_lora_B_3D, ) return x_B_D_T_H_W ================================================ FILE: comfy/ldm/cosmos/position_embedding.py ================================================ # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Optional import torch from einops import rearrange, repeat from torch import nn import math def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0) -> torch.Tensor: """ Normalizes the input tensor along specified dimensions such that the average square norm of elements is adjusted. Args: x (torch.Tensor): The input tensor to normalize. dim (list, optional): The dimensions over which to normalize. If None, normalizes over all dimensions except the first. eps (float, optional): A small constant to ensure numerical stability during division. Returns: torch.Tensor: The normalized tensor. """ if dim is None: dim = list(range(1, x.ndim)) norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32) norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel())) return x / norm.to(x.dtype) class VideoPositionEmb(nn.Module): def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: """ It delegates the embedding generation to generate_embeddings function. """ B_T_H_W_C = x_B_T_H_W_C.shape embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype) return embeddings def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None): raise NotImplementedError class VideoRopePosition3DEmb(VideoPositionEmb): def __init__( self, *, # enforce keyword arguments head_dim: int, len_h: int, len_w: int, len_t: int, base_fps: int = 24, h_extrapolation_ratio: float = 1.0, w_extrapolation_ratio: float = 1.0, t_extrapolation_ratio: float = 1.0, enable_fps_modulation: bool = True, device=None, **kwargs, # used for compatibility with other positional embeddings; unused in this class ): del kwargs super().__init__() self.base_fps = base_fps self.max_h = len_h self.max_w = len_w self.enable_fps_modulation = enable_fps_modulation dim = head_dim dim_h = dim // 6 * 2 dim_w = dim_h dim_t = dim - 2 * dim_h assert dim == dim_h + dim_w + dim_t, f"bad dim: {dim} != {dim_h} + {dim_w} + {dim_t}" self.register_buffer( "dim_spatial_range", torch.arange(0, dim_h, 2, device=device)[: (dim_h // 2)].float() / dim_h, persistent=False, ) self.register_buffer( "dim_temporal_range", torch.arange(0, dim_t, 2, device=device)[: (dim_t // 2)].float() / dim_t, persistent=False, ) self.h_ntk_factor = h_extrapolation_ratio ** (dim_h / (dim_h - 2)) self.w_ntk_factor = w_extrapolation_ratio ** (dim_w / (dim_w - 2)) self.t_ntk_factor = t_extrapolation_ratio ** (dim_t / (dim_t - 2)) def generate_embeddings( self, B_T_H_W_C: torch.Size, fps: Optional[torch.Tensor] = None, h_ntk_factor: Optional[float] = None, w_ntk_factor: Optional[float] = None, t_ntk_factor: Optional[float] = None, device=None, dtype=None, ): """ Generate embeddings for the given input size. Args: B_T_H_W_C (torch.Size): Input tensor size (Batch, Time, Height, Width, Channels). fps (Optional[torch.Tensor], optional): Frames per second. Defaults to None. h_ntk_factor (Optional[float], optional): Height NTK factor. If None, uses self.h_ntk_factor. w_ntk_factor (Optional[float], optional): Width NTK factor. If None, uses self.w_ntk_factor. t_ntk_factor (Optional[float], optional): Time NTK factor. If None, uses self.t_ntk_factor. Returns: Not specified in the original code snippet. """ h_ntk_factor = h_ntk_factor if h_ntk_factor is not None else self.h_ntk_factor w_ntk_factor = w_ntk_factor if w_ntk_factor is not None else self.w_ntk_factor t_ntk_factor = t_ntk_factor if t_ntk_factor is not None else self.t_ntk_factor h_theta = 10000.0 * h_ntk_factor w_theta = 10000.0 * w_ntk_factor t_theta = 10000.0 * t_ntk_factor h_spatial_freqs = 1.0 / (h_theta**self.dim_spatial_range.to(device=device)) w_spatial_freqs = 1.0 / (w_theta**self.dim_spatial_range.to(device=device)) temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device)) B, T, H, W, _ = B_T_H_W_C seq = torch.arange(max(H, W, T), dtype=torch.float, device=device) uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max()) assert ( uniform_fps or B == 1 or T == 1 ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1" half_emb_h = torch.outer(seq[:H].to(device=device), h_spatial_freqs) half_emb_w = torch.outer(seq[:W].to(device=device), w_spatial_freqs) # apply sequence scaling in temporal dimension if fps is None or self.enable_fps_modulation is False: # image case half_emb_t = torch.outer(seq[:T].to(device=device), temporal_freqs) else: half_emb_t = torch.outer(seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs) half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1) half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1) half_emb_t = torch.stack([torch.cos(half_emb_t), -torch.sin(half_emb_t), torch.sin(half_emb_t), torch.cos(half_emb_t)], dim=-1) em_T_H_W_D = torch.cat( [ repeat(half_emb_t, "t d x -> t h w d x", h=H, w=W), repeat(half_emb_h, "h d x -> t h w d x", t=T, w=W), repeat(half_emb_w, "w d x -> t h w d x", t=T, h=H), ] , dim=-2, ) return rearrange(em_T_H_W_D, "t h w d (i j) -> (t h w) d i j", i=2, j=2).float() class LearnablePosEmbAxis(VideoPositionEmb): def __init__( self, *, # enforce keyword arguments interpolation: str, model_channels: int, len_h: int, len_w: int, len_t: int, device=None, dtype=None, **kwargs, ): """ Args: interpolation (str): we curretly only support "crop", ideally when we need extrapolation capacity, we should adjust frequency or other more advanced methods. they are not implemented yet. """ del kwargs # unused super().__init__() self.interpolation = interpolation assert self.interpolation in ["crop"], f"Unknown interpolation method {self.interpolation}" self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device, dtype=dtype)) self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype)) self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype)) def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: B, T, H, W, _ = B_T_H_W_C if self.interpolation == "crop": emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype) emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype) emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype) emb = ( repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W) + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W) + repeat(emb_w_W, "w d-> b t h w d", b=B, t=T, h=H) ) assert list(emb.shape)[:4] == [B, T, H, W], f"bad shape: {list(emb.shape)[:4]} != {B, T, H, W}" else: raise ValueError(f"Unknown interpolation method {self.interpolation}") return normalize(emb, dim=-1, eps=1e-6) ================================================ FILE: comfy/ldm/cosmos/predict2.py ================================================ # original code from: https://github.com/nvidia-cosmos/cosmos-predict2 import torch from torch import nn from einops import rearrange from einops.layers.torch import Rearrange import logging from typing import Callable, Optional, Tuple import math from .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis from torchvision import transforms import comfy.patcher_extension from comfy.ldm.modules.attention import optimized_attention import comfy.ldm.common_dit def apply_rotary_pos_emb( t: torch.Tensor, freqs: torch.Tensor, ) -> torch.Tensor: t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float() t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1] t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t) return t_out # ---------------------- Feed Forward Network ----------------------- class GPT2FeedForward(nn.Module): def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, operations=None) -> None: super().__init__() self.activation = nn.GELU() self.layer1 = operations.Linear(d_model, d_ff, bias=False, device=device, dtype=dtype) self.layer2 = operations.Linear(d_ff, d_model, bias=False, device=device, dtype=dtype) self._layer_id = None self._dim = d_model self._hidden_dim = d_ff def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.layer1(x) x = self.activation(x) x = self.layer2(x) return x def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor: """Computes multi-head attention using PyTorch's native implementation. This function provides a PyTorch backend alternative to Transformer Engine's attention operation. It rearranges the input tensors to match PyTorch's expected format, computes scaled dot-product attention, and rearranges the output back to the original format. The input tensor names use the following dimension conventions: - B: batch size - S: sequence length - H: number of attention heads - D: head dimension Args: q_B_S_H_D: Query tensor with shape (batch, seq_len, n_heads, head_dim) k_B_S_H_D: Key tensor with shape (batch, seq_len, n_heads, head_dim) v_B_S_H_D: Value tensor with shape (batch, seq_len, n_heads, head_dim) Returns: Attention output tensor with shape (batch, seq_len, n_heads * head_dim) """ in_q_shape = q_B_S_H_D.shape in_k_shape = k_B_S_H_D.shape q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1]) k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options) class Attention(nn.Module): """ A flexible attention module supporting both self-attention and cross-attention mechanisms. This module implements a multi-head attention layer that can operate in either self-attention or cross-attention mode. The mode is determined by whether a context dimension is provided. The implementation uses scaled dot-product attention and supports optional bias terms and dropout regularization. Args: query_dim (int): The dimensionality of the query vectors. context_dim (int, optional): The dimensionality of the context (key/value) vectors. If None, the module operates in self-attention mode using query_dim. Default: None n_heads (int, optional): Number of attention heads for multi-head attention. Default: 8 head_dim (int, optional): The dimension of each attention head. Default: 64 dropout (float, optional): Dropout probability applied to the output. Default: 0.0 qkv_format (str, optional): Format specification for QKV tensors. Default: "bshd" backend (str, optional): Backend to use for the attention operation. Default: "transformer_engine" Examples: >>> # Self-attention with 512 dimensions and 8 heads >>> self_attn = Attention(query_dim=512) >>> x = torch.randn(32, 16, 512) # (batch_size, seq_len, dim) >>> out = self_attn(x) # (32, 16, 512) >>> # Cross-attention >>> cross_attn = Attention(query_dim=512, context_dim=256) >>> query = torch.randn(32, 16, 512) >>> context = torch.randn(32, 8, 256) >>> out = cross_attn(query, context) # (32, 16, 512) """ def __init__( self, query_dim: int, context_dim: Optional[int] = None, n_heads: int = 8, head_dim: int = 64, dropout: float = 0.0, device=None, dtype=None, operations=None, ) -> None: super().__init__() logging.debug( f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " f"{n_heads} heads with a dimension of {head_dim}." ) self.is_selfattn = context_dim is None # self attention context_dim = query_dim if context_dim is None else context_dim inner_dim = head_dim * n_heads self.n_heads = n_heads self.head_dim = head_dim self.query_dim = query_dim self.context_dim = context_dim self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype) self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) self.v_norm = nn.Identity() self.output_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype) self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity() self.attn_op = torch_attention_op self._query_dim = query_dim self._context_dim = context_dim self._inner_dim = inner_dim def compute_qkv( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, rope_emb: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: q = self.q_proj(x) context = x if context is None else context k = self.k_proj(context) v = self.v_proj(context) q, k, v = map( lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim), (q, k, v), ) def apply_norm_and_rotary_pos_emb( q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor] ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: q = self.q_norm(q) k = self.k_norm(k) v = self.v_norm(v) if self.is_selfattn and rope_emb is not None: # only apply to self-attention! q = apply_rotary_pos_emb(q, rope_emb) k = apply_rotary_pos_emb(k, rope_emb) return q, k, v q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb) return q, k, v def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor: result = self.attn_op(q, k, v, transformer_options=transformer_options) # [B, S, H, D] return self.output_dropout(self.output_proj(result)) def forward( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, rope_emb: Optional[torch.Tensor] = None, transformer_options: Optional[dict] = {}, ) -> torch.Tensor: """ Args: x (Tensor): The query tensor of shape [B, Mq, K] context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None """ q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb) return self.compute_attention(q, k, v, transformer_options=transformer_options) class Timesteps(nn.Module): def __init__(self, num_channels: int): super().__init__() self.num_channels = num_channels def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor: assert timesteps_B_T.ndim == 2, f"Expected 2D input, got {timesteps_B_T.ndim}" timesteps = timesteps_B_T.flatten().float() half_dim = self.num_channels // 2 exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) exponent = exponent / (half_dim - 0.0) emb = torch.exp(exponent) emb = timesteps[:, None].float() * emb[None, :] sin_emb = torch.sin(emb) cos_emb = torch.cos(emb) emb = torch.cat([cos_emb, sin_emb], dim=-1) return rearrange(emb, "(b t) d -> b t d", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1]) class TimestepEmbedding(nn.Module): def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, device=None, dtype=None, operations=None): super().__init__() logging.debug( f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." ) self.in_dim = in_features self.out_dim = out_features self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, device=device, dtype=dtype) self.activation = nn.SiLU() self.use_adaln_lora = use_adaln_lora if use_adaln_lora: self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, device=device, dtype=dtype) else: self.linear_2 = operations.Linear(out_features, out_features, bias=False, device=device, dtype=dtype) def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: emb = self.linear_1(sample) emb = self.activation(emb) emb = self.linear_2(emb) if self.use_adaln_lora: adaln_lora_B_T_3D = emb emb_B_T_D = sample else: adaln_lora_B_T_3D = None emb_B_T_D = emb return emb_B_T_D, adaln_lora_B_T_3D class PatchEmbed(nn.Module): """ PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, making it suitable for video and image processing tasks. It supports dividing the input into patches and embedding each patch into a vector of size `out_channels`. Parameters: - spatial_patch_size (int): The size of each spatial patch. - temporal_patch_size (int): The size of each temporal patch. - in_channels (int): Number of input channels. Default: 3. - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. """ def __init__( self, spatial_patch_size: int, temporal_patch_size: int, in_channels: int = 3, out_channels: int = 768, device=None, dtype=None, operations=None ): super().__init__() self.spatial_patch_size = spatial_patch_size self.temporal_patch_size = temporal_patch_size self.proj = nn.Sequential( Rearrange( "b c (t r) (h m) (w n) -> b t h w (c r m n)", r=temporal_patch_size, m=spatial_patch_size, n=spatial_patch_size, ), operations.Linear( in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False, device=device, dtype=dtype ), ) self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size def forward(self, x: torch.Tensor) -> torch.Tensor: """ Forward pass of the PatchEmbed module. Parameters: - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where B is the batch size, C is the number of channels, T is the temporal dimension, H is the height, and W is the width of the input. Returns: - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. """ assert x.dim() == 5 _, _, T, H, W = x.shape assert ( H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 ), f"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}" assert T % self.temporal_patch_size == 0 x = self.proj(x) return x class FinalLayer(nn.Module): """ The final layer of video DiT. """ def __init__( self, hidden_size: int, spatial_patch_size: int, temporal_patch_size: int, out_channels: int, use_adaln_lora: bool = False, adaln_lora_dim: int = 256, device=None, dtype=None, operations=None ): super().__init__() self.layer_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.linear = operations.Linear( hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype ) self.hidden_size = hidden_size self.n_adaln_chunks = 2 self.use_adaln_lora = use_adaln_lora self.adaln_lora_dim = adaln_lora_dim if use_adaln_lora: self.adaln_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, adaln_lora_dim, bias=False, device=device, dtype=dtype), operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype), ) else: self.adaln_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype) ) def forward( self, x_B_T_H_W_D: torch.Tensor, emb_B_T_D: torch.Tensor, adaln_lora_B_T_3D: Optional[torch.Tensor] = None, ): if self.use_adaln_lora: assert adaln_lora_B_T_3D is not None shift_B_T_D, scale_B_T_D = ( self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size] ).chunk(2, dim=-1) else: shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1) shift_B_T_1_1_D, scale_B_T_1_1_D = rearrange(shift_B_T_D, "b t d -> b t 1 1 d"), rearrange( scale_B_T_D, "b t d -> b t 1 1 d" ) def _fn( _x_B_T_H_W_D: torch.Tensor, _norm_layer: nn.Module, _scale_B_T_1_1_D: torch.Tensor, _shift_B_T_1_1_D: torch.Tensor, ) -> torch.Tensor: return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D x_B_T_H_W_D = _fn(x_B_T_H_W_D, self.layer_norm, scale_B_T_1_1_D, shift_B_T_1_1_D) x_B_T_H_W_O = self.linear(x_B_T_H_W_D) return x_B_T_H_W_O class Block(nn.Module): """ A transformer block that combines self-attention, cross-attention and MLP layers with AdaLN modulation. Each component (self-attention, cross-attention, MLP) has its own layer normalization and AdaLN modulation. Parameters: x_dim (int): Dimension of input features context_dim (int): Dimension of context features for cross-attention num_heads (int): Number of attention heads mlp_ratio (float): Multiplier for MLP hidden dimension. Default: 4.0 use_adaln_lora (bool): Whether to use AdaLN-LoRA modulation. Default: False adaln_lora_dim (int): Hidden dimension for AdaLN-LoRA layers. Default: 256 The block applies the following sequence: 1. Self-attention with AdaLN modulation 2. Cross-attention with AdaLN modulation 3. MLP with AdaLN modulation Each component uses skip connections and layer normalization. """ def __init__( self, x_dim: int, context_dim: int, num_heads: int, mlp_ratio: float = 4.0, use_adaln_lora: bool = False, adaln_lora_dim: int = 256, device=None, dtype=None, operations=None, ): super().__init__() self.x_dim = x_dim self.layer_norm_self_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) self.self_attn = Attention(x_dim, None, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations) self.layer_norm_cross_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) self.cross_attn = Attention( x_dim, context_dim, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations ) self.layer_norm_mlp = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), device=device, dtype=dtype, operations=operations) self.use_adaln_lora = use_adaln_lora if self.use_adaln_lora: self.adaln_modulation_self_attn = nn.Sequential( nn.SiLU(), operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), ) self.adaln_modulation_cross_attn = nn.Sequential( nn.SiLU(), operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), ) self.adaln_modulation_mlp = nn.Sequential( nn.SiLU(), operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), ) else: self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) def forward( self, x_B_T_H_W_D: torch.Tensor, emb_B_T_D: torch.Tensor, crossattn_emb: torch.Tensor, rope_emb_L_1_1_D: Optional[torch.Tensor] = None, adaln_lora_B_T_3D: Optional[torch.Tensor] = None, extra_per_block_pos_emb: Optional[torch.Tensor] = None, transformer_options: Optional[dict] = {}, ) -> torch.Tensor: residual_dtype = x_B_T_H_W_D.dtype compute_dtype = emb_B_T_D.dtype if extra_per_block_pos_emb is not None: x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb if self.use_adaln_lora: shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = ( self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D ).chunk(3, dim=-1) shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = ( self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D ).chunk(3, dim=-1) shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = ( self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D ).chunk(3, dim=-1) else: shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn( emb_B_T_D ).chunk(3, dim=-1) shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn( emb_B_T_D ).chunk(3, dim=-1) shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1) # Reshape tensors from (B, T, D) to (B, T, 1, 1, D) for broadcasting shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, "b t d -> b t 1 1 d") scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, "b t d -> b t 1 1 d") gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, "b t d -> b t 1 1 d") shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, "b t d -> b t 1 1 d") scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, "b t d -> b t 1 1 d") gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, "b t d -> b t 1 1 d") shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, "b t d -> b t 1 1 d") scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, "b t d -> b t 1 1 d") gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, "b t d -> b t 1 1 d") B, T, H, W, D = x_B_T_H_W_D.shape def _fn(_x_B_T_H_W_D, _norm_layer, _scale_B_T_1_1_D, _shift_B_T_1_1_D): return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D normalized_x_B_T_H_W_D = _fn( x_B_T_H_W_D, self.layer_norm_self_attn, scale_self_attn_B_T_1_1_D, shift_self_attn_B_T_1_1_D, ) result_B_T_H_W_D = rearrange( self.self_attn( # normalized_x_B_T_HW_D, rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"), None, rope_emb=rope_emb_L_1_1_D, transformer_options=transformer_options, ), "b (t h w) d -> b t h w d", t=T, h=H, w=W, ) x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype) def _x_fn( _x_B_T_H_W_D: torch.Tensor, layer_norm_cross_attn: Callable, _scale_cross_attn_B_T_1_1_D: torch.Tensor, _shift_cross_attn_B_T_1_1_D: torch.Tensor, transformer_options: Optional[dict] = {}, ) -> torch.Tensor: _normalized_x_B_T_H_W_D = _fn( _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D ) _result_B_T_H_W_D = rearrange( self.cross_attn( rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"), crossattn_emb, rope_emb=rope_emb_L_1_1_D, transformer_options=transformer_options, ), "b (t h w) d -> b t h w d", t=T, h=H, w=W, ) return _result_B_T_H_W_D result_B_T_H_W_D = _x_fn( x_B_T_H_W_D, self.layer_norm_cross_attn, scale_cross_attn_B_T_1_1_D, shift_cross_attn_B_T_1_1_D, transformer_options=transformer_options, ) x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D normalized_x_B_T_H_W_D = _fn( x_B_T_H_W_D, self.layer_norm_mlp, scale_mlp_B_T_1_1_D, shift_mlp_B_T_1_1_D, ) result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype)) x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype) return x_B_T_H_W_D class MiniTrainDIT(nn.Module): """ A clean impl of DIT that can load and reproduce the training results of the original DIT model in~(cosmos 1) A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. Args: max_img_h (int): Maximum height of the input images. max_img_w (int): Maximum width of the input images. max_frames (int): Maximum number of frames in the video sequence. in_channels (int): Number of input channels (e.g., RGB channels for color images). out_channels (int): Number of output channels. patch_spatial (tuple): Spatial resolution of patches for input processing. patch_temporal (int): Temporal resolution of patches for input processing. concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. model_channels (int): Base number of channels used throughout the model. num_blocks (int): Number of transformer blocks. num_heads (int): Number of heads in the multi-head attention layers. mlp_ratio (float): Expansion ratio for MLP blocks. crossattn_emb_channels (int): Number of embedding channels for cross-attention. pos_emb_cls (str): Type of positional embeddings. pos_emb_learnable (bool): Whether positional embeddings are learnable. pos_emb_interpolation (str): Method for interpolating positional embeddings. min_fps (int): Minimum frames per second. max_fps (int): Maximum frames per second. use_adaln_lora (bool): Whether to use AdaLN-LoRA. adaln_lora_dim (int): Dimension for AdaLN-LoRA. rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. """ def __init__( self, max_img_h: int, max_img_w: int, max_frames: int, in_channels: int, out_channels: int, patch_spatial: int, # tuple, patch_temporal: int, concat_padding_mask: bool = True, # attention settings model_channels: int = 768, num_blocks: int = 10, num_heads: int = 16, mlp_ratio: float = 4.0, # cross attention settings crossattn_emb_channels: int = 1024, # positional embedding settings pos_emb_cls: str = "sincos", pos_emb_learnable: bool = False, pos_emb_interpolation: str = "crop", min_fps: int = 1, max_fps: int = 30, use_adaln_lora: bool = False, adaln_lora_dim: int = 256, rope_h_extrapolation_ratio: float = 1.0, rope_w_extrapolation_ratio: float = 1.0, rope_t_extrapolation_ratio: float = 1.0, extra_per_block_abs_pos_emb: bool = False, extra_h_extrapolation_ratio: float = 1.0, extra_w_extrapolation_ratio: float = 1.0, extra_t_extrapolation_ratio: float = 1.0, rope_enable_fps_modulation: bool = True, image_model=None, device=None, dtype=None, operations=None, ) -> None: super().__init__() self.dtype = dtype self.max_img_h = max_img_h self.max_img_w = max_img_w self.max_frames = max_frames self.in_channels = in_channels self.out_channels = out_channels self.patch_spatial = patch_spatial self.patch_temporal = patch_temporal self.num_heads = num_heads self.num_blocks = num_blocks self.model_channels = model_channels self.concat_padding_mask = concat_padding_mask # positional embedding settings self.pos_emb_cls = pos_emb_cls self.pos_emb_learnable = pos_emb_learnable self.pos_emb_interpolation = pos_emb_interpolation self.min_fps = min_fps self.max_fps = max_fps self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio self.rope_enable_fps_modulation = rope_enable_fps_modulation self.build_pos_embed(device=device, dtype=dtype) self.use_adaln_lora = use_adaln_lora self.adaln_lora_dim = adaln_lora_dim self.t_embedder = nn.Sequential( Timesteps(model_channels), TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, device=device, dtype=dtype, operations=operations,), ) in_channels = in_channels + 1 if concat_padding_mask else in_channels self.x_embedder = PatchEmbed( spatial_patch_size=patch_spatial, temporal_patch_size=patch_temporal, in_channels=in_channels, out_channels=model_channels, device=device, dtype=dtype, operations=operations, ) self.blocks = nn.ModuleList( [ Block( x_dim=model_channels, context_dim=crossattn_emb_channels, num_heads=num_heads, mlp_ratio=mlp_ratio, use_adaln_lora=use_adaln_lora, adaln_lora_dim=adaln_lora_dim, device=device, dtype=dtype, operations=operations, ) for _ in range(num_blocks) ] ) self.final_layer = FinalLayer( hidden_size=self.model_channels, spatial_patch_size=self.patch_spatial, temporal_patch_size=self.patch_temporal, out_channels=self.out_channels, use_adaln_lora=self.use_adaln_lora, adaln_lora_dim=self.adaln_lora_dim, device=device, dtype=dtype, operations=operations, ) self.t_embedding_norm = operations.RMSNorm(model_channels, eps=1e-6, device=device, dtype=dtype) def build_pos_embed(self, device=None, dtype=None) -> None: if self.pos_emb_cls == "rope3d": cls_type = VideoRopePosition3DEmb else: raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") kwargs = dict( model_channels=self.model_channels, len_h=self.max_img_h // self.patch_spatial, len_w=self.max_img_w // self.patch_spatial, len_t=self.max_frames // self.patch_temporal, max_fps=self.max_fps, min_fps=self.min_fps, is_learnable=self.pos_emb_learnable, interpolation=self.pos_emb_interpolation, head_dim=self.model_channels // self.num_heads, h_extrapolation_ratio=self.rope_h_extrapolation_ratio, w_extrapolation_ratio=self.rope_w_extrapolation_ratio, t_extrapolation_ratio=self.rope_t_extrapolation_ratio, enable_fps_modulation=self.rope_enable_fps_modulation, device=device, ) self.pos_embedder = cls_type( **kwargs, # type: ignore ) if self.extra_per_block_abs_pos_emb: kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio kwargs["device"] = device kwargs["dtype"] = dtype self.extra_pos_embedder = LearnablePosEmbAxis( **kwargs, # type: ignore ) def prepare_embedded_sequence( self, x_B_C_T_H_W: torch.Tensor, fps: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: """ Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. Args: x_B_C_T_H_W (torch.Tensor): video fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. If None, a default value (`self.base_fps`) will be used. padding_mask (Optional[torch.Tensor]): current it is not used Returns: Tuple[torch.Tensor, Optional[torch.Tensor]]: - A tensor of shape (B, T, H, W, D) with the embedded sequence. - An optional positional embedding tensor, returned only if the positional embedding class (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. Notes: - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using the `self.pos_embedder` with the shape [T, H, W]. - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the `self.pos_embedder` with the fps tensor. - Otherwise, the positional embeddings are generated without considering fps. """ if self.concat_padding_mask: if padding_mask is None: padding_mask = torch.zeros(x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[3], x_B_C_T_H_W.shape[4], dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) else: padding_mask = transforms.functional.resize( padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST ) x_B_C_T_H_W = torch.cat( [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 ) x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) if self.extra_per_block_abs_pos_emb: extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) else: extra_pos_emb = None if "rope" in self.pos_emb_cls.lower(): return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] return x_B_T_H_W_D, None, extra_pos_emb def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor: x_B_C_Tt_Hp_Wp = rearrange( x_B_T_H_W_M, "B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)", p1=self.patch_spatial, p2=self.patch_spatial, t=self.patch_temporal, ) return x_B_C_Tt_Hp_Wp def forward(self, x: torch.Tensor, timesteps: torch.Tensor, context: torch.Tensor, fps: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, **kwargs, ): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) ).execute(x, timesteps, context, fps, padding_mask, **kwargs) def _forward( self, x: torch.Tensor, timesteps: torch.Tensor, context: torch.Tensor, fps: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, **kwargs, ): orig_shape = list(x.shape) x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_temporal, self.patch_spatial, self.patch_spatial)) x_B_C_T_H_W = x timesteps_B_T = timesteps crossattn_emb = context """ Args: x: (B, C, T, H, W) tensor of spatial-temp inputs timesteps: (B, ) tensor of timesteps crossattn_emb: (B, N, D) tensor of cross-attention embeddings """ x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( x_B_C_T_H_W, fps=fps, padding_mask=padding_mask, ) if timesteps_B_T.ndim == 1: timesteps_B_T = timesteps_B_T.unsqueeze(1) t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder[1](self.t_embedder[0](timesteps_B_T).to(x_B_T_H_W_D.dtype)) t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D) # for logging purpose affline_scale_log_info = {} affline_scale_log_info["t_embedding_B_T_D"] = t_embedding_B_T_D.detach() self.affline_scale_log_info = affline_scale_log_info self.affline_emb = t_embedding_B_T_D self.crossattn_emb = crossattn_emb if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: assert ( x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape ), f"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}" block_kwargs = { "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0), "adaln_lora_B_T_3D": adaln_lora_B_T_3D, "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "transformer_options": kwargs.get("transformer_options", {}), } # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream # in fp32, but run attention and MLP modules in fp16. # An alternate method that clamps fp16 values "works" in the sense that it makes coherent images, but there is noticeable # quality degradation and visual artifacts. if x_B_T_H_W_D.dtype == torch.float16: x_B_T_H_W_D = x_B_T_H_W_D.float() for block in self.blocks: x_B_T_H_W_D = block( x_B_T_H_W_D, t_embedding_B_T_D, crossattn_emb, **block_kwargs, ) x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D.to(crossattn_emb.dtype), t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D) x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]] return x_B_C_Tt_Hp_Wp ================================================ FILE: comfy/ldm/cosmos/vae.py ================================================ # SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """The causal continuous video tokenizer with VAE or AE formulation for 3D data..""" import logging import torch from torch import nn from enum import Enum import math from .cosmos_tokenizer.layers3d import ( EncoderFactorized, DecoderFactorized, CausalConv3d, ) class IdentityDistribution(torch.nn.Module): def __init__(self): super().__init__() def forward(self, parameters): return parameters, (torch.tensor([0.0]), torch.tensor([0.0])) class GaussianDistribution(torch.nn.Module): def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0): super().__init__() self.min_logvar = min_logvar self.max_logvar = max_logvar def sample(self, mean, logvar): std = torch.exp(0.5 * logvar) return mean + std * torch.randn_like(mean) def forward(self, parameters): mean, logvar = torch.chunk(parameters, 2, dim=1) logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar) return self.sample(mean, logvar), (mean, logvar) class ContinuousFormulation(Enum): VAE = GaussianDistribution AE = IdentityDistribution class CausalContinuousVideoTokenizer(nn.Module): def __init__( self, z_channels: int, z_factor: int, latent_channels: int, **kwargs ) -> None: super().__init__() self.name = kwargs.get("name", "CausalContinuousVideoTokenizer") self.latent_channels = latent_channels self.sigma_data = 0.5 # encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name) self.encoder = EncoderFactorized( z_channels=z_factor * z_channels, **kwargs ) if kwargs.get("temporal_compression", 4) == 4: kwargs["channels_mult"] = [2, 4] # decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name) self.decoder = DecoderFactorized( z_channels=z_channels, **kwargs ) self.quant_conv = CausalConv3d( z_factor * z_channels, z_factor * latent_channels, kernel_size=1, padding=0, ) self.post_quant_conv = CausalConv3d( latent_channels, z_channels, kernel_size=1, padding=0 ) # formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name) self.distribution = IdentityDistribution() # ContinuousFormulation[formulation_name].value() num_parameters = sum(param.numel() for param in self.parameters()) logging.debug(f"model={self.name}, num_parameters={num_parameters:,}") logging.debug( f"z_channels={z_channels}, latent_channels={self.latent_channels}." ) latent_temporal_chunk = 16 self.latent_mean = nn.Parameter(torch.zeros([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) self.latent_std = nn.Parameter(torch.ones([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) def encode(self, x): h = self.encoder(x) moments = self.quant_conv(h) z, posteriors = self.distribution(moments) latent_ch = z.shape[1] latent_t = z.shape[2] in_dtype = z.dtype mean = self.latent_mean.view(latent_ch, -1) std = self.latent_std.view(latent_ch, -1) mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) return ((z - mean) / std) * self.sigma_data def decode(self, z): in_dtype = z.dtype latent_ch = z.shape[1] latent_t = z.shape[2] mean = self.latent_mean.view(latent_ch, -1) std = self.latent_std.view(latent_ch, -1) mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) z = z / self.sigma_data z = z * std + mean z = self.post_quant_conv(z) return self.decoder(z) ================================================ FILE: comfy/ldm/flux/controlnet.py ================================================ #Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py #modified to support different types of flux controlnets import torch import math from torch import Tensor, nn from einops import rearrange, repeat from .layers import (timestep_embedding) from .model import Flux import comfy.ldm.common_dit class MistolineCondDownsamplBlock(nn.Module): def __init__(self, dtype=None, device=None, operations=None): super().__init__() self.encoder = nn.Sequential( operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) ) def forward(self, x): return self.encoder(x) class MistolineControlnetBlock(nn.Module): def __init__(self, hidden_size, dtype=None, device=None, operations=None): super().__init__() self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device) self.act = nn.SiLU() def forward(self, x): return self.act(self.linear(x)) class ControlNetFlux(Flux): def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs): super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) self.main_model_double = 19 self.main_model_single = 38 self.mistoline = mistoline # add ControlNet blocks if self.mistoline: control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations) else: control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) self.controlnet_blocks = nn.ModuleList([]) for _ in range(self.params.depth): self.controlnet_blocks.append(control_block()) self.controlnet_single_blocks = nn.ModuleList([]) for _ in range(self.params.depth_single_blocks): self.controlnet_single_blocks.append(control_block()) self.num_union_modes = num_union_modes self.controlnet_mode_embedder = None if self.num_union_modes > 0: self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device) self.gradient_checkpointing = False self.latent_input = latent_input if control_latent_channels is None: control_latent_channels = self.in_channels else: control_latent_channels *= 2 * 2 #patch size self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device) if not self.latent_input: if self.mistoline: self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations) else: self.input_hint_block = nn.Sequential( operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), nn.SiLU(), operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) ) def forward_orig( self, img: Tensor, img_ids: Tensor, controlnet_cond: Tensor, txt: Tensor, txt_ids: Tensor, timesteps: Tensor, y: Tensor, guidance: Tensor = None, control_type: Tensor = None, ) -> Tensor: if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") if y is None: y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) else: y = y[:, :self.params.vec_in_dim] # running on sequences img img = self.img_in(img) controlnet_cond = self.pos_embed_input(controlnet_cond) img = img + controlnet_cond vec = self.time_in(timestep_embedding(timesteps, 256)) if self.params.guidance_embed: vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) vec = vec + self.vector_in(y) txt = self.txt_in(txt) if self.controlnet_mode_embedder is not None and len(control_type) > 0: control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1)) txt = torch.cat([control_cond, txt], dim=1) txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1) ids = torch.cat((txt_ids, img_ids), dim=1) pe = self.pe_embedder(ids) controlnet_double = () for i in range(len(self.double_blocks)): img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe) controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),) img = torch.cat((txt, img), 1) controlnet_single = () for i in range(len(self.single_blocks)): img = self.single_blocks[i](img, vec=vec, pe=pe) controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),) repeat = math.ceil(self.main_model_double / len(controlnet_double)) if self.latent_input: out_input = () for x in controlnet_double: out_input += (x,) * repeat else: out_input = (controlnet_double * repeat) out = {"input": out_input[:self.main_model_double]} if len(controlnet_single) > 0: repeat = math.ceil(self.main_model_single / len(controlnet_single)) out_output = () if self.latent_input: for x in controlnet_single: out_output += (x,) * repeat else: out_output = (controlnet_single * repeat) out["output"] = out_output[:self.main_model_single] return out def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs): patch_size = 2 if self.latent_input: hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size)) elif self.mistoline: hint = hint * 2.0 - 1.0 hint = self.input_cond_block(hint) else: hint = hint * 2.0 - 1.0 hint = self.input_hint_block(hint) hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) bs, c, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) h_len = ((h + (patch_size // 2)) // patch_size) w_len = ((w + (patch_size // 2)) // patch_size) img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None] img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :] img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", [])) ================================================ FILE: comfy/ldm/flux/layers.py ================================================ import math from dataclasses import dataclass import torch from torch import Tensor, nn from .math import attention, rope # Fix import for some custom nodes, TODO: delete eventually. RMSNorm = None class EmbedND(nn.Module): def __init__(self, dim: int, theta: int, axes_dim: list): super().__init__() self.dim = dim self.theta = theta self.axes_dim = axes_dim def forward(self, ids: Tensor) -> Tensor: n_axes = ids.shape[-1] emb = torch.cat( [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3, ) return emb.unsqueeze(1) def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): """ Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an (N, D) Tensor of positional embeddings. """ t = time_factor * t half = dim // 2 freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half) args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) if torch.is_floating_point(t): embedding = embedding.to(t) return embedding class MLPEmbedder(nn.Module): def __init__(self, in_dim: int, hidden_dim: int, bias=True, dtype=None, device=None, operations=None): super().__init__() self.in_layer = operations.Linear(in_dim, hidden_dim, bias=bias, dtype=dtype, device=device) self.silu = nn.SiLU() self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=bias, dtype=dtype, device=device) def forward(self, x: Tensor) -> Tensor: return self.out_layer(self.silu(self.in_layer(x))) class YakMLP(nn.Module): def __init__(self, hidden_size: int, intermediate_size: int, dtype=None, device=None, operations=None): super().__init__() self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device) self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device) self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=True, dtype=dtype, device=device) self.act_fn = nn.SiLU() def forward(self, x: Tensor) -> Tensor: down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=False, dtype=None, device=None, operations=None): if yak_mlp: return YakMLP(hidden_size, mlp_hidden_dim, dtype=dtype, device=device, operations=operations) if mlp_silu_act: return nn.Sequential( operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device), SiLUActivation(), operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device), ) else: return nn.Sequential( operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), nn.GELU(approximate="tanh"), operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), ) class QKNorm(torch.nn.Module): def __init__(self, dim: int, dtype=None, device=None, operations=None): super().__init__() self.query_norm = operations.RMSNorm(dim, dtype=dtype, device=device) self.key_norm = operations.RMSNorm(dim, dtype=dtype, device=device) def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple: q = self.query_norm(q) k = self.key_norm(k) return q.to(v), k.to(v) class SelfAttention(nn.Module): def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bias: bool = True, dtype=None, device=None, operations=None): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) self.proj = operations.Linear(dim, dim, bias=proj_bias, dtype=dtype, device=device) @dataclass class ModulationOut: shift: Tensor scale: Tensor gate: Tensor class Modulation(nn.Module): def __init__(self, dim: int, double: bool, bias=True, dtype=None, device=None, operations=None): super().__init__() self.is_double = double self.multiplier = 6 if double else 3 self.lin = operations.Linear(dim, self.multiplier * dim, bias=bias, dtype=dtype, device=device) def forward(self, vec: Tensor) -> tuple: if vec.ndim == 2: vec = vec[:, None, :] out = self.lin(nn.functional.silu(vec)).chunk(self.multiplier, dim=-1) return ( ModulationOut(*out[:3]), ModulationOut(*out[3:]) if self.is_double else None, ) def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None): if modulation_dims is None: if m_add is not None: return torch.addcmul(m_add, tensor, m_mult) else: return tensor * m_mult else: for d in modulation_dims: tensor[:, d[0]:d[1]] *= m_mult[:, d[2]:d[2] + 1] if m_add is not None: tensor[:, d[0]:d[1]] += m_add[:, d[2]:d[2] + 1] return tensor class SiLUActivation(nn.Module): def __init__(self): super().__init__() self.gate_fn = nn.SiLU() def forward(self, x: Tensor) -> Tensor: x1, x2 = x.chunk(2, dim=-1) return self.gate_fn(x1) * x2 class DoubleStreamBlock(nn.Module): def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None): super().__init__() mlp_hidden_dim = int(hidden_size * mlp_ratio) self.num_heads = num_heads self.hidden_size = hidden_size self.modulation = modulation if self.modulation: self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations) self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.img_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations) if self.modulation: self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations) self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.txt_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations) def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}): if self.modulation: img_mod1, img_mod2 = self.img_mod(vec) txt_mod1, txt_mod2 = self.txt_mod(vec) else: (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec transformer_patches = transformer_options.get("patches", {}) extra_options = transformer_options.copy() # prepare image for attention img_modulated = self.img_norm1(img) img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img) img_qkv = self.img_attn.qkv(img_modulated) del img_modulated img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) del img_qkv img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) # prepare txt for attention txt_modulated = self.txt_norm1(txt) txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt) txt_qkv = self.txt_attn.qkv(txt_modulated) del txt_modulated txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) del txt_qkv txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) q = torch.cat((txt_q, img_q), dim=2) del txt_q, img_q k = torch.cat((txt_k, img_k), dim=2) del txt_k, img_k v = torch.cat((txt_v, img_v), dim=2) del txt_v, img_v extra_options["img_slice"] = [txt.shape[1], q.shape[2]] if "attn1_patch" in transformer_patches: patch = transformer_patches["attn1_patch"] for p in patch: out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options) q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask) # run actual attention attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) del q, k, v if "attn1_output_patch" in transformer_patches: patch = transformer_patches["attn1_output_patch"] for p in patch: attn = p(attn, extra_options) txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] # calculate the img bloks img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img) del img_attn img += apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img) # calculate the txt bloks txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt) del txt_attn txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt) if txt.dtype == torch.float16: txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) return img, txt class SingleStreamBlock(nn.Module): """ A DiT block with parallel linear layers as described in https://arxiv.org/abs/2302.05442 and adapted modulation interface. """ def __init__( self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, qk_scale: float = None, modulation=True, mlp_silu_act=False, bias=True, yak_mlp=False, dtype=None, device=None, operations=None ): super().__init__() self.hidden_dim = hidden_size self.num_heads = num_heads head_dim = hidden_size // num_heads self.scale = qk_scale or head_dim**-0.5 self.mlp_hidden_dim = int(hidden_size * mlp_ratio) self.mlp_hidden_dim_first = self.mlp_hidden_dim self.yak_mlp = yak_mlp if mlp_silu_act: self.mlp_hidden_dim_first = int(hidden_size * mlp_ratio * 2) self.mlp_act = SiLUActivation() else: self.mlp_act = nn.GELU(approximate="tanh") if self.yak_mlp: self.mlp_hidden_dim_first *= 2 self.mlp_act = nn.SiLU() # qkv and mlp_in self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim_first, bias=bias, dtype=dtype, device=device) # proj and mlp_out self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, bias=bias, dtype=dtype, device=device) self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) self.hidden_size = hidden_size self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) if modulation: self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) else: self.modulation = None def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor: if self.modulation: mod, _ = self.modulation(vec) else: mod = vec transformer_patches = transformer_options.get("patches", {}) extra_options = transformer_options.copy() qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim_first], dim=-1) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) del qkv q, k = self.norm(q, k, v) if "attn1_patch" in transformer_patches: patch = transformer_patches["attn1_patch"] for p in patch: out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options) q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask) # compute attention attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) del q, k, v if "attn1_output_patch" in transformer_patches: patch = transformer_patches["attn1_output_patch"] for p in patch: attn = p(attn, extra_options) # compute activation in mlp stream, cat again and run second linear layer if self.yak_mlp: mlp = self.mlp_act(mlp[..., self.mlp_hidden_dim_first // 2:]) * mlp[..., :self.mlp_hidden_dim_first // 2] else: mlp = self.mlp_act(mlp) output = self.linear2(torch.cat((attn, mlp), 2)) x += apply_mod(output, mod.gate, None, modulation_dims) if x.dtype == torch.float16: x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x class LastLayer(nn.Module): def __init__(self, hidden_size: int, patch_size: int, out_channels: int, bias=True, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=bias, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=bias, dtype=dtype, device=device)) def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor: if vec.ndim == 2: vec = vec[:, None, :] shift, scale = self.adaLN_modulation(vec).chunk(2, dim=-1) x = apply_mod(self.norm_final(x), (1 + scale), shift, modulation_dims) x = self.linear(x) return x ================================================ FILE: comfy/ldm/flux/math.py ================================================ import torch from einops import rearrange from torch import Tensor from comfy.ldm.modules.attention import optimized_attention import comfy.model_management import logging def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor: if pe is not None: q, k = apply_rope(q, k, pe) heads = q.shape[1] x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options) return x def rope(pos: Tensor, dim: int, theta: int) -> Tensor: assert dim % 2 == 0 if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled(): device = torch.device("cpu") else: device = pos.device scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) omega = 1.0 / (theta**scale) out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) return out.to(dtype=torch.float32, device=pos.device) def _apply_rope1(x: Tensor, freqs_cis: Tensor): x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2) if x_.shape[2] != 1 and freqs_cis.shape[2] != 1 and x_.shape[2] != freqs_cis.shape[2]: freqs_cis = freqs_cis[:, :, :x_.shape[2]] x_out = freqs_cis[..., 0] * x_[..., 0] x_out.addcmul_(freqs_cis[..., 1], x_[..., 1]) return x_out.reshape(*x.shape).type_as(x) def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis) try: import comfy.quant_ops q_apply_rope = comfy.quant_ops.ck.apply_rope q_apply_rope1 = comfy.quant_ops.ck.apply_rope1 def apply_rope(xq, xk, freqs_cis): if comfy.model_management.in_training: return _apply_rope(xq, xk, freqs_cis) else: return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis) def apply_rope1(x, freqs_cis): if comfy.model_management.in_training: return _apply_rope1(x, freqs_cis) else: return q_apply_rope1(x, freqs_cis) except: logging.warning("No comfy kitchen, using old apply_rope functions.") apply_rope = _apply_rope apply_rope1 = _apply_rope1 ================================================ FILE: comfy/ldm/flux/model.py ================================================ #Original code can be found on: https://github.com/black-forest-labs/flux from dataclasses import dataclass import torch from torch import Tensor, nn from einops import rearrange, repeat import comfy.ldm.common_dit import comfy.patcher_extension from .layers import ( DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock, timestep_embedding, Modulation, ) @dataclass class FluxParams: in_channels: int out_channels: int vec_in_dim: int context_in_dim: int hidden_size: int mlp_ratio: float num_heads: int depth: int depth_single_blocks: int axes_dim: list theta: int patch_size: int qkv_bias: bool guidance_embed: bool txt_ids_dims: list global_modulation: bool = False mlp_silu_act: bool = False ops_bias: bool = True default_ref_method: str = "offset" ref_index_scale: float = 1.0 yak_mlp: bool = False txt_norm: bool = False def invert_slices(slices, length): sorted_slices = sorted(slices) result = [] current = 0 for start, end in sorted_slices: if current < start: result.append((current, start)) current = max(current, end) if current < length: result.append((current, length)) return result class Flux(nn.Module): """ Transformer model for flow matching on sequences. """ def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): super().__init__() self.dtype = dtype params = FluxParams(**kwargs) self.params = params self.patch_size = params.patch_size self.in_channels = params.in_channels * params.patch_size * params.patch_size self.out_channels = params.out_channels * params.patch_size * params.patch_size if params.hidden_size % params.num_heads != 0: raise ValueError( f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" ) pe_dim = params.hidden_size // params.num_heads if sum(params.axes_dim) != pe_dim: raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") self.hidden_size = params.hidden_size self.num_heads = params.num_heads self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device) self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) if params.vec_in_dim is not None: self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) else: self.vector_in = None self.guidance_in = ( MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() ) self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device) if params.txt_norm: self.txt_norm = operations.RMSNorm(params.context_in_dim, dtype=dtype, device=device) else: self.txt_norm = None self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, proj_bias=params.ops_bias, yak_mlp=params.yak_mlp, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, yak_mlp=params.yak_mlp, dtype=dtype, device=device, operations=operations) for _ in range(params.depth_single_blocks) ] ) if final_layer: self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) if params.global_modulation: self.double_stream_modulation_img = Modulation( self.hidden_size, double=True, bias=False, dtype=dtype, device=device, operations=operations ) self.double_stream_modulation_txt = Modulation( self.hidden_size, double=True, bias=False, dtype=dtype, device=device, operations=operations ) self.single_stream_modulation = Modulation( self.hidden_size, double=False, bias=False, dtype=dtype, device=device, operations=operations ) def forward_orig( self, img: Tensor, img_ids: Tensor, txt: Tensor, txt_ids: Tensor, timesteps: Tensor, y: Tensor, guidance: Tensor = None, control = None, timestep_zero_index=None, transformer_options={}, attn_mask: Tensor = None, ) -> Tensor: transformer_options = transformer_options.copy() patches = transformer_options.get("patches", {}) patches_replace = transformer_options.get("patches_replace", {}) if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") # running on sequences img img = self.img_in(img) vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) if self.params.guidance_embed: if guidance is not None: vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) if self.vector_in is not None: if y is None: y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) if self.txt_norm is not None: txt = self.txt_norm(txt) txt = self.txt_in(txt) if "post_input" in patches: for p in patches["post_input"]: out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options}) img = out["img"] txt = out["txt"] img_ids = out["img_ids"] txt_ids = out["txt_ids"] if img_ids is not None: ids = torch.cat((txt_ids, img_ids), dim=1) pe = self.pe_embedder(ids) else: pe = None vec_orig = vec txt_vec = vec extra_kwargs = {} if timestep_zero_index is not None: modulation_dims = [] batch = vec.shape[0] // 2 vec_orig = vec_orig.reshape(2, batch, vec.shape[1]).movedim(0, 1) invert = invert_slices(timestep_zero_index, img.shape[1]) for s in invert: modulation_dims.append((s[0], s[1], 0)) for s in timestep_zero_index: modulation_dims.append((s[0], s[1], 1)) extra_kwargs["modulation_dims_img"] = modulation_dims txt_vec = vec[:batch] if self.params.global_modulation: vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(txt_vec)) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.double_blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.double_blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args.get("transformer_options"), **extra_kwargs) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] else: img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options, **extra_kwargs) if control is not None: # Controlnet control_i = control.get("input") if i < len(control_i): add = control_i[i] if add is not None: img[:, :add.shape[1]] += add if img.dtype == torch.float16: img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504) img = torch.cat((txt, img), 1) if self.params.global_modulation: vec, _ = self.single_stream_modulation(vec_orig) extra_kwargs = {} if timestep_zero_index is not None: lambda a: 0 if a == 0 else a + txt.shape[1] modulation_dims_combined = list(map(lambda x: (0 if x[0] == 0 else x[0] + txt.shape[1], x[1] + txt.shape[1], x[2]), modulation_dims)) extra_kwargs["modulation_dims"] = modulation_dims_combined transformer_options["total_blocks"] = len(self.single_blocks) transformer_options["block_type"] = "single" transformer_options["img_slice"] = [txt.shape[1], img.shape[1]] for i, block in enumerate(self.single_blocks): transformer_options["block_index"] = i if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args.get("transformer_options"), **extra_kwargs) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options, **extra_kwargs) if control is not None: # Controlnet control_o = control.get("output") if i < len(control_o): add = control_o[i] if add is not None: img[:, txt.shape[1] : txt.shape[1] + add.shape[1], ...] += add img = img[:, txt.shape[1] :, ...] extra_kwargs = {} if timestep_zero_index is not None: extra_kwargs["modulation_dims"] = modulation_dims img = self.final_layer(img, vec_orig, **extra_kwargs) # (N, T, patch_size ** 2 * out_channels) return img def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}): bs, c, h, w = x.shape patch_size = self.patch_size x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) h_len = ((h + (patch_size // 2)) // patch_size) w_len = ((w + (patch_size // 2)) // patch_size) h_offset = ((h_offset + (patch_size // 2)) // patch_size) w_offset = ((w_offset + (patch_size // 2)) // patch_size) steps_h = h_len steps_w = w_len rope_options = transformer_options.get("rope_options", None) if rope_options is not None: h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0 w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0 index += rope_options.get("shift_t", 0.0) h_offset += rope_options.get("shift_y", 0.0) w_offset += rope_options.get("shift_x", 0.0) img_ids = torch.zeros((steps_h, steps_w, len(self.params.axes_dim)), device=x.device, dtype=torch.float32) img_ids[:, :, 0] = img_ids[:, :, 1] + index img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=torch.float32).unsqueeze(1) img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=torch.float32).unsqueeze(0) return img, repeat(img_ids, "h w c -> b (h w) c", b=bs) def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, y, guidance, ref_latents, control, transformer_options, **kwargs) def _forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): bs, c, h_orig, w_orig = x.shape patch_size = self.patch_size h_len = ((h_orig + (patch_size // 2)) // patch_size) w_len = ((w_orig + (patch_size // 2)) // patch_size) img, img_ids = self.process_img(x, transformer_options=transformer_options) img_tokens = img.shape[1] timestep_zero_index = None if ref_latents is not None: ref_num_tokens = [] h = 0 w = 0 index = 0 ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method) timestep_zero = ref_latents_method == "index_timestep_zero" for ref in ref_latents: if ref_latents_method in ("index", "index_timestep_zero"): index += self.params.ref_index_scale h_offset = 0 w_offset = 0 elif ref_latents_method == "uxo": index = 0 h_offset = h_len * patch_size + h w_offset = w_len * patch_size + w h += ref.shape[-2] w += ref.shape[-1] else: index = 1 h_offset = 0 w_offset = 0 if ref.shape[-2] + h > ref.shape[-1] + w: w_offset = w else: h_offset = h h = max(h, ref.shape[-2] + h_offset) w = max(w, ref.shape[-1] + w_offset) kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset) img = torch.cat([img, kontext], dim=1) img_ids = torch.cat([img_ids, kontext_ids], dim=1) ref_num_tokens.append(kontext.shape[1]) if timestep_zero: if index > 0: timestep = torch.cat([timestep, timestep * 0], dim=0) timestep_zero_index = [[img_tokens, img_ids.shape[1]]] transformer_options = transformer_options.copy() transformer_options["reference_image_num_tokens"] = ref_num_tokens txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32) if len(self.params.txt_ids_dims) > 0: for i in self.params.txt_ids_dims: txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32) out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options, attn_mask=kwargs.get("attention_mask", None)) out = out[:, :img_tokens] return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig] ================================================ FILE: comfy/ldm/flux/redux.py ================================================ import torch import comfy.ops ops = comfy.ops.manual_cast class ReduxImageEncoder(torch.nn.Module): def __init__( self, redux_dim: int = 1152, txt_in_features: int = 4096, device=None, dtype=None, ) -> None: super().__init__() self.redux_dim = redux_dim self.device = device self.dtype = dtype self.redux_up = ops.Linear(redux_dim, txt_in_features * 3, dtype=dtype) self.redux_down = ops.Linear(txt_in_features * 3, txt_in_features, dtype=dtype) def forward(self, sigclip_embeds) -> torch.Tensor: projected_x = self.redux_down(torch.nn.functional.silu(self.redux_up(sigclip_embeds))) return projected_x ================================================ FILE: comfy/ldm/genmo/joint_model/asymm_models_joint.py ================================================ #original code from https://github.com/genmoai/models under apache 2.0 license #adapted to ComfyUI from typing import Dict, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange # from flash_attn import flash_attn_varlen_qkvpacked_func from comfy.ldm.modules.attention import optimized_attention from .layers import ( FeedForward, PatchEmbed, TimestepEmbedder, ) from .rope_mixed import ( compute_mixed_rotation, create_position_matrix, ) from .temporal_rope import apply_rotary_emb_qk_real from .utils import ( AttentionPool, modulate, ) import comfy.ldm.common_dit import comfy.ops def modulated_rmsnorm(x, scale, eps=1e-6): # Normalize and modulate x_normed = comfy.ldm.common_dit.rms_norm(x, eps=eps) x_modulated = x_normed * (1 + scale.unsqueeze(1)) return x_modulated def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6): # Apply tanh to gate tanh_gate = torch.tanh(gate).unsqueeze(1) # Normalize and apply gated scaling x_normed = comfy.ldm.common_dit.rms_norm(x_res, eps=eps) * tanh_gate # Apply residual connection output = x + x_normed return output class AsymmetricAttention(nn.Module): def __init__( self, dim_x: int, dim_y: int, num_heads: int = 8, qkv_bias: bool = True, qk_norm: bool = False, attn_drop: float = 0.0, update_y: bool = True, out_bias: bool = True, attend_to_padding: bool = False, softmax_scale: Optional[float] = None, device: Optional[torch.device] = None, dtype=None, operations=None, ): super().__init__() self.dim_x = dim_x self.dim_y = dim_y self.num_heads = num_heads self.head_dim = dim_x // num_heads self.attn_drop = attn_drop self.update_y = update_y self.attend_to_padding = attend_to_padding self.softmax_scale = softmax_scale if dim_x % num_heads != 0: raise ValueError( f"dim_x={dim_x} should be divisible by num_heads={num_heads}" ) # Input layers. self.qkv_bias = qkv_bias self.qkv_x = operations.Linear(dim_x, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) # Project text features to match visual features (dim_y -> dim_x) self.qkv_y = operations.Linear(dim_y, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) # Query and key normalization for stability. assert qk_norm self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) # Output layers. y features go back down from dim_x -> dim_y. self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype) self.proj_y = ( operations.Linear(dim_x, dim_y, bias=out_bias, device=device, dtype=dtype) if update_y else nn.Identity() ) def forward( self, x: torch.Tensor, # (B, N, dim_x) y: torch.Tensor, # (B, L, dim_y) scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm. scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm. crop_y, transformer_options={}, **rope_rotation, ) -> Tuple[torch.Tensor, torch.Tensor]: rope_cos = rope_rotation.get("rope_cos") rope_sin = rope_rotation.get("rope_sin") # Pre-norm for visual features x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size # Process visual features # qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x) # assert qkv_x.dtype == torch.bfloat16 # qkv_x = all_to_all_collect_tokens( # qkv_x, self.num_heads # ) # (3, B, N, local_h, head_dim) # Process text features y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y) q_y, k_y, v_y = self.qkv_y(y).view(y.shape[0], y.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) q_y = self.q_norm_y(q_y) k_y = self.k_norm_y(k_y) # Split qkv_x into q, k, v q_x, k_x, v_x = self.qkv_x(x).view(x.shape[0], x.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) q_x = self.q_norm_x(q_x) q_x = apply_rotary_emb_qk_real(q_x, rope_cos, rope_sin) k_x = self.k_norm_x(k_x) k_x = apply_rotary_emb_qk_real(k_x, rope_cos, rope_sin) q = torch.cat([q_x, q_y[:, :crop_y]], dim=1).transpose(1, 2) k = torch.cat([k_x, k_y[:, :crop_y]], dim=1).transpose(1, 2) v = torch.cat([v_x, v_y[:, :crop_y]], dim=1).transpose(1, 2) xy = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, transformer_options=transformer_options) x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1) x = self.proj_x(x) o = torch.zeros(y.shape[0], q_y.shape[1], y.shape[-1], device=y.device, dtype=y.dtype) o[:, :y.shape[1]] = y y = self.proj_y(o) # print("ox", x) # print("oy", y) return x, y class AsymmetricJointBlock(nn.Module): def __init__( self, hidden_size_x: int, hidden_size_y: int, num_heads: int, *, mlp_ratio_x: float = 8.0, # Ratio of hidden size to d_model for MLP for visual tokens. mlp_ratio_y: float = 4.0, # Ratio of hidden size to d_model for MLP for text tokens. update_y: bool = True, # Whether to update text tokens in this block. device: Optional[torch.device] = None, dtype=None, operations=None, **block_kwargs, ): super().__init__() self.update_y = update_y self.hidden_size_x = hidden_size_x self.hidden_size_y = hidden_size_y self.mod_x = operations.Linear(hidden_size_x, 4 * hidden_size_x, device=device, dtype=dtype) if self.update_y: self.mod_y = operations.Linear(hidden_size_x, 4 * hidden_size_y, device=device, dtype=dtype) else: self.mod_y = operations.Linear(hidden_size_x, hidden_size_y, device=device, dtype=dtype) # Self-attention: self.attn = AsymmetricAttention( hidden_size_x, hidden_size_y, num_heads=num_heads, update_y=update_y, device=device, dtype=dtype, operations=operations, **block_kwargs, ) # MLP. mlp_hidden_dim_x = int(hidden_size_x * mlp_ratio_x) assert mlp_hidden_dim_x == int(1536 * 8) self.mlp_x = FeedForward( in_features=hidden_size_x, hidden_size=mlp_hidden_dim_x, multiple_of=256, ffn_dim_multiplier=None, device=device, dtype=dtype, operations=operations, ) # MLP for text not needed in last block. if self.update_y: mlp_hidden_dim_y = int(hidden_size_y * mlp_ratio_y) self.mlp_y = FeedForward( in_features=hidden_size_y, hidden_size=mlp_hidden_dim_y, multiple_of=256, ffn_dim_multiplier=None, device=device, dtype=dtype, operations=operations, ) def forward( self, x: torch.Tensor, c: torch.Tensor, y: torch.Tensor, transformer_options={}, **attn_kwargs, ): """Forward pass of a block. Args: x: (B, N, dim) tensor of visual tokens c: (B, dim) tensor of conditioned features y: (B, L, dim) tensor of text tokens num_frames: Number of frames in the video. N = num_frames * num_spatial_tokens Returns: x: (B, N, dim) tensor of visual tokens after block y: (B, L, dim) tensor of text tokens after block """ N = x.size(1) c = F.silu(c) mod_x = self.mod_x(c) scale_msa_x, gate_msa_x, scale_mlp_x, gate_mlp_x = mod_x.chunk(4, dim=1) mod_y = self.mod_y(c) if self.update_y: scale_msa_y, gate_msa_y, scale_mlp_y, gate_mlp_y = mod_y.chunk(4, dim=1) else: scale_msa_y = mod_y # Self-attention block. x_attn, y_attn = self.attn( x, y, scale_x=scale_msa_x, scale_y=scale_msa_y, transformer_options=transformer_options, **attn_kwargs, ) assert x_attn.size(1) == N x = residual_tanh_gated_rmsnorm(x, x_attn, gate_msa_x) if self.update_y: y = residual_tanh_gated_rmsnorm(y, y_attn, gate_msa_y) # MLP block. x = self.ff_block_x(x, scale_mlp_x, gate_mlp_x) if self.update_y: y = self.ff_block_y(y, scale_mlp_y, gate_mlp_y) return x, y def ff_block_x(self, x, scale_x, gate_x): x_mod = modulated_rmsnorm(x, scale_x) x_res = self.mlp_x(x_mod) x = residual_tanh_gated_rmsnorm(x, x_res, gate_x) # Sandwich norm return x def ff_block_y(self, y, scale_y, gate_y): y_mod = modulated_rmsnorm(y, scale_y) y_res = self.mlp_y(y_mod) y = residual_tanh_gated_rmsnorm(y, y_res, gate_y) # Sandwich norm return y class FinalLayer(nn.Module): """ The final layer of DiT. """ def __init__( self, hidden_size, patch_size, out_channels, device: Optional[torch.device] = None, dtype=None, operations=None, ): super().__init__() self.norm_final = operations.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype ) self.mod = operations.Linear(hidden_size, 2 * hidden_size, device=device, dtype=dtype) self.linear = operations.Linear( hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype ) def forward(self, x, c): c = F.silu(c) shift, scale = self.mod(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class AsymmDiTJoint(nn.Module): """ Diffusion model with a Transformer backbone. Ingests text embeddings instead of a label. """ def __init__( self, *, patch_size=2, in_channels=4, hidden_size_x=1152, hidden_size_y=1152, depth=48, num_heads=16, mlp_ratio_x=8.0, mlp_ratio_y=4.0, use_t5: bool = False, t5_feat_dim: int = 4096, t5_token_length: int = 256, learn_sigma=True, patch_embed_bias: bool = True, timestep_mlp_bias: bool = True, attend_to_padding: bool = False, timestep_scale: Optional[float] = None, use_extended_posenc: bool = False, posenc_preserve_area: bool = False, rope_theta: float = 10000.0, image_model=None, device: Optional[torch.device] = None, dtype=None, operations=None, **block_kwargs, ): super().__init__() self.dtype = dtype self.learn_sigma = learn_sigma self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma else in_channels self.patch_size = patch_size self.num_heads = num_heads self.hidden_size_x = hidden_size_x self.hidden_size_y = hidden_size_y self.head_dim = ( hidden_size_x // num_heads ) # Head dimension and count is determined by visual. self.attend_to_padding = attend_to_padding self.use_extended_posenc = use_extended_posenc self.posenc_preserve_area = posenc_preserve_area self.use_t5 = use_t5 self.t5_token_length = t5_token_length self.t5_feat_dim = t5_feat_dim self.rope_theta = ( rope_theta # Scaling factor for frequency computation for temporal RoPE. ) self.x_embedder = PatchEmbed( patch_size=patch_size, in_chans=in_channels, embed_dim=hidden_size_x, bias=patch_embed_bias, dtype=dtype, device=device, operations=operations ) # Conditionings # Timestep self.t_embedder = TimestepEmbedder( hidden_size_x, bias=timestep_mlp_bias, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations ) if self.use_t5: # Caption Pooling (T5) self.t5_y_embedder = AttentionPool( t5_feat_dim, num_heads=8, output_dim=hidden_size_x, dtype=dtype, device=device, operations=operations ) # Dense Embedding Projection (T5) self.t5_yproj = operations.Linear( t5_feat_dim, hidden_size_y, bias=True, dtype=dtype, device=device ) # Initialize pos_frequencies as an empty parameter. self.pos_frequencies = nn.Parameter( torch.empty(3, self.num_heads, self.head_dim // 2, dtype=dtype, device=device) ) assert not self.attend_to_padding # for depth 48: # b = 0: AsymmetricJointBlock, update_y=True # b = 1: AsymmetricJointBlock, update_y=True # ... # b = 46: AsymmetricJointBlock, update_y=True # b = 47: AsymmetricJointBlock, update_y=False. No need to update text features. blocks = [] for b in range(depth): # Joint multi-modal block update_y = b < depth - 1 block = AsymmetricJointBlock( hidden_size_x, hidden_size_y, num_heads, mlp_ratio_x=mlp_ratio_x, mlp_ratio_y=mlp_ratio_y, update_y=update_y, attend_to_padding=attend_to_padding, device=device, dtype=dtype, operations=operations, **block_kwargs, ) blocks.append(block) self.blocks = nn.ModuleList(blocks) self.final_layer = FinalLayer( hidden_size_x, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations ) def embed_x(self, x: torch.Tensor) -> torch.Tensor: """ Args: x: (B, C=12, T, H, W) tensor of visual tokens Returns: x: (B, C=3072, N) tensor of visual tokens with positional embedding. """ return self.x_embedder(x) # Convert BcTHW to BCN def prepare( self, x: torch.Tensor, sigma: torch.Tensor, t5_feat: torch.Tensor, t5_mask: torch.Tensor, ): """Prepare input and conditioning embeddings.""" # Visual patch embeddings with positional encoding. T, H, W = x.shape[-3:] pH, pW = H // self.patch_size, W // self.patch_size x = self.embed_x(x) # (B, N, D), where N = T * H * W / patch_size ** 2 assert x.ndim == 3 pH, pW = H // self.patch_size, W // self.patch_size N = T * pH * pW assert x.size(1) == N pos = create_position_matrix( T, pH=pH, pW=pW, device=x.device, dtype=torch.float32 ) # (N, 3) rope_cos, rope_sin = compute_mixed_rotation( freqs=comfy.ops.cast_to(self.pos_frequencies, dtype=x.dtype, device=x.device), pos=pos ) # Each are (N, num_heads, dim // 2) c_t = self.t_embedder(1 - sigma, out_dtype=x.dtype) # (B, D) t5_y_pool = self.t5_y_embedder(t5_feat, t5_mask) # (B, D) c = c_t + t5_y_pool y_feat = self.t5_yproj(t5_feat) # (B, L, t5_feat_dim) --> (B, L, D) return x, c, y_feat, rope_cos, rope_sin def forward( self, x: torch.Tensor, timestep: torch.Tensor, context: List[torch.Tensor], attention_mask: List[torch.Tensor], num_tokens=256, packed_indices: Dict[str, torch.Tensor] = None, rope_cos: torch.Tensor = None, rope_sin: torch.Tensor = None, control=None, transformer_options={}, **kwargs ): patches_replace = transformer_options.get("patches_replace", {}) y_feat = context y_mask = attention_mask sigma = timestep """Forward pass of DiT. Args: x: (B, C, T, H, W) tensor of spatial inputs (images or latent representations of images) sigma: (B,) tensor of noise standard deviations y_feat: List((B, L, y_feat_dim) tensor of caption token features. For SDXL text encoders: L=77, y_feat_dim=2048) y_mask: List((B, L) boolean tensor indicating which tokens are not padding) packed_indices: Dict with keys for Flash Attention. Result of compute_packed_indices. """ B, _, T, H, W = x.shape x, c, y_feat, rope_cos, rope_sin = self.prepare( x, sigma, y_feat, y_mask ) del y_mask blocks_replace = patches_replace.get("dit", {}) for i, block in enumerate(self.blocks): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"], out["txt"] = block( args["img"], args["vec"], args["txt"], rope_cos=args["rope_cos"], rope_sin=args["rope_sin"], crop_y=args["num_tokens"], transformer_options=args["transformer_options"] ) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap}) y_feat = out["txt"] x = out["img"] else: x, y_feat = block( x, c, y_feat, rope_cos=rope_cos, rope_sin=rope_sin, crop_y=num_tokens, transformer_options=transformer_options, ) # (B, M, D), (B, L, D) del y_feat # Final layers don't use dense text features. x = self.final_layer(x, c) # (B, M, patch_size ** 2 * out_channels) x = rearrange( x, "B (T hp wp) (p1 p2 c) -> B c T (hp p1) (wp p2)", T=T, hp=H // self.patch_size, wp=W // self.patch_size, p1=self.patch_size, p2=self.patch_size, c=self.out_channels, ) return -x ================================================ FILE: comfy/ldm/genmo/joint_model/layers.py ================================================ #original code from https://github.com/genmoai/models under apache 2.0 license #adapted to ComfyUI import collections.abc import math from itertools import repeat from typing import Callable, Optional import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange import comfy.ldm.common_dit # From PyTorch internals def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): return tuple(x) return tuple(repeat(x, n)) return parse to_2tuple = _ntuple(2) class TimestepEmbedder(nn.Module): def __init__( self, hidden_size: int, frequency_embedding_size: int = 256, *, bias: bool = True, timestep_scale: Optional[float] = None, dtype=None, device=None, operations=None, ): super().__init__() self.mlp = nn.Sequential( operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size self.timestep_scale = timestep_scale @staticmethod def timestep_embedding(t, dim, max_period=10000): half = dim // 2 freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) freqs.mul_(-math.log(max_period) / half).exp_() args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat( [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 ) return embedding def forward(self, t, out_dtype): if self.timestep_scale is not None: t = t * self.timestep_scale t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype) t_emb = self.mlp(t_freq) return t_emb class FeedForward(nn.Module): def __init__( self, in_features: int, hidden_size: int, multiple_of: int, ffn_dim_multiplier: Optional[float], device: Optional[torch.device] = None, dtype=None, operations=None, ): super().__init__() # keep parameter count and computation constant compared to standard FFN hidden_size = int(2 * hidden_size / 3) # custom dim factor multiplier if ffn_dim_multiplier is not None: hidden_size = int(ffn_dim_multiplier * hidden_size) hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of) self.hidden_dim = hidden_size self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype) self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype) def forward(self, x): x, gate = self.w1(x).chunk(2, dim=-1) x = self.w2(F.silu(x) * gate) return x class PatchEmbed(nn.Module): def __init__( self, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768, norm_layer: Optional[Callable] = None, flatten: bool = True, bias: bool = True, dynamic_img_pad: bool = False, dtype=None, device=None, operations=None, ): super().__init__() self.patch_size = to_2tuple(patch_size) self.flatten = flatten self.dynamic_img_pad = dynamic_img_pad self.proj = operations.Conv2d( in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, device=device, dtype=dtype, ) assert norm_layer is None self.norm = ( norm_layer(embed_dim, device=device) if norm_layer else nn.Identity() ) def forward(self, x): B, _C, T, H, W = x.shape if not self.dynamic_img_pad: assert H % self.patch_size[0] == 0, f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})." assert W % self.patch_size[1] == 0, f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})." else: pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0] pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1] x = F.pad(x, (0, pad_w, 0, pad_h)) x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T) x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular') x = self.proj(x) # Flatten temporal and spatial dimensions. if not self.flatten: raise NotImplementedError("Must flatten output.") x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T) x = self.norm(x) return x ================================================ FILE: comfy/ldm/genmo/joint_model/rope_mixed.py ================================================ #original code from https://github.com/genmoai/models under apache 2.0 license # import functools import math import torch def centers(start: float, stop, num, dtype=None, device=None): """linspace through bin centers. Args: start (float): Start of the range. stop (float): End of the range. num (int): Number of points. dtype (torch.dtype): Data type of the points. device (torch.device): Device of the points. Returns: centers (Tensor): Centers of the bins. Shape: (num,). """ edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device) return (edges[:-1] + edges[1:]) / 2 # @functools.lru_cache(maxsize=1) def create_position_matrix( T: int, pH: int, pW: int, device: torch.device, dtype: torch.dtype, *, target_area: float = 36864, ): """ Args: T: int - Temporal dimension pH: int - Height dimension after patchify pW: int - Width dimension after patchify Returns: pos: [T * pH * pW, 3] - position matrix """ # Create 1D tensors for each dimension t = torch.arange(T, dtype=dtype) # Positionally interpolate to area 36864. # (3072x3072 frame with 16x16 patches = 192x192 latents). # This automatically scales rope positions when the resolution changes. # We use a large target area so the model is more sensitive # to changes in the learned pos_frequencies matrix. scale = math.sqrt(target_area / (pW * pH)) w = centers(-pW * scale / 2, pW * scale / 2, pW) h = centers(-pH * scale / 2, pH * scale / 2, pH) # Use meshgrid to create 3D grids grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij") # Stack and reshape the grids. pos = torch.stack([grid_t, grid_h, grid_w], dim=-1) # [T, pH, pW, 3] pos = pos.view(-1, 3) # [T * pH * pW, 3] pos = pos.to(dtype=dtype, device=device) return pos def compute_mixed_rotation( freqs: torch.Tensor, pos: torch.Tensor, ): """ Project each 3-dim position into per-head, per-head-dim 1D frequencies. Args: freqs: [3, num_heads, num_freqs] - learned rotation frequency (for t, row, col) for each head position pos: [N, 3] - position of each token num_heads: int Returns: freqs_cos: [N, num_heads, num_freqs] - cosine components freqs_sin: [N, num_heads, num_freqs] - sine components """ assert freqs.ndim == 3 freqs_sum = torch.einsum("Nd,dhf->Nhf", pos.to(freqs), freqs) freqs_cos = torch.cos(freqs_sum) freqs_sin = torch.sin(freqs_sum) return freqs_cos, freqs_sin ================================================ FILE: comfy/ldm/genmo/joint_model/temporal_rope.py ================================================ #original code from https://github.com/genmoai/models under apache 2.0 license # Based on Llama3 Implementation. import torch def apply_rotary_emb_qk_real( xqk: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor, ) -> torch.Tensor: """ Apply rotary embeddings to input tensors using the given frequency tensor without complex numbers. Args: xqk (torch.Tensor): Query and/or Key tensors to apply rotary embeddings. Shape: (B, S, *, num_heads, D) Can be either just query or just key, or both stacked along some batch or * dim. freqs_cos (torch.Tensor): Precomputed cosine frequency tensor. freqs_sin (torch.Tensor): Precomputed sine frequency tensor. Returns: torch.Tensor: The input tensor with rotary embeddings applied. """ # Split the last dimension into even and odd parts xqk_even = xqk[..., 0::2] xqk_odd = xqk[..., 1::2] # Apply rotation cos_part = (xqk_even * freqs_cos - xqk_odd * freqs_sin).type_as(xqk) sin_part = (xqk_even * freqs_sin + xqk_odd * freqs_cos).type_as(xqk) # Interleave the results back into the original shape out = torch.stack([cos_part, sin_part], dim=-1).flatten(-2) return out ================================================ FILE: comfy/ldm/genmo/joint_model/utils.py ================================================ #original code from https://github.com/genmoai/models under apache 2.0 license #adapted to ComfyUI from typing import Optional import torch import torch.nn as nn import torch.nn.functional as F def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor: """ Pool tokens in x using mask. NOTE: We assume x does not require gradients. Args: x: (B, L, D) tensor of tokens. mask: (B, L) boolean tensor indicating which tokens are not padding. Returns: pooled: (B, D) tensor of pooled tokens. """ assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens. assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens. mask = mask[:, :, None].to(dtype=x.dtype) mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1) pooled = (x * mask).sum(dim=1, keepdim=keepdim) return pooled class AttentionPool(nn.Module): def __init__( self, embed_dim: int, num_heads: int, output_dim: int = None, device: Optional[torch.device] = None, dtype=None, operations=None, ): """ Args: spatial_dim (int): Number of tokens in sequence length. embed_dim (int): Dimensionality of input tokens. num_heads (int): Number of attention heads. output_dim (int): Dimensionality of output tokens. Defaults to embed_dim. """ super().__init__() self.num_heads = num_heads self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype) self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype) self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype) def forward(self, x, mask): """ Args: x (torch.Tensor): (B, L, D) tensor of input tokens. mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding. NOTE: We assume x does not require gradients. Returns: x (torch.Tensor): (B, D) tensor of pooled tokens. """ D = x.size(2) # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L). attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L). attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L). # Average non-padding token features. These will be used as the query. x_pool = pool_tokens(x, mask, keepdim=True) # (B, 1, D) # Concat pooled features to input sequence. x = torch.cat([x_pool, x], dim=1) # (B, L+1, D) # Compute queries, keys, values. Only the mean token is used to create a query. kv = self.to_kv(x) # (B, L+1, 2 * D) q = self.to_q(x[:, 0]) # (B, D) # Extract heads. head_dim = D // self.num_heads kv = kv.unflatten(2, (2, self.num_heads, head_dim)) # (B, 1+L, 2, H, head_dim) kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim) k, v = kv.unbind(2) # (B, H, 1+L, head_dim) q = q.unflatten(1, (self.num_heads, head_dim)) # (B, H, head_dim) q = q.unsqueeze(2) # (B, H, 1, head_dim) # Compute attention. x = F.scaled_dot_product_attention( q, k, v, attn_mask=attn_mask, dropout_p=0.0 ) # (B, H, 1, head_dim) # Concatenate heads and run output. x = x.squeeze(2).flatten(1, 2) # (B, D = H * head_dim) x = self.to_out(x) return x ================================================ FILE: comfy/ldm/genmo/vae/model.py ================================================ #original code from https://github.com/genmoai/models under apache 2.0 license #adapted to ComfyUI from typing import List, Optional, Tuple, Union from functools import partial import math import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from comfy.ldm.modules.attention import optimized_attention import comfy.ops ops = comfy.ops.disable_weight_init # import mochi_preview.dit.joint_model.context_parallel as cp # from mochi_preview.vae.cp_conv import cp_pass_frames, gather_all_frames def cast_tuple(t, length=1): return t if isinstance(t, tuple) else ((t,) * length) class GroupNormSpatial(ops.GroupNorm): """ GroupNorm applied per-frame. """ def forward(self, x: torch.Tensor, *, chunk_size: int = 8): B, C, T, H, W = x.shape x = rearrange(x, "B C T H W -> (B T) C H W") # Run group norm in chunks. output = torch.empty_like(x) for b in range(0, B * T, chunk_size): output[b : b + chunk_size] = super().forward(x[b : b + chunk_size]) return rearrange(output, "(B T) C H W -> B C T H W", B=B, T=T) class PConv3d(ops.Conv3d): def __init__( self, in_channels, out_channels, kernel_size: Union[int, Tuple[int, int, int]], stride: Union[int, Tuple[int, int, int]], causal: bool = True, context_parallel: bool = True, **kwargs, ): self.causal = causal self.context_parallel = context_parallel kernel_size = cast_tuple(kernel_size, 3) stride = cast_tuple(stride, 3) height_pad = (kernel_size[1] - 1) // 2 width_pad = (kernel_size[2] - 1) // 2 super().__init__( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, dilation=(1, 1, 1), padding=(0, height_pad, width_pad), **kwargs, ) def forward(self, x: torch.Tensor): # Compute padding amounts. context_size = self.kernel_size[0] - 1 if self.causal: pad_front = context_size pad_back = 0 else: pad_front = context_size // 2 pad_back = context_size - pad_front # Apply padding. assert self.padding_mode == "replicate" # DEBUG mode = "constant" if self.padding_mode == "zeros" else self.padding_mode x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode) return super().forward(x) class Conv1x1(ops.Linear): """*1x1 Conv implemented with a linear layer.""" def __init__(self, in_features: int, out_features: int, *args, **kwargs): super().__init__(in_features, out_features, *args, **kwargs) def forward(self, x: torch.Tensor): """Forward pass. Args: x: Input tensor. Shape: [B, C, *] or [B, *, C]. Returns: x: Output tensor. Shape: [B, C', *] or [B, *, C']. """ x = x.movedim(1, -1) x = super().forward(x) x = x.movedim(-1, 1) return x class DepthToSpaceTime(nn.Module): def __init__( self, temporal_expansion: int, spatial_expansion: int, ): super().__init__() self.temporal_expansion = temporal_expansion self.spatial_expansion = spatial_expansion # When printed, this module should show the temporal and spatial expansion factors. def extra_repr(self): return f"texp={self.temporal_expansion}, sexp={self.spatial_expansion}" def forward(self, x: torch.Tensor): """Forward pass. Args: x: Input tensor. Shape: [B, C, T, H, W]. Returns: x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s]. """ x = rearrange( x, "B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)", st=self.temporal_expansion, sh=self.spatial_expansion, sw=self.spatial_expansion, ) # cp_rank, _ = cp.get_cp_rank_size() if self.temporal_expansion > 1: # and cp_rank == 0: # Drop the first self.temporal_expansion - 1 frames. # This is because we always want the 3x3x3 conv filter to only apply # to the first frame, and the first frame doesn't need to be repeated. assert all(x.shape) x = x[:, :, self.temporal_expansion - 1 :] assert all(x.shape) return x def norm_fn( in_channels: int, affine: bool = True, ): return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels) class ResBlock(nn.Module): """Residual block that preserves the spatial dimensions.""" def __init__( self, channels: int, *, affine: bool = True, attn_block: Optional[nn.Module] = None, causal: bool = True, prune_bottleneck: bool = False, padding_mode: str, bias: bool = True, ): super().__init__() self.channels = channels assert causal self.stack = nn.Sequential( norm_fn(channels, affine=affine), nn.SiLU(inplace=True), PConv3d( in_channels=channels, out_channels=channels // 2 if prune_bottleneck else channels, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding_mode=padding_mode, bias=bias, causal=causal, ), norm_fn(channels, affine=affine), nn.SiLU(inplace=True), PConv3d( in_channels=channels // 2 if prune_bottleneck else channels, out_channels=channels, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding_mode=padding_mode, bias=bias, causal=causal, ), ) self.attn_block = attn_block if attn_block else nn.Identity() def forward(self, x: torch.Tensor): """Forward pass. Args: x: Input tensor. Shape: [B, C, T, H, W]. """ residual = x x = self.stack(x) x = x + residual del residual return self.attn_block(x) class Attention(nn.Module): def __init__( self, dim: int, head_dim: int = 32, qkv_bias: bool = False, out_bias: bool = True, qk_norm: bool = True, ) -> None: super().__init__() self.head_dim = head_dim self.num_heads = dim // head_dim self.qk_norm = qk_norm self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) self.out = nn.Linear(dim, dim, bias=out_bias) def forward( self, x: torch.Tensor, ) -> torch.Tensor: """Compute temporal self-attention. Args: x: Input tensor. Shape: [B, C, T, H, W]. chunk_size: Chunk size for large tensors. Returns: x: Output tensor. Shape: [B, C, T, H, W]. """ B, _, T, H, W = x.shape if T == 1: # No attention for single frame. x = x.movedim(1, -1) # [B, C, T, H, W] -> [B, T, H, W, C] qkv = self.qkv(x) _, _, x = qkv.chunk(3, dim=-1) # Throw away queries and keys. x = self.out(x) return x.movedim(-1, 1) # [B, T, H, W, C] -> [B, C, T, H, W] # 1D temporal attention. x = rearrange(x, "B C t h w -> (B h w) t C") qkv = self.qkv(x) # Input: qkv with shape [B, t, 3 * num_heads * head_dim] # Output: x with shape [B, num_heads, t, head_dim] q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2) if self.qk_norm: q = F.normalize(q, p=2, dim=-1) k = F.normalize(k, p=2, dim=-1) x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True) assert x.size(0) == q.size(0) x = self.out(x) x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W) return x class AttentionBlock(nn.Module): def __init__( self, dim: int, **attn_kwargs, ) -> None: super().__init__() self.norm = norm_fn(dim) self.attn = Attention(dim, **attn_kwargs) def forward(self, x: torch.Tensor) -> torch.Tensor: return x + self.attn(self.norm(x)) class CausalUpsampleBlock(nn.Module): def __init__( self, in_channels: int, out_channels: int, num_res_blocks: int, *, temporal_expansion: int = 2, spatial_expansion: int = 2, **block_kwargs, ): super().__init__() blocks = [] for _ in range(num_res_blocks): blocks.append(block_fn(in_channels, **block_kwargs)) self.blocks = nn.Sequential(*blocks) self.temporal_expansion = temporal_expansion self.spatial_expansion = spatial_expansion # Change channels in the final convolution layer. self.proj = Conv1x1( in_channels, out_channels * temporal_expansion * (spatial_expansion**2), ) self.d2st = DepthToSpaceTime( temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion ) def forward(self, x): x = self.blocks(x) x = self.proj(x) x = self.d2st(x) return x def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs): attn_block = AttentionBlock(channels) if has_attention else None return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs) class DownsampleBlock(nn.Module): def __init__( self, in_channels: int, out_channels: int, num_res_blocks, *, temporal_reduction=2, spatial_reduction=2, **block_kwargs, ): """ Downsample block for the VAE encoder. Args: in_channels: Number of input channels. out_channels: Number of output channels. num_res_blocks: Number of residual blocks. temporal_reduction: Temporal reduction factor. spatial_reduction: Spatial reduction factor. """ super().__init__() layers = [] # Change the channel count in the strided convolution. # This lets the ResBlock have uniform channel count, # as in ConvNeXt. assert in_channels != out_channels layers.append( PConv3d( in_channels=in_channels, out_channels=out_channels, kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction), stride=(temporal_reduction, spatial_reduction, spatial_reduction), # First layer in each block always uses replicate padding padding_mode="replicate", bias=block_kwargs["bias"], ) ) for _ in range(num_res_blocks): layers.append(block_fn(out_channels, **block_kwargs)) self.layers = nn.Sequential(*layers) def forward(self, x): return self.layers(x) def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1): num_freqs = (stop - start) // step assert inputs.ndim == 5 C = inputs.size(1) # Create Base 2 Fourier features. freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device) assert num_freqs == len(freqs) w = torch.pow(2.0, freqs) * (2 * torch.pi) # [num_freqs] C = inputs.shape[1] w = w.repeat(C)[None, :, None, None, None] # [1, C * num_freqs, 1, 1, 1] # Interleaved repeat of input channels to match w. h = inputs.repeat_interleave(num_freqs, dim=1) # [B, C * num_freqs, T, H, W] # Scale channels by frequency. h = w * h return torch.cat( [ inputs, torch.sin(h), torch.cos(h), ], dim=1, ) class FourierFeatures(nn.Module): def __init__(self, start: int = 6, stop: int = 8, step: int = 1): super().__init__() self.start = start self.stop = stop self.step = step def forward(self, inputs): """Add Fourier features to inputs. Args: inputs: Input tensor. Shape: [B, C, T, H, W] Returns: h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W] """ return add_fourier_features(inputs, self.start, self.stop, self.step) class Decoder(nn.Module): def __init__( self, *, out_channels: int = 3, latent_dim: int, base_channels: int, channel_multipliers: List[int], num_res_blocks: List[int], temporal_expansions: Optional[List[int]] = None, spatial_expansions: Optional[List[int]] = None, has_attention: List[bool], output_norm: bool = True, nonlinearity: str = "silu", output_nonlinearity: str = "silu", causal: bool = True, **block_kwargs, ): super().__init__() self.input_channels = latent_dim self.base_channels = base_channels self.channel_multipliers = channel_multipliers self.num_res_blocks = num_res_blocks self.output_nonlinearity = output_nonlinearity assert nonlinearity == "silu" assert causal ch = [mult * base_channels for mult in channel_multipliers] self.num_up_blocks = len(ch) - 1 assert len(num_res_blocks) == self.num_up_blocks + 2 blocks = [] first_block = [ ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1)) ] # Input layer. # First set of blocks preserve channel count. for _ in range(num_res_blocks[-1]): first_block.append( block_fn( ch[-1], has_attention=has_attention[-1], causal=causal, **block_kwargs, ) ) blocks.append(nn.Sequential(*first_block)) assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2 upsample_block_fn = CausalUpsampleBlock for i in range(self.num_up_blocks): block = upsample_block_fn( ch[-i - 1], ch[-i - 2], num_res_blocks=num_res_blocks[-i - 2], has_attention=has_attention[-i - 2], temporal_expansion=temporal_expansions[-i - 1], spatial_expansion=spatial_expansions[-i - 1], causal=causal, **block_kwargs, ) blocks.append(block) assert not output_norm # Last block. Preserve channel count. last_block = [] for _ in range(num_res_blocks[0]): last_block.append( block_fn( ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs ) ) blocks.append(nn.Sequential(*last_block)) self.blocks = nn.ModuleList(blocks) self.output_proj = Conv1x1(ch[0], out_channels) def forward(self, x): """Forward pass. Args: x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1]. Returns: x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]. T + 1 = (t - 1) * 4. H = h * 16, W = w * 16. """ for block in self.blocks: x = block(x) if self.output_nonlinearity == "silu": x = F.silu(x, inplace=not self.training) else: assert ( not self.output_nonlinearity ) # StyleGAN3 omits the to-RGB nonlinearity. return self.output_proj(x).contiguous() class LatentDistribution: def __init__(self, mean: torch.Tensor, logvar: torch.Tensor): """Initialize latent distribution. Args: mean: Mean of the distribution. Shape: [B, C, T, H, W]. logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W]. """ assert mean.shape == logvar.shape self.mean = mean self.logvar = logvar def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None): if temperature == 0.0: return self.mean if noise is None: noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator) else: assert noise.device == self.mean.device noise = noise.to(self.mean.dtype) if temperature != 1.0: raise NotImplementedError(f"Temperature {temperature} is not supported.") # Just Gaussian sample with no scaling of variance. return noise * torch.exp(self.logvar * 0.5) + self.mean def mode(self): return self.mean class Encoder(nn.Module): def __init__( self, *, in_channels: int, base_channels: int, channel_multipliers: List[int], num_res_blocks: List[int], latent_dim: int, temporal_reductions: List[int], spatial_reductions: List[int], prune_bottlenecks: List[bool], has_attentions: List[bool], affine: bool = True, bias: bool = True, input_is_conv_1x1: bool = False, padding_mode: str, ): super().__init__() self.temporal_reductions = temporal_reductions self.spatial_reductions = spatial_reductions self.base_channels = base_channels self.channel_multipliers = channel_multipliers self.num_res_blocks = num_res_blocks self.latent_dim = latent_dim self.fourier_features = FourierFeatures() ch = [mult * base_channels for mult in channel_multipliers] num_down_blocks = len(ch) - 1 assert len(num_res_blocks) == num_down_blocks + 2 layers = ( [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)] if not input_is_conv_1x1 else [Conv1x1(in_channels, ch[0])] ) assert len(prune_bottlenecks) == num_down_blocks + 2 assert len(has_attentions) == num_down_blocks + 2 block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias) for _ in range(num_res_blocks[0]): layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0])) prune_bottlenecks = prune_bottlenecks[1:] has_attentions = has_attentions[1:] assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1 for i in range(num_down_blocks): layer = DownsampleBlock( ch[i], ch[i + 1], num_res_blocks=num_res_blocks[i + 1], temporal_reduction=temporal_reductions[i], spatial_reduction=spatial_reductions[i], prune_bottleneck=prune_bottlenecks[i], has_attention=has_attentions[i], affine=affine, bias=bias, padding_mode=padding_mode, ) layers.append(layer) # Additional blocks. for _ in range(num_res_blocks[-1]): layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1])) self.layers = nn.Sequential(*layers) # Output layers. self.output_norm = norm_fn(ch[-1]) self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False) @property def temporal_downsample(self): return math.prod(self.temporal_reductions) @property def spatial_downsample(self): return math.prod(self.spatial_reductions) def forward(self, x) -> LatentDistribution: """Forward pass. Args: x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1] Returns: means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1]. h = H // 8, w = W // 8, t - 1 = (T - 1) // 6 logvar: Shape: [B, latent_dim, t, h, w]. """ assert x.ndim == 5, f"Expected 5D input, got {x.shape}" x = self.fourier_features(x) x = self.layers(x) x = self.output_norm(x) x = F.silu(x, inplace=True) x = self.output_proj(x) means, logvar = torch.chunk(x, 2, dim=1) assert means.ndim == 5 assert logvar.shape == means.shape assert means.size(1) == self.latent_dim return LatentDistribution(means, logvar) class VideoVAE(nn.Module): def __init__(self): super().__init__() self.encoder = Encoder( in_channels=15, base_channels=64, channel_multipliers=[1, 2, 4, 6], num_res_blocks=[3, 3, 4, 6, 3], latent_dim=12, temporal_reductions=[1, 2, 3], spatial_reductions=[2, 2, 2], prune_bottlenecks=[False, False, False, False, False], has_attentions=[False, True, True, True, True], affine=True, bias=True, input_is_conv_1x1=True, padding_mode="replicate" ) self.decoder = Decoder( out_channels=3, base_channels=128, channel_multipliers=[1, 2, 4, 6], temporal_expansions=[1, 2, 3], spatial_expansions=[2, 2, 2], num_res_blocks=[3, 3, 4, 6, 3], latent_dim=12, has_attention=[False, False, False, False, False], padding_mode="replicate", output_norm=False, nonlinearity="silu", output_nonlinearity="silu", causal=True, ) def encode(self, x): return self.encoder(x).mode() def decode(self, x): return self.decoder(x) ================================================ FILE: comfy/ldm/hidream/model.py ================================================ from typing import Optional, Tuple, List import torch import torch.nn as nn import einops from einops import repeat from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps import torch.nn.functional as F from comfy.ldm.flux.math import apply_rope, rope from comfy.ldm.flux.layers import LastLayer from comfy.ldm.modules.attention import optimized_attention import comfy.model_management import comfy.patcher_extension import comfy.ldm.common_dit # Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py class EmbedND(nn.Module): def __init__(self, theta: int, axes_dim: List[int]): super().__init__() self.theta = theta self.axes_dim = axes_dim def forward(self, ids: torch.Tensor) -> torch.Tensor: n_axes = ids.shape[-1] emb = torch.cat( [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3, ) return emb.unsqueeze(2) class PatchEmbed(nn.Module): def __init__( self, patch_size=2, in_channels=4, out_channels=1024, dtype=None, device=None, operations=None ): super().__init__() self.patch_size = patch_size self.out_channels = out_channels self.proj = operations.Linear(in_channels * patch_size * patch_size, out_channels, bias=True, dtype=dtype, device=device) def forward(self, latent): latent = self.proj(latent) return latent class PooledEmbed(nn.Module): def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None, operations=None): super().__init__() self.pooled_embedder = TimestepEmbedding(in_channels=text_emb_dim, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) def forward(self, pooled_embed): return self.pooled_embedder(pooled_embed) class TimestepEmbed(nn.Module): def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): super().__init__() self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0) self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) def forward(self, timesteps, wdtype): t_emb = self.time_proj(timesteps).to(dtype=wdtype) t_emb = self.timestep_embedder(t_emb) return t_emb def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}): return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options) class HiDreamAttnProcessor_flashattn: """Attention processor used typically in processing the SD3-like self-attention projections.""" def __call__( self, attn, image_tokens: torch.FloatTensor, image_tokens_masks: Optional[torch.FloatTensor] = None, text_tokens: Optional[torch.FloatTensor] = None, rope: torch.FloatTensor = None, transformer_options={}, *args, **kwargs, ) -> torch.FloatTensor: dtype = image_tokens.dtype batch_size = image_tokens.shape[0] query_i = attn.q_rms_norm(attn.to_q(image_tokens)).to(dtype=dtype) key_i = attn.k_rms_norm(attn.to_k(image_tokens)).to(dtype=dtype) value_i = attn.to_v(image_tokens) inner_dim = key_i.shape[-1] head_dim = inner_dim // attn.heads query_i = query_i.view(batch_size, -1, attn.heads, head_dim) key_i = key_i.view(batch_size, -1, attn.heads, head_dim) value_i = value_i.view(batch_size, -1, attn.heads, head_dim) if image_tokens_masks is not None: key_i = key_i * image_tokens_masks.view(batch_size, -1, 1, 1) if not attn.single: query_t = attn.q_rms_norm_t(attn.to_q_t(text_tokens)).to(dtype=dtype) key_t = attn.k_rms_norm_t(attn.to_k_t(text_tokens)).to(dtype=dtype) value_t = attn.to_v_t(text_tokens) query_t = query_t.view(batch_size, -1, attn.heads, head_dim) key_t = key_t.view(batch_size, -1, attn.heads, head_dim) value_t = value_t.view(batch_size, -1, attn.heads, head_dim) num_image_tokens = query_i.shape[1] num_text_tokens = query_t.shape[1] query = torch.cat([query_i, query_t], dim=1) key = torch.cat([key_i, key_t], dim=1) value = torch.cat([value_i, value_t], dim=1) else: query = query_i key = key_i value = value_i if query.shape[-1] == rope.shape[-3] * 2: query, key = apply_rope(query, key, rope) else: query_1, query_2 = query.chunk(2, dim=-1) key_1, key_2 = key.chunk(2, dim=-1) query_1, key_1 = apply_rope(query_1, key_1, rope) query = torch.cat([query_1, query_2], dim=-1) key = torch.cat([key_1, key_2], dim=-1) hidden_states = attention(query, key, value, transformer_options=transformer_options) if not attn.single: hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1) hidden_states_i = attn.to_out(hidden_states_i) hidden_states_t = attn.to_out_t(hidden_states_t) return hidden_states_i, hidden_states_t else: hidden_states = attn.to_out(hidden_states) return hidden_states class HiDreamAttention(nn.Module): def __init__( self, query_dim: int, heads: int = 8, dim_head: int = 64, upcast_attention: bool = False, upcast_softmax: bool = False, scale_qk: bool = True, eps: float = 1e-5, processor = None, out_dim: int = None, single: bool = False, dtype=None, device=None, operations=None ): # super(Attention, self).__init__() super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads self.query_dim = query_dim self.upcast_attention = upcast_attention self.upcast_softmax = upcast_softmax self.out_dim = out_dim if out_dim is not None else query_dim self.scale_qk = scale_qk self.scale = dim_head**-0.5 if self.scale_qk else 1.0 self.heads = out_dim // dim_head if out_dim is not None else heads self.sliceable_head_dim = heads self.single = single linear_cls = operations.Linear self.linear_cls = linear_cls self.to_q = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) self.to_k = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) self.to_v = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) self.to_out = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) self.q_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) self.k_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) if not single: self.to_q_t = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) self.to_k_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) self.to_v_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) self.to_out_t = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) self.q_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) self.k_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) self.processor = processor def forward( self, norm_image_tokens: torch.FloatTensor, image_tokens_masks: torch.FloatTensor = None, norm_text_tokens: torch.FloatTensor = None, rope: torch.FloatTensor = None, transformer_options={}, ) -> torch.Tensor: return self.processor( self, image_tokens = norm_image_tokens, image_tokens_masks = image_tokens_masks, text_tokens = norm_text_tokens, rope = rope, transformer_options=transformer_options, ) class FeedForwardSwiGLU(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int = 256, ffn_dim_multiplier: Optional[float] = None, dtype=None, device=None, operations=None ): super().__init__() hidden_dim = int(2 * hidden_dim / 3) # custom dim factor multiplier if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ( (hidden_dim + multiple_of - 1) // multiple_of ) self.w1 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) self.w2 = operations.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device) self.w3 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) def forward(self, x): return self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x)) # Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py class MoEGate(nn.Module): def __init__(self, embed_dim, num_routed_experts=4, num_activated_experts=2, aux_loss_alpha=0.01, dtype=None, device=None, operations=None): super().__init__() self.top_k = num_activated_experts self.n_routed_experts = num_routed_experts self.scoring_func = 'softmax' self.alpha = aux_loss_alpha self.seq_aux = False # topk selection algorithm self.norm_topk_prob = False self.gating_dim = embed_dim self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), dtype=dtype, device=device)) self.reset_parameters() def reset_parameters(self) -> None: pass # import torch.nn.init as init # init.kaiming_uniform_(self.weight, a=math.sqrt(5)) def forward(self, hidden_states): bsz, seq_len, h = hidden_states.shape ### compute gating score hidden_states = hidden_states.view(-1, h) logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), None) if self.scoring_func == 'softmax': scores = logits.softmax(dim=-1) else: raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') ### select top-k experts topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) ### norm gate to sum 1 if self.top_k > 1 and self.norm_topk_prob: denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 topk_weight = topk_weight / denominator aux_loss = None return topk_idx, topk_weight, aux_loss # Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py class MOEFeedForwardSwiGLU(nn.Module): def __init__( self, dim: int, hidden_dim: int, num_routed_experts: int, num_activated_experts: int, dtype=None, device=None, operations=None ): super().__init__() self.shared_experts = FeedForwardSwiGLU(dim, hidden_dim // 2, dtype=dtype, device=device, operations=operations) self.experts = nn.ModuleList([FeedForwardSwiGLU(dim, hidden_dim, dtype=dtype, device=device, operations=operations) for i in range(num_routed_experts)]) self.gate = MoEGate( embed_dim = dim, num_routed_experts = num_routed_experts, num_activated_experts = num_activated_experts, dtype=dtype, device=device, operations=operations ) self.num_activated_experts = num_activated_experts def forward(self, x): wtype = x.dtype identity = x orig_shape = x.shape topk_idx, topk_weight, aux_loss = self.gate(x) x = x.view(-1, x.shape[-1]) flat_topk_idx = topk_idx.view(-1) if True: # self.training: # TODO: check which branch performs faster x = x.repeat_interleave(self.num_activated_experts, dim=0) y = torch.empty_like(x, dtype=wtype) for i, expert in enumerate(self.experts): y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(dtype=wtype) y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) y = y.view(*orig_shape).to(dtype=wtype) #y = AddAuxiliaryLoss.apply(y, aux_loss) else: y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) y = y + self.shared_experts(identity) return y @torch.no_grad() def moe_infer(self, x, flat_expert_indices, flat_expert_weights): expert_cache = torch.zeros_like(x) idxs = flat_expert_indices.argsort() tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) token_idxs = idxs // self.num_activated_experts for i, end_idx in enumerate(tokens_per_expert): start_idx = 0 if i == 0 else tokens_per_expert[i-1] if start_idx == end_idx: continue expert = self.experts[i] exp_token_idx = token_idxs[start_idx:end_idx] expert_tokens = x[exp_token_idx] expert_out = expert(expert_tokens) expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) # for fp16 and other dtype expert_cache = expert_cache.to(expert_out.dtype) expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum') return expert_cache class TextProjection(nn.Module): def __init__(self, in_features, hidden_size, dtype=None, device=None, operations=None): super().__init__() self.linear = operations.Linear(in_features=in_features, out_features=hidden_size, bias=False, dtype=dtype, device=device) def forward(self, caption): hidden_states = self.linear(caption) return hidden_states class BlockType: TransformerBlock = 1 SingleTransformerBlock = 2 class HiDreamImageSingleTransformerBlock(nn.Module): def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, num_routed_experts: int = 4, num_activated_experts: int = 2, dtype=None, device=None, operations=None ): super().__init__() self.num_attention_heads = num_attention_heads self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device) ) # 1. Attention self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) self.attn1 = HiDreamAttention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, processor = HiDreamAttnProcessor_flashattn(), single = True, dtype=dtype, device=device, operations=operations ) # 3. Feed-forward self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) if num_routed_experts > 0: self.ff_i = MOEFeedForwardSwiGLU( dim = dim, hidden_dim = 4 * dim, num_routed_experts = num_routed_experts, num_activated_experts = num_activated_experts, dtype=dtype, device=device, operations=operations ) else: self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) def forward( self, image_tokens: torch.FloatTensor, image_tokens_masks: Optional[torch.FloatTensor] = None, text_tokens: Optional[torch.FloatTensor] = None, adaln_input: Optional[torch.FloatTensor] = None, rope: torch.FloatTensor = None, transformer_options={}, ) -> torch.FloatTensor: wtype = image_tokens.dtype shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \ self.adaLN_modulation(adaln_input)[:,None].chunk(6, dim=-1) # 1. MM-Attention norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i attn_output_i = self.attn1( norm_image_tokens, image_tokens_masks, rope = rope, transformer_options=transformer_options, ) image_tokens = gate_msa_i * attn_output_i + image_tokens # 2. Feed-forward norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens.to(dtype=wtype)) image_tokens = ff_output_i + image_tokens return image_tokens class HiDreamImageTransformerBlock(nn.Module): def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, num_routed_experts: int = 4, num_activated_experts: int = 2, dtype=None, device=None, operations=None ): super().__init__() self.num_attention_heads = num_attention_heads self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(dim, 12 * dim, bias=True, dtype=dtype, device=device) ) # nn.init.zeros_(self.adaLN_modulation[1].weight) # nn.init.zeros_(self.adaLN_modulation[1].bias) # 1. Attention self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) self.norm1_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) self.attn1 = HiDreamAttention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, processor = HiDreamAttnProcessor_flashattn(), single = False, dtype=dtype, device=device, operations=operations ) # 3. Feed-forward self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) if num_routed_experts > 0: self.ff_i = MOEFeedForwardSwiGLU( dim = dim, hidden_dim = 4 * dim, num_routed_experts = num_routed_experts, num_activated_experts = num_activated_experts, dtype=dtype, device=device, operations=operations ) else: self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) self.norm3_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False) self.ff_t = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) def forward( self, image_tokens: torch.FloatTensor, image_tokens_masks: Optional[torch.FloatTensor] = None, text_tokens: Optional[torch.FloatTensor] = None, adaln_input: Optional[torch.FloatTensor] = None, rope: torch.FloatTensor = None, transformer_options={}, ) -> torch.FloatTensor: wtype = image_tokens.dtype shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \ shift_msa_t, scale_msa_t, gate_msa_t, shift_mlp_t, scale_mlp_t, gate_mlp_t = \ self.adaLN_modulation(adaln_input)[:,None].chunk(12, dim=-1) # 1. MM-Attention norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i norm_text_tokens = self.norm1_t(text_tokens).to(dtype=wtype) norm_text_tokens = norm_text_tokens * (1 + scale_msa_t) + shift_msa_t attn_output_i, attn_output_t = self.attn1( norm_image_tokens, image_tokens_masks, norm_text_tokens, rope = rope, transformer_options=transformer_options, ) image_tokens = gate_msa_i * attn_output_i + image_tokens text_tokens = gate_msa_t * attn_output_t + text_tokens # 2. Feed-forward norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i norm_text_tokens = self.norm3_t(text_tokens).to(dtype=wtype) norm_text_tokens = norm_text_tokens * (1 + scale_mlp_t) + shift_mlp_t ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens) ff_output_t = gate_mlp_t * self.ff_t(norm_text_tokens) image_tokens = ff_output_i + image_tokens text_tokens = ff_output_t + text_tokens return image_tokens, text_tokens class HiDreamImageBlock(nn.Module): def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, num_routed_experts: int = 4, num_activated_experts: int = 2, block_type: BlockType = BlockType.TransformerBlock, dtype=None, device=None, operations=None ): super().__init__() block_classes = { BlockType.TransformerBlock: HiDreamImageTransformerBlock, BlockType.SingleTransformerBlock: HiDreamImageSingleTransformerBlock, } self.block = block_classes[block_type]( dim, num_attention_heads, attention_head_dim, num_routed_experts, num_activated_experts, dtype=dtype, device=device, operations=operations ) def forward( self, image_tokens: torch.FloatTensor, image_tokens_masks: Optional[torch.FloatTensor] = None, text_tokens: Optional[torch.FloatTensor] = None, adaln_input: torch.FloatTensor = None, rope: torch.FloatTensor = None, transformer_options={}, ) -> torch.FloatTensor: return self.block( image_tokens, image_tokens_masks, text_tokens, adaln_input, rope, transformer_options=transformer_options, ) class HiDreamImageTransformer2DModel(nn.Module): def __init__( self, patch_size: Optional[int] = None, in_channels: int = 64, out_channels: Optional[int] = None, num_layers: int = 16, num_single_layers: int = 32, attention_head_dim: int = 128, num_attention_heads: int = 20, caption_channels: List[int] = None, text_emb_dim: int = 2048, num_routed_experts: int = 4, num_activated_experts: int = 2, axes_dims_rope: Tuple[int, int] = (32, 32), max_resolution: Tuple[int, int] = (128, 128), llama_layers: List[int] = None, image_model=None, dtype=None, device=None, operations=None ): self.patch_size = patch_size self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim self.num_layers = num_layers self.num_single_layers = num_single_layers self.gradient_checkpointing = False super().__init__() self.dtype = dtype self.out_channels = out_channels or in_channels self.inner_dim = self.num_attention_heads * self.attention_head_dim self.llama_layers = llama_layers self.t_embedder = TimestepEmbed(self.inner_dim, dtype=dtype, device=device, operations=operations) self.p_embedder = PooledEmbed(text_emb_dim, self.inner_dim, dtype=dtype, device=device, operations=operations) self.x_embedder = PatchEmbed( patch_size = patch_size, in_channels = in_channels, out_channels = self.inner_dim, dtype=dtype, device=device, operations=operations ) self.pe_embedder = EmbedND(theta=10000, axes_dim=axes_dims_rope) self.double_stream_blocks = nn.ModuleList( [ HiDreamImageBlock( dim = self.inner_dim, num_attention_heads = self.num_attention_heads, attention_head_dim = self.attention_head_dim, num_routed_experts = num_routed_experts, num_activated_experts = num_activated_experts, block_type = BlockType.TransformerBlock, dtype=dtype, device=device, operations=operations ) for i in range(self.num_layers) ] ) self.single_stream_blocks = nn.ModuleList( [ HiDreamImageBlock( dim = self.inner_dim, num_attention_heads = self.num_attention_heads, attention_head_dim = self.attention_head_dim, num_routed_experts = num_routed_experts, num_activated_experts = num_activated_experts, block_type = BlockType.SingleTransformerBlock, dtype=dtype, device=device, operations=operations ) for i in range(self.num_single_layers) ] ) self.final_layer = LastLayer(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) caption_channels = [caption_channels[1], ] * (num_layers + num_single_layers) + [caption_channels[0], ] caption_projection = [] for caption_channel in caption_channels: caption_projection.append(TextProjection(in_features=caption_channel, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations)) self.caption_projection = nn.ModuleList(caption_projection) self.max_seq = max_resolution[0] * max_resolution[1] // (patch_size * patch_size) def expand_timesteps(self, timesteps, batch_size, device): if not torch.is_tensor(timesteps): is_mps = device.type == "mps" if isinstance(timesteps, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 timesteps = torch.tensor([timesteps], dtype=dtype, device=device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(batch_size) return timesteps def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]]) -> List[torch.Tensor]: x_arr = [] for i, img_size in enumerate(img_sizes): pH, pW = img_size x_arr.append( einops.rearrange(x[i, :pH*pW].reshape(1, pH, pW, -1), 'B H W (p1 p2 C) -> B C (H p1) (W p2)', p1=self.patch_size, p2=self.patch_size) ) x = torch.cat(x_arr, dim=0) return x def patchify(self, x, max_seq, img_sizes=None): pz2 = self.patch_size * self.patch_size if isinstance(x, torch.Tensor): B = x.shape[0] device = x.device dtype = x.dtype else: B = len(x) device = x[0].device dtype = x[0].dtype x_masks = torch.zeros((B, max_seq), dtype=dtype, device=device) if img_sizes is not None: for i, img_size in enumerate(img_sizes): x_masks[i, 0:img_size[0] * img_size[1]] = 1 x = einops.rearrange(x, 'B C S p -> B S (p C)', p=pz2) elif isinstance(x, torch.Tensor): pH, pW = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size x = einops.rearrange(x, 'B C (H p1) (W p2) -> B (H W) (p1 p2 C)', p1=self.patch_size, p2=self.patch_size) img_sizes = [[pH, pW]] * B x_masks = None else: raise NotImplementedError return x, x_masks, img_sizes def forward(self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, encoder_hidden_states_llama3=None, image_cond=None, control = None, transformer_options = {}, ): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, t, y, context, encoder_hidden_states_llama3, image_cond, control, transformer_options) def _forward( self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, encoder_hidden_states_llama3=None, image_cond=None, control = None, transformer_options = {}, ) -> torch.Tensor: bs, c, h, w = x.shape if image_cond is not None: x = torch.cat([x, image_cond], dim=-1) hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) timesteps = t pooled_embeds = y T5_encoder_hidden_states = context img_sizes = None # spatial forward batch_size = hidden_states.shape[0] hidden_states_type = hidden_states.dtype # 0. time timesteps = self.expand_timesteps(timesteps, batch_size, hidden_states.device) timesteps = self.t_embedder(timesteps, hidden_states_type) p_embedder = self.p_embedder(pooled_embeds) adaln_input = timesteps + p_embedder hidden_states, image_tokens_masks, img_sizes = self.patchify(hidden_states, self.max_seq, img_sizes) if image_tokens_masks is None: pH, pW = img_sizes[0] img_ids = torch.zeros(pH, pW, 3, device=hidden_states.device) img_ids[..., 1] = img_ids[..., 1] + torch.arange(pH, device=hidden_states.device)[:, None] img_ids[..., 2] = img_ids[..., 2] + torch.arange(pW, device=hidden_states.device)[None, :] img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size) hidden_states = self.x_embedder(hidden_states) # T5_encoder_hidden_states = encoder_hidden_states[0] encoder_hidden_states = encoder_hidden_states_llama3.movedim(1, 0) encoder_hidden_states = [encoder_hidden_states[k] for k in self.llama_layers] if self.caption_projection is not None: new_encoder_hidden_states = [] for i, enc_hidden_state in enumerate(encoder_hidden_states): enc_hidden_state = self.caption_projection[i](enc_hidden_state) enc_hidden_state = enc_hidden_state.view(batch_size, -1, hidden_states.shape[-1]) new_encoder_hidden_states.append(enc_hidden_state) encoder_hidden_states = new_encoder_hidden_states T5_encoder_hidden_states = self.caption_projection[-1](T5_encoder_hidden_states) T5_encoder_hidden_states = T5_encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) encoder_hidden_states.append(T5_encoder_hidden_states) txt_ids = torch.zeros( batch_size, encoder_hidden_states[-1].shape[1] + encoder_hidden_states[-2].shape[1] + encoder_hidden_states[0].shape[1], 3, device=img_ids.device, dtype=img_ids.dtype ) ids = torch.cat((img_ids, txt_ids), dim=1) rope = self.pe_embedder(ids) # 2. Blocks block_id = 0 initial_encoder_hidden_states = torch.cat([encoder_hidden_states[-1], encoder_hidden_states[-2]], dim=1) initial_encoder_hidden_states_seq_len = initial_encoder_hidden_states.shape[1] for bid, block in enumerate(self.double_stream_blocks): cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] cur_encoder_hidden_states = torch.cat([initial_encoder_hidden_states, cur_llama31_encoder_hidden_states], dim=1) hidden_states, initial_encoder_hidden_states = block( image_tokens = hidden_states, image_tokens_masks = image_tokens_masks, text_tokens = cur_encoder_hidden_states, adaln_input = adaln_input, rope = rope, transformer_options=transformer_options, ) initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len] block_id += 1 image_tokens_seq_len = hidden_states.shape[1] hidden_states = torch.cat([hidden_states, initial_encoder_hidden_states], dim=1) hidden_states_seq_len = hidden_states.shape[1] if image_tokens_masks is not None: encoder_attention_mask_ones = torch.ones( (batch_size, initial_encoder_hidden_states.shape[1] + cur_llama31_encoder_hidden_states.shape[1]), device=image_tokens_masks.device, dtype=image_tokens_masks.dtype ) image_tokens_masks = torch.cat([image_tokens_masks, encoder_attention_mask_ones], dim=1) for bid, block in enumerate(self.single_stream_blocks): cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] hidden_states = torch.cat([hidden_states, cur_llama31_encoder_hidden_states], dim=1) hidden_states = block( image_tokens=hidden_states, image_tokens_masks=image_tokens_masks, text_tokens=None, adaln_input=adaln_input, rope=rope, transformer_options=transformer_options, ) hidden_states = hidden_states[:, :hidden_states_seq_len] block_id += 1 hidden_states = hidden_states[:, :image_tokens_seq_len, ...] output = self.final_layer(hidden_states, adaln_input) output = self.unpatchify(output, img_sizes) return -output[:, :, :h, :w] ================================================ FILE: comfy/ldm/hunyuan3d/model.py ================================================ import torch from torch import nn from comfy.ldm.flux.layers import ( DoubleStreamBlock, LastLayer, MLPEmbedder, SingleStreamBlock, timestep_embedding, ) import comfy.patcher_extension class Hunyuan3Dv2(nn.Module): def __init__( self, in_channels=64, context_in_dim=1536, hidden_size=1024, mlp_ratio=4.0, num_heads=16, depth=16, depth_single_blocks=32, qkv_bias=True, guidance_embed=False, image_model=None, dtype=None, device=None, operations=None ): super().__init__() self.dtype = dtype if hidden_size % num_heads != 0: raise ValueError( f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}" ) self.max_period = 1000 # While reimplementing the model I noticed that they messed up. This 1000 value was meant to be the time_factor but they set the max_period instead self.latent_in = operations.Linear(in_channels, hidden_size, bias=True, dtype=dtype, device=device) self.time_in = MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) self.guidance_in = ( MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) if guidance_embed else None ) self.cond_in = operations.Linear(context_in_dim, hidden_size, dtype=dtype, device=device) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( hidden_size, num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations ) for _ in range(depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock( hidden_size, num_heads, mlp_ratio=mlp_ratio, dtype=dtype, device=device, operations=operations ) for _ in range(depth_single_blocks) ] ) self.final_layer = LastLayer(hidden_size, 1, in_channels, dtype=dtype, device=device, operations=operations) def forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, guidance, transformer_options, **kwargs) def _forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs): x = x.movedim(-1, -2) timestep = 1.0 - timestep txt = context img = self.latent_in(x) vec = self.time_in(timestep_embedding(timestep, 256, self.max_period).to(dtype=img.dtype)) if self.guidance_in is not None: if guidance is not None: vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.max_period).to(img.dtype)) txt = self.cond_in(txt) pe = None attn_mask = None patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) for i, block in enumerate(self.double_blocks): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] else: img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) img = torch.cat((txt, img), 1) for i, block in enumerate(self.single_blocks): if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args["transformer_options"]) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) img = img[:, txt.shape[1]:, ...] img = self.final_layer(img, vec) return img.movedim(-2, -1) * (-1.0) ================================================ FILE: comfy/ldm/hunyuan3d/vae.py ================================================ # Original: https://github.com/Tencent/Hunyuan3D-2/blob/main/hy3dgen/shapegen/models/autoencoders/model.py # Since the header on their VAE source file was a bit confusing we asked for permission to use this code from tencent under the GPL license used in ComfyUI. import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import math from tqdm import tqdm from typing import Optional import logging import comfy.ops ops = comfy.ops.disable_weight_init def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, start_random: bool = True): # manually create the pointer vector assert src.size(0) == batch.numel() batch_size = int(batch.max()) + 1 deg = src.new_zeros(batch_size, dtype = torch.long) deg.scatter_add_(0, batch, torch.ones_like(batch)) ptr_vec = deg.new_zeros(batch_size + 1) torch.cumsum(deg, 0, out=ptr_vec[1:]) #return fps_sampling(src, ptr_vec, ratio) sampled_indicies = [] for b in range(batch_size): # start and the end of each batch start, end = ptr_vec[b].item(), ptr_vec[b + 1].item() # points from the point cloud points = src[start:end] num_points = points.size(0) num_samples = max(1, math.ceil(num_points * sampling_ratio)) selected = torch.zeros(num_samples, device = src.device, dtype = torch.long) distances = torch.full((num_points,), float("inf"), device = src.device) # select a random start point if start_random: farthest = torch.randint(0, num_points, (1,), device = src.device) else: farthest = torch.tensor([0], device = src.device, dtype = torch.long) for i in range(num_samples): selected[i] = farthest centroid = points[farthest].squeeze(0) dist = torch.norm(points - centroid, dim = 1) # compute euclidean distance distances = torch.minimum(distances, dist) farthest = torch.argmax(distances) sampled_indicies.append(torch.arange(start, end)[selected]) return torch.cat(sampled_indicies, dim = 0) class PointCrossAttention(nn.Module): def __init__(self, num_latents: int, downsample_ratio: float, pc_size: int, pc_sharpedge_size: int, point_feats: int, width: int, heads: int, layers: int, fourier_embedder, normal_pe: bool = False, qkv_bias: bool = False, use_ln_post: bool = True, qk_norm: bool = True): super().__init__() self.fourier_embedder = fourier_embedder self.pc_size = pc_size self.normal_pe = normal_pe self.downsample_ratio = downsample_ratio self.pc_sharpedge_size = pc_sharpedge_size self.num_latents = num_latents self.point_feats = point_feats self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width) self.cross_attn = ResidualCrossAttentionBlock( width = width, heads = heads, qkv_bias = qkv_bias, qk_norm = qk_norm ) self.self_attn = None if layers > 0: self.self_attn = Transformer( width = width, heads = heads, qkv_bias = qkv_bias, qk_norm = qk_norm, layers = layers ) if use_ln_post: self.ln_post = nn.LayerNorm(width) else: self.ln_post = None def sample_points_and_latents(self, point_cloud: torch.Tensor, features: torch.Tensor): """ Subsample points randomly from the point cloud (input_pc) Further sample the subsampled points to get query_pc take the fourier embeddings for both input and query pc Mental Note: FPS-sampled points (query_pc) act as latent tokens that attend to and learn from the broader context in input_pc. Goal: get a smaller represenation (query_pc) to represent the entire scence structure by learning from a broader subset (input_pc). More computationally efficient. Features are additional information for each point in the cloud """ B, _, D = point_cloud.shape num_latents = int(self.num_latents) num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents num_sharpedge_query = num_latents - num_random_query # Split random and sharpedge surface points random_pc, sharpedge_pc = torch.split(point_cloud, [self.pc_size, self.pc_sharpedge_size], dim=1) # assert statements assert random_pc.shape[1] <= self.pc_size, "Random surface points size must be less than or equal to pc_size" assert sharpedge_pc.shape[1] <= self.pc_sharpedge_size, "Sharpedge surface points size must be less than or equal to pc_sharpedge_size" input_random_pc_size = int(num_random_query * self.downsample_ratio) random_query_pc, random_input_pc, random_idx_pc, random_idx_query = \ self.subsample(pc = random_pc, num_query = num_random_query, input_pc_size = input_random_pc_size) input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio) if input_sharpedge_pc_size == 0: sharpedge_input_pc = torch.zeros(B, 0, D, dtype = random_input_pc.dtype).to(point_cloud.device) sharpedge_query_pc = torch.zeros(B, 0, D, dtype= random_query_pc.dtype).to(point_cloud.device) else: sharpedge_query_pc, sharpedge_input_pc, sharpedge_idx_pc, sharpedge_idx_query = \ self.subsample(pc = sharpedge_pc, num_query = num_sharpedge_query, input_pc_size = input_sharpedge_pc_size) # concat the random and sharpedges query_pc = torch.cat([random_query_pc, sharpedge_query_pc], dim = 1) input_pc = torch.cat([random_input_pc, sharpedge_input_pc], dim = 1) query = self.fourier_embedder(query_pc) data = self.fourier_embedder(input_pc) if self.point_feats > 0: random_surface_features, sharpedge_surface_features = torch.split(features, [self.pc_size, self.pc_sharpedge_size], dim = 1) input_random_surface_features, query_random_features = \ self.handle_features(features = random_surface_features, idx_pc = random_idx_pc, batch_size = B, input_pc_size = input_random_pc_size, idx_query = random_idx_query) if input_sharpedge_pc_size == 0: input_sharpedge_surface_features = torch.zeros(B, 0, self.point_feats, dtype = input_random_surface_features.dtype, device = point_cloud.device) query_sharpedge_features = torch.zeros(B, 0, self.point_feats, dtype = query_random_features.dtype, device = point_cloud.device) else: input_sharpedge_surface_features, query_sharpedge_features = \ self.handle_features(idx_pc = sharpedge_idx_pc, features = sharpedge_surface_features, batch_size = B, idx_query = sharpedge_idx_query, input_pc_size = input_sharpedge_pc_size) query_features = torch.cat([query_random_features, query_sharpedge_features], dim = 1) input_features = torch.cat([input_random_surface_features, input_sharpedge_surface_features], dim = 1) if self.normal_pe: # apply the fourier embeddings on the first 3 dims (xyz) input_features_pe = self.fourier_embedder(input_features[..., :3]) query_features_pe = self.fourier_embedder(query_features[..., :3]) # replace the first 3 dims with the new PE ones input_features = torch.cat([input_features_pe, input_features[..., :3]], dim = -1) query_features = torch.cat([query_features_pe, query_features[..., :3]], dim = -1) # concat at the channels dim query = torch.cat([query, query_features], dim = -1) data = torch.cat([data, input_features], dim = -1) # don't return pc_info to avoid unnecessary memory usuage return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1]) def forward(self, point_cloud: torch.Tensor, features: torch.Tensor): query, data = self.sample_points_and_latents(point_cloud = point_cloud, features = features) # apply projections query = self.input_proj(query) data = self.input_proj(data) # apply cross attention between query and data latents = self.cross_attn(query, data) if self.self_attn is not None: latents = self.self_attn(latents) if self.ln_post is not None: latents = self.ln_post(latents) return latents def subsample(self, pc, num_query, input_pc_size: int): """ num_query: number of points to keep after FPS input_pc_size: number of points to select before FPS """ B, _, D = pc.shape query_ratio = num_query / input_pc_size # random subsampling of points inside the point cloud idx_pc = torch.randperm(pc.shape[1], device = pc.device)[:input_pc_size] input_pc = pc[:, idx_pc, :] # flatten to allow applying fps across the whole batch flattent_input_pc = input_pc.view(B * input_pc_size, D) # construct a batch_down tensor to tell fps # which points belong to which batch N_down = int(flattent_input_pc.shape[0] / B) batch_down = torch.arange(B).to(pc.device) batch_down = torch.repeat_interleave(batch_down, N_down) idx_query = fps(flattent_input_pc, batch_down, sampling_ratio = query_ratio) query_pc = flattent_input_pc[idx_query].view(B, -1, D) return query_pc, input_pc, idx_pc, idx_query def handle_features(self, features, idx_pc, input_pc_size, batch_size: int, idx_query): B = batch_size input_surface_features = features[:, idx_pc, :] flattent_input_features = input_surface_features.view(B * input_pc_size, -1) query_features = flattent_input_features[idx_query].view(B, -1, flattent_input_features.shape[-1]) return input_surface_features, query_features def normalize_mesh(mesh, scale = 0.9999): """Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]""" bbox = mesh.bounds center = (bbox[1] + bbox[0]) / 2 max_extent = (bbox[1] - bbox[0]).max() mesh.apply_translation(-center) mesh.apply_scale((2 * scale) / max_extent) return mesh def sample_pointcloud(mesh, num = 200000): """ Uniformly sample points from the surface of the mesh """ points, face_idx = mesh.sample(num, return_index = True) normals = mesh.face_normals[face_idx] return torch.from_numpy(points.astype(np.float32)), torch.from_numpy(normals.astype(np.float32)) def detect_sharp_edges(mesh, threshold=0.985): """Return edge indices (a, b) that lie on sharp boundaries of the mesh.""" V, F = mesh.vertices, mesh.faces VN, FN = mesh.vertex_normals, mesh.face_normals sharp_mask = np.ones(V.shape[0]) for i in range(3): indices = F[:, i] alignment = np.einsum('ij,ij->i', VN[indices], FN) dot_stack = np.stack((sharp_mask[indices], alignment), axis=-1) sharp_mask[indices] = np.min(dot_stack, axis=-1) edge_a = np.concatenate([F[:, 0], F[:, 1], F[:, 2]]) edge_b = np.concatenate([F[:, 1], F[:, 2], F[:, 0]]) sharp_edges = (sharp_mask[edge_a] < threshold) & (sharp_mask[edge_b] < threshold) return edge_a[sharp_edges], edge_b[sharp_edges] def sharp_sample_pointcloud(mesh, num = 16384): """ Sample points preferentially from sharp edges in the mesh. """ edge_a, edge_b = detect_sharp_edges(mesh) V, VN = mesh.vertices, mesh.vertex_normals va, vb = V[edge_a], V[edge_b] na, nb = VN[edge_a], VN[edge_b] edge_lengths = np.linalg.norm(vb - va, axis=-1) weights = edge_lengths / edge_lengths.sum() indices = np.searchsorted(np.cumsum(weights), np.random.rand(num)) t = np.random.rand(num, 1) samples = t * va[indices] + (1 - t) * vb[indices] normals = t * na[indices] + (1 - t) * nb[indices] return samples.astype(np.float32), normals.astype(np.float32) def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = "cuda"): """Load a surface with optional sharp-edge annotations from a trimesh mesh.""" import trimesh try: mesh_full = trimesh.util.concatenate(mesh.dump()) except Exception: mesh_full = trimesh.util.concatenate(mesh) mesh_full = normalize_mesh(mesh_full) faces = mesh_full.faces vertices = mesh_full.vertices origin_face_count = faces.shape[0] mesh_surface = trimesh.Trimesh(vertices=vertices, faces=faces[:origin_face_count]) mesh_fill = trimesh.Trimesh(vertices=vertices, faces=faces[origin_face_count:]) area_surface = mesh_surface.area area_fill = mesh_fill.area total_area = area_surface + area_fill sample_num = 499712 // 2 fill_ratio = area_fill / total_area if total_area > 0 else 0 num_fill = int(sample_num * fill_ratio) num_surface = sample_num - num_fill surf_pts, surf_normals = sample_pointcloud(mesh_surface, num_surface) fill_pts, fill_normals = (torch.zeros(0, 3), torch.zeros(0, 3)) if num_fill == 0 else sample_pointcloud(mesh_fill, num_fill) sharp_pts, sharp_normals = sharp_sample_pointcloud(mesh_surface, sample_num) def assemble_tensor(points, normals, label=None): data = torch.cat([points, normals], dim=1).half().to(device) if label is not None: label_tensor = torch.full((data.shape[0], 1), float(label), dtype=torch.float16).to(device) data = torch.cat([data, label_tensor], dim=1) return data surface = assemble_tensor(torch.cat([surf_pts.to(device), fill_pts.to(device)], dim=0), torch.cat([surf_normals.to(device), fill_normals.to(device)], dim=0), label = 0 if sharpedge_flag else None) sharp_surface = assemble_tensor(torch.from_numpy(sharp_pts), torch.from_numpy(sharp_normals), label = 1 if sharpedge_flag else None) rng = np.random.default_rng() surface = surface[rng.choice(surface.shape[0], num_points, replace = False)] sharp_surface = sharp_surface[rng.choice(sharp_surface.shape[0], num_sharp_points, replace = False)] full = torch.cat([surface, sharp_surface], dim = 0).unsqueeze(0) return full class SharpEdgeSurfaceLoader: """ Load mesh surface and sharp edge samples. """ def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192): self.num_uniform_points = num_uniform_points self.num_sharp_points = num_sharp_points self.total_points = num_uniform_points + num_sharp_points def __call__(self, mesh_input, device = "cuda"): mesh = self._load_mesh(mesh_input) return load_surface_sharpedge(mesh, self.num_uniform_points, self.num_sharp_points, device = device) @staticmethod def _load_mesh(mesh_input): import trimesh if isinstance(mesh_input, str): mesh = trimesh.load(mesh_input, force="mesh", merge_primitives = True) else: mesh = mesh_input if isinstance(mesh, trimesh.Scene): combined = None for obj in mesh.geometry.values(): combined = obj if combined is None else combined + obj return combined return mesh class DiagonalGaussianDistribution: def __init__(self, params: torch.Tensor, feature_dim: int = -1): # divide quant channels (8) into mean and log variance self.mean, self.logvar = torch.chunk(params, 2, dim = feature_dim) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.std = torch.exp(0.5 * self.logvar) def sample(self): eps = torch.randn_like(self.std) z = self.mean + eps * self.std return z ################################################ # Volume Decoder ################################################ class VanillaVolumeDecoder(): @torch.no_grad() def __call__(self, latents: torch.Tensor, geo_decoder: callable, octree_resolution: int, bounds = 1.01, num_chunks: int = 10_000, enable_pbar: bool = True, **kwargs): if isinstance(bounds, float): bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] bbox_min, bbox_max = torch.tensor(bounds[:3]), torch.tensor(bounds[3:]) x = torch.linspace(bbox_min[0], bbox_max[0], int(octree_resolution) + 1, dtype = torch.float32) y = torch.linspace(bbox_min[1], bbox_max[1], int(octree_resolution) + 1, dtype = torch.float32) z = torch.linspace(bbox_min[2], bbox_max[2], int(octree_resolution) + 1, dtype = torch.float32) [xs, ys, zs] = torch.meshgrid(x, y, z, indexing = "ij") xyz = torch.stack((xs, ys, zs), axis=-1).to(latents.device, dtype = latents.dtype).contiguous().reshape(-1, 3) grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1] batch_logits = [] for start in tqdm(range(0, xyz.shape[0], num_chunks), desc="Volume Decoding", disable=not enable_pbar): chunk_queries = xyz[start: start + num_chunks, :] chunk_queries = chunk_queries.unsqueeze(0).repeat(latents.shape[0], 1, 1) logits = geo_decoder(queries = chunk_queries, latents = latents) batch_logits.append(logits) grid_logits = torch.cat(batch_logits, dim = 1) grid_logits = grid_logits.view((latents.shape[0], *grid_size)).float() return grid_logits class FourierEmbedder(nn.Module): """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts each feature dimension of `x[..., i]` into: [ sin(x[..., i]), sin(f_1*x[..., i]), sin(f_2*x[..., i]), ... sin(f_N * x[..., i]), cos(x[..., i]), cos(f_1*x[..., i]), cos(f_2*x[..., i]), ... cos(f_N * x[..., i]), x[..., i] # only present if include_input is True. ], here f_i is the frequency. Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs]. If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...]; Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]. Args: num_freqs (int): the number of frequencies, default is 6; logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]; input_dim (int): the input dimension, default is 3; include_input (bool): include the input tensor or not, default is True. Attributes: frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1); out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1), otherwise, it is input_dim * num_freqs * 2. """ def __init__(self, num_freqs: int = 6, logspace: bool = True, input_dim: int = 3, include_input: bool = True, include_pi: bool = True) -> None: """The initialization""" super().__init__() if logspace: frequencies = 2.0 ** torch.arange( num_freqs, dtype=torch.float32 ) else: frequencies = torch.linspace( 1.0, 2.0 ** (num_freqs - 1), num_freqs, dtype=torch.float32 ) if include_pi: frequencies *= torch.pi self.register_buffer("frequencies", frequencies, persistent=False) self.include_input = include_input self.num_freqs = num_freqs self.out_dim = self.get_dims(input_dim) def get_dims(self, input_dim): temp = 1 if self.include_input or self.num_freqs == 0 else 0 out_dim = input_dim * (self.num_freqs * 2 + temp) return out_dim def forward(self, x: torch.Tensor) -> torch.Tensor: """ Forward process. Args: x: tensor of shape [..., dim] Returns: embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)] where temp is 1 if include_input is True and 0 otherwise. """ if self.num_freqs > 0: embed = (x[..., None].contiguous() * self.frequencies.to(device=x.device, dtype=x.dtype)).view(*x.shape[:-1], -1) if self.include_input: return torch.cat((x, embed.sin(), embed.cos()), dim=-1) else: return torch.cat((embed.sin(), embed.cos()), dim=-1) else: return x class CrossAttentionProcessor: def __call__(self, attn, q, k, v): out = comfy.ops.scaled_dot_product_attention(q, k, v) return out class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True): super(DropPath, self).__init__() self.drop_prob = drop_prob self.scale_by_keep = scale_by_keep def forward(self, x): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if self.drop_prob == 0. or not self.training: return x keep_prob = 1 - self.drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and self.scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor def extra_repr(self): return f'drop_prob={round(self.drop_prob, 3):0.3f}' class MLP(nn.Module): def __init__( self, *, width: int, expand_ratio: int = 4, output_width: int = None, drop_path_rate: float = 0.0 ): super().__init__() self.width = width self.c_fc = ops.Linear(width, width * expand_ratio) self.c_proj = ops.Linear(width * expand_ratio, output_width if output_width is not None else width) self.gelu = nn.GELU() self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() def forward(self, x): return self.drop_path(self.c_proj(self.gelu(self.c_fc(x)))) class QKVMultiheadCrossAttention(nn.Module): def __init__( self, heads: int, n_data = None, width=None, qk_norm=False, norm_layer=ops.LayerNorm ): super().__init__() self.heads = heads self.n_data = n_data self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() def forward(self, q, kv): _, n_ctx, _ = q.shape bs, n_data, width = kv.shape attn_ch = width // self.heads // 2 q = q.view(bs, n_ctx, self.heads, -1) kv = kv.view(bs, n_data, self.heads, -1) k, v = torch.split(kv, attn_ch, dim=-1) q = self.q_norm(q) k = self.k_norm(k) q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)] out = F.scaled_dot_product_attention(q, k, v) out = out.transpose(1, 2).reshape(bs, n_ctx, -1) return out class MultiheadCrossAttention(nn.Module): def __init__( self, *, width: int, heads: int, qkv_bias: bool = True, data_width: Optional[int] = None, norm_layer=ops.LayerNorm, qk_norm: bool = False, kv_cache: bool = False, ): super().__init__() self.width = width self.heads = heads self.data_width = width if data_width is None else data_width self.c_q = ops.Linear(width, width, bias=qkv_bias) self.c_kv = ops.Linear(self.data_width, width * 2, bias=qkv_bias) self.c_proj = ops.Linear(width, width) self.attention = QKVMultiheadCrossAttention( heads=heads, width=width, norm_layer=norm_layer, qk_norm=qk_norm ) self.kv_cache = kv_cache self.data = None def forward(self, x, data): x = self.c_q(x) if self.kv_cache: if self.data is None: self.data = self.c_kv(data) logging.info('Save kv cache,this should be called only once for one mesh') data = self.data else: data = self.c_kv(data) x = self.attention(x, data) x = self.c_proj(x) return x class ResidualCrossAttentionBlock(nn.Module): def __init__( self, *, width: int, heads: int, mlp_expand_ratio: int = 4, data_width: Optional[int] = None, qkv_bias: bool = True, norm_layer=ops.LayerNorm, qk_norm: bool = False ): super().__init__() if data_width is None: data_width = width self.attn = MultiheadCrossAttention( width=width, heads=heads, data_width=data_width, qkv_bias=qkv_bias, norm_layer=norm_layer, qk_norm=qk_norm ) self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6) self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6) self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio) def forward(self, x: torch.Tensor, data: torch.Tensor): x = x + self.attn(self.ln_1(x), self.ln_2(data)) x = x + self.mlp(self.ln_3(x)) return x class QKVMultiheadAttention(nn.Module): def __init__( self, *, heads: int, width=None, qk_norm=False, norm_layer=ops.LayerNorm ): super().__init__() self.heads = heads self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() def forward(self, qkv): bs, n_ctx, width = qkv.shape attn_ch = width // self.heads // 3 qkv = qkv.view(bs, n_ctx, self.heads, -1) q, k, v = torch.split(qkv, attn_ch, dim=-1) q = self.q_norm(q) k = self.k_norm(k) q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)] out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) return out class MultiheadAttention(nn.Module): def __init__( self, *, width: int, heads: int, qkv_bias: bool, norm_layer=ops.LayerNorm, qk_norm: bool = False, drop_path_rate: float = 0.0 ): super().__init__() self.c_qkv = ops.Linear(width, width * 3, bias=qkv_bias) self.c_proj = ops.Linear(width, width) self.attention = QKVMultiheadAttention( heads=heads, width=width, norm_layer=norm_layer, qk_norm=qk_norm ) self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() def forward(self, x): x = self.c_qkv(x) x = self.attention(x) x = self.drop_path(self.c_proj(x)) return x class ResidualAttentionBlock(nn.Module): def __init__( self, *, width: int, heads: int, qkv_bias: bool = True, norm_layer=ops.LayerNorm, qk_norm: bool = False, drop_path_rate: float = 0.0, ): super().__init__() self.attn = MultiheadAttention( width=width, heads=heads, qkv_bias=qkv_bias, norm_layer=norm_layer, qk_norm=qk_norm, drop_path_rate=drop_path_rate ) self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) self.mlp = MLP(width=width, drop_path_rate=drop_path_rate) self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6) def forward(self, x: torch.Tensor): x = x + self.attn(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class Transformer(nn.Module): def __init__( self, *, width: int, layers: int, heads: int, qkv_bias: bool = True, norm_layer=ops.LayerNorm, qk_norm: bool = False, drop_path_rate: float = 0.0 ): super().__init__() self.width = width self.layers = layers self.resblocks = nn.ModuleList( [ ResidualAttentionBlock( width=width, heads=heads, qkv_bias=qkv_bias, norm_layer=norm_layer, qk_norm=qk_norm, drop_path_rate=drop_path_rate ) for _ in range(layers) ] ) def forward(self, x: torch.Tensor): for block in self.resblocks: x = block(x) return x class CrossAttentionDecoder(nn.Module): def __init__( self, *, out_channels: int, fourier_embedder: FourierEmbedder, width: int, heads: int, mlp_expand_ratio: int = 4, downsample_ratio: int = 1, enable_ln_post: bool = True, qkv_bias: bool = True, qk_norm: bool = False, label_type: str = "binary" ): super().__init__() self.enable_ln_post = enable_ln_post self.fourier_embedder = fourier_embedder self.downsample_ratio = downsample_ratio self.query_proj = ops.Linear(self.fourier_embedder.out_dim, width) if self.downsample_ratio != 1: self.latents_proj = ops.Linear(width * downsample_ratio, width) if not self.enable_ln_post: qk_norm = False self.cross_attn_decoder = ResidualCrossAttentionBlock( width=width, mlp_expand_ratio=mlp_expand_ratio, heads=heads, qkv_bias=qkv_bias, qk_norm=qk_norm ) if self.enable_ln_post: self.ln_post = ops.LayerNorm(width) self.output_proj = ops.Linear(width, out_channels) self.label_type = label_type self.count = 0 def forward(self, queries=None, query_embeddings=None, latents=None): if query_embeddings is None: query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype)) self.count += query_embeddings.shape[1] if self.downsample_ratio != 1: latents = self.latents_proj(latents) x = self.cross_attn_decoder(query_embeddings, latents) if self.enable_ln_post: x = self.ln_post(x) occ = self.output_proj(x) return occ class ShapeVAE(nn.Module): def __init__( self, *, num_latents: int = 4096, embed_dim: int = 64, width: int = 1024, heads: int = 16, num_decoder_layers: int = 16, num_encoder_layers: int = 8, pc_size: int = 81920, pc_sharpedge_size: int = 0, point_feats: int = 4, downsample_ratio: int = 20, geo_decoder_downsample_ratio: int = 1, geo_decoder_mlp_expand_ratio: int = 4, geo_decoder_ln_post: bool = True, num_freqs: int = 8, qkv_bias: bool = False, qk_norm: bool = True, drop_path_rate: float = 0.0, include_pi: bool = False, scale_factor: float = 1.0039506158752403, label_type: str = "binary", ): super().__init__() self.geo_decoder_ln_post = geo_decoder_ln_post self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) self.encoder = PointCrossAttention(layers = num_encoder_layers, num_latents = num_latents, downsample_ratio = downsample_ratio, heads = heads, pc_size = pc_size, width = width, point_feats = point_feats, fourier_embedder = self.fourier_embedder, pc_sharpedge_size = pc_sharpedge_size) self.post_kl = ops.Linear(embed_dim, width) self.transformer = Transformer( width=width, layers=num_decoder_layers, heads=heads, qkv_bias=qkv_bias, qk_norm=qk_norm, drop_path_rate=drop_path_rate ) self.geo_decoder = CrossAttentionDecoder( fourier_embedder=self.fourier_embedder, out_channels=1, mlp_expand_ratio=geo_decoder_mlp_expand_ratio, downsample_ratio=geo_decoder_downsample_ratio, enable_ln_post=self.geo_decoder_ln_post, width=width // geo_decoder_downsample_ratio, heads=heads // geo_decoder_downsample_ratio, qkv_bias=qkv_bias, qk_norm=qk_norm, label_type=label_type, ) self.volume_decoder = VanillaVolumeDecoder() self.scale_factor = scale_factor def decode(self, latents, **kwargs): latents = self.post_kl(latents.movedim(-2, -1)) latents = self.transformer(latents) bounds = kwargs.get("bounds", 1.01) num_chunks = kwargs.get("num_chunks", 8000) octree_resolution = kwargs.get("octree_resolution", 256) enable_pbar = kwargs.get("enable_pbar", True) grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar) return grid_logits.movedim(-2, -1) def encode(self, surface): pc, feats = surface[:, :, :3], surface[:, :, 3:] latents = self.encoder(pc, feats) moments = self.pre_kl(latents) posterior = DiagonalGaussianDistribution(moments, feature_dim = -1) latents = posterior.sample() return latents ================================================ FILE: comfy/ldm/hunyuan3dv2_1/hunyuandit.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F from comfy.ldm.modules.attention import optimized_attention import comfy.model_management class GELU(nn.Module): def __init__(self, dim_in: int, dim_out: int, operations, device, dtype): super().__init__() self.proj = operations.Linear(dim_in, dim_out, device = device, dtype = dtype) def gelu(self, gate: torch.Tensor) -> torch.Tensor: if gate.device.type == "mps": return F.gelu(gate.to(dtype = torch.float32)).to(dtype = gate.dtype) return F.gelu(gate) def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states = self.gelu(hidden_states) return hidden_states class FeedForward(nn.Module): def __init__(self, dim: int, dim_out = None, mult: int = 4, dropout: float = 0.0, inner_dim = None, operations = None, device = None, dtype = None): super().__init__() if inner_dim is None: inner_dim = int(dim * mult) dim_out = dim_out if dim_out is not None else dim act_fn = GELU(dim, inner_dim, operations = operations, device = device, dtype = dtype) self.net = nn.ModuleList([]) self.net.append(act_fn) self.net.append(nn.Dropout(dropout)) self.net.append(operations.Linear(inner_dim, dim_out, device = device, dtype = dtype)) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: for module in self.net: hidden_states = module(hidden_states) return hidden_states class AddAuxLoss(torch.autograd.Function): @staticmethod def forward(ctx, x, loss): # do nothing in forward (no computation) ctx.requires_aux_loss = loss.requires_grad ctx.dtype = loss.dtype return x @staticmethod def backward(ctx, grad_output): # add the aux loss gradients grad_loss = None # put the aux grad the same as the main grad loss # aux grad contributes equally if ctx.requires_aux_loss: grad_loss = torch.ones(1, dtype = ctx.dtype, device = grad_output.device) return grad_output, grad_loss class MoEGate(nn.Module): def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01, device = None, dtype = None): super().__init__() self.top_k = num_experts_per_tok self.n_routed_experts = num_experts self.alpha = aux_loss_alpha self.gating_dim = embed_dim self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), device = device, dtype = dtype)) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # flatten hidden states hidden_states = hidden_states.view(-1, hidden_states.size(-1)) # get logits and pass it to softmax logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), bias = None) scores = logits.softmax(dim = -1) topk_weight, topk_idx = torch.topk(scores, k = self.top_k, dim = -1, sorted = False) if self.training and self.alpha > 0.0: scores_for_aux = scores # used bincount instead of one hot encoding counts = torch.bincount(topk_idx.view(-1), minlength = self.n_routed_experts).float() ce = counts / topk_idx.numel() # normalized expert usage # mean expert score Pi = scores_for_aux.mean(0) # expert balance loss aux_loss = (Pi * ce * self.n_routed_experts).sum() * self.alpha else: aux_loss = None return topk_idx, topk_weight, aux_loss class MoEBlock(nn.Module): def __init__(self, dim, num_experts: int = 6, moe_top_k: int = 2, dropout: float = 0.0, ff_inner_dim: int = None, operations = None, device = None, dtype = None): super().__init__() self.moe_top_k = moe_top_k self.num_experts = num_experts self.experts = nn.ModuleList([ FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype) for _ in range(num_experts) ]) self.gate = MoEGate(dim, num_experts = num_experts, num_experts_per_tok = moe_top_k, device = device, dtype = dtype) self.shared_experts = FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype) def forward(self, hidden_states) -> torch.Tensor: identity = hidden_states orig_shape = hidden_states.shape topk_idx, topk_weight, aux_loss = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) flat_topk_idx = topk_idx.view(-1) if self.training: hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim = 0) y = torch.empty_like(hidden_states, dtype = hidden_states.dtype) for i, expert in enumerate(self.experts): tmp = expert(hidden_states[flat_topk_idx == i]) y[flat_topk_idx == i] = tmp.to(hidden_states.dtype) y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim = 1) y = y.view(*orig_shape) y = AddAuxLoss.apply(y, aux_loss) else: y = self.moe_infer(hidden_states, flat_expert_indices = flat_topk_idx,flat_expert_weights = topk_weight.view(-1, 1)).view(*orig_shape) y = y + self.shared_experts(identity) return y @torch.no_grad() def moe_infer(self, x, flat_expert_indices, flat_expert_weights): expert_cache = torch.zeros_like(x) idxs = flat_expert_indices.argsort() # no need for .numpy().cpu() here tokens_per_expert = flat_expert_indices.bincount().cumsum(0) token_idxs = idxs // self.moe_top_k for i, end_idx in enumerate(tokens_per_expert): start_idx = 0 if i == 0 else tokens_per_expert[i-1] if start_idx == end_idx: continue expert = self.experts[i] exp_token_idx = token_idxs[start_idx:end_idx] expert_tokens = x[exp_token_idx] expert_out = expert(expert_tokens) expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) # use index_add_ with a 1-D index tensor directly avoids building a large [N, D] index map and extra memcopy required by scatter_reduce_ # + avoid dtype conversion expert_cache.index_add_(0, exp_token_idx, expert_out) return expert_cache class Timesteps(nn.Module): def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0, scale: float = 1.0, max_period: int = 10000): super().__init__() self.num_channels = num_channels half_dim = num_channels // 2 # precompute the “inv_freq” vector once exponent = -math.log(max_period) * torch.arange( half_dim, dtype=torch.float32 ) / (half_dim - downscale_freq_shift) inv_freq = torch.exp(exponent) # pad if num_channels % 2 == 1: # we’ll pad a zero at the end of the cos-half inv_freq = torch.cat([inv_freq, inv_freq.new_zeros(1)]) # register to buffer so it moves with the device self.register_buffer("inv_freq", inv_freq, persistent = False) self.scale = scale def forward(self, timesteps: torch.Tensor): x = timesteps.float().unsqueeze(1) * self.inv_freq.to(timesteps.device).unsqueeze(0) # fused CUDA kernels for sin and cos sin_emb = x.sin() cos_emb = x.cos() emb = torch.cat([sin_emb, cos_emb], dim = 1) # scale factor if self.scale != 1.0: emb = emb * self.scale # If we padded inv_freq for odd, emb is already wide enough; otherwise: if emb.shape[1] > self.num_channels: emb = emb[:, :self.num_channels] return emb class TimestepEmbedder(nn.Module): def __init__(self, hidden_size, frequency_embedding_size = 256, cond_proj_dim = None, operations = None, device = None, dtype = None): super().__init__() self.mlp = nn.Sequential( operations.Linear(hidden_size, frequency_embedding_size, bias=True, device = device, dtype = dtype), nn.GELU(), operations.Linear(frequency_embedding_size, hidden_size, bias=True, device = device, dtype = dtype), ) self.frequency_embedding_size = frequency_embedding_size if cond_proj_dim is not None: self.cond_proj = operations.Linear(cond_proj_dim, frequency_embedding_size, bias=False, device = device, dtype = dtype) self.time_embed = Timesteps(hidden_size) def forward(self, timesteps, condition): timestep_embed = self.time_embed(timesteps).type(self.mlp[0].weight.dtype) if condition is not None: cond_embed = self.cond_proj(condition) timestep_embed = timestep_embed + cond_embed time_conditioned = self.mlp(timestep_embed) # for broadcasting with image tokens return time_conditioned.unsqueeze(1) class MLP(nn.Module): def __init__(self, *, width: int, operations = None, device = None, dtype = None): super().__init__() self.width = width self.fc1 = operations.Linear(width, width * 4, device = device, dtype = dtype) self.fc2 = operations.Linear(width * 4, width, device = device, dtype = dtype) self.gelu = nn.GELU() def forward(self, x): return self.fc2(self.gelu(self.fc1(x))) class CrossAttention(nn.Module): def __init__( self, qdim, kdim, num_heads, qkv_bias=True, qk_norm=False, norm_layer=nn.LayerNorm, use_fp16: bool = False, operations = None, dtype = None, device = None, **kwargs, ): super().__init__() self.qdim = qdim self.kdim = kdim self.num_heads = num_heads self.head_dim = self.qdim // num_heads self.scale = self.head_dim ** -0.5 self.to_q = operations.Linear(qdim, qdim, bias=qkv_bias, device = device, dtype = dtype) self.to_k = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype) self.to_v = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype) if use_fp16: eps = 1.0 / 65504 else: eps = 1e-6 if norm_layer == nn.LayerNorm: norm_layer = operations.LayerNorm else: norm_layer = operations.RMSNorm self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() self.out_proj = operations.Linear(qdim, qdim, bias=True, device = device, dtype = dtype) def forward(self, x, y): b, s1, _ = x.shape _, s2, _ = y.shape y = y.to(next(self.to_k.parameters()).dtype) q = self.to_q(x) k = self.to_k(y) v = self.to_v(y) kv = torch.cat((k, v), dim=-1) split_size = kv.shape[-1] // self.num_heads // 2 kv = kv.view(1, -1, self.num_heads, split_size * 2) k, v = torch.split(kv, split_size, dim=-1) q = q.view(b, s1, self.num_heads, self.head_dim) k = k.view(b, s2, self.num_heads, self.head_dim) v = v.reshape(b, s2, self.num_heads * self.head_dim) q = self.q_norm(q) k = self.k_norm(k) x = optimized_attention( q.reshape(b, s1, self.num_heads * self.head_dim), k.reshape(b, s2, self.num_heads * self.head_dim), v, heads=self.num_heads, low_precision_attention=False, ) out = self.out_proj(x) return out class Attention(nn.Module): def __init__( self, dim, num_heads, qkv_bias = True, qk_norm = False, norm_layer = nn.LayerNorm, use_fp16: bool = False, operations = None, device = None, dtype = None ): super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = self.dim // num_heads self.scale = self.head_dim ** -0.5 self.to_q = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype) self.to_k = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype) self.to_v = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype) if use_fp16: eps = 1.0 / 65504 else: eps = 1e-6 if norm_layer == nn.LayerNorm: norm_layer = operations.LayerNorm else: norm_layer = operations.RMSNorm self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() self.out_proj = operations.Linear(dim, dim, device = device, dtype = dtype) def forward(self, x): B, N, _ = x.shape query = self.to_q(x) key = self.to_k(x) value = self.to_v(x) qkv_combined = torch.cat((query, key, value), dim=-1) split_size = qkv_combined.shape[-1] // self.num_heads // 3 qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3) query, key, value = torch.split(qkv, split_size, dim=-1) query = query.reshape(B, N, self.num_heads, self.head_dim) key = key.reshape(B, N, self.num_heads, self.head_dim) value = value.reshape(B, N, self.num_heads * self.head_dim) query = self.q_norm(query) key = self.k_norm(key) x = optimized_attention( query.reshape(B, N, self.num_heads * self.head_dim), key.reshape(B, N, self.num_heads * self.head_dim), value, heads=self.num_heads, low_precision_attention=False, ) x = self.out_proj(x) return x class HunYuanDiTBlock(nn.Module): def __init__( self, hidden_size, c_emb_size, num_heads, text_states_dim=1024, qk_norm=False, norm_layer=nn.LayerNorm, qk_norm_layer=True, qkv_bias=True, skip_connection=True, timested_modulate=False, use_moe: bool = False, num_experts: int = 8, moe_top_k: int = 2, use_fp16: bool = False, operations = None, device = None, dtype = None ): super().__init__() # eps can't be 1e-6 in fp16 mode because of numerical stability issues if use_fp16: eps = 1.0 / 65504 else: eps = 1e-6 self.norm1 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm, norm_layer=qk_norm_layer, use_fp16 = use_fp16, device = device, dtype = dtype, operations = operations) self.norm2 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) self.timested_modulate = timested_modulate if self.timested_modulate: self.default_modulation = nn.Sequential( nn.SiLU(), operations.Linear(c_emb_size, hidden_size, bias=True, device = device, dtype = dtype) ) self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm, norm_layer=qk_norm_layer, use_fp16 = use_fp16, device = device, dtype = dtype, operations = operations) self.norm3 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) if skip_connection: self.skip_norm = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, device = device, dtype = dtype) else: self.skip_linear = None self.use_moe = use_moe if self.use_moe: self.moe = MoEBlock( hidden_size, num_experts = num_experts, moe_top_k = moe_top_k, dropout = 0.0, ff_inner_dim = int(hidden_size * 4.0), device = device, dtype = dtype, operations = operations ) else: self.mlp = MLP(width=hidden_size, operations=operations, device = device, dtype = dtype) def forward(self, hidden_states, conditioning=None, text_states=None, skip_tensor=None): if self.skip_linear is not None: combined = torch.cat([skip_tensor, hidden_states], dim=-1) hidden_states = self.skip_linear(combined) hidden_states = self.skip_norm(hidden_states) # self attention if self.timested_modulate: modulation_shift = self.default_modulation(conditioning).unsqueeze(dim=1) hidden_states = hidden_states + modulation_shift self_attn_out = self.attn1(self.norm1(hidden_states)) hidden_states = hidden_states + self_attn_out # cross attention hidden_states = hidden_states + self.attn2(self.norm2(hidden_states), text_states) # MLP Layer mlp_input = self.norm3(hidden_states) if self.use_moe: hidden_states = hidden_states + self.moe(mlp_input) else: hidden_states = hidden_states + self.mlp(mlp_input) return hidden_states class FinalLayer(nn.Module): def __init__(self, final_hidden_size, out_channels, operations, use_fp16: bool = False, device = None, dtype = None): super().__init__() if use_fp16: eps = 1.0 / 65504 else: eps = 1e-6 self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) self.linear = operations.Linear(final_hidden_size, out_channels, bias = True, device = device, dtype = dtype) def forward(self, x): x = self.norm_final(x) x = x[:, 1:] x = self.linear(x) return x class HunYuanDiTPlain(nn.Module): # init with the defaults values from https://huggingface.co/tencent/Hunyuan3D-2.1/blob/main/hunyuan3d-dit-v2-1/config.yaml def __init__( self, in_channels: int = 64, hidden_size: int = 2048, context_dim: int = 1024, depth: int = 21, num_heads: int = 16, qk_norm: bool = True, qkv_bias: bool = False, num_moe_layers: int = 6, guidance_cond_proj_dim = 2048, norm_type = 'layer', num_experts: int = 8, moe_top_k: int = 2, use_fp16: bool = False, dtype = None, device = None, operations = None, **kwargs ): self.dtype = dtype super().__init__() self.depth = depth self.in_channels = in_channels self.out_channels = in_channels self.num_heads = num_heads self.hidden_size = hidden_size norm = operations.LayerNorm if norm_type == 'layer' else operations.RMSNorm qk_norm = operations.RMSNorm self.context_dim = context_dim self.guidance_cond_proj_dim = guidance_cond_proj_dim self.x_embedder = operations.Linear(in_channels, hidden_size, bias = True, device = device, dtype = dtype) self.t_embedder = TimestepEmbedder(hidden_size, hidden_size * 4, cond_proj_dim = guidance_cond_proj_dim, device = device, dtype = dtype, operations = operations) # HUnYuanDiT Blocks self.blocks = nn.ModuleList([ HunYuanDiTBlock(hidden_size=hidden_size, c_emb_size=hidden_size, num_heads=num_heads, text_states_dim=context_dim, qk_norm=qk_norm, norm_layer = norm, qk_norm_layer = qk_norm, skip_connection=layer > depth // 2, qkv_bias=qkv_bias, use_moe=True if depth - layer <= num_moe_layers else False, num_experts=num_experts, moe_top_k=moe_top_k, use_fp16 = use_fp16, device = device, dtype = dtype, operations = operations) for layer in range(depth) ]) self.depth = depth self.final_layer = FinalLayer(hidden_size, self.out_channels, use_fp16 = use_fp16, operations = operations, device = device, dtype = dtype) def forward(self, x, t, context, transformer_options = {}, **kwargs): x = x.movedim(-1, -2) uncond_emb, cond_emb = context.chunk(2, dim = 0) context = torch.cat([cond_emb, uncond_emb], dim = 0) main_condition = context t = 1.0 - t time_embedded = self.t_embedder(t, condition = kwargs.get('guidance_cond')) x = x.to(dtype = next(self.x_embedder.parameters()).dtype) x_embedded = self.x_embedder(x) combined = torch.cat([time_embedded, x_embedded], dim=1) def block_wrap(args): return block( args["x"], args["t"], args["cond"], skip_tensor=args.get("skip"),) skip_stack = [] patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) for idx, block in enumerate(self.blocks): if idx <= self.depth // 2: skip_input = None else: skip_input = skip_stack.pop() if ("block", idx) in blocks_replace: combined = blocks_replace[("block", idx)]( { "x": combined, "t": time_embedded, "cond": main_condition, "skip": skip_input, }, {"original_block": block_wrap}, ) else: combined = block(combined, time_embedded, main_condition, skip_tensor=skip_input) if idx < self.depth // 2: skip_stack.append(combined) output = self.final_layer(combined) output = output.movedim(-2, -1) * (-1.0) cond_emb, uncond_emb = output.chunk(2, dim = 0) return torch.cat([uncond_emb, cond_emb]) ================================================ FILE: comfy/ldm/hunyuan_video/model.py ================================================ #Based on Flux code because of weird hunyuan video code license. import torch import comfy.patcher_extension import comfy.ldm.flux.layers import comfy.ldm.modules.diffusionmodules.mmdit from comfy.ldm.modules.attention import optimized_attention from dataclasses import dataclass from einops import repeat from torch import Tensor, nn from comfy.ldm.flux.layers import ( DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock, timestep_embedding ) import comfy.ldm.common_dit @dataclass class HunyuanVideoParams: in_channels: int out_channels: int vec_in_dim: int context_in_dim: int hidden_size: int mlp_ratio: float num_heads: int depth: int depth_single_blocks: int axes_dim: list theta: int patch_size: list qkv_bias: bool guidance_embed: bool byt5: bool meanflow: bool use_cond_type_embedding: bool vision_in_dim: int meanflow_sum: bool class SelfAttentionRef(nn.Module): def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, device=None, operations=None): super().__init__() self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) class TokenRefinerBlock(nn.Module): def __init__( self, hidden_size, heads, dtype=None, device=None, operations=None ): super().__init__() self.heads = heads mlp_hidden_dim = hidden_size * 4 self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device), ) self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) self.self_attn = SelfAttentionRef(hidden_size, True, dtype=dtype, device=device, operations=operations) self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) self.mlp = nn.Sequential( operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), ) def forward(self, x, c, mask, transformer_options={}): mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1) norm_x = self.norm1(x) qkv = self.self_attn.qkv(norm_x) q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4) attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options) x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1) x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1) return x class IndividualTokenRefiner(nn.Module): def __init__( self, hidden_size, heads, num_blocks, dtype=None, device=None, operations=None ): super().__init__() self.blocks = nn.ModuleList( [ TokenRefinerBlock( hidden_size=hidden_size, heads=heads, dtype=dtype, device=device, operations=operations ) for _ in range(num_blocks) ] ) def forward(self, x, c, mask, transformer_options={}): m = None if mask is not None: m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1) m = m + m.transpose(2, 3) for block in self.blocks: x = block(x, c, m, transformer_options=transformer_options) return x class TokenRefiner(nn.Module): def __init__( self, text_dim, hidden_size, heads, num_blocks, dtype=None, device=None, operations=None ): super().__init__() self.input_embedder = operations.Linear(text_dim, hidden_size, bias=True, dtype=dtype, device=device) self.t_embedder = MLPEmbedder(256, hidden_size, dtype=dtype, device=device, operations=operations) self.c_embedder = MLPEmbedder(text_dim, hidden_size, dtype=dtype, device=device, operations=operations) self.individual_token_refiner = IndividualTokenRefiner(hidden_size, heads, num_blocks, dtype=dtype, device=device, operations=operations) def forward( self, x, timesteps, mask, transformer_options={}, ): t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype)) # m = mask.float().unsqueeze(-1) # c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise if x.dtype == torch.float16: c = x.float().sum(dim=1) / x.shape[1] else: c = x.sum(dim=1) / x.shape[1] c = t + self.c_embedder(c.to(x.dtype)) x = self.input_embedder(x) x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options) return x class ByT5Mapper(nn.Module): def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=False, dtype=None, device=None, operations=None): super().__init__() self.layernorm = operations.LayerNorm(in_dim, dtype=dtype, device=device) self.fc1 = operations.Linear(in_dim, hidden_dim, dtype=dtype, device=device) self.fc2 = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) self.fc3 = operations.Linear(out_dim, out_dim1, dtype=dtype, device=device) self.use_res = use_res self.act_fn = nn.GELU() def forward(self, x): if self.use_res: res = x x = self.layernorm(x) x = self.fc1(x) x = self.act_fn(x) x = self.fc2(x) x2 = self.act_fn(x) x2 = self.fc3(x2) if self.use_res: x2 = x2 + res return x2 class HunyuanVideo(nn.Module): """ Transformer model for flow matching on sequences. """ def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): super().__init__() self.dtype = dtype operation_settings = {"operations": operations, "device": device, "dtype": dtype} params = HunyuanVideoParams(**kwargs) self.params = params self.patch_size = params.patch_size self.in_channels = params.in_channels self.out_channels = params.out_channels self.use_cond_type_embedding = params.use_cond_type_embedding self.vision_in_dim = params.vision_in_dim if params.hidden_size % params.num_heads != 0: raise ValueError( f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" ) pe_dim = params.hidden_size // params.num_heads if sum(params.axes_dim) != pe_dim: raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") self.hidden_size = params.hidden_size self.num_heads = params.num_heads self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=len(self.patch_size) == 3, dtype=dtype, device=device, operations=operations) self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.vec_in_dim is not None: self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) else: self.vector_in = None self.guidance_in = ( MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() ) self.txt_in = TokenRefiner(params.context_in_dim, self.hidden_size, self.num_heads, 2, dtype=dtype, device=device, operations=operations) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) for _ in range(params.depth_single_blocks) ] ) if params.byt5: self.byt5_in = ByT5Mapper( in_dim=1472, out_dim=2048, hidden_dim=2048, out_dim1=self.hidden_size, use_res=False, dtype=dtype, device=device, operations=operations ) else: self.byt5_in = None if params.meanflow: self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) else: self.time_r_in = None if final_layer: self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations) # HunyuanVideo 1.5 specific modules if self.vision_in_dim is not None: from comfy.ldm.wan.model import MLPProj self.vision_in = MLPProj(in_dim=self.vision_in_dim, out_dim=self.hidden_size, operation_settings=operation_settings) else: self.vision_in = None if self.use_cond_type_embedding: # 0: text_encoder feature 1: byt5 feature 2: vision_encoder feature self.cond_type_embedding = nn.Embedding(3, self.hidden_size) else: self.cond_type_embedding = None def forward_orig( self, img: Tensor, img_ids: Tensor, txt: Tensor, txt_ids: Tensor, txt_mask: Tensor, timesteps: Tensor, y: Tensor = None, txt_byt5=None, clip_fea=None, guidance: Tensor = None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, ) -> Tensor: transformer_options = transformer_options.copy() patches_replace = transformer_options.get("patches_replace", {}) initial_shape = list(img.shape) # running on sequences img img = self.img_in(img) vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype)) if (self.time_r_in is not None) and (not disable_time_r): w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0] # This most likely could be improved if len(w) > 0: timesteps_r = transformer_options['sample_sigmas'][w[0] + 1] timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype) vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype)) vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2 if ref_latent is not None: ref_latent_ids = self.img_ids(ref_latent) ref_latent = self.img_in(ref_latent) img = torch.cat([ref_latent, img], dim=-2) ref_latent_ids[..., 0] = -1 ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1]) img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2) if guiding_frame_index is not None: token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0)) if self.vector_in is not None: vec_ = self.vector_in(y[:, :self.params.vec_in_dim]) vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1) else: vec = torch.cat([(token_replace_vec).unsqueeze(1), (vec).unsqueeze(1)], dim=1) frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2]) modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)] modulation_dims_txt = [(0, None, 1)] else: if self.vector_in is not None: vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) modulation_dims = None modulation_dims_txt = None if self.params.guidance_embed: if guidance is not None: vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) if txt_mask is not None and not torch.is_floating_point(txt_mask): txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options) if self.cond_type_embedding is not None: self.cond_type_embedding.to(txt.device) cond_emb = self.cond_type_embedding(torch.zeros_like(txt[:, :, 0], device=txt.device, dtype=torch.long)) txt = txt + cond_emb.to(txt.dtype) if self.byt5_in is not None and txt_byt5 is not None: txt_byt5 = self.byt5_in(txt_byt5) if self.cond_type_embedding is not None: cond_emb = self.cond_type_embedding(torch.ones_like(txt_byt5[:, :, 0], device=txt_byt5.device, dtype=torch.long)) txt_byt5 = txt_byt5 + cond_emb.to(txt_byt5.dtype) txt = torch.cat((txt_byt5, txt), dim=1) # byt5 first for HunyuanVideo1.5 else: txt = torch.cat((txt, txt_byt5), dim=1) txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype) txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1) if clip_fea is not None: txt_vision_states = self.vision_in(clip_fea) if self.cond_type_embedding is not None: cond_emb = self.cond_type_embedding(2 * torch.ones_like(txt_vision_states[:, :, 0], dtype=torch.long, device=txt_vision_states.device)) txt_vision_states = txt_vision_states + cond_emb txt = torch.cat((txt_vision_states.to(txt.dtype), txt), dim=1) extra_txt_ids = torch.zeros((txt_ids.shape[0], txt_vision_states.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype) txt_ids = torch.cat((txt_ids, extra_txt_ids), dim=1) ids = torch.cat((txt_ids, img_ids), dim=1) pe = self.pe_embedder(ids) img_len = img.shape[1] if txt_mask is not None: attn_mask_len = img_len + txt.shape[1] attn_mask = torch.zeros((1, 1, attn_mask_len), dtype=img.dtype, device=img.device) attn_mask[:, 0, :txt.shape[1]] = txt_mask else: attn_mask = None blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.double_blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.double_blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] else: img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") if i < len(control_i): add = control_i[i] if add is not None: img += add img = torch.cat((txt, img), 1) transformer_options["total_blocks"] = len(self.single_blocks) transformer_options["block_type"] = "single" transformer_options["img_slice"] = [txt.shape[1], img.shape[1]] for i, block in enumerate(self.single_blocks): transformer_options["block_index"] = i if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"]) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap}) img = out["img"] else: img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") if i < len(control_o): add = control_o[i] if add is not None: img[:, txt.shape[1]: img_len + txt.shape[1]] += add img = img[:, txt.shape[1]: img_len + txt.shape[1]] if ref_latent is not None: img = img[:, ref_latent.shape[1]:] img = self.final_layer(img, vec, modulation_dims=modulation_dims) # (N, T, patch_size ** 2 * out_channels) shape = initial_shape[-len(self.patch_size):] for i in range(len(shape)): shape[i] = shape[i] // self.patch_size[i] img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size) if img.ndim == 8: img = img.permute(0, 4, 1, 5, 2, 6, 3, 7) img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4]) else: img = img.permute(0, 3, 1, 4, 2, 5) img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3]) return img def img_ids(self, x): bs, c, t, h, w = x.shape patch_size = self.patch_size t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype) img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) return repeat(img_ids, "t h w c -> b (t h w) c", b=bs) def img_ids_2d(self, x): bs, c, h, w = x.shape patch_size = self.patch_size h_len = ((h + (patch_size[0] // 2)) // patch_size[0]) w_len = ((w + (patch_size[1] // 2)) // patch_size[1]) img_ids = torch.zeros((h_len, w_len, 2), device=x.device, dtype=x.dtype) img_ids[:, :, 0] = img_ids[:, :, 0] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) return repeat(img_ids, "h w c -> b (h w) c", b=bs) def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, y, txt_byt5, clip_fea, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs) def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): bs = x.shape[0] if len(self.patch_size) == 3: img_ids = self.img_ids(x) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) else: img_ids = self.img_ids_2d(x) txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype) out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, clip_fea, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options) return out ================================================ FILE: comfy/ldm/hunyuan_video/upsampler.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm import comfy.model_management import comfy.model_patcher class SRResidualCausalBlock3D(nn.Module): def __init__(self, channels: int): super().__init__() self.block = nn.Sequential( VideoConv3d(channels, channels, kernel_size=3), nn.SiLU(inplace=True), VideoConv3d(channels, channels, kernel_size=3), nn.SiLU(inplace=True), VideoConv3d(channels, channels, kernel_size=3), ) def forward(self, x: torch.Tensor) -> torch.Tensor: return x + self.block(x) class SRModel3DV2(nn.Module): def __init__( self, in_channels: int, out_channels: int, hidden_channels: int = 64, num_blocks: int = 6, global_residual: bool = False, ): super().__init__() self.in_conv = VideoConv3d(in_channels, hidden_channels, kernel_size=3) self.blocks = nn.ModuleList([SRResidualCausalBlock3D(hidden_channels) for _ in range(num_blocks)]) self.out_conv = VideoConv3d(hidden_channels, out_channels, kernel_size=3) self.global_residual = bool(global_residual) def forward(self, x: torch.Tensor) -> torch.Tensor: residual = x y = self.in_conv(x) for blk in self.blocks: y = blk(y) y = self.out_conv(y) if self.global_residual and (y.shape == residual.shape): y = y + residual return y class Upsampler(nn.Module): def __init__( self, z_channels: int, out_channels: int, block_out_channels: tuple[int, ...], num_res_blocks: int = 2, ): super().__init__() self.num_res_blocks = num_res_blocks self.block_out_channels = block_out_channels self.z_channels = z_channels ch = block_out_channels[0] self.conv_in = VideoConv3d(z_channels, ch, kernel_size=3) self.up = nn.ModuleList() for i, tgt in enumerate(block_out_channels): stage = nn.Module() stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, out_channels=tgt, temb_channels=0, conv_shortcut=False, conv_op=VideoConv3d, norm_op=RMS_norm) for j in range(num_res_blocks + 1)]) ch = tgt self.up.append(stage) self.norm_out = RMS_norm(ch) self.conv_out = VideoConv3d(ch, out_channels, kernel_size=3) def forward(self, z): """ Args: z: (B, C, T, H, W) target_shape: (H, W) """ # z to block_in repeats = self.block_out_channels[0] // (self.z_channels) x = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1) # upsampling for stage in self.up: for blk in stage.block: x = blk(x) out = self.conv_out(F.silu(self.norm_out(x))) return out UPSAMPLERS = { "720p": SRModel3DV2, "1080p": Upsampler, } class HunyuanVideo15SRModel(): def __init__(self, model_type, config): self.load_device = comfy.model_management.vae_device() offload_device = comfy.model_management.vae_offload_device() self.dtype = comfy.model_management.vae_dtype(self.load_device) self.model_class = UPSAMPLERS.get(model_type) self.model = self.model_class(**config).eval() self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) def load_sd(self, sd): return self.model.load_state_dict(sd, strict=True, assign=self.patcher.is_dynamic()) def get_sd(self): return self.model.state_dict() def resample_latent(self, latent): comfy.model_management.load_model_gpu(self.patcher) return self.model(latent.to(self.load_device)) ================================================ FILE: comfy/ldm/hunyuan_video/vae.py ================================================ import torch.nn as nn import torch.nn.functional as F from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock import comfy.ops ops = comfy.ops.disable_weight_init class PixelShuffle2D(nn.Module): def __init__(self, in_dim, out_dim, op=ops.Conv2d): super().__init__() self.conv = op(in_dim, out_dim >> 2, 3, 1, 1) self.ratio = (in_dim << 2) // out_dim def forward(self, x): b, c, h, w = x.shape h2, w2 = h >> 1, w >> 1 y = self.conv(x).view(b, -1, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, -1, h2, w2) r = x.view(b, c, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, c << 2, h2, w2) return y + r.view(b, y.shape[1], self.ratio, h2, w2).mean(2) class PixelUnshuffle2D(nn.Module): def __init__(self, in_dim, out_dim, op=ops.Conv2d): super().__init__() self.conv = op(in_dim, out_dim << 2, 3, 1, 1) self.scale = (out_dim << 2) // in_dim def forward(self, x): b, c, h, w = x.shape h2, w2 = h << 1, w << 1 y = self.conv(x).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2) r = x.repeat_interleave(self.scale, 1).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2) return y + r class Encoder(nn.Module): def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks, ffactor_spatial, downsample_match_channel=True, **_): super().__init__() self.z_channels = z_channels self.block_out_channels = block_out_channels self.num_res_blocks = num_res_blocks self.conv_in = ops.Conv2d(in_channels, block_out_channels[0], 3, 1, 1) self.down = nn.ModuleList() ch = block_out_channels[0] depth = (ffactor_spatial >> 1).bit_length() for i, tgt in enumerate(block_out_channels): stage = nn.Module() stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, out_channels=tgt, temb_channels=0, conv_op=ops.Conv2d) for j in range(num_res_blocks)]) ch = tgt if i < depth: nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch stage.downsample = PixelShuffle2D(ch, nxt, ops.Conv2d) ch = nxt self.down.append(stage) self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d) self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) self.norm_out = ops.GroupNorm(32, ch, 1e-6, True) self.conv_out = ops.Conv2d(ch, z_channels << 1, 3, 1, 1) def forward(self, x): x = self.conv_in(x) for stage in self.down: for blk in stage.block: x = blk(x) if hasattr(stage, 'downsample'): x = stage.downsample(x) x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) b, c, h, w = x.shape grp = c // (self.z_channels << 1) skip = x.view(b, c // grp, grp, h, w).mean(2) return self.conv_out(F.silu(self.norm_out(x))) + skip class Decoder(nn.Module): def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks, ffactor_spatial, upsample_match_channel=True, **_): super().__init__() block_out_channels = block_out_channels[::-1] self.z_channels = z_channels self.block_out_channels = block_out_channels self.num_res_blocks = num_res_blocks ch = block_out_channels[0] self.conv_in = ops.Conv2d(z_channels, ch, 3, 1, 1) self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d) self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) self.up = nn.ModuleList() depth = (ffactor_spatial >> 1).bit_length() for i, tgt in enumerate(block_out_channels): stage = nn.Module() stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, out_channels=tgt, temb_channels=0, conv_op=ops.Conv2d) for j in range(num_res_blocks + 1)]) ch = tgt if i < depth: nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch stage.upsample = PixelUnshuffle2D(ch, nxt, ops.Conv2d) ch = nxt self.up.append(stage) self.norm_out = ops.GroupNorm(32, ch, 1e-6, True) self.conv_out = ops.Conv2d(ch, out_channels, 3, 1, 1) def forward(self, z): x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1) x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) for stage in self.up: for blk in stage.block: x = blk(x) if hasattr(stage, 'upsample'): x = stage.upsample(x) return self.conv_out(F.silu(self.norm_out(x))) ================================================ FILE: comfy/ldm/hunyuan_video/vae_refiner.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, CarriedConv3d, Normalize, conv_carry_causal_3d, torch_cat_if_needed import comfy.ops import comfy.ldm.models.autoencoder import comfy.model_management ops = comfy.ops.disable_weight_init class RMS_norm(nn.Module): def __init__(self, dim): super().__init__() shape = (dim, 1, 1, 1) self.scale = dim**0.5 self.gamma = nn.Parameter(torch.empty(shape)) def forward(self, x): return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) class DnSmpl(nn.Module): def __init__(self, ic, oc, tds, refiner_vae, op): super().__init__() fct = 2 * 2 * 2 if tds else 1 * 2 * 2 assert oc % fct == 0 self.conv = op(ic, oc // fct, kernel_size=3, stride=1, padding=1) self.refiner_vae = refiner_vae self.tds = tds self.gs = fct * ic // oc def forward(self, x, conv_carry_in=None, conv_carry_out=None): r1 = 2 if self.tds else 1 h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out) if self.tds and self.refiner_vae and conv_carry_in is None: hf = h[:, :, :1, :, :] b, c, f, ht, wd = hf.shape hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2) hf = hf.permute(0, 4, 6, 1, 2, 3, 5) hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2) hf = torch.cat([hf, hf], dim=1) h = h[:, :, 1:, :, :] xf = x[:, :, :1, :, :] b, ci, f, ht, wd = xf.shape xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2) xf = xf.permute(0, 4, 6, 1, 2, 3, 5) xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2) B, C, T, H, W = xf.shape xf = xf.view(B, hf.shape[1], self.gs // 2, T, H, W).mean(dim=2) x = x[:, :, 1:, :, :] if h.shape[2] == 0: return hf + xf b, c, frms, ht, wd = h.shape nf = frms // r1 h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) h = h.permute(0, 3, 5, 7, 1, 2, 4, 6) h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) b, ci, frms, ht, wd = x.shape nf = frms // r1 x = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) x = x.permute(0, 3, 5, 7, 1, 2, 4, 6) x = x.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) B, C, T, H, W = x.shape x = x.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) if self.tds and self.refiner_vae and conv_carry_in is None: h = torch.cat([hf, h], dim=2) x = torch.cat([xf, x], dim=2) return h + x class UpSmpl(nn.Module): def __init__(self, ic, oc, tus, refiner_vae, op): super().__init__() fct = 2 * 2 * 2 if tus else 1 * 2 * 2 self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1) self.refiner_vae = refiner_vae self.tus = tus self.rp = fct * oc // ic def forward(self, x, conv_carry_in=None, conv_carry_out=None): r1 = 2 if self.tus else 1 h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out) if self.tus and self.refiner_vae and conv_carry_in is None: hf = h[:, :, :1, :, :] b, c, f, ht, wd = hf.shape nc = c // (2 * 2) hf = hf.reshape(b, 2, 2, nc, f, ht, wd) hf = hf.permute(0, 3, 4, 5, 1, 6, 2) hf = hf.reshape(b, nc, f, ht * 2, wd * 2) hf = hf[:, : hf.shape[1] // 2] h = h[:, :, 1:, :, :] xf = x[:, :, :1, :, :] b, ci, f, ht, wd = xf.shape xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1) b, c, f, ht, wd = xf.shape nc = c // (2 * 2) xf = xf.reshape(b, 2, 2, nc, f, ht, wd) xf = xf.permute(0, 3, 4, 5, 1, 6, 2) xf = xf.reshape(b, nc, f, ht * 2, wd * 2) x = x[:, :, 1:, :, :] b, c, frms, ht, wd = h.shape nc = c // (r1 * 2 * 2) h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd) h = h.permute(0, 4, 5, 1, 6, 2, 7, 3) h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2) x = x.repeat_interleave(repeats=self.rp, dim=1) b, c, frms, ht, wd = x.shape nc = c // (r1 * 2 * 2) x = x.reshape(b, r1, 2, 2, nc, frms, ht, wd) x = x.permute(0, 4, 5, 1, 6, 2, 7, 3) x = x.reshape(b, nc, frms * r1, ht * 2, wd * 2) if self.tus and self.refiner_vae and conv_carry_in is None: h = torch.cat([hf, h], dim=2) x = torch.cat([xf, x], dim=2) return h + x class Encoder(nn.Module): def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks, ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_): super().__init__() self.z_channels = z_channels self.block_out_channels = block_out_channels self.num_res_blocks = num_res_blocks self.ffactor_temporal = ffactor_temporal self.refiner_vae = refiner_vae if self.refiner_vae: conv_op = CarriedConv3d norm_op = RMS_norm else: conv_op = ops.Conv3d norm_op = Normalize self.conv_in = conv_op(in_channels, block_out_channels[0], 3, 1, 1) self.down = nn.ModuleList() ch = block_out_channels[0] depth = (ffactor_spatial >> 1).bit_length() depth_temporal = ((ffactor_spatial // self.ffactor_temporal) >> 1).bit_length() for i, tgt in enumerate(block_out_channels): stage = nn.Module() stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, out_channels=tgt, temb_channels=0, conv_op=conv_op, norm_op=norm_op) for j in range(num_res_blocks)]) ch = tgt if i < depth: nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal, refiner_vae=self.refiner_vae, op=conv_op) ch = nxt self.down.append(stage) self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op) self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.norm_out = norm_op(ch) self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1) self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer() def forward(self, x): if not self.refiner_vae and x.shape[2] == 1: x = x.expand(-1, -1, self.ffactor_temporal, -1, -1) if self.refiner_vae: xl = [x[:, :, :1, :, :]] if x.shape[2] > self.ffactor_temporal: xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // self.ffactor_temporal) * self.ffactor_temporal, :, :], self.ffactor_temporal * 2, dim=2) x = xl else: x = [x] out = [] conv_carry_in = None for i, x1 in enumerate(x): conv_carry_out = [] if i == len(x) - 1: conv_carry_out = None x1 = [ x1 ] x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out) for stage in self.down: for blk in stage.block: x1 = blk(x1, None, conv_carry_in, conv_carry_out) if hasattr(stage, 'downsample'): x1 = stage.downsample(x1, conv_carry_in, conv_carry_out) out.append(x1) conv_carry_in = conv_carry_out out = torch_cat_if_needed(out, dim=2) x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out))) del out b, c, t, h, w = x.shape grp = c // (self.z_channels << 1) skip = x.view(b, c // grp, grp, t, h, w).mean(2) out = conv_carry_causal_3d([F.silu(self.norm_out(x))], self.conv_out) + skip if self.refiner_vae: out = self.regul(out)[0] return out class Decoder(nn.Module): def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks, ffactor_spatial, ffactor_temporal, upsample_match_channel=True, refiner_vae=True, **_): super().__init__() block_out_channels = block_out_channels[::-1] self.z_channels = z_channels self.block_out_channels = block_out_channels self.num_res_blocks = num_res_blocks self.refiner_vae = refiner_vae if self.refiner_vae: conv_op = CarriedConv3d norm_op = RMS_norm else: conv_op = ops.Conv3d norm_op = Normalize ch = block_out_channels[0] self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1) self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op) self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.up = nn.ModuleList() depth = (ffactor_spatial >> 1).bit_length() depth_temporal = (ffactor_temporal >> 1).bit_length() for i, tgt in enumerate(block_out_channels): stage = nn.Module() stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, out_channels=tgt, temb_channels=0, conv_op=conv_op, norm_op=norm_op) for j in range(num_res_blocks + 1)]) ch = tgt if i < depth: nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal, refiner_vae=self.refiner_vae, op=conv_op) ch = nxt self.up.append(stage) self.norm_out = norm_op(ch) self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1) def forward(self, z): x = conv_carry_causal_3d([z], self.conv_in) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1) x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) if self.refiner_vae: x = torch.split(x, 2, dim=2) else: x = [ x ] out = [] conv_carry_in = None for i, x1 in enumerate(x): conv_carry_out = [] if i == len(x) - 1: conv_carry_out = None for stage in self.up: for blk in stage.block: x1 = blk(x1, None, conv_carry_in, conv_carry_out) if hasattr(stage, 'upsample'): x1 = stage.upsample(x1, conv_carry_in, conv_carry_out) x1 = [ F.silu(self.norm_out(x1)) ] x1 = conv_carry_causal_3d(x1, self.conv_out, conv_carry_in, conv_carry_out) out.append(x1) conv_carry_in = conv_carry_out del x out = torch_cat_if_needed(out, dim=2) if not self.refiner_vae: if z.shape[-3] == 1: out = out[:, :, -1:] return out ================================================ FILE: comfy/ldm/hydit/attn_layers.py ================================================ import torch import torch.nn as nn from typing import Tuple, Union, Optional from comfy.ldm.modules.attention import optimized_attention def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False): """ Reshape frequency tensor for broadcasting it with another tensor. This function reshapes the frequency tensor to have the same shape as the target tensor 'x' for the purpose of broadcasting the frequency tensor during element-wise operations. Args: freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. x (torch.Tensor): Target tensor for broadcasting compatibility. head_first (bool): head dimension first (except batch dim) or not. Returns: torch.Tensor: Reshaped frequency tensor. Raises: AssertionError: If the frequency tensor doesn't match the expected shape. AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. """ ndim = x.ndim assert 0 <= 1 < ndim if isinstance(freqs_cis, tuple): # freqs_cis: (cos, sin) in real space if head_first: assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] else: assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) else: # freqs_cis: values in complex space if head_first: assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] else: assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis.view(*shape) def rotate_half(x): x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] return torch.stack([-x_imag, x_real], dim=-1).flatten(3) def apply_rotary_emb( xq: torch.Tensor, xk: Optional[torch.Tensor], freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], head_first: bool = False, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are returned as real tensors. Args: xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials. head_first (bool): head dimension first (except batch dim) or not. Returns: Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. """ xk_out = None if isinstance(freqs_cis, tuple): cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] xq_out = (xq * cos + rotate_half(xq) * sin) if xk is not None: xk_out = (xk * cos + rotate_half(xk) * sin) else: xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) if xk is not None: xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) return xq_out, xk_out class CrossAttention(nn.Module): """ Use QK Normalization. """ def __init__(self, qdim, kdim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0.0, proj_drop=0.0, attn_precision=None, device=None, dtype=None, operations=None, ): factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() self.attn_precision = attn_precision self.qdim = qdim self.kdim = kdim self.num_heads = num_heads assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" self.head_dim = self.qdim // num_heads assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" self.scale = self.head_dim ** -0.5 self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) # TODO: eps should be 1 / 65530 if using fp16 self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() self.attn_drop = nn.Dropout(attn_drop) self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, y, freqs_cis_img=None): """ Parameters ---------- x: torch.Tensor (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) y: torch.Tensor (batch, seqlen2, hidden_dim2) freqs_cis_img: torch.Tensor (batch, hidden_dim // 2), RoPE for image """ b, s1, c = x.shape # [b, s1, D] _, s2, c = y.shape # [b, s2, 1024] q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] k, v = kv.unbind(dim=2) # [b, s, h, d] q = self.q_norm(q) k = self.k_norm(k) # Apply RoPE if needed if freqs_cis_img is not None: qq, _ = apply_rotary_emb(q, None, freqs_cis_img) assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' q = qq q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2 v = v.transpose(-2, -3).contiguous() context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) out = self.out_proj(context) # context.reshape - B, L1, -1 out = self.proj_drop(out) out_tuple = (out,) return out_tuple class Attention(nn.Module): """ We rename some layer names to align with flash attention """ def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None): super().__init__() self.attn_precision = attn_precision self.dim = dim self.num_heads = num_heads assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' self.head_dim = self.dim // num_heads # This assertion is aligned with flash attention assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" self.scale = self.head_dim ** -0.5 # qkv --> Wqkv self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) # TODO: eps should be 1 / 65530 if using fp16 self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() self.attn_drop = nn.Dropout(attn_drop) self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, freqs_cis_img=None): B, N, C = x.shape qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d] q, k, v = qkv.unbind(0) # [b, h, s, d] q = self.q_norm(q) # [b, h, s, d] k = self.k_norm(k) # [b, h, s, d] # Apply RoPE if needed if freqs_cis_img is not None: qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True) assert qq.shape == q.shape and kk.shape == k.shape, \ f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' q, k = qq, kk x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) x = self.out_proj(x) x = self.proj_drop(x) out_tuple = (x,) return out_tuple ================================================ FILE: comfy/ldm/hydit/controlnet.py ================================================ import torch import torch.nn as nn from comfy.ldm.modules.diffusionmodules.mmdit import ( TimestepEmbedder, PatchEmbed, ) from .poolers import AttentionPool import comfy.latent_formats from .models import HunYuanDiTBlock, calc_rope class HunYuanControlNet(nn.Module): """ HunYuanDiT: Diffusion model with a Transformer backbone. Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. Parameters ---------- args: argparse.Namespace The arguments parsed by argparse. input_size: tuple The size of the input image. patch_size: int The size of the patch. in_channels: int The number of input channels. hidden_size: int The hidden size of the transformer backbone. depth: int The number of transformer blocks. num_heads: int The number of attention heads. mlp_ratio: float The ratio of the hidden size of the MLP in the transformer block. log_fn: callable The logging function. """ def __init__( self, input_size: tuple = 128, patch_size: int = 2, in_channels: int = 4, hidden_size: int = 1408, depth: int = 40, num_heads: int = 16, mlp_ratio: float = 4.3637, text_states_dim=1024, text_states_dim_t5=2048, text_len=77, text_len_t5=256, qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details. size_cond=False, use_style_cond=False, learn_sigma=True, norm="layer", log_fn: callable = print, attn_precision=None, dtype=None, device=None, operations=None, **kwargs, ): super().__init__() self.log_fn = log_fn self.depth = depth self.learn_sigma = learn_sigma self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma else in_channels self.patch_size = patch_size self.num_heads = num_heads self.hidden_size = hidden_size self.text_states_dim = text_states_dim self.text_states_dim_t5 = text_states_dim_t5 self.text_len = text_len self.text_len_t5 = text_len_t5 self.size_cond = size_cond self.use_style_cond = use_style_cond self.norm = norm self.dtype = dtype self.latent_format = comfy.latent_formats.SDXL self.mlp_t5 = nn.Sequential( nn.Linear( self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device, ), nn.SiLU(), nn.Linear( self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device, ), ) # learnable replace self.text_embedding_padding = nn.Parameter( torch.randn( self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device, ) ) # Attention pooling pooler_out_dim = 1024 self.pooler = AttentionPool( self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations, ) # Dimension of the extra input vectors self.extra_in_dim = pooler_out_dim if self.size_cond: # Image size and crop size conditions self.extra_in_dim += 6 * 256 if self.use_style_cond: # Here we use a default learned embedder layer for future extension. self.style_embedder = nn.Embedding( 1, hidden_size, dtype=dtype, device=device ) self.extra_in_dim += hidden_size # Text embedding for `add` self.x_embedder = PatchEmbed( input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations, ) self.t_embedder = TimestepEmbedder( hidden_size, dtype=dtype, device=device, operations=operations ) self.extra_embedder = nn.Sequential( operations.Linear( self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device ), nn.SiLU(), operations.Linear( hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device ), ) # HUnYuanDiT Blocks self.blocks = nn.ModuleList( [ HunYuanDiTBlock( hidden_size=hidden_size, c_emb_size=hidden_size, num_heads=num_heads, mlp_ratio=mlp_ratio, text_states_dim=self.text_states_dim, qk_norm=qk_norm, norm_type=self.norm, skip=False, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations, ) for _ in range(19) ] ) # Input zero linear for the first block self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) # Output zero linear for the every block self.after_proj_list = nn.ModuleList( [ operations.Linear( self.hidden_size, self.hidden_size, dtype=dtype, device=device ) for _ in range(len(self.blocks)) ] ) def forward( self, x, hint, timesteps, context,#encoder_hidden_states=None, text_embedding_mask=None, encoder_hidden_states_t5=None, text_embedding_mask_t5=None, image_meta_size=None, style=None, return_dict=False, **kwarg, ): """ Forward pass of the encoder. Parameters ---------- x: torch.Tensor (B, D, H, W) t: torch.Tensor (B) encoder_hidden_states: torch.Tensor CLIP text embedding, (B, L_clip, D) text_embedding_mask: torch.Tensor CLIP text embedding mask, (B, L_clip) encoder_hidden_states_t5: torch.Tensor T5 text embedding, (B, L_t5, D) text_embedding_mask_t5: torch.Tensor T5 text embedding mask, (B, L_t5) image_meta_size: torch.Tensor (B, 6) style: torch.Tensor (B) cos_cis_img: torch.Tensor sin_cis_img: torch.Tensor return_dict: bool Whether to return a dictionary. """ condition = hint if condition.shape[0] == 1: condition = torch.repeat_interleave(condition, x.shape[0], dim=0) text_states = context # 2,77,1024 text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 text_states_mask = text_embedding_mask.bool() # 2,77 text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 b_t5, l_t5, c_t5 = text_states_t5.shape text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) text_states[:, -self.text_len :] = torch.where( text_states_mask[:, -self.text_len :].unsqueeze(2), text_states[:, -self.text_len :], padding[: self.text_len], ) text_states_t5[:, -self.text_len_t5 :] = torch.where( text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2), text_states_t5[:, -self.text_len_t5 :], padding[self.text_len :], ) text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 # _, _, oh, ow = x.shape # th, tw = oh // self.patch_size, ow // self.patch_size # Get image RoPE embedding according to `reso`lution. freqs_cis_img = calc_rope( x, self.patch_size, self.hidden_size // self.num_heads ) # (cos_cis_img, sin_cis_img) # ========================= Build time and image embedding ========================= t = self.t_embedder(timesteps, dtype=self.dtype) x = self.x_embedder(x) # ========================= Concatenate all extra vectors ========================= # Build text tokens with pooling extra_vec = self.pooler(encoder_hidden_states_t5) # Build image meta size tokens if applicable # if image_meta_size is not None: # image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256] # if image_meta_size.dtype != self.dtype: # image_meta_size = image_meta_size.half() # image_meta_size = image_meta_size.view(-1, 6 * 256) # extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] # Build style tokens if style is not None: style_embedding = self.style_embedder(style) extra_vec = torch.cat([extra_vec, style_embedding], dim=1) # Concatenate all extra vectors c = t + self.extra_embedder(extra_vec) # [B, D] # ========================= Deal with Condition ========================= condition = self.x_embedder(condition) # ========================= Forward pass through HunYuanDiT blocks ========================= controls = [] x = x + self.before_proj(condition) # add condition for layer, block in enumerate(self.blocks): x = block(x, c, text_states, freqs_cis_img) controls.append(self.after_proj_list[layer](x)) # zero linear for output return {"output": controls} ================================================ FILE: comfy/ldm/hydit/models.py ================================================ import torch import torch.nn as nn import comfy.ops from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed from comfy.ldm.modules.diffusionmodules.util import timestep_embedding from torch.utils import checkpoint from .attn_layers import Attention, CrossAttention from .poolers import AttentionPool from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop def calc_rope(x, patch_size, head_size): th = (x.shape[2] + (patch_size // 2)) // patch_size tw = (x.shape[3] + (patch_size // 2)) // patch_size base_size = 512 // 8 // patch_size start, stop = get_fill_resize_and_crop((th, tw), base_size) sub_args = [start, stop, (th, tw)] # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads'] rope = get_2d_rotary_pos_embed(head_size, *sub_args) rope = (rope[0].to(x), rope[1].to(x)) return rope def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) class HunYuanDiTBlock(nn.Module): """ A HunYuanDiT block with `add` conditioning. """ def __init__(self, hidden_size, c_emb_size, num_heads, mlp_ratio=4.0, text_states_dim=1024, qk_norm=False, norm_type="layer", skip=False, attn_precision=None, dtype=None, device=None, operations=None, ): super().__init__() use_ele_affine = True if norm_type == "layer": norm_layer = operations.LayerNorm elif norm_type == "rms": norm_layer = operations.RMSNorm else: raise ValueError(f"Unknown norm_type: {norm_type}") # ========================= Self-Attention ========================= self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) # ========================= FFN ========================= self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) mlp_hidden_dim = int(hidden_size * mlp_ratio) approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations) # ========================= Add ========================= # Simply use add like SDXL. self.default_modulation = nn.Sequential( nn.SiLU(), operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device) ) # ========================= Cross-Attention ========================= self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) # ========================= Skip Connection ========================= if skip: self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device) else: self.skip_linear = None self.gradient_checkpointing = False def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): # Long Skip Connection if self.skip_linear is not None: cat = torch.cat([x, skip], dim=-1) if cat.dtype != x.dtype: cat = cat.to(x.dtype) cat = self.skip_norm(cat) x = self.skip_linear(cat) # Self-Attention shift_msa = self.default_modulation(c).unsqueeze(dim=1) attn_inputs = ( self.norm1(x) + shift_msa, freq_cis_img, ) x = x + self.attn1(*attn_inputs)[0] # Cross-Attention cross_inputs = ( self.norm3(x), text_states, freq_cis_img ) x = x + self.attn2(*cross_inputs)[0] # FFN Layer mlp_inputs = self.norm2(x) x = x + self.mlp(mlp_inputs) return x def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): if self.gradient_checkpointing and self.training: return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip) return self._forward(x, c, text_states, freq_cis_img, skip) class FinalLayer(nn.Module): """ The final layer of HunYuanDiT. """ def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x, c): shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class HunYuanDiT(nn.Module): """ HunYuanDiT: Diffusion model with a Transformer backbone. Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. Parameters ---------- args: argparse.Namespace The arguments parsed by argparse. input_size: tuple The size of the input image. patch_size: int The size of the patch. in_channels: int The number of input channels. hidden_size: int The hidden size of the transformer backbone. depth: int The number of transformer blocks. num_heads: int The number of attention heads. mlp_ratio: float The ratio of the hidden size of the MLP in the transformer block. log_fn: callable The logging function. """ #@register_to_config def __init__(self, input_size: tuple = 32, patch_size: int = 2, in_channels: int = 4, hidden_size: int = 1152, depth: int = 28, num_heads: int = 16, mlp_ratio: float = 4.0, text_states_dim = 1024, text_states_dim_t5 = 2048, text_len = 77, text_len_t5 = 256, qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details. size_cond = False, use_style_cond = False, learn_sigma = True, norm = "layer", log_fn: callable = print, attn_precision=None, dtype=None, device=None, operations=None, **kwargs, ): super().__init__() self.log_fn = log_fn self.depth = depth self.learn_sigma = learn_sigma self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma else in_channels self.patch_size = patch_size self.num_heads = num_heads self.hidden_size = hidden_size self.text_states_dim = text_states_dim self.text_states_dim_t5 = text_states_dim_t5 self.text_len = text_len self.text_len_t5 = text_len_t5 self.size_cond = size_cond self.use_style_cond = use_style_cond self.norm = norm self.dtype = dtype #import pdb #pdb.set_trace() self.mlp_t5 = nn.Sequential( operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device), ) # learnable replace self.text_embedding_padding = nn.Parameter( torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device)) # Attention pooling pooler_out_dim = 1024 self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations) # Dimension of the extra input vectors self.extra_in_dim = pooler_out_dim if self.size_cond: # Image size and crop size conditions self.extra_in_dim += 6 * 256 if self.use_style_cond: # Here we use a default learned embedder layer for future extension. self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device) self.extra_in_dim += hidden_size # Text embedding for `add` self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations) self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations) self.extra_embedder = nn.Sequential( operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device), ) # HUnYuanDiT Blocks self.blocks = nn.ModuleList([ HunYuanDiTBlock(hidden_size=hidden_size, c_emb_size=hidden_size, num_heads=num_heads, mlp_ratio=mlp_ratio, text_states_dim=self.text_states_dim, qk_norm=qk_norm, norm_type=self.norm, skip=layer > depth // 2, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations, ) for layer in range(depth) ]) self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) self.unpatchify_channels = self.out_channels def forward(self, x, t, context,#encoder_hidden_states=None, text_embedding_mask=None, encoder_hidden_states_t5=None, text_embedding_mask_t5=None, image_meta_size=None, style=None, return_dict=False, control=None, transformer_options={}, ): """ Forward pass of the encoder. Parameters ---------- x: torch.Tensor (B, D, H, W) t: torch.Tensor (B) encoder_hidden_states: torch.Tensor CLIP text embedding, (B, L_clip, D) text_embedding_mask: torch.Tensor CLIP text embedding mask, (B, L_clip) encoder_hidden_states_t5: torch.Tensor T5 text embedding, (B, L_t5, D) text_embedding_mask_t5: torch.Tensor T5 text embedding mask, (B, L_t5) image_meta_size: torch.Tensor (B, 6) style: torch.Tensor (B) cos_cis_img: torch.Tensor sin_cis_img: torch.Tensor return_dict: bool Whether to return a dictionary. """ patches_replace = transformer_options.get("patches_replace", {}) encoder_hidden_states = context text_states = encoder_hidden_states # 2,77,1024 text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 text_states_mask = text_embedding_mask.bool() # 2,77 text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 b_t5, l_t5, c_t5 = text_states_t5.shape text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len]) text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:]) text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1) _, _, oh, ow = x.shape th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size # Get image RoPE embedding according to `reso`lution. freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img) # ========================= Build time and image embedding ========================= t = self.t_embedder(t, dtype=x.dtype) x = self.x_embedder(x) # ========================= Concatenate all extra vectors ========================= # Build text tokens with pooling extra_vec = self.pooler(encoder_hidden_states_t5) # Build image meta size tokens if applicable if self.size_cond: image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype) # [B * 6, 256] image_meta_size = image_meta_size.view(-1, 6 * 256) extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] # Build style tokens if self.use_style_cond: if style is None: style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int) style_embedding = self.style_embedder(style, out_dtype=x.dtype) extra_vec = torch.cat([extra_vec, style_embedding], dim=1) # Concatenate all extra vectors c = t + self.extra_embedder(extra_vec) # [B, D] blocks_replace = patches_replace.get("dit", {}) controls = None if control: controls = control.get("output", None) # ========================= Forward pass through HunYuanDiT blocks ========================= skips = [] for layer, block in enumerate(self.blocks): if layer > self.depth // 2: if controls is not None: skip = skips.pop() + controls.pop().to(dtype=x.dtype) else: skip = skips.pop() else: skip = None if ("double_block", layer) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], args["vec"], args["txt"], args["pe"], args["skip"]) return out out = blocks_replace[("double_block", layer)]({"img": x, "txt": text_states, "vec": c, "pe": freqs_cis_img, "skip": skip}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D) if layer < (self.depth // 2 - 1): skips.append(x) if controls is not None and len(controls) != 0: raise ValueError("The number of controls is not equal to the number of skip connections.") # ========================= Final layer ========================= x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels) x = self.unpatchify(x, th, tw) # (N, out_channels, H, W) if return_dict: return {'x': x} if self.learn_sigma: return x[:,:self.out_channels // 2,:oh,:ow] return x[:,:,:oh,:ow] def unpatchify(self, x, h, w): """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ c = self.unpatchify_channels p = self.x_embedder.patch_size[0] # h = w = int(x.shape[1] ** 0.5) assert h * w == x.shape[1] x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) x = torch.einsum('nhwpqc->nchpwq', x) imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) return imgs ================================================ FILE: comfy/ldm/hydit/poolers.py ================================================ import torch import torch.nn as nn from comfy.ldm.modules.attention import optimized_attention import comfy.ops class AttentionPool(nn.Module): def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None): super().__init__() self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device)) self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device) self.num_heads = num_heads self.embed_dim = embed_dim def forward(self, x): x = x[:,:self.positional_embedding.shape[0] - 1] x = x.permute(1, 0, 2) # NLC -> LNC x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC q = self.q_proj(x[:1]) k = self.k_proj(x) v = self.v_proj(x) batch_size = q.shape[1] head_dim = self.embed_dim // self.num_heads q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1) attn_output = self.c_proj(attn_output) return attn_output.squeeze(0) ================================================ FILE: comfy/ldm/hydit/posemb_layers.py ================================================ import torch import numpy as np from typing import Union def _to_tuple(x): if isinstance(x, int): return x, x else: return x def get_fill_resize_and_crop(src, tgt): th, tw = _to_tuple(tgt) h, w = _to_tuple(src) tr = th / tw # base resolution r = h / w # target resolution # resize if r > tr: resize_height = th resize_width = int(round(th / h * w)) else: resize_width = tw resize_height = int(round(tw / w * h)) # resize the target resolution down based on the base resolution crop_top = int(round((th - resize_height) / 2.0)) crop_left = int(round((tw - resize_width) / 2.0)) return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) def get_meshgrid(start, *args): if len(args) == 0: # start is grid_size num = _to_tuple(start) start = (0, 0) stop = num elif len(args) == 1: # start is start, args[0] is stop, step is 1 start = _to_tuple(start) stop = _to_tuple(args[0]) num = (stop[0] - start[0], stop[1] - start[1]) elif len(args) == 2: # start is start, args[0] is stop, args[1] is num start = _to_tuple(start) stop = _to_tuple(args[0]) num = _to_tuple(args[1]) else: raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) # [2, W, H] return grid ################################################################################# # Sine/Cosine Positional Embedding Functions # ################################################################################# # https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ grid = get_meshgrid(start, *args) # [2, H, w] # grid_h = np.arange(grid_size, dtype=np.float32) # grid_w = np.arange(grid_size, dtype=np.float32) # grid = np.meshgrid(grid_w, grid_h) # here w goes first # grid = np.stack(grid, axis=0) # [2, W, H] grid = grid.reshape([2, 1, *grid.shape[1:]]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token and extra_tokens > 0: pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (W,H) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float64) omega /= embed_dim / 2. omega = 1. / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb ################################################################################# # Rotary Positional Embedding Functions # ################################################################################# # https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443 def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True): """ This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure. Parameters ---------- embed_dim: int embedding dimension size start: int or tuple of int If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. use_real: bool If True, return real part and imaginary part separately. Otherwise, return complex numbers. Returns ------- pos_embed: torch.Tensor [HW, D/2] """ grid = get_meshgrid(start, *args) # [2, H, w] grid = grid.reshape([2, 1, *grid.shape[1:]]) # Returns a sampling matrix with the same resolution as the target resolution pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) return pos_embed def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): assert embed_dim % 4 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) if use_real: cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) return cos, sin else: emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) return emb def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False): """ Precompute the frequency tensor for complex exponentials (cis) with given dimensions. This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64 data type. Args: dim (int): Dimension of the frequency tensor. pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. use_real (bool, optional): If True, return real part and imaginary part separately. Otherwise, return complex numbers. Returns: torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2] """ if isinstance(pos, int): pos = np.arange(pos) freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] if use_real: freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] return freqs_cos, freqs_sin else: freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] return freqs_cis def calc_sizes(rope_img, patch_size, th, tw): if rope_img == 'extend': # Expansion mode sub_args = [(th, tw)] elif rope_img.startswith('base'): # Based on the specified dimensions, other dimensions are obtained through interpolation. base_size = int(rope_img[4:]) // 8 // patch_size start, stop = get_fill_resize_and_crop((th, tw), base_size) sub_args = [start, stop, (th, tw)] else: raise ValueError(f"Unknown rope_img: {rope_img}") return sub_args def init_image_posemb(rope_img, resolutions, patch_size, hidden_size, num_heads, log_fn, rope_real=True, ): freqs_cis_img = {} for reso in resolutions: th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size sub_args = calc_sizes(rope_img, patch_size, th, tw) freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real) log_fn(f" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) " f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}") return freqs_cis_img ================================================ FILE: comfy/ldm/kandinsky5/model.py ================================================ import torch from torch import nn import math import comfy.ldm.common_dit from comfy.ldm.modules.attention import optimized_attention from comfy.ldm.flux.math import apply_rope1 from comfy.ldm.flux.layers import EmbedND def attention(q, k, v, heads, transformer_options={}): return optimized_attention( q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), heads=heads, skip_reshape=True, transformer_options=transformer_options ) def apply_scale_shift_norm(norm, x, scale, shift): return torch.addcmul(shift, norm(x), scale + 1.0) def apply_gate_sum(x, out, gate): return torch.addcmul(x, gate, out) def get_shift_scale_gate(params): shift, scale, gate = torch.chunk(params, 3, dim=-1) return tuple(x.unsqueeze(1) for x in (shift, scale, gate)) def get_freqs(dim, max_period=10000.0): return torch.exp(-math.log(max_period) * torch.arange(start=0, end=dim, dtype=torch.float32) / dim) class TimeEmbeddings(nn.Module): def __init__(self, model_dim, time_dim, max_period=10000.0, operation_settings=None): super().__init__() assert model_dim % 2 == 0 self.model_dim = model_dim self.max_period = max_period self.register_buffer("freqs", get_freqs(model_dim // 2, max_period), persistent=False) operations = operation_settings.get("operations") self.in_layer = operations.Linear(model_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.activation = nn.SiLU() self.out_layer = operations.Linear(time_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, timestep, dtype): args = torch.outer(timestep, self.freqs.to(device=timestep.device)) time_embed = torch.cat([torch.cos(args), torch.sin(args)], dim=-1).to(dtype) time_embed = self.out_layer(self.activation(self.in_layer(time_embed))) return time_embed class TextEmbeddings(nn.Module): def __init__(self, text_dim, model_dim, operation_settings=None): super().__init__() operations = operation_settings.get("operations") self.in_layer = operations.Linear(text_dim, model_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.norm = operations.LayerNorm(model_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, text_embed): text_embed = self.in_layer(text_embed) return self.norm(text_embed).type_as(text_embed) class VisualEmbeddings(nn.Module): def __init__(self, visual_dim, model_dim, patch_size, operation_settings=None): super().__init__() self.patch_size = patch_size operations = operation_settings.get("operations") self.in_layer = operations.Linear(visual_dim, model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, x): x = x.movedim(1, -1) # B C T H W -> B T H W C B, T, H, W, dim = x.shape pt, ph, pw = self.patch_size x = x.view( B, T // pt, pt, H // ph, ph, W // pw, pw, dim, ).permute(0, 1, 3, 5, 2, 4, 6, 7).flatten(4, 7) return self.in_layer(x) class Modulation(nn.Module): def __init__(self, time_dim, model_dim, num_params, operation_settings=None): super().__init__() self.activation = nn.SiLU() self.out_layer = operation_settings.get("operations").Linear(time_dim, num_params * model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, x): return self.out_layer(self.activation(x)) class SelfAttention(nn.Module): def __init__(self, num_channels, head_dim, operation_settings=None): super().__init__() assert num_channels % head_dim == 0 self.num_heads = num_channels // head_dim self.head_dim = head_dim operations = operation_settings.get("operations") self.to_query = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.to_key = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.to_value = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.query_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.key_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.out_layer = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.num_chunks = 2 def _compute_qk(self, x, freqs, proj_fn, norm_fn): result = proj_fn(x).view(*x.shape[:-1], self.num_heads, -1) return apply_rope1(norm_fn(result), freqs) def _forward(self, x, freqs, transformer_options={}): q = self._compute_qk(x, freqs, self.to_query, self.query_norm) k = self._compute_qk(x, freqs, self.to_key, self.key_norm) v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1) out = attention(q, k, v, self.num_heads, transformer_options=transformer_options) return self.out_layer(out) def _forward_chunked(self, x, freqs, transformer_options={}): def process_chunks(proj_fn, norm_fn): x_chunks = torch.chunk(x, self.num_chunks, dim=1) freqs_chunks = torch.chunk(freqs, self.num_chunks, dim=1) chunks = [] for x_chunk, freqs_chunk in zip(x_chunks, freqs_chunks): chunks.append(self._compute_qk(x_chunk, freqs_chunk, proj_fn, norm_fn)) return torch.cat(chunks, dim=1) q = process_chunks(self.to_query, self.query_norm) k = process_chunks(self.to_key, self.key_norm) v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1) out = attention(q, k, v, self.num_heads, transformer_options=transformer_options) return self.out_layer(out) def forward(self, x, freqs, transformer_options={}): if x.shape[1] > 8192: return self._forward_chunked(x, freqs, transformer_options=transformer_options) else: return self._forward(x, freqs, transformer_options=transformer_options) class CrossAttention(SelfAttention): def get_qkv(self, x, context): q = self.to_query(x).view(*x.shape[:-1], self.num_heads, -1) k = self.to_key(context).view(*context.shape[:-1], self.num_heads, -1) v = self.to_value(context).view(*context.shape[:-1], self.num_heads, -1) return q, k, v def forward(self, x, context, transformer_options={}): q, k, v = self.get_qkv(x, context) out = attention(self.query_norm(q), self.key_norm(k), v, self.num_heads, transformer_options=transformer_options) return self.out_layer(out) class FeedForward(nn.Module): def __init__(self, dim, ff_dim, operation_settings=None): super().__init__() operations = operation_settings.get("operations") self.in_layer = operations.Linear(dim, ff_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.activation = nn.GELU() self.out_layer = operations.Linear(ff_dim, dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.num_chunks = 4 def _forward(self, x): return self.out_layer(self.activation(self.in_layer(x))) def _forward_chunked(self, x): chunks = torch.chunk(x, self.num_chunks, dim=1) output_chunks = [] for chunk in chunks: output_chunks.append(self._forward(chunk)) return torch.cat(output_chunks, dim=1) def forward(self, x): if x.shape[1] > 8192: return self._forward_chunked(x) else: return self._forward(x) class OutLayer(nn.Module): def __init__(self, model_dim, time_dim, visual_dim, patch_size, operation_settings=None): super().__init__() self.patch_size = patch_size self.modulation = Modulation(time_dim, model_dim, 2, operation_settings=operation_settings) operations = operation_settings.get("operations") self.norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.out_layer = operations.Linear(model_dim, math.prod(patch_size) * visual_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, visual_embed, time_embed): B, T, H, W, _ = visual_embed.shape shift, scale = torch.chunk(self.modulation(time_embed), 2, dim=-1) scale = scale[:, None, None, None, :] shift = shift[:, None, None, None, :] visual_embed = apply_scale_shift_norm(self.norm, visual_embed, scale, shift) x = self.out_layer(visual_embed) out_dim = x.shape[-1] // (self.patch_size[0] * self.patch_size[1] * self.patch_size[2]) x = x.view( B, T, H, W, out_dim, self.patch_size[0], self.patch_size[1], self.patch_size[2] ) return x.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(2, 3).flatten(3, 4).flatten(4, 5) class TransformerEncoderBlock(nn.Module): def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None): super().__init__() self.text_modulation = Modulation(time_dim, model_dim, 6, operation_settings=operation_settings) operations = operation_settings.get("operations") self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings) self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings) def forward(self, x, time_embed, freqs, transformer_options={}): self_attn_params, ff_params = torch.chunk(self.text_modulation(time_embed), 2, dim=-1) shift, scale, gate = get_shift_scale_gate(self_attn_params) out = apply_scale_shift_norm(self.self_attention_norm, x, scale, shift) out = self.self_attention(out, freqs, transformer_options=transformer_options) x = apply_gate_sum(x, out, gate) shift, scale, gate = get_shift_scale_gate(ff_params) out = apply_scale_shift_norm(self.feed_forward_norm, x, scale, shift) out = self.feed_forward(out) x = apply_gate_sum(x, out, gate) return x class TransformerDecoderBlock(nn.Module): def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None): super().__init__() self.visual_modulation = Modulation(time_dim, model_dim, 9, operation_settings=operation_settings) operations = operation_settings.get("operations") self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings) self.cross_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.cross_attention = CrossAttention(model_dim, head_dim, operation_settings=operation_settings) self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings) def forward(self, visual_embed, text_embed, time_embed, freqs, transformer_options={}): self_attn_params, cross_attn_params, ff_params = torch.chunk(self.visual_modulation(time_embed), 3, dim=-1) # self attention shift, scale, gate = get_shift_scale_gate(self_attn_params) visual_out = apply_scale_shift_norm(self.self_attention_norm, visual_embed, scale, shift) visual_out = self.self_attention(visual_out, freqs, transformer_options=transformer_options) visual_embed = apply_gate_sum(visual_embed, visual_out, gate) # cross attention shift, scale, gate = get_shift_scale_gate(cross_attn_params) visual_out = apply_scale_shift_norm(self.cross_attention_norm, visual_embed, scale, shift) visual_out = self.cross_attention(visual_out, text_embed, transformer_options=transformer_options) visual_embed = apply_gate_sum(visual_embed, visual_out, gate) # feed forward shift, scale, gate = get_shift_scale_gate(ff_params) visual_out = apply_scale_shift_norm(self.feed_forward_norm, visual_embed, scale, shift) visual_out = self.feed_forward(visual_out) visual_embed = apply_gate_sum(visual_embed, visual_out, gate) return visual_embed class Kandinsky5(nn.Module): def __init__( self, in_visual_dim=16, out_visual_dim=16, in_text_dim=3584, in_text_dim2=768, time_dim=512, model_dim=1792, ff_dim=7168, visual_embed_dim=132, patch_size=(1, 2, 2), num_text_blocks=2, num_visual_blocks=32, axes_dims=(16, 24, 24), rope_scale_factor=(1.0, 2.0, 2.0), dtype=None, device=None, operations=None, **kwargs ): super().__init__() head_dim = sum(axes_dims) self.rope_scale_factor = rope_scale_factor self.in_visual_dim = in_visual_dim self.model_dim = model_dim self.patch_size = patch_size self.visual_embed_dim = visual_embed_dim self.dtype = dtype self.device = device operation_settings = {"operations": operations, "device": device, "dtype": dtype} self.time_embeddings = TimeEmbeddings(model_dim, time_dim, operation_settings=operation_settings) self.text_embeddings = TextEmbeddings(in_text_dim, model_dim, operation_settings=operation_settings) self.pooled_text_embeddings = TextEmbeddings(in_text_dim2, time_dim, operation_settings=operation_settings) self.visual_embeddings = VisualEmbeddings(visual_embed_dim, model_dim, patch_size, operation_settings=operation_settings) self.text_transformer_blocks = nn.ModuleList( [TransformerEncoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_text_blocks)] ) self.visual_transformer_blocks = nn.ModuleList( [TransformerDecoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_visual_blocks)] ) self.out_layer = OutLayer(model_dim, time_dim, out_visual_dim, patch_size, operation_settings=operation_settings) self.rope_embedder_3d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=axes_dims) self.rope_embedder_1d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=[head_dim]) def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None, dtype=None, transformer_options={}): steps = seq_len if steps is None else steps seq_ids = torch.linspace(seq_start, seq_start + (seq_len - 1), steps=steps, device=device, dtype=dtype) seq_ids = seq_ids.reshape(-1, 1).unsqueeze(0) # Shape: (1, steps, 1) freqs = self.rope_embedder_1d(seq_ids).movedim(1, 2) return freqs def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}): patch_size = self.patch_size t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) if steps_t is None: steps_t = t_len if steps_h is None: steps_h = h_len if steps_w is None: steps_w = w_len h_start = 0 w_start = 0 rope_options = transformer_options.get("rope_options", None) if rope_options is not None: t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0 h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0 w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0 t_start += rope_options.get("shift_t", 0.0) h_start += rope_options.get("shift_y", 0.0) w_start += rope_options.get("shift_x", 0.0) else: rope_scale_factor = self.rope_scale_factor if self.model_dim == 4096: # pro video model uses different rope scaling at higher resolutions if h * w >= 14080: rope_scale_factor = (1.0, 3.16, 3.16) t_len = (t_len - 1.0) / rope_scale_factor[0] + 1.0 h_len = (h_len - 1.0) / rope_scale_factor[1] + 1.0 w_len = (w_len - 1.0) / rope_scale_factor[2] + 1.0 img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype) img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1) img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1) img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1) img_ids = img_ids.reshape(1, -1, img_ids.shape[-1]) freqs = self.rope_embedder_3d(img_ids).movedim(1, 2) return freqs def forward_orig(self, x, timestep, context, y, freqs, freqs_text, transformer_options={}, **kwargs): patches_replace = transformer_options.get("patches_replace", {}) context = self.text_embeddings(context) time_embed = self.time_embeddings(timestep, x.dtype) + self.pooled_text_embeddings(y) for block in self.text_transformer_blocks: context = block(context, time_embed, freqs_text, transformer_options=transformer_options) visual_embed = self.visual_embeddings(x) visual_shape = visual_embed.shape[:-1] visual_embed = visual_embed.flatten(1, -2) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.visual_transformer_blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.visual_transformer_blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): return block(x=args["x"], context=args["context"], time_embed=args["time_embed"], freqs=args["freqs"], transformer_options=args.get("transformer_options")) visual_embed = blocks_replace[("double_block", i)]({"x": visual_embed, "context": context, "time_embed": time_embed, "freqs": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})["x"] else: visual_embed = block(visual_embed, context, time_embed, freqs=freqs, transformer_options=transformer_options) visual_embed = visual_embed.reshape(*visual_shape, -1) return self.out_layer(visual_embed, time_embed) def _forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs): original_dims = x.ndim if original_dims == 4: x = x.unsqueeze(2) bs, c, t_len, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) if time_dim_replace is not None: time_dim_replace = comfy.ldm.common_dit.pad_to_patch_size(time_dim_replace, self.patch_size) x[:, :time_dim_replace.shape[1], :time_dim_replace.shape[2]] = time_dim_replace freqs = self.rope_encode_3d(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options) freqs_text = self.rope_encode_1d(context.shape[1], device=x.device, dtype=x.dtype, transformer_options=transformer_options) out = self.forward_orig(x, timestep, context, y, freqs, freqs_text, transformer_options=transformer_options, **kwargs) if original_dims == 4: out = out.squeeze(2) return out def forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, y, time_dim_replace=time_dim_replace, transformer_options=transformer_options, **kwargs) ================================================ FILE: comfy/ldm/lightricks/av_model.py ================================================ from typing import Tuple import torch import torch.nn as nn from comfy.ldm.lightricks.model import ( ADALN_BASE_PARAMS_COUNT, ADALN_CROSS_ATTN_PARAMS_COUNT, CrossAttention, FeedForward, AdaLayerNormSingle, PixArtAlphaTextProjection, NormSingleLinearTextProjection, LTXVModel, apply_cross_attention_adaln, compute_prompt_timestep, ) from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector import comfy.ldm.common_dit class CompressedTimestep: """Store video timestep embeddings in compressed form using per-frame indexing.""" __slots__ = ('data', 'batch_size', 'num_frames', 'patches_per_frame', 'feature_dim') def __init__(self, tensor: torch.Tensor, patches_per_frame: int): """ tensor: [batch_size, num_tokens, feature_dim] tensor where num_tokens = num_frames * patches_per_frame patches_per_frame: Number of spatial patches per frame (height * width in latent space), or None to disable compression """ self.batch_size, num_tokens, self.feature_dim = tensor.shape # Check if compression is valid (num_tokens must be divisible by patches_per_frame) if patches_per_frame is not None and num_tokens % patches_per_frame == 0 and num_tokens >= patches_per_frame: self.patches_per_frame = patches_per_frame self.num_frames = num_tokens // patches_per_frame # Reshape to [batch, frames, patches_per_frame, feature_dim] and store one value per frame # All patches in a frame are identical, so we only keep the first one reshaped = tensor.view(self.batch_size, self.num_frames, patches_per_frame, self.feature_dim) self.data = reshaped[:, :, 0, :].contiguous() # [batch, frames, feature_dim] else: # Not divisible or too small - store directly without compression self.patches_per_frame = 1 self.num_frames = num_tokens self.data = tensor def expand(self): """Expand back to original tensor.""" if self.patches_per_frame == 1: return self.data # [batch, frames, feature_dim] -> [batch, frames, patches_per_frame, feature_dim] -> [batch, tokens, feature_dim] expanded = self.data.unsqueeze(2).expand(self.batch_size, self.num_frames, self.patches_per_frame, self.feature_dim) return expanded.reshape(self.batch_size, -1, self.feature_dim) def expand_for_computation(self, scale_shift_table: torch.Tensor, batch_size: int, indices: slice = slice(None, None)): """Compute ada values on compressed per-frame data, then expand spatially.""" num_ada_params = scale_shift_table.shape[0] # No compression - compute directly if self.patches_per_frame == 1: num_tokens = self.data.shape[1] dim_per_param = self.feature_dim // num_ada_params reshaped = self.data.reshape(batch_size, num_tokens, num_ada_params, dim_per_param)[:, :, indices, :] table_values = scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=self.data.device, dtype=self.data.dtype) ada_values = (table_values + reshaped).unbind(dim=2) return ada_values # Compressed: compute on per-frame data then expand spatially # Reshape: [batch, frames, feature_dim] -> [batch, frames, num_ada_params, dim_per_param] frame_reshaped = self.data.reshape(batch_size, self.num_frames, num_ada_params, -1)[:, :, indices, :] table_values = scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to( device=self.data.device, dtype=self.data.dtype ) frame_ada = (table_values + frame_reshaped).unbind(dim=2) # Expand each ada parameter spatially: [batch, frames, dim] -> [batch, frames, patches, dim] -> [batch, tokens, dim] return tuple( frame_val.unsqueeze(2).expand(batch_size, self.num_frames, self.patches_per_frame, -1) .reshape(batch_size, -1, frame_val.shape[-1]) for frame_val in frame_ada ) class BasicAVTransformerBlock(nn.Module): def __init__( self, v_dim, a_dim, v_heads, a_heads, vd_head, ad_head, v_context_dim=None, a_context_dim=None, attn_precision=None, apply_gated_attention=False, cross_attention_adaln=False, dtype=None, device=None, operations=None, ): super().__init__() self.attn_precision = attn_precision self.cross_attention_adaln = cross_attention_adaln self.attn1 = CrossAttention( query_dim=v_dim, heads=v_heads, dim_head=vd_head, context_dim=None, attn_precision=self.attn_precision, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) self.audio_attn1 = CrossAttention( query_dim=a_dim, heads=a_heads, dim_head=ad_head, context_dim=None, attn_precision=self.attn_precision, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) self.attn2 = CrossAttention( query_dim=v_dim, context_dim=v_context_dim, heads=v_heads, dim_head=vd_head, attn_precision=self.attn_precision, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) self.audio_attn2 = CrossAttention( query_dim=a_dim, context_dim=a_context_dim, heads=a_heads, dim_head=ad_head, attn_precision=self.attn_precision, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) # Q: Video, K,V: Audio self.audio_to_video_attn = CrossAttention( query_dim=v_dim, context_dim=a_dim, heads=a_heads, dim_head=ad_head, attn_precision=self.attn_precision, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) # Q: Audio, K,V: Video self.video_to_audio_attn = CrossAttention( query_dim=a_dim, context_dim=v_dim, heads=a_heads, dim_head=ad_head, attn_precision=self.attn_precision, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) self.ff = FeedForward( v_dim, dim_out=v_dim, glu=True, dtype=dtype, device=device, operations=operations ) self.audio_ff = FeedForward( a_dim, dim_out=a_dim, glu=True, dtype=dtype, device=device, operations=operations ) num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, v_dim, device=device, dtype=dtype)) self.audio_scale_shift_table = nn.Parameter( torch.empty(num_ada_params, a_dim, device=device, dtype=dtype) ) if cross_attention_adaln: self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, v_dim, device=device, dtype=dtype)) self.audio_prompt_scale_shift_table = nn.Parameter(torch.empty(2, a_dim, device=device, dtype=dtype)) self.scale_shift_table_a2v_ca_audio = nn.Parameter( torch.empty(5, a_dim, device=device, dtype=dtype) ) self.scale_shift_table_a2v_ca_video = nn.Parameter( torch.empty(5, v_dim, device=device, dtype=dtype) ) def get_ada_values( self, scale_shift_table: torch.Tensor, batch_size: int, timestep: torch.Tensor, indices: slice = slice(None, None) ): if isinstance(timestep, CompressedTimestep): return timestep.expand_for_computation(scale_shift_table, batch_size, indices) num_ada_params = scale_shift_table.shape[0] ada_values = ( scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=timestep.device, dtype=timestep.dtype) + timestep.reshape(batch_size, timestep.shape[1], num_ada_params, -1)[:, :, indices, :] ).unbind(dim=2) return ada_values def get_av_ca_ada_values( self, scale_shift_table: torch.Tensor, batch_size: int, scale_shift_timestep: torch.Tensor, gate_timestep: torch.Tensor, num_scale_shift_values: int = 4, ): scale_shift_ada_values = self.get_ada_values( scale_shift_table[:num_scale_shift_values, :], batch_size, scale_shift_timestep, ) gate_ada_values = self.get_ada_values( scale_shift_table[num_scale_shift_values:, :], batch_size, gate_timestep, ) return (*scale_shift_ada_values, *gate_ada_values) def _apply_text_cross_attention( self, x, context, attn, scale_shift_table, prompt_scale_shift_table, timestep, prompt_timestep, attention_mask, transformer_options, ): """Apply text cross-attention, with optional ADaLN modulation.""" if self.cross_attention_adaln: shift_q, scale_q, gate = self.get_ada_values( scale_shift_table, x.shape[0], timestep, slice(6, 9) ) return apply_cross_attention_adaln( x, context, attn, shift_q, scale_q, gate, prompt_scale_shift_table, prompt_timestep, attention_mask, transformer_options, ) return attn( comfy.ldm.common_dit.rms_norm(x), context=context, mask=attention_mask, transformer_options=transformer_options, ) def forward( self, x: Tuple[torch.Tensor, torch.Tensor], v_context=None, a_context=None, attention_mask=None, v_timestep=None, a_timestep=None, v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None, v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None, v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None, self_attention_mask=None, v_prompt_timestep=None, a_prompt_timestep=None, ) -> Tuple[torch.Tensor, torch.Tensor]: run_vx = transformer_options.get("run_vx", True) run_ax = transformer_options.get("run_ax", True) vx, ax = x run_ax = run_ax and ax.numel() > 0 run_a2v = run_vx and transformer_options.get("a2v_cross_attn", True) and ax.numel() > 0 run_v2a = run_ax and transformer_options.get("v2a_cross_attn", True) # video if run_vx: # video self-attention vshift_msa, vscale_msa = (self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 2))) norm_vx = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_msa) + vshift_msa del vshift_msa, vscale_msa attn1_out = self.attn1(norm_vx, pe=v_pe, mask=self_attention_mask, transformer_options=transformer_options) del norm_vx # video cross-attention vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0] vx.addcmul_(attn1_out, vgate_msa) del vgate_msa, attn1_out vx.add_(self._apply_text_cross_attention( vx, v_context, self.attn2, self.scale_shift_table, getattr(self, 'prompt_scale_shift_table', None), v_timestep, v_prompt_timestep, attention_mask, transformer_options,) ) # audio if run_ax: # audio self-attention ashift_msa, ascale_msa = (self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 2))) norm_ax = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_msa) + ashift_msa del ashift_msa, ascale_msa attn1_out = self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options) del norm_ax # audio cross-attention agate_msa = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(2, 3))[0] ax.addcmul_(attn1_out, agate_msa) del agate_msa, attn1_out ax.add_(self._apply_text_cross_attention( ax, a_context, self.audio_attn2, self.audio_scale_shift_table, getattr(self, 'audio_prompt_scale_shift_table', None), a_timestep, a_prompt_timestep, attention_mask, transformer_options,) ) # video - audio cross attention. if run_a2v or run_v2a: vx_norm3 = comfy.ldm.common_dit.rms_norm(vx) ax_norm3 = comfy.ldm.common_dit.rms_norm(ax) # audio to video cross attention if run_a2v: scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v = self.get_ada_values( self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[:2] scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v = self.get_ada_values( self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[:2] vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_a2v_v) + shift_ca_video_hidden_states_a2v_v ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v) + shift_ca_audio_hidden_states_a2v del scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v, scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v a2v_out = self.audio_to_video_attn(vx_scaled, context=ax_scaled, pe=v_cross_pe, k_pe=a_cross_pe, transformer_options=transformer_options) del vx_scaled, ax_scaled gate_out_a2v = self.get_ada_values(self.scale_shift_table_a2v_ca_video[4:, :], vx.shape[0], v_cross_gate_timestep)[0] vx.addcmul_(a2v_out, gate_out_a2v) del gate_out_a2v, a2v_out # video to audio cross attention if run_v2a: scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a = self.get_ada_values( self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[2:4] scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a = self.get_ada_values( self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[2:4] ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a) + shift_ca_audio_hidden_states_v2a vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_v2a) + shift_ca_video_hidden_states_v2a del scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a, scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a v2a_out = self.video_to_audio_attn(ax_scaled, context=vx_scaled, pe=a_cross_pe, k_pe=v_cross_pe, transformer_options=transformer_options) del ax_scaled, vx_scaled gate_out_v2a = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[4:, :], ax.shape[0], a_cross_gate_timestep)[0] ax.addcmul_(v2a_out, gate_out_v2a) del gate_out_v2a, v2a_out del vx_norm3, ax_norm3 # video feedforward if run_vx: vshift_mlp, vscale_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, 5)) vx_scaled = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_mlp) + vshift_mlp del vshift_mlp, vscale_mlp ff_out = self.ff(vx_scaled) del vx_scaled vgate_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(5, 6))[0] vx.addcmul_(ff_out, vgate_mlp) del vgate_mlp, ff_out # audio feedforward if run_ax: ashift_mlp, ascale_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, 5)) ax_scaled = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_mlp) + ashift_mlp del ashift_mlp, ascale_mlp ff_out = self.audio_ff(ax_scaled) del ax_scaled agate_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(5, 6))[0] ax.addcmul_(ff_out, agate_mlp) del agate_mlp, ff_out return vx, ax class LTXAVModel(LTXVModel): """LTXAV model for audio-video generation.""" def __init__( self, in_channels=128, audio_in_channels=128, cross_attention_dim=4096, audio_cross_attention_dim=2048, attention_head_dim=128, audio_attention_head_dim=64, num_attention_heads=32, audio_num_attention_heads=32, caption_channels=3840, num_layers=48, positional_embedding_theta=10000.0, positional_embedding_max_pos=[20, 2048, 2048], audio_positional_embedding_max_pos=[20], causal_temporal_positioning=False, vae_scale_factors=(8, 32, 32), use_middle_indices_grid=False, timestep_scale_multiplier=1000.0, av_ca_timestep_scale_multiplier=1.0, apply_gated_attention=False, caption_proj_before_connector=False, cross_attention_adaln=False, dtype=None, device=None, operations=None, **kwargs, ): # Store audio-specific parameters self.audio_in_channels = audio_in_channels self.audio_cross_attention_dim = audio_cross_attention_dim self.audio_attention_head_dim = audio_attention_head_dim self.audio_num_attention_heads = audio_num_attention_heads self.audio_positional_embedding_max_pos = audio_positional_embedding_max_pos self.apply_gated_attention = apply_gated_attention # Calculate audio dimensions self.audio_inner_dim = audio_num_attention_heads * audio_attention_head_dim self.audio_out_channels = audio_in_channels # Audio-specific constants self.num_audio_channels = 8 self.audio_frequency_bins = 16 self.av_ca_timestep_scale_multiplier = av_ca_timestep_scale_multiplier super().__init__( in_channels=in_channels, cross_attention_dim=cross_attention_dim, attention_head_dim=attention_head_dim, num_attention_heads=num_attention_heads, caption_channels=caption_channels, num_layers=num_layers, positional_embedding_theta=positional_embedding_theta, positional_embedding_max_pos=positional_embedding_max_pos, causal_temporal_positioning=causal_temporal_positioning, vae_scale_factors=vae_scale_factors, use_middle_indices_grid=use_middle_indices_grid, timestep_scale_multiplier=timestep_scale_multiplier, caption_proj_before_connector=caption_proj_before_connector, cross_attention_adaln=cross_attention_adaln, dtype=dtype, device=device, operations=operations, **kwargs, ) def _init_model_components(self, device, dtype, **kwargs): """Initialize LTXAV-specific components.""" # Audio-specific projections self.audio_patchify_proj = self.operations.Linear( self.audio_in_channels, self.audio_inner_dim, bias=True, dtype=dtype, device=device ) # Audio-specific AdaLN audio_embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT self.audio_adaln_single = AdaLayerNormSingle( self.audio_inner_dim, embedding_coefficient=audio_embedding_coefficient, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations, ) if self.cross_attention_adaln: self.audio_prompt_adaln_single = AdaLayerNormSingle( self.audio_inner_dim, embedding_coefficient=2, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations, ) else: self.audio_prompt_adaln_single = None num_scale_shift_values = 4 self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle( self.inner_dim, use_additional_conditions=False, embedding_coefficient=num_scale_shift_values, dtype=dtype, device=device, operations=self.operations, ) self.av_ca_a2v_gate_adaln_single = AdaLayerNormSingle( self.inner_dim, use_additional_conditions=False, embedding_coefficient=1, dtype=dtype, device=device, operations=self.operations, ) self.av_ca_audio_scale_shift_adaln_single = AdaLayerNormSingle( self.audio_inner_dim, use_additional_conditions=False, embedding_coefficient=num_scale_shift_values, dtype=dtype, device=device, operations=self.operations, ) self.av_ca_v2a_gate_adaln_single = AdaLayerNormSingle( self.audio_inner_dim, use_additional_conditions=False, embedding_coefficient=1, dtype=dtype, device=device, operations=self.operations, ) # Audio caption projection if self.caption_proj_before_connector: if self.caption_projection_first_linear: self.audio_caption_projection = NormSingleLinearTextProjection( in_features=self.caption_channels, hidden_size=self.audio_inner_dim, dtype=dtype, device=device, operations=self.operations, ) else: self.audio_caption_projection = lambda a: a else: self.audio_caption_projection = PixArtAlphaTextProjection( in_features=self.caption_channels, hidden_size=self.audio_inner_dim, dtype=dtype, device=device, operations=self.operations, ) connector_split_rope = kwargs.get("rope_type", "split") == "split" connector_gated_attention = kwargs.get("connector_apply_gated_attention", False) attention_head_dim = kwargs.get("connector_attention_head_dim", 128) num_attention_heads = kwargs.get("connector_num_attention_heads", 30) num_layers = kwargs.get("connector_num_layers", 2) self.audio_embeddings_connector = Embeddings1DConnector( attention_head_dim=kwargs.get("audio_connector_attention_head_dim", attention_head_dim), num_attention_heads=kwargs.get("audio_connector_num_attention_heads", num_attention_heads), num_layers=num_layers, split_rope=connector_split_rope, double_precision_rope=True, apply_gated_attention=connector_gated_attention, dtype=dtype, device=device, operations=self.operations, ) self.video_embeddings_connector = Embeddings1DConnector( attention_head_dim=attention_head_dim, num_attention_heads=num_attention_heads, num_layers=num_layers, split_rope=connector_split_rope, double_precision_rope=True, apply_gated_attention=connector_gated_attention, dtype=dtype, device=device, operations=self.operations, ) def preprocess_text_embeds(self, context, unprocessed=False): # LTXv2 fully processed context has dimension of self.caption_channels * 2 # LTXv2.3 fully processed context has dimension of self.cross_attention_dim + self.audio_cross_attention_dim if not unprocessed: if context.shape[-1] in (self.cross_attention_dim + self.audio_cross_attention_dim, self.caption_channels * 2): return context if context.shape[-1] == self.cross_attention_dim + self.audio_cross_attention_dim: context_vid = context[:, :, :self.cross_attention_dim] context_audio = context[:, :, self.cross_attention_dim:] else: context_vid = context context_audio = context if self.caption_proj_before_connector: context_vid = self.caption_projection(context_vid) context_audio = self.audio_caption_projection(context_audio) out_vid = self.video_embeddings_connector(context_vid)[0] out_audio = self.audio_embeddings_connector(context_audio)[0] return torch.concat((out_vid, out_audio), dim=-1) def _init_transformer_blocks(self, device, dtype, **kwargs): """Initialize transformer blocks for LTXAV.""" self.transformer_blocks = nn.ModuleList( [ BasicAVTransformerBlock( v_dim=self.inner_dim, a_dim=self.audio_inner_dim, v_heads=self.num_attention_heads, a_heads=self.audio_num_attention_heads, vd_head=self.attention_head_dim, ad_head=self.audio_attention_head_dim, v_context_dim=self.cross_attention_dim, a_context_dim=self.audio_cross_attention_dim, apply_gated_attention=self.apply_gated_attention, cross_attention_adaln=self.cross_attention_adaln, dtype=dtype, device=device, operations=self.operations, ) for _ in range(self.num_layers) ] ) def _init_output_components(self, device, dtype): """Initialize output components for LTXAV.""" # Video output components super()._init_output_components(device, dtype) # Audio output components self.audio_scale_shift_table = nn.Parameter( torch.empty(2, self.audio_inner_dim, dtype=dtype, device=device) ) self.audio_norm_out = self.operations.LayerNorm( self.audio_inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device ) self.audio_proj_out = self.operations.Linear( self.audio_inner_dim, self.audio_out_channels, dtype=dtype, device=device ) self.a_patchifier = AudioPatchifier(1, start_end=True) def separate_audio_and_video_latents(self, x, audio_length): """Separate audio and video latents from combined input.""" # vx = x[:, : self.in_channels] # ax = x[:, self.in_channels :] # # ax = ax.reshape(ax.shape[0], -1) # ax = ax[:, : audio_length * self.num_audio_channels * self.audio_frequency_bins] # # ax = ax.reshape( # ax.shape[0], self.num_audio_channels, audio_length, self.audio_frequency_bins # ) vx = x[0] ax = x[1] if len(x) > 1 else torch.zeros( (vx.shape[0], self.num_audio_channels, 0, self.audio_frequency_bins), device=vx.device, dtype=vx.dtype ) return vx, ax def recombine_audio_and_video_latents(self, vx, ax, target_shape=None): if ax.numel() == 0: return vx else: return [vx, ax] """Recombine audio and video latents for output.""" # if ax.device != vx.device or ax.dtype != vx.dtype: # logging.warning("Audio and video latents are on different devices or dtypes.") # ax = ax.to(device=vx.device, dtype=vx.dtype) # logging.warning(f"Audio audio latent moved to device: {ax.device}, dtype: {ax.dtype}") # # ax = ax.reshape(ax.shape[0], -1) # # pad to f x h x w of the video latents # divisor = vx.shape[-1] * vx.shape[-2] * vx.shape[-3] # if target_shape is None: # repetitions = math.ceil(ax.shape[-1] / divisor) # else: # repetitions = target_shape[1] - vx.shape[1] # padded_len = repetitions * divisor # ax = F.pad(ax, (0, padded_len - ax.shape[-1])) # ax = ax.reshape(ax.shape[0], -1, vx.shape[-3], vx.shape[-2], vx.shape[-1]) # return torch.cat([vx, ax], dim=1) def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs): """Process input for LTXAV - separate audio and video, then patchify.""" audio_length = kwargs.get("audio_length", 0) # Separate audio and video latents vx, ax = self.separate_audio_and_video_latents(x, audio_length) has_spatial_mask = False if denoise_mask is not None: # check if any frame has spatial variation (inpainting) for frame_idx in range(denoise_mask.shape[2]): frame_mask = denoise_mask[0, 0, frame_idx] if frame_mask.numel() > 0 and frame_mask.min() != frame_mask.max(): has_spatial_mask = True break [vx, v_pixel_coords, additional_args] = super()._process_input( vx, keyframe_idxs, denoise_mask, **kwargs ) additional_args["has_spatial_mask"] = has_spatial_mask ax, a_latent_coords = self.a_patchifier.patchify(ax) ax = self.audio_patchify_proj(ax) # additional_args.update({"av_orig_shape": list(x.shape)}) return [vx, ax], [v_pixel_coords, a_latent_coords], additional_args def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs): """Prepare timestep embeddings.""" # TODO: some code reuse is needed here. grid_mask = kwargs.get("grid_mask", None) if grid_mask is not None: timestep = timestep[:, grid_mask] timestep_scaled = timestep * self.timestep_scale_multiplier v_timestep, v_embedded_timestep = self.adaln_single( timestep_scaled.flatten(), {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) # Calculate patches_per_frame from orig_shape: [batch, channels, frames, height, width] # Video tokens are arranged as (frames * height * width), so patches_per_frame = height * width orig_shape = kwargs.get("orig_shape") has_spatial_mask = kwargs.get("has_spatial_mask", None) v_patches_per_frame = None if not has_spatial_mask and orig_shape is not None and len(orig_shape) == 5: # orig_shape[3] = height, orig_shape[4] = width (in latent space) v_patches_per_frame = orig_shape[3] * orig_shape[4] # Reshape to [batch_size, num_tokens, dim] and compress for storage v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame) v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame) v_prompt_timestep = compute_prompt_timestep( self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype ) # Prepare audio timestep a_timestep = kwargs.get("a_timestep") if a_timestep is not None: a_timestep_scaled = a_timestep * self.timestep_scale_multiplier a_timestep_flat = a_timestep_scaled.flatten() timestep_flat = timestep_scaled.flatten() av_ca_factor = self.av_ca_timestep_scale_multiplier / self.timestep_scale_multiplier # Cross-attention timesteps - compress these too av_ca_audio_scale_shift_timestep, _ = self.av_ca_audio_scale_shift_adaln_single( timestep.max().expand_as(a_timestep_flat), {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) av_ca_video_scale_shift_timestep, _ = self.av_ca_video_scale_shift_adaln_single( a_timestep.max().expand_as(timestep_flat), {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) av_ca_a2v_gate_noise_timestep, _ = self.av_ca_a2v_gate_adaln_single( a_timestep.max().expand_as(timestep_flat) * av_ca_factor, {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) av_ca_v2a_gate_noise_timestep, _ = self.av_ca_v2a_gate_adaln_single( timestep.max().expand_as(a_timestep_flat) * av_ca_factor, {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) # Compress cross-attention timesteps (only video side, audio is too small to benefit) # v_patches_per_frame is None for spatial masks, set for temporal masks or no mask cross_av_timestep_ss = [ av_ca_audio_scale_shift_timestep.view(batch_size, -1, av_ca_audio_scale_shift_timestep.shape[-1]), CompressedTimestep(av_ca_video_scale_shift_timestep.view(batch_size, -1, av_ca_video_scale_shift_timestep.shape[-1]), v_patches_per_frame), # video - compressed if possible CompressedTimestep(av_ca_a2v_gate_noise_timestep.view(batch_size, -1, av_ca_a2v_gate_noise_timestep.shape[-1]), v_patches_per_frame), # video - compressed if possible av_ca_v2a_gate_noise_timestep.view(batch_size, -1, av_ca_v2a_gate_noise_timestep.shape[-1]), ] a_timestep, a_embedded_timestep = self.audio_adaln_single( a_timestep_flat, {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) # Audio timesteps a_timestep = a_timestep.view(batch_size, -1, a_timestep.shape[-1]) a_embedded_timestep = a_embedded_timestep.view(batch_size, -1, a_embedded_timestep.shape[-1]) a_prompt_timestep = compute_prompt_timestep( self.audio_prompt_adaln_single, a_timestep_scaled, batch_size, hidden_dtype ) else: a_timestep = timestep_scaled a_embedded_timestep = kwargs.get("embedded_timestep") cross_av_timestep_ss = [] a_prompt_timestep = None return [v_timestep, a_timestep, cross_av_timestep_ss, v_prompt_timestep, a_prompt_timestep], [ v_embedded_timestep, a_embedded_timestep, ], None def _prepare_context(self, context, batch_size, x, attention_mask=None): vx = x[0] ax = x[1] video_dim = vx.shape[-1] audio_dim = ax.shape[-1] v_context_dim = self.caption_channels if self.caption_proj_before_connector is False else video_dim a_context_dim = self.caption_channels if self.caption_proj_before_connector is False else audio_dim v_context, a_context = torch.split( context, [v_context_dim, a_context_dim], len(context.shape) - 1 ) v_context, attention_mask = super()._prepare_context( v_context, batch_size, vx, attention_mask ) if self.caption_proj_before_connector is False: a_context = self.audio_caption_projection(a_context) a_context = a_context.view(batch_size, -1, audio_dim) return [v_context, a_context], attention_mask def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype): v_pixel_coords = pixel_coords[0] v_pe = super()._prepare_positional_embeddings(v_pixel_coords, frame_rate, x_dtype) a_latent_coords = pixel_coords[1] a_pe = self._precompute_freqs_cis( a_latent_coords, dim=self.audio_inner_dim, out_dtype=x_dtype, max_pos=self.audio_positional_embedding_max_pos, use_middle_indices_grid=self.use_middle_indices_grid, num_attention_heads=self.audio_num_attention_heads, ) # calculate positional embeddings for the middle of the token duration, to use in av cross attention layers. max_pos = max( self.positional_embedding_max_pos[0], self.audio_positional_embedding_max_pos[0] ) v_pixel_coords = v_pixel_coords.to(torch.float32) v_pixel_coords[:, 0] = v_pixel_coords[:, 0] * (1.0 / frame_rate) av_cross_video_freq_cis = self._precompute_freqs_cis( v_pixel_coords[:, 0:1, :], dim=self.audio_cross_attention_dim, out_dtype=x_dtype, max_pos=[max_pos], use_middle_indices_grid=True, num_attention_heads=self.audio_num_attention_heads, ) av_cross_audio_freq_cis = self._precompute_freqs_cis( a_latent_coords[:, 0:1, :], dim=self.audio_cross_attention_dim, out_dtype=x_dtype, max_pos=[max_pos], use_middle_indices_grid=True, num_attention_heads=self.audio_num_attention_heads, ) return [(v_pe, av_cross_video_freq_cis), (a_pe, av_cross_audio_freq_cis)] def _process_transformer_blocks( self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs ): vx = x[0] ax = x[1] v_context = context[0] a_context = context[1] v_timestep = timestep[0] a_timestep = timestep[1] v_pe, av_cross_video_freq_cis = pe[0] a_pe, av_cross_audio_freq_cis = pe[1] ( av_ca_audio_scale_shift_timestep, av_ca_video_scale_shift_timestep, av_ca_a2v_gate_noise_timestep, av_ca_v2a_gate_noise_timestep, ) = timestep[2] v_prompt_timestep = timestep[3] a_prompt_timestep = timestep[4] """Process transformer blocks for LTXAV.""" patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) # Process transformer blocks for i, block in enumerate(self.transformer_blocks): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block( args["img"], v_context=args["v_context"], a_context=args["a_context"], attention_mask=args["attention_mask"], v_timestep=args["v_timestep"], a_timestep=args["a_timestep"], v_pe=args["v_pe"], a_pe=args["a_pe"], v_cross_pe=args["v_cross_pe"], a_cross_pe=args["a_cross_pe"], v_cross_scale_shift_timestep=args["v_cross_scale_shift_timestep"], a_cross_scale_shift_timestep=args["a_cross_scale_shift_timestep"], v_cross_gate_timestep=args["v_cross_gate_timestep"], a_cross_gate_timestep=args["a_cross_gate_timestep"], transformer_options=args["transformer_options"], self_attention_mask=args.get("self_attention_mask"), v_prompt_timestep=args.get("v_prompt_timestep"), a_prompt_timestep=args.get("a_prompt_timestep"), ) return out out = blocks_replace[("double_block", i)]( { "img": (vx, ax), "v_context": v_context, "a_context": a_context, "attention_mask": attention_mask, "v_timestep": v_timestep, "a_timestep": a_timestep, "v_pe": v_pe, "a_pe": a_pe, "v_cross_pe": av_cross_video_freq_cis, "a_cross_pe": av_cross_audio_freq_cis, "v_cross_scale_shift_timestep": av_ca_video_scale_shift_timestep, "a_cross_scale_shift_timestep": av_ca_audio_scale_shift_timestep, "v_cross_gate_timestep": av_ca_a2v_gate_noise_timestep, "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask, "v_prompt_timestep": v_prompt_timestep, "a_prompt_timestep": a_prompt_timestep, }, {"original_block": block_wrap}, ) vx, ax = out["img"] else: vx, ax = block( (vx, ax), v_context=v_context, a_context=a_context, attention_mask=attention_mask, v_timestep=v_timestep, a_timestep=a_timestep, v_pe=v_pe, a_pe=a_pe, v_cross_pe=av_cross_video_freq_cis, a_cross_pe=av_cross_audio_freq_cis, v_cross_scale_shift_timestep=av_ca_video_scale_shift_timestep, a_cross_scale_shift_timestep=av_ca_audio_scale_shift_timestep, v_cross_gate_timestep=av_ca_a2v_gate_noise_timestep, a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep, transformer_options=transformer_options, self_attention_mask=self_attention_mask, v_prompt_timestep=v_prompt_timestep, a_prompt_timestep=a_prompt_timestep, ) return [vx, ax] def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs): vx = x[0] ax = x[1] v_embedded_timestep = embedded_timestep[0] a_embedded_timestep = embedded_timestep[1] # Expand compressed video timestep if needed if isinstance(v_embedded_timestep, CompressedTimestep): v_embedded_timestep = v_embedded_timestep.expand() vx = super()._process_output(vx, v_embedded_timestep, keyframe_idxs, **kwargs) # Process audio output a_scale_shift_values = ( self.audio_scale_shift_table[None, None].to(device=a_embedded_timestep.device, dtype=a_embedded_timestep.dtype) + a_embedded_timestep[:, :, None] ) a_shift, a_scale = a_scale_shift_values[:, :, 0], a_scale_shift_values[:, :, 1] ax = self.audio_norm_out(ax) ax = ax * (1 + a_scale) + a_shift ax = self.audio_proj_out(ax) # Unpatchify audio ax = self.a_patchifier.unpatchify( ax, channels=self.num_audio_channels, freq=self.audio_frequency_bins ) # Recombine audio and video original_shape = kwargs.get("av_orig_shape") return self.recombine_audio_and_video_latents(vx, ax, original_shape) def forward( self, x, timestep, context, attention_mask=None, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs, ): """ Forward pass for LTXAV model. Args: x: Combined audio-video input tensor timestep: Tuple of (video_timestep, audio_timestep) or single timestep context: Context tensor (e.g., text embeddings) attention_mask: Attention mask tensor frame_rate: Frame rate for temporal processing transformer_options: Additional options for transformer blocks keyframe_idxs: Keyframe indices for temporal processing **kwargs: Additional keyword arguments including audio_length Returns: Combined audio-video output tensor """ # Handle timestep format if isinstance(timestep, (tuple, list)) and len(timestep) == 2: v_timestep, a_timestep = timestep kwargs["a_timestep"] = a_timestep timestep = v_timestep else: kwargs["a_timestep"] = timestep # Call parent forward method return super().forward( x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, **kwargs, ) ================================================ FILE: comfy/ldm/lightricks/embeddings_connector.py ================================================ import math from typing import Optional import comfy.ldm.common_dit import torch from comfy.ldm.lightricks.model import ( CrossAttention, FeedForward, generate_freq_grid_np, interleaved_freqs_cis, split_freqs_cis, ) from torch import nn class BasicTransformerBlock1D(nn.Module): r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads (`int`): The number of heads to use for multi-head attention. attention_head_dim (`int`): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. attention_bias (: obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. upcast_attention (`bool`, *optional*): Whether to upcast the attention computation to float32. This is useful for mixed precision training. norm_elementwise_affine (`bool`, *optional*, defaults to `True`): Whether to use learnable elementwise affine parameters for normalization. standardization_norm (`str`, *optional*, defaults to `"layer_norm"`): The type of pre-normalization to use. Can be `"layer_norm"` or `"rms_norm"`. norm_eps (`float`, *optional*, defaults to 1e-5): Epsilon value for normalization layers. qk_norm (`str`, *optional*, defaults to None): Set to 'layer_norm' or `rms_norm` to perform query and key normalization. final_dropout (`bool` *optional*, defaults to False): Whether to apply a final dropout after the last feed-forward layer. ff_inner_dim (`int`, *optional*): Dimension of the inner feed-forward layer. If not provided, defaults to `dim * 4`. ff_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the feed-forward layer. attention_out_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the attention output layer. use_rope (`bool`, *optional*, defaults to `False`): Whether to use Rotary Position Embeddings (RoPE). ffn_dim_mult (`int`, *optional*, defaults to 4): Multiplier for the inner dimension of the feed-forward layer. """ def __init__( self, dim, n_heads, d_head, context_dim=None, attn_precision=None, apply_gated_attention=False, dtype=None, device=None, operations=None, ): super().__init__() # Define 3 blocks. Each block has its own normalization layer. # 1. Self-Attn self.attn1 = CrossAttention( query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) # 3. Feed-forward self.ff = FeedForward( dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations, ) def forward(self, hidden_states, attention_mask=None, pe=None) -> torch.FloatTensor: # Notice that normalization is always applied before the real computation in the following blocks. # 1. Normalization Before Self-Attention norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states) norm_hidden_states = norm_hidden_states.squeeze(1) # 2. Self-Attention attn_output = self.attn1(norm_hidden_states, mask=attention_mask, pe=pe) hidden_states = attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 3. Normalization before Feed-Forward norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states) # 4. Feed-forward ff_output = self.ff(norm_hidden_states) hidden_states = ff_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) return hidden_states class Embeddings1DConnector(nn.Module): _supports_gradient_checkpointing = True def __init__( self, in_channels=128, cross_attention_dim=2048, attention_head_dim=128, num_attention_heads=30, num_layers=2, positional_embedding_theta=10000.0, positional_embedding_max_pos=[4096], causal_temporal_positioning=False, num_learnable_registers: Optional[int] = 128, apply_gated_attention=False, dtype=None, device=None, operations=None, split_rope=False, double_precision_rope=False, **kwargs, ): super().__init__() self.dtype = dtype self.out_channels = in_channels self.num_attention_heads = num_attention_heads self.inner_dim = num_attention_heads * attention_head_dim self.causal_temporal_positioning = causal_temporal_positioning self.positional_embedding_theta = positional_embedding_theta self.positional_embedding_max_pos = positional_embedding_max_pos self.split_rope = split_rope self.double_precision_rope = double_precision_rope self.transformer_1d_blocks = nn.ModuleList( [ BasicTransformerBlock1D( self.inner_dim, num_attention_heads, attention_head_dim, context_dim=cross_attention_dim, apply_gated_attention=apply_gated_attention, dtype=dtype, device=device, operations=operations, ) for _ in range(num_layers) ] ) inner_dim = num_attention_heads * attention_head_dim self.num_learnable_registers = num_learnable_registers if self.num_learnable_registers: self.learnable_registers = nn.Parameter( torch.empty( self.num_learnable_registers, inner_dim, dtype=dtype, device=device ) ) def get_fractional_positions(self, indices_grid): fractional_positions = torch.stack( [ indices_grid[:, i] / self.positional_embedding_max_pos[i] for i in range(1) ], dim=-1, ) return fractional_positions def precompute_freqs(self, indices_grid, spacing): source_dtype = indices_grid.dtype dtype = ( torch.float32 if source_dtype in (torch.bfloat16, torch.float16) else source_dtype ) fractional_positions = self.get_fractional_positions(indices_grid) indices = ( generate_freq_grid_np( self.positional_embedding_theta, indices_grid.shape[1], self.inner_dim, ) if self.double_precision_rope else self.generate_freq_grid(spacing, dtype, fractional_positions.device) ).to(device=fractional_positions.device) if spacing == "exp_2": freqs = ( (indices * fractional_positions.unsqueeze(-1)) .transpose(-1, -2) .flatten(2) ) else: freqs = ( (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)) .transpose(-1, -2) .flatten(2) ) return freqs def generate_freq_grid(self, spacing, dtype, device): dim = self.inner_dim theta = self.positional_embedding_theta n_pos_dims = 1 n_elem = 2 * n_pos_dims # 2 for cos and sin e.g. x 3 = 6 start = 1 end = theta if spacing == "exp": indices = theta ** (torch.arange(0, dim, n_elem, device="cpu", dtype=torch.float32) / (dim - n_elem)) indices = indices.to(dtype=dtype, device=device) elif spacing == "exp_2": indices = 1.0 / theta ** (torch.arange(0, dim, n_elem, device=device) / dim) indices = indices.to(dtype=dtype) elif spacing == "linear": indices = torch.linspace( start, end, dim // n_elem, device=device, dtype=dtype ) elif spacing == "sqrt": indices = torch.linspace( start**2, end**2, dim // n_elem, device=device, dtype=dtype ).sqrt() indices = indices * math.pi / 2 return indices def precompute_freqs_cis(self, indices_grid, spacing="exp", out_dtype=None): dim = self.inner_dim n_elem = 2 # 2 because of cos and sin freqs = self.precompute_freqs(indices_grid, spacing) if self.split_rope: expected_freqs = dim // 2 current_freqs = freqs.shape[-1] pad_size = expected_freqs - current_freqs cos_freq, sin_freq = split_freqs_cis( freqs, pad_size, self.num_attention_heads ) else: cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem) return cos_freq.to(dtype=out_dtype), sin_freq.to(dtype=out_dtype), self.split_rope def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, ): """ The [`Transformer2DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): Input `hidden_states`. indices_grid (`torch.LongTensor` of shape `(batch size, 3, num latent pixels)`): attention_mask ( `torch.Tensor`, *optional*): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. Returns: If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ # 1. Input if self.num_learnable_registers: num_registers_duplications = math.ceil( max(1024, hidden_states.shape[1]) / self.num_learnable_registers ) learnable_registers = torch.tile( self.learnable_registers.to(hidden_states), (num_registers_duplications, 1) ) hidden_states = torch.cat((hidden_states, learnable_registers[hidden_states.shape[1]:].unsqueeze(0).repeat(hidden_states.shape[0], 1, 1)), dim=1) if attention_mask is not None: attention_mask = torch.zeros([1, 1, 1, hidden_states.shape[1]], dtype=attention_mask.dtype, device=attention_mask.device) indices_grid = torch.arange( hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device ) indices_grid = indices_grid[None, None, :] freqs_cis = self.precompute_freqs_cis(indices_grid, out_dtype=hidden_states.dtype) # 2. Blocks for block_idx, block in enumerate(self.transformer_1d_blocks): hidden_states = block( hidden_states, attention_mask=attention_mask, pe=freqs_cis ) # 3. Output # if self.output_scale is not None: # hidden_states = hidden_states / self.output_scale hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states) return hidden_states, attention_mask ================================================ FILE: comfy/ldm/lightricks/latent_upsampler.py ================================================ from typing import Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange def _rational_for_scale(scale: float) -> Tuple[int, int]: mapping = {0.75: (3, 4), 1.5: (3, 2), 2.0: (2, 1), 4.0: (4, 1)} if float(scale) not in mapping: raise ValueError( f"Unsupported spatial_scale {scale}. Choose from {list(mapping.keys())}" ) return mapping[float(scale)] class PixelShuffleND(nn.Module): def __init__(self, dims, upscale_factors=(2, 2, 2)): super().__init__() assert dims in [1, 2, 3], "dims must be 1, 2, or 3" self.dims = dims self.upscale_factors = upscale_factors def forward(self, x): if self.dims == 3: return rearrange( x, "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)", p1=self.upscale_factors[0], p2=self.upscale_factors[1], p3=self.upscale_factors[2], ) elif self.dims == 2: return rearrange( x, "b (c p1 p2) h w -> b c (h p1) (w p2)", p1=self.upscale_factors[0], p2=self.upscale_factors[1], ) elif self.dims == 1: return rearrange( x, "b (c p1) f h w -> b c (f p1) h w", p1=self.upscale_factors[0], ) class BlurDownsample(nn.Module): """ Anti-aliased spatial downsampling by integer stride using a fixed separable binomial kernel. Applies only on H,W. Works for dims=2 or dims=3 (per-frame). """ def __init__(self, dims: int, stride: int): super().__init__() assert dims in (2, 3) assert stride >= 1 and isinstance(stride, int) self.dims = dims self.stride = stride # 5x5 separable binomial kernel [1,4,6,4,1] (outer product), normalized k = torch.tensor([1.0, 4.0, 6.0, 4.0, 1.0]) k2d = k[:, None] @ k[None, :] k2d = (k2d / k2d.sum()).float() # shape (5,5) self.register_buffer("kernel", k2d[None, None, :, :]) # (1,1,5,5) def forward(self, x: torch.Tensor) -> torch.Tensor: if self.stride == 1: return x def _apply_2d(x2d: torch.Tensor) -> torch.Tensor: # x2d: (B, C, H, W) B, C, H, W = x2d.shape weight = self.kernel.expand(C, 1, 5, 5) # depthwise x2d = F.conv2d( x2d, weight=weight, bias=None, stride=self.stride, padding=2, groups=C ) return x2d if self.dims == 2: return _apply_2d(x) else: # dims == 3: apply per-frame on H,W b, c, f, h, w = x.shape x = rearrange(x, "b c f h w -> (b f) c h w") x = _apply_2d(x) h2, w2 = x.shape[-2:] x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f, h=h2, w=w2) return x class SpatialRationalResampler(nn.Module): """ Fully-learned rational spatial scaling: up by 'num' via PixelShuffle, then anti-aliased downsample by 'den' using fixed blur + stride. Operates on H,W only. For dims==3, work per-frame for spatial scaling (temporal axis untouched). """ def __init__(self, mid_channels: int, scale: float): super().__init__() self.scale = float(scale) self.num, self.den = _rational_for_scale(self.scale) self.conv = nn.Conv2d( mid_channels, (self.num**2) * mid_channels, kernel_size=3, padding=1 ) self.pixel_shuffle = PixelShuffleND(2, upscale_factors=(self.num, self.num)) self.blur_down = BlurDownsample(dims=2, stride=self.den) def forward(self, x: torch.Tensor) -> torch.Tensor: b, c, f, h, w = x.shape x = rearrange(x, "b c f h w -> (b f) c h w") x = self.conv(x) x = self.pixel_shuffle(x) x = self.blur_down(x) x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f) return x class ResBlock(nn.Module): def __init__( self, channels: int, mid_channels: Optional[int] = None, dims: int = 3 ): super().__init__() if mid_channels is None: mid_channels = channels Conv = nn.Conv2d if dims == 2 else nn.Conv3d self.conv1 = Conv(channels, mid_channels, kernel_size=3, padding=1) self.norm1 = nn.GroupNorm(32, mid_channels) self.conv2 = Conv(mid_channels, channels, kernel_size=3, padding=1) self.norm2 = nn.GroupNorm(32, channels) self.activation = nn.SiLU() def forward(self, x: torch.Tensor) -> torch.Tensor: residual = x x = self.conv1(x) x = self.norm1(x) x = self.activation(x) x = self.conv2(x) x = self.norm2(x) x = self.activation(x + residual) return x class LatentUpsampler(nn.Module): """ Model to spatially upsample VAE latents. Args: in_channels (`int`): Number of channels in the input latent mid_channels (`int`): Number of channels in the middle layers num_blocks_per_stage (`int`): Number of ResBlocks to use in each stage (pre/post upsampling) dims (`int`): Number of dimensions for convolutions (2 or 3) spatial_upsample (`bool`): Whether to spatially upsample the latent temporal_upsample (`bool`): Whether to temporally upsample the latent """ def __init__( self, in_channels: int = 128, mid_channels: int = 512, num_blocks_per_stage: int = 4, dims: int = 3, spatial_upsample: bool = True, temporal_upsample: bool = False, spatial_scale: float = 2.0, rational_resampler: bool = False, ): super().__init__() self.in_channels = in_channels self.mid_channels = mid_channels self.num_blocks_per_stage = num_blocks_per_stage self.dims = dims self.spatial_upsample = spatial_upsample self.temporal_upsample = temporal_upsample self.spatial_scale = float(spatial_scale) self.rational_resampler = rational_resampler Conv = nn.Conv2d if dims == 2 else nn.Conv3d self.initial_conv = Conv(in_channels, mid_channels, kernel_size=3, padding=1) self.initial_norm = nn.GroupNorm(32, mid_channels) self.initial_activation = nn.SiLU() self.res_blocks = nn.ModuleList( [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)] ) if spatial_upsample and temporal_upsample: self.upsampler = nn.Sequential( nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1), PixelShuffleND(3), ) elif spatial_upsample: if rational_resampler: self.upsampler = SpatialRationalResampler( mid_channels=mid_channels, scale=self.spatial_scale ) else: self.upsampler = nn.Sequential( nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1), PixelShuffleND(2), ) elif temporal_upsample: self.upsampler = nn.Sequential( nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1), PixelShuffleND(1), ) else: raise ValueError( "Either spatial_upsample or temporal_upsample must be True" ) self.post_upsample_res_blocks = nn.ModuleList( [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)] ) self.final_conv = Conv(mid_channels, in_channels, kernel_size=3, padding=1) def forward(self, latent: torch.Tensor) -> torch.Tensor: b, c, f, h, w = latent.shape if self.dims == 2: x = rearrange(latent, "b c f h w -> (b f) c h w") x = self.initial_conv(x) x = self.initial_norm(x) x = self.initial_activation(x) for block in self.res_blocks: x = block(x) x = self.upsampler(x) for block in self.post_upsample_res_blocks: x = block(x) x = self.final_conv(x) x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f) else: x = self.initial_conv(latent) x = self.initial_norm(x) x = self.initial_activation(x) for block in self.res_blocks: x = block(x) if self.temporal_upsample: x = self.upsampler(x) x = x[:, :, 1:, :, :] else: if isinstance(self.upsampler, SpatialRationalResampler): x = self.upsampler(x) else: x = rearrange(x, "b c f h w -> (b f) c h w") x = self.upsampler(x) x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f) for block in self.post_upsample_res_blocks: x = block(x) x = self.final_conv(x) return x @classmethod def from_config(cls, config): return cls( in_channels=config.get("in_channels", 4), mid_channels=config.get("mid_channels", 128), num_blocks_per_stage=config.get("num_blocks_per_stage", 4), dims=config.get("dims", 2), spatial_upsample=config.get("spatial_upsample", True), temporal_upsample=config.get("temporal_upsample", False), spatial_scale=config.get("spatial_scale", 2.0), rational_resampler=config.get("rational_resampler", False), ) def config(self): return { "_class_name": "LatentUpsampler", "in_channels": self.in_channels, "mid_channels": self.mid_channels, "num_blocks_per_stage": self.num_blocks_per_stage, "dims": self.dims, "spatial_upsample": self.spatial_upsample, "temporal_upsample": self.temporal_upsample, "spatial_scale": self.spatial_scale, "rational_resampler": self.rational_resampler, } ================================================ FILE: comfy/ldm/lightricks/model.py ================================================ from abc import ABC, abstractmethod from enum import Enum import functools import logging import math from typing import Dict, Optional, Tuple from einops import rearrange import numpy as np import torch from torch import nn import comfy.patcher_extension import comfy.ldm.modules.attention import comfy.ldm.common_dit from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords logger = logging.getLogger(__name__) def _log_base(x, base): return np.log(x) / np.log(base) class LTXRopeType(str, Enum): INTERLEAVED = "interleaved" SPLIT = "split" KEY = "rope_type" @classmethod def from_dict(cls, kwargs, default=None): if default is None: default = cls.INTERLEAVED return cls(kwargs.get(cls.KEY, default)) class LTXFrequenciesPrecision(str, Enum): FLOAT32 = "float32" FLOAT64 = "float64" KEY = "frequencies_precision" @classmethod def from_dict(cls, kwargs, default=None): if default is None: default = cls.FLOAT32 return cls(kwargs.get(cls.KEY, default)) def get_timestep_embedding( timesteps: torch.Tensor, embedding_dim: int, flip_sin_to_cos: bool = False, downscale_freq_shift: float = 1, scale: float = 1, max_period: int = 10000, ): """ This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. Args timesteps (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional. embedding_dim (int): the dimension of the output. flip_sin_to_cos (bool): Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False) downscale_freq_shift (float): Controls the delta between frequencies between dimensions scale (float): Scaling factor applied to the embeddings. max_period (int): Controls the maximum frequency of the embeddings Returns torch.Tensor: an [N x dim] Tensor of positional embeddings. """ assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" half_dim = embedding_dim // 2 exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) exponent = exponent / (half_dim - downscale_freq_shift) emb = torch.exp(exponent) emb = timesteps[:, None].float() * emb[None, :] # scale embeddings emb = scale * emb # concat sine and cosine embeddings emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) # flip sine and cosine embeddings if flip_sin_to_cos: emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) # zero pad if embedding_dim % 2 == 1: emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) return emb class TimestepEmbedding(nn.Module): def __init__( self, in_channels: int, time_embed_dim: int, act_fn: str = "silu", out_dim: int = None, post_act_fn: Optional[str] = None, cond_proj_dim=None, sample_proj_bias=True, dtype=None, device=None, operations=None, ): super().__init__() self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device) if cond_proj_dim is not None: self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device) else: self.cond_proj = None self.act = nn.SiLU() if out_dim is not None: time_embed_dim_out = out_dim else: time_embed_dim_out = time_embed_dim self.linear_2 = operations.Linear( time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device ) if post_act_fn is None: self.post_act = None # else: # self.post_act = get_activation(post_act_fn) def forward(self, sample, condition=None): if condition is not None: sample = sample + self.cond_proj(condition) sample = self.linear_1(sample) if self.act is not None: sample = self.act(sample) sample = self.linear_2(sample) if self.post_act is not None: sample = self.post_act(sample) return sample class Timesteps(nn.Module): def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1): super().__init__() self.num_channels = num_channels self.flip_sin_to_cos = flip_sin_to_cos self.downscale_freq_shift = downscale_freq_shift self.scale = scale def forward(self, timesteps): t_emb = get_timestep_embedding( timesteps, self.num_channels, flip_sin_to_cos=self.flip_sin_to_cos, downscale_freq_shift=self.downscale_freq_shift, scale=self.scale, ) return t_emb class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): """ For PixArt-Alpha. Reference: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29 """ def __init__( self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None, ): super().__init__() self.outdim = size_emb_dim self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.timestep_embedder = TimestepEmbedding( in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations ) def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype): timesteps_proj = self.time_proj(timestep) timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) return timesteps_emb class AdaLayerNormSingle(nn.Module): r""" Norm layer adaptive layer norm single (adaLN-single). As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3). Parameters: embedding_dim (`int`): The size of each embedding vector. use_additional_conditions (`bool`): To use additional conditions for normalization or not. """ def __init__( self, embedding_dim: int, embedding_coefficient: int = 6, use_additional_conditions: bool = False, dtype=None, device=None, operations=None ): super().__init__() self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings( embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations, ) self.silu = nn.SiLU() self.linear = operations.Linear(embedding_dim, embedding_coefficient * embedding_dim, bias=True, dtype=dtype, device=device) def forward( self, timestep: torch.Tensor, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, batch_size: Optional[int] = None, hidden_dtype: Optional[torch.dtype] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: # No modulation happening here. added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None} embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype) return self.linear(self.silu(embedded_timestep)), embedded_timestep class PixArtAlphaTextProjection(nn.Module): """ Projects caption embeddings. Also handles dropout for classifier-free guidance. Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py """ def __init__( self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None ): super().__init__() if out_features is None: out_features = hidden_size self.linear_1 = operations.Linear( in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device ) if act_fn == "gelu_tanh": self.act_1 = nn.GELU(approximate="tanh") elif act_fn == "silu": self.act_1 = nn.SiLU() else: raise ValueError(f"Unknown activation function: {act_fn}") self.linear_2 = operations.Linear( in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device ) def forward(self, caption): hidden_states = self.linear_1(caption) hidden_states = self.act_1(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states class NormSingleLinearTextProjection(nn.Module): """Text projection for 20B models - single linear with RMSNorm (no activation).""" def __init__( self, in_features, hidden_size, dtype=None, device=None, operations=None ): super().__init__() if operations is None: operations = comfy.ops.disable_weight_init self.in_norm = operations.RMSNorm( in_features, eps=1e-6, elementwise_affine=False ) self.linear_1 = operations.Linear( in_features, hidden_size, bias=True, dtype=dtype, device=device ) self.hidden_size = hidden_size self.in_features = in_features def forward(self, caption): caption = self.in_norm(caption) caption = caption * (self.hidden_size / self.in_features) ** 0.5 return self.linear_1(caption) class GELU_approx(nn.Module): def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None): super().__init__() self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device) def forward(self, x): return torch.nn.functional.gelu(self.proj(x), approximate="tanh") class FeedForward(nn.Module): def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() inner_dim = int(dim * mult) project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations) self.net = nn.Sequential( project_in, nn.Dropout(dropout), operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) ) def forward(self, x): return self.net(x) def apply_rotary_emb(input_tensor, freqs_cis): cos_freqs, sin_freqs = freqs_cis[0], freqs_cis[1] split_pe = freqs_cis[2] if len(freqs_cis) > 2 else False return ( apply_split_rotary_emb(input_tensor, cos_freqs, sin_freqs) if split_pe else apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs) ) def apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs): # TODO: remove duplicate funcs and pick the best/fastest one t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2) t1, t2 = t_dup.unbind(dim=-1) t_dup = torch.stack((-t2, t1), dim=-1) input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)") out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs return out def apply_split_rotary_emb(input_tensor, cos, sin): needs_reshape = False if input_tensor.ndim != 4 and cos.ndim == 4: B, H, T, _ = cos.shape input_tensor = input_tensor.reshape(B, T, H, -1).swapaxes(1, 2) needs_reshape = True split_input = rearrange(input_tensor, "... (d r) -> ... d r", d=2) first_half_input = split_input[..., :1, :] second_half_input = split_input[..., 1:, :] output = split_input * cos.unsqueeze(-2) first_half_output = output[..., :1, :] second_half_output = output[..., 1:, :] first_half_output.addcmul_(-sin.unsqueeze(-2), second_half_input) second_half_output.addcmul_(sin.unsqueeze(-2), first_half_input) output = rearrange(output, "... d r -> ... (d r)") return output.swapaxes(1, 2).reshape(B, T, -1) if needs_reshape else output class CrossAttention(nn.Module): def __init__( self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, attn_precision=None, apply_gated_attention=False, dtype=None, device=None, operations=None, ): super().__init__() inner_dim = dim_head * heads context_dim = query_dim if context_dim is None else context_dim self.attn_precision = attn_precision self.heads = heads self.dim_head = dim_head self.q_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device) self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) # Optional per-head gating if apply_gated_attention: self.to_gate_logits = operations.Linear(query_dim, heads, bias=True, dtype=dtype, device=device) else: self.to_gate_logits = None self.to_out = nn.Sequential( operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout) ) def forward(self, x, context=None, mask=None, pe=None, k_pe=None, transformer_options={}): q = self.to_q(x) context = x if context is None else context k = self.to_k(context) v = self.to_v(context) q = self.q_norm(q) k = self.k_norm(k) if pe is not None: q = apply_rotary_emb(q, pe) k = apply_rotary_emb(k, pe if k_pe is None else k_pe) if mask is None: out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options) else: out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) # Apply per-head gating if enabled if self.to_gate_logits is not None: gate_logits = self.to_gate_logits(x) # (B, T, H) b, t, _ = out.shape out = out.view(b, t, self.heads, self.dim_head) gates = 2.0 * torch.sigmoid(gate_logits) # zero-init -> identity out = out * gates.unsqueeze(-1) out = out.view(b, t, self.heads * self.dim_head) return self.to_out(out) # 6 base ADaLN params (shift/scale/gate for MSA + MLP), +3 for cross-attention Q (shift/scale/gate) ADALN_BASE_PARAMS_COUNT = 6 ADALN_CROSS_ATTN_PARAMS_COUNT = 9 class BasicTransformerBlock(nn.Module): def __init__( self, dim, n_heads, d_head, context_dim=None, attn_precision=None, cross_attention_adaln=False, dtype=None, device=None, operations=None ): super().__init__() self.attn_precision = attn_precision self.cross_attention_adaln = cross_attention_adaln self.attn1 = CrossAttention( query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations, ) self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations) self.attn2 = CrossAttention( query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations, ) num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, dim, device=device, dtype=dtype)) if cross_attention_adaln: self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, dim, device=device, dtype=dtype)) def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None, prompt_timestep=None): shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None, :6].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, :6, :]).unbind(dim=2) x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, mask=self_attention_mask, transformer_options=transformer_options) * gate_msa if self.cross_attention_adaln: shift_q_mca, scale_q_mca, gate_mca = (self.scale_shift_table[None, None, 6:9].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, 6:9, :]).unbind(dim=2) x += apply_cross_attention_adaln( x, context, self.attn2, shift_q_mca, scale_q_mca, gate_mca, self.prompt_scale_shift_table, prompt_timestep, attention_mask, transformer_options, ) else: x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options) y = comfy.ldm.common_dit.rms_norm(x) y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp) x.addcmul_(self.ff(y), gate_mlp) return x def compute_prompt_timestep(adaln_module, timestep_scaled, batch_size, hidden_dtype): """Compute a single global prompt timestep for cross-attention ADaLN. Uses the max across tokens (matching JAX max_per_segment) and broadcasts over text tokens. Returns None when *adaln_module* is None. """ if adaln_module is None: return None ts_input = ( timestep_scaled.max(dim=1, keepdim=True).values.flatten() if timestep_scaled.dim() > 1 else timestep_scaled.flatten() ) prompt_ts, _ = adaln_module( ts_input, {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) return prompt_ts.view(batch_size, 1, prompt_ts.shape[-1]) def apply_cross_attention_adaln( x, context, attn, q_shift, q_scale, q_gate, prompt_scale_shift_table, prompt_timestep, attention_mask=None, transformer_options={}, ): """Apply cross-attention with ADaLN modulation (shift/scale/gate on Q and KV). Q params (q_shift, q_scale, q_gate) are pre-extracted by the caller so that both regular tensors and CompressedTimestep are supported. """ batch_size = x.shape[0] shift_kv, scale_kv = ( prompt_scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + prompt_timestep.reshape(batch_size, prompt_timestep.shape[1], 2, -1) ).unbind(dim=2) attn_input = comfy.ldm.common_dit.rms_norm(x) * (1 + q_scale) + q_shift encoder_hidden_states = context * (1 + scale_kv) + shift_kv return attn(attn_input, context=encoder_hidden_states, mask=attention_mask, transformer_options=transformer_options) * q_gate def get_fractional_positions(indices_grid, max_pos): n_pos_dims = indices_grid.shape[1] assert n_pos_dims == len(max_pos), f'Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})' fractional_positions = torch.stack( [indices_grid[:, i] / max_pos[i] for i in range(n_pos_dims)], axis=-1, ) return fractional_positions @functools.lru_cache(maxsize=5) def generate_freq_grid_np(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, _ = None): theta = positional_embedding_theta start = 1 end = theta n_elem = 2 * positional_embedding_max_pos_count pow_indices = np.power( theta, np.linspace( _log_base(start, theta), _log_base(end, theta), inner_dim // n_elem, dtype=np.float64, ), ) return torch.tensor(pow_indices * math.pi / 2, dtype=torch.float32) def generate_freq_grid_pytorch(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, device): theta = positional_embedding_theta start = 1 end = theta n_elem = 2 * positional_embedding_max_pos_count indices = theta ** ( torch.linspace( math.log(start, theta), math.log(end, theta), inner_dim // n_elem, device=device, dtype=torch.float32, ) ) indices = indices.to(dtype=torch.float32) indices = indices * math.pi / 2 return indices def generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid): if use_middle_indices_grid: assert(len(indices_grid.shape) == 4 and indices_grid.shape[-1] ==2) indices_grid_start, indices_grid_end = indices_grid[..., 0], indices_grid[..., 1] indices_grid = (indices_grid_start + indices_grid_end) / 2.0 elif len(indices_grid.shape) == 4: indices_grid = indices_grid[..., 0] # Get fractional positions and compute frequency indices fractional_positions = get_fractional_positions(indices_grid, max_pos) indices = indices.to(device=fractional_positions.device) freqs = ( (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)) .transpose(-1, -2) .flatten(2) ) return freqs def interleaved_freqs_cis(freqs, pad_size): cos_freq = freqs.cos().repeat_interleave(2, dim=-1) sin_freq = freqs.sin().repeat_interleave(2, dim=-1) if pad_size != 0: cos_padding = torch.ones_like(cos_freq[:, :, : pad_size]) sin_padding = torch.zeros_like(cos_freq[:, :, : pad_size]) cos_freq = torch.cat([cos_padding, cos_freq], dim=-1) sin_freq = torch.cat([sin_padding, sin_freq], dim=-1) return cos_freq, sin_freq def split_freqs_cis(freqs, pad_size, num_attention_heads): cos_freq = freqs.cos() sin_freq = freqs.sin() if pad_size != 0: cos_padding = torch.ones_like(cos_freq[:, :, :pad_size]) sin_padding = torch.zeros_like(sin_freq[:, :, :pad_size]) cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1) sin_freq = torch.concatenate([sin_padding, sin_freq], axis=-1) # Reshape freqs to be compatible with multi-head attention B , T, half_HD = cos_freq.shape cos_freq = cos_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads) sin_freq = sin_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads) cos_freq = torch.swapaxes(cos_freq, 1, 2) # (B,H,T,D//2) sin_freq = torch.swapaxes(sin_freq, 1, 2) # (B,H,T,D//2) return cos_freq, sin_freq class LTXBaseModel(torch.nn.Module, ABC): """ Abstract base class for LTX models (Lightricks Transformer models). This class defines the common interface and shared functionality for all LTX models, including LTXV (video) and LTXAV (audio-video) variants. """ def __init__( self, in_channels: int, cross_attention_dim: int, attention_head_dim: int, num_attention_heads: int, caption_channels: int, num_layers: int, positional_embedding_theta: float = 10000.0, positional_embedding_max_pos: list = [20, 2048, 2048], causal_temporal_positioning: bool = False, vae_scale_factors: tuple = (8, 32, 32), use_middle_indices_grid=False, timestep_scale_multiplier = 1000.0, caption_proj_before_connector=False, cross_attention_adaln=False, caption_projection_first_linear=True, dtype=None, device=None, operations=None, **kwargs, ): super().__init__() self.generator = None self.vae_scale_factors = vae_scale_factors self.use_middle_indices_grid = use_middle_indices_grid self.dtype = dtype self.in_channels = in_channels self.cross_attention_dim = cross_attention_dim self.attention_head_dim = attention_head_dim self.num_attention_heads = num_attention_heads self.caption_channels = caption_channels self.num_layers = num_layers self.positional_embedding_theta = positional_embedding_theta self.positional_embedding_max_pos = positional_embedding_max_pos self.split_positional_embedding = LTXRopeType.from_dict(kwargs) self.freq_grid_generator = ( generate_freq_grid_np if LTXFrequenciesPrecision.from_dict(kwargs) == LTXFrequenciesPrecision.FLOAT64 else generate_freq_grid_pytorch ) self.causal_temporal_positioning = causal_temporal_positioning self.operations = operations self.timestep_scale_multiplier = timestep_scale_multiplier self.caption_proj_before_connector = caption_proj_before_connector self.cross_attention_adaln = cross_attention_adaln self.caption_projection_first_linear = caption_projection_first_linear # Common dimensions self.inner_dim = num_attention_heads * attention_head_dim self.out_channels = in_channels # Initialize common components self._init_common_components(device, dtype) # Initialize model-specific components self._init_model_components(device, dtype, **kwargs) # Initialize transformer blocks self._init_transformer_blocks(device, dtype, **kwargs) # Initialize output components self._init_output_components(device, dtype) def _init_common_components(self, device, dtype): """Initialize components common to all LTX models - patchify_proj: Linear projection for patchifying input - adaln_single: AdaLN layer for timestep embedding - caption_projection: Linear projection for caption embedding """ self.patchify_proj = self.operations.Linear( self.in_channels, self.inner_dim, bias=True, dtype=dtype, device=device ) embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT self.adaln_single = AdaLayerNormSingle( self.inner_dim, embedding_coefficient=embedding_coefficient, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations ) if self.cross_attention_adaln: self.prompt_adaln_single = AdaLayerNormSingle( self.inner_dim, embedding_coefficient=2, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations ) else: self.prompt_adaln_single = None if self.caption_proj_before_connector: if self.caption_projection_first_linear: self.caption_projection = NormSingleLinearTextProjection( in_features=self.caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=self.operations, ) else: self.caption_projection = lambda a: a else: self.caption_projection = PixArtAlphaTextProjection( in_features=self.caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=self.operations, ) @abstractmethod def _init_model_components(self, device, dtype, **kwargs): """Initialize model-specific components. Must be implemented by subclasses.""" pass @abstractmethod def _init_transformer_blocks(self, device, dtype, **kwargs): """Initialize transformer blocks. Must be implemented by subclasses.""" pass @abstractmethod def _init_output_components(self, device, dtype): """Initialize output components. Must be implemented by subclasses.""" pass @abstractmethod def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs): """Process input data. Must be implemented by subclasses.""" pass def _build_guide_self_attention_mask(self, x, transformer_options, merged_args): """Build self-attention mask for per-guide attention attenuation. Base implementation returns None (no attenuation). Subclasses that support guide-based attention control should override this. """ return None @abstractmethod def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, self_attention_mask=None, **kwargs): """Process transformer blocks. Must be implemented by subclasses.""" pass @abstractmethod def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs): """Process output data. Must be implemented by subclasses.""" pass def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs): """Prepare timestep embeddings.""" grid_mask = kwargs.get("grid_mask", None) if grid_mask is not None: timestep = timestep[:, grid_mask] timestep_scaled = timestep * self.timestep_scale_multiplier timestep, embedded_timestep = self.adaln_single( timestep_scaled.flatten(), {"resolution": None, "aspect_ratio": None}, batch_size=batch_size, hidden_dtype=hidden_dtype, ) # Second dimension is 1 or number of tokens (if timestep_per_token) timestep = timestep.view(batch_size, -1, timestep.shape[-1]) embedded_timestep = embedded_timestep.view(batch_size, -1, embedded_timestep.shape[-1]) prompt_timestep = compute_prompt_timestep( self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype ) return timestep, embedded_timestep, prompt_timestep def _prepare_context(self, context, batch_size, x, attention_mask=None): """Prepare context for transformer blocks.""" if self.caption_proj_before_connector is False: context = self.caption_projection(context) context = context.view(batch_size, -1, x.shape[-1]) return context, attention_mask def _precompute_freqs_cis( self, indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048], use_middle_indices_grid=False, num_attention_heads=32, ): split_mode = self.split_positional_embedding == LTXRopeType.SPLIT indices = self.freq_grid_generator(theta, indices_grid.shape[1], dim, indices_grid.device) freqs = generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid) if split_mode: expected_freqs = dim // 2 current_freqs = freqs.shape[-1] pad_size = expected_freqs - current_freqs cos_freq, sin_freq = split_freqs_cis(freqs, pad_size, num_attention_heads) else: # 2 because of cos and sin by 3 for (t, x, y), 1 for temporal only n_elem = 2 * indices_grid.shape[1] cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem) return cos_freq.to(out_dtype), sin_freq.to(out_dtype), split_mode def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype): """Prepare positional embeddings.""" fractional_coords = pixel_coords.to(torch.float32) fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate) pe = self._precompute_freqs_cis( fractional_coords, dim=self.inner_dim, out_dtype=x_dtype, max_pos=self.positional_embedding_max_pos, use_middle_indices_grid=self.use_middle_indices_grid, num_attention_heads=self.num_attention_heads, ) return pe def _prepare_attention_mask(self, attention_mask, x_dtype): """Prepare attention mask.""" if attention_mask is not None and not torch.is_floating_point(attention_mask): attention_mask = (attention_mask - 1).to(x_dtype).reshape( (attention_mask.shape[0], 1, -1, attention_mask.shape[-1]) ) * torch.finfo(x_dtype).max return attention_mask def forward( self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs ): """ Forward pass for LTX models. Args: x: Input tensor timestep: Timestep tensor context: Context tensor (e.g., text embeddings) attention_mask: Attention mask tensor frame_rate: Frame rate for temporal processing transformer_options: Additional options for transformer blocks keyframe_idxs: Keyframe indices for temporal processing **kwargs: Additional keyword arguments Returns: Processed output tensor """ return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers( comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options ), ).execute(x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, denoise_mask=denoise_mask, **kwargs) def _forward( self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs ): """ Internal forward pass for LTX models. Args: x: Input tensor timestep: Timestep tensor context: Context tensor (e.g., text embeddings) attention_mask: Attention mask tensor frame_rate: Frame rate for temporal processing transformer_options: Additional options for transformer blocks keyframe_idxs: Keyframe indices for temporal processing **kwargs: Additional keyword arguments Returns: Processed output tensor """ if isinstance(x, list): input_dtype = x[0].dtype batch_size = x[0].shape[0] else: input_dtype = x.dtype batch_size = x.shape[0] # Process input merged_args = {**transformer_options, **kwargs} x, pixel_coords, additional_args = self._process_input(x, keyframe_idxs, denoise_mask, **merged_args) merged_args.update(additional_args) # Prepare timestep and context timestep, embedded_timestep, prompt_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args) merged_args["prompt_timestep"] = prompt_timestep context, attention_mask = self._prepare_context(context, batch_size, x, attention_mask) # Prepare attention mask and positional embeddings attention_mask = self._prepare_attention_mask(attention_mask, input_dtype) pe = self._prepare_positional_embeddings(pixel_coords, frame_rate, input_dtype) # Build self-attention mask for per-guide attenuation self_attention_mask = self._build_guide_self_attention_mask( x, transformer_options, merged_args ) # Process transformer blocks x = self._process_transformer_blocks( x, context, attention_mask, timestep, pe, transformer_options=transformer_options, self_attention_mask=self_attention_mask, **merged_args, ) # Process output x = self._process_output(x, embedded_timestep, keyframe_idxs, **merged_args) return x class LTXVModel(LTXBaseModel): """LTXV model for video generation.""" def __init__( self, in_channels=128, cross_attention_dim=2048, attention_head_dim=64, num_attention_heads=32, caption_channels=4096, num_layers=28, positional_embedding_theta=10000.0, positional_embedding_max_pos=[20, 2048, 2048], causal_temporal_positioning=False, vae_scale_factors=(8, 32, 32), use_middle_indices_grid=False, timestep_scale_multiplier=1000.0, caption_proj_before_connector=False, cross_attention_adaln=False, dtype=None, device=None, operations=None, **kwargs, ): super().__init__( in_channels=in_channels, cross_attention_dim=cross_attention_dim, attention_head_dim=attention_head_dim, num_attention_heads=num_attention_heads, caption_channels=caption_channels, num_layers=num_layers, positional_embedding_theta=positional_embedding_theta, positional_embedding_max_pos=positional_embedding_max_pos, causal_temporal_positioning=causal_temporal_positioning, vae_scale_factors=vae_scale_factors, use_middle_indices_grid=use_middle_indices_grid, timestep_scale_multiplier=timestep_scale_multiplier, caption_proj_before_connector=caption_proj_before_connector, cross_attention_adaln=cross_attention_adaln, dtype=dtype, device=device, operations=operations, **kwargs, ) def _init_model_components(self, device, dtype, **kwargs): """Initialize LTXV-specific components.""" pass def _init_transformer_blocks(self, device, dtype, **kwargs): """Initialize transformer blocks for LTXV.""" self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( self.inner_dim, self.num_attention_heads, self.attention_head_dim, context_dim=self.cross_attention_dim, cross_attention_adaln=self.cross_attention_adaln, dtype=dtype, device=device, operations=self.operations, ) for _ in range(self.num_layers) ] ) def _init_output_components(self, device, dtype): """Initialize output components for LTXV.""" self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device)) self.norm_out = self.operations.LayerNorm( self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device ) self.proj_out = self.operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device) self.patchifier = SymmetricPatchifier(1, start_end=True) def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs): """Process input for LTXV.""" additional_args = {"orig_shape": list(x.shape)} x, latent_coords = self.patchifier.patchify(x) pixel_coords = latent_to_pixel_coords( latent_coords=latent_coords, scale_factors=self.vae_scale_factors, causal_fix=self.causal_temporal_positioning, ) grid_mask = None if keyframe_idxs is not None: additional_args.update({ "orig_patchified_shape": list(x.shape)}) denoise_mask = self.patchifier.patchify(denoise_mask)[0] grid_mask = ~torch.any(denoise_mask < 0, dim=-1)[0] additional_args.update({"grid_mask": grid_mask}) x = x[:, grid_mask, :] pixel_coords = pixel_coords[:, :, grid_mask, ...] kf_grid_mask = grid_mask[-keyframe_idxs.shape[2]:] # Compute per-guide surviving token counts from guide_attention_entries. # Each entry tracks one guide reference; they are appended in order and # their pre_filter_counts partition the kf_grid_mask. guide_entries = kwargs.get("guide_attention_entries", None) if guide_entries: total_pfc = sum(e["pre_filter_count"] for e in guide_entries) if total_pfc != len(kf_grid_mask): raise ValueError( f"guide pre_filter_counts ({total_pfc}) != " f"keyframe grid mask length ({len(kf_grid_mask)})" ) resolved_entries = [] offset = 0 for entry in guide_entries: pfc = entry["pre_filter_count"] entry_mask = kf_grid_mask[offset:offset + pfc] surviving = int(entry_mask.sum().item()) resolved_entries.append({ **entry, "surviving_count": surviving, }) offset += pfc additional_args["resolved_guide_entries"] = resolved_entries keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :] pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs # Total surviving guide tokens (all guides) additional_args["num_guide_tokens"] = keyframe_idxs.shape[2] x = self.patchify_proj(x) return x, pixel_coords, additional_args def _build_guide_self_attention_mask(self, x, transformer_options, merged_args): """Build self-attention mask for per-guide attention attenuation. Reads resolved_guide_entries from merged_args (computed in _process_input) to build a log-space additive bias mask that attenuates noisy ↔ guide attention for each guide reference independently. Returns None if no attenuation is needed (all strengths == 1.0 and no spatial masks, or no guide tokens). """ if isinstance(x, list): # AV model: x = [vx, ax]; use vx for token count and device total_tokens = x[0].shape[1] device = x[0].device dtype = x[0].dtype else: total_tokens = x.shape[1] device = x.device dtype = x.dtype num_guide_tokens = merged_args.get("num_guide_tokens", 0) if num_guide_tokens == 0: return None resolved_entries = merged_args.get("resolved_guide_entries", None) if not resolved_entries: return None # Check if any attenuation is actually needed needs_attenuation = any( e["strength"] < 1.0 or e.get("pixel_mask") is not None for e in resolved_entries ) if not needs_attenuation: return None # Build per-guide-token weights for all tracked guide tokens. # Guides are appended in order at the end of the sequence. guide_start = total_tokens - num_guide_tokens all_weights = [] total_tracked = 0 for entry in resolved_entries: surviving = entry["surviving_count"] if surviving == 0: continue strength = entry["strength"] pixel_mask = entry.get("pixel_mask") latent_shape = entry.get("latent_shape") if pixel_mask is not None and latent_shape is not None: f_lat, h_lat, w_lat = latent_shape per_token = self._downsample_mask_to_latent( pixel_mask.to(device=device, dtype=dtype), f_lat, h_lat, w_lat, ) # per_token shape: (B, f_lat*h_lat*w_lat). # Collapse batch dim — the mask is assumed identical across the # batch; validate and take the first element to get (1, tokens). if per_token.shape[0] > 1: ref = per_token[0] for bi in range(1, per_token.shape[0]): if not torch.equal(ref, per_token[bi]): logger.warning( "pixel_mask differs across batch elements; " "using first element only." ) break per_token = per_token[:1] # `surviving` is the post-grid_mask token count. # Clamp to surviving to handle any mismatch safely. n_weights = min(per_token.shape[1], surviving) weights = per_token[:, :n_weights] * strength # (1, n_weights) else: weights = torch.full( (1, surviving), strength, device=device, dtype=dtype ) all_weights.append(weights) total_tracked += weights.shape[1] if not all_weights: return None # Concatenate per-token weights for all tracked guides tracked_weights = torch.cat(all_weights, dim=1) # (1, total_tracked) # Check if any weight is actually < 1.0 (otherwise no attenuation needed) if (tracked_weights >= 1.0).all(): return None # Build the mask: guide tokens are at the end of the sequence. # Tracked guides come first (in order), untracked follow. return self._build_self_attention_mask( total_tokens, num_guide_tokens, total_tracked, tracked_weights, guide_start, device, dtype, ) @staticmethod def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat): """Downsample a pixel-space mask to per-token latent weights. Args: mask: (B, 1, F_pix, H_pix, W_pix) pixel-space mask with values in [0, 1]. f_lat: Number of latent frames (pre-dilation original count). h_lat: Latent height (pre-dilation original height). w_lat: Latent width (pre-dilation original width). Returns: (B, F_lat * H_lat * W_lat) flattened per-token weights. """ b = mask.shape[0] f_pix = mask.shape[2] # Spatial downsampling: area interpolation per frame spatial_down = torch.nn.functional.interpolate( rearrange(mask, "b 1 f h w -> (b f) 1 h w"), size=(h_lat, w_lat), mode="area", ) spatial_down = rearrange(spatial_down, "(b f) 1 h w -> b 1 f h w", b=b) # Temporal downsampling: first pixel frame maps to first latent frame, # remaining pixel frames are averaged in groups for causal temporal structure. first_frame = spatial_down[:, :, :1, :, :] if f_pix > 1 and f_lat > 1: remaining_pix = f_pix - 1 remaining_lat = f_lat - 1 t = remaining_pix // remaining_lat if t < 1: # Fewer pixel frames than latent frames — upsample by repeating # the available pixel frames via nearest interpolation. rest_flat = rearrange( spatial_down[:, :, 1:, :, :], "b 1 f h w -> (b h w) 1 f", ) rest_up = torch.nn.functional.interpolate( rest_flat, size=remaining_lat, mode="nearest", ) rest = rearrange( rest_up, "(b h w) 1 f -> b 1 f h w", b=b, h=h_lat, w=w_lat, ) else: # Trim trailing pixel frames that don't fill a complete group usable = remaining_lat * t rest = rearrange( spatial_down[:, :, 1:1 + usable, :, :], "b 1 (f t) h w -> b 1 f t h w", t=t, ) rest = rest.mean(dim=3) latent_mask = torch.cat([first_frame, rest], dim=2) elif f_lat > 1: # Single pixel frame but multiple latent frames — repeat the # single frame across all latent frames. latent_mask = first_frame.expand(-1, -1, f_lat, -1, -1) else: latent_mask = first_frame return rearrange(latent_mask, "b 1 f h w -> b (f h w)") @staticmethod def _build_self_attention_mask(total_tokens, num_guide_tokens, tracked_count, tracked_weights, guide_start, device, dtype): """Build a log-space additive self-attention bias mask. Attenuates attention between noisy tokens and tracked guide tokens. Untracked guide tokens (at the end of the guide portion) keep full attention. Args: total_tokens: Total sequence length. num_guide_tokens: Total guide tokens (all guides) at end of sequence. tracked_count: Number of tracked guide tokens (first in the guide portion). tracked_weights: (1, tracked_count) tensor, values in [0, 1]. guide_start: Index where guide tokens begin in the sequence. device: Target device. dtype: Target dtype. Returns: (1, 1, total_tokens, total_tokens) additive bias mask. 0.0 = full attention, negative = attenuated, finfo.min = effectively fully masked. """ finfo = torch.finfo(dtype) mask = torch.zeros((1, 1, total_tokens, total_tokens), device=device, dtype=dtype) tracked_end = guide_start + tracked_count # Convert weights to log-space bias w = tracked_weights.to(device=device, dtype=dtype) # (1, tracked_count) log_w = torch.full_like(w, finfo.min) positive_mask = w > 0 if positive_mask.any(): log_w[positive_mask] = torch.log(w[positive_mask].clamp(min=finfo.tiny)) # noisy → tracked guides: each noisy row gets the same per-guide weight mask[:, :, :guide_start, guide_start:tracked_end] = log_w.view(1, 1, 1, -1) # tracked guides → noisy: each guide row broadcasts its weight across noisy cols mask[:, :, guide_start:tracked_end, :guide_start] = log_w.view(1, 1, -1, 1) return mask def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs): """Process transformer blocks for LTXV.""" patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) prompt_timestep = kwargs.get("prompt_timestep", None) for i, block in enumerate(self.transformer_blocks): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"], self_attention_mask=args.get("self_attention_mask"), prompt_timestep=args.get("prompt_timestep")) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask, "prompt_timestep": prompt_timestep}, {"original_block": block_wrap}) x = out["img"] else: x = block( x, context=context, attention_mask=attention_mask, timestep=timestep, pe=pe, transformer_options=transformer_options, self_attention_mask=self_attention_mask, prompt_timestep=prompt_timestep, ) return x def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs): """Process output for LTXV.""" # Apply scale-shift modulation scale_shift_values = ( self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None] ) shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1] x = self.norm_out(x) x = x * (1 + scale) + shift x = self.proj_out(x) if keyframe_idxs is not None: grid_mask = kwargs["grid_mask"] orig_patchified_shape = kwargs["orig_patchified_shape"] full_x = torch.zeros(orig_patchified_shape, dtype=x.dtype, device=x.device) full_x[:, grid_mask, :] = x x = full_x # Unpatchify to restore original dimensions orig_shape = kwargs["orig_shape"] x = self.patchifier.unpatchify( latents=x, output_height=orig_shape[3], output_width=orig_shape[4], output_num_frames=orig_shape[2], out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size), ) return x ================================================ FILE: comfy/ldm/lightricks/symmetric_patchifier.py ================================================ from abc import ABC, abstractmethod from typing import Tuple import torch from einops import rearrange from torch import Tensor def latent_to_pixel_coords( latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False ) -> Tensor: """ Converts latent coordinates to pixel coordinates by scaling them according to the VAE's configuration. Args: latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents] containing the latent corner coordinates of each token. scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space. causal_fix (bool): Whether to take into account the different temporal scale of the first frame. Default = False for backwards compatibility. Returns: Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates. """ shape = [1] * latent_coords.ndim shape[1] = -1 pixel_coords = ( latent_coords * torch.tensor(scale_factors, device=latent_coords.device).view(*shape) ) if causal_fix: # Fix temporal scale for first frame to 1 due to causality pixel_coords[:, 0, ...] = (pixel_coords[:, 0, ...] + 1 - scale_factors[0]).clamp(min=0) return pixel_coords class Patchifier(ABC): def __init__(self, patch_size: int, start_end: bool=False): super().__init__() self._patch_size = (1, patch_size, patch_size) self.start_end = start_end @abstractmethod def patchify( self, latents: Tensor, frame_rates: Tensor, scale_grid: bool ) -> Tuple[Tensor, Tensor]: pass @abstractmethod def unpatchify( self, latents: Tensor, output_height: int, output_width: int, output_num_frames: int, out_channels: int, ) -> Tuple[Tensor, Tensor]: pass @property def patch_size(self): return self._patch_size def get_latent_coords( self, latent_num_frames, latent_height, latent_width, batch_size, device ): """ Return a tensor of shape [batch_size, 3, num_patches] containing the top-left corner latent coordinates of each latent patch. The tensor is repeated for each batch element. """ latent_sample_coords = torch.meshgrid( torch.arange(0, latent_num_frames, self._patch_size[0], device=device), torch.arange(0, latent_height, self._patch_size[1], device=device), torch.arange(0, latent_width, self._patch_size[2], device=device), indexing="ij", ) latent_sample_coords_start = torch.stack(latent_sample_coords, dim=0) delta = torch.tensor(self._patch_size, device=latent_sample_coords_start.device, dtype=latent_sample_coords_start.dtype)[:, None, None, None] latent_sample_coords_end = latent_sample_coords_start + delta latent_sample_coords_start = latent_sample_coords_start.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) latent_sample_coords_start = rearrange( latent_sample_coords_start, "b c f h w -> b c (f h w)", b=batch_size ) if self.start_end: latent_sample_coords_end = latent_sample_coords_end.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) latent_sample_coords_end = rearrange( latent_sample_coords_end, "b c f h w -> b c (f h w)", b=batch_size ) latent_coords = torch.stack((latent_sample_coords_start, latent_sample_coords_end), dim=-1) else: latent_coords = latent_sample_coords_start return latent_coords class SymmetricPatchifier(Patchifier): def patchify( self, latents: Tensor, ) -> Tuple[Tensor, Tensor]: b, _, f, h, w = latents.shape latent_coords = self.get_latent_coords(f, h, w, b, latents.device) latents = rearrange( latents, "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)", p1=self._patch_size[0], p2=self._patch_size[1], p3=self._patch_size[2], ) return latents, latent_coords def unpatchify( self, latents: Tensor, output_height: int, output_width: int, output_num_frames: int, out_channels: int, ) -> Tuple[Tensor, Tensor]: output_height = output_height // self._patch_size[1] output_width = output_width // self._patch_size[2] latents = rearrange( latents, "b (f h w) (c p q) -> b c f (h p) (w q) ", f=output_num_frames, h=output_height, w=output_width, p=self._patch_size[1], q=self._patch_size[2], ) return latents class AudioPatchifier(Patchifier): def __init__(self, patch_size: int, sample_rate=16000, hop_length=160, audio_latent_downsample_factor=4, is_causal=True, start_end=False, shift = 0 ): super().__init__(patch_size, start_end=start_end) self.hop_length = hop_length self.sample_rate = sample_rate self.audio_latent_downsample_factor = audio_latent_downsample_factor self.is_causal = is_causal self.shift = shift def copy_with_shift(self, shift): return AudioPatchifier( self.patch_size, self.sample_rate, self.hop_length, self.audio_latent_downsample_factor, self.is_causal, self.start_end, shift ) def _get_audio_latent_time_in_sec(self, start_latent, end_latent: int, dtype: torch.dtype, device=torch.device): audio_latent_frame = torch.arange(start_latent, end_latent, dtype=dtype, device=device) audio_mel_frame = audio_latent_frame * self.audio_latent_downsample_factor if self.is_causal: audio_mel_frame = (audio_mel_frame + 1 - self.audio_latent_downsample_factor).clip(min=0) return audio_mel_frame * self.hop_length / self.sample_rate def patchify(self, audio_latents: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # audio_latents: (batch, channels, time, freq) b, _, t, _ = audio_latents.shape audio_latents = rearrange( audio_latents, "b c t f -> b t (c f)", ) audio_latents_start_timings = self._get_audio_latent_time_in_sec(self.shift, t + self.shift, torch.float32, audio_latents.device) audio_latents_start_timings = audio_latents_start_timings.unsqueeze(0).expand(b, -1).unsqueeze(1) if self.start_end: audio_latents_end_timings = self._get_audio_latent_time_in_sec(self.shift + 1, t + self.shift + 1, torch.float32, audio_latents.device) audio_latents_end_timings = audio_latents_end_timings.unsqueeze(0).expand(b, -1).unsqueeze(1) audio_latents_timings = torch.stack([audio_latents_start_timings, audio_latents_end_timings], dim=-1) else: audio_latents_timings = audio_latents_start_timings return audio_latents, audio_latents_timings def unpatchify(self, audio_latents: torch.Tensor, channels: int, freq: int) -> torch.Tensor: # audio_latents: (batch, time, freq * channels) audio_latents = rearrange( audio_latents, "b t (c f) -> b c t f", c=channels, f=freq ) return audio_latents ================================================ FILE: comfy/ldm/lightricks/vae/audio_vae.py ================================================ import json from dataclasses import dataclass import math import torch import torchaudio import comfy.model_management import comfy.model_patcher import comfy.utils as utils from comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier from comfy.ldm.lightricks.vae.causal_audio_autoencoder import ( CausalityAxis, CausalAudioAutoencoder, ) from comfy.ldm.lightricks.vocoders.vocoder import Vocoder, VocoderWithBWE LATENT_DOWNSAMPLE_FACTOR = 4 @dataclass(frozen=True) class AudioVAEComponentConfig: """Container for model component configuration extracted from metadata.""" autoencoder: dict vocoder: dict @classmethod def from_metadata(cls, metadata: dict) -> "AudioVAEComponentConfig": assert metadata is not None and "config" in metadata, "Metadata is required for audio VAE" raw_config = metadata["config"] if isinstance(raw_config, str): parsed_config = json.loads(raw_config) else: parsed_config = raw_config audio_config = parsed_config.get("audio_vae") vocoder_config = parsed_config.get("vocoder") assert audio_config is not None, "Audio VAE config is required for audio VAE" assert vocoder_config is not None, "Vocoder config is required for audio VAE" return cls(autoencoder=audio_config, vocoder=vocoder_config) class ModelDeviceManager: """Manages device placement and GPU residency for the composed model.""" def __init__(self, module: torch.nn.Module): load_device = comfy.model_management.get_torch_device() offload_device = comfy.model_management.vae_offload_device() self.patcher = comfy.model_patcher.ModelPatcher(module, load_device, offload_device) def ensure_model_loaded(self) -> None: comfy.model_management.free_memory( self.patcher.model_size(), self.patcher.load_device, ) comfy.model_management.load_model_gpu(self.patcher) def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor: return tensor.to(self.patcher.load_device) @property def load_device(self): return self.patcher.load_device class AudioLatentNormalizer: """Applies per-channel statistics in patch space and restores original layout.""" def __init__(self, patchfier: AudioPatchifier, statistics_processor: torch.nn.Module): self.patchifier = patchfier self.statistics = statistics_processor def normalize(self, latents: torch.Tensor) -> torch.Tensor: channels = latents.shape[1] freq = latents.shape[3] patched, _ = self.patchifier.patchify(latents) normalized = self.statistics.normalize(patched) return self.patchifier.unpatchify(normalized, channels=channels, freq=freq) def denormalize(self, latents: torch.Tensor) -> torch.Tensor: channels = latents.shape[1] freq = latents.shape[3] patched, _ = self.patchifier.patchify(latents) denormalized = self.statistics.un_normalize(patched) return self.patchifier.unpatchify(denormalized, channels=channels, freq=freq) class AudioPreprocessor: """Prepares raw waveforms for the autoencoder by matching training conditions.""" def __init__(self, target_sample_rate: int, mel_bins: int, mel_hop_length: int, n_fft: int): self.target_sample_rate = target_sample_rate self.mel_bins = mel_bins self.mel_hop_length = mel_hop_length self.n_fft = n_fft def resample(self, waveform: torch.Tensor, source_rate: int) -> torch.Tensor: if source_rate == self.target_sample_rate: return waveform return torchaudio.functional.resample(waveform, source_rate, self.target_sample_rate) def waveform_to_mel( self, waveform: torch.Tensor, waveform_sample_rate: int, device ) -> torch.Tensor: waveform = self.resample(waveform, waveform_sample_rate) mel_transform = torchaudio.transforms.MelSpectrogram( sample_rate=self.target_sample_rate, n_fft=self.n_fft, win_length=self.n_fft, hop_length=self.mel_hop_length, f_min=0.0, f_max=self.target_sample_rate / 2.0, n_mels=self.mel_bins, window_fn=torch.hann_window, center=True, pad_mode="reflect", power=1.0, mel_scale="slaney", norm="slaney", ).to(device) mel = mel_transform(waveform) mel = torch.log(torch.clamp(mel, min=1e-5)) return mel.permute(0, 1, 3, 2).contiguous() class AudioVAE(torch.nn.Module): """High-level Audio VAE wrapper exposing encode and decode entry points.""" def __init__(self, state_dict: dict, metadata: dict): super().__init__() component_config = AudioVAEComponentConfig.from_metadata(metadata) vae_sd = utils.state_dict_prefix_replace(state_dict, {"audio_vae.": ""}, filter_keys=True) vocoder_sd = utils.state_dict_prefix_replace(state_dict, {"vocoder.": ""}, filter_keys=True) self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder) if "bwe" in component_config.vocoder: self.vocoder = VocoderWithBWE(config=component_config.vocoder) else: self.vocoder = Vocoder(config=component_config.vocoder) self.autoencoder.load_state_dict(vae_sd, strict=False) self.vocoder.load_state_dict(vocoder_sd, strict=False) autoencoder_config = self.autoencoder.get_config() self.normalizer = AudioLatentNormalizer( AudioPatchifier( patch_size=1, audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR, sample_rate=autoencoder_config["sampling_rate"], hop_length=autoencoder_config["mel_hop_length"], is_causal=autoencoder_config["is_causal"], ), self.autoencoder.per_channel_statistics, ) self.preprocessor = AudioPreprocessor( target_sample_rate=autoencoder_config["sampling_rate"], mel_bins=autoencoder_config["mel_bins"], mel_hop_length=autoencoder_config["mel_hop_length"], n_fft=autoencoder_config["n_fft"], ) self.device_manager = ModelDeviceManager(self) def encode(self, audio: dict) -> torch.Tensor: """Encode a waveform dictionary into normalized latent tensors.""" waveform = audio["waveform"] waveform_sample_rate = audio["sample_rate"] input_device = waveform.device # Ensure that Audio VAE is loaded on the correct device. self.device_manager.ensure_model_loaded() waveform = self.device_manager.move_to_load_device(waveform) expected_channels = self.autoencoder.encoder.in_channels if waveform.shape[1] != expected_channels: if waveform.shape[1] == 1: waveform = waveform.expand(-1, expected_channels, *waveform.shape[2:]) else: raise ValueError( f"Input audio must have {expected_channels} channels, got {waveform.shape[1]}" ) mel_spec = self.preprocessor.waveform_to_mel( waveform, waveform_sample_rate, device=self.device_manager.load_device ) latents = self.autoencoder.encode(mel_spec) posterior = DiagonalGaussianDistribution(latents) latent_mode = posterior.mode() normalized = self.normalizer.normalize(latent_mode) return normalized.to(input_device) def decode(self, latents: torch.Tensor) -> torch.Tensor: """Decode normalized latent tensors into an audio waveform.""" original_shape = latents.shape # Ensure that Audio VAE is loaded on the correct device. self.device_manager.ensure_model_loaded() latents = self.device_manager.move_to_load_device(latents) latents = self.normalizer.denormalize(latents) target_shape = self.target_shape_from_latents(original_shape) mel_spec = self.autoencoder.decode(latents, target_shape=target_shape) waveform = self.run_vocoder(mel_spec) return self.device_manager.move_to_load_device(waveform) def target_shape_from_latents(self, latents_shape): batch, _, time, _ = latents_shape target_length = time * LATENT_DOWNSAMPLE_FACTOR if self.autoencoder.causality_axis != CausalityAxis.NONE: target_length -= LATENT_DOWNSAMPLE_FACTOR - 1 return ( batch, self.autoencoder.decoder.out_ch, target_length, self.autoencoder.mel_bins, ) def num_of_latents_from_frames(self, frames_number: int, frame_rate: int) -> int: return math.ceil((float(frames_number) / frame_rate) * self.latents_per_second) def run_vocoder(self, mel_spec: torch.Tensor) -> torch.Tensor: audio_channels = self.autoencoder.decoder.out_ch vocoder_input = mel_spec.transpose(2, 3) if audio_channels == 1: vocoder_input = vocoder_input.squeeze(1) elif audio_channels != 2: raise ValueError(f"Unsupported audio_channels: {audio_channels}") return self.vocoder(vocoder_input) @property def sample_rate(self) -> int: return int(self.autoencoder.sampling_rate) @property def mel_hop_length(self) -> int: return int(self.autoencoder.mel_hop_length) @property def mel_bins(self) -> int: return int(self.autoencoder.mel_bins) @property def latent_channels(self) -> int: return int(self.autoencoder.decoder.z_channels) @property def latent_frequency_bins(self) -> int: return int(self.mel_bins // LATENT_DOWNSAMPLE_FACTOR) @property def latents_per_second(self) -> float: return self.sample_rate / self.mel_hop_length / LATENT_DOWNSAMPLE_FACTOR @property def output_sample_rate(self) -> int: output_rate = getattr(self.vocoder, "output_sample_rate", None) if output_rate is not None: return int(output_rate) upsample_factor = getattr(self.vocoder, "upsample_factor", None) if upsample_factor is None: raise AttributeError( "Vocoder is missing upsample_factor; cannot infer output sample rate" ) return int(self.sample_rate * upsample_factor / self.mel_hop_length) def memory_required(self, input_shape): return self.device_manager.patcher.model_size() ================================================ FILE: comfy/ldm/lightricks/vae/causal_audio_autoencoder.py ================================================ from __future__ import annotations import torch from torch import nn from torch.nn import functional as F from typing import Optional from enum import Enum from .pixel_norm import PixelNorm import comfy.ops import logging ops = comfy.ops.disable_weight_init class StringConvertibleEnum(Enum): """ Base enum class that provides string-to-enum conversion functionality. This mixin adds a str_to_enum() class method that handles conversion from strings, None, or existing enum instances with case-insensitive matching. """ @classmethod def str_to_enum(cls, value): """ Convert a string, enum instance, or None to the appropriate enum member. Args: value: Can be an enum instance of this class, a string, or None Returns: Enum member of this class Raises: ValueError: If the value cannot be converted to a valid enum member """ # Already an enum instance of this class if isinstance(value, cls): return value # None maps to NONE member if it exists if value is None: if hasattr(cls, "NONE"): return cls.NONE raise ValueError(f"{cls.__name__} does not have a NONE member to map None to") # String conversion (case-insensitive) if isinstance(value, str): value_lower = value.lower() # Try to match against enum values for member in cls: # Handle members with None values if member.value is None: if value_lower == "none": return member # Handle members with string values elif isinstance(member.value, str) and member.value.lower() == value_lower: return member # Build helpful error message with valid values valid_values = [] for member in cls: if member.value is None: valid_values.append("none") elif isinstance(member.value, str): valid_values.append(member.value) raise ValueError(f"Invalid {cls.__name__} string: '{value}'. " f"Valid values are: {valid_values}") raise ValueError( f"Cannot convert type {type(value).__name__} to {cls.__name__} enum. " f"Expected string, None, or {cls.__name__} instance." ) class AttentionType(StringConvertibleEnum): """Enum for specifying the attention mechanism type.""" VANILLA = "vanilla" LINEAR = "linear" NONE = "none" class CausalityAxis(StringConvertibleEnum): """Enum for specifying the causality axis in causal convolutions.""" NONE = None WIDTH = "width" HEIGHT = "height" WIDTH_COMPATIBILITY = "width-compatibility" def Normalize(in_channels, *, num_groups=32, normtype="group"): if normtype == "group": return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) elif normtype == "pixel": return PixelNorm(dim=1, eps=1e-6) else: raise ValueError(f"Invalid normalization type: {normtype}") class CausalConv2d(nn.Module): """ A causal 2D convolution. This layer ensures that the output at time `t` only depends on inputs at time `t` and earlier. It achieves this by applying asymmetric padding to the time dimension (width) before the convolution. """ def __init__( self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True, causality_axis: CausalityAxis = CausalityAxis.HEIGHT, ): super().__init__() self.causality_axis = causality_axis # Ensure kernel_size and dilation are tuples kernel_size = nn.modules.utils._pair(kernel_size) dilation = nn.modules.utils._pair(dilation) # Calculate padding dimensions pad_h = (kernel_size[0] - 1) * dilation[0] pad_w = (kernel_size[1] - 1) * dilation[1] # The padding tuple for F.pad is (pad_left, pad_right, pad_top, pad_bottom) match self.causality_axis: case CausalityAxis.NONE: self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2) case CausalityAxis.WIDTH | CausalityAxis.WIDTH_COMPATIBILITY: self.padding = (pad_w, 0, pad_h // 2, pad_h - pad_h // 2) case CausalityAxis.HEIGHT: self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h, 0) case _: raise ValueError(f"Invalid causality_axis: {causality_axis}") # The internal convolution layer uses no padding, as we handle it manually self.conv = ops.Conv2d( in_channels, out_channels, kernel_size, stride=stride, padding=0, dilation=dilation, groups=groups, bias=bias, ) def forward(self, x): # Apply causal padding before convolution x = F.pad(x, self.padding) return self.conv(x) def make_conv2d( in_channels, out_channels, kernel_size, stride=1, padding=None, dilation=1, groups=1, bias=True, causality_axis: Optional[CausalityAxis] = None, ): """ Create a 2D convolution layer that can be either causal or non-causal. Args: in_channels: Number of input channels out_channels: Number of output channels kernel_size: Size of the convolution kernel stride: Convolution stride padding: Padding (if None, will be calculated based on causal flag) dilation: Dilation rate groups: Number of groups for grouped convolution bias: Whether to use bias causality_axis: Dimension along which to apply causality. Returns: Either a regular Conv2d or CausalConv2d layer """ if causality_axis is not None: # For causal convolution, padding is handled internally by CausalConv2d return CausalConv2d(in_channels, out_channels, kernel_size, stride, dilation, groups, bias, causality_axis) else: # For non-causal convolution, use symmetric padding if not specified if padding is None: if isinstance(kernel_size, int): padding = kernel_size // 2 else: padding = tuple(k // 2 for k in kernel_size) return ops.Conv2d( in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, ) class Upsample(nn.Module): def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.HEIGHT): super().__init__() self.with_conv = with_conv self.causality_axis = causality_axis if self.with_conv: self.conv = make_conv2d(in_channels, in_channels, kernel_size=3, stride=1, causality_axis=causality_axis) def forward(self, x): x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") if self.with_conv: x = self.conv(x) # Drop FIRST element in the causal axis to undo encoder's padding, while keeping the length 1 + 2 * n. # For example, if the input is [0, 1, 2], after interpolation, the output is [0, 0, 1, 1, 2, 2]. # The causal convolution will pad the first element as [-, -, 0, 0, 1, 1, 2, 2], # So the output elements rely on the following windows: # 0: [-,-,0] # 1: [-,0,0] # 2: [0,0,1] # 3: [0,1,1] # 4: [1,1,2] # 5: [1,2,2] # Notice that the first and second elements in the output rely only on the first element in the input, # while all other elements rely on two elements in the input. # So we can drop the first element to undo the padding (rather than the last element). # This is a no-op for non-causal convolutions. match self.causality_axis: case CausalityAxis.NONE: pass # x remains unchanged case CausalityAxis.HEIGHT: x = x[:, :, 1:, :] case CausalityAxis.WIDTH: x = x[:, :, :, 1:] case CausalityAxis.WIDTH_COMPATIBILITY: pass # x remains unchanged case _: raise ValueError(f"Invalid causality_axis: {self.causality_axis}") return x class Downsample(nn.Module): """ A downsampling layer that can use either a strided convolution or average pooling. Supports standard and causal padding for the convolutional mode. """ def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.WIDTH): super().__init__() self.with_conv = with_conv self.causality_axis = causality_axis if self.causality_axis != CausalityAxis.NONE and not self.with_conv: raise ValueError("causality is only supported when `with_conv=True`.") if self.with_conv: # Do time downsampling here # no asymmetric padding in torch conv, must do it ourselves self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): if self.with_conv: # (pad_left, pad_right, pad_top, pad_bottom) match self.causality_axis: case CausalityAxis.NONE: pad = (0, 1, 0, 1) case CausalityAxis.WIDTH: pad = (2, 0, 0, 1) case CausalityAxis.HEIGHT: pad = (0, 1, 2, 0) case CausalityAxis.WIDTH_COMPATIBILITY: pad = (1, 0, 0, 1) case _: raise ValueError(f"Invalid causality_axis: {self.causality_axis}") x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) else: # This branch is only taken if with_conv=False, which implies causality_axis is NONE. x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) return x class ResnetBlock(nn.Module): def __init__( self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512, norm_type="group", causality_axis: CausalityAxis = CausalityAxis.HEIGHT, ): super().__init__() self.causality_axis = causality_axis if self.causality_axis != CausalityAxis.NONE and norm_type == "group": raise ValueError("Causal ResnetBlock with GroupNorm is not supported.") self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.norm1 = Normalize(in_channels, normtype=norm_type) self.non_linearity = nn.SiLU() self.conv1 = make_conv2d(in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis) if temb_channels > 0: self.temb_proj = ops.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels, normtype=norm_type) self.dropout = torch.nn.Dropout(dropout) self.conv2 = make_conv2d(out_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = make_conv2d( in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis ) else: self.nin_shortcut = make_conv2d( in_channels, out_channels, kernel_size=1, stride=1, causality_axis=causality_axis ) def forward(self, x, temb): h = x h = self.norm1(h) h = self.non_linearity(h) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(self.non_linearity(temb))[:, :, None, None] h = self.norm2(h) h = self.non_linearity(h) h = self.dropout(h) h = self.conv2(h) if self.in_channels != self.out_channels: if self.use_conv_shortcut: x = self.conv_shortcut(x) else: x = self.nin_shortcut(x) return x + h class AttnBlock(nn.Module): def __init__(self, in_channels, norm_type="group"): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels, normtype=norm_type) self.q = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b, c, h, w = q.shape q = q.reshape(b, c, h * w).contiguous() q = q.permute(0, 2, 1).contiguous() # b,hw,c k = k.reshape(b, c, h * w).contiguous() # b,c,hw w_ = torch.bmm(q, k).contiguous() # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] w_ = w_ * (int(c) ** (-0.5)) w_ = torch.nn.functional.softmax(w_, dim=2) # attend to values v = v.reshape(b, c, h * w).contiguous() w_ = w_.permute(0, 2, 1).contiguous() # b,hw,hw (first hw of k, second of q) h_ = torch.bmm(v, w_).contiguous() # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] h_ = h_.reshape(b, c, h, w).contiguous() h_ = self.proj_out(h_) return x + h_ def make_attn(in_channels, attn_type="vanilla", norm_type="group"): # Convert string to enum if needed attn_type = AttentionType.str_to_enum(attn_type) if attn_type != AttentionType.NONE: logging.info(f"making attention of type '{attn_type.value}' with {in_channels} in_channels") else: logging.info(f"making identity attention with {in_channels} in_channels") match attn_type: case AttentionType.VANILLA: return AttnBlock(in_channels, norm_type=norm_type) case AttentionType.NONE: return nn.Identity(in_channels) case AttentionType.LINEAR: raise NotImplementedError(f"Attention type {attn_type.value} is not supported yet.") case _: raise ValueError(f"Unknown attention type: {attn_type}") class Encoder(nn.Module): def __init__( self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, attn_type="vanilla", mid_block_add_attention=True, norm_type="group", causality_axis=CausalityAxis.WIDTH.value, **ignore_kwargs, ): super().__init__() self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.z_channels = z_channels self.double_z = double_z self.norm_type = norm_type # Convert string to enum if needed (for config loading) causality_axis = CausalityAxis.str_to_enum(causality_axis) self.attn_type = AttentionType.str_to_enum(attn_type) # downsampling self.conv_in = make_conv2d( in_channels, self.ch, kernel_size=3, stride=1, causality_axis=causality_axis, ) self.non_linearity = nn.SiLU() curr_res = resolution in_ch_mult = (1,) + tuple(ch_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch * in_ch_mult[i_level] block_out = ch * ch_mult[i_level] for _ in range(self.num_res_blocks): block.append( ResnetBlock( in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout, norm_type=self.norm_type, causality_axis=causality_axis, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: down.downsample = Downsample(block_in, resamp_with_conv, causality_axis=causality_axis) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, norm_type=self.norm_type, causality_axis=causality_axis, ) if mid_block_add_attention: self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type) else: self.mid.attn_1 = nn.Identity() self.mid.block_2 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, norm_type=self.norm_type, causality_axis=causality_axis, ) # end self.norm_out = Normalize(block_in, normtype=self.norm_type) self.conv_out = make_conv2d( block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, causality_axis=causality_axis, ) def forward(self, x): """ Forward pass through the encoder. Args: x: Input tensor of shape [batch, channels, time, n_mels] Returns: Encoded latent representation """ feature_maps = [self.conv_in(x)] # Process each resolution level (from high to low resolution) for resolution_level in range(self.num_resolutions): # Apply residual blocks at current resolution level for block_idx in range(self.num_res_blocks): # Apply ResNet block with optional timestep embedding current_features = self.down[resolution_level].block[block_idx](feature_maps[-1], temb=None) # Apply attention if configured for this resolution level if len(self.down[resolution_level].attn) > 0: current_features = self.down[resolution_level].attn[block_idx](current_features) # Store processed features feature_maps.append(current_features) # Downsample spatial dimensions (except at the final resolution level) if resolution_level != self.num_resolutions - 1: downsampled_features = self.down[resolution_level].downsample(feature_maps[-1]) feature_maps.append(downsampled_features) # === MIDDLE PROCESSING PHASE === # Take the lowest resolution features for middle processing bottleneck_features = feature_maps[-1] # Apply first middle ResNet block bottleneck_features = self.mid.block_1(bottleneck_features, temb=None) # Apply middle attention block bottleneck_features = self.mid.attn_1(bottleneck_features) # Apply second middle ResNet block bottleneck_features = self.mid.block_2(bottleneck_features, temb=None) # === OUTPUT PHASE === # Normalize the bottleneck features output_features = self.norm_out(bottleneck_features) # Apply non-linearity (SiLU activation) output_features = self.non_linearity(output_features) # Final convolution to produce latent representation # [batch, channels, time, n_mels] -> [batch, 2 * z_channels if double_z else z_channels, time, n_mels] return self.conv_out(output_features) class Decoder(nn.Module): def __init__( self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, give_pre_end=False, tanh_out=False, attn_type="vanilla", mid_block_add_attention=True, norm_type="group", causality_axis=CausalityAxis.WIDTH.value, **ignorekwargs, ): super().__init__() self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.out_ch = out_ch self.give_pre_end = give_pre_end self.tanh_out = tanh_out self.norm_type = norm_type self.z_channels = z_channels # Convert string to enum if needed (for config loading) causality_axis = CausalityAxis.str_to_enum(causality_axis) self.attn_type = AttentionType.str_to_enum(attn_type) # compute block_in and curr_res at lowest res block_in = ch * ch_mult[self.num_resolutions - 1] curr_res = resolution // 2 ** (self.num_resolutions - 1) self.z_shape = (1, z_channels, curr_res, curr_res) # z to block_in self.conv_in = make_conv2d(z_channels, block_in, kernel_size=3, stride=1, causality_axis=causality_axis) self.non_linearity = nn.SiLU() # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, norm_type=self.norm_type, causality_axis=causality_axis, ) if mid_block_add_attention: self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type) else: self.mid.attn_1 = nn.Identity() self.mid.block_2 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, norm_type=self.norm_type, causality_axis=causality_axis, ) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch * ch_mult[i_level] for _ in range(self.num_res_blocks + 1): block.append( ResnetBlock( in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout, norm_type=self.norm_type, causality_axis=causality_axis, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv, causality_axis=causality_axis) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in, normtype=self.norm_type) self.conv_out = make_conv2d(block_in, out_ch, kernel_size=3, stride=1, causality_axis=causality_axis) def _adjust_output_shape(self, decoded_output, target_shape): """ Adjust output shape to match target dimensions for variable-length audio. This function handles the common case where decoded audio spectrograms need to be resized to match a specific target shape. Args: decoded_output: Tensor of shape (batch, channels, time, frequency) target_shape: Target shape tuple (batch, channels, time, frequency) Returns: Tensor adjusted to match target_shape exactly """ # Current output shape: (batch, channels, time, frequency) _, _, current_time, current_freq = decoded_output.shape _, target_channels, target_time, target_freq = target_shape # Step 1: Crop first to avoid exceeding target dimensions decoded_output = decoded_output[ :, :target_channels, : min(current_time, target_time), : min(current_freq, target_freq) ] # Step 2: Calculate padding needed for time and frequency dimensions time_padding_needed = target_time - decoded_output.shape[2] freq_padding_needed = target_freq - decoded_output.shape[3] # Step 3: Apply padding if needed if time_padding_needed > 0 or freq_padding_needed > 0: # PyTorch padding format: (pad_left, pad_right, pad_top, pad_bottom) # For audio: pad_left/right = frequency, pad_top/bottom = time padding = ( 0, max(freq_padding_needed, 0), # frequency padding (left, right) 0, max(time_padding_needed, 0), # time padding (top, bottom) ) decoded_output = F.pad(decoded_output, padding) # Step 4: Final safety crop to ensure exact target shape decoded_output = decoded_output[:, :target_channels, :target_time, :target_freq] return decoded_output def get_config(self): return { "ch": self.ch, "out_ch": self.out_ch, "ch_mult": self.ch_mult, "num_res_blocks": self.num_res_blocks, "in_channels": self.in_channels, "resolution": self.resolution, "z_channels": self.z_channels, } def forward(self, latent_features, target_shape=None): """ Decode latent features back to audio spectrograms. Args: latent_features: Encoded latent representation of shape (batch, channels, height, width) target_shape: Optional target output shape (batch, channels, time, frequency) If provided, output will be cropped/padded to match this shape Returns: Reconstructed audio spectrogram of shape (batch, channels, time, frequency) """ assert target_shape is not None, "Target shape is required for CausalAudioAutoencoder Decoder" # Transform latent features to decoder's internal feature dimension hidden_features = self.conv_in(latent_features) # Middle processing hidden_features = self.mid.block_1(hidden_features, temb=None) hidden_features = self.mid.attn_1(hidden_features) hidden_features = self.mid.block_2(hidden_features, temb=None) # Upsampling # Progressively increase spatial resolution from lowest to highest for resolution_level in reversed(range(self.num_resolutions)): # Apply residual blocks at current resolution level for block_index in range(self.num_res_blocks + 1): hidden_features = self.up[resolution_level].block[block_index](hidden_features, temb=None) if len(self.up[resolution_level].attn) > 0: hidden_features = self.up[resolution_level].attn[block_index](hidden_features) if resolution_level != 0: hidden_features = self.up[resolution_level].upsample(hidden_features) # Output if self.give_pre_end: # Return intermediate features before final processing (for debugging/analysis) decoded_output = hidden_features else: # Standard output path: normalize, activate, and convert to output channels # Final normalization layer hidden_features = self.norm_out(hidden_features) # Apply SiLU (Swish) activation function hidden_features = self.non_linearity(hidden_features) # Final convolution to map to output channels (typically 2 for stereo audio) decoded_output = self.conv_out(hidden_features) # Optional tanh activation to bound output values to [-1, 1] range if self.tanh_out: decoded_output = torch.tanh(decoded_output) # Adjust shape for audio data if target_shape is not None: decoded_output = self._adjust_output_shape(decoded_output, target_shape) return decoded_output class processor(nn.Module): def __init__(self): super().__init__() self.register_buffer("std-of-means", torch.empty(128)) self.register_buffer("mean-of-means", torch.empty(128)) def un_normalize(self, x): return (x * self.get_buffer("std-of-means").to(x)) + self.get_buffer("mean-of-means").to(x) def normalize(self, x): return (x - self.get_buffer("mean-of-means").to(x)) / self.get_buffer("std-of-means").to(x) class CausalAudioAutoencoder(nn.Module): def __init__(self, config=None): super().__init__() if config is None: config = self.get_default_config() model_config = config.get("model", {}).get("params", {}) self.sampling_rate = model_config.get( "sampling_rate", config.get("sampling_rate", 16000) ) encoder_config = model_config.get("encoder", model_config.get("ddconfig", {})) decoder_config = model_config.get("decoder", encoder_config) # Load mel spectrogram parameters self.mel_bins = encoder_config.get("mel_bins", 64) self.mel_hop_length = config.get("preprocessing", {}).get("stft", {}).get("hop_length", 160) self.n_fft = config.get("preprocessing", {}).get("stft", {}).get("filter_length", 1024) # Store causality configuration at VAE level (not just in encoder internals) causality_axis_value = encoder_config.get("causality_axis", CausalityAxis.HEIGHT.value) self.causality_axis = CausalityAxis.str_to_enum(causality_axis_value) self.is_causal = self.causality_axis == CausalityAxis.HEIGHT self.encoder = Encoder(**encoder_config) self.decoder = Decoder(**decoder_config) self.per_channel_statistics = processor() def get_default_config(self): ddconfig = { "double_z": True, "mel_bins": 64, "z_channels": 8, "resolution": 256, "downsample_time": False, "in_channels": 2, "out_ch": 2, "ch": 128, "ch_mult": [1, 2, 4], "num_res_blocks": 2, "attn_resolutions": [], "dropout": 0.0, "mid_block_add_attention": False, "norm_type": "pixel", "causality_axis": "height", } config = { "model": { "params": { "ddconfig": ddconfig, "sampling_rate": 16000, } }, "preprocessing": { "stft": { "filter_length": 1024, "hop_length": 160, }, }, } return config def get_config(self): return { "sampling_rate": self.sampling_rate, "mel_bins": self.mel_bins, "mel_hop_length": self.mel_hop_length, "n_fft": self.n_fft, "causality_axis": self.causality_axis.value, "is_causal": self.is_causal, } def encode(self, x): return self.encoder(x) def decode(self, x, target_shape=None): return self.decoder(x, target_shape=target_shape) ================================================ FILE: comfy/ldm/lightricks/vae/causal_conv3d.py ================================================ from typing import Tuple, Union import threading import torch import torch.nn as nn import comfy.ops ops = comfy.ops.disable_weight_init class CausalConv3d(nn.Module): def __init__( self, in_channels, out_channels, kernel_size: int = 3, stride: Union[int, Tuple[int]] = 1, dilation: int = 1, groups: int = 1, spatial_padding_mode: str = "zeros", **kwargs, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels if isinstance(stride, int): self.time_stride = stride else: self.time_stride = stride[0] kernel_size = (kernel_size, kernel_size, kernel_size) self.time_kernel_size = kernel_size[0] dilation = (dilation, 1, 1) height_pad = kernel_size[1] // 2 width_pad = kernel_size[2] // 2 padding = (0, height_pad, width_pad) self.conv = ops.Conv3d( in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, padding_mode=spatial_padding_mode, groups=groups, ) self.temporal_cache_state={} def forward(self, x, causal: bool = True): tid = threading.get_ident() cached, is_end = self.temporal_cache_state.get(tid, (None, False)) if cached is None: padding_length = self.time_kernel_size - 1 if not causal: padding_length = padding_length // 2 if x.shape[2] == 0: return x cached = x[:, :, :1, :, :].repeat((1, 1, padding_length, 1, 1)) pieces = [ cached, x ] if is_end and not causal: pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1))) input_length = sum([piece.shape[2] for piece in pieces]) cache_length = (self.time_kernel_size - self.time_stride) + ((input_length - self.time_kernel_size) % self.time_stride) needs_caching = not is_end if needs_caching and cache_length == 0: self.temporal_cache_state[tid] = (x[:, :, :0, :, :], False) needs_caching = False if needs_caching and x.shape[2] >= cache_length: needs_caching = False self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False) x = torch.cat(pieces, dim=2) del pieces del cached if needs_caching: self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False) elif is_end: self.temporal_cache_state[tid] = (None, True) return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :] @property def weight(self): return self.conv.weight ================================================ FILE: comfy/ldm/lightricks/vae/causal_video_autoencoder.py ================================================ from __future__ import annotations import threading import torch from torch import nn from functools import partial import math from einops import rearrange from typing import List, Optional, Tuple, Union from .conv_nd_factory import make_conv_nd, make_linear_nd from .causal_conv3d import CausalConv3d from .pixel_norm import PixelNorm from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings import comfy.ops import comfy.model_management from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed ops = comfy.ops.disable_weight_init def in_meta_context(): return torch.device("meta") == torch.empty(0).device def mark_conv3d_ended(module): tid = threading.get_ident() for _, m in module.named_modules(): if isinstance(m, CausalConv3d): current = m.temporal_cache_state.get(tid, (None, False)) m.temporal_cache_state[tid] = (current[0], True) def split2(tensor, split_point, dim=2): return torch.split(tensor, [split_point, tensor.shape[dim] - split_point], dim=dim) def add_exchange_cache(dest, cache_in, new_input, dim=2): if dest is not None: if cache_in is not None: cache_to_dest = min(dest.shape[dim], cache_in.shape[dim]) lead_in_dest, dest = split2(dest, cache_to_dest, dim=dim) lead_in_source, cache_in = split2(cache_in, cache_to_dest, dim=dim) lead_in_dest.add_(lead_in_source) body, new_input = split2(new_input, dest.shape[dim], dim) dest.add_(body) return torch_cat_if_needed([cache_in, new_input], dim=dim) class Encoder(nn.Module): r""" The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation. Args: dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3): The number of dimensions to use in convolutions. in_channels (`int`, *optional*, defaults to 3): The number of input channels. out_channels (`int`, *optional*, defaults to 3): The number of output channels. blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`): The blocks to use. Each block is a tuple of the block name and the number of layers. base_channels (`int`, *optional*, defaults to 128): The number of output channels for the first convolutional layer. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups for normalization. patch_size (`int`, *optional*, defaults to 1): The patch size to use. Should be a power of 2. norm_layer (`str`, *optional*, defaults to `group_norm`): The normalization layer to use. Can be either `group_norm` or `pixel_norm`. latent_log_var (`str`, *optional*, defaults to `per_channel`): The number of channels for the log variance. Can be either `per_channel`, `uniform`, `constant` or `none`. """ def __init__( self, dims: Union[int, Tuple[int, int]] = 3, in_channels: int = 3, out_channels: int = 3, blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], base_channels: int = 128, norm_num_groups: int = 32, patch_size: Union[int, Tuple[int]] = 1, norm_layer: str = "group_norm", # group_norm, pixel_norm latent_log_var: str = "per_channel", spatial_padding_mode: str = "zeros", ): super().__init__() self.patch_size = patch_size self.norm_layer = norm_layer self.latent_channels = out_channels self.latent_log_var = latent_log_var self.blocks_desc = blocks in_channels = in_channels * patch_size**2 output_channel = base_channels self.conv_in = make_conv_nd( dims=dims, in_channels=in_channels, out_channels=output_channel, kernel_size=3, stride=1, padding=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) self.down_blocks = nn.ModuleList([]) for block_name, block_params in blocks: input_channel = output_channel if isinstance(block_params, int): block_params = {"num_layers": block_params} if block_name == "res_x": block = UNetMidBlock3D( dims=dims, in_channels=input_channel, num_layers=block_params["num_layers"], resnet_eps=1e-6, resnet_groups=norm_num_groups, norm_layer=norm_layer, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "res_x_y": output_channel = block_params.get("multiplier", 2) * output_channel block = ResnetBlock3D( dims=dims, in_channels=input_channel, out_channels=output_channel, eps=1e-6, groups=norm_num_groups, norm_layer=norm_layer, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_time": block = make_conv_nd( dims=dims, in_channels=input_channel, out_channels=output_channel, kernel_size=3, stride=(2, 1, 1), causal=True, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_space": block = make_conv_nd( dims=dims, in_channels=input_channel, out_channels=output_channel, kernel_size=3, stride=(1, 2, 2), causal=True, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all": block = make_conv_nd( dims=dims, in_channels=input_channel, out_channels=output_channel, kernel_size=3, stride=(2, 2, 2), causal=True, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all_x_y": output_channel = block_params.get("multiplier", 2) * output_channel block = make_conv_nd( dims=dims, in_channels=input_channel, out_channels=output_channel, kernel_size=3, stride=(2, 2, 2), causal=True, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all_res": output_channel = block_params.get("multiplier", 2) * output_channel block = SpaceToDepthDownsample( dims=dims, in_channels=input_channel, out_channels=output_channel, stride=(2, 2, 2), spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_space_res": output_channel = block_params.get("multiplier", 2) * output_channel block = SpaceToDepthDownsample( dims=dims, in_channels=input_channel, out_channels=output_channel, stride=(1, 2, 2), spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_time_res": output_channel = block_params.get("multiplier", 2) * output_channel block = SpaceToDepthDownsample( dims=dims, in_channels=input_channel, out_channels=output_channel, stride=(2, 1, 1), spatial_padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unknown block: {block_name}") self.down_blocks.append(block) # out if norm_layer == "group_norm": self.conv_norm_out = nn.GroupNorm( num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6 ) elif norm_layer == "pixel_norm": self.conv_norm_out = PixelNorm() elif norm_layer == "layer_norm": self.conv_norm_out = LayerNorm(output_channel, eps=1e-6) self.conv_act = nn.SiLU() conv_out_channels = out_channels if latent_log_var == "per_channel": conv_out_channels *= 2 elif latent_log_var == "uniform": conv_out_channels += 1 elif latent_log_var == "constant": conv_out_channels += 1 elif latent_log_var != "none": raise ValueError(f"Invalid latent_log_var: {latent_log_var}") self.conv_out = make_conv_nd( dims, output_channel, conv_out_channels, 3, padding=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) self.gradient_checkpointing = False def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch.FloatTensor]: sample = self.conv_in(sample) checkpoint_fn = ( partial(torch.utils.checkpoint.checkpoint, use_reentrant=False) if self.gradient_checkpointing and self.training else lambda x: x ) for down_block in self.down_blocks: sample = checkpoint_fn(down_block)(sample) if sample is None or sample.shape[2] == 0: return None sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if sample is None or sample.shape[2] == 0: return None if self.latent_log_var == "uniform": last_channel = sample[:, -1:, ...] num_dims = sample.dim() if num_dims == 4: # For shape (B, C, H, W) repeated_last_channel = last_channel.repeat( 1, sample.shape[1] - 2, 1, 1 ) sample = torch.cat([sample, repeated_last_channel], dim=1) elif num_dims == 5: # For shape (B, C, F, H, W) repeated_last_channel = last_channel.repeat( 1, sample.shape[1] - 2, 1, 1, 1 ) sample = torch.cat([sample, repeated_last_channel], dim=1) else: raise ValueError(f"Invalid input shape: {sample.shape}") elif self.latent_log_var == "constant": sample = sample[:, :-1, ...] approx_ln_0 = ( -30 ) # this is the minimal clamp value in DiagonalGaussianDistribution objects sample = torch.cat( [sample, torch.ones_like(sample, device=sample.device) * approx_ln_0], dim=1, ) return sample def forward_orig(self, sample: torch.FloatTensor, device=None) -> torch.FloatTensor: r"""The forward method of the `Encoder` class.""" max_chunk_size = get_max_chunk_size(sample.device if device is None else device) * 2 # encoder is more memory-efficient than decoder frame_size = sample[:, :, :1, :, :].numel() * sample.element_size() frame_size = int(frame_size * (self.conv_in.out_channels / self.conv_in.in_channels)) outputs = [] samples = [sample[:, :, :1, :, :]] if sample.shape[2] > 1: chunk_t = max(2, max_chunk_size // frame_size) if chunk_t < 4: chunk_t = 2 elif chunk_t < 8: chunk_t = 4 else: chunk_t = (chunk_t // 8) * 8 samples += list(torch.split(sample[:, :, 1:, :, :], chunk_t, dim=2)) for chunk_idx, chunk in enumerate(samples): if chunk_idx == len(samples) - 1: mark_conv3d_ended(self) chunk = patchify(chunk, patch_size_hw=self.patch_size, patch_size_t=1).to(device=device) output = self._forward_chunk(chunk) if output is not None: outputs.append(output) return torch_cat_if_needed(outputs, dim=2) def forward(self, *args, **kwargs): try: return self.forward_orig(*args, **kwargs) finally: tid = threading.get_ident() for _, module in self.named_modules(): # ComfyUI doesn't thread this kind of stuff today, but just in case # we key on the thread to make it thread safe. tid = threading.get_ident() if hasattr(module, "temporal_cache_state"): module.temporal_cache_state.pop(tid, None) MIN_VRAM_FOR_CHUNK_SCALING = 6 * 1024 ** 3 MAX_VRAM_FOR_CHUNK_SCALING = 24 * 1024 ** 3 MIN_CHUNK_SIZE = 32 * 1024 ** 2 MAX_CHUNK_SIZE = 128 * 1024 ** 2 def get_max_chunk_size(device: torch.device) -> int: total_memory = comfy.model_management.get_total_memory(dev=device) if total_memory <= MIN_VRAM_FOR_CHUNK_SCALING: return MIN_CHUNK_SIZE if total_memory >= MAX_VRAM_FOR_CHUNK_SCALING: return MAX_CHUNK_SIZE interp = (total_memory - MIN_VRAM_FOR_CHUNK_SCALING) / ( MAX_VRAM_FOR_CHUNK_SCALING - MIN_VRAM_FOR_CHUNK_SCALING ) return int(MIN_CHUNK_SIZE + interp * (MAX_CHUNK_SIZE - MIN_CHUNK_SIZE)) class Decoder(nn.Module): r""" The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample. Args: dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3): The number of dimensions to use in convolutions. in_channels (`int`, *optional*, defaults to 3): The number of input channels. out_channels (`int`, *optional*, defaults to 3): The number of output channels. blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`): The blocks to use. Each block is a tuple of the block name and the number of layers. base_channels (`int`, *optional*, defaults to 128): The number of output channels for the first convolutional layer. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups for normalization. patch_size (`int`, *optional*, defaults to 1): The patch size to use. Should be a power of 2. norm_layer (`str`, *optional*, defaults to `group_norm`): The normalization layer to use. Can be either `group_norm` or `pixel_norm`. causal (`bool`, *optional*, defaults to `True`): Whether to use causal convolutions or not. """ def __init__( self, dims, in_channels: int = 3, out_channels: int = 3, blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], base_channels: int = 128, layers_per_block: int = 2, norm_num_groups: int = 32, patch_size: int = 1, norm_layer: str = "group_norm", causal: bool = True, timestep_conditioning: bool = False, spatial_padding_mode: str = "zeros", ): super().__init__() self.patch_size = patch_size self.layers_per_block = layers_per_block out_channels = out_channels * patch_size**2 self.causal = causal self.blocks_desc = blocks # Compute output channel to be product of all channel-multiplier blocks output_channel = base_channels for block_name, block_params in list(reversed(blocks)): block_params = block_params if isinstance(block_params, dict) else {} if block_name == "res_x_y": output_channel = output_channel * block_params.get("multiplier", 2) if block_name == "compress_all": output_channel = output_channel * block_params.get("multiplier", 1) if block_name == "compress_space": output_channel = output_channel * block_params.get("multiplier", 1) if block_name == "compress_time": output_channel = output_channel * block_params.get("multiplier", 1) self.conv_in = make_conv_nd( dims, in_channels, output_channel, kernel_size=3, stride=1, padding=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) self.up_blocks = nn.ModuleList([]) for block_name, block_params in list(reversed(blocks)): input_channel = output_channel if isinstance(block_params, int): block_params = {"num_layers": block_params} if block_name == "res_x": block = UNetMidBlock3D( dims=dims, in_channels=input_channel, num_layers=block_params["num_layers"], resnet_eps=1e-6, resnet_groups=norm_num_groups, norm_layer=norm_layer, inject_noise=block_params.get("inject_noise", False), timestep_conditioning=timestep_conditioning, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "res_x_y": output_channel = output_channel // block_params.get("multiplier", 2) block = ResnetBlock3D( dims=dims, in_channels=input_channel, out_channels=output_channel, eps=1e-6, groups=norm_num_groups, norm_layer=norm_layer, inject_noise=block_params.get("inject_noise", False), timestep_conditioning=False, spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_time": output_channel = output_channel // block_params.get("multiplier", 1) block = DepthToSpaceUpsample( dims=dims, in_channels=input_channel, stride=(2, 1, 1), out_channels_reduction_factor=block_params.get("multiplier", 1), spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_space": output_channel = output_channel // block_params.get("multiplier", 1) block = DepthToSpaceUpsample( dims=dims, in_channels=input_channel, stride=(1, 2, 2), out_channels_reduction_factor=block_params.get("multiplier", 1), spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all": output_channel = output_channel // block_params.get("multiplier", 1) block = DepthToSpaceUpsample( dims=dims, in_channels=input_channel, stride=(2, 2, 2), residual=block_params.get("residual", False), out_channels_reduction_factor=block_params.get("multiplier", 1), spatial_padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unknown layer: {block_name}") self.up_blocks.append(block) if norm_layer == "group_norm": self.conv_norm_out = nn.GroupNorm( num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6 ) elif norm_layer == "pixel_norm": self.conv_norm_out = PixelNorm() elif norm_layer == "layer_norm": self.conv_norm_out = LayerNorm(output_channel, eps=1e-6) self.conv_act = nn.SiLU() self.conv_out = make_conv_nd( dims, output_channel, out_channels, 3, padding=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) self.gradient_checkpointing = False # Precompute output scale factors: (channels, (t_scale, h_scale, w_scale), t_offset) ts, hs, ws, to = 1, 1, 1, 0 for block in self.up_blocks: if isinstance(block, DepthToSpaceUpsample): ts *= block.stride[0] hs *= block.stride[1] ws *= block.stride[2] if block.stride[0] > 1: to = to * block.stride[0] + 1 self._output_scale = (out_channels // (patch_size ** 2), (ts, hs * patch_size, ws * patch_size), to) self.timestep_conditioning = timestep_conditioning if timestep_conditioning: self.timestep_scale_multiplier = nn.Parameter( torch.tensor(1000.0, dtype=torch.float32) ) self.last_time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings( output_channel * 2, 0, operations=ops, ) self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel)) else: self.register_buffer( "last_scale_shift_table", torch.tensor( [0.0, 0.0], device="cpu" if in_meta_context() else None ).unsqueeze(1).expand(2, output_channel), persistent=False, ) def decode_output_shape(self, input_shape): c, (ts, hs, ws), to = self._output_scale return (input_shape[0], c, input_shape[2] * ts - to, input_shape[3] * hs, input_shape[4] * ws) def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size): sample = sample_ref[0] sample_ref[0] = None if idx >= len(self.up_blocks): sample = self.conv_norm_out(sample) if timestep_shift_scale is not None: shift, scale = timestep_shift_scale sample = sample * (1 + scale) + shift sample = self.conv_act(sample) if ended: mark_conv3d_ended(self.conv_out) sample = self.conv_out(sample, causal=self.causal) if sample is not None and sample.shape[2] > 0: sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) t = sample.shape[2] output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample) output_offset[0] += t return up_block = self.up_blocks[idx] if ended: mark_conv3d_ended(up_block) if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D): sample = checkpoint_fn(up_block)( sample, causal=self.causal, timestep=scaled_timestep ) else: sample = checkpoint_fn(up_block)(sample, causal=self.causal) if sample is None or sample.shape[2] == 0: return total_bytes = sample.numel() * sample.element_size() num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size if num_chunks == 1: # when we are not chunking, detach our x so the callee can free it as soon as they are done next_sample_ref = [sample] del sample self.run_up(idx + 1, next_sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size) return else: samples = torch.chunk(sample, chunks=num_chunks, dim=2) for chunk_idx, sample1 in enumerate(samples): self.run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size) def forward_orig( self, sample: torch.FloatTensor, timestep: Optional[torch.Tensor] = None, output_buffer: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: r"""The forward method of the `Decoder` class.""" batch_size = sample.shape[0] mark_conv3d_ended(self.conv_in) sample = self.conv_in(sample, causal=self.causal) checkpoint_fn = ( partial(torch.utils.checkpoint.checkpoint, use_reentrant=False) if self.gradient_checkpointing and self.training else lambda x: x ) timestep_shift_scale = None scaled_timestep = None if self.timestep_conditioning: assert ( timestep is not None ), "should pass timestep with timestep_conditioning=True" scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device) embedded_timestep = self.last_time_embedder( timestep=scaled_timestep.flatten(), resolution=None, aspect_ratio=None, batch_size=sample.shape[0], hidden_dtype=sample.dtype, ) embedded_timestep = embedded_timestep.view( batch_size, embedded_timestep.shape[-1], 1, 1, 1 ) ada_values = self.last_scale_shift_table[ None, ..., None, None, None ].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape( batch_size, 2, -1, embedded_timestep.shape[-3], embedded_timestep.shape[-2], embedded_timestep.shape[-1], ) timestep_shift_scale = ada_values.unbind(dim=1) if output_buffer is None: output_buffer = torch.empty( self.decode_output_shape(sample.shape), dtype=sample.dtype, device=comfy.model_management.intermediate_device(), ) output_offset = [0] max_chunk_size = get_max_chunk_size(sample.device) self.run_up(0, [sample], True, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size) return output_buffer def forward(self, *args, **kwargs): try: return self.forward_orig(*args, **kwargs) finally: for _, module in self.named_modules(): #ComfyUI doesn't thread this kind of stuff today, but just incase #we key on the thread to make it thread safe. tid = threading.get_ident() if hasattr(module, "temporal_cache_state"): module.temporal_cache_state.pop(tid, None) class UNetMidBlock3D(nn.Module): """ A 3D UNet mid-block [`UNetMidBlock3D`] with multiple residual blocks. Args: in_channels (`int`): The number of input channels. dropout (`float`, *optional*, defaults to 0.0): The dropout rate. num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. resnet_groups (`int`, *optional*, defaults to 32): The number of groups to use in the group normalization layers of the resnet blocks. norm_layer (`str`, *optional*, defaults to `group_norm`): The normalization layer to use. Can be either `group_norm` or `pixel_norm`. inject_noise (`bool`, *optional*, defaults to `False`): Whether to inject noise into the hidden states. timestep_conditioning (`bool`, *optional*, defaults to `False`): Whether to condition the hidden states on the timestep. Returns: `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, in_channels, height, width)`. """ def __init__( self, dims: Union[int, Tuple[int, int]], in_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_groups: int = 32, norm_layer: str = "group_norm", inject_noise: bool = False, timestep_conditioning: bool = False, spatial_padding_mode: str = "zeros", ): super().__init__() resnet_groups = ( resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) ) self.timestep_conditioning = timestep_conditioning if timestep_conditioning: self.time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings( in_channels * 4, 0, operations=ops, ) self.res_blocks = nn.ModuleList( [ ResnetBlock3D( dims=dims, in_channels=in_channels, out_channels=in_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, norm_layer=norm_layer, inject_noise=inject_noise, timestep_conditioning=timestep_conditioning, spatial_padding_mode=spatial_padding_mode, ) for _ in range(num_layers) ] ) def forward( self, hidden_states: torch.FloatTensor, causal: bool = True, timestep: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: timestep_embed = None if self.timestep_conditioning: assert ( timestep is not None ), "should pass timestep with timestep_conditioning=True" batch_size = hidden_states.shape[0] timestep_embed = self.time_embedder( timestep=timestep.flatten(), resolution=None, aspect_ratio=None, batch_size=batch_size, hidden_dtype=hidden_states.dtype, ) timestep_embed = timestep_embed.view( batch_size, timestep_embed.shape[-1], 1, 1, 1 ) for resnet in self.res_blocks: hidden_states = resnet(hidden_states, causal=causal, timestep=timestep_embed) return hidden_states class SpaceToDepthDownsample(nn.Module): def __init__(self, dims, in_channels, out_channels, stride, spatial_padding_mode): super().__init__() self.stride = stride self.group_size = in_channels * math.prod(stride) // out_channels self.conv = make_conv_nd( dims=dims, in_channels=in_channels, out_channels=out_channels // math.prod(stride), kernel_size=3, stride=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) self.temporal_cache_state = {} def forward(self, x, causal: bool = True): tid = threading.get_ident() cached, pad_first, cached_x, cached_input = self.temporal_cache_state.get(tid, (None, True, None, None)) if cached_input is not None: x = torch_cat_if_needed([cached_input, x], dim=2) cached_input = None if self.stride[0] == 2 and pad_first: x = torch.cat( [x[:, :, :1, :, :], x], dim=2 ) # duplicate first frames for padding pad_first = False if x.shape[2] < self.stride[0]: cached_input = x self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input) return None # skip connection x_in = rearrange( x, "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", p1=self.stride[0], p2=self.stride[1], p3=self.stride[2], ) x_in = rearrange(x_in, "b (c g) d h w -> b c g d h w", g=self.group_size) x_in = x_in.mean(dim=2) # conv x = self.conv(x, causal=causal) if self.stride[0] == 2 and x.shape[2] == 1: if cached_x is not None: x = torch_cat_if_needed([cached_x, x], dim=2) cached_x = None else: cached_x = x x = None if x is not None: x = rearrange( x, "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", p1=self.stride[0], p2=self.stride[1], p3=self.stride[2], ) cached = add_exchange_cache(x, cached, x_in, dim=2) self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input) return x class DepthToSpaceUpsample(nn.Module): def __init__( self, dims, in_channels, stride, residual=False, out_channels_reduction_factor=1, spatial_padding_mode="zeros", ): super().__init__() self.stride = stride self.out_channels = ( math.prod(stride) * in_channels // out_channels_reduction_factor ) self.conv = make_conv_nd( dims=dims, in_channels=in_channels, out_channels=self.out_channels, kernel_size=3, stride=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) self.residual = residual self.out_channels_reduction_factor = out_channels_reduction_factor self.temporal_cache_state = {} def forward(self, x, causal: bool = True, timestep: Optional[torch.Tensor] = None): tid = threading.get_ident() cached, drop_first_conv, drop_first_res = self.temporal_cache_state.get(tid, (None, True, True)) y = self.conv(x, causal=causal) y = rearrange( y, "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)", p1=self.stride[0], p2=self.stride[1], p3=self.stride[2], ) if self.stride[0] == 2 and y.shape[2] > 0 and drop_first_conv: y = y[:, :, 1:, :, :] drop_first_conv = False if self.residual: # Reshape and duplicate the input to match the output shape x_in = rearrange( x, "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)", p1=self.stride[0], p2=self.stride[1], p3=self.stride[2], ) num_repeat = math.prod(self.stride) // self.out_channels_reduction_factor x_in = x_in.repeat(1, num_repeat, 1, 1, 1) if self.stride[0] == 2 and x_in.shape[2] > 0 and drop_first_res: x_in = x_in[:, :, 1:, :, :] drop_first_res = False if y.shape[2] == 0: y = None cached = add_exchange_cache(y, cached, x_in, dim=2) self.temporal_cache_state[tid] = (cached, drop_first_conv, drop_first_res) else: self.temporal_cache_state[tid] = (None, drop_first_conv, False) return y class LayerNorm(nn.Module): def __init__(self, dim, eps, elementwise_affine=True) -> None: super().__init__() self.norm = ops.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine) def forward(self, x): x = rearrange(x, "b c d h w -> b d h w c") x = self.norm(x) x = rearrange(x, "b d h w c -> b c d h w") return x class ResnetBlock3D(nn.Module): r""" A Resnet block. Parameters: in_channels (`int`): The number of channels in the input. out_channels (`int`, *optional*, default to be `None`): The number of output channels for the first conv layer. If None, same as `in_channels`. dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use. groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer. eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization. """ def __init__( self, dims: Union[int, Tuple[int, int]], in_channels: int, out_channels: Optional[int] = None, dropout: float = 0.0, groups: int = 32, eps: float = 1e-6, norm_layer: str = "group_norm", inject_noise: bool = False, timestep_conditioning: bool = False, spatial_padding_mode: str = "zeros", ): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.inject_noise = inject_noise if norm_layer == "group_norm": self.norm1 = nn.GroupNorm( num_groups=groups, num_channels=in_channels, eps=eps, affine=True ) elif norm_layer == "pixel_norm": self.norm1 = PixelNorm() elif norm_layer == "layer_norm": self.norm1 = LayerNorm(in_channels, eps=eps, elementwise_affine=True) self.non_linearity = nn.SiLU() self.conv1 = make_conv_nd( dims, in_channels, out_channels, kernel_size=3, stride=1, padding=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) if inject_noise: self.per_channel_scale1 = nn.Parameter(torch.zeros((in_channels, 1, 1))) if norm_layer == "group_norm": self.norm2 = nn.GroupNorm( num_groups=groups, num_channels=out_channels, eps=eps, affine=True ) elif norm_layer == "pixel_norm": self.norm2 = PixelNorm() elif norm_layer == "layer_norm": self.norm2 = LayerNorm(out_channels, eps=eps, elementwise_affine=True) self.dropout = torch.nn.Dropout(dropout) self.conv2 = make_conv_nd( dims, out_channels, out_channels, kernel_size=3, stride=1, padding=1, causal=True, spatial_padding_mode=spatial_padding_mode, ) if inject_noise: self.per_channel_scale2 = nn.Parameter(torch.zeros((in_channels, 1, 1))) self.conv_shortcut = ( make_linear_nd( dims=dims, in_channels=in_channels, out_channels=out_channels ) if in_channels != out_channels else nn.Identity() ) self.norm3 = ( LayerNorm(in_channels, eps=eps, elementwise_affine=True) if in_channels != out_channels else nn.Identity() ) self.timestep_conditioning = timestep_conditioning if timestep_conditioning: self.scale_shift_table = nn.Parameter( torch.randn(4, in_channels) / in_channels**0.5 ) else: self.register_buffer( "scale_shift_table", torch.tensor( [0.0, 0.0, 0.0, 0.0], device="cpu" if in_meta_context() else None ).unsqueeze(1).expand(4, in_channels), persistent=False, ) self.temporal_cache_state={} def _feed_spatial_noise( self, hidden_states: torch.FloatTensor, per_channel_scale: torch.FloatTensor ) -> torch.FloatTensor: spatial_shape = hidden_states.shape[-2:] device = hidden_states.device dtype = hidden_states.dtype # similar to the "explicit noise inputs" method in style-gan spatial_noise = torch.randn(spatial_shape, device=device, dtype=dtype)[None] scaled_noise = (spatial_noise * per_channel_scale)[None, :, None, ...] hidden_states = hidden_states + scaled_noise return hidden_states def forward( self, input_tensor: torch.FloatTensor, causal: bool = True, timestep: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: hidden_states = input_tensor batch_size = hidden_states.shape[0] hidden_states = self.norm1(hidden_states) if self.timestep_conditioning: assert ( timestep is not None ), "should pass timestep with timestep_conditioning=True" ada_values = self.scale_shift_table[ None, ..., None, None, None ].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape( batch_size, 4, -1, timestep.shape[-3], timestep.shape[-2], timestep.shape[-1], ) shift1, scale1, shift2, scale2 = ada_values.unbind(dim=1) hidden_states = hidden_states * (1 + scale1) + shift1 hidden_states = self.non_linearity(hidden_states) hidden_states = self.conv1(hidden_states, causal=causal) if self.inject_noise: hidden_states = self._feed_spatial_noise( hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype) ) hidden_states = self.norm2(hidden_states) if self.timestep_conditioning: hidden_states = hidden_states * (1 + scale2) + shift2 hidden_states = self.non_linearity(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states, causal=causal) if self.inject_noise: hidden_states = self._feed_spatial_noise( hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype) ) input_tensor = self.norm3(input_tensor) batch_size = input_tensor.shape[0] input_tensor = self.conv_shortcut(input_tensor) tid = threading.get_ident() cached = self.temporal_cache_state.get(tid, None) cached = add_exchange_cache(hidden_states, cached, input_tensor, dim=2) self.temporal_cache_state[tid] = cached return hidden_states def patchify(x, patch_size_hw, patch_size_t=1): if patch_size_hw == 1 and patch_size_t == 1: return x if x.dim() == 4: x = rearrange( x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size_hw, r=patch_size_hw ) elif x.dim() == 5: x = rearrange( x, "b c (f p) (h q) (w r) -> b (c p r q) f h w", p=patch_size_t, q=patch_size_hw, r=patch_size_hw, ) else: raise ValueError(f"Invalid input shape: {x.shape}") return x def unpatchify(x, patch_size_hw, patch_size_t=1): if patch_size_hw == 1 and patch_size_t == 1: return x if x.dim() == 4: x = rearrange( x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size_hw, r=patch_size_hw ) elif x.dim() == 5: x = rearrange( x, "b (c p r q) f h w -> b c (f p) (h q) (w r)", p=patch_size_t, q=patch_size_hw, r=patch_size_hw, ) return x class processor(nn.Module): def __init__(self): super().__init__() self.register_buffer("std-of-means", torch.empty(128)) self.register_buffer("mean-of-means", torch.empty(128)) def un_normalize(self, x): return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x) def normalize(self, x): return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x) class VideoVAE(nn.Module): comfy_has_chunked_io = True def __init__(self, version=0, config=None): super().__init__() if config is None: config = self.get_default_config(version) self.config = config self.timestep_conditioning = config.get("timestep_conditioning", False) self.decode_noise_scale = config.get("decode_noise_scale", 0.025) self.decode_timestep = config.get("decode_timestep", 0.05) double_z = config.get("double_z", True) latent_log_var = config.get( "latent_log_var", "per_channel" if double_z else "none" ) self.encoder = Encoder( dims=config["dims"], in_channels=config.get("in_channels", 3), out_channels=config["latent_channels"], blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))), patch_size=config.get("patch_size", 1), latent_log_var=latent_log_var, norm_layer=config.get("norm_layer", "group_norm"), spatial_padding_mode=config.get("spatial_padding_mode", "zeros"), base_channels=config.get("encoder_base_channels", 128), ) self.decoder = Decoder( dims=config["dims"], in_channels=config["latent_channels"], out_channels=config.get("out_channels", 3), blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))), base_channels=config.get("decoder_base_channels", 128), patch_size=config.get("patch_size", 1), norm_layer=config.get("norm_layer", "group_norm"), causal=config.get("causal_decoder", False), timestep_conditioning=self.timestep_conditioning, spatial_padding_mode=config.get("spatial_padding_mode", "reflect"), ) self.per_channel_statistics = processor() def get_default_config(self, version): if version == 0: config = { "_class_name": "CausalVideoAutoencoder", "dims": 3, "in_channels": 3, "out_channels": 3, "latent_channels": 128, "blocks": [ ["res_x", 4], ["compress_all", 1], ["res_x_y", 1], ["res_x", 3], ["compress_all", 1], ["res_x_y", 1], ["res_x", 3], ["compress_all", 1], ["res_x", 3], ["res_x", 4], ], "scaling_factor": 1.0, "norm_layer": "pixel_norm", "patch_size": 4, "latent_log_var": "uniform", "use_quant_conv": False, "causal_decoder": False, } elif version == 1: config = { "_class_name": "CausalVideoAutoencoder", "dims": 3, "in_channels": 3, "out_channels": 3, "latent_channels": 128, "decoder_blocks": [ ["res_x", {"num_layers": 5, "inject_noise": True}], ["compress_all", {"residual": True, "multiplier": 2}], ["res_x", {"num_layers": 6, "inject_noise": True}], ["compress_all", {"residual": True, "multiplier": 2}], ["res_x", {"num_layers": 7, "inject_noise": True}], ["compress_all", {"residual": True, "multiplier": 2}], ["res_x", {"num_layers": 8, "inject_noise": False}] ], "encoder_blocks": [ ["res_x", {"num_layers": 4}], ["compress_all", {}], ["res_x_y", 1], ["res_x", {"num_layers": 3}], ["compress_all", {}], ["res_x_y", 1], ["res_x", {"num_layers": 3}], ["compress_all", {}], ["res_x", {"num_layers": 3}], ["res_x", {"num_layers": 4}] ], "scaling_factor": 1.0, "norm_layer": "pixel_norm", "patch_size": 4, "latent_log_var": "uniform", "use_quant_conv": False, "causal_decoder": False, "timestep_conditioning": True, } else: config = { "_class_name": "CausalVideoAutoencoder", "dims": 3, "in_channels": 3, "out_channels": 3, "latent_channels": 128, "encoder_blocks": [ ["res_x", {"num_layers": 4}], ["compress_space_res", {"multiplier": 2}], ["res_x", {"num_layers": 6}], ["compress_time_res", {"multiplier": 2}], ["res_x", {"num_layers": 6}], ["compress_all_res", {"multiplier": 2}], ["res_x", {"num_layers": 2}], ["compress_all_res", {"multiplier": 2}], ["res_x", {"num_layers": 2}] ], "decoder_blocks": [ ["res_x", {"num_layers": 5, "inject_noise": False}], ["compress_all", {"residual": True, "multiplier": 2}], ["res_x", {"num_layers": 5, "inject_noise": False}], ["compress_all", {"residual": True, "multiplier": 2}], ["res_x", {"num_layers": 5, "inject_noise": False}], ["compress_all", {"residual": True, "multiplier": 2}], ["res_x", {"num_layers": 5, "inject_noise": False}] ], "scaling_factor": 1.0, "norm_layer": "pixel_norm", "patch_size": 4, "latent_log_var": "uniform", "use_quant_conv": False, "causal_decoder": False, "timestep_conditioning": True } return config def encode(self, x, device=None): x = x[:, :, :max(1, 1 + ((x.shape[2] - 1) // 8) * 8), :, :] means, logvar = torch.chunk(self.encoder(x, device=device), 2, dim=1) return self.per_channel_statistics.normalize(means) def decode_output_shape(self, input_shape): return self.decoder.decode_output_shape(input_shape) def decode(self, x, output_buffer=None): if self.timestep_conditioning: #TODO: seed x = torch.randn_like(x) * self.decode_noise_scale + (1.0 - self.decode_noise_scale) * x return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep, output_buffer=output_buffer) ================================================ FILE: comfy/ldm/lightricks/vae/conv_nd_factory.py ================================================ from typing import Tuple, Union from .dual_conv3d import DualConv3d from .causal_conv3d import CausalConv3d import comfy.ops ops = comfy.ops.disable_weight_init def make_conv_nd( dims: Union[int, Tuple[int, int]], in_channels: int, out_channels: int, kernel_size: int, stride=1, padding=0, dilation=1, groups=1, bias=True, causal=False, spatial_padding_mode="zeros", temporal_padding_mode="zeros", ): if not (spatial_padding_mode == temporal_padding_mode or causal): raise NotImplementedError("spatial and temporal padding modes must be equal") if dims == 2: return ops.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=spatial_padding_mode, ) elif dims == 3: if causal: return CausalConv3d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, spatial_padding_mode=spatial_padding_mode, ) return ops.Conv3d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=spatial_padding_mode, ) elif dims == (2, 1): return DualConv3d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias, padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unsupported dimensions: {dims}") def make_linear_nd( dims: int, in_channels: int, out_channels: int, bias=True, ): if dims == 2: return ops.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias ) elif dims == 3 or dims == (2, 1): return ops.Conv3d( in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias ) else: raise ValueError(f"unsupported dimensions: {dims}") ================================================ FILE: comfy/ldm/lightricks/vae/dual_conv3d.py ================================================ import math from typing import Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange class DualConv3d(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, stride: Union[int, Tuple[int, int, int]] = 1, padding: Union[int, Tuple[int, int, int]] = 0, dilation: Union[int, Tuple[int, int, int]] = 1, groups=1, bias=True, padding_mode="zeros", ): super(DualConv3d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.padding_mode = padding_mode # Ensure kernel_size, stride, padding, and dilation are tuples of length 3 if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size, kernel_size) if kernel_size == (1, 1, 1): raise ValueError( "kernel_size must be greater than 1. Use make_linear_nd instead." ) if isinstance(stride, int): stride = (stride, stride, stride) if isinstance(padding, int): padding = (padding, padding, padding) if isinstance(dilation, int): dilation = (dilation, dilation, dilation) # Set parameters for convolutions self.groups = groups self.bias = bias # Define the size of the channels after the first convolution intermediate_channels = ( out_channels if in_channels < out_channels else in_channels ) # Define parameters for the first convolution self.weight1 = nn.Parameter( torch.Tensor( intermediate_channels, in_channels // groups, 1, kernel_size[1], kernel_size[2], ) ) self.stride1 = (1, stride[1], stride[2]) self.padding1 = (0, padding[1], padding[2]) self.dilation1 = (1, dilation[1], dilation[2]) if bias: self.bias1 = nn.Parameter(torch.Tensor(intermediate_channels)) else: self.register_parameter("bias1", None) # Define parameters for the second convolution self.weight2 = nn.Parameter( torch.Tensor( out_channels, intermediate_channels // groups, kernel_size[0], 1, 1 ) ) self.stride2 = (stride[0], 1, 1) self.padding2 = (padding[0], 0, 0) self.dilation2 = (dilation[0], 1, 1) if bias: self.bias2 = nn.Parameter(torch.Tensor(out_channels)) else: self.register_parameter("bias2", None) # Initialize weights and biases self.reset_parameters() def reset_parameters(self): nn.init.kaiming_uniform_(self.weight1, a=math.sqrt(5)) nn.init.kaiming_uniform_(self.weight2, a=math.sqrt(5)) if self.bias: fan_in1, _ = nn.init._calculate_fan_in_and_fan_out(self.weight1) bound1 = 1 / math.sqrt(fan_in1) nn.init.uniform_(self.bias1, -bound1, bound1) fan_in2, _ = nn.init._calculate_fan_in_and_fan_out(self.weight2) bound2 = 1 / math.sqrt(fan_in2) nn.init.uniform_(self.bias2, -bound2, bound2) def forward(self, x, use_conv3d=False, skip_time_conv=False): if use_conv3d: return self.forward_with_3d(x=x, skip_time_conv=skip_time_conv) else: return self.forward_with_2d(x=x, skip_time_conv=skip_time_conv) def forward_with_3d(self, x, skip_time_conv): # First convolution x = F.conv3d( x, self.weight1, self.bias1, self.stride1, self.padding1, self.dilation1, self.groups, padding_mode=self.padding_mode, ) if skip_time_conv: return x # Second convolution x = F.conv3d( x, self.weight2, self.bias2, self.stride2, self.padding2, self.dilation2, self.groups, padding_mode=self.padding_mode, ) return x def forward_with_2d(self, x, skip_time_conv): b, c, d, h, w = x.shape # First 2D convolution x = rearrange(x, "b c d h w -> (b d) c h w") # Squeeze the depth dimension out of weight1 since it's 1 weight1 = self.weight1.squeeze(2) # Select stride, padding, and dilation for the 2D convolution stride1 = (self.stride1[1], self.stride1[2]) padding1 = (self.padding1[1], self.padding1[2]) dilation1 = (self.dilation1[1], self.dilation1[2]) x = F.conv2d( x, weight1, self.bias1, stride1, padding1, dilation1, self.groups, padding_mode=self.padding_mode, ) _, _, h, w = x.shape if skip_time_conv: x = rearrange(x, "(b d) c h w -> b c d h w", b=b) return x # Second convolution which is essentially treated as a 1D convolution across the 'd' dimension x = rearrange(x, "(b d) c h w -> (b h w) c d", b=b) # Reshape weight2 to match the expected dimensions for conv1d weight2 = self.weight2.squeeze(-1).squeeze(-1) # Use only the relevant dimension for stride, padding, and dilation for the 1D convolution stride2 = self.stride2[0] padding2 = self.padding2[0] dilation2 = self.dilation2[0] x = F.conv1d( x, weight2, self.bias2, stride2, padding2, dilation2, self.groups, padding_mode=self.padding_mode, ) x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w) return x @property def weight(self): return self.weight2 def test_dual_conv3d_consistency(): # Initialize parameters in_channels = 3 out_channels = 5 kernel_size = (3, 3, 3) stride = (2, 2, 2) padding = (1, 1, 1) # Create an instance of the DualConv3d class dual_conv3d = DualConv3d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=True, ) # Example input tensor test_input = torch.randn(1, 3, 10, 10, 10) # Perform forward passes with both 3D and 2D settings output_conv3d = dual_conv3d(test_input, use_conv3d=True) output_2d = dual_conv3d(test_input, use_conv3d=False) # Assert that the outputs from both methods are sufficiently close assert torch.allclose( output_conv3d, output_2d, atol=1e-6 ), "Outputs are not consistent between 3D and 2D convolutions." ================================================ FILE: comfy/ldm/lightricks/vae/pixel_norm.py ================================================ import torch from torch import nn class PixelNorm(nn.Module): def __init__(self, dim=1, eps=1e-8): super(PixelNorm, self).__init__() self.dim = dim self.eps = eps def forward(self, x): return x / torch.sqrt(torch.mean(x**2, dim=self.dim, keepdim=True) + self.eps) ================================================ FILE: comfy/ldm/lightricks/vocoders/vocoder.py ================================================ import torch import torch.nn.functional as F import torch.nn as nn import comfy.ops import comfy.model_management import numpy as np import math ops = comfy.ops.disable_weight_init LRELU_SLOPE = 0.1 def get_padding(kernel_size, dilation=1): return int((kernel_size * dilation - dilation) / 2) # --------------------------------------------------------------------------- # Anti-aliased resampling helpers (kaiser-sinc filters) for BigVGAN v2 # Adopted from https://github.com/NVIDIA/BigVGAN # --------------------------------------------------------------------------- def _sinc(x: torch.Tensor): return torch.where( x == 0, torch.tensor(1.0, device=x.device, dtype=x.dtype), torch.sin(math.pi * x) / math.pi / x, ) def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): even = kernel_size % 2 == 0 half_size = kernel_size // 2 delta_f = 4 * half_width A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 if A > 50.0: beta = 0.1102 * (A - 8.7) elif A >= 21.0: beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0) else: beta = 0.0 window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) if even: time = torch.arange(-half_size, half_size) + 0.5 else: time = torch.arange(kernel_size) - half_size if cutoff == 0: filter_ = torch.zeros_like(time) else: filter_ = 2 * cutoff * window * _sinc(2 * cutoff * time) filter_ /= filter_.sum() filter = filter_.view(1, 1, kernel_size) return filter class LowPassFilter1d(nn.Module): def __init__( self, cutoff=0.5, half_width=0.6, stride=1, padding=True, padding_mode="replicate", kernel_size=12, ): super().__init__() if cutoff < -0.0: raise ValueError("Minimum cutoff must be larger than zero.") if cutoff > 0.5: raise ValueError("A cutoff above 0.5 does not make sense.") self.kernel_size = kernel_size self.even = kernel_size % 2 == 0 self.pad_left = kernel_size // 2 - int(self.even) self.pad_right = kernel_size // 2 self.stride = stride self.padding = padding self.padding_mode = padding_mode filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) self.register_buffer("filter", filter) def forward(self, x): _, C, _ = x.shape if self.padding: x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode) return F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C) class UpSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None, persistent=True, window_type="kaiser"): super().__init__() self.ratio = ratio self.stride = ratio if window_type == "hann": # Hann-windowed sinc filter — identical to torchaudio.functional.resample # with its default parameters (rolloff=0.99, lowpass_filter_width=6). # Uses replicate boundary padding, matching the reference resampler exactly. rolloff = 0.99 lowpass_filter_width = 6 width = math.ceil(lowpass_filter_width / rolloff) self.kernel_size = 2 * width * ratio + 1 self.pad = width self.pad_left = 2 * width * ratio self.pad_right = self.kernel_size - ratio t = (torch.arange(self.kernel_size) / ratio - width) * rolloff t_clamped = t.clamp(-lowpass_filter_width, lowpass_filter_width) window = torch.cos(t_clamped * math.pi / lowpass_filter_width / 2) ** 2 filter = (torch.sinc(t) * window * rolloff / ratio).view(1, 1, -1) else: # Kaiser-windowed sinc filter (BigVGAN default). self.kernel_size = ( int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size ) self.pad = self.kernel_size // ratio - 1 self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 self.pad_right = ( self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 ) filter = kaiser_sinc_filter1d( cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size ) self.register_buffer("filter", filter, persistent=persistent) def forward(self, x): _, C, _ = x.shape x = F.pad(x, (self.pad, self.pad), mode="replicate") x = self.ratio * F.conv_transpose1d( x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C ) x = x[..., self.pad_left : -self.pad_right] return x class DownSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None): super().__init__() self.ratio = ratio self.kernel_size = ( int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size ) self.lowpass = LowPassFilter1d( cutoff=0.5 / ratio, half_width=0.6 / ratio, stride=ratio, kernel_size=self.kernel_size, ) def forward(self, x): return self.lowpass(x) class Activation1d(nn.Module): def __init__( self, activation, up_ratio=2, down_ratio=2, up_kernel_size=12, down_kernel_size=12, ): super().__init__() self.act = activation self.upsample = UpSample1d(up_ratio, up_kernel_size) self.downsample = DownSample1d(down_ratio, down_kernel_size) def forward(self, x): x = self.upsample(x) x = self.act(x) x = self.downsample(x) return x # --------------------------------------------------------------------------- # BigVGAN v2 activations (Snake / SnakeBeta) # --------------------------------------------------------------------------- class Snake(nn.Module): def __init__( self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True ): super().__init__() self.alpha_logscale = alpha_logscale self.alpha = nn.Parameter( torch.zeros(in_features) if alpha_logscale else torch.ones(in_features) * alpha ) self.alpha.requires_grad = alpha_trainable self.eps = 1e-9 def forward(self, x): a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device) if self.alpha_logscale: a = torch.exp(a) return x + (1.0 / (a + self.eps)) * torch.sin(x * a).pow(2) class SnakeBeta(nn.Module): def __init__( self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True ): super().__init__() self.alpha_logscale = alpha_logscale self.alpha = nn.Parameter( torch.zeros(in_features) if alpha_logscale else torch.ones(in_features) * alpha ) self.alpha.requires_grad = alpha_trainable self.beta = nn.Parameter( torch.zeros(in_features) if alpha_logscale else torch.ones(in_features) * alpha ) self.beta.requires_grad = alpha_trainable self.eps = 1e-9 def forward(self, x): a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device) b = comfy.model_management.cast_to(self.beta.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device) if self.alpha_logscale: a = torch.exp(a) b = torch.exp(b) return x + (1.0 / (b + self.eps)) * torch.sin(x * a).pow(2) # --------------------------------------------------------------------------- # BigVGAN v2 AMPBlock (Anti-aliased Multi-Periodicity) # --------------------------------------------------------------------------- class AMPBlock1(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation="snake"): super().__init__() act_cls = SnakeBeta if activation == "snakebeta" else Snake self.convs1 = nn.ModuleList( [ ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]), ), ] ) self.convs2 = nn.ModuleList( [ ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ), ] ) self.acts1 = nn.ModuleList( [Activation1d(act_cls(channels)) for _ in range(len(self.convs1))] ) self.acts2 = nn.ModuleList( [Activation1d(act_cls(channels)) for _ in range(len(self.convs2))] ) def forward(self, x): for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.acts1, self.acts2): xt = a1(x) xt = c1(xt) xt = a2(xt) xt = c2(xt) x = x + xt return x # --------------------------------------------------------------------------- # HiFi-GAN residual blocks # --------------------------------------------------------------------------- class ResBlock1(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): super(ResBlock1, self).__init__() self.convs1 = nn.ModuleList( [ ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]), ), ] ) self.convs2 = nn.ModuleList( [ ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1), ), ] ) def forward(self, x): for c1, c2 in zip(self.convs1, self.convs2): xt = F.leaky_relu(x, LRELU_SLOPE) xt = c1(xt) xt = F.leaky_relu(xt, LRELU_SLOPE) xt = c2(xt) x = xt + x return x class ResBlock2(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3)): super(ResBlock2, self).__init__() self.convs = nn.ModuleList( [ ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]), ), ops.Conv1d( channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]), ), ] ) def forward(self, x): for c in self.convs: xt = F.leaky_relu(x, LRELU_SLOPE) xt = c(xt) x = xt + x return x class Vocoder(torch.nn.Module): """ Vocoder model for synthesizing audio from spectrograms, based on: https://github.com/jik876/hifi-gan. Supports both HiFi-GAN (resblock "1"/"2") and BigVGAN v2 (resblock "AMP1"). """ def __init__(self, config=None): super(Vocoder, self).__init__() if config is None: config = self.get_default_config() resblock_kernel_sizes = config.get("resblock_kernel_sizes", [3, 7, 11]) upsample_rates = config.get("upsample_rates", [5, 4, 2, 2, 2]) upsample_kernel_sizes = config.get("upsample_kernel_sizes", [16, 16, 8, 4, 4]) resblock_dilation_sizes = config.get("resblock_dilation_sizes", [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) upsample_initial_channel = config.get("upsample_initial_channel", 1024) stereo = config.get("stereo", True) activation = config.get("activation", "snake") use_bias_at_final = config.get("use_bias_at_final", True) # "output_sample_rate" is not present in recent checkpoint configs. # When absent (None), AudioVAE.output_sample_rate computes it as: # sample_rate * vocoder.upsample_factor / mel_hop_length # where upsample_factor = product of all upsample stride lengths, # and mel_hop_length is loaded from the autoencoder config at # preprocessing.stft.hop_length (see CausalAudioAutoencoder). self.output_sample_rate = config.get("output_sample_rate") self.resblock = config.get("resblock", "1") self.use_tanh_at_final = config.get("use_tanh_at_final", True) self.apply_final_activation = config.get("apply_final_activation", True) self.num_kernels = len(resblock_kernel_sizes) self.num_upsamples = len(upsample_rates) in_channels = 128 if stereo else 64 self.conv_pre = ops.Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3) if self.resblock == "1": resblock_cls = ResBlock1 elif self.resblock == "2": resblock_cls = ResBlock2 elif self.resblock == "AMP1": resblock_cls = AMPBlock1 else: raise ValueError(f"Unknown resblock type: {self.resblock}") self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): self.ups.append( ops.ConvTranspose1d( upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2, ) ) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = upsample_initial_channel // (2 ** (i + 1)) for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): if self.resblock == "AMP1": self.resblocks.append(resblock_cls(ch, k, d, activation=activation)) else: self.resblocks.append(resblock_cls(ch, k, d)) out_channels = 2 if stereo else 1 if self.resblock == "AMP1": act_cls = SnakeBeta if activation == "snakebeta" else Snake self.act_post = Activation1d(act_cls(ch)) else: self.act_post = nn.LeakyReLU() self.conv_post = ops.Conv1d( ch, out_channels, 7, 1, padding=3, bias=use_bias_at_final ) self.upsample_factor = np.prod([self.ups[i].stride[0] for i in range(len(self.ups))]) def get_default_config(self): """Generate default configuration for the vocoder.""" config = { "resblock_kernel_sizes": [3, 7, 11], "upsample_rates": [5, 4, 2, 2, 2], "upsample_kernel_sizes": [16, 16, 8, 4, 4], "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], "upsample_initial_channel": 1024, "stereo": True, "resblock": "1", "activation": "snake", "use_bias_at_final": True, "use_tanh_at_final": True, } return config def forward(self, x): """ Forward pass of the vocoder. Args: x: Input spectrogram tensor. Can be: - 3D: (batch_size, channels, time_steps) for mono - 4D: (batch_size, 2, channels, time_steps) for stereo Returns: Audio tensor of shape (batch_size, out_channels, audio_length) """ if x.dim() == 4: # stereo assert x.shape[1] == 2, "Input must have 2 channels for stereo" x = torch.cat((x[:, 0, :, :], x[:, 1, :, :]), dim=1) x = self.conv_pre(x) for i in range(self.num_upsamples): if self.resblock != "AMP1": x = F.leaky_relu(x, LRELU_SLOPE) x = self.ups[i](x) xs = None for j in range(self.num_kernels): if xs is None: xs = self.resblocks[i * self.num_kernels + j](x) else: xs += self.resblocks[i * self.num_kernels + j](x) x = xs / self.num_kernels x = self.act_post(x) x = self.conv_post(x) if self.apply_final_activation: if self.use_tanh_at_final: x = torch.tanh(x) else: x = torch.clamp(x, -1, 1) return x class _STFTFn(nn.Module): """Implements STFT as a convolution with precomputed DFT × Hann-window bases. The DFT basis rows (real and imaginary parts interleaved) multiplied by the causal Hann window are stored as buffers and loaded from the checkpoint. Using the exact bfloat16 bases from training ensures the mel values fed to the BWE generator are bit-identical to what it was trained on. """ def __init__(self, filter_length: int, hop_length: int, win_length: int): super().__init__() self.hop_length = hop_length self.win_length = win_length n_freqs = filter_length // 2 + 1 self.register_buffer("forward_basis", torch.zeros(n_freqs * 2, 1, filter_length)) self.register_buffer("inverse_basis", torch.zeros(n_freqs * 2, 1, filter_length)) def forward(self, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """Compute magnitude and phase spectrogram from a batch of waveforms. Applies causal (left-only) padding of win_length - hop_length samples so that each output frame depends only on past and present input — no lookahead. The STFT is computed by convolving the padded signal with forward_basis. Args: y: Waveform tensor of shape (B, T). Returns: magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames). phase: Phase spectrogram in radians, shape (B, n_freqs, T_frames). Computed in float32 for numerical stability, then cast back to the input dtype. """ if y.dim() == 2: y = y.unsqueeze(1) # (B, 1, T) left_pad = max(0, self.win_length - self.hop_length) # causal: left-only y = F.pad(y, (left_pad, 0)) spec = F.conv1d(y, comfy.model_management.cast_to(self.forward_basis, dtype=y.dtype, device=y.device), stride=self.hop_length, padding=0) n_freqs = spec.shape[1] // 2 real, imag = spec[:, :n_freqs], spec[:, n_freqs:] magnitude = torch.sqrt(real ** 2 + imag ** 2) phase = torch.atan2(imag.float(), real.float()).to(real.dtype) return magnitude, phase class MelSTFT(nn.Module): """Causal log-mel spectrogram module whose buffers are loaded from the checkpoint. Computes a log-mel spectrogram by running the causal STFT (_STFTFn) on the input waveform and projecting the linear magnitude spectrum onto the mel filterbank. The module's state dict layout matches the 'mel_stft.*' keys stored in the checkpoint (mel_basis, stft_fn.forward_basis, stft_fn.inverse_basis). """ def __init__( self, filter_length: int, hop_length: int, win_length: int, n_mel_channels: int, sampling_rate: int, mel_fmin: float, mel_fmax: float, ): super().__init__() self.stft_fn = _STFTFn(filter_length, hop_length, win_length) n_freqs = filter_length // 2 + 1 self.register_buffer("mel_basis", torch.zeros(n_mel_channels, n_freqs)) def mel_spectrogram( self, y: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """Compute log-mel spectrogram and auxiliary spectral quantities. Args: y: Waveform tensor of shape (B, T). Returns: log_mel: Log-compressed mel spectrogram, shape (B, n_mel_channels, T_frames). Computed as log(clamp(mel_basis @ magnitude, min=1e-5)). magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames). phase: Phase spectrogram in radians, shape (B, n_freqs, T_frames). energy: Per-frame energy (L2 norm over frequency), shape (B, T_frames). """ magnitude, phase = self.stft_fn(y) energy = torch.norm(magnitude, dim=1) mel = torch.matmul(comfy.model_management.cast_to(self.mel_basis, dtype=magnitude.dtype, device=y.device), magnitude) log_mel = torch.log(torch.clamp(mel, min=1e-5)) return log_mel, magnitude, phase, energy class VocoderWithBWE(torch.nn.Module): """Vocoder with bandwidth extension (BWE) for higher sample rate output. Chains a base vocoder (mel → low-rate waveform) with a BWE stage that upsamples to a higher rate. The BWE computes a mel spectrogram from the low-rate waveform. """ def __init__(self, config): super().__init__() vocoder_config = config["vocoder"] bwe_config = config["bwe"] self.vocoder = Vocoder(config=vocoder_config) self.bwe_generator = Vocoder( config={**bwe_config, "apply_final_activation": False} ) self.input_sample_rate = bwe_config["input_sampling_rate"] self.output_sample_rate = bwe_config["output_sampling_rate"] self.hop_length = bwe_config["hop_length"] self.mel_stft = MelSTFT( filter_length=bwe_config["n_fft"], hop_length=bwe_config["hop_length"], win_length=bwe_config["n_fft"], n_mel_channels=bwe_config["num_mels"], sampling_rate=bwe_config["input_sampling_rate"], mel_fmin=0.0, mel_fmax=bwe_config["input_sampling_rate"] / 2.0, ) self.resampler = UpSample1d( ratio=bwe_config["output_sampling_rate"] // bwe_config["input_sampling_rate"], persistent=False, window_type="hann", ) def _compute_mel(self, audio): """Compute log-mel spectrogram from waveform using causal STFT bases.""" B, C, T = audio.shape flat = audio.reshape(B * C, -1) # (B*C, T) mel, _, _, _ = self.mel_stft.mel_spectrogram(flat) # (B*C, n_mels, T_frames) return mel.reshape(B, C, mel.shape[1], mel.shape[2]) # (B, C, n_mels, T_frames) def forward(self, mel_spec): x = self.vocoder(mel_spec) _, _, T_low = x.shape T_out = T_low * self.output_sample_rate // self.input_sample_rate remainder = T_low % self.hop_length if remainder != 0: x = F.pad(x, (0, self.hop_length - remainder)) mel = self._compute_mel(x) residual = self.bwe_generator(mel) skip = self.resampler(x) assert residual.shape == skip.shape, f"residual {residual.shape} != skip {skip.shape}" return torch.clamp(residual + skip, -1, 1)[..., :T_out] ================================================ FILE: comfy/ldm/lumina/controlnet.py ================================================ import torch from torch import nn from .model import JointTransformerBlock class ZImageControlTransformerBlock(JointTransformerBlock): def __init__( self, layer_id: int, dim: int, n_heads: int, n_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, qk_norm: bool, modulation=True, block_id=0, operation_settings=None, ): super().__init__(layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation, z_image_modulation=True, operation_settings=operation_settings) self.block_id = block_id if block_id == 0: self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, c, x, **kwargs): if self.block_id == 0: c = self.before_proj(c) + x c = super().forward(c, **kwargs) c_skip = self.after_proj(c) return c_skip, c class ZImage_Control(torch.nn.Module): def __init__( self, dim: int = 3840, n_heads: int = 30, n_kv_heads: int = 30, multiple_of: int = 256, ffn_dim_multiplier: float = (8.0 / 3.0), norm_eps: float = 1e-5, qk_norm: bool = True, n_control_layers=6, control_in_dim=16, additional_in_dim=0, broken=False, refiner_control=False, dtype=None, device=None, operations=None, **kwargs ): super().__init__() operation_settings = {"operations": operations, "device": device, "dtype": dtype} self.broken = broken self.additional_in_dim = additional_in_dim self.control_in_dim = control_in_dim n_refiner_layers = 2 self.n_control_layers = n_control_layers self.control_layers = nn.ModuleList( [ ZImageControlTransformerBlock( i, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, block_id=i, operation_settings=operation_settings, ) for i in range(self.n_control_layers) ] ) all_x_embedder = {} patch_size = 2 f_patch_size = 1 x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * (self.control_in_dim + self.additional_in_dim), dim, bias=True, device=device, dtype=dtype) all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder self.refiner_control = refiner_control self.control_all_x_embedder = nn.ModuleDict(all_x_embedder) if self.refiner_control: self.control_noise_refiner = nn.ModuleList( [ ZImageControlTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, block_id=layer_id, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) ] ) else: self.control_noise_refiner = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation=True, z_image_modulation=True, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) ] ) def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input): patch_size = 2 f_patch_size = 1 pH = pW = patch_size B, C, H, W = control_context.shape control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2)) x_attn_mask = None if not self.refiner_control: for layer in self.control_noise_refiner: control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input) return control_context def forward_noise_refiner_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input): if self.refiner_control: if self.broken: if layer_id == 0: return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input) if layer_id > 0: out = None for i in range(1, len(self.control_layers)): o, control_context = self.control_layers[i](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input) if out is None: out = o return (out, control_context) else: return self.control_noise_refiner[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input) else: return (None, control_context) def forward_control_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input): return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input) ================================================ FILE: comfy/ldm/lumina/model.py ================================================ # Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py from __future__ import annotations from typing import List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F import comfy.ldm.common_dit from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder from comfy.ldm.modules.attention import optimized_attention_masked from comfy.ldm.flux.layers import EmbedND from comfy.ldm.flux.math import apply_rope import comfy.patcher_extension import comfy.utils from comfy.ldm.chroma_radiance.layers import NerfEmbedder def invert_slices(slices, length): sorted_slices = sorted(slices) result = [] current = 0 for start, end in sorted_slices: if current < start: result.append((current, start)) current = max(current, end) if current < length: result.append((current, length)) return result def modulate(x, scale, timestep_zero_index=None): if timestep_zero_index is None: return x * (1 + scale.unsqueeze(1)) else: scale = (1 + scale.unsqueeze(1)) actual_batch = scale.size(0) // 2 slices = timestep_zero_index invert = invert_slices(timestep_zero_index, x.shape[1]) for s in slices: x[:, s[0]:s[1]] *= scale[actual_batch:] for s in invert: x[:, s[0]:s[1]] *= scale[:actual_batch] return x def apply_gate(gate, x, timestep_zero_index=None): if timestep_zero_index is None: return gate * x else: actual_batch = gate.size(0) // 2 slices = timestep_zero_index invert = invert_slices(timestep_zero_index, x.shape[1]) for s in slices: x[:, s[0]:s[1]] *= gate[actual_batch:] for s in invert: x[:, s[0]:s[1]] *= gate[:actual_batch] return x ############################################################################# # Core NextDiT Model # ############################################################################# def clamp_fp16(x): if x.dtype == torch.float16: return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x class JointAttention(nn.Module): """Multi-head attention module.""" def __init__( self, dim: int, n_heads: int, n_kv_heads: Optional[int], qk_norm: bool, out_bias: bool = False, operation_settings={}, ): """ Initialize the Attention module. Args: dim (int): Number of input dimensions. n_heads (int): Number of heads. n_kv_heads (Optional[int]): Number of kv heads, if using GQA. """ super().__init__() self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads self.n_local_heads = n_heads self.n_local_kv_heads = self.n_kv_heads self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = dim // n_heads self.qkv = operation_settings.get("operations").Linear( dim, (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.out = operation_settings.get("operations").Linear( n_heads * self.head_dim, dim, bias=out_bias, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) if qk_norm: self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) else: self.q_norm = self.k_norm = nn.Identity() def forward( self, x: torch.Tensor, x_mask: torch.Tensor, freqs_cis: torch.Tensor, transformer_options={}, ) -> torch.Tensor: """ Args: x: x_mask: freqs_cis: Returns: """ bsz, seqlen, _ = x.shape xq, xk, xv = torch.split( self.qkv(x), [ self.n_local_heads * self.head_dim, self.n_local_kv_heads * self.head_dim, self.n_local_kv_heads * self.head_dim, ], dim=-1, ) xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xq = self.q_norm(xq) xk = self.k_norm(xk) xq, xk = apply_rope(xq, xk, freqs_cis) n_rep = self.n_local_heads // self.n_local_kv_heads if n_rep >= 1: xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options) return self.out(output) class FeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplier: Optional[float], operation_settings={}, ): """ Initialize the FeedForward module. Args: dim (int): Input dimension. hidden_dim (int): Hidden dimension of the feedforward layer. multiple_of (int): Value to ensure hidden dimension is a multiple of this value. ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. """ super().__init__() # custom dim factor multiplier if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) self.w1 = operation_settings.get("operations").Linear( dim, hidden_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.w2 = operation_settings.get("operations").Linear( hidden_dim, dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.w3 = operation_settings.get("operations").Linear( dim, hidden_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) # @torch.compile def _forward_silu_gating(self, x1, x3): return clamp_fp16(F.silu(x1) * x3) def forward(self, x): return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) class JointTransformerBlock(nn.Module): def __init__( self, layer_id: int, dim: int, n_heads: int, n_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, qk_norm: bool, modulation=True, z_image_modulation=False, attn_out_bias=False, operation_settings={}, ) -> None: """ Initialize a TransformerBlock. Args: layer_id (int): Identifier for the layer. dim (int): Embedding dimension of the input features. n_heads (int): Number of attention heads. n_kv_heads (Optional[int]): Number of attention heads in key and value features (if using GQA), or set to None for the same as query. multiple_of (int): ffn_dim_multiplier (float): norm_eps (float): """ super().__init__() self.dim = dim self.head_dim = dim // n_heads self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, out_bias=attn_out_bias, operation_settings=operation_settings) self.feed_forward = FeedForward( dim=dim, hidden_dim=dim, multiple_of=multiple_of, ffn_dim_multiplier=ffn_dim_multiplier, operation_settings=operation_settings, ) self.layer_id = layer_id self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.modulation = modulation if modulation: if z_image_modulation: self.adaLN_modulation = nn.Sequential( operation_settings.get("operations").Linear( min(dim, 256), 4 * dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) else: self.adaLN_modulation = nn.Sequential( nn.SiLU(), operation_settings.get("operations").Linear( min(dim, 1024), 4 * dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) def forward( self, x: torch.Tensor, x_mask: torch.Tensor, freqs_cis: torch.Tensor, adaln_input: Optional[torch.Tensor]=None, timestep_zero_index=None, transformer_options={}, ): """ Perform a forward pass through the TransformerBlock. Args: x (torch.Tensor): Input tensor. freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. Returns: torch.Tensor: Output tensor after applying attention and feedforward layers. """ if self.modulation: assert adaln_input is not None scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) x = x + apply_gate(gate_msa.unsqueeze(1).tanh(), self.attention_norm2( clamp_fp16(self.attention( modulate(self.attention_norm1(x), scale_msa, timestep_zero_index=timestep_zero_index), x_mask, freqs_cis, transformer_options=transformer_options, ))), timestep_zero_index=timestep_zero_index ) x = x + apply_gate(gate_mlp.unsqueeze(1).tanh(), self.ffn_norm2( clamp_fp16(self.feed_forward( modulate(self.ffn_norm1(x), scale_mlp, timestep_zero_index=timestep_zero_index), ))), timestep_zero_index=timestep_zero_index ) else: assert adaln_input is None x = x + self.attention_norm2( clamp_fp16(self.attention( self.attention_norm1(x), x_mask, freqs_cis, transformer_options=transformer_options, )) ) x = x + self.ffn_norm2( self.feed_forward( self.ffn_norm1(x), ) ) return x class FinalLayer(nn.Module): """ The final layer of NextDiT. """ def __init__(self, hidden_size, patch_size, out_channels, z_image_modulation=False, operation_settings={}): super().__init__() self.norm_final = operation_settings.get("operations").LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.linear = operation_settings.get("operations").Linear( hidden_size, patch_size * patch_size * out_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) if z_image_modulation: min_mod = 256 else: min_mod = 1024 self.adaLN_modulation = nn.Sequential( nn.SiLU(), operation_settings.get("operations").Linear( min(hidden_size, min_mod), hidden_size, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) def forward(self, x, c, timestep_zero_index=None): scale = self.adaLN_modulation(c) x = modulate(self.norm_final(x), scale, timestep_zero_index=timestep_zero_index) x = self.linear(x) return x def pad_zimage(feats, pad_token, pad_tokens_multiple): pad_extra = (-feats.shape[1]) % pad_tokens_multiple return torch.cat((feats, pad_token.to(device=feats.device, dtype=feats.dtype, copy=True).unsqueeze(0).repeat(feats.shape[0], pad_extra, 1)), dim=1), pad_extra def pos_ids_x(start_t, H_tokens, W_tokens, batch_size, device, transformer_options={}): rope_options = transformer_options.get("rope_options", None) h_scale = 1.0 w_scale = 1.0 h_start = 0 w_start = 0 if rope_options is not None: h_scale = rope_options.get("scale_y", 1.0) w_scale = rope_options.get("scale_x", 1.0) h_start = rope_options.get("shift_y", 0.0) w_start = rope_options.get("shift_x", 0.0) x_pos_ids = torch.zeros((batch_size, H_tokens * W_tokens, 3), dtype=torch.float32, device=device) x_pos_ids[:, :, 0] = start_t x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten() x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten() return x_pos_ids class NextDiT(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, patch_size: int = 2, in_channels: int = 4, dim: int = 4096, n_layers: int = 32, n_refiner_layers: int = 2, n_heads: int = 32, n_kv_heads: Optional[int] = None, multiple_of: int = 256, ffn_dim_multiplier: float = 4.0, norm_eps: float = 1e-5, qk_norm: bool = False, cap_feat_dim: int = 5120, axes_dims: List[int] = (16, 56, 56), axes_lens: List[int] = (1, 512, 512), rope_theta=10000.0, z_image_modulation=False, time_scale=1.0, pad_tokens_multiple=None, clip_text_dim=None, siglip_feat_dim=None, image_model=None, device=None, dtype=None, operations=None, **kwargs, ) -> None: super().__init__() self.dtype = dtype operation_settings = {"operations": operations, "device": device, "dtype": dtype} self.in_channels = in_channels self.out_channels = in_channels self.patch_size = patch_size self.time_scale = time_scale self.pad_tokens_multiple = pad_tokens_multiple self.x_embedder = operation_settings.get("operations").Linear( in_features=patch_size * patch_size * in_channels, out_features=dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ) self.noise_refiner = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation=True, z_image_modulation=z_image_modulation, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) ] ) self.context_refiner = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation=False, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) ] ) self.t_embedder = TimestepEmbedder(min(dim, 1024), output_size=256 if z_image_modulation else None, **operation_settings) self.cap_embedder = nn.Sequential( operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear( cap_feat_dim, dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) self.clip_text_pooled_proj = None if clip_text_dim is not None: self.clip_text_dim = clip_text_dim self.clip_text_pooled_proj = nn.Sequential( operation_settings.get("operations").RMSNorm(clip_text_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear( clip_text_dim, clip_text_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) self.time_text_embed = nn.Sequential( nn.SiLU(), operation_settings.get("operations").Linear( min(dim, 1024) + clip_text_dim, min(dim, 1024), bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) self.layers = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, z_image_modulation=z_image_modulation, attn_out_bias=False, operation_settings=operation_settings, ) for layer_id in range(n_layers) ] ) if siglip_feat_dim is not None: self.siglip_embedder = nn.Sequential( operation_settings.get("operations").RMSNorm(siglip_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear( siglip_feat_dim, dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"), ), ) self.siglip_refiner = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation=False, operation_settings=operation_settings, ) for layer_id in range(n_refiner_layers) ] ) self.siglip_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype)) else: self.siglip_embedder = None self.siglip_refiner = None self.siglip_pad_token = None # This norm final is in the lumina 2.0 code but isn't actually used for anything. # self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings) if self.pad_tokens_multiple is not None: self.x_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype)) self.cap_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype)) assert (dim // n_heads) == sum(axes_dims) self.axes_dims = axes_dims self.axes_lens = axes_lens self.rope_embedder = EmbedND(dim=dim // n_heads, theta=rope_theta, axes_dim=axes_dims) self.dim = dim self.n_heads = n_heads def unpatchify( self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False ) -> List[torch.Tensor]: """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ pH = pW = self.patch_size imgs = [] for i in range(x.size(0)): H, W = img_size[i] begin = cap_size[i] end = begin + (H // pH) * (W // pW) imgs.append( x[i][begin:end] .view(H // pH, W // pW, pH, pW, self.out_channels) .permute(4, 0, 2, 1, 3) .flatten(3, 4) .flatten(1, 2) ) if return_tensor: imgs = torch.stack(imgs, dim=0) return imgs def embed_cap(self, cap_feats=None, offset=0, bsz=1, device=None, dtype=None): if cap_feats is not None: cap_feats = self.cap_embedder(cap_feats) cap_feats_len = cap_feats.shape[1] if self.pad_tokens_multiple is not None: cap_feats, _ = pad_zimage(cap_feats, self.cap_pad_token, self.pad_tokens_multiple) else: cap_feats_len = 0 cap_feats = self.cap_pad_token.to(device=device, dtype=dtype, copy=True).unsqueeze(0).repeat(bsz, self.pad_tokens_multiple, 1) cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device) cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0 + offset embeds = (cap_feats,) freqs_cis = (self.rope_embedder(cap_pos_ids).movedim(1, 2),) return embeds, freqs_cis, cap_feats_len def embed_all(self, x, cap_feats=None, siglip_feats=None, offset=0, omni=False, transformer_options={}): bsz = 1 pH = pW = self.patch_size device = x.device embeds, freqs_cis, cap_feats_len = self.embed_cap(cap_feats, offset=offset, bsz=bsz, device=device, dtype=x.dtype) if (not omni) or self.siglip_embedder is None: cap_feats_len = embeds[0].shape[1] + offset embeds += (None,) freqs_cis += (None,) else: cap_feats_len += offset if siglip_feats is not None: b, h, w, c = siglip_feats.shape siglip_feats = siglip_feats.permute(0, 3, 1, 2).reshape(b, h * w, c) siglip_feats = self.siglip_embedder(siglip_feats) siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device) siglip_pos_ids[:, :, 0] = cap_feats_len + 2 siglip_pos_ids[:, :, 1] = (torch.linspace(0, h * 8 - 1, steps=h, dtype=torch.float32, device=device).floor()).view(-1, 1).repeat(1, w).flatten() siglip_pos_ids[:, :, 2] = (torch.linspace(0, w * 8 - 1, steps=w, dtype=torch.float32, device=device).floor()).view(1, -1).repeat(h, 1).flatten() if self.siglip_pad_token is not None: siglip_feats, pad_extra = pad_zimage(siglip_feats, self.siglip_pad_token, self.pad_tokens_multiple) # TODO: double check siglip_pos_ids = torch.nn.functional.pad(siglip_pos_ids, (0, 0, 0, pad_extra)) else: if self.siglip_pad_token is not None: siglip_feats = self.siglip_pad_token.to(device=device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(bsz, self.pad_tokens_multiple, 1) siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device) if siglip_feats is None: embeds += (None,) freqs_cis += (None,) else: embeds += (siglip_feats,) freqs_cis += (self.rope_embedder(siglip_pos_ids).movedim(1, 2),) B, C, H, W = x.shape x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2)) x_pos_ids = pos_ids_x(cap_feats_len + 1, H // pH, W // pW, bsz, device, transformer_options=transformer_options) if self.pad_tokens_multiple is not None: x, pad_extra = pad_zimage(x, self.x_pad_token, self.pad_tokens_multiple) x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra)) embeds += (x,) freqs_cis += (self.rope_embedder(x_pos_ids).movedim(1, 2),) return embeds, freqs_cis, cap_feats_len + len(freqs_cis) - 1 def patchify_and_embed( self, x: torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={} ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]: bsz = x.shape[0] cap_mask = None # TODO? main_siglip = None orig_x = x embeds = ([], [], []) freqs_cis = ([], [], []) leftover_cap = [] start_t = 0 omni = len(ref_latents) > 0 if omni: for i, ref in enumerate(ref_latents): if i < len(ref_contexts): ref_con = ref_contexts[i] else: ref_con = None if i < len(siglip_feats): sig_feat = siglip_feats[i] else: sig_feat = None out = self.embed_all(ref, ref_con, sig_feat, offset=start_t, omni=omni, transformer_options=transformer_options) for i, e in enumerate(out[0]): if e is not None: embeds[i].append(comfy.utils.repeat_to_batch_size(e, bsz)) freqs_cis[i].append(out[1][i]) start_t = out[2] leftover_cap = ref_contexts[len(ref_latents):] H, W = x.shape[-2], x.shape[-1] img_sizes = [(H, W)] * bsz out = self.embed_all(x, cap_feats, main_siglip, offset=start_t, omni=omni, transformer_options=transformer_options) img_len = out[0][-1].shape[1] cap_len = out[0][0].shape[1] for i, e in enumerate(out[0]): if e is not None: e = comfy.utils.repeat_to_batch_size(e, bsz) embeds[i].append(e) freqs_cis[i].append(out[1][i]) start_t = out[2] for cap in leftover_cap: out = self.embed_cap(cap, offset=start_t, bsz=bsz, device=x.device, dtype=x.dtype) cap_len += out[0][0].shape[1] embeds[0].append(comfy.utils.repeat_to_batch_size(out[0][0], bsz)) freqs_cis[0].append(out[1][0]) start_t += out[2] patches = transformer_options.get("patches", {}) # refine context cap_feats = torch.cat(embeds[0], dim=1) cap_freqs_cis = torch.cat(freqs_cis[0], dim=1) for layer in self.context_refiner: cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options) feats = (cap_feats,) fc = (cap_freqs_cis,) if omni and len(embeds[1]) > 0: siglip_mask = None siglip_feats_combined = torch.cat(embeds[1], dim=1) siglip_feats_freqs_cis = torch.cat(freqs_cis[1], dim=1) if self.siglip_refiner is not None: for layer in self.siglip_refiner: siglip_feats_combined = layer(siglip_feats_combined, siglip_mask, siglip_feats_freqs_cis, transformer_options=transformer_options) feats += (siglip_feats_combined,) fc += (siglip_feats_freqs_cis,) padded_img_mask = None x = torch.cat(embeds[-1], dim=1) fc_x = torch.cat(freqs_cis[-1], dim=1) if omni: timestep_zero_index = [(x.shape[1] - img_len, x.shape[1])] else: timestep_zero_index = None x_input = x for i, layer in enumerate(self.noise_refiner): x = layer(x, padded_img_mask, fc_x, t, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options) if "noise_refiner" in patches: for p in patches["noise_refiner"]: out = p({"img": x, "img_input": x_input, "txt": cap_feats, "pe": fc_x, "vec": t, "x": orig_x, "block_index": i, "transformer_options": transformer_options, "block_type": "noise_refiner"}) if "img" in out: x = out["img"] padded_full_embed = torch.cat(feats + (x,), dim=1) if timestep_zero_index is not None: ind = padded_full_embed.shape[1] - x.shape[1] timestep_zero_index = [(ind + x.shape[1] - img_len, ind + x.shape[1])] timestep_zero_index.append((feats[0].shape[1] - cap_len, feats[0].shape[1])) mask = None l_effective_cap_len = [padded_full_embed.shape[1] - img_len] * bsz return padded_full_embed, mask, img_sizes, l_effective_cap_len, torch.cat(fc + (fc_x,), dim=1), timestep_zero_index def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs) # def forward(self, x, t, cap_feats, cap_mask): def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs): omni = len(ref_latents) > 0 if omni: timesteps = torch.cat([timesteps * 0, timesteps], dim=0) t = 1.0 - timesteps cap_feats = context cap_mask = attention_mask bs, c, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) """ Forward pass of NextDiT. t: (N,) tensor of diffusion timesteps y: (N,) tensor of text tokens/features """ t = self.t_embedder(t * self.time_scale, dtype=x.dtype) # (N, D) adaln_input = t if self.clip_text_pooled_proj is not None: pooled = kwargs.get("clip_text_pooled", None) if pooled is not None: pooled = self.clip_text_pooled_proj(pooled) else: pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype) adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1)) patches = transformer_options.get("patches", {}) x_is_tensor = isinstance(x, torch.Tensor) img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, ref_latents=ref_latents, ref_contexts=ref_contexts, siglip_feats=siglip_feats, transformer_options=transformer_options) freqs_cis = freqs_cis.to(img.device) transformer_options["total_blocks"] = len(self.layers) transformer_options["block_type"] = "double" img_input = img for i, layer in enumerate(self.layers): transformer_options["block_index"] = i img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options) if "double_block" in patches: for p in patches["double_block"]: out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options}) if "img" in out: img[:, cap_size[0]:] = out["img"] if "txt" in out: img[:, :cap_size[0]] = out["txt"] img = self.final_layer(img, adaln_input, timestep_zero_index=timestep_zero_index) img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w] return -img ############################################################################# # Pixel Space Decoder Components # ############################################################################# def _modulate_shift_scale(x, shift, scale): return x * (1 + scale) + shift class PixelResBlock(nn.Module): """ Residual block with AdaLN modulation, zero-initialised so it starts as an identity at the beginning of training. """ def __init__(self, channels: int, dtype=None, device=None, operations=None): super().__init__() self.in_ln = operations.LayerNorm(channels, eps=1e-6, dtype=dtype, device=device) self.mlp = nn.Sequential( operations.Linear(channels, channels, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(channels, channels, bias=True, dtype=dtype, device=device), ) self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(channels, 3 * channels, bias=True, dtype=dtype, device=device), ) def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: shift, scale, gate = self.adaLN_modulation(y).chunk(3, dim=-1) h = _modulate_shift_scale(self.in_ln(x), shift, scale) h = self.mlp(h) return x + gate * h class DCTFinalLayer(nn.Module): """Zero-initialised output projection (adopted from DiT).""" def __init__(self, model_channels: int, out_channels: int, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(model_channels, out_channels, bias=True, dtype=dtype, device=device) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.linear(self.norm_final(x)) class SimpleMLPAdaLN(nn.Module): """ Small MLP decoder head for the pixel-space variant. Takes per-patch pixel values and a per-patch conditioning vector from the transformer backbone and predicts the denoised pixel values. x : [B*N, P^2, C] – noisy pixel values per patch position c : [B*N, dim] – backbone hidden state per patch (conditioning) → [B*N, P^2, C] """ def __init__( self, in_channels: int, model_channels: int, out_channels: int, z_channels: int, num_res_blocks: int, max_freqs: int = 8, dtype=None, device=None, operations=None, ): super().__init__() self.dtype = dtype # Project backbone hidden state → per-patch conditioning self.cond_embed = operations.Linear(z_channels, model_channels, dtype=dtype, device=device) # Input projection with DCT positional encoding self.input_embedder = NerfEmbedder( in_channels=in_channels, hidden_size_input=model_channels, max_freqs=max_freqs, dtype=dtype, device=device, operations=operations, ) # Residual blocks self.res_blocks = nn.ModuleList([ PixelResBlock(model_channels, dtype=dtype, device=device, operations=operations) for _ in range(num_res_blocks) ]) # Output projection self.final_layer = DCTFinalLayer(model_channels, out_channels, dtype=dtype, device=device, operations=operations) def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: # x: [B*N, 1, P^2*C], c: [B*N, dim] original_dtype = x.dtype weight_dtype = self.cond_embed.weight.dtype if hasattr(self.cond_embed, "weight") and self.cond_embed.weight is not None else (self.dtype or x.dtype) x = self.input_embedder(x) # [B*N, 1, model_channels] y = self.cond_embed(c.to(weight_dtype)).unsqueeze(1) # [B*N, 1, model_channels] x = x.to(weight_dtype) for block in self.res_blocks: x = block(x, y) return self.final_layer(x).to(original_dtype) # [B*N, 1, P^2*C] ############################################################################# # NextDiT – Pixel Space # ############################################################################# class NextDiTPixelSpace(NextDiT): """ Pixel-space variant of NextDiT. Identical transformer backbone to NextDiT, but the output head is replaced with a small MLP decoder (SimpleMLPAdaLN) that operates on raw pixel values per patch rather than a single affine projection. Key differences vs NextDiT: • ``final_layer`` is removed; ``dec_net`` (SimpleMLPAdaLN) is used instead. • ``_forward`` stores the raw patchified pixel values before the backbone embedding and feeds them to ``dec_net`` together with the per-patch backbone hidden states. • Supports optional x0 prediction via ``use_x0``. """ def __init__( self, # decoder-specific decoder_hidden_size: int = 3840, decoder_num_res_blocks: int = 4, decoder_max_freqs: int = 8, decoder_in_channels: int = None, # full flattened patch size (patch_size^2 * in_channels) use_x0: bool = False, # all NextDiT args forwarded unchanged **kwargs, ): super().__init__(**kwargs) # Remove the latent-space final layer – not used in pixel space del self.final_layer patch_size = kwargs.get("patch_size", 2) in_channels = kwargs.get("in_channels", 4) dim = kwargs.get("dim", 4096) # decoder_in_channels is the full flattened patch: patch_size^2 * in_channels dec_in_ch = decoder_in_channels if decoder_in_channels is not None else patch_size ** 2 * in_channels self.dec_net = SimpleMLPAdaLN( in_channels=dec_in_ch, model_channels=decoder_hidden_size, out_channels=dec_in_ch, z_channels=dim, num_res_blocks=decoder_num_res_blocks, max_freqs=decoder_max_freqs, dtype=kwargs.get("dtype"), device=kwargs.get("device"), operations=kwargs.get("operations"), ) if use_x0: self.register_buffer("__x0__", torch.tensor([])) # ------------------------------------------------------------------ # Forward — mirrors NextDiT._forward exactly, replacing final_layer # with the pixel-space dec_net decoder. # ------------------------------------------------------------------ def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs): omni = len(ref_latents) > 0 if omni: timesteps = torch.cat([timesteps * 0, timesteps], dim=0) t = 1.0 - timesteps cap_feats = context cap_mask = attention_mask bs, c, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) t = self.t_embedder(t * self.time_scale, dtype=x.dtype) adaln_input = t if self.clip_text_pooled_proj is not None: pooled = kwargs.get("clip_text_pooled", None) if pooled is not None: pooled = self.clip_text_pooled_proj(pooled) else: pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype) adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1)) # ---- capture raw pixel patches before patchify_and_embed embeds them ---- pH = pW = self.patch_size B, C, H, W = x.shape pixel_patches = ( x.view(B, C, H // pH, pH, W // pW, pW) .permute(0, 2, 4, 3, 5, 1) # [B, Ht, Wt, pH, pW, C] .flatten(3) # [B, Ht, Wt, pH*pW*C] .flatten(1, 2) # [B, N, pH*pW*C] ) N = pixel_patches.shape[1] # decoder sees one token per patch: [B*N, 1, P^2*C] pixel_values = pixel_patches.reshape(B * N, 1, pH * pW * C) patches = transformer_options.get("patches", {}) x_is_tensor = isinstance(x, torch.Tensor) img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed( x, cap_feats, cap_mask, adaln_input, num_tokens, ref_latents=ref_latents, ref_contexts=ref_contexts, siglip_feats=siglip_feats, transformer_options=transformer_options ) freqs_cis = freqs_cis.to(img.device) transformer_options["total_blocks"] = len(self.layers) transformer_options["block_type"] = "double" img_input = img for i, layer in enumerate(self.layers): transformer_options["block_index"] = i img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options) if "double_block" in patches: for p in patches["double_block"]: out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options}) if "img" in out: img[:, cap_size[0]:] = out["img"] if "txt" in out: img[:, :cap_size[0]] = out["txt"] # ---- pixel-space decoder (replaces final_layer + unpatchify) ---- # img may have padding tokens beyond N; only the first N are real image patches img_hidden = img[:, cap_size[0]:cap_size[0] + N, :] # [B, N, dim] decoder_cond = img_hidden.reshape(B * N, self.dim) # [B*N, dim] output = self.dec_net(pixel_values, decoder_cond) # [B*N, 1, P^2*C] output = output.reshape(B, N, -1) # [B, N, P^2*C] # prepend zero cap placeholder so unpatchify indexing works unchanged cap_placeholder = torch.zeros( B, cap_size[0], output.shape[-1], device=output.device, dtype=output.dtype ) img_out = self.unpatchify( torch.cat([cap_placeholder, output], dim=1), img_size, cap_size, return_tensor=x_is_tensor )[:, :, :h, :w] return -img_out def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): # _forward returns neg_x0 = -x0 (negated decoder output). # # Reference inference (working_inference_reference.py): # out = _forward(img, t) # = -x0 # pred = (img - out) / t # = (img + x0) / t [_apply_x0_residual] # img += (t_prev - t_curr) * pred # Euler step # # ComfyUI's Euler sampler does the same: # x_next = x + (sigma_next - sigma) * model_output # So model_output must equal pred = (x - neg_x0) / t = (x - (-x0)) / t = (x + x0) / t neg_x0 = comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs) return (x - neg_x0) / timesteps.view(-1, 1, 1, 1) ================================================ FILE: comfy/ldm/mmaudio/vae/__init__.py ================================================ ================================================ FILE: comfy/ldm/mmaudio/vae/activations.py ================================================ # Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. # LICENSE is in incl_licenses directory. import torch from torch import nn, sin, pow from torch.nn import Parameter import comfy.model_management class Snake(nn.Module): ''' Implementation of a sine-based periodic activation function Shape: - Input: (B, C, T) - Output: (B, C, T), same shape as the input Parameters: - alpha - trainable parameter References: - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195 Examples: >>> a1 = snake(256) >>> x = torch.randn(256) >>> x = a1(x) ''' def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): ''' Initialization. INPUT: - in_features: shape of the input - alpha: trainable parameter alpha is initialized to 1 by default, higher values = higher-frequency. alpha will be trained along with the rest of your model. ''' super(Snake, self).__init__() self.in_features = in_features # initialize alpha self.alpha_logscale = alpha_logscale if self.alpha_logscale: self.alpha = Parameter(torch.empty(in_features)) else: self.alpha = Parameter(torch.empty(in_features)) self.alpha.requires_grad = alpha_trainable self.no_div_by_zero = 0.000000001 def forward(self, x): ''' Forward pass of the function. Applies the function to the input elementwise. Snake ∶= x + 1/a * sin^2 (xa) ''' alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] if self.alpha_logscale: alpha = torch.exp(alpha) x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) return x class SnakeBeta(nn.Module): ''' A modified Snake function which uses separate parameters for the magnitude of the periodic components Shape: - Input: (B, C, T) - Output: (B, C, T), same shape as the input Parameters: - alpha - trainable parameter that controls frequency - beta - trainable parameter that controls magnitude References: - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195 Examples: >>> a1 = snakebeta(256) >>> x = torch.randn(256) >>> x = a1(x) ''' def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): ''' Initialization. INPUT: - in_features: shape of the input - alpha - trainable parameter that controls frequency - beta - trainable parameter that controls magnitude alpha is initialized to 1 by default, higher values = higher-frequency. beta is initialized to 1 by default, higher values = higher-magnitude. alpha will be trained along with the rest of your model. ''' super(SnakeBeta, self).__init__() self.in_features = in_features # initialize alpha self.alpha_logscale = alpha_logscale if self.alpha_logscale: self.alpha = Parameter(torch.empty(in_features)) self.beta = Parameter(torch.empty(in_features)) else: self.alpha = Parameter(torch.empty(in_features)) self.beta = Parameter(torch.empty(in_features)) self.alpha.requires_grad = alpha_trainable self.beta.requires_grad = alpha_trainable self.no_div_by_zero = 0.000000001 def forward(self, x): ''' Forward pass of the function. Applies the function to the input elementwise. SnakeBeta ∶= x + 1/b * sin^2 (xa) ''' alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] beta = comfy.model_management.cast_to(self.beta, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) if self.alpha_logscale: alpha = torch.exp(alpha) beta = torch.exp(beta) x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) return x ================================================ FILE: comfy/ldm/mmaudio/vae/alias_free_torch.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import math import comfy.model_management if 'sinc' in dir(torch): sinc = torch.sinc else: # This code is adopted from adefossez's julius.core.sinc under the MIT License # https://adefossez.github.io/julius/julius/core.html # LICENSE is in incl_licenses directory. def sinc(x: torch.Tensor): """ Implementation of sinc, i.e. sin(pi * x) / (pi * x) __Warning__: Different to julius.sinc, the input is multiplied by `pi`! """ return torch.where(x == 0, torch.tensor(1., device=x.device, dtype=x.dtype), torch.sin(math.pi * x) / math.pi / x) # This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License # https://adefossez.github.io/julius/julius/lowpass.html # LICENSE is in incl_licenses directory. def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size] even = (kernel_size % 2 == 0) half_size = kernel_size // 2 #For kaiser window delta_f = 4 * half_width A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 if A > 50.: beta = 0.1102 * (A - 8.7) elif A >= 21.: beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.) else: beta = 0. window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio if even: time = (torch.arange(-half_size, half_size) + 0.5) else: time = torch.arange(kernel_size) - half_size if cutoff == 0: filter_ = torch.zeros_like(time) else: filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) # Normalize filter to have sum = 1, otherwise we will have a small leakage # of the constant component in the input signal. filter_ /= filter_.sum() filter = filter_.view(1, 1, kernel_size) return filter class LowPassFilter1d(nn.Module): def __init__(self, cutoff=0.5, half_width=0.6, stride: int = 1, padding: bool = True, padding_mode: str = 'replicate', kernel_size: int = 12): # kernel_size should be even number for stylegan3 setup, # in this implementation, odd number is also possible. super().__init__() if cutoff < -0.: raise ValueError("Minimum cutoff must be larger than zero.") if cutoff > 0.5: raise ValueError("A cutoff above 0.5 does not make sense.") self.kernel_size = kernel_size self.even = (kernel_size % 2 == 0) self.pad_left = kernel_size // 2 - int(self.even) self.pad_right = kernel_size // 2 self.stride = stride self.padding = padding self.padding_mode = padding_mode filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) self.register_buffer("filter", filter) #input [B, C, T] def forward(self, x): _, C, _ = x.shape if self.padding: x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode) out = F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C) return out class UpSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None): super().__init__() self.ratio = ratio self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size self.stride = ratio self.pad = self.kernel_size // ratio - 1 self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size) self.register_buffer("filter", filter) # x: [B, C, T] def forward(self, x): _, C, _ = x.shape x = F.pad(x, (self.pad, self.pad), mode='replicate') x = self.ratio * F.conv_transpose1d( x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C) x = x[..., self.pad_left:-self.pad_right] return x class DownSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None): super().__init__() self.ratio = ratio self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, stride=ratio, kernel_size=self.kernel_size) def forward(self, x): xx = self.lowpass(x) return xx class Activation1d(nn.Module): def __init__(self, activation, up_ratio: int = 2, down_ratio: int = 2, up_kernel_size: int = 12, down_kernel_size: int = 12): super().__init__() self.up_ratio = up_ratio self.down_ratio = down_ratio self.act = activation self.upsample = UpSample1d(up_ratio, up_kernel_size) self.downsample = DownSample1d(down_ratio, down_kernel_size) # x: [B,C,T] def forward(self, x): x = self.upsample(x) x = self.act(x) x = self.downsample(x) return x ================================================ FILE: comfy/ldm/mmaudio/vae/autoencoder.py ================================================ from typing import Literal import torch import torch.nn as nn from .distributions import DiagonalGaussianDistribution from .vae import VAE_16k from .bigvgan import BigVGANVocoder import logging try: import torchaudio except: logging.warning("torchaudio missing, MMAudio VAE model will be broken") def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn): return norm_fn(torch.clamp(x, min=clip_val) * C) def spectral_normalize_torch(magnitudes, norm_fn): output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn) return output class MelConverter(nn.Module): def __init__( self, *, sampling_rate: float, n_fft: int, num_mels: int, hop_size: int, win_size: int, fmin: float, fmax: float, norm_fn, ): super().__init__() self.sampling_rate = sampling_rate self.n_fft = n_fft self.num_mels = num_mels self.hop_size = hop_size self.win_size = win_size self.fmin = fmin self.fmax = fmax self.norm_fn = norm_fn # mel = librosa_mel_fn(sr=self.sampling_rate, # n_fft=self.n_fft, # n_mels=self.num_mels, # fmin=self.fmin, # fmax=self.fmax) # mel_basis = torch.from_numpy(mel).float() mel_basis = torch.empty((num_mels, 1 + n_fft // 2)) hann_window = torch.hann_window(self.win_size) self.register_buffer('mel_basis', mel_basis) self.register_buffer('hann_window', hann_window) @property def device(self): return self.mel_basis.device def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor: waveform = waveform.clamp(min=-1., max=1.).to(self.device) waveform = torch.nn.functional.pad( waveform.unsqueeze(1), [int((self.n_fft - self.hop_size) / 2), int((self.n_fft - self.hop_size) / 2)], mode='reflect') waveform = waveform.squeeze(1) spec = torch.stft(waveform, self.n_fft, hop_length=self.hop_size, win_length=self.win_size, window=self.hann_window, center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) spec = torch.view_as_real(spec) spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) spec = torch.matmul(self.mel_basis, spec) spec = spectral_normalize_torch(spec, self.norm_fn) return spec class AudioAutoencoder(nn.Module): def __init__( self, *, # ckpt_path: str, mode=Literal['16k', '44k'], need_vae_encoder: bool = True, ): super().__init__() assert mode == "16k", "Only 16k mode is supported currently." self.mel_converter = MelConverter(sampling_rate=16_000, n_fft=1024, num_mels=80, hop_size=256, win_size=1024, fmin=0, fmax=8_000, norm_fn=torch.log10) self.vae = VAE_16k().eval() bigvgan_config = { "resblock": "1", "num_mels": 80, "upsample_rates": [4, 4, 2, 2, 2, 2], "upsample_kernel_sizes": [8, 8, 4, 4, 4, 4], "upsample_initial_channel": 1536, "resblock_kernel_sizes": [3, 7, 11], "resblock_dilation_sizes": [ [1, 3, 5], [1, 3, 5], [1, 3, 5], ], "activation": "snakebeta", "snake_logscale": True, } self.vocoder = BigVGANVocoder( bigvgan_config ).eval() @torch.inference_mode() def encode_audio(self, x) -> DiagonalGaussianDistribution: # x: (B * L) mel = self.mel_converter(x) dist = self.vae.encode(mel) return dist @torch.no_grad() def decode(self, z): mel_decoded = self.vae.decode(z) audio = self.vocoder(mel_decoded) audio = torchaudio.functional.resample(audio, 16000, 44100) return audio @torch.no_grad() def encode(self, audio): audio = audio.mean(dim=1) audio = torchaudio.functional.resample(audio, 44100, 16000) dist = self.encode_audio(audio) return dist.mean ================================================ FILE: comfy/ldm/mmaudio/vae/bigvgan.py ================================================ # Copyright (c) 2022 NVIDIA CORPORATION. # Licensed under the MIT license. # Adapted from https://github.com/jik876/hifi-gan under the MIT license. # LICENSE is in incl_licenses directory. import torch import torch.nn as nn from types import SimpleNamespace from . import activations from .alias_free_torch import Activation1d import comfy.ops ops = comfy.ops.disable_weight_init def get_padding(kernel_size, dilation=1): return int((kernel_size * dilation - dilation) / 2) class AMPBlock1(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): super(AMPBlock1, self).__init__() self.h = h self.convs1 = nn.ModuleList([ ops.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0])), ops.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1])), ops.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2])) ]) self.convs2 = nn.ModuleList([ ops.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)), ops.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)), ops.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)) ]) self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) else: raise NotImplementedError( "activation incorrectly specified. check the config file and look for 'activation'." ) def forward(self, x): acts1, acts2 = self.activations[::2], self.activations[1::2] for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): xt = a1(x) xt = c1(xt) xt = a2(xt) xt = c2(xt) x = xt + x return x class AMPBlock2(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None): super(AMPBlock2, self).__init__() self.h = h self.convs = nn.ModuleList([ ops.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0])), ops.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1])) ]) self.num_layers = len(self.convs) # total number of conv layers if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) else: raise NotImplementedError( "activation incorrectly specified. check the config file and look for 'activation'." ) def forward(self, x): for c, a in zip(self.convs, self.activations): xt = a(x) xt = c(xt) x = xt + x return x class BigVGANVocoder(torch.nn.Module): # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. def __init__(self, h): super().__init__() if isinstance(h, dict): h = SimpleNamespace(**h) self.h = h self.num_kernels = len(h.resblock_kernel_sizes) self.num_upsamples = len(h.upsample_rates) # pre conv self.conv_pre = ops.Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3) # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2 # transposed conv-based upsamplers. does not apply anti-aliasing self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): self.ups.append( nn.ModuleList([ ops.ConvTranspose1d(h.upsample_initial_channel // (2**i), h.upsample_initial_channel // (2**(i + 1)), k, u, padding=(k - u) // 2) ])) # residual blocks using anti-aliased multi-periodicity composition modules (AMP) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = h.upsample_initial_channel // (2**(i + 1)) for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): self.resblocks.append(resblock(h, ch, k, d, activation=h.activation)) # post conv if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale) self.activation_post = Activation1d(activation=activation_post) elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale) self.activation_post = Activation1d(activation=activation_post) else: raise NotImplementedError( "activation incorrectly specified. check the config file and look for 'activation'." ) self.conv_post = ops.Conv1d(ch, 1, 7, 1, padding=3) def forward(self, x): # pre conv x = self.conv_pre(x) for i in range(self.num_upsamples): # upsampling for i_up in range(len(self.ups[i])): x = self.ups[i][i_up](x) # AMP blocks xs = None for j in range(self.num_kernels): if xs is None: xs = self.resblocks[i * self.num_kernels + j](x) else: xs += self.resblocks[i * self.num_kernels + j](x) x = xs / self.num_kernels # post conv x = self.activation_post(x) x = self.conv_post(x) x = torch.tanh(x) return x ================================================ FILE: comfy/ldm/mmaudio/vae/distributions.py ================================================ import torch import numpy as np class AbstractDistribution: def sample(self): raise NotImplementedError() def mode(self): raise NotImplementedError() class DiracDistribution(AbstractDistribution): def __init__(self, value): self.value = value def sample(self): return self.value def mode(self): return self.value class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device) def sample(self): x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device) return x def kl(self, other=None): if self.deterministic: return torch.Tensor([0.]) else: if other is None: return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) else: return 0.5 * torch.sum( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, dim=[1, 2, 3]) def nll(self, sample, dims=[1,2,3]): if self.deterministic: return torch.Tensor([0.]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) def mode(self): return self.mean def normal_kl(mean1, logvar1, mean2, logvar2): """ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases. """ tensor = None for obj in (mean1, logvar1, mean2, logvar2): if isinstance(obj, torch.Tensor): tensor = obj break assert tensor is not None, "at least one argument must be a Tensor" # Force variances to be Tensors. Broadcasting helps convert scalars to # Tensors, but it does not work for torch.exp(). logvar1, logvar2 = [ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2) ] return 0.5 * ( -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) ) ================================================ FILE: comfy/ldm/mmaudio/vae/vae.py ================================================ import logging from typing import Optional import torch import torch.nn as nn from .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D, Upsample1D, nonlinearity) from .distributions import DiagonalGaussianDistribution import comfy.ops ops = comfy.ops.disable_weight_init log = logging.getLogger() DATA_MEAN_80D = [ -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927, -1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728, -1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131, -1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280, -1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643, -1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436, -2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282, -2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673 ] DATA_STD_80D = [ 1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263, 0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194, 0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043, 0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973, 0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939, 0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604, 1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070 ] DATA_MEAN_128D = [ -3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597, -2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033, -2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157, -3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782, -3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647, -3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795, -3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121, -4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960, -4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712, -5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120, -6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663, -7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628, -9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861 ] DATA_STD_128D = [ 2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659, 2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557, 2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182, 2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991, 2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900, 2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817, 2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609, 2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812, 2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451, 2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877, 2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164 ] class VAE(nn.Module): def __init__( self, *, data_dim: int, embed_dim: int, hidden_dim: int, ): super().__init__() if data_dim == 80: self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32)) self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32)) elif data_dim == 128: self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32)) self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32)) self.data_mean = self.data_mean.view(1, -1, 1) self.data_std = self.data_std.view(1, -1, 1) self.encoder = Encoder1D( dim=hidden_dim, ch_mult=(1, 2, 4), num_res_blocks=2, attn_layers=[3], down_layers=[0], in_dim=data_dim, embed_dim=embed_dim, ) self.decoder = Decoder1D( dim=hidden_dim, ch_mult=(1, 2, 4), num_res_blocks=2, attn_layers=[3], down_layers=[0], in_dim=data_dim, out_dim=data_dim, embed_dim=embed_dim, ) self.embed_dim = embed_dim # self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1) # self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1) self.initialize_weights() def initialize_weights(self): pass def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution: if normalize: x = self.normalize(x) moments = self.encoder(x) posterior = DiagonalGaussianDistribution(moments) return posterior def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor: dec = self.decoder(z) if unnormalize: dec = self.unnormalize(dec) return dec def normalize(self, x: torch.Tensor) -> torch.Tensor: return (x - comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) def unnormalize(self, x: torch.Tensor) -> torch.Tensor: return x * comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device) def forward( self, x: torch.Tensor, sample_posterior: bool = True, rng: Optional[torch.Generator] = None, normalize: bool = True, unnormalize: bool = True, ) -> tuple[torch.Tensor, DiagonalGaussianDistribution]: posterior = self.encode(x, normalize=normalize) if sample_posterior: z = posterior.sample(rng) else: z = posterior.mode() dec = self.decode(z, unnormalize=unnormalize) return dec, posterior def load_weights(self, src_dict) -> None: self.load_state_dict(src_dict, strict=True) @property def device(self) -> torch.device: return next(self.parameters()).device def get_last_layer(self): return self.decoder.conv_out.weight def remove_weight_norm(self): return self class Encoder1D(nn.Module): def __init__(self, *, dim: int, ch_mult: tuple[int] = (1, 2, 4, 8), num_res_blocks: int, attn_layers: list[int] = [], down_layers: list[int] = [], resamp_with_conv: bool = True, in_dim: int, embed_dim: int, double_z: bool = True, kernel_size: int = 3, clip_act: float = 256.0): super().__init__() self.dim = dim self.num_layers = len(ch_mult) self.num_res_blocks = num_res_blocks self.in_channels = in_dim self.clip_act = clip_act self.down_layers = down_layers self.attn_layers = attn_layers self.conv_in = ops.Conv1d(in_dim, self.dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) in_ch_mult = (1, ) + tuple(ch_mult) self.in_ch_mult = in_ch_mult # downsampling self.down = nn.ModuleList() for i_level in range(self.num_layers): block = nn.ModuleList() attn = nn.ModuleList() block_in = dim * in_ch_mult[i_level] block_out = dim * ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append( ResnetBlock1D(in_dim=block_in, out_dim=block_out, kernel_size=kernel_size, use_norm=True)) block_in = block_out if i_level in attn_layers: attn.append(AttnBlock1D(block_in)) down = nn.Module() down.block = block down.attn = attn if i_level in down_layers: down.downsample = Downsample1D(block_in, resamp_with_conv) self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, kernel_size=kernel_size, use_norm=True) self.mid.attn_1 = AttnBlock1D(block_in) self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, kernel_size=kernel_size, use_norm=True) # end self.conv_out = ops.Conv1d(block_in, 2 * embed_dim if double_z else embed_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) self.learnable_gain = nn.Parameter(torch.zeros([])) def forward(self, x): # downsampling h = self.conv_in(x) for i_level in range(self.num_layers): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](h) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) h = h.clamp(-self.clip_act, self.clip_act) if i_level in self.down_layers: h = self.down[i_level].downsample(h) # middle h = self.mid.block_1(h) h = self.mid.attn_1(h) h = self.mid.block_2(h) h = h.clamp(-self.clip_act, self.clip_act) # end h = nonlinearity(h) h = self.conv_out(h) * (self.learnable_gain + 1) return h class Decoder1D(nn.Module): def __init__(self, *, dim: int, out_dim: int, ch_mult: tuple[int] = (1, 2, 4, 8), num_res_blocks: int, attn_layers: list[int] = [], down_layers: list[int] = [], kernel_size: int = 3, resamp_with_conv: bool = True, in_dim: int, embed_dim: int, clip_act: float = 256.0): super().__init__() self.ch = dim self.num_layers = len(ch_mult) self.num_res_blocks = num_res_blocks self.in_channels = in_dim self.clip_act = clip_act self.down_layers = [i + 1 for i in down_layers] # each downlayer add one # compute in_ch_mult, block_in and curr_res at lowest res block_in = dim * ch_mult[self.num_layers - 1] # z to block_in self.conv_in = ops.Conv1d(embed_dim, block_in, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True) self.mid.attn_1 = AttnBlock1D(block_in) self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_layers)): block = nn.ModuleList() attn = nn.ModuleList() block_out = dim * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True)) block_in = block_out if i_level in attn_layers: attn.append(AttnBlock1D(block_in)) up = nn.Module() up.block = block up.attn = attn if i_level in self.down_layers: up.upsample = Upsample1D(block_in, resamp_with_conv) self.up.insert(0, up) # prepend to get consistent order # end self.conv_out = ops.Conv1d(block_in, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) self.learnable_gain = nn.Parameter(torch.zeros([])) def forward(self, z): # z to block_in h = self.conv_in(z) # middle h = self.mid.block_1(h) h = self.mid.attn_1(h) h = self.mid.block_2(h) h = h.clamp(-self.clip_act, self.clip_act) # upsampling for i_level in reversed(range(self.num_layers)): for i_block in range(self.num_res_blocks + 1): h = self.up[i_level].block[i_block](h) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) h = h.clamp(-self.clip_act, self.clip_act) if i_level in self.down_layers: h = self.up[i_level].upsample(h) h = nonlinearity(h) h = self.conv_out(h) * (self.learnable_gain + 1) return h def VAE_16k(**kwargs) -> VAE: return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs) def VAE_44k(**kwargs) -> VAE: return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs) def get_my_vae(name: str, **kwargs) -> VAE: if name == '16k': return VAE_16k(**kwargs) if name == '44k': return VAE_44k(**kwargs) raise ValueError(f'Unknown model: {name}') ================================================ FILE: comfy/ldm/mmaudio/vae/vae_modules.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from comfy.ldm.modules.diffusionmodules.model import vae_attention import math import comfy.ops ops = comfy.ops.disable_weight_init def nonlinearity(x): # swish return torch.nn.functional.silu(x) / 0.596 def mp_sum(a, b, t=0.5): return a.lerp(b, t) / math.sqrt((1 - t)**2 + t**2) def normalize(x, dim=None, eps=1e-4): if dim is None: dim = list(range(1, x.ndim)) norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32) norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel())) return x / norm.to(x.dtype) class ResnetBlock1D(nn.Module): def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True): super().__init__() self.in_dim = in_dim out_dim = in_dim if out_dim is None else out_dim self.out_dim = out_dim self.use_conv_shortcut = conv_shortcut self.use_norm = use_norm self.conv1 = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) self.conv2 = ops.Conv1d(out_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) if self.in_dim != self.out_dim: if self.use_conv_shortcut: self.conv_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) else: self.nin_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=1, padding=0, bias=False) def forward(self, x: torch.Tensor) -> torch.Tensor: # pixel norm if self.use_norm: x = normalize(x, dim=1) h = x h = nonlinearity(h) h = self.conv1(h) h = nonlinearity(h) h = self.conv2(h) if self.in_dim != self.out_dim: if self.use_conv_shortcut: x = self.conv_shortcut(x) else: x = self.nin_shortcut(x) return mp_sum(x, h, t=0.3) class AttnBlock1D(nn.Module): def __init__(self, in_channels, num_heads=1): super().__init__() self.in_channels = in_channels self.num_heads = num_heads self.qkv = ops.Conv1d(in_channels, in_channels * 3, kernel_size=1, padding=0, bias=False) self.proj_out = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False) self.optimized_attention = vae_attention() def forward(self, x): h = x y = self.qkv(h) y = y.reshape(y.shape[0], -1, 3, y.shape[-1]) q, k, v = normalize(y, dim=1).unbind(2) h = self.optimized_attention(q, k, v) h = self.proj_out(h) return mp_sum(x, h, t=0.3) class Upsample1D(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = ops.Conv1d(in_channels, in_channels, kernel_size=3, padding=1, bias=False) def forward(self, x): x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact') # support 3D tensor(B,C,T) if self.with_conv: x = self.conv(x) return x class Downsample1D(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: # no asymmetric padding in torch conv, must do it ourselves self.conv1 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False) self.conv2 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False) def forward(self, x): if self.with_conv: x = self.conv1(x) x = F.avg_pool1d(x, kernel_size=2, stride=2) if self.with_conv: x = self.conv2(x) return x ================================================ FILE: comfy/ldm/models/autoencoder.py ================================================ import logging import math import torch from contextlib import contextmanager from typing import Any, Dict, Tuple, Union from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution from comfy.ldm.util import get_obj_from_str, instantiate_from_config from comfy.ldm.modules.ema import LitEma import comfy.ops from einops import rearrange import comfy.model_management class DiagonalGaussianRegularizer(torch.nn.Module): def __init__(self, sample: bool = False): super().__init__() self.sample = sample def get_trainable_parameters(self) -> Any: yield from () def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: posterior = DiagonalGaussianDistribution(z) if self.sample: z = posterior.sample() else: z = posterior.mode() return z, None class EmptyRegularizer(torch.nn.Module): def __init__(self): super().__init__() def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: return z, None class AbstractAutoencoder(torch.nn.Module): """ This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators, unCLIP models, etc. Hence, it is fairly general, and specific features (e.g. discriminator training, encoding, decoding) must be implemented in subclasses. """ def __init__( self, ema_decay: Union[None, float] = None, monitor: Union[None, str] = None, input_key: str = "jpg", **kwargs, ): super().__init__() self.input_key = input_key self.use_ema = ema_decay is not None if monitor is not None: self.monitor = monitor if self.use_ema: self.model_ema = LitEma(self, decay=ema_decay) logging.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") def get_input(self, batch) -> Any: raise NotImplementedError() def on_train_batch_end(self, *args, **kwargs): # for EMA computation if self.use_ema: self.model_ema(self) @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.parameters()) self.model_ema.copy_to(self) if context is not None: logging.info(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.parameters()) if context is not None: logging.info(f"{context}: Restored training weights") def encode(self, *args, **kwargs) -> torch.Tensor: raise NotImplementedError("encode()-method of abstract base class called") def decode(self, *args, **kwargs) -> torch.Tensor: raise NotImplementedError("decode()-method of abstract base class called") def instantiate_optimizer_from_config(self, params, lr, cfg): logging.info(f"loading >>> {cfg['target']} <<< optimizer from config") return get_obj_from_str(cfg["target"])( params, lr=lr, **cfg.get("params", dict()) ) def configure_optimizers(self) -> Any: raise NotImplementedError() class AutoencodingEngine(AbstractAutoencoder): """ Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL (we also restore them explicitly as special cases for legacy reasons). Regularizations such as KL or VQ are moved to the regularizer class. """ def __init__( self, *args, encoder_config: Dict, decoder_config: Dict, regularizer_config: Dict, **kwargs, ): super().__init__(*args, **kwargs) self.encoder: torch.nn.Module = instantiate_from_config(encoder_config) self.decoder: torch.nn.Module = instantiate_from_config(decoder_config) self.regularization = instantiate_from_config( regularizer_config ) def get_last_layer(self): return self.decoder.get_last_layer() def encode( self, x: torch.Tensor, return_reg_log: bool = False, unregularized: bool = False, ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: z = self.encoder(x) if unregularized: return z, dict() z, reg_log = self.regularization(z) if return_reg_log: return z, reg_log return z def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor: x = self.decoder(z, **kwargs) return x def forward( self, x: torch.Tensor, **additional_decode_kwargs ) -> Tuple[torch.Tensor, torch.Tensor, dict]: z, reg_log = self.encode(x, return_reg_log=True) dec = self.decode(z, **additional_decode_kwargs) return z, dec, reg_log class AutoencodingEngineLegacy(AutoencodingEngine): def __init__(self, embed_dim: int, **kwargs): self.max_batch_size = kwargs.pop("max_batch_size", None) ddconfig = kwargs.pop("ddconfig") super().__init__( encoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Encoder", "params": ddconfig, }, decoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Decoder", "params": ddconfig, }, **kwargs, ) if ddconfig.get("conv3d", False): conv_op = comfy.ops.disable_weight_init.Conv3d else: conv_op = comfy.ops.disable_weight_init.Conv2d self.quant_conv = conv_op( (1 + ddconfig["double_z"]) * ddconfig["z_channels"], (1 + ddconfig["double_z"]) * embed_dim, 1, ) self.post_quant_conv = conv_op(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim if ddconfig.get("batch_norm_latent", False): self.bn_eps = 1e-4 self.bn_momentum = 0.1 self.ps = [2, 2] self.bn = torch.nn.BatchNorm2d(math.prod(self.ps) * ddconfig["z_channels"], eps=self.bn_eps, momentum=self.bn_momentum, affine=False, track_running_stats=True, ) self.bn.eval() else: self.bn = None def get_autoencoder_params(self) -> list: params = super().get_autoencoder_params() return params def encode( self, x: torch.Tensor, return_reg_log: bool = False ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: if self.max_batch_size is None: z = self.encoder(x) z = self.quant_conv(z) else: N = x.shape[0] bs = self.max_batch_size n_batches = int(math.ceil(N / bs)) z = list() for i_batch in range(n_batches): z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs]) z_batch = self.quant_conv(z_batch) z.append(z_batch) z = torch.cat(z, 0) z, reg_log = self.regularization(z) if self.bn is not None: z = rearrange(z, "... c (i pi) (j pj) -> ... (c pi pj) i j", pi=self.ps[0], pj=self.ps[1], ) z = torch.nn.functional.batch_norm(z, comfy.model_management.cast_to(self.bn.running_mean, dtype=z.dtype, device=z.device), comfy.model_management.cast_to(self.bn.running_var, dtype=z.dtype, device=z.device), momentum=self.bn_momentum, eps=self.bn_eps) if return_reg_log: return z, reg_log return z def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: if self.bn is not None: s = torch.sqrt(comfy.model_management.cast_to(self.bn.running_var.view(1, -1, 1, 1), dtype=z.dtype, device=z.device) + self.bn_eps) m = comfy.model_management.cast_to(self.bn.running_mean.view(1, -1, 1, 1), dtype=z.dtype, device=z.device) z = z * s + m z = rearrange( z, "... (c pi pj) i j -> ... c (i pi) (j pj)", pi=self.ps[0], pj=self.ps[1], ) if self.max_batch_size is None: dec = self.post_quant_conv(z) dec = self.decoder(dec, **decoder_kwargs) else: N = z.shape[0] bs = self.max_batch_size n_batches = int(math.ceil(N / bs)) dec = list() for i_batch in range(n_batches): dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs]) dec_batch = self.decoder(dec_batch, **decoder_kwargs) dec.append(dec_batch) dec = torch.cat(dec, 0) return dec class AutoencoderKL(AutoencodingEngineLegacy): def __init__(self, **kwargs): if "lossconfig" in kwargs: kwargs["loss_config"] = kwargs.pop("lossconfig") super().__init__( regularizer_config={ "target": ( "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer" ) }, **kwargs, ) ================================================ FILE: comfy/ldm/modules/attention.py ================================================ import math import sys import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from typing import Optional, Any, Callable, Union import logging import functools from .diffusionmodules.util import AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention from comfy import model_management if model_management.xformers_enabled(): import xformers import xformers.ops SAGE_ATTENTION_IS_AVAILABLE = False try: from sageattention import sageattn SAGE_ATTENTION_IS_AVAILABLE = True except ImportError as e: if model_management.sage_attention_enabled(): if e.name == "sageattention": logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") else: raise e exit(-1) SAGE_ATTENTION3_IS_AVAILABLE = False try: from sageattn3 import sageattn3_blackwell SAGE_ATTENTION3_IS_AVAILABLE = True except ImportError: pass FLASH_ATTENTION_IS_AVAILABLE = False try: from flash_attn import flash_attn_func FLASH_ATTENTION_IS_AVAILABLE = True except ImportError: if model_management.flash_attention_enabled(): logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn") exit(-1) REGISTERED_ATTENTION_FUNCTIONS = {} def register_attention_function(name: str, func: Callable): # avoid replacing existing functions if name not in REGISTERED_ATTENTION_FUNCTIONS: REGISTERED_ATTENTION_FUNCTIONS[name] = func else: logging.warning(f"Attention function {name} already registered, skipping registration.") def get_attention_function(name: str, default: Any=...) -> Union[Callable, None]: if name == "optimized": return optimized_attention elif name not in REGISTERED_ATTENTION_FUNCTIONS: if default is ...: raise KeyError(f"Attention function {name} not found.") else: return default return REGISTERED_ATTENTION_FUNCTIONS[name] from comfy.cli_args import args import comfy.ops ops = comfy.ops.disable_weight_init FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype() def get_attn_precision(attn_precision, current_dtype): if args.dont_upcast_attention: return None if FORCE_UPCAST_ATTENTION_DTYPE is not None and current_dtype in FORCE_UPCAST_ATTENTION_DTYPE: return FORCE_UPCAST_ATTENTION_DTYPE[current_dtype] return attn_precision def exists(val): return val is not None def default(val, d): if exists(val): return val return d # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): super().__init__() self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( operations.Linear(dim, inner_dim, dtype=dtype, device=device), nn.GELU() ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) self.net = nn.Sequential( project_in, nn.Dropout(dropout), operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) ) def forward(self, x): return self.net(x) def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) def wrap_attn(func): @functools.wraps(func) def wrapper(*args, **kwargs): remove_attn_wrapper_key = False try: if "_inside_attn_wrapper" not in kwargs: transformer_options = kwargs.get("transformer_options", None) remove_attn_wrapper_key = True kwargs["_inside_attn_wrapper"] = True if transformer_options is not None: if "optimized_attention_override" in transformer_options: return transformer_options["optimized_attention_override"](func, *args, **kwargs) return func(*args, **kwargs) finally: if remove_attn_wrapper_key: del kwargs["_inside_attn_wrapper"] return wrapper @wrap_attn def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): attn_precision = get_attn_precision(attn_precision, q.dtype) if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 h = heads if skip_reshape: q, k, v = map( lambda t: t.reshape(b * heads, -1, dim_head), (q, k, v), ) else: q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) # force cast to fp32 to avoid overflowing if attn_precision == torch.float32: sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale del q, k if exists(mask): if mask.dtype == torch.bool: mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) else: if len(mask.shape) == 2: bs = 1 else: bs = mask.shape[0] mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) sim.add_(mask) # attention, what we cannot get enough of sim = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) if skip_output_reshape: out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) ) else: out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return out @wrap_attn def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): attn_precision = get_attn_precision(attn_precision, query.dtype) if skip_reshape: b, _, _, dim_head = query.shape else: b, _, dim_head = query.shape dim_head //= heads if skip_reshape: query = query.reshape(b * heads, -1, dim_head) value = value.reshape(b * heads, -1, dim_head) key = key.reshape(b * heads, -1, dim_head).movedim(1, 2) else: query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) dtype = query.dtype upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32 if upcast_attention: bytes_per_token = torch.finfo(torch.float32).bits//8 else: bytes_per_token = torch.finfo(query.dtype).bits//8 batch_x_heads, q_tokens, _ = query.shape _, _, k_tokens = key.shape mem_free_total, _ = model_management.get_free_memory(query.device, True) kv_chunk_size_min = None kv_chunk_size = None query_chunk_size = None for x in [4096, 2048, 1024, 512, 256]: count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0) if count >= k_tokens: kv_chunk_size = k_tokens query_chunk_size = x break if query_chunk_size is None: query_chunk_size = 512 if mask is not None: if len(mask.shape) == 2: bs = 1 else: bs = mask.shape[0] mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) hidden_states = efficient_dot_product_attention( query, key, value, query_chunk_size=query_chunk_size, kv_chunk_size=kv_chunk_size, kv_chunk_size_min=kv_chunk_size_min, use_checkpoint=False, upcast_attention=upcast_attention, mask=mask, ) hidden_states = hidden_states.to(dtype) if skip_output_reshape: hidden_states = hidden_states.unflatten(0, (-1, heads)) else: hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) return hidden_states @wrap_attn def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): attn_precision = get_attn_precision(attn_precision, q.dtype) if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 if skip_reshape: q, k, v = map( lambda t: t.reshape(b * heads, -1, dim_head), (q, k, v), ) else: q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) mem_free_total = model_management.get_free_memory(q.device) if attn_precision == torch.float32: element_size = 4 upcast = True else: element_size = q.element_size() upcast = False gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size modifier = 3 mem_required = tensor_size * modifier steps = 1 if mem_required > mem_free_total: steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") if steps > 64: max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') if mask is not None: if len(mask.shape) == 2: bs = 1 else: bs = mask.shape[0] mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) first_op_done = False cleared_cache = False while True: try: slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size if upcast: with torch.autocast(enabled=False, device_type = 'cuda'): s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale else: s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale if mask is not None: if len(mask.shape) == 2: s1 += mask[i:end] else: if mask.shape[1] == 1: s1 += mask else: s1 += mask[:, i:end] s2 = s1.softmax(dim=-1).to(v.dtype) del s1 first_op_done = True r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) del s2 break except Exception as e: model_management.raise_non_oom(e) if first_op_done == False: model_management.soft_empty_cache(True) if cleared_cache == False: cleared_cache = True logging.warning("out of memory error, emptying cache and trying again") continue steps *= 2 if steps > 64: raise e logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) else: raise e del q, k, v if skip_output_reshape: r1 = ( r1.unsqueeze(0) .reshape(b, heads, -1, dim_head) ) else: r1 = ( r1.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return r1 BROKEN_XFORMERS = False try: x_vers = xformers.__version__ # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error) BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") except: pass @wrap_attn def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): b = q.shape[0] dim_head = q.shape[-1] # check to make sure xformers isn't broken disabled_xformers = False if BROKEN_XFORMERS: if b * heads > 65535: disabled_xformers = True if not disabled_xformers: if torch.jit.is_tracing() or torch.jit.is_scripting(): disabled_xformers = True if disabled_xformers: return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs) if skip_reshape: # b h k d -> b k h d q, k, v = map( lambda t: t.permute(0, 2, 1, 3), (q, k, v), ) # actually do the reshaping else: dim_head //= heads q, k, v = map( lambda t: t.reshape(b, -1, heads, dim_head), (q, k, v), ) if mask is not None: # add a singleton batch dimension if mask.ndim == 2: mask = mask.unsqueeze(0) # add a singleton heads dimension if mask.ndim == 3: mask = mask.unsqueeze(1) # pad to a multiple of 8 pad = 8 - mask.shape[-1] % 8 # the xformers docs says that it's allowed to have a mask of shape (1, Nq, Nk) # but when using separated heads, the shape has to be (B, H, Nq, Nk) # in flux, this matrix ends up being over 1GB # here, we create a mask with the same batch/head size as the input mask (potentially singleton or full) mask_out = torch.empty([mask.shape[0], mask.shape[1], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device) mask_out[..., :mask.shape[-1]] = mask # doesn't this remove the padding again?? mask = mask_out[..., :mask.shape[-1]] mask = mask.expand(b, heads, -1, -1) out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) if skip_output_reshape: out = out.permute(0, 2, 1, 3) else: out = ( out.reshape(b, -1, heads * dim_head) ) return out if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue. SDP_BATCH_LIMIT = 2**15 else: #TODO: other GPUs ? SDP_BATCH_LIMIT = 2**31 @wrap_attn def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head //= heads q, k, v = map( lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v), ) if mask is not None: # add a batch dimension if there isn't already one if mask.ndim == 2: mask = mask.unsqueeze(0) # add a heads dimension if there isn't already one if mask.ndim == 3: mask = mask.unsqueeze(1) if SDP_BATCH_LIMIT >= b: out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) if not skip_output_reshape: out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) ) else: out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device) for i in range(0, b, SDP_BATCH_LIMIT): m = mask if mask is not None: if mask.shape[0] > 1: m = mask[i : i + SDP_BATCH_LIMIT] out[i : i + SDP_BATCH_LIMIT] = comfy.ops.scaled_dot_product_attention( q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=m, dropout_p=0.0, is_causal=False ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) return out @wrap_attn def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if kwargs.get("low_precision_attention", True) is False: return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs) exception_fallback = False if skip_reshape: b, _, _, dim_head = q.shape tensor_layout = "HND" else: b, _, dim_head = q.shape dim_head //= heads q, k, v = map( lambda t: t.view(b, -1, heads, dim_head), (q, k, v), ) tensor_layout = "NHD" if mask is not None: # add a batch dimension if there isn't already one if mask.ndim == 2: mask = mask.unsqueeze(0) # add a heads dimension if there isn't already one if mask.ndim == 3: mask = mask.unsqueeze(1) try: out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) except Exception as e: logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e)) exception_fallback = True if exception_fallback: if tensor_layout == "NHD": q, k, v = map( lambda t: t.transpose(1, 2), (q, k, v), ) return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs) if tensor_layout == "HND": if not skip_output_reshape: out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) ) else: if skip_output_reshape: out = out.transpose(1, 2) else: out = out.reshape(b, -1, heads * dim_head) return out @wrap_attn def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): exception_fallback = False if (q.device.type != "cuda" or q.dtype not in (torch.float16, torch.bfloat16) or mask is not None): return attention_pytorch( q, k, v, heads, mask=mask, attn_precision=attn_precision, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs ) if skip_reshape: B, H, L, D = q.shape if H != heads: return attention_pytorch( q, k, v, heads, mask=mask, attn_precision=attn_precision, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs ) q_s, k_s, v_s = q, k, v N = q.shape[2] dim_head = D else: B, N, inner_dim = q.shape if inner_dim % heads != 0: return attention_pytorch( q, k, v, heads, mask=mask, attn_precision=attn_precision, skip_reshape=False, skip_output_reshape=skip_output_reshape, **kwargs ) dim_head = inner_dim // heads if dim_head >= 256 or N <= 1024: return attention_pytorch( q, k, v, heads, mask=mask, attn_precision=attn_precision, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs ) if not skip_reshape: q_s, k_s, v_s = map( lambda t: t.view(B, -1, heads, dim_head).permute(0, 2, 1, 3).contiguous(), (q, k, v), ) B, H, L, D = q_s.shape try: out = sageattn3_blackwell(q_s, k_s, v_s, is_causal=False) except Exception as e: exception_fallback = True logging.error("Error running SageAttention3: %s, falling back to pytorch attention.", e) if exception_fallback: if not skip_reshape: del q_s, k_s, v_s return attention_pytorch( q, k, v, heads, mask=mask, attn_precision=attn_precision, skip_reshape=False, skip_output_reshape=skip_output_reshape, **kwargs ) if skip_reshape: if not skip_output_reshape: out = out.permute(0, 2, 1, 3).reshape(B, L, H * D) else: if skip_output_reshape: pass else: out = out.permute(0, 2, 1, 3).reshape(B, L, H * D) return out try: @torch.library.custom_op("flash_attention::flash_attn", mutates_args=()) def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal) @flash_attn_wrapper.register_fake def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False): # Output shape is the same as q return q.new_empty(q.shape) except AttributeError as error: FLASH_ATTN_ERROR = error def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}" @wrap_attn def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head //= heads q, k, v = map( lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v), ) if mask is not None: # add a batch dimension if there isn't already one if mask.ndim == 2: mask = mask.unsqueeze(0) # add a heads dimension if there isn't already one if mask.ndim == 3: mask = mask.unsqueeze(1) try: if mask is not None: raise RuntimeError("Mask must not be set for Flash attention") out = flash_attn_wrapper( q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), dropout_p=0.0, causal=False, ).transpose(1, 2) except Exception as e: logging.warning(f"Flash Attention failed, using default SDPA: {e}") out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) if not skip_output_reshape: out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) ) return out optimized_attention = attention_basic if model_management.sage_attention_enabled(): logging.info("Using sage attention") optimized_attention = attention_sage elif model_management.xformers_enabled(): logging.info("Using xformers attention") optimized_attention = attention_xformers elif model_management.flash_attention_enabled(): logging.info("Using Flash Attention") optimized_attention = attention_flash elif model_management.pytorch_attention_enabled(): logging.info("Using pytorch attention") optimized_attention = attention_pytorch else: if args.use_split_cross_attention: logging.info("Using split optimization for attention") optimized_attention = attention_split else: logging.info("Using sub quadratic optimization for attention, if you have memory or speed issues try using: --use-split-cross-attention") optimized_attention = attention_sub_quad optimized_attention_masked = optimized_attention # register core-supported attention functions if SAGE_ATTENTION_IS_AVAILABLE: register_attention_function("sage", attention_sage) if SAGE_ATTENTION3_IS_AVAILABLE: register_attention_function("sage3", attention3_sage) if FLASH_ATTENTION_IS_AVAILABLE: register_attention_function("flash", attention_flash) if model_management.xformers_enabled(): register_attention_function("xformers", attention_xformers) register_attention_function("pytorch", attention_pytorch) register_attention_function("sub_quad", attention_sub_quad) register_attention_function("split", attention_split) def optimized_attention_for_device(device, mask=False, small_input=False): if small_input: if model_management.pytorch_attention_enabled(): return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases else: return attention_basic if device == torch.device("cpu"): return attention_sub_quad if mask: return optimized_attention_masked return optimized_attention class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.attn_precision = attn_precision self.heads = heads self.dim_head = dim_head self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) def forward(self, x, context=None, value=None, mask=None, transformer_options={}): q = self.to_q(x) context = default(context, x) k = self.to_k(context) if value is not None: v = self.to_v(value) del value else: v = self.to_v(context) if mask is None: out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options) else: out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) return self.to_out(out) class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() self.ff_in = ff_in or inner_dim is not None if inner_dim is None: inner_dim = dim self.is_res = inner_dim == dim self.attn_precision = attn_precision if self.ff_in: self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) self.disable_self_attn = disable_self_attn self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) if disable_temporal_crossattention: if switch_temporal_ca_to_sa: raise ValueError else: self.attn2 = None else: context_dim_attn2 = None if not switch_temporal_ca_to_sa: context_dim_attn2 = context_dim self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.n_heads = n_heads self.d_head = d_head self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa def forward(self, x, context=None, transformer_options={}): extra_options = {} block = transformer_options.get("block", None) block_index = transformer_options.get("block_index", 0) transformer_patches = {} transformer_patches_replace = {} for k in transformer_options: if k == "patches": transformer_patches = transformer_options[k] elif k == "patches_replace": transformer_patches_replace = transformer_options[k] else: extra_options[k] = transformer_options[k] extra_options["n_heads"] = self.n_heads extra_options["dim_head"] = self.d_head extra_options["attn_precision"] = self.attn_precision if self.ff_in: x_skip = x x = self.ff_in(self.norm_in(x)) if self.is_res: x += x_skip n = self.norm1(x) if self.disable_self_attn: context_attn1 = context else: context_attn1 = None value_attn1 = None if "attn1_patch" in transformer_patches: patch = transformer_patches["attn1_patch"] if context_attn1 is None: context_attn1 = n value_attn1 = context_attn1 for p in patch: n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) if block is not None: transformer_block = (block[0], block[1], block_index) else: transformer_block = None attn1_replace_patch = transformer_patches_replace.get("attn1", {}) block_attn1 = transformer_block if block_attn1 not in attn1_replace_patch: block_attn1 = block if block_attn1 in attn1_replace_patch: if context_attn1 is None: context_attn1 = n value_attn1 = n n = self.attn1.to_q(n) context_attn1 = self.attn1.to_k(context_attn1) value_attn1 = self.attn1.to_v(value_attn1) n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) n = self.attn1.to_out(n) else: n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=transformer_options) if "attn1_output_patch" in transformer_patches: patch = transformer_patches["attn1_output_patch"] for p in patch: n = p(n, extra_options) x = n + x if "middle_patch" in transformer_patches: patch = transformer_patches["middle_patch"] for p in patch: x = p(x, extra_options) if self.attn2 is not None: n = self.norm2(x) if self.switch_temporal_ca_to_sa: context_attn2 = n else: context_attn2 = context value_attn2 = None if "attn2_patch" in transformer_patches: patch = transformer_patches["attn2_patch"] value_attn2 = context_attn2 for p in patch: n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) attn2_replace_patch = transformer_patches_replace.get("attn2", {}) block_attn2 = transformer_block if block_attn2 not in attn2_replace_patch: block_attn2 = block if block_attn2 in attn2_replace_patch: if value_attn2 is None: value_attn2 = context_attn2 n = self.attn2.to_q(n) context_attn2 = self.attn2.to_k(context_attn2) value_attn2 = self.attn2.to_v(value_attn2) n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) n = self.attn2.to_out(n) else: n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=transformer_options) if "attn2_output_patch" in transformer_patches: patch = transformer_patches["attn2_output_patch"] for p in patch: n = p(n, extra_options) x = n + x if self.is_res: x_skip = x x = self.ff(self.norm3(x)) if self.is_res: x = x_skip + x return x class SpatialTransformer(nn.Module): """ Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image NEW: use_linear for more efficiency instead of the 1x1 convs """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, disable_self_attn=False, use_linear=False, use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] * depth self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) if not use_linear: self.proj_in = operations.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) else: self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) for d in range(depth)] ) if not use_linear: self.proj_out = operations.Conv2d(inner_dim,in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) else: self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.use_linear = use_linear def forward(self, x, context=None, transformer_options={}): # note: if no context is given, cross-attention defaults to self-attention if not isinstance(context, list): context = [context] * len(self.transformer_blocks) b, c, h, w = x.shape transformer_options["activations_shape"] = list(x.shape) x_in = x x = self.norm(x) if not self.use_linear: x = self.proj_in(x) x = x.movedim(1, 3).flatten(1, 2).contiguous() if self.use_linear: x = self.proj_in(x) for i, block in enumerate(self.transformer_blocks): transformer_options["block_index"] = i x = block(x, context=context[i], transformer_options=transformer_options) if self.use_linear: x = self.proj_out(x) x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous() if not self.use_linear: x = self.proj_out(x) return x + x_in class SpatialVideoTransformer(SpatialTransformer): def __init__( self, in_channels, n_heads, d_head, depth=1, dropout=0.0, use_linear=False, context_dim=None, use_spatial_context=False, timesteps=None, merge_strategy: str = "fixed", merge_factor: float = 0.5, time_context_dim=None, ff_in=False, checkpoint=False, time_depth=1, disable_self_attn=False, disable_temporal_crossattention=False, max_time_embed_period: int = 10000, attn_precision=None, dtype=None, device=None, operations=ops ): super().__init__( in_channels, n_heads, d_head, depth=depth, dropout=dropout, use_checkpoint=checkpoint, context_dim=context_dim, use_linear=use_linear, disable_self_attn=disable_self_attn, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations ) self.time_depth = time_depth self.depth = depth self.max_time_embed_period = max_time_embed_period time_mix_d_head = d_head n_time_mix_heads = n_heads time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) inner_dim = n_heads * d_head if use_spatial_context: time_context_dim = context_dim self.time_stack = nn.ModuleList( [ BasicTransformerBlock( inner_dim, n_time_mix_heads, time_mix_d_head, dropout=dropout, context_dim=time_context_dim, # timesteps=timesteps, checkpoint=checkpoint, ff_in=ff_in, inner_dim=time_mix_inner_dim, disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations ) for _ in range(self.depth) ] ) assert len(self.time_stack) == len(self.transformer_blocks) self.use_spatial_context = use_spatial_context self.in_channels = in_channels time_embed_dim = self.in_channels * 4 self.time_pos_embed = nn.Sequential( operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy ) def forward( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, time_context: Optional[torch.Tensor] = None, timesteps: Optional[int] = None, image_only_indicator: Optional[torch.Tensor] = None, transformer_options={} ) -> torch.Tensor: _, _, h, w = x.shape transformer_options["activations_shape"] = list(x.shape) x_in = x spatial_context = None if exists(context): spatial_context = context if self.use_spatial_context: assert ( context.ndim == 3 ), f"n dims of spatial context should be 3 but are {context.ndim}" if time_context is None: time_context = context time_context_first_timestep = time_context[::timesteps] time_context = repeat( time_context_first_timestep, "b ... -> (b n) ...", n=h * w ) elif time_context is not None and not self.use_spatial_context: time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) if time_context.ndim == 2: time_context = rearrange(time_context, "b c -> b 1 c") x = self.norm(x) if not self.use_linear: x = self.proj_in(x) x = rearrange(x, "b c h w -> b (h w) c") if self.use_linear: x = self.proj_in(x) num_frames = torch.arange(timesteps, device=x.device) num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) num_frames = rearrange(num_frames, "b t -> (b t)") t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) emb = self.time_pos_embed(t_emb) emb = emb[:, None, :] for it_, (block, mix_block) in enumerate( zip(self.transformer_blocks, self.time_stack) ): transformer_options["block_index"] = it_ x = block( x, context=spatial_context, transformer_options=transformer_options, ) x_mix = x x_mix = x_mix + emb B, S, C = x_mix.shape x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) x_mix = mix_block(x_mix, context=time_context, transformer_options=transformer_options) x_mix = rearrange( x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps ) x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) if self.use_linear: x = self.proj_out(x) x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) if not self.use_linear: x = self.proj_out(x) out = x + x_in return out ================================================ FILE: comfy/ldm/modules/diffusionmodules/__init__.py ================================================ ================================================ FILE: comfy/ldm/modules/diffusionmodules/mmdit.py ================================================ from functools import partial from typing import Dict, Optional, List import numpy as np import torch import torch.nn as nn from ..attention import optimized_attention from einops import rearrange, repeat from .util import timestep_embedding import comfy.ops import comfy.ldm.common_dit def default(x, y): if x is not None: return x return y class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, norm_layer=None, bias=True, drop=0., use_conv=False, dtype=None, device=None, operations=None, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features drop_probs = drop linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs) self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device) self.drop2 = nn.Dropout(drop_probs) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop1(x) x = self.norm(x) x = self.fc2(x) x = self.drop2(x) return x class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ dynamic_img_pad: torch.jit.Final[bool] def __init__( self, img_size: Optional[int] = 224, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768, norm_layer = None, flatten: bool = True, bias: bool = True, strict_img_size: bool = True, dynamic_img_pad: bool = True, padding_mode='circular', conv3d=False, dtype=None, device=None, operations=None, ): super().__init__() try: len(patch_size) self.patch_size = patch_size except: if conv3d: self.patch_size = (patch_size, patch_size, patch_size) else: self.patch_size = (patch_size, patch_size) self.padding_mode = padding_mode # flatten spatial dim and transpose to channels last, kept for bwd compat self.flatten = flatten self.strict_img_size = strict_img_size self.dynamic_img_pad = dynamic_img_pad if conv3d: self.proj = operations.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) else: self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): if self.dynamic_img_pad: x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode) x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) # NCHW -> NLC x = self.norm(x) return x def modulate(x, shift, scale): if shift is None: shift = torch.zeros_like(scale) return torch.addcmul(shift.unsqueeze(1), x, 1+ scale.unsqueeze(1)) ################################################################################# # Sine/Cosine Positional Embedding Functions # ################################################################################# def get_2d_sincos_pos_embed( embed_dim, grid_size, cls_token=False, extra_tokens=0, scaling_factor=None, offset=None, ): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ grid_h = np.arange(grid_size, dtype=np.float32) grid_w = np.arange(grid_size, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) if scaling_factor is not None: grid = grid / scaling_factor if offset is not None: grid = grid - offset grid = grid.reshape([2, 1, grid_size, grid_size]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token and extra_tokens > 0: pos_embed = np.concatenate( [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0 ) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float64) omega /= embed_dim / 2.0 omega = 1.0 / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32): omega = torch.arange(embed_dim // 2, device=device, dtype=dtype) omega /= embed_dim / 2.0 omega = 1.0 / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product emb_sin = torch.sin(out) # (M, D/2) emb_cos = torch.cos(out) # (M, D/2) emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D) return emb def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32): small = min(h, w) val_h = (h / small) * val_magnitude val_w = (w / small) * val_magnitude grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij') emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) return emb ################################################################################# # Embedding Layers for Timesteps and Class Labels # ################################################################################# class TimestepEmbedder(nn.Module): """ Embeds scalar timesteps into vector representations. """ def __init__(self, hidden_size, frequency_embedding_size=256, output_size=None, dtype=None, device=None, operations=None): super().__init__() if output_size is None: output_size = hidden_size self.mlp = nn.Sequential( operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size, output_size, bias=True, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size def forward(self, t, dtype, **kwargs): t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype) t_emb = self.mlp(t_freq) return t_emb class VectorEmbedder(nn.Module): """ Embeds a flat vector of dimension input_dim """ def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None): super().__init__() self.mlp = nn.Sequential( operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), ) def forward(self, x: torch.Tensor) -> torch.Tensor: emb = self.mlp(x) return emb ################################################################################# # Core DiT Model # ################################################################################# def split_qkv(qkv, head_dim): qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0) return qkv[0], qkv[1], qkv[2] class SelfAttention(nn.Module): ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, qk_scale: Optional[float] = None, proj_drop: float = 0.0, attn_mode: str = "xformers", pre_only: bool = False, qk_norm: Optional[str] = None, rmsnorm: bool = False, dtype=None, device=None, operations=None, ): super().__init__() self.num_heads = num_heads self.head_dim = dim // num_heads self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) if not pre_only: self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) self.proj_drop = nn.Dropout(proj_drop) assert attn_mode in self.ATTENTION_MODES self.attn_mode = attn_mode self.pre_only = pre_only if qk_norm == "rms": self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) elif qk_norm == "ln": self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) elif qk_norm is None: self.ln_q = nn.Identity() self.ln_k = nn.Identity() else: raise ValueError(qk_norm) def pre_attention(self, x: torch.Tensor) -> torch.Tensor: B, L, C = x.shape qkv = self.qkv(x) q, k, v = split_qkv(qkv, self.head_dim) q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1) k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1) return (q, k, v) def post_attention(self, x: torch.Tensor) -> torch.Tensor: assert not self.pre_only x = self.proj(x) x = self.proj_drop(x) return x def forward(self, x: torch.Tensor) -> torch.Tensor: q, k, v = self.pre_attention(x) x = optimized_attention( q, k, v, heads=self.num_heads ) x = self.post_attention(x) return x class RMSNorm(torch.nn.Module): def __init__( self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs ): """ Initialize the RMSNorm normalization layer. Args: dim (int): The dimension of the input tensor. eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. Attributes: eps (float): A small value added to the denominator for numerical stability. weight (nn.Parameter): Learnable scaling parameter. """ super().__init__() self.eps = eps self.learnable_scale = elementwise_affine if self.learnable_scale: self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) else: self.register_parameter("weight", None) def forward(self, x): return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) class SwiGLUFeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplier: Optional[float] = None, ): """ Initialize the FeedForward module. Args: dim (int): Input dimension. hidden_dim (int): Hidden dimension of the feedforward layer. multiple_of (int): Value to ensure hidden dimension is a multiple of this value. ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. Attributes: w1 (ColumnParallelLinear): Linear transformation for the first layer. w2 (RowParallelLinear): Linear transformation for the second layer. w3 (ColumnParallelLinear): Linear transformation for the third layer. """ super().__init__() hidden_dim = int(2 * hidden_dim / 3) # custom dim factor multiplier if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) self.w1 = nn.Linear(dim, hidden_dim, bias=False) self.w2 = nn.Linear(hidden_dim, dim, bias=False) self.w3 = nn.Linear(dim, hidden_dim, bias=False) def forward(self, x): return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) class DismantledBlock(nn.Module): """ A DiT block with gated adaptive layer norm (adaLN) conditioning. """ ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") def __init__( self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, attn_mode: str = "xformers", qkv_bias: bool = False, pre_only: bool = False, rmsnorm: bool = False, scale_mod_only: bool = False, swiglu: bool = False, qk_norm: Optional[str] = None, x_block_self_attn: bool = False, dtype=None, device=None, operations=None, **block_kwargs, ): super().__init__() assert attn_mode in self.ATTENTION_MODES if not rmsnorm: self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) else: self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.attn = SelfAttention( dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=pre_only, qk_norm=qk_norm, rmsnorm=rmsnorm, dtype=dtype, device=device, operations=operations ) if x_block_self_attn: assert not pre_only assert not scale_mod_only self.x_block_self_attn = True self.attn2 = SelfAttention( dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=False, qk_norm=qk_norm, rmsnorm=rmsnorm, dtype=dtype, device=device, operations=operations ) else: self.x_block_self_attn = False if not pre_only: if not rmsnorm: self.norm2 = operations.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device ) else: self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) mlp_hidden_dim = int(hidden_size * mlp_ratio) if not pre_only: if not swiglu: self.mlp = Mlp( in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations ) else: self.mlp = SwiGLUFeedForward( dim=hidden_size, hidden_dim=mlp_hidden_dim, multiple_of=256, ) self.scale_mod_only = scale_mod_only if x_block_self_attn: assert not pre_only assert not scale_mod_only n_mods = 9 elif not scale_mod_only: n_mods = 6 if not pre_only else 2 else: n_mods = 4 if not pre_only else 1 self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device) ) self.pre_only = pre_only def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: if not self.pre_only: if not self.scale_mod_only: ( shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, ) = self.adaLN_modulation(c).chunk(6, dim=1) else: shift_msa = None shift_mlp = None ( scale_msa, gate_msa, scale_mlp, gate_mlp, ) = self.adaLN_modulation( c ).chunk(4, dim=1) qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) return qkv, ( x, gate_msa, shift_mlp, scale_mlp, gate_mlp, ) else: if not self.scale_mod_only: ( shift_msa, scale_msa, ) = self.adaLN_modulation( c ).chunk(2, dim=1) else: shift_msa = None scale_msa = self.adaLN_modulation(c) qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) return qkv, None def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp): assert not self.pre_only x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn) x = x + gate_mlp.unsqueeze(1) * self.mlp( modulate(self.norm2(x), shift_mlp, scale_mlp) ) return x def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: assert self.x_block_self_attn ( shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2, ) = self.adaLN_modulation(c).chunk(9, dim=1) x_norm = self.norm1(x) qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa)) qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2)) return qkv, qkv2, ( x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2, ) def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2): assert not self.pre_only attn1 = self.attn.post_attention(attn) attn2 = self.attn2.post_attention(attn2) x = gate_cat(x, gate_msa, gate_msa2, attn1, attn2) x = x + gate_mlp.unsqueeze(1) * self.mlp( modulate(self.norm2(x), shift_mlp, scale_mlp) ) return x def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: assert not self.pre_only if self.x_block_self_attn: qkv, qkv2, intermediates = self.pre_attention_x(x, c) attn, _ = optimized_attention( qkv[0], qkv[1], qkv[2], num_heads=self.attn.num_heads, ) attn2, _ = optimized_attention( qkv2[0], qkv2[1], qkv2[2], num_heads=self.attn2.num_heads, ) return self.post_attention_x(attn, attn2, *intermediates) else: qkv, intermediates = self.pre_attention(x, c) attn = optimized_attention( qkv[0], qkv[1], qkv[2], heads=self.attn.num_heads, ) return self.post_attention(attn, *intermediates) def gate_cat(x, gate_msa, gate_msa2, attn1, attn2): out1 = gate_msa.unsqueeze(1) * attn1 out2 = gate_msa2.unsqueeze(1) * attn2 x = torch.stack([x, out1, out2], dim=0).sum(dim=0) return x def block_mixing(*args, use_checkpoint=True, **kwargs): if use_checkpoint: return torch.utils.checkpoint.checkpoint( _block_mixing, *args, use_reentrant=False, **kwargs ) else: return _block_mixing(*args, **kwargs) def _block_mixing(context, x, context_block, x_block, c, transformer_options={}): context_qkv, context_intermediates = context_block.pre_attention(context, c) if x_block.x_block_self_attn: x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c) else: x_qkv, x_intermediates = x_block.pre_attention(x, c) o = [] for t in range(3): o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1)) qkv = tuple(o) attn = optimized_attention( qkv[0], qkv[1], qkv[2], heads=x_block.attn.num_heads, transformer_options=transformer_options, ) context_attn, x_attn = ( attn[:, : context_qkv[0].shape[1]], attn[:, context_qkv[0].shape[1] :], ) if not context_block.pre_only: context = context_block.post_attention(context_attn, *context_intermediates) else: context = None if x_block.x_block_self_attn: attn2 = optimized_attention( x_qkv2[0], x_qkv2[1], x_qkv2[2], heads=x_block.attn2.num_heads, transformer_options=transformer_options, ) x = x_block.post_attention_x(x_attn, attn2, *x_intermediates) else: x = x_block.post_attention(x_attn, *x_intermediates) return context, x class JointBlock(nn.Module): """just a small wrapper to serve as a fsdp unit""" def __init__( self, *args, **kwargs, ): super().__init__() pre_only = kwargs.pop("pre_only") qk_norm = kwargs.pop("qk_norm", None) x_block_self_attn = kwargs.pop("x_block_self_attn", False) self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs) self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, x_block_self_attn=x_block_self_attn, **kwargs) def forward(self, *args, **kwargs): return block_mixing( *args, context_block=self.context_block, x_block=self.x_block, **kwargs ) class FinalLayer(nn.Module): """ The final layer of DiT. """ def __init__( self, hidden_size: int, patch_size: int, out_channels: int, total_out_channels: Optional[int] = None, dtype=None, device=None, operations=None, ): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = ( operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) if (total_out_channels is None) else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device) ) self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class SelfAttentionContext(nn.Module): def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None): super().__init__() dim_head = dim // heads inner_dim = dim self.heads = heads self.dim_head = dim_head self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device) self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device) def forward(self, x): qkv = self.qkv(x) q, k, v = split_qkv(qkv, self.dim_head) x = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads) return self.proj(x) class ContextProcessorBlock(nn.Module): def __init__(self, context_size, dtype=None, device=None, operations=None): super().__init__() self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations) self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations) def forward(self, x): x += self.attn(self.norm1(x)) x += self.mlp(self.norm2(x)) return x class ContextProcessor(nn.Module): def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None): super().__init__() self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)]) self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) def forward(self, x): for i, l in enumerate(self.layers): x = l(x) return self.norm(x) class MMDiT(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, input_size: int = 32, patch_size: int = 2, in_channels: int = 4, depth: int = 28, # hidden_size: Optional[int] = None, # num_heads: Optional[int] = None, mlp_ratio: float = 4.0, learn_sigma: bool = False, adm_in_channels: Optional[int] = None, context_embedder_config: Optional[Dict] = None, compile_core: bool = False, use_checkpoint: bool = False, register_length: int = 0, attn_mode: str = "torch", rmsnorm: bool = False, scale_mod_only: bool = False, swiglu: bool = False, out_channels: Optional[int] = None, pos_embed_scaling_factor: Optional[float] = None, pos_embed_offset: Optional[float] = None, pos_embed_max_size: Optional[int] = None, num_patches = None, qk_norm: Optional[str] = None, qkv_bias: bool = True, context_processor_layers = None, x_block_self_attn: bool = False, x_block_self_attn_layers: Optional[List[int]] = [], context_size = 4096, num_blocks = None, final_layer = True, skip_blocks = False, dtype = None, #TODO device = None, operations = None, ): super().__init__() self.dtype = dtype self.learn_sigma = learn_sigma self.in_channels = in_channels default_out_channels = in_channels * 2 if learn_sigma else in_channels self.out_channels = default(out_channels, default_out_channels) self.patch_size = patch_size self.pos_embed_scaling_factor = pos_embed_scaling_factor self.pos_embed_offset = pos_embed_offset self.pos_embed_max_size = pos_embed_max_size self.x_block_self_attn_layers = x_block_self_attn_layers # hidden_size = default(hidden_size, 64 * depth) # num_heads = default(num_heads, hidden_size // 64) # apply magic --> this defines a head_size of 64 self.hidden_size = 64 * depth num_heads = depth if num_blocks is None: num_blocks = depth self.depth = depth self.num_heads = num_heads self.x_embedder = PatchEmbed( input_size, patch_size, in_channels, self.hidden_size, bias=True, strict_img_size=self.pos_embed_max_size is None, dtype=dtype, device=device, operations=operations ) self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) self.y_embedder = None if adm_in_channels is not None: assert isinstance(adm_in_channels, int) self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations) if context_processor_layers is not None: self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations) else: self.context_processor = None self.context_embedder = nn.Identity() if context_embedder_config is not None: if context_embedder_config["target"] == "torch.nn.Linear": self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device) self.register_length = register_length if self.register_length > 0: self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device)) # num_patches = self.x_embedder.num_patches # Will use fixed sin-cos embedding: # just use a buffer already if num_patches is not None: self.register_buffer( "pos_embed", torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device), ) else: self.pos_embed = None self.use_checkpoint = use_checkpoint if not skip_blocks: self.joint_blocks = nn.ModuleList( [ JointBlock( self.hidden_size, num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=(i == num_blocks - 1) and final_layer, rmsnorm=rmsnorm, scale_mod_only=scale_mod_only, swiglu=swiglu, qk_norm=qk_norm, x_block_self_attn=(i in self.x_block_self_attn_layers) or x_block_self_attn, dtype=dtype, device=device, operations=operations, ) for i in range(num_blocks) ] ) if final_layer: self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) if compile_core: assert False self.forward_core_with_concat = torch.compile(self.forward_core_with_concat) def cropped_pos_embed(self, hw, device=None): p = self.x_embedder.patch_size[0] h, w = hw # patched size h = (h + 1) // p w = (w + 1) // p if self.pos_embed is None: return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device) assert self.pos_embed_max_size is not None assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size) assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size) top = (self.pos_embed_max_size - h) // 2 left = (self.pos_embed_max_size - w) // 2 spatial_pos_embed = rearrange( self.pos_embed, "1 (h w) c -> 1 h w c", h=self.pos_embed_max_size, w=self.pos_embed_max_size, ) spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c") # print(spatial_pos_embed, top, left, h, w) # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better # # print(t) # return t return spatial_pos_embed def unpatchify(self, x, hw=None): """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ c = self.out_channels p = self.x_embedder.patch_size[0] if hw is None: h = w = int(x.shape[1] ** 0.5) else: h, w = hw h = (h + 1) // p w = (w + 1) // p assert h * w == x.shape[1] x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) x = torch.einsum("nhwpqc->nchpwq", x) imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) return imgs def forward_core_with_concat( self, x: torch.Tensor, c_mod: torch.Tensor, context: Optional[torch.Tensor] = None, control = None, transformer_options = {}, ) -> torch.Tensor: patches_replace = transformer_options.get("patches_replace", {}) if self.register_length > 0: context = torch.cat( ( repeat(self.register, "1 ... -> b ...", b=x.shape[0]), default(context, torch.Tensor([]).type_as(x)), ), 1, ) # context is B, L', D # x is B, L, D blocks_replace = patches_replace.get("dit", {}) blocks = len(self.joint_blocks) for i in range(blocks): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"], transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod, "transformer_options": transformer_options}, {"original_block": block_wrap}) context = out["txt"] x = out["img"] else: context, x = self.joint_blocks[i]( context, x, c=c_mod, use_checkpoint=self.use_checkpoint, transformer_options=transformer_options, ) if control is not None: control_o = control.get("output") if i < len(control_o): add = control_o[i] if add is not None: x += add x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels) return x def forward( self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, control = None, transformer_options = {}, ) -> torch.Tensor: """ Forward pass of DiT. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) t: (N,) tensor of diffusion timesteps y: (N,) tensor of class labels """ if self.context_processor is not None: context = self.context_processor(context) hw = x.shape[-2:] x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x) c = self.t_embedder(t, dtype=x.dtype) # (N, D) if y is not None and self.y_embedder is not None: y = self.y_embedder(y) # (N, D) c = c + y # (N, D) if context is not None: context = self.context_embedder(context) x = self.forward_core_with_concat(x, c, context, control, transformer_options) x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W) return x[:,:,:hw[-2],:hw[-1]] class OpenAISignatureMMDITWrapper(MMDiT): def forward( self, x: torch.Tensor, timesteps: torch.Tensor, context: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, control = None, transformer_options = {}, **kwargs, ) -> torch.Tensor: return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options) ================================================ FILE: comfy/ldm/modules/diffusionmodules/model.py ================================================ # pytorch_diffusion + derived encoder decoder import math import torch import torch.nn as nn import numpy as np import logging from comfy import model_management import comfy.ops ops = comfy.ops.disable_weight_init if model_management.xformers_enabled_vae(): import xformers import xformers.ops def torch_cat_if_needed(xl, dim): xl = [x for x in xl if x is not None and x.shape[dim] > 0] if len(xl) > 1: return torch.cat(xl, dim) elif len(xl) == 1: return xl[0] else: return None def get_timestep_embedding(timesteps, embedding_dim): """ This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ assert len(timesteps.shape) == 1 half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) emb = emb.to(device=timesteps.device) emb = timesteps.float()[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0,1,0,0)) return emb def nonlinearity(x): # swish return torch.nn.functional.silu(x) def Normalize(in_channels, num_groups=32): return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) class CarriedConv3d(nn.Module): def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs): super().__init__() self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): return self.conv(x) def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None): x = xl[0] xl.clear() if isinstance(op, CarriedConv3d): if conv_carry_in is None: x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate') else: carry_len = conv_carry_in[0].shape[2] x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate') x = torch.cat([conv_carry_in.pop(0), x], dim=2) if conv_carry_out is not None: to_push = x[:, :, -2:, :, :].clone() conv_carry_out.append(to_push) out = op(x) return out class VideoConv3d(nn.Module): def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs): super().__init__() self.padding_mode = padding_mode if padding != 0: padding = (padding, padding, padding, padding, kernel_size - 1, 0) else: kwargs["padding"] = padding self.padding = padding self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): if self.padding != 0: x = torch.nn.functional.pad(x, self.padding, mode=self.padding_mode) return self.conv(x) def interpolate_up(x, scale_factor): return torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="nearest") class Upsample(nn.Module): def __init__(self, in_channels, with_conv, conv_op=ops.Conv2d, scale_factor=2.0): super().__init__() self.with_conv = with_conv self.scale_factor = scale_factor if self.with_conv: self.conv = conv_op(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x, conv_carry_in=None, conv_carry_out=None): scale_factor = self.scale_factor if isinstance(scale_factor, (int, float)): scale_factor = (scale_factor,) * (x.ndim - 2) if x.ndim == 5 and scale_factor[0] > 1.0: results = [] if conv_carry_in is None: first = x[:, :, :1, :, :] results.append(interpolate_up(first.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2)) x = x[:, :, 1:, :, :] if x.shape[2] > 0: results.append(interpolate_up(x, scale_factor)) x = torch_cat_if_needed(results, dim=2) else: x = interpolate_up(x, scale_factor) if self.with_conv: x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out) return x class Downsample(nn.Module): def __init__(self, in_channels, with_conv, stride=2, conv_op=ops.Conv2d): super().__init__() self.with_conv = with_conv if self.with_conv: # no asymmetric padding in torch conv, must do it ourselves self.conv = conv_op(in_channels, in_channels, kernel_size=3, stride=stride, padding=0) def forward(self, x, conv_carry_in=None, conv_carry_out=None): if self.with_conv: if isinstance(self.conv, CarriedConv3d): x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out) elif x.ndim == 4: pad = (0, 1, 0, 1) mode = "constant" x = torch.nn.functional.pad(x, pad, mode=mode, value=0) x = self.conv(x) elif x.ndim == 5: pad = (1, 1, 1, 1, 2, 0) mode = "replicate" x = torch.nn.functional.pad(x, pad, mode=mode) x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) return x class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.swish = torch.nn.SiLU(inplace=True) self.norm1 = norm_op(in_channels) self.conv1 = conv_op(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels > 0: self.temb_proj = ops.Linear(temb_channels, out_channels) self.norm2 = norm_op(out_channels) self.dropout = torch.nn.Dropout(dropout, inplace=True) self.conv2 = conv_op(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = conv_op(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = conv_op(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, temb=None, conv_carry_in=None, conv_carry_out=None): h = x h = self.norm1(h) h = [ self.swish(h) ] h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out) if temb is not None: h = h + self.temb_proj(self.swish(temb))[:,:,None,None] h = self.norm2(h) h = self.swish(h) h = [ self.dropout(h) ] h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out) if self.in_channels != self.out_channels: if self.use_conv_shortcut: x = conv_carry_causal_3d([x], self.conv_shortcut, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out) else: x = self.nin_shortcut(x) return x+h def slice_attention(q, k, v): r1 = torch.zeros_like(k, device=q.device) scale = (int(q.shape[-1])**(-0.5)) mem_free_total = model_management.get_free_memory(q.device) tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() modifier = 3 if q.element_size() == 2 else 2.5 mem_required = tensor_size * modifier steps = 1 if mem_required > mem_free_total: steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) while True: try: slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size s1 = torch.bmm(q[:, i:end], k) * scale s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) del s1 r1[:, :, i:end] = torch.bmm(v, s2) del s2 break except Exception as e: model_management.raise_non_oom(e) model_management.soft_empty_cache(True) steps *= 2 if steps > 128: raise e logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) return r1 def normal_attention(q, k, v): # compute attention orig_shape = q.shape b = orig_shape[0] c = orig_shape[1] q = q.reshape(b, c, -1) q = q.permute(0, 2, 1) # b,hw,c k = k.reshape(b, c, -1) # b,c,hw v = v.reshape(b, c, -1) r1 = slice_attention(q, k, v) h_ = r1.reshape(orig_shape) del r1 return h_ def xformers_attention(q, k, v): # compute attention orig_shape = q.shape B = orig_shape[0] C = orig_shape[1] q, k, v = map( lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), (q, k, v), ) try: out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) out = out.transpose(1, 2).reshape(orig_shape) except NotImplementedError: out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape) return out def pytorch_attention(q, k, v): # compute attention orig_shape = q.shape B = orig_shape[0] C = orig_shape[1] oom_fallback = False q, k, v = map( lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), (q, k, v), ) try: out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) out = out.transpose(2, 3).reshape(orig_shape) except Exception as e: model_management.raise_non_oom(e) logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") oom_fallback = True if oom_fallback: out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape) return out def vae_attention(): if model_management.xformers_enabled_vae(): logging.info("Using xformers attention in VAE") return xformers_attention elif model_management.pytorch_attention_enabled_vae(): logging.info("Using pytorch attention in VAE") return pytorch_attention else: logging.info("Using split attention in VAE") return normal_attention class AttnBlock(nn.Module): def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize): super().__init__() self.in_channels = in_channels self.norm = norm_op(in_channels) self.q = conv_op(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = conv_op(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = conv_op(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = conv_op(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.optimized_attention = vae_attention() def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) h_ = self.optimized_attention(q, k, v) h_ = self.proj_out(h_) return x+h_ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None, conv_op=ops.Conv2d): return AttnBlock(in_channels, conv_op=conv_op) class Model(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = self.ch*4 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.use_timestep = use_timestep if self.use_timestep: # timestep embedding self.temb = nn.Module() self.temb.dense = nn.ModuleList([ ops.Linear(self.ch, self.temb_ch), ops.Linear(self.temb_ch, self.temb_ch), ]) # downsampling self.conv_in = ops.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] skip_in = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): if i_block == self.num_res_blocks: skip_in = ch*in_ch_mult[i_level] block.append(ResnetBlock(in_channels=block_in+skip_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = ops.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, x, t=None, context=None): #assert x.shape[2] == x.shape[3] == self.resolution if context is not None: # assume aligned context, cat along channel axis x = torch.cat((x, context), dim=1) if self.use_timestep: # timestep embedding assert t is not None temb = get_timestep_embedding(t, self.ch) temb = self.temb.dense[0](temb) temb = nonlinearity(temb) temb = self.temb.dense[1](temb) else: temb = None # downsampling hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1], temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions-1: hs.append(self.down[i_level].downsample(hs[-1])) # middle h = hs[-1] h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block]( torch.cat([h, hs.pop()], dim=1), temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h def get_last_layer(self): return self.conv_out.weight class Encoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", conv3d=False, time_compress=None, **ignore_kwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.carried = False if conv3d: if not attn_resolutions: conv_op = CarriedConv3d self.carried = True else: conv_op = VideoConv3d mid_attn_conv_op = ops.Conv3d else: conv_op = ops.Conv2d mid_attn_conv_op = ops.Conv2d # downsampling self.conv_in = conv_op(in_channels, self.ch, kernel_size=3, stride=1, padding=1) self.time_compress = 1 curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout, conv_op=conv_op)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type, conv_op=conv_op)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: stride = 2 if time_compress is not None: if (self.num_resolutions - 1 - i_level) > math.log2(time_compress): stride = (1, 2, 2) else: self.time_compress *= 2 down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op) curr_res = curr_res // 2 self.down.append(down) if time_compress is not None: self.time_compress = time_compress # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, conv_op=conv_op) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, conv_op=mid_attn_conv_op) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, conv_op=conv_op) # end self.norm_out = Normalize(block_in) self.conv_out = conv_op(block_in, 2*z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # timestep embedding temb = None if self.carried: xl = [x[:, :, :1, :, :]] if x.shape[2] > self.time_compress: tc = self.time_compress xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // tc) * tc, :, :], tc * 2, dim = 2) x = xl else: x = [x] out = [] conv_carry_in = None for i, x1 in enumerate(x): conv_carry_out = [] if i == len(x) - 1: conv_carry_out = None # downsampling x1 = [ x1 ] h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out) for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h1 = self.down[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out) if len(self.down[i_level].attn) > 0: assert i == 0 #carried should not happen if attn exists h1 = self.down[i_level].attn[i_block](h1) if i_level != self.num_resolutions-1: h1 = self.down[i_level].downsample(h1, conv_carry_in, conv_carry_out) out.append(h1) conv_carry_in = conv_carry_out h = torch_cat_if_needed(out, dim=2) del out # middle h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # end h = self.norm_out(h) h = [ nonlinearity(h) ] h = conv_carry_causal_3d(h, self.conv_out) return h class Decoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, tanh_out=False, use_linear_attn=False, conv_out_op=ops.Conv2d, resnet_op=ResnetBlock, attn_op=AttnBlock, conv3d=False, time_compress=None, **ignorekwargs): super().__init__() self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.tanh_out = tanh_out self.carried = False if conv3d: if not attn_resolutions and resnet_op == ResnetBlock: conv_op = CarriedConv3d conv_out_op = CarriedConv3d self.carried = True else: conv_op = VideoConv3d conv_out_op = VideoConv3d mid_attn_conv_op = ops.Conv3d else: conv_op = ops.Conv2d mid_attn_conv_op = ops.Conv2d # compute block_in and curr_res at lowest res block_in = ch*ch_mult[self.num_resolutions-1] curr_res = resolution // 2**(self.num_resolutions-1) self.z_shape = (1,z_channels,curr_res,curr_res) logging.debug("Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) # z to block_in self.conv_in = conv_op(z_channels, block_in, kernel_size=3, stride=1, padding=1) # middle self.mid = nn.Module() self.mid.block_1 = resnet_op(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, conv_op=conv_op) self.mid.attn_1 = attn_op(block_in, conv_op=mid_attn_conv_op) self.mid.block_2 = resnet_op(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, conv_op=conv_op) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): block.append(resnet_op(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout, conv_op=conv_op)) block_in = block_out if curr_res in attn_resolutions: attn.append(attn_op(block_in, conv_op=conv_op)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: scale_factor = 2.0 if time_compress is not None: if i_level > math.log2(time_compress): scale_factor = (1.0, 2.0, 2.0) up.upsample = Upsample(block_in, resamp_with_conv, conv_op=conv_op, scale_factor=scale_factor) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = conv_out_op(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, z, **kwargs): # timestep embedding temb = None # z to block_in h = conv_carry_causal_3d([z], self.conv_in) # middle h = self.mid.block_1(h, temb, **kwargs) h = self.mid.attn_1(h, **kwargs) h = self.mid.block_2(h, temb, **kwargs) if self.carried: h = torch.split(h, 2, dim=2) else: h = [ h ] out = [] conv_carry_in = None # upsampling for i, h1 in enumerate(h): conv_carry_out = [] if i == len(h) - 1: conv_carry_out = None for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h1 = self.up[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out, **kwargs) if len(self.up[i_level].attn) > 0: assert i == 0 #carried should not happen if attn exists h1 = self.up[i_level].attn[i_block](h1, **kwargs) if i_level != 0: h1 = self.up[i_level].upsample(h1, conv_carry_in, conv_carry_out) h1 = self.norm_out(h1) h1 = [ nonlinearity(h1) ] h1 = conv_carry_causal_3d(h1, self.conv_out, conv_carry_in, conv_carry_out) if self.tanh_out: h1 = torch.tanh(h1) out.append(h1) conv_carry_in = conv_carry_out out = torch_cat_if_needed(out, dim=2) return out ================================================ FILE: comfy/ldm/modules/diffusionmodules/openaimodel.py ================================================ from abc import abstractmethod import torch as th import torch.nn as nn import torch.nn.functional as F from einops import rearrange import logging from .util import ( checkpoint, avg_pool_nd, timestep_embedding, AlphaBlender, ) from ..attention import SpatialTransformer, SpatialVideoTransformer, default from comfy.ldm.util import exists import comfy.patcher_extension import comfy.ops ops = comfy.ops.disable_weight_init from ..sdpose import HeatmapHead class TimestepBlock(nn.Module): """ Any module where forward() takes timestep embeddings as a second argument. """ @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ #This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): for layer in ts: if isinstance(layer, VideoResBlock): x = layer(x, emb, num_video_frames, image_only_indicator) elif isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialVideoTransformer): x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) if "transformer_index" in transformer_options: transformer_options["transformer_index"] += 1 elif isinstance(layer, SpatialTransformer): x = layer(x, context, transformer_options) if "transformer_index" in transformer_options: transformer_options["transformer_index"] += 1 elif isinstance(layer, Upsample): x = layer(x, output_shape=output_shape) else: if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]: found_patched = False for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]: if isinstance(layer, class_type): x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator) found_patched = True break if found_patched: continue x = layer(x) return x class TimestepEmbedSequential(nn.Sequential, TimestepBlock): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward(self, *args, **kwargs): return forward_timestep_embed(self, *args, **kwargs) class Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) def forward(self, x, output_shape=None): assert x.shape[1] == self.channels if self.dims == 3: shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] if output_shape is not None: shape[1] = output_shape[3] shape[2] = output_shape[4] else: shape = [x.shape[2] * 2, x.shape[3] * 2] if output_shape is not None: shape[0] = output_shape[2] shape[1] = output_shape[3] x = F.interpolate(x, size=shape, mode="nearest") if self.use_conv: x = self.conv(x) return x class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = operations.conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class ResBlock(TimestepBlock): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, use_checkpoint=False, up=False, down=False, kernel_size=3, exchange_temb_dims=False, skip_t_emb=False, dtype=None, device=None, operations=ops ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm self.exchange_temb_dims = exchange_temb_dims if isinstance(kernel_size, list): padding = [k // 2 for k in kernel_size] else: padding = kernel_size // 2 self.in_layers = nn.Sequential( operations.GroupNorm(32, channels, dtype=dtype, device=device), nn.SiLU(), operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) elif down: self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) else: self.h_upd = self.x_upd = nn.Identity() self.skip_t_emb = skip_t_emb if self.skip_t_emb: self.emb_layers = None self.exchange_temb_dims = False else: self.emb_layers = nn.Sequential( nn.SiLU(), operations.Linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device ), ) self.out_layers = nn.Sequential( operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), nn.SiLU(), nn.Dropout(p=dropout), operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) , ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = operations.conv_nd( dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device ) else: self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, (x, emb), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = None if not self.skip_t_emb: emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] h = out_norm(h) if emb_out is not None: scale, shift = th.chunk(emb_out, 2, dim=1) h *= (1 + scale) h += shift h = out_rest(h) else: if emb_out is not None: if self.exchange_temb_dims: emb_out = emb_out.movedim(1, 2) h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class VideoResBlock(ResBlock): def __init__( self, channels: int, emb_channels: int, dropout: float, video_kernel_size=3, merge_strategy: str = "fixed", merge_factor: float = 0.5, out_channels=None, use_conv: bool = False, use_scale_shift_norm: bool = False, dims: int = 2, use_checkpoint: bool = False, up: bool = False, down: bool = False, dtype=None, device=None, operations=ops ): super().__init__( channels, emb_channels, dropout, out_channels=out_channels, use_conv=use_conv, use_scale_shift_norm=use_scale_shift_norm, dims=dims, use_checkpoint=use_checkpoint, up=up, down=down, dtype=dtype, device=device, operations=operations ) self.time_stack = ResBlock( default(out_channels, channels), emb_channels, dropout=dropout, dims=3, out_channels=default(out_channels, channels), use_scale_shift_norm=False, use_conv=False, up=False, down=False, kernel_size=video_kernel_size, use_checkpoint=use_checkpoint, exchange_temb_dims=True, dtype=dtype, device=device, operations=operations ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy, rearrange_pattern="b t -> b 1 t 1 1", ) def forward( self, x: th.Tensor, emb: th.Tensor, num_video_frames: int, image_only_indicator = None, ) -> th.Tensor: x = super().forward(x, emb) x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = self.time_stack( x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) ) x = self.time_mixer( x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator ) x = rearrange(x, "b c t h w -> (b t) c h w") return x class Timestep(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, t): return timestep_embedding(t, self.dim) def apply_control(h, control, name): if control is not None and name in control and len(control[name]) > 0: ctrl = control[name].pop() if ctrl is not None: try: h += ctrl except: logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape)) return h class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, dtype=th.float32, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, use_temporal_resblock=False, use_temporal_attention=False, time_context_dim=None, extra_ff_mix_layer=False, use_spatial_context=False, merge_strategy=None, merge_factor=0.0, video_kernel_size=None, disable_temporal_crossattention=False, max_ddpm_temb_period=10000, attn_precision=None, heatmap_head=False, device=None, operations=ops, ): super().__init__() if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' # from omegaconf.listconfig import ListConfig # if type(context_dim) == ListConfig: # context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) transformer_depth = transformer_depth[:] transformer_depth_output = transformer_depth_output[:] self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = dtype self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.use_temporal_resblocks = use_temporal_resblock self.predict_codebook_ids = n_embed is not None self.default_num_video_frames = None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) elif self.num_classes == "continuous": logging.debug("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 def get_attention_layer( ch, num_heads, dim_head, depth=1, context_dim=None, use_checkpoint=False, disable_self_attn=False, ): if use_temporal_attention: return SpatialVideoTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, time_context_dim=time_context_dim, dropout=dropout, ff_in=extra_ff_mix_layer, use_spatial_context=use_spatial_context, merge_strategy=merge_strategy, merge_factor=merge_factor, checkpoint=use_checkpoint, use_linear=use_linear_in_transformer, disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, max_time_embed_period=max_ddpm_temb_period, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) else: return SpatialTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) def get_resblock( merge_factor, merge_strategy, video_kernel_size, ch, time_embed_dim, dropout, out_channels, dims, use_checkpoint, use_scale_shift_norm, down=False, up=False, dtype=None, device=None, operations=ops ): if self.use_temporal_resblocks: return VideoResBlock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, channels=ch, emb_channels=time_embed_dim, dropout=dropout, out_channels=out_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=down, up=up, dtype=dtype, device=device, operations=operations ) else: return ResBlock( channels=ch, emb_channels=time_embed_dim, dropout=dropout, out_channels=out_channels, use_checkpoint=use_checkpoint, dims=dims, use_scale_shift_norm=use_scale_shift_norm, down=down, up=up, dtype=dtype, device=device, operations=operations ) for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations, ) ] ch = mult * model_channels num_transformers = transformer_depth.pop(0) if num_transformers > 0: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append(get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels mid_block = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = None if transformer_depth_middle >= -1: if transformer_depth_middle >= 0: mid_block += [get_attention_layer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint ), get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = TimestepEmbedSequential(*mid_block) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch + ich, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations ) ] ch = model_channels * mult num_transformers = transformer_depth_output.pop() if num_transformers > 0: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or i < num_attention_blocks[level]: layers.append( get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint ) ) if level and i == self.num_res_blocks[level]: out_ch = ch layers.append( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( operations.GroupNorm(32, ch, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( operations.GroupNorm(32, ch, dtype=self.dtype, device=device), operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) if heatmap_head: self.heatmap_head = HeatmapHead(device=device, dtype=self.dtype, operations=operations) def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timesteps, context, y, control, transformer_options, **kwargs) def _forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ transformer_options["original_shape"] = list(x.shape) transformer_options["transformer_index"] = 0 transformer_patches = transformer_options.get("patches", {}) num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) image_only_indicator = kwargs.get("image_only_indicator", None) time_context = kwargs.get("time_context", None) assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) if "emb_patch" in transformer_patches: patch = transformer_patches["emb_patch"] for p in patch: emb = p(emb, self.model_channels, transformer_options) if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for id, module in enumerate(self.input_blocks): transformer_options["block"] = ("input", id) h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'input') if "input_block_patch" in transformer_patches: patch = transformer_patches["input_block_patch"] for p in patch: h = p(h, transformer_options) hs.append(h) if "input_block_patch_after_skip" in transformer_patches: patch = transformer_patches["input_block_patch_after_skip"] for p in patch: h = p(h, transformer_options) transformer_options["block"] = ("middle", 0) if self.middle_block is not None: h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') for id, module in enumerate(self.output_blocks): transformer_options["block"] = ("output", id) hsp = hs.pop() hsp = apply_control(hsp, control, 'output') if "output_block_patch" in transformer_patches: patch = transformer_patches["output_block_patch"] for p in patch: h, hsp = p(h, hsp, transformer_options) h = th.cat([h, hsp], dim=1) del hsp if len(hs) > 0: output_shape = hs[-1].shape else: output_shape = None h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h) ================================================ FILE: comfy/ldm/modules/diffusionmodules/upscaling.py ================================================ import torch import torch.nn as nn import numpy as np from functools import partial from .util import extract_into_tensor, make_beta_schedule class AbstractLowScaleModel(nn.Module): # for concatenating a downsampled image to the latent representation def __init__(self, noise_schedule_config=None): super(AbstractLowScaleModel, self).__init__() if noise_schedule_config is not None: self.register_schedule(**noise_schedule_config) def register_schedule(self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) def q_sample(self, x_start, t, noise=None, seed=None): if noise is None: if seed is None: noise = torch.randn_like(x_start) else: noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device) return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) def forward(self, x): return x, None def decode(self, x): return x class SimpleImageConcat(AbstractLowScaleModel): # no noise level conditioning def __init__(self): super(SimpleImageConcat, self).__init__(noise_schedule_config=None) self.max_noise_level = 0 def forward(self, x): # fix to constant noise level return x, torch.zeros(x.shape[0], device=x.device).long() class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): super().__init__(noise_schedule_config=noise_schedule_config) self.max_noise_level = max_noise_level def forward(self, x, noise_level=None, seed=None): if noise_level is None: noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() else: assert isinstance(noise_level, torch.Tensor) z = self.q_sample(x, noise_level, seed=seed) return z, noise_level ================================================ FILE: comfy/ldm/modules/diffusionmodules/util.py ================================================ # adopted from # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py # and # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py # and # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py # # thanks! import math import logging import torch import torch.nn as nn import numpy as np from einops import repeat, rearrange from comfy.ldm.util import instantiate_from_config class AlphaBlender(nn.Module): strategies = ["learned", "fixed", "learned_with_images"] def __init__( self, alpha: float, merge_strategy: str = "learned_with_images", rearrange_pattern: str = "b t -> (b t) 1 1", ): super().__init__() self.merge_strategy = merge_strategy self.rearrange_pattern = rearrange_pattern assert ( merge_strategy in self.strategies ), f"merge_strategy needs to be in {self.strategies}" if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif ( self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images" ): self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor: # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) if self.merge_strategy == "fixed": # make shape compatible # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) alpha = self.mix_factor.to(device) elif self.merge_strategy == "learned": alpha = torch.sigmoid(self.mix_factor.to(device)) # make shape compatible # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) elif self.merge_strategy == "learned_with_images": if image_only_indicator is None: alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1") else: alpha = torch.where( image_only_indicator.bool(), torch.ones(1, 1, device=image_only_indicator.device), rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), ) alpha = rearrange(alpha, self.rearrange_pattern) # make shape compatible # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) else: raise NotImplementedError() return alpha def forward( self, x_spatial, x_temporal, image_only_indicator=None, ) -> torch.Tensor: alpha = self.get_alpha(image_only_indicator, x_spatial.device) x = ( alpha.to(x_spatial.dtype) * x_spatial + (1.0 - alpha).to(x_spatial.dtype) * x_temporal ) return x def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == "linear": betas = ( torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 ) elif schedule == "cosine": timesteps = ( torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s ) alphas = timesteps / (1 + cosine_s) * np.pi / 2 alphas = torch.cos(alphas).pow(2) alphas = alphas / alphas[0] betas = 1 - alphas[1:] / alphas[:-1] betas = torch.clamp(betas, min=0, max=0.999) elif schedule == "squaredcos_cap_v2": # used for karlo prior # return early return betas_for_alpha_bar( n_timestep, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, ) elif schedule == "sqrt_linear": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) elif schedule == "sqrt": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 else: raise ValueError(f"schedule '{schedule}' unknown.") return betas def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): if ddim_discr_method == 'uniform': c = num_ddpm_timesteps // num_ddim_timesteps ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) elif ddim_discr_method == 'quad': ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) else: raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') # assert ddim_timesteps.shape[0] == num_ddim_timesteps # add one to get the final alpha values right (the ones from first scale to data during sampling) steps_out = ddim_timesteps + 1 if verbose: logging.info(f'Selected timesteps for ddim sampler: {steps_out}') return steps_out def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): # select alphas for computing the variance schedule alphas = alphacums[ddim_timesteps] alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) # according the the formula provided in https://arxiv.org/abs/2010.02502 sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) if verbose: logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') logging.info(f'For the chosen value of eta, which is {eta}, ' f'this results in the following sigma_t schedule for ddim sampler {sigmas}') return sigmas, alphas, alphas_prev def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): """ Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities. """ betas = [] for i in range(num_diffusion_timesteps): t1 = i / num_diffusion_timesteps t2 = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) return np.array(betas) def extract_into_tensor(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) def checkpoint(func, inputs, params, flag): """ Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing. """ if flag: args = tuple(inputs) + tuple(params) return CheckpointFunction.apply(func, len(inputs), *args) else: return func(*inputs) class CheckpointFunction(torch.autograd.Function): @staticmethod def forward(ctx, run_function, length, *args): ctx.run_function = run_function ctx.input_tensors = list(args[:length]) ctx.input_params = list(args[length:]) ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), "dtype": torch.get_autocast_gpu_dtype(), "cache_enabled": torch.is_autocast_cache_enabled()} with torch.no_grad(): output_tensors = ctx.run_function(*ctx.input_tensors) return output_tensors @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] with torch.enable_grad(), \ torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): # Fixes a bug where the first op in run_function modifies the # Tensor storage in place, which is not allowed for detach()'d # Tensors. shallow_copies = [x.view_as(x) for x in ctx.input_tensors] output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True, ) del ctx.input_tensors del ctx.input_params del output_tensors return (None, None) + input_grads def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): """ Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ if not repeat_only: half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half ) args = timesteps[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) else: embedding = repeat(timesteps, 'b -> b d', d=dim) return embedding def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def scale_module(module, scale): """ Scale the parameters of a module and return it. """ for p in module.parameters(): p.detach().mul_(scale) return module def mean_flat(tensor): """ Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class HybridConditioner(nn.Module): def __init__(self, c_concat_config, c_crossattn_config): super().__init__() self.concat_conditioner = instantiate_from_config(c_concat_config) self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) def forward(self, c_concat, c_crossattn): c_concat = self.concat_conditioner(c_concat) c_crossattn = self.crossattn_conditioner(c_crossattn) return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise() ================================================ FILE: comfy/ldm/modules/distributions/__init__.py ================================================ ================================================ FILE: comfy/ldm/modules/distributions/distributions.py ================================================ import torch import numpy as np class AbstractDistribution: def sample(self): raise NotImplementedError() def mode(self): raise NotImplementedError() class DiracDistribution(AbstractDistribution): def __init__(self, value): self.value = value def sample(self): return self.value def mode(self): return self.value class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device) def sample(self): x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device) return x def kl(self, other=None): if self.deterministic: return torch.Tensor([0.]) else: if other is None: return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) else: return 0.5 * torch.sum( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, dim=[1, 2, 3]) def nll(self, sample, dims=[1,2,3]): if self.deterministic: return torch.Tensor([0.]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) def mode(self): return self.mean def normal_kl(mean1, logvar1, mean2, logvar2): """ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases. """ tensor = None for obj in (mean1, logvar1, mean2, logvar2): if isinstance(obj, torch.Tensor): tensor = obj break assert tensor is not None, "at least one argument must be a Tensor" # Force variances to be Tensors. Broadcasting helps convert scalars to # Tensors, but it does not work for torch.exp(). logvar1, logvar2 = [ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2) ] return 0.5 * ( -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) ) ================================================ FILE: comfy/ldm/modules/ema.py ================================================ import torch from torch import nn class LitEma(nn.Module): def __init__(self, model, decay=0.9999, use_num_upates=True): super().__init__() if decay < 0.0 or decay > 1.0: raise ValueError('Decay must be between 0 and 1') self.m_name2s_name = {} self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates else torch.tensor(-1, dtype=torch.int)) for name, p in model.named_parameters(): if p.requires_grad: # remove as '.'-character is not allowed in buffers s_name = name.replace('.', '') self.m_name2s_name.update({name: s_name}) self.register_buffer(s_name, p.clone().detach().data) self.collected_params = [] def reset_num_updates(self): del self.num_updates self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) def forward(self, model): decay = self.decay if self.num_updates >= 0: self.num_updates += 1 decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) one_minus_decay = 1.0 - decay with torch.no_grad(): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: sname = self.m_name2s_name[key] shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) else: assert key not in self.m_name2s_name def copy_to(self, model): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) else: assert key not in self.m_name2s_name def store(self, parameters): """ Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.collected_params = [param.clone() for param in parameters] def restore(self, parameters): """ Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before the `copy_to` method. After validation (or model saving), use this to restore the former parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. """ for c_param, param in zip(self.collected_params, parameters): param.data.copy_(c_param.data) ================================================ FILE: comfy/ldm/modules/encoders/__init__.py ================================================ ================================================ FILE: comfy/ldm/modules/encoders/noise_aug_modules.py ================================================ from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from ..diffusionmodules.openaimodel import Timestep import torch class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): super().__init__(*args, **kwargs) if clip_stats_path is None: clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) else: clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") self.register_buffer("data_mean", clip_mean[None, :], persistent=False) self.register_buffer("data_std", clip_std[None, :], persistent=False) self.time_embed = Timestep(timestep_dim) def scale(self, x): # re-normalize to centered mean and unit variance x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) return x def unscale(self, x): # back to original data stats x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) return x def forward(self, x, noise_level=None, seed=None): if noise_level is None: noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() else: assert isinstance(noise_level, torch.Tensor) x = self.scale(x) z = self.q_sample(x, noise_level, seed=seed) z = self.unscale(z) noise_level = self.time_embed(noise_level) return z, noise_level ================================================ FILE: comfy/ldm/modules/sdpose.py ================================================ import torch import numpy as np from scipy.ndimage import gaussian_filter class HeatmapHead(torch.nn.Module): def __init__( self, in_channels=640, out_channels=133, input_size=(768, 1024), heatmap_scale=4, deconv_out_channels=(640,), deconv_kernel_sizes=(4,), conv_out_channels=(640,), conv_kernel_sizes=(1,), final_layer_kernel_size=1, device=None, dtype=None, operations=None ): super().__init__() self.heatmap_size = (input_size[0] // heatmap_scale, input_size[1] // heatmap_scale) self.scale_factor = ((np.array(input_size) - 1) / (np.array(self.heatmap_size) - 1)).astype(np.float32) # Deconv layers if deconv_out_channels: deconv_layers = [] for out_ch, kernel_size in zip(deconv_out_channels, deconv_kernel_sizes): if kernel_size == 4: padding, output_padding = 1, 0 elif kernel_size == 3: padding, output_padding = 1, 1 elif kernel_size == 2: padding, output_padding = 0, 0 else: raise ValueError(f'Unsupported kernel size {kernel_size}') deconv_layers.extend([ operations.ConvTranspose2d(in_channels, out_ch, kernel_size, stride=2, padding=padding, output_padding=output_padding, bias=False, device=device, dtype=dtype), torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype), torch.nn.SiLU(inplace=True) ]) in_channels = out_ch self.deconv_layers = torch.nn.Sequential(*deconv_layers) else: self.deconv_layers = torch.nn.Identity() # Conv layers if conv_out_channels: conv_layers = [] for out_ch, kernel_size in zip(conv_out_channels, conv_kernel_sizes): padding = (kernel_size - 1) // 2 conv_layers.extend([ operations.Conv2d(in_channels, out_ch, kernel_size, stride=1, padding=padding, device=device, dtype=dtype), torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype), torch.nn.SiLU(inplace=True) ]) in_channels = out_ch self.conv_layers = torch.nn.Sequential(*conv_layers) else: self.conv_layers = torch.nn.Identity() self.final_layer = operations.Conv2d(in_channels, out_channels, kernel_size=final_layer_kernel_size, padding=final_layer_kernel_size // 2, device=device, dtype=dtype) def forward(self, x): # Decode heatmaps to keypoints heatmaps = self.final_layer(self.conv_layers(self.deconv_layers(x))) heatmaps_np = heatmaps.float().cpu().numpy() # (B, K, H, W) B, K, H, W = heatmaps_np.shape batch_keypoints = [] batch_scores = [] for b in range(B): hm = heatmaps_np[b].copy() # (K, H, W) # --- vectorised argmax --- flat = hm.reshape(K, -1) idx = np.argmax(flat, axis=1) scores = flat[np.arange(K), idx].copy() y_locs, x_locs = np.unravel_index(idx, (H, W)) keypoints = np.stack([x_locs, y_locs], axis=-1).astype(np.float32) # (K, 2) in heatmap space invalid = scores <= 0. keypoints[invalid] = -1 # --- DARK sub-pixel refinement (UDP) --- # 1. Gaussian blur with max-preserving normalisation border = 5 # (kernel-1)//2 for kernel=11 for k in range(K): origin_max = np.max(hm[k]) dr = np.zeros((H + 2 * border, W + 2 * border), dtype=np.float32) dr[border:-border, border:-border] = hm[k].copy() dr = gaussian_filter(dr, sigma=2.0) hm[k] = dr[border:-border, border:-border].copy() cur_max = np.max(hm[k]) if cur_max > 0: hm[k] *= origin_max / cur_max # 2. Log-space for Taylor expansion np.clip(hm, 1e-3, 50., hm) np.log(hm, hm) # 3. Hessian-based Newton step hm_pad = np.pad(hm, ((0, 0), (1, 1), (1, 1)), mode='edge').flatten() index = keypoints[:, 0] + 1 + (keypoints[:, 1] + 1) * (W + 2) index += (W + 2) * (H + 2) * np.arange(0, K) index = index.astype(int).reshape(-1, 1) i_ = hm_pad[index] ix1 = hm_pad[index + 1] iy1 = hm_pad[index + W + 2] ix1y1 = hm_pad[index + W + 3] ix1_y1_ = hm_pad[index - W - 3] ix1_ = hm_pad[index - 1] iy1_ = hm_pad[index - 2 - W] dx = 0.5 * (ix1 - ix1_) dy = 0.5 * (iy1 - iy1_) derivative = np.concatenate([dx, dy], axis=1).reshape(K, 2, 1) dxx = ix1 - 2 * i_ + ix1_ dyy = iy1 - 2 * i_ + iy1_ dxy = 0.5 * (ix1y1 - ix1 - iy1 + i_ + i_ - ix1_ - iy1_ + ix1_y1_) hessian = np.concatenate([dxx, dxy, dxy, dyy], axis=1).reshape(K, 2, 2) hessian = np.linalg.inv(hessian + np.finfo(np.float32).eps * np.eye(2)) keypoints -= np.einsum('imn,ink->imk', hessian, derivative).squeeze(axis=-1) # --- restore to input image space --- keypoints = keypoints * self.scale_factor keypoints[invalid] = -1 batch_keypoints.append(keypoints) batch_scores.append(scores) return batch_keypoints, batch_scores ================================================ FILE: comfy/ldm/modules/sub_quadratic_attention.py ================================================ # original source: # https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py # license: # MIT # credit: # Amin Rezaei (original author) # Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) # implementation of: # Self-attention Does Not Need O(n2) Memory": # https://arxiv.org/abs/2112.05682v2 from functools import partial import torch from torch import Tensor from torch.utils.checkpoint import checkpoint import math import logging try: from typing import Optional, NamedTuple, List, Protocol except ImportError: from typing import Optional, NamedTuple, List from typing_extensions import Protocol from typing import List from comfy import model_management def dynamic_slice( x: Tensor, starts: List[int], sizes: List[int], ) -> Tensor: slicing = tuple(slice(start, start + size) for start, size in zip(starts, sizes)) return x[slicing] class AttnChunk(NamedTuple): exp_values: Tensor exp_weights_sum: Tensor max_score: Tensor class SummarizeChunk(Protocol): @staticmethod def __call__( query: Tensor, key_t: Tensor, value: Tensor, ) -> AttnChunk: ... class ComputeQueryChunkAttn(Protocol): @staticmethod def __call__( query: Tensor, key_t: Tensor, value: Tensor, ) -> Tensor: ... def _summarize_chunk( query: Tensor, key_t: Tensor, value: Tensor, scale: float, upcast_attention: bool, mask, ) -> AttnChunk: if upcast_attention: with torch.autocast(enabled=False, device_type = 'cuda'): query = query.float() key_t = key_t.float() attn_weights = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) else: attn_weights = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) max_score, _ = torch.max(attn_weights, -1, keepdim=True) max_score = max_score.detach() attn_weights -= max_score if mask is not None: attn_weights += mask torch.exp(attn_weights, out=attn_weights) exp_weights = attn_weights.to(value.dtype) exp_values = torch.bmm(exp_weights, value) max_score = max_score.squeeze(-1) return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) def _query_chunk_attention( query: Tensor, key_t: Tensor, value: Tensor, summarize_chunk: SummarizeChunk, kv_chunk_size: int, mask, ) -> Tensor: batch_x_heads, k_channels_per_head, k_tokens = key_t.shape _, _, v_channels_per_head = value.shape def chunk_scanner(chunk_idx: int, mask) -> AttnChunk: key_chunk = dynamic_slice( key_t, (0, 0, chunk_idx), (batch_x_heads, k_channels_per_head, kv_chunk_size) ) value_chunk = dynamic_slice( value, (0, chunk_idx, 0), (batch_x_heads, kv_chunk_size, v_channels_per_head) ) if mask is not None: mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size] return summarize_chunk(query, key_chunk, value_chunk, mask=mask) chunks: List[AttnChunk] = [ chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size) ] acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) chunk_values, chunk_weights, chunk_max = acc_chunk global_max, _ = torch.max(chunk_max, 0, keepdim=True) max_diffs = torch.exp(chunk_max - global_max) chunk_values *= torch.unsqueeze(max_diffs, -1) chunk_weights *= max_diffs all_values = chunk_values.sum(dim=0) all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) return all_values / all_weights # TODO: refactor CrossAttention#get_attention_scores to share code with this def _get_attention_scores_no_kv_chunking( query: Tensor, key_t: Tensor, value: Tensor, scale: float, upcast_attention: bool, mask, ) -> Tensor: if upcast_attention: with torch.autocast(enabled=False, device_type = 'cuda'): query = query.float() key_t = key_t.float() attn_scores = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) else: attn_scores = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) if mask is not None: attn_scores += mask try: attn_probs = attn_scores.softmax(dim=-1) del attn_scores except Exception as e: model_management.raise_non_oom(e) logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") attn_scores -= attn_scores.max(dim=-1, keepdim=True).values # noqa: F821 attn_scores is not defined torch.exp(attn_scores, out=attn_scores) summed = torch.sum(attn_scores, dim=-1, keepdim=True) attn_scores /= summed attn_probs = attn_scores hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) return hidden_states_slice class ScannedChunk(NamedTuple): chunk_idx: int attn_chunk: AttnChunk def efficient_dot_product_attention( query: Tensor, key_t: Tensor, value: Tensor, query_chunk_size=1024, kv_chunk_size: Optional[int] = None, kv_chunk_size_min: Optional[int] = None, use_checkpoint=True, upcast_attention=False, mask = None, ): """Computes efficient dot-product attention given query, transposed key, and value. This is efficient version of attention presented in https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. Args: query: queries for calculating attention with shape of `[batch * num_heads, tokens, channels_per_head]`. key_t: keys for calculating attention with shape of `[batch * num_heads, channels_per_head, tokens]`. value: values to be used in attention with shape of `[batch * num_heads, tokens, channels_per_head]`. query_chunk_size: int: query chunks size kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) Returns: Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. """ batch_x_heads, q_tokens, q_channels_per_head = query.shape _, _, k_tokens = key_t.shape scale = q_channels_per_head ** -0.5 kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) if kv_chunk_size_min is not None: kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) if mask is not None and len(mask.shape) == 2: mask = mask.unsqueeze(0) def get_query_chunk(chunk_idx: int) -> Tensor: return dynamic_slice( query, (0, chunk_idx, 0), (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) ) def get_mask_chunk(chunk_idx: int) -> Tensor: if mask is None: return None if mask.shape[1] == 1: return mask chunk = min(query_chunk_size, q_tokens) return mask[:,chunk_idx:chunk_idx + chunk] summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk compute_query_chunk_attn: ComputeQueryChunkAttn = partial( _get_attention_scores_no_kv_chunking, scale=scale, upcast_attention=upcast_attention ) if k_tokens <= kv_chunk_size else ( # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) partial( _query_chunk_attention, kv_chunk_size=kv_chunk_size, summarize_chunk=summarize_chunk, ) ) if q_tokens <= query_chunk_size: # fast-path for when there's just 1 query chunk return compute_query_chunk_attn( query=query, key_t=key_t, value=value, mask=mask, ) # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, # and pass slices to be mutated, instead of torch.cat()ing the returned slices res = torch.cat([ compute_query_chunk_attn( query=get_query_chunk(i * query_chunk_size), key_t=key_t, value=value, mask=get_mask_chunk(i * query_chunk_size) ) for i in range(math.ceil(q_tokens / query_chunk_size)) ], dim=1) return res ================================================ FILE: comfy/ldm/modules/temporal_ae.py ================================================ import functools from typing import Iterable, Union import torch from einops import rearrange, repeat import comfy.ops ops = comfy.ops.disable_weight_init from .diffusionmodules.model import ( AttnBlock, Decoder, ResnetBlock, ) from .diffusionmodules.openaimodel import ResBlock, timestep_embedding from .attention import BasicTransformerBlock def partialclass(cls, *args, **kwargs): class NewCls(cls): __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) return NewCls class VideoResBlock(ResnetBlock): def __init__( self, out_channels, *args, dropout=0.0, video_kernel_size=3, alpha=0.0, merge_strategy="learned", **kwargs, ): super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) if video_kernel_size is None: video_kernel_size = [3, 1, 1] self.time_stack = ResBlock( channels=out_channels, emb_channels=0, dropout=dropout, dims=3, use_scale_shift_norm=False, use_conv=False, up=False, down=False, kernel_size=video_kernel_size, use_checkpoint=False, skip_t_emb=True, ) self.merge_strategy = merge_strategy if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif self.merge_strategy == "learned": self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def get_alpha(self, bs): if self.merge_strategy == "fixed": return self.mix_factor elif self.merge_strategy == "learned": return torch.sigmoid(self.mix_factor) else: raise NotImplementedError() def forward(self, x, temb, skip_video=False, timesteps=None): b, c, h, w = x.shape if timesteps is None: timesteps = b x = super().forward(x, temb) if not skip_video: x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = self.time_stack(x, temb) alpha = self.get_alpha(bs=b // timesteps).to(x.device) x = alpha * x + (1.0 - alpha) * x_mix x = rearrange(x, "b c t h w -> (b t) c h w") return x class AE3DConv(ops.Conv2d): def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): super().__init__(in_channels, out_channels, *args, **kwargs) if isinstance(video_kernel_size, Iterable): padding = [int(k // 2) for k in video_kernel_size] else: padding = int(video_kernel_size // 2) self.time_mix_conv = ops.Conv3d( in_channels=out_channels, out_channels=out_channels, kernel_size=video_kernel_size, padding=padding, ) def forward(self, input, timesteps=None, skip_video=False): if timesteps is None: timesteps = input.shape[0] x = super().forward(input) if skip_video: return x x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = self.time_mix_conv(x) return rearrange(x, "b c t h w -> (b t) c h w") class AttnVideoBlock(AttnBlock): def __init__( self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" ): super().__init__(in_channels) # no context, single headed, as in base class self.time_mix_block = BasicTransformerBlock( dim=in_channels, n_heads=1, d_head=in_channels, checkpoint=False, ff_in=True, ) time_embed_dim = self.in_channels * 4 self.video_time_embed = torch.nn.Sequential( ops.Linear(self.in_channels, time_embed_dim), torch.nn.SiLU(), ops.Linear(time_embed_dim, self.in_channels), ) self.merge_strategy = merge_strategy if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif self.merge_strategy == "learned": self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def forward(self, x, timesteps=None, skip_time_block=False): if skip_time_block: return super().forward(x) if timesteps is None: timesteps = x.shape[0] x_in = x x = self.attention(x) h, w = x.shape[2:] x = rearrange(x, "b c h w -> b (h w) c") x_mix = x num_frames = torch.arange(timesteps, device=x.device) num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) num_frames = rearrange(num_frames, "b t -> (b t)") t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) emb = self.video_time_embed(t_emb) # b, n_channels emb = emb[:, None, :] x_mix = x_mix + emb alpha = self.get_alpha().to(x.device) x_mix = self.time_mix_block(x_mix, timesteps=timesteps) x = alpha * x + (1.0 - alpha) * x_mix # alpha merge x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) x = self.proj_out(x) return x_in + x def get_alpha( self, ): if self.merge_strategy == "fixed": return self.mix_factor elif self.merge_strategy == "learned": return torch.sigmoid(self.mix_factor) else: raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") def make_time_attn( in_channels, attn_type="vanilla", attn_kwargs=None, alpha: float = 0, merge_strategy: str = "learned", conv_op=ops.Conv2d, ): return partialclass( AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy ) class Conv2DWrapper(torch.nn.Conv2d): def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: return super().forward(input) class VideoDecoder(Decoder): available_time_modes = ["all", "conv-only", "attn-only"] def __init__( self, *args, video_kernel_size: Union[int, list] = 3, alpha: float = 0.0, merge_strategy: str = "learned", time_mode: str = "conv-only", **kwargs, ): self.video_kernel_size = video_kernel_size self.alpha = alpha self.merge_strategy = merge_strategy self.time_mode = time_mode assert ( self.time_mode in self.available_time_modes ), f"time_mode parameter has to be in {self.available_time_modes}" if self.time_mode != "attn-only": kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) if self.time_mode not in ["conv-only", "only-last-conv"]: kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) if self.time_mode not in ["attn-only", "only-last-conv"]: kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) super().__init__(*args, **kwargs) def get_last_layer(self, skip_time_mix=False, **kwargs): if self.time_mode == "attn-only": raise NotImplementedError("TODO") else: return ( self.conv_out.time_mix_conv.weight if not skip_time_mix else self.conv_out.weight ) ================================================ FILE: comfy/ldm/omnigen/omnigen2.py ================================================ # Original code: https://github.com/VectorSpaceLab/OmniGen2 from typing import Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from comfy.ldm.lightricks.model import Timesteps from comfy.ldm.flux.layers import EmbedND from comfy.ldm.modules.attention import optimized_attention_masked import comfy.model_management import comfy.ldm.common_dit def apply_rotary_emb(x, freqs_cis): if x.shape[1] == 0: return x t_ = x.reshape(*x.shape[:-1], -1, 1, 2) t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] return t_out.reshape(*x.shape).to(dtype=x.dtype) def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: return F.silu(x) * y class TimestepEmbedding(nn.Module): def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None): super().__init__() self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device) self.act = nn.SiLU() self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device) def forward(self, sample: torch.Tensor) -> torch.Tensor: sample = self.linear_1(sample) sample = self.act(sample) sample = self.linear_2(sample) return sample class LuminaRMSNormZero(nn.Module): def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None): super().__init__() self.silu = nn.SiLU() self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device) self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device) def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: emb = self.linear(self.silu(emb)) scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1) x = self.norm(x) * (1 + scale_msa[:, None]) return x, gate_msa, scale_mlp, gate_mlp class LuminaLayerNormContinuous(nn.Module): def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None): super().__init__() self.silu = nn.SiLU() self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device) self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device) self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor: emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype)) x = self.norm(x) * (1 + emb)[:, None, :] if self.linear_2 is not None: x = self.linear_2(x) return x class LuminaFeedForward(nn.Module): def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None): super().__init__() inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of) self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device) self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) def forward(self, x: torch.Tensor) -> torch.Tensor: h1, h2 = self.linear_1(x), self.linear_3(x) return self.linear_2(swiglu(h1, h2)) class Lumina2CombinedTimestepCaptionEmbedding(nn.Module): def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None): super().__init__() self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale) self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations) self.caption_embedder = nn.Sequential( operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device), operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device), ) def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]: timestep_proj = self.time_proj(timestep).to(dtype=dtype) time_embed = self.timestep_embedder(timestep_proj) caption_embed = self.caption_embedder(text_hidden_states) return time_embed, caption_embed class Attention(nn.Module): def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None): super().__init__() self.heads = heads self.kv_heads = kv_heads self.dim_head = dim_head self.scale = dim_head ** -0.5 self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device) self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) self.to_out = nn.Sequential( operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device), nn.Dropout(0.0) ) def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor: batch_size, sequence_length, _ = hidden_states.shape query = self.to_q(hidden_states) key = self.to_k(encoder_hidden_states) value = self.to_v(encoder_hidden_states) query = query.view(batch_size, -1, self.heads, self.dim_head) key = key.view(batch_size, -1, self.kv_heads, self.dim_head) value = value.view(batch_size, -1, self.kv_heads, self.dim_head) query = self.norm_q(query) key = self.norm_k(key) if image_rotary_emb is not None: query = apply_rotary_emb(query, image_rotary_emb) key = apply_rotary_emb(key, image_rotary_emb) query = query.transpose(1, 2) key = key.transpose(1, 2) value = value.transpose(1, 2) if self.kv_heads < self.heads: key = key.repeat_interleave(self.heads // self.kv_heads, dim=1) value = value.repeat_interleave(self.heads // self.kv_heads, dim=1) hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options) hidden_states = self.to_out[0](hidden_states) return hidden_states class OmniGen2TransformerBlock(nn.Module): def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None): super().__init__() self.modulation = modulation self.attn = Attention( query_dim=dim, dim_head=dim // num_attention_heads, heads=num_attention_heads, kv_heads=num_kv_heads, eps=1e-5, bias=False, dtype=dtype, device=device, operations=operations, ) self.feed_forward = LuminaFeedForward( dim=dim, inner_dim=4 * dim, multiple_of=multiple_of, dtype=dtype, device=device, operations=operations ) if modulation: self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) else: self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor: if self.modulation: norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options) hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output) mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1))) hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) else: norm_hidden_states = self.norm1(hidden_states) attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options) hidden_states = hidden_states + self.norm2(attn_output) mlp_output = self.feed_forward(self.ffn_norm1(hidden_states)) hidden_states = hidden_states + self.ffn_norm2(mlp_output) return hidden_states class OmniGen2RotaryPosEmbed(nn.Module): def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2): super().__init__() self.theta = theta self.axes_dim = axes_dim self.axes_lens = axes_lens self.patch_size = patch_size self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim) def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device): p = self.patch_size seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)] max_seq_len = max(seq_lengths) max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len]) max_img_len = max(l_effective_img_len) position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device) for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3") pe_shift = cap_seq_len pe_shift_len = cap_seq_len if ref_img_sizes[i] is not None: for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]): H, W = ref_img_size ref_H_tokens, ref_W_tokens = H // p, W // p row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten() col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten() position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids pe_shift += max(ref_H_tokens, ref_W_tokens) pe_shift_len += ref_img_len H, W = img_sizes[i] H_tokens, W_tokens = H // p, W // p row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten() col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten() position_ids[i, pe_shift_len: seq_len, 0] = pe_shift position_ids[i, pe_shift_len: seq_len, 1] = row_ids position_ids[i, pe_shift_len: seq_len, 2] = col_ids freqs_cis = self.rope_embedder(position_ids).movedim(1, 2) cap_freqs_cis_shape = list(freqs_cis.shape) cap_freqs_cis_shape[1] = encoder_seq_len cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) ref_img_freqs_cis_shape = list(freqs_cis.shape) ref_img_freqs_cis_shape[1] = max_ref_img_len ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) img_freqs_cis_shape = list(freqs_cis.shape) img_freqs_cis_shape[1] = max_img_len img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)): cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len] ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)] img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len] return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths class OmniGen2Transformer2DModel(nn.Module): def __init__( self, patch_size: int = 2, in_channels: int = 16, out_channels: Optional[int] = None, hidden_size: int = 2304, num_layers: int = 26, num_refiner_layers: int = 2, num_attention_heads: int = 24, num_kv_heads: int = 8, multiple_of: int = 256, ffn_dim_multiplier: Optional[float] = None, norm_eps: float = 1e-5, axes_dim_rope: Tuple[int, int, int] = (32, 32, 32), axes_lens: Tuple[int, int, int] = (300, 512, 512), text_feat_dim: int = 1024, timestep_scale: float = 1.0, image_model=None, device=None, dtype=None, operations=None, ): super().__init__() self.patch_size = patch_size self.out_channels = out_channels or in_channels self.hidden_size = hidden_size self.dtype = dtype self.rope_embedder = OmniGen2RotaryPosEmbed( theta=10000, axes_dim=axes_dim_rope, axes_lens=axes_lens, patch_size=patch_size, ) self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding( hidden_size=hidden_size, text_feat_dim=text_feat_dim, norm_eps=norm_eps, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations ) self.noise_refiner = nn.ModuleList([ OmniGen2TransformerBlock( hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations ) for _ in range(num_refiner_layers) ]) self.ref_image_refiner = nn.ModuleList([ OmniGen2TransformerBlock( hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations ) for _ in range(num_refiner_layers) ]) self.context_refiner = nn.ModuleList([ OmniGen2TransformerBlock( hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations ) for _ in range(num_refiner_layers) ]) self.layers = nn.ModuleList([ OmniGen2TransformerBlock( hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations ) for _ in range(num_layers) ]) self.norm_out = LuminaLayerNormContinuous( embedding_dim=hidden_size, conditioning_embedding_dim=min(hidden_size, 1024), elementwise_affine=False, eps=1e-6, out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations ) self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype)) def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states): batch_size = len(hidden_states) p = self.patch_size img_sizes = [(img.size(1), img.size(2)) for img in hidden_states] l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes] if ref_image_hidden_states is not None: ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states)) ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes] else: ref_img_sizes = [None for _ in range(batch_size)] l_effective_ref_img_len = [[0] for _ in range(batch_size)] flat_ref_img_hidden_states = None if ref_image_hidden_states is not None: imgs = [] for ref_img in ref_image_hidden_states: B, C, H, W = ref_img.size() ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) imgs.append(ref_img) flat_ref_img_hidden_states = torch.cat(imgs, dim=1) img = hidden_states B, C, H, W = img.size() flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) return ( flat_hidden_states, flat_ref_img_hidden_states, None, None, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, ) def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options={}): batch_size = len(hidden_states) hidden_states = self.x_embedder(hidden_states) if ref_image_hidden_states is not None: ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states) image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device) for i in range(batch_size): shift = 0 for j, ref_img_len in enumerate(l_effective_ref_img_len[i]): ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j] shift += ref_img_len for layer in self.noise_refiner: hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options) if ref_image_hidden_states is not None: for layer in self.ref_image_refiner: ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options) hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1) return hidden_states def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs): B, C, H, W = x.shape hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) _, _, H_padded, W_padded = hidden_states.shape timestep = 1.0 - timesteps text_hidden_states = context text_attention_mask = attention_mask ref_image_hidden_states = ref_latents device = hidden_states.device temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype) ( hidden_states, ref_image_hidden_states, img_mask, ref_img_mask, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states) ( context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb, rotary_emb, encoder_seq_lengths, seq_lengths, ) = self.rope_embedder( hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0], l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device, ) for layer in self.context_refiner: text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options) img_len = hidden_states.shape[1] combined_img_hidden_states = self.img_patch_embed_and_refine( hidden_states, ref_image_hidden_states, img_mask, ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options=transformer_options, ) hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1) attention_mask = None for layer in self.layers: hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options) hidden_states = self.norm_out(hidden_states, temb) p = self.patch_size output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W] return -output ================================================ FILE: comfy/ldm/pixart/blocks.py ================================================ # Based on: # https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] # https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, Mlp, timestep_embedding from comfy.ldm.modules.attention import optimized_attention # if model_management.xformers_enabled(): # import xformers.ops # if int((xformers.__version__).split(".")[2].split("+")[0]) >= 28: # block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens # else: # block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) def t2i_modulate(x, shift, scale): return x * (1 + scale) + shift class MultiHeadCrossAttention(nn.Module): def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **kwargs): super(MultiHeadCrossAttention, self).__init__() assert d_model % num_heads == 0, "d_model must be divisible by num_heads" self.d_model = d_model self.num_heads = num_heads self.head_dim = d_model // num_heads self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device) self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device) self.attn_drop = nn.Dropout(attn_drop) self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, cond, mask=None): # query/value: img tokens; key: condition; mask: if padding tokens B, N, C = x.shape q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim) kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim) k, v = kv.unbind(2) assert mask is None # TODO? # # TODO: xformers needs separate mask logic here # if model_management.xformers_enabled(): # attn_bias = None # if mask is not None: # attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask) # x = xformers.ops.memory_efficient_attention(q, k, v, p=0, attn_bias=attn_bias) # else: # q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),) # attn_mask = None # mask = torch.ones(()) # if mask is not None and len(mask) > 1: # # Create equivalent of xformer diagonal block mask, still only correct for square masks # # But depth doesn't matter as tensors can expand in that dimension # attn_mask_template = torch.ones( # [q.shape[2] // B, mask[0]], # dtype=torch.bool, # device=q.device # ) # attn_mask = torch.block_diag(attn_mask_template) # # # create a mask on the diagonal for each mask in the batch # for _ in range(B - 1): # attn_mask = torch.block_diag(attn_mask, attn_mask_template) # x = optimized_attention(q, k, v, self.num_heads, mask=attn_mask, skip_reshape=True) x = optimized_attention(q.view(B, -1, C), k.view(B, -1, C), v.view(B, -1, C), self.num_heads, mask=None) x = self.proj(x) x = self.proj_drop(x) return x class AttentionKVCompress(nn.Module): """Multi-head Attention block with KV token compression and qk norm.""" def __init__(self, dim, num_heads=8, qkv_bias=True, sampling='conv', sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **kwargs): """ Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. qkv_bias (bool: If True, add a learnable bias to query, key, value. """ super().__init__() assert dim % num_heads == 0, 'dim should be divisible by num_heads' self.num_heads = num_heads self.head_dim = dim // num_heads self.scale = self.head_dim ** -0.5 self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) self.sampling=sampling # ['conv', 'ave', 'uniform', 'uniform_every'] self.sr_ratio = sr_ratio if sr_ratio > 1 and sampling == 'conv': # Avg Conv Init. self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device) # self.sr.weight.data.fill_(1/sr_ratio**2) # self.sr.bias.data.zero_() self.norm = operations.LayerNorm(dim, dtype=dtype, device=device) if qk_norm: self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device) self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device) else: self.q_norm = nn.Identity() self.k_norm = nn.Identity() def downsample_2d(self, tensor, H, W, scale_factor, sampling=None): if sampling is None or scale_factor == 1: return tensor B, N, C = tensor.shape if sampling == 'uniform_every': return tensor[:, ::scale_factor], int(N // scale_factor) tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2) new_H, new_W = int(H / scale_factor), int(W / scale_factor) new_N = new_H * new_W if sampling == 'ave': tensor = F.interpolate( tensor, scale_factor=1 / scale_factor, mode='nearest' ).permute(0, 2, 3, 1) elif sampling == 'uniform': tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1) elif sampling == 'conv': tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1) tensor = self.norm(tensor) else: raise ValueError return tensor.reshape(B, new_N, C).contiguous(), new_N def forward(self, x, mask=None, HW=None, block_id=None): B, N, C = x.shape # 2 4096 1152 new_N = N if HW is None: H = W = int(N ** 0.5) else: H, W = HW qkv = self.qkv(x).reshape(B, N, 3, C) q, k, v = qkv.unbind(2) q = self.q_norm(q) k = self.k_norm(k) # KV compression if self.sr_ratio > 1: k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling) v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling) q = q.reshape(B, N, self.num_heads, C // self.num_heads) k = k.reshape(B, new_N, self.num_heads, C // self.num_heads) v = v.reshape(B, new_N, self.num_heads, C // self.num_heads) if mask is not None: raise NotImplementedError("Attn mask logic not added for self attention") # This is never called at the moment # attn_bias = None # if mask is not None: # attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device) # attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf')) # attention 2 q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),) x = optimized_attention(q, k, v, self.num_heads, mask=None, skip_reshape=True) x = x.view(B, N, C) x = self.proj(x) return x class FinalLayer(nn.Module): """ The final layer of PixArt. """ def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x, c): shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class T2IFinalLayer(nn.Module): """ The final layer of PixArt. """ def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5) self.out_channels = out_channels def forward(self, x, t): shift, scale = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t[:, None]).chunk(2, dim=1) x = t2i_modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class MaskFinalLayer(nn.Module): """ The final layer of PixArt. """ def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x, t): shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class DecoderLayer(nn.Module): """ The final layer of PixArt. """ def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None): super().__init__() self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x, t): shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) x = modulate(self.norm_decoder(x), shift, scale) x = self.linear(x) return x class SizeEmbedder(TimestepEmbedder): """ Embeds scalar timesteps into vector representations. """ def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations) self.mlp = nn.Sequential( operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size self.outdim = hidden_size def forward(self, s, bs): if s.ndim == 1: s = s[:, None] assert s.ndim == 2 if s.shape[0] != bs: s = s.repeat(bs//s.shape[0], 1) assert s.shape[0] == bs b, dims = s.shape[0], s.shape[1] s = rearrange(s, "b d -> (b d)") s_freq = timestep_embedding(s, self.frequency_embedding_size) s_emb = self.mlp(s_freq.to(s.dtype)) s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim) return s_emb class LabelEmbedder(nn.Module): """ Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None): super().__init__() use_cfg_embedding = dropout_prob > 0 self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device), self.num_classes = num_classes self.dropout_prob = dropout_prob def token_drop(self, labels, force_drop_ids=None): """ Drops labels to enable classifier-free guidance. """ if force_drop_ids is None: drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob else: drop_ids = force_drop_ids == 1 labels = torch.where(drop_ids, self.num_classes, labels) return labels def forward(self, labels, train, force_drop_ids=None): use_dropout = self.dropout_prob > 0 if (train and use_dropout) or (force_drop_ids is not None): labels = self.token_drop(labels, force_drop_ids) embeddings = self.embedding_table(labels) return embeddings class CaptionEmbedder(nn.Module): """ Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): super().__init__() self.y_proj = Mlp( in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, dtype=dtype, device=device, operations=operations, ) self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5)) self.uncond_prob = uncond_prob def token_drop(self, caption, force_drop_ids=None): """ Drops labels to enable classifier-free guidance. """ if force_drop_ids is None: drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob else: drop_ids = force_drop_ids == 1 caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) return caption def forward(self, caption, train, force_drop_ids=None): if train: assert caption.shape[2:] == self.y_embedding.shape use_dropout = self.uncond_prob > 0 if (train and use_dropout) or (force_drop_ids is not None): caption = self.token_drop(caption, force_drop_ids) caption = self.y_proj(caption) return caption class CaptionEmbedderDoubleBr(nn.Module): """ Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): super().__init__() self.proj = Mlp( in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, dtype=dtype, device=device, operations=operations, ) self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5) self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5) self.uncond_prob = uncond_prob def token_drop(self, global_caption, caption, force_drop_ids=None): """ Drops labels to enable classifier-free guidance. """ if force_drop_ids is None: drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob else: drop_ids = force_drop_ids == 1 global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption) caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) return global_caption, caption def forward(self, caption, train, force_drop_ids=None): assert caption.shape[2: ] == self.y_embedding.shape global_caption = caption.mean(dim=2).squeeze() use_dropout = self.uncond_prob > 0 if (train and use_dropout) or (force_drop_ids is not None): global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids) y_embed = self.proj(global_caption) return y_embed, caption ================================================ FILE: comfy/ldm/pixart/pixartms.py ================================================ # Based on: # https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] # https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] import torch import torch.nn as nn from .blocks import ( t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, SizeEmbedder, ) from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32): grid_h, grid_w = torch.meshgrid( torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation, torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation, indexing='ij' ) emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) return emb class PixArtMSBlock(nn.Module): """ A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning. """ def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None, sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs): super().__init__() self.hidden_size = hidden_size self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.attn = AttentionKVCompress( hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs ) self.cross_attn = MultiHeadCrossAttention( hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs ) self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) # to be compatible with lower version pytorch approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = Mlp( in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, dtype=dtype, device=device, operations=operations ) self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) def forward(self, x, y, t, mask=None, HW=None, **kwargs): B, N, C = x.shape shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1) x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) x = x + self.cross_attn(x, y, mask) x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) return x ### Core PixArt Model ### class PixArtMS(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, input_size=32, patch_size=2, in_channels=4, hidden_size=1152, depth=28, num_heads=16, mlp_ratio=4.0, class_dropout_prob=0.1, learn_sigma=True, pred_sigma=True, drop_path: float = 0., caption_channels=4096, pe_interpolation=None, pe_precision=None, config=None, model_max_length=120, micro_condition=True, qk_norm=False, kv_compress_config=None, dtype=None, device=None, operations=None, **kwargs, ): nn.Module.__init__(self) self.dtype = dtype self.pred_sigma = pred_sigma self.in_channels = in_channels self.out_channels = in_channels * 2 if pred_sigma else in_channels self.patch_size = patch_size self.num_heads = num_heads self.pe_interpolation = pe_interpolation self.pe_precision = pe_precision self.hidden_size = hidden_size self.depth = depth approx_gelu = lambda: nn.GELU(approximate="tanh") self.t_block = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) ) self.x_embedder = PatchEmbed( patch_size=patch_size, in_chans=in_channels, embed_dim=hidden_size, bias=True, dtype=dtype, device=device, operations=operations ) self.t_embedder = TimestepEmbedder( hidden_size, dtype=dtype, device=device, operations=operations, ) self.y_embedder = CaptionEmbedder( in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length, dtype=dtype, device=device, operations=operations, ) self.micro_conditioning = micro_condition if self.micro_conditioning: self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) # For fixed sin-cos embedding: # num_patches = (input_size // patch_size) * (input_size // patch_size) # self.base_size = input_size // self.patch_size # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule if kv_compress_config is None: kv_compress_config = { 'sampling': None, 'scale_factor': 1, 'kv_compress_layer': [], } self.blocks = nn.ModuleList([ PixArtMSBlock( hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], sampling=kv_compress_config['sampling'], sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1, qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, ) for i in range(depth) ]) self.final_layer = T2IFinalLayer( hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations ) def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs): """ Original forward pass of PixArt. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) t: (N,) tensor of diffusion timesteps y: (N, 1, 120, C) conditioning ar: (N, 1): aspect ratio cs: (N ,2) size conditioning for height/width """ B, C, H, W = x.shape c_res = (H + W) // 2 pe_interpolation = self.pe_interpolation if pe_interpolation is None or self.pe_precision is not None: # calculate pe_interpolation on-the-fly pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0) pos_embed = get_2d_sincos_pos_embed_torch( self.hidden_size, h=(H // self.patch_size), w=(W // self.patch_size), pe_interpolation=pe_interpolation, base_size=((round(c_res / 64) * 64) // self.patch_size), device=x.device, dtype=x.dtype, ).unsqueeze(0) x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 t = self.t_embedder(timestep, x.dtype) # (N, D) if self.micro_conditioning and (c_size is not None and c_ar is not None): bs = x.shape[0] c_size = self.csize_embedder(c_size, bs) # (N, D) c_ar = self.ar_embedder(c_ar, bs) # (N, D) t = t + torch.cat([c_size, c_ar], dim=1) t0 = self.t_block(t) y = self.y_embedder(y, self.training) # (N, D) if mask is not None: if mask.shape[0] != y.shape[0]: mask = mask.repeat(y.shape[0] // mask.shape[0], 1) mask = mask.squeeze(1).squeeze(1) y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) y_lens = mask.sum(dim=1).tolist() else: y_lens = None y = y.squeeze(1).view(1, -1, x.shape[-1]) for block in self.blocks: x = block(x, y, t0, y_lens, (H, W), **kwargs) # (N, T, D) x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) x = self.unpatchify(x, H, W) # (N, out_channels, H, W) return x def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs): B, C, H, W = x.shape # Fallback for missing microconds if self.micro_conditioning: if c_size is None: c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1) if c_ar is None: c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1) ## Still accepts the input w/o that dim but returns garbage if len(context.shape) == 3: context = context.unsqueeze(1) ## run original forward pass out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar) ## only return EPS if self.pred_sigma: return out[:, :self.in_channels] return out def unpatchify(self, x, h, w): """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ c = self.out_channels p = self.x_embedder.patch_size[0] h = h // self.patch_size w = w // self.patch_size assert h * w == x.shape[1] x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) x = torch.einsum('nhwpqc->nchpwq', x) imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) return imgs ================================================ FILE: comfy/ldm/qwen_image/controlnet.py ================================================ import torch import math from .model import QwenImageTransformer2DModel from .model import QwenImageTransformerBlock class QwenImageFunControlBlock(QwenImageTransformerBlock): def __init__(self, dim, num_attention_heads, attention_head_dim, has_before_proj=False, dtype=None, device=None, operations=None): super().__init__( dim=dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, dtype=dtype, device=device, operations=operations, ) self.has_before_proj = has_before_proj if has_before_proj: self.before_proj = operations.Linear(dim, dim, device=device, dtype=dtype) self.after_proj = operations.Linear(dim, dim, device=device, dtype=dtype) class QwenImageFunControlNetModel(torch.nn.Module): def __init__( self, control_in_features=132, inner_dim=3072, num_attention_heads=24, attention_head_dim=128, num_control_blocks=5, main_model_double=60, injection_layers=(0, 12, 24, 36, 48), dtype=None, device=None, operations=None, ): super().__init__() self.dtype = dtype self.main_model_double = main_model_double self.injection_layers = tuple(injection_layers) # Keep base hint scaling at 1.0 so user-facing strength behaves similarly # to the reference Gen2/VideoX implementation around strength=1. self.hint_scale = 1.0 self.control_img_in = operations.Linear(control_in_features, inner_dim, device=device, dtype=dtype) self.control_blocks = torch.nn.ModuleList([]) for i in range(num_control_blocks): self.control_blocks.append( QwenImageFunControlBlock( dim=inner_dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, has_before_proj=(i == 0), dtype=dtype, device=device, operations=operations, ) ) def _process_hint_tokens(self, hint): if hint is None: return None if hint.ndim == 4: hint = hint.unsqueeze(2) # Fun checkpoints are trained with 33 latent channels before 2x2 packing: # [control_latent(16), mask(1), inpaint_latent(16)] -> 132 features. # Default behavior (no inpaint input in stock Apply ControlNet) should use # zeros for mask/inpaint branches, matching VideoX fallback semantics. expected_c = self.control_img_in.weight.shape[1] // 4 if hint.shape[1] == 16 and expected_c == 33: zeros_mask = torch.zeros_like(hint[:, :1]) zeros_inpaint = torch.zeros_like(hint) hint = torch.cat([hint, zeros_mask, zeros_inpaint], dim=1) bs, c, t, h, w = hint.shape hidden_states = torch.nn.functional.pad(hint, (0, w % 2, 0, h % 2)) orig_shape = hidden_states.shape hidden_states = hidden_states.view( orig_shape[0], orig_shape[1], orig_shape[-3], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2, ) hidden_states = hidden_states.permute(0, 2, 3, 5, 1, 4, 6) hidden_states = hidden_states.reshape( bs, t * ((h + 1) // 2) * ((w + 1) // 2), c * 4, ) expected_in = self.control_img_in.weight.shape[1] cur_in = hidden_states.shape[-1] if cur_in < expected_in: pad = torch.zeros( (hidden_states.shape[0], hidden_states.shape[1], expected_in - cur_in), device=hidden_states.device, dtype=hidden_states.dtype, ) hidden_states = torch.cat([hidden_states, pad], dim=-1) elif cur_in > expected_in: hidden_states = hidden_states[:, :, :expected_in] return hidden_states def forward( self, x, timesteps, context, attention_mask=None, guidance: torch.Tensor = None, hint=None, transformer_options={}, base_model=None, **kwargs, ): if base_model is None: raise RuntimeError("Qwen Fun ControlNet requires a QwenImage base model at runtime.") encoder_hidden_states_mask = attention_mask # Keep attention mask disabled inside Fun control blocks to mirror # VideoX behavior (they rely on seq lengths for RoPE, not masked attention). encoder_hidden_states_mask = None hidden_states, img_ids, _ = base_model.process_img(x) hint_tokens = self._process_hint_tokens(hint) if hint_tokens is None: raise RuntimeError("Qwen Fun ControlNet requires a control hint image.") if hint_tokens.shape[1] != hidden_states.shape[1]: max_tokens = min(hint_tokens.shape[1], hidden_states.shape[1]) hint_tokens = hint_tokens[:, :max_tokens] hidden_states = hidden_states[:, :max_tokens] img_ids = img_ids[:, :max_tokens] txt_start = round( max( ((x.shape[-1] + (base_model.patch_size // 2)) // base_model.patch_size) // 2, ((x.shape[-2] + (base_model.patch_size // 2)) // base_model.patch_size) // 2, ) ) txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3) ids = torch.cat((txt_ids, img_ids), dim=1) image_rotary_emb = base_model.pe_embedder(ids).to(x.dtype).contiguous() hidden_states = base_model.img_in(hidden_states) encoder_hidden_states = base_model.txt_norm(context) encoder_hidden_states = base_model.txt_in(encoder_hidden_states) if guidance is not None: guidance = guidance * 1000 temb = ( base_model.time_text_embed(timesteps, hidden_states) if guidance is None else base_model.time_text_embed(timesteps, guidance, hidden_states) ) c = self.control_img_in(hint_tokens) for i, block in enumerate(self.control_blocks): if i == 0: c_in = block.before_proj(c) + hidden_states all_c = [] else: all_c = list(torch.unbind(c, dim=0)) c_in = all_c.pop(-1) encoder_hidden_states, c_out = block( hidden_states=c_in, encoder_hidden_states=encoder_hidden_states, encoder_hidden_states_mask=encoder_hidden_states_mask, temb=temb, image_rotary_emb=image_rotary_emb, transformer_options=transformer_options, ) c_skip = block.after_proj(c_out) * self.hint_scale all_c += [c_skip, c_out] c = torch.stack(all_c, dim=0) hints = torch.unbind(c, dim=0)[:-1] controlnet_block_samples = [None] * self.main_model_double for local_idx, base_idx in enumerate(self.injection_layers): if local_idx < len(hints) and base_idx < len(controlnet_block_samples): controlnet_block_samples[base_idx] = hints[local_idx] return {"input": controlnet_block_samples} class QwenImageControlNetModel(QwenImageTransformer2DModel): def __init__( self, extra_condition_channels=0, dtype=None, device=None, operations=None, **kwargs ): super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) self.main_model_double = 60 # controlnet_blocks self.controlnet_blocks = torch.nn.ModuleList([]) for _ in range(len(self.transformer_blocks)): self.controlnet_blocks.append(operations.Linear(self.inner_dim, self.inner_dim, device=device, dtype=dtype)) self.controlnet_x_embedder = operations.Linear(self.in_channels + extra_condition_channels, self.inner_dim, device=device, dtype=dtype) def forward( self, x, timesteps, context, attention_mask=None, guidance: torch.Tensor = None, ref_latents=None, hint=None, transformer_options={}, **kwargs ): timestep = timesteps encoder_hidden_states = context encoder_hidden_states_mask = attention_mask hidden_states, img_ids, orig_shape = self.process_img(x) hint, _, _ = self.process_img(hint) txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2)) txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3) ids = torch.cat((txt_ids, img_ids), dim=1) image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous() del ids, txt_ids, img_ids hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint) encoder_hidden_states = self.txt_norm(encoder_hidden_states) encoder_hidden_states = self.txt_in(encoder_hidden_states) if guidance is not None: guidance = guidance * 1000 temb = ( self.time_text_embed(timestep, hidden_states) if guidance is None else self.time_text_embed(timestep, guidance, hidden_states) ) repeat = math.ceil(self.main_model_double / len(self.controlnet_blocks)) controlnet_block_samples = () for i, block in enumerate(self.transformer_blocks): encoder_hidden_states, hidden_states = block( hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, encoder_hidden_states_mask=encoder_hidden_states_mask, temb=temb, image_rotary_emb=image_rotary_emb, ) controlnet_block_samples = controlnet_block_samples + (self.controlnet_blocks[i](hidden_states),) * repeat return {"input": controlnet_block_samples[:self.main_model_double]} ================================================ FILE: comfy/ldm/qwen_image/model.py ================================================ # https://github.com/QwenLM/Qwen-Image (Apache 2.0) import torch import torch.nn as nn import torch.nn.functional as F from typing import Optional, Tuple from einops import repeat from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps from comfy.ldm.modules.attention import optimized_attention_masked from comfy.ldm.flux.layers import EmbedND import comfy.ldm.common_dit import comfy.patcher_extension from comfy.ldm.flux.math import apply_rope1 class GELU(nn.Module): def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None): super().__init__() self.proj = operations.Linear(dim_in, dim_out, bias=bias, dtype=dtype, device=device) self.approximate = approximate def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states = F.gelu(hidden_states, approximate=self.approximate) return hidden_states class FeedForward(nn.Module): def __init__( self, dim: int, dim_out: Optional[int] = None, mult: int = 4, dropout: float = 0.0, inner_dim=None, bias: bool = True, dtype=None, device=None, operations=None ): super().__init__() if inner_dim is None: inner_dim = int(dim * mult) dim_out = dim_out if dim_out is not None else dim self.net = nn.ModuleList([]) self.net.append(GELU(dim, inner_dim, approximate="tanh", bias=bias, dtype=dtype, device=device, operations=operations)) self.net.append(nn.Dropout(dropout)) self.net.append(operations.Linear(inner_dim, dim_out, bias=bias, dtype=dtype, device=device)) def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor: for module in self.net: hidden_states = module(hidden_states) return hidden_states def apply_rotary_emb(x, freqs_cis): if x.shape[1] == 0: return x t_ = x.reshape(*x.shape[:-1], -1, 1, 2) t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] return t_out.reshape(*x.shape) class QwenTimestepProjEmbeddings(nn.Module): def __init__(self, embedding_dim, pooled_projection_dim, use_additional_t_cond=False, dtype=None, device=None, operations=None): super().__init__() self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000) self.timestep_embedder = TimestepEmbedding( in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations ) self.use_additional_t_cond = use_additional_t_cond if self.use_additional_t_cond: self.addition_t_embedding = operations.Embedding(2, embedding_dim, device=device, dtype=dtype) def forward(self, timestep, hidden_states, addition_t_cond=None): timesteps_proj = self.time_proj(timestep) timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype)) if self.use_additional_t_cond: if addition_t_cond is None: addition_t_cond = torch.zeros((timesteps_emb.shape[0]), device=timesteps_emb.device, dtype=torch.long) timesteps_emb += self.addition_t_embedding(addition_t_cond, out_dtype=timesteps_emb.dtype) return timesteps_emb class Attention(nn.Module): def __init__( self, query_dim: int, dim_head: int = 64, heads: int = 8, dropout: float = 0.0, bias: bool = False, eps: float = 1e-5, out_bias: bool = True, out_dim: int = None, out_context_dim: int = None, dtype=None, device=None, operations=None ): super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads self.inner_kv_dim = self.inner_dim self.heads = heads self.dim_head = dim_head self.out_dim = out_dim if out_dim is not None else query_dim self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim self.dropout = dropout # Q/K normalization self.norm_q = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device) self.norm_k = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device) self.norm_added_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) self.norm_added_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) # Image stream projections self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) self.to_k = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) self.to_v = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) # Text stream projections self.add_q_proj = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) self.add_k_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) self.add_v_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) # Output projections self.to_out = nn.ModuleList([ operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device), nn.Dropout(dropout) ]) self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) def forward( self, hidden_states: torch.FloatTensor, # Image stream encoder_hidden_states: torch.FloatTensor = None, # Text stream encoder_hidden_states_mask: torch.FloatTensor = None, attention_mask: Optional[torch.FloatTensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}, ) -> Tuple[torch.Tensor, torch.Tensor]: batch_size = hidden_states.shape[0] seq_img = hidden_states.shape[1] seq_txt = encoder_hidden_states.shape[1] transformer_patches = transformer_options.get("patches", {}) extra_options = transformer_options.copy() # Project and reshape to BHND format (batch, heads, seq, dim) img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous() img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous() img_value = self.to_v(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2) txt_query = self.add_q_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous() txt_key = self.add_k_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous() txt_value = self.add_v_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2) img_query = self.norm_q(img_query) img_key = self.norm_k(img_key) txt_query = self.norm_added_q(txt_query) txt_key = self.norm_added_k(txt_key) joint_query = torch.cat([txt_query, img_query], dim=2) joint_key = torch.cat([txt_key, img_key], dim=2) joint_value = torch.cat([txt_value, img_value], dim=2) if encoder_hidden_states_mask is not None: attn_mask = torch.zeros((batch_size, 1, seq_txt + seq_img), dtype=hidden_states.dtype, device=hidden_states.device) attn_mask[:, 0, :seq_txt] = encoder_hidden_states_mask else: attn_mask = None extra_options["img_slice"] = [txt_query.shape[2], joint_query.shape[2]] if "attn1_patch" in transformer_patches: patch = transformer_patches["attn1_patch"] for p in patch: out = p(joint_query, joint_key, joint_value, pe=image_rotary_emb, attn_mask=encoder_hidden_states_mask, extra_options=extra_options) joint_query, joint_key, joint_value, image_rotary_emb, encoder_hidden_states_mask = out.get("q", joint_query), out.get("k", joint_key), out.get("v", joint_value), out.get("pe", image_rotary_emb), out.get("attn_mask", encoder_hidden_states_mask) joint_query = apply_rope1(joint_query, image_rotary_emb) joint_key = apply_rope1(joint_key, image_rotary_emb) joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attn_mask, transformer_options=transformer_options, skip_reshape=True) txt_attn_output = joint_hidden_states[:, :seq_txt, :] img_attn_output = joint_hidden_states[:, seq_txt:, :] img_attn_output = self.to_out[0](img_attn_output) img_attn_output = self.to_out[1](img_attn_output) txt_attn_output = self.to_add_out(txt_attn_output) return img_attn_output, txt_attn_output class QwenImageTransformerBlock(nn.Module): def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, eps: float = 1e-6, dtype=None, device=None, operations=None ): super().__init__() self.dim = dim self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim self.img_mod = nn.Sequential( nn.SiLU(), operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device), ) self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) self.img_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations) self.txt_mod = nn.Sequential( nn.SiLU(), operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device), ) self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) self.txt_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations) self.attn = Attention( query_dim=dim, dim_head=attention_head_dim, heads=num_attention_heads, out_dim=dim, bias=True, eps=eps, dtype=dtype, device=device, operations=operations, ) def _apply_gate(self, x, y, gate, timestep_zero_index=None): if timestep_zero_index is not None: return y + torch.cat((x[:, :timestep_zero_index] * gate[0], x[:, timestep_zero_index:] * gate[1]), dim=1) else: return torch.addcmul(y, gate, x) def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor, timestep_zero_index=None) -> Tuple[torch.Tensor, torch.Tensor]: shift, scale, gate = torch.chunk(mod_params, 3, dim=-1) if timestep_zero_index is not None: actual_batch = shift.size(0) // 2 shift, shift_0 = shift[:actual_batch], shift[actual_batch:] scale, scale_0 = scale[:actual_batch], scale[actual_batch:] gate, gate_0 = gate[:actual_batch], gate[actual_batch:] reg = torch.addcmul(shift.unsqueeze(1), x[:, :timestep_zero_index], 1 + scale.unsqueeze(1)) zero = torch.addcmul(shift_0.unsqueeze(1), x[:, timestep_zero_index:], 1 + scale_0.unsqueeze(1)) return torch.cat((reg, zero), dim=1), (gate.unsqueeze(1), gate_0.unsqueeze(1)) else: return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1) def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_hidden_states_mask: torch.Tensor, temb: torch.Tensor, image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, timestep_zero_index=None, transformer_options={}, ) -> Tuple[torch.Tensor, torch.Tensor]: img_mod_params = self.img_mod(temb) if timestep_zero_index is not None: temb = temb.chunk(2, dim=0)[0] txt_mod_params = self.txt_mod(temb) img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1) txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1) img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1, timestep_zero_index) del img_mod1 txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1) del txt_mod1 img_attn_output, txt_attn_output = self.attn( hidden_states=img_modulated, encoder_hidden_states=txt_modulated, encoder_hidden_states_mask=encoder_hidden_states_mask, image_rotary_emb=image_rotary_emb, transformer_options=transformer_options, ) del img_modulated del txt_modulated hidden_states = self._apply_gate(img_attn_output, hidden_states, img_gate1, timestep_zero_index) encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output del img_attn_output del txt_attn_output del img_gate1 del txt_gate1 img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2, timestep_zero_index) hidden_states = self._apply_gate(self.img_mlp(img_modulated2), hidden_states, img_gate2, timestep_zero_index) txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2) encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2)) return encoder_hidden_states, hidden_states class LastLayer(nn.Module): def __init__( self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine=False, eps=1e-6, bias=True, dtype=None, device=None, operations=None ): super().__init__() self.silu = nn.SiLU() self.linear = operations.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias, dtype=dtype, device=device) self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine=False, bias=bias, dtype=dtype, device=device) def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor: emb = self.linear(self.silu(conditioning_embedding)) scale, shift = torch.chunk(emb, 2, dim=1) x = torch.addcmul(shift[:, None, :], self.norm(x), (1 + scale)[:, None, :]) return x class QwenImageTransformer2DModel(nn.Module): def __init__( self, patch_size: int = 2, in_channels: int = 64, out_channels: Optional[int] = 16, num_layers: int = 60, attention_head_dim: int = 128, num_attention_heads: int = 24, joint_attention_dim: int = 3584, pooled_projection_dim: int = 768, axes_dims_rope: Tuple[int, int, int] = (16, 56, 56), default_ref_method="index", image_model=None, final_layer=True, use_additional_t_cond=False, dtype=None, device=None, operations=None, ): super().__init__() self.dtype = dtype self.patch_size = patch_size self.in_channels = in_channels self.out_channels = out_channels or in_channels self.inner_dim = num_attention_heads * attention_head_dim self.default_ref_method = default_ref_method self.pe_embedder = EmbedND(dim=attention_head_dim, theta=10000, axes_dim=list(axes_dims_rope)) self.time_text_embed = QwenTimestepProjEmbeddings( embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim, use_additional_t_cond=use_additional_t_cond, dtype=dtype, device=device, operations=operations ) self.txt_norm = operations.RMSNorm(joint_attention_dim, eps=1e-6, dtype=dtype, device=device) self.img_in = operations.Linear(in_channels, self.inner_dim, dtype=dtype, device=device) self.txt_in = operations.Linear(joint_attention_dim, self.inner_dim, dtype=dtype, device=device) self.transformer_blocks = nn.ModuleList([ QwenImageTransformerBlock( dim=self.inner_dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, dtype=dtype, device=device, operations=operations ) for _ in range(num_layers) ]) if self.default_ref_method == "index_timestep_zero": self.register_buffer("__index_timestep_zero__", torch.tensor([])) if final_layer: self.norm_out = LastLayer(self.inner_dim, self.inner_dim, dtype=dtype, device=device, operations=operations) self.proj_out = operations.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True, dtype=dtype, device=device) def process_img(self, x, index=0, h_offset=0, w_offset=0): bs, c, t, h, w = x.shape patch_size = self.patch_size hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (1, self.patch_size, self.patch_size)) orig_shape = hidden_states.shape hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-3], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2) hidden_states = hidden_states.permute(0, 2, 3, 5, 1, 4, 6) hidden_states = hidden_states.reshape(orig_shape[0], orig_shape[-3] * (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4) t_len = t h_len = ((h + (patch_size // 2)) // patch_size) w_len = ((w + (patch_size // 2)) // patch_size) h_offset = ((h_offset + (patch_size // 2)) // patch_size) w_offset = ((w_offset + (patch_size // 2)) // patch_size) img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device) if t_len > 1: img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).unsqueeze(1).unsqueeze(1) else: img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + index img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1).unsqueeze(0) - (h_len // 2) img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0).unsqueeze(0) - (w_len // 2) return hidden_states, repeat(img_ids, "t h w c -> b (t h w) c", b=bs), orig_shape def forward(self, x, timestep, context, attention_mask=None, ref_latents=None, additional_t_cond=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, attention_mask, ref_latents, additional_t_cond, transformer_options, **kwargs) def _forward( self, x, timesteps, context, attention_mask=None, ref_latents=None, additional_t_cond=None, transformer_options={}, control=None, **kwargs ): timestep = timesteps encoder_hidden_states = context encoder_hidden_states_mask = attention_mask if encoder_hidden_states_mask is not None and not torch.is_floating_point(encoder_hidden_states_mask): encoder_hidden_states_mask = (encoder_hidden_states_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max hidden_states, img_ids, orig_shape = self.process_img(x) num_embeds = hidden_states.shape[1] timestep_zero_index = None if ref_latents is not None: ref_num_tokens = [] h = 0 w = 0 index = 0 ref_method = kwargs.get("ref_latents_method", self.default_ref_method) index_ref_method = (ref_method == "index") or (ref_method == "index_timestep_zero") negative_ref_method = ref_method == "negative_index" timestep_zero = ref_method == "index_timestep_zero" for ref in ref_latents: if index_ref_method: index += 1 h_offset = 0 w_offset = 0 elif negative_ref_method: index -= 1 h_offset = 0 w_offset = 0 else: index = 1 h_offset = 0 w_offset = 0 if ref.shape[-2] + h > ref.shape[-1] + w: w_offset = w else: h_offset = h h = max(h, ref.shape[-2] + h_offset) w = max(w, ref.shape[-1] + w_offset) kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset) hidden_states = torch.cat([hidden_states, kontext], dim=1) img_ids = torch.cat([img_ids, kontext_ids], dim=1) ref_num_tokens.append(kontext.shape[1]) if timestep_zero: if index > 0: timestep = torch.cat([timestep, timestep * 0], dim=0) timestep_zero_index = num_embeds transformer_options = transformer_options.copy() transformer_options["reference_image_num_tokens"] = ref_num_tokens txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2)) txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3) hidden_states = self.img_in(hidden_states) encoder_hidden_states = self.txt_norm(encoder_hidden_states) encoder_hidden_states = self.txt_in(encoder_hidden_states) temb = self.time_text_embed(timestep, hidden_states, additional_t_cond) patches_replace = transformer_options.get("patches_replace", {}) patches = transformer_options.get("patches", {}) blocks_replace = patches_replace.get("dit", {}) if "post_input" in patches: for p in patches["post_input"]: out = p({"img": hidden_states, "txt": encoder_hidden_states, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options}) hidden_states = out["img"] encoder_hidden_states = out["txt"] img_ids = out["img_ids"] txt_ids = out["txt_ids"] ids = torch.cat((txt_ids, img_ids), dim=1) image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous() del ids, txt_ids, img_ids transformer_options["total_blocks"] = len(self.transformer_blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.transformer_blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], timestep_zero_index=timestep_zero_index, transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap}) hidden_states = out["img"] encoder_hidden_states = out["txt"] else: encoder_hidden_states, hidden_states = block( hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, encoder_hidden_states_mask=encoder_hidden_states_mask, temb=temb, image_rotary_emb=image_rotary_emb, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options, ) if "double_block" in patches: for p in patches["double_block"]: out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i, "transformer_options": transformer_options}) hidden_states = out["img"] encoder_hidden_states = out["txt"] if control is not None: # Controlnet control_i = control.get("input") if i < len(control_i): add = control_i[i] if add is not None: hidden_states[:, :add.shape[1]] += add if timestep_zero_index is not None: temb = temb.chunk(2, dim=0)[0] hidden_states = self.norm_out(hidden_states, temb) hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states[:, :num_embeds].view(orig_shape[0], orig_shape[-3], orig_shape[-2] // 2, orig_shape[-1] // 2, orig_shape[1], 2, 2) hidden_states = hidden_states.permute(0, 4, 1, 2, 5, 3, 6) return hidden_states.reshape(orig_shape)[:, :, :, :x.shape[-2], :x.shape[-1]] ================================================ FILE: comfy/ldm/util.py ================================================ import importlib import logging import torch from torch import optim import numpy as np from inspect import isfunction from PIL import Image, ImageDraw, ImageFont def log_txt_as_img(wh, xc, size=10): # wh a tuple of (width, height) # xc a list of captions to plot b = len(xc) txts = list() for bi in range(b): txt = Image.new("RGB", wh, color="white") draw = ImageDraw.Draw(txt) font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) nc = int(40 * (wh[0] / 256)) lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) try: draw.text((0, 0), lines, fill="black", font=font) except UnicodeEncodeError: logging.warning("Cant encode string for logging. Skipping.") txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 txts.append(txt) txts = np.stack(txts) txts = torch.tensor(txts) return txts def ismap(x): if not isinstance(x, torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] > 3) def isimage(x): if not isinstance(x,torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def mean_flat(tensor): """ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def count_params(model, verbose=False): total_params = sum(p.numel() for p in model.parameters()) if verbose: logging.info(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") return total_params def instantiate_from_config(config): if "target" not in config: if config == '__is_first_stage__': return None elif config == "__is_unconditional__": return None raise KeyError("Expected key `target` to instantiate.") return get_obj_from_str(config["target"])(**config.get("params", dict())) def get_obj_from_str(string, reload=False): module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) class AdamWwithEMAandWings(optim.Optimizer): # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code ema_power=1., param_names=()): """AdamW that saves EMA versions of the parameters.""" if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= ema_decay <= 1.0: raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, ema_power=ema_power, param_names=param_names) super().__init__(params, defaults) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False) @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params_with_grad = [] grads = [] exp_avgs = [] exp_avg_sqs = [] ema_params_with_grad = [] max_exp_avg_sqs = [] state_steps = [] amsgrad = group['amsgrad'] beta1, beta2 = group['betas'] ema_decay = group['ema_decay'] ema_power = group['ema_power'] for p in group['params']: if p.grad is None: continue params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError('AdamW does not support sparse gradients') grads.append(p.grad) state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) # Exponential moving average of parameter values state['param_exp_avg'] = p.detach().float().clone() exp_avgs.append(state['exp_avg']) exp_avg_sqs.append(state['exp_avg_sq']) ema_params_with_grad.append(state['param_exp_avg']) if amsgrad: max_exp_avg_sqs.append(state['max_exp_avg_sq']) # update the steps for each param group update state['step'] += 1 # record the step after step update state_steps.append(state['step']) optim._functional.adamw(params_with_grad, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad=amsgrad, beta1=beta1, beta2=beta2, lr=group['lr'], weight_decay=group['weight_decay'], eps=group['eps'], maximize=False) cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) for param, ema_param in zip(params_with_grad, ema_params_with_grad): ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) return loss ================================================ FILE: comfy/ldm/wan/model.py ================================================ # original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math import torch import torch.nn as nn from einops import rearrange from comfy.ldm.modules.attention import optimized_attention from comfy.ldm.flux.layers import EmbedND from comfy.ldm.flux.math import apply_rope1 import comfy.ldm.common_dit import comfy.model_management import comfy.patcher_extension def sinusoidal_embedding_1d(dim, position): # preprocess assert dim % 2 == 0 half = dim // 2 position = position.type(torch.float32) # calculation sinusoid = torch.outer( position, torch.pow(10000, -torch.arange(half).to(position).div(half))) x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) return x class WanSelfAttention(nn.Module): def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, kv_dim=None, operation_settings={}): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.window_size = window_size self.qk_norm = qk_norm self.eps = eps if kv_dim is None: kv_dim = dim # layers self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.k = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.v = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() def forward(self, x, freqs, transformer_options={}): r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ patches = transformer_options.get("patches", {}) b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim def qkv_fn_q(x): q = self.norm_q(self.q(x)).view(b, s, n, d) return apply_rope1(q, freqs) def qkv_fn_k(x): k = self.norm_k(self.k(x)).view(b, s, n, d) return apply_rope1(k, freqs) #These two are VRAM hogs, so we want to do all of q computation and #have pytorch garbage collect the intermediates on the sub function #return before we touch k q = qkv_fn_q(x) k = qkv_fn_k(x) x = optimized_attention( q.view(b, s, n * d), k.view(b, s, n * d), self.v(x).view(b, s, n * d), heads=self.num_heads, transformer_options=transformer_options, ) if "attn1_patch" in patches: for p in patches["attn1_patch"]: x = p({"x": x, "q": q, "k": k, "transformer_options": transformer_options}) x = self.o(x) return x class WanT2VCrossAttention(WanSelfAttention): def forward(self, x, context, transformer_options={}, **kwargs): r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] """ # compute query, key, value q = self.norm_q(self.q(x)) k = self.norm_k(self.k(context)) v = self.v(context) # compute attention x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options) x = self.o(x) return x class WanI2VCrossAttention(WanSelfAttention): def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, operation_settings={}): super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings) self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) # self.alpha = nn.Parameter(torch.zeros((1, ))) self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() def forward(self, x, context, context_img_len, transformer_options={}): r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] """ context_img = context[:, :context_img_len] context = context[:, context_img_len:] # compute query, key, value q = self.norm_q(self.q(x)) k = self.norm_k(self.k(context)) v = self.v(context) k_img = self.norm_k_img(self.k_img(context_img)) v_img = self.v_img(context_img) img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options) # compute attention x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options) # output x = x + img_x x = self.o(x) return x WAN_CROSSATTENTION_CLASSES = { 't2v_cross_attn': WanT2VCrossAttention, 'i2v_cross_attn': WanI2VCrossAttention, } def repeat_e(e, x): repeats = 1 if e.size(1) > 1: repeats = x.size(1) // e.size(1) if repeats == 1: return e if repeats * e.size(1) == x.size(1): return torch.repeat_interleave(e, repeats, dim=1) else: return torch.repeat_interleave(e, repeats + 1, dim=1)[:, :x.size(1)] class WanAttentionBlock(nn.Module): def __init__(self, cross_attn_type, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6, operation_settings={}): super().__init__() self.dim = dim self.ffn_dim = ffn_dim self.num_heads = num_heads self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # layers self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings) self.norm3 = operation_settings.get("operations").LayerNorm( dim, eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity() self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, num_heads, (-1, -1), qk_norm, eps, operation_settings=operation_settings) self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.ffn = nn.Sequential( operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) # modulation self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) def forward( self, x, e, freqs, context, context_img_len=257, transformer_options={}, ): r""" Args: x(Tensor): Shape [B, L, C] e(Tensor): Shape [B, 6, C] freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ # assert e.dtype == torch.float32 patches = transformer_options.get("patches", {}) if e.ndim < 4: e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1) else: e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2) # assert e[0].dtype == torch.float32 # self-attention x = x.contiguous() # otherwise implicit in LayerNorm y = self.self_attn( torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)), freqs, transformer_options=transformer_options) x = torch.addcmul(x, y, repeat_e(e[2], x)) del y # cross-attention & ffn x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options) if "attn2_patch" in patches: for p in patches["attn2_patch"]: x = p({"x": x, "transformer_options": transformer_options}) y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x))) x = torch.addcmul(x, y, repeat_e(e[5], x)) return x class VaceWanAttentionBlock(WanAttentionBlock): def __init__( self, cross_attn_type, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6, block_id=0, operation_settings={} ): super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) self.block_id = block_id if block_id == 0: self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, c, x, **kwargs): if self.block_id == 0: c = self.before_proj(c) + x c = super().forward(c, **kwargs) c_skip = self.after_proj(c) return c_skip, c class WanCamAdapter(nn.Module): def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}): super(WanCamAdapter, self).__init__() # Pixel Unshuffle: reduce spatial dimensions by a factor of 8 self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8) # Convolution: reduce spatial dimensions by a factor # of 2 (without overlap) self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) # Residual blocks for feature extraction self.residual_blocks = nn.Sequential( *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)] ) def forward(self, x): # Reshape to merge the frame dimension into batch bs, c, f, h, w = x.size() x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w) # Pixel Unshuffle operation x_unshuffled = self.pixel_unshuffle(x) # Convolution operation x_conv = self.conv(x_unshuffled) # Feature extraction with residual blocks out = self.residual_blocks(x_conv) # Reshape to restore original bf dimension out = out.view(bs, f, out.size(1), out.size(2), out.size(3)) # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames out = out.permute(0, 2, 1, 3, 4) return out class WanCamResidualBlock(nn.Module): def __init__(self, dim, operation_settings={}): super(WanCamResidualBlock, self).__init__() self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.relu = nn.ReLU(inplace=True) self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, x): residual = x out = self.relu(self.conv1(x)) out = self.conv2(out) out += residual return out class Head(nn.Module): def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}): super().__init__() self.dim = dim self.out_dim = out_dim self.patch_size = patch_size self.eps = eps # layers out_dim = math.prod(patch_size) * out_dim self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.head = operation_settings.get("operations").Linear(dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) # modulation self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) def forward(self, x, e): r""" Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, C] """ # assert e.dtype == torch.float32 if e.ndim < 3: e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1) else: e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2) x = (self.head(torch.addcmul(repeat_e(e[0], x), self.norm(x), 1 + repeat_e(e[1], x)))) return x class MLPProj(torch.nn.Module): def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, operation_settings={}): super().__init__() self.proj = torch.nn.Sequential( operation_settings.get("operations").LayerNorm(in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear(in_dim, in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) if flf_pos_embed_token_number is not None: self.emb_pos = nn.Parameter(torch.empty((1, flf_pos_embed_token_number, in_dim), device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) else: self.emb_pos = None def forward(self, image_embeds): if self.emb_pos is not None: image_embeds = image_embeds[:, :self.emb_pos.shape[1]] + comfy.model_management.cast_to(self.emb_pos[:, :image_embeds.shape[1]], dtype=image_embeds.dtype, device=image_embeds.device) clip_extra_context_tokens = self.proj(image_embeds) return clip_extra_context_tokens class WanModel(torch.nn.Module): r""" Wan diffusion backbone supporting both text-to-video and image-to-video. """ def __init__(self, model_type='t2v', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, flf_pos_embed_token_number=None, in_dim_ref_conv=None, wan_attn_block_class=WanAttentionBlock, image_model=None, device=None, dtype=None, operations=None, ): r""" Initialize the diffusion model backbone. Args: model_type (`str`, *optional*, defaults to 't2v'): Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) text_len (`int`, *optional*, defaults to 512): Fixed length for text embeddings in_dim (`int`, *optional*, defaults to 16): Input video channels (C_in) dim (`int`, *optional*, defaults to 2048): Hidden dimension of the transformer ffn_dim (`int`, *optional*, defaults to 8192): Intermediate dimension in feed-forward network freq_dim (`int`, *optional*, defaults to 256): Dimension for sinusoidal time embeddings text_dim (`int`, *optional*, defaults to 4096): Input dimension for text embeddings out_dim (`int`, *optional*, defaults to 16): Output video channels (C_out) num_heads (`int`, *optional*, defaults to 16): Number of attention heads num_layers (`int`, *optional*, defaults to 32): Number of transformer blocks window_size (`tuple`, *optional*, defaults to (-1, -1)): Window size for local attention (-1 indicates global attention) qk_norm (`bool`, *optional*, defaults to True): Enable query/key normalization cross_attn_norm (`bool`, *optional*, defaults to False): Enable cross-attention normalization eps (`float`, *optional*, defaults to 1e-6): Epsilon value for normalization layers """ super().__init__() self.dtype = dtype operation_settings = {"operations": operations, "device": device, "dtype": dtype} assert model_type in ['t2v', 'i2v'] self.model_type = model_type self.patch_size = patch_size self.text_len = text_len self.in_dim = in_dim self.dim = dim self.ffn_dim = ffn_dim self.freq_dim = freq_dim self.text_dim = text_dim self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # embeddings self.patch_embedding = operations.Conv3d( in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32) self.text_embedding = nn.Sequential( operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) self.time_embedding = nn.Sequential( operations.Linear(freq_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) # blocks cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' self.blocks = nn.ModuleList([ wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) for i in range(num_layers) ]) # head self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings) d = dim // num_heads self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]) if model_type == 'i2v': self.img_emb = MLPProj(1280, dim, flf_pos_embed_token_number=flf_pos_embed_token_number, operation_settings=operation_settings) else: self.img_emb = None if in_dim_ref_conv is not None: self.ref_conv = operations.Conv2d(in_dim_ref_conv, dim, kernel_size=patch_size[1:], stride=patch_size[1:], device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) else: self.ref_conv = None def forward_orig( self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, **kwargs, ): r""" Forward pass through the diffusion model Args: x (Tensor): List of input video tensors with shape [B, C_in, F, H, W] t (Tensor): Diffusion timesteps tensor of shape [B] context (List[Tensor]): List of text embeddings each with shape [B, L, C] seq_len (`int`): Maximum sequence length for positional encoding clip_fea (Tensor, *optional*): CLIP image features for image-to-video mode y (List[Tensor], *optional*): Conditional video inputs for image-to-video mode, same shape as x Returns: List[Tensor]: List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] """ # embeddings x = self.patch_embedding(x.float()).to(x.dtype) grid_sizes = x.shape[2:] transformer_options["grid_sizes"] = grid_sizes x = x.flatten(2).transpose(1, 2) # time embeddings e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) e = e.reshape(t.shape[0], -1, e.shape[-1]) e0 = self.time_projection(e).unflatten(2, (6, self.dim)) full_ref = None if self.ref_conv is not None: full_ref = kwargs.get("reference_latent", None) if full_ref is not None: full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2) x = torch.concat((full_ref, x), dim=1) # context context = self.text_embedding(context) context_img_len = None if clip_fea is not None: if self.img_emb is not None: context_clip = self.img_emb(clip_fea) # bs x 257 x dim context = torch.concat([context_clip, context], dim=1) context_img_len = clip_fea.shape[-2] patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) # head x = self.head(x, e) if full_ref is not None: x = x[:, full_ref.shape[1]:] # unpatchify x = self.unpatchify(x, grid_sizes) return x def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}): patch_size = self.patch_size t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) if steps_t is None: steps_t = t_len if steps_h is None: steps_h = h_len if steps_w is None: steps_w = w_len h_start = 0 w_start = 0 rope_options = transformer_options.get("rope_options", None) if rope_options is not None: t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0 h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0 w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0 t_start += rope_options.get("shift_t", 0.0) h_start += rope_options.get("shift_y", 0.0) w_start += rope_options.get("shift_x", 0.0) img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype) img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1) img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1) img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1) img_ids = img_ids.reshape(1, -1, img_ids.shape[-1]) freqs = self.rope_embedder(img_ids).movedim(1, 2) return freqs def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, clip_fea, time_dim_concat, transformer_options, **kwargs) def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): bs, c, t, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) t_len = t if time_dim_concat is not None: time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size) x = torch.cat([x, time_dim_concat], dim=2) t_len = x.shape[2] if self.ref_conv is not None and "reference_latent" in kwargs: t_len += 1 freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options) return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w] def unpatchify(self, x, grid_sizes): r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features, each with shape [L, C_out * prod(patch_size)] grid_sizes (Tensor): Original spatial-temporal grid dimensions before patching, shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) Returns: List[Tensor]: Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8] """ c = self.out_dim u = x b = u.shape[0] u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c) u = torch.einsum('bfhwpqrc->bcfphqwr', u) u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)]) return u class VaceWanModel(WanModel): r""" Wan diffusion backbone supporting both text-to-video and image-to-video. """ def __init__(self, model_type='vace', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, flf_pos_embed_token_number=None, image_model=None, vace_layers=None, vace_in_dim=None, device=None, dtype=None, operations=None, ): super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) operation_settings = {"operations": operations, "device": device, "dtype": dtype} # Vace if vace_layers is not None: self.vace_layers = vace_layers self.vace_in_dim = vace_in_dim # vace blocks self.vace_blocks = nn.ModuleList([ VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, self.cross_attn_norm, self.eps, block_id=i, operation_settings=operation_settings) for i in range(self.vace_layers) ]) self.vace_layers_mapping = {i: n for n, i in enumerate(range(0, self.num_layers, self.num_layers // self.vace_layers))} # vace patch embeddings self.vace_patch_embedding = operations.Conv3d( self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size, device=device, dtype=torch.float32 ) def forward_orig( self, x, t, context, vace_context, vace_strength, clip_fea=None, freqs=None, transformer_options={}, **kwargs, ): # embeddings x = self.patch_embedding(x.float()).to(x.dtype) grid_sizes = x.shape[2:] transformer_options["grid_sizes"] = grid_sizes x = x.flatten(2).transpose(1, 2) # time embeddings e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) e0 = self.time_projection(e).unflatten(1, (6, self.dim)) # context context = self.text_embedding(context) context_img_len = None if clip_fea is not None: if self.img_emb is not None: context_clip = self.img_emb(clip_fea) # bs x 257 x dim context = torch.concat([context_clip, context], dim=1) context_img_len = clip_fea.shape[-2] orig_shape = list(vace_context.shape) vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:]) c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype) c = c.flatten(2).transpose(1, 2) c = list(c.split(orig_shape[0], dim=0)) # arguments x_orig = x patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) ii = self.vace_layers_mapping.get(i, None) if ii is not None: for iii in range(len(c)): c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) x += c_skip * vace_strength[iii] del c_skip # head x = self.head(x, e) # unpatchify x = self.unpatchify(x, grid_sizes) return x class CameraWanModel(WanModel): r""" Wan diffusion backbone supporting both text-to-video and image-to-video. """ def __init__(self, model_type='camera', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, flf_pos_embed_token_number=None, image_model=None, in_dim_control_adapter=24, device=None, dtype=None, operations=None, ): if model_type == 'camera': model_type = 'i2v' else: model_type = 't2v' super().__init__(model_type=model_type, patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) operation_settings = {"operations": operations, "device": device, "dtype": dtype} self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings) def forward_orig( self, x, t, context, clip_fea=None, freqs=None, camera_conditions = None, transformer_options={}, **kwargs, ): # embeddings x = self.patch_embedding(x.float()).to(x.dtype) if self.control_adapter is not None and camera_conditions is not None: x = x + self.control_adapter(camera_conditions).to(x.dtype) grid_sizes = x.shape[2:] x = x.flatten(2).transpose(1, 2) # time embeddings e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) e0 = self.time_projection(e).unflatten(1, (6, self.dim)) # context context = self.text_embedding(context) context_img_len = None if clip_fea is not None: if self.img_emb is not None: context_clip = self.img_emb(clip_fea) # bs x 257 x dim context = torch.concat([context_clip, context], dim=1) context_img_len = clip_fea.shape[-2] patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) # head x = self.head(x, e) # unpatchify x = self.unpatchify(x, grid_sizes) return x class CausalConv1d(nn.Module): def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode='replicate', operations=None, **kwargs): super().__init__() self.pad_mode = pad_mode padding = (kernel_size - 1, 0) # T self.time_causal_padding = padding self.conv = operations.Conv1d( chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): x = torch.nn.functional.pad(x, self.time_causal_padding, mode=self.pad_mode) return self.conv(x) class MotionEncoder_tc(nn.Module): def __init__(self, in_dim: int, hidden_dim: int, num_heads: int, need_global=True, dtype=None, device=None, operations=None,): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.num_heads = num_heads self.need_global = need_global self.conv1_local = CausalConv1d(in_dim, hidden_dim // 4 * num_heads, 3, stride=1, operations=operations, **factory_kwargs) if need_global: self.conv1_global = CausalConv1d( in_dim, hidden_dim // 4, 3, stride=1, operations=operations, **factory_kwargs) self.norm1 = operations.LayerNorm( hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.act = nn.SiLU() self.conv2 = CausalConv1d(hidden_dim // 4, hidden_dim // 2, 3, stride=2, operations=operations, **factory_kwargs) self.conv3 = CausalConv1d(hidden_dim // 2, hidden_dim, 3, stride=2, operations=operations, **factory_kwargs) if need_global: self.final_linear = operations.Linear(hidden_dim, hidden_dim, **factory_kwargs) self.norm1 = operations.LayerNorm( hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm2 = operations.LayerNorm( hidden_dim // 2, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm3 = operations.LayerNorm( hidden_dim, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs)) def forward(self, x): x = rearrange(x, 'b t c -> b c t') x_ori = x.clone() b, c, t = x.shape x = self.conv1_local(x) x = rearrange(x, 'b (n c) t -> (b n) t c', n=self.num_heads) x = self.norm1(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv2(x) x = rearrange(x, 'b c t -> b t c') x = self.norm2(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv3(x) x = rearrange(x, 'b c t -> b t c') x = self.norm3(x) x = self.act(x) x = rearrange(x, '(b n) t c -> b t n c', b=b) padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1) x = torch.cat([x, padding], dim=-2) x_local = x.clone() if not self.need_global: return x_local x = self.conv1_global(x_ori) x = rearrange(x, 'b c t -> b t c') x = self.norm1(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv2(x) x = rearrange(x, 'b c t -> b t c') x = self.norm2(x) x = self.act(x) x = rearrange(x, 'b t c -> b c t') x = self.conv3(x) x = rearrange(x, 'b c t -> b t c') x = self.norm3(x) x = self.act(x) x = self.final_linear(x) x = rearrange(x, '(b n) t c -> b t n c', b=b) return x, x_local class CausalAudioEncoder(nn.Module): def __init__(self, dim=5120, num_layers=25, out_dim=2048, video_rate=8, num_token=4, need_global=False, dtype=None, device=None, operations=None): super().__init__() self.encoder = MotionEncoder_tc( in_dim=dim, hidden_dim=out_dim, num_heads=num_token, need_global=need_global, dtype=dtype, device=device, operations=operations) weight = torch.empty((1, num_layers, 1, 1), dtype=dtype, device=device) self.weights = torch.nn.Parameter(weight) self.act = torch.nn.SiLU() def forward(self, features): # features B * num_layers * dim * video_length weights = self.act(comfy.model_management.cast_to(self.weights, dtype=features.dtype, device=features.device)) weights_sum = weights.sum(dim=1, keepdims=True) weighted_feat = ((features * weights) / weights_sum).sum( dim=1) # b dim f weighted_feat = weighted_feat.permute(0, 2, 1) # b f dim res = self.encoder(weighted_feat) # b f n dim return res # b f n dim class AdaLayerNorm(nn.Module): def __init__(self, embedding_dim, output_dim=None, norm_elementwise_affine=False, norm_eps=1e-5, dtype=None, device=None, operations=None): super().__init__() output_dim = output_dim or embedding_dim * 2 self.silu = nn.SiLU() self.linear = operations.Linear(embedding_dim, output_dim, dtype=dtype, device=device) self.norm = operations.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine, dtype=dtype, device=device) def forward(self, x, temb): temb = self.linear(self.silu(temb)) shift, scale = temb.chunk(2, dim=1) shift = shift[:, None, :] scale = scale[:, None, :] x = self.norm(x) * (1 + scale) + shift return x class AudioInjector_WAN(nn.Module): def __init__(self, dim=2048, num_heads=32, inject_layer=[0, 27], root_net=None, enable_adain=False, adain_dim=2048, adain_mode=None, dtype=None, device=None, operations=None): super().__init__() self.enable_adain = enable_adain self.adain_mode = adain_mode self.injected_block_id = {} audio_injector_id = 0 for inject_id in inject_layer: self.injected_block_id[inject_id] = audio_injector_id audio_injector_id += 1 self.injector = nn.ModuleList([ WanT2VCrossAttention( dim=dim, num_heads=num_heads, qk_norm=True, operation_settings={"operations": operations, "device": device, "dtype": dtype} ) for _ in range(audio_injector_id) ]) self.injector_pre_norm_feat = nn.ModuleList([ operations.LayerNorm( dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device ) for _ in range(audio_injector_id) ]) self.injector_pre_norm_vec = nn.ModuleList([ operations.LayerNorm( dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device ) for _ in range(audio_injector_id) ]) if enable_adain: self.injector_adain_layers = nn.ModuleList([ AdaLayerNorm( output_dim=dim * 2, embedding_dim=adain_dim, dtype=dtype, device=device, operations=operations) for _ in range(audio_injector_id) ]) if adain_mode != "attn_norm": self.injector_adain_output_layers = nn.ModuleList( [operations.Linear(dim, dim, dtype=dtype, device=device) for _ in range(audio_injector_id)]) def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len): audio_attn_id = self.injected_block_id.get(block_id, None) if audio_attn_id is None: return x num_frames = audio_emb.shape[1] input_hidden_states = rearrange(x[:, :seq_len], "b (t n) c -> (b t) n c", t=num_frames) if self.enable_adain and self.adain_mode == "attn_norm": audio_emb_global = rearrange(audio_emb_global, "b t n c -> (b t) n c") adain_hidden_states = self.injector_adain_layers[audio_attn_id](input_hidden_states, temb=audio_emb_global[:, 0]) attn_hidden_states = adain_hidden_states else: attn_hidden_states = self.injector_pre_norm_feat[audio_attn_id](input_hidden_states) audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames) attn_audio_emb = audio_emb residual_out = self.injector[audio_attn_id](x=attn_hidden_states, context=attn_audio_emb) residual_out = rearrange( residual_out, "(b t) n c -> b (t n) c", t=num_frames) x[:, :seq_len] = x[:, :seq_len] + residual_out return x class FramePackMotioner(nn.Module): def __init__( self, inner_dim=1024, num_heads=16, # Used to indicate the number of heads in the backbone network; unrelated to this module's design zip_frame_buckets=[ 1, 2, 16 ], # Three numbers representing the number of frames sampled for patch operations from the nearest to the farthest frames drop_mode="drop", # If not "drop", it will use "padd", meaning padding instead of deletion dtype=None, device=None, operations=None): super().__init__() self.proj = operations.Conv3d(16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2), dtype=dtype, device=device) self.proj_2x = operations.Conv3d(16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4), dtype=dtype, device=device) self.proj_4x = operations.Conv3d(16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8), dtype=dtype, device=device) self.zip_frame_buckets = zip_frame_buckets self.inner_dim = inner_dim self.num_heads = num_heads self.drop_mode = drop_mode def forward(self, motion_latents, rope_embedder, add_last_motion=2): lat_height, lat_width = motion_latents.shape[3], motion_latents.shape[4] padd_lat = torch.zeros(motion_latents.shape[0], 16, sum(self.zip_frame_buckets), lat_height, lat_width).to(device=motion_latents.device, dtype=motion_latents.dtype) overlap_frame = min(padd_lat.shape[2], motion_latents.shape[2]) if overlap_frame > 0: padd_lat[:, :, -overlap_frame:] = motion_latents[:, :, -overlap_frame:] if add_last_motion < 2 and self.drop_mode != "drop": zero_end_frame = sum(self.zip_frame_buckets[:len(self.zip_frame_buckets) - add_last_motion - 1]) padd_lat[:, :, -zero_end_frame:] = 0 clean_latents_4x, clean_latents_2x, clean_latents_post = padd_lat[:, :, -sum(self.zip_frame_buckets):, :, :].split(self.zip_frame_buckets[::-1], dim=2) # 16, 2 ,1 # patchfy clean_latents_post = self.proj(clean_latents_post).flatten(2).transpose(1, 2) clean_latents_2x = self.proj_2x(clean_latents_2x) l_2x_shape = clean_latents_2x.shape clean_latents_2x = clean_latents_2x.flatten(2).transpose(1, 2) clean_latents_4x = self.proj_4x(clean_latents_4x) l_4x_shape = clean_latents_4x.shape clean_latents_4x = clean_latents_4x.flatten(2).transpose(1, 2) if add_last_motion < 2 and self.drop_mode == "drop": clean_latents_post = clean_latents_post[:, : 0] if add_last_motion < 2 else clean_latents_post clean_latents_2x = clean_latents_2x[:, : 0] if add_last_motion < 1 else clean_latents_2x motion_lat = torch.cat([clean_latents_post, clean_latents_2x, clean_latents_4x], dim=1) rope_post = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-1, device=motion_latents.device, dtype=motion_latents.dtype) rope_2x = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-3, steps_h=l_2x_shape[-2], steps_w=l_2x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype) rope_4x = rope_embedder.rope_encode(4, lat_height, lat_width, t_start=-19, steps_h=l_4x_shape[-2], steps_w=l_4x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype) rope = torch.cat([rope_post, rope_2x, rope_4x], dim=1) return motion_lat, rope class WanModel_S2V(WanModel): def __init__(self, model_type='s2v', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, audio_dim=1024, num_audio_token=4, enable_adain=True, cond_dim=16, audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39], adain_mode="attn_norm", framepack_drop_mode="padd", image_model=None, device=None, dtype=None, operations=None, ): super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model, device=device, dtype=dtype, operations=operations) self.trainable_cond_mask = operations.Embedding(3, self.dim, device=device, dtype=dtype) self.casual_audio_encoder = CausalAudioEncoder( dim=audio_dim, out_dim=self.dim, num_token=num_audio_token, need_global=enable_adain, dtype=dtype, device=device, operations=operations) if cond_dim > 0: self.cond_encoder = operations.Conv3d( cond_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size, device=device, dtype=dtype) self.audio_injector = AudioInjector_WAN( dim=self.dim, num_heads=self.num_heads, inject_layer=audio_inject_layers, root_net=self, enable_adain=enable_adain, adain_dim=self.dim, adain_mode=adain_mode, dtype=dtype, device=device, operations=operations ) self.frame_packer = FramePackMotioner( inner_dim=self.dim, num_heads=self.num_heads, zip_frame_buckets=[1, 2, 16], drop_mode=framepack_drop_mode, dtype=dtype, device=device, operations=operations) def forward_orig( self, x, t, context, audio_embed=None, reference_latent=None, control_video=None, reference_motion=None, clip_fea=None, freqs=None, transformer_options={}, **kwargs, ): if audio_embed is not None: num_embeds = x.shape[-3] * 4 audio_emb_global, audio_emb = self.casual_audio_encoder(audio_embed[:, :, :, :num_embeds]) else: audio_emb = None # embeddings bs, _, time, height, width = x.shape x = self.patch_embedding(x.float()).to(x.dtype) if control_video is not None: x = x + self.cond_encoder(control_video) if t.ndim == 1: t = t.unsqueeze(1).repeat(1, x.shape[2]) grid_sizes = x.shape[2:] x = x.flatten(2).transpose(1, 2) seq_len = x.size(1) cond_mask_weight = comfy.model_management.cast_to(self.trainable_cond_mask.weight, dtype=x.dtype, device=x.device).unsqueeze(1).unsqueeze(1) x = x + cond_mask_weight[0] if reference_latent is not None: ref = self.patch_embedding(reference_latent.float()).to(x.dtype) ref = ref.flatten(2).transpose(1, 2) freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=max(30, time + 9), device=x.device, dtype=x.dtype) ref = ref + cond_mask_weight[1] x = torch.cat([x, ref], dim=1) freqs = torch.cat([freqs, freqs_ref], dim=1) t = torch.cat([t, torch.zeros((t.shape[0], reference_latent.shape[-3]), device=t.device, dtype=t.dtype)], dim=1) del ref, freqs_ref if reference_motion is not None: motion_encoded, freqs_motion = self.frame_packer(reference_motion, self) motion_encoded = motion_encoded + cond_mask_weight[2] x = torch.cat([x, motion_encoded], dim=1) freqs = torch.cat([freqs, freqs_motion], dim=1) t = torch.repeat_interleave(t, 2, dim=1) t = torch.cat([t, torch.zeros((t.shape[0], 3), device=t.device, dtype=t.dtype)], dim=1) del motion_encoded, freqs_motion # time embeddings e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) e = e.reshape(t.shape[0], -1, e.shape[-1]) e0 = self.time_projection(e).unflatten(2, (6, self.dim)) # context context = self.text_embedding(context) patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, e=e0, freqs=freqs, context=context, transformer_options=transformer_options) if audio_emb is not None: x = self.audio_injector(x, i, audio_emb, audio_emb_global, seq_len) # head x = self.head(x, e) # unpatchify x = self.unpatchify(x, grid_sizes) return x class WanT2VCrossAttentionGather(WanSelfAttention): def forward(self, x, context, transformer_options={}, **kwargs): r""" Args: x(Tensor): Shape [B, L1, C] - video tokens context(Tensor): Shape [B, L2, C] - audio tokens with shape [B, frames*16, 1536] """ b, n, d = x.size(0), self.num_heads, self.head_dim q = self.norm_q(self.q(x)) k = self.norm_k(self.k(context)) v = self.v(context) # Handle audio temporal structure (16 tokens per frame) k = k.reshape(-1, 16, n, d).transpose(1, 2) v = v.reshape(-1, 16, n, d).transpose(1, 2) # Handle video spatial structure q = q.reshape(k.shape[0], -1, n, d).transpose(1, 2) x = optimized_attention(q, k, v, heads=self.num_heads, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options) x = x.transpose(1, 2).reshape(b, -1, n * d) x = self.o(x) return x class AudioCrossAttentionWrapper(nn.Module): def __init__(self, dim, kv_dim, num_heads, qk_norm=True, eps=1e-6, operation_settings={}): super().__init__() self.audio_cross_attn = WanT2VCrossAttentionGather(dim, num_heads, qk_norm=qk_norm, kv_dim=kv_dim, eps=eps, operation_settings=operation_settings) self.norm1_audio = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) def forward(self, x, audio, transformer_options={}): x = x + self.audio_cross_attn(self.norm1_audio(x), audio, transformer_options=transformer_options) return x class WanAttentionBlockAudio(WanAttentionBlock): def __init__(self, cross_attn_type, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6, operation_settings={}): super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings) self.audio_cross_attn_wrapper = AudioCrossAttentionWrapper(dim, 1536, num_heads, qk_norm, eps, operation_settings=operation_settings) def forward( self, x, e, freqs, context, context_img_len=257, audio=None, transformer_options={}, ): r""" Args: x(Tensor): Shape [B, L, C] e(Tensor): Shape [B, 6, C] freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ # assert e.dtype == torch.float32 if e.ndim < 4: e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1) else: e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2) # assert e[0].dtype == torch.float32 # self-attention y = self.self_attn( torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)), freqs, transformer_options=transformer_options) x = torch.addcmul(x, y, repeat_e(e[2], x)) # cross-attention & ffn x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options) if audio is not None: x = self.audio_cross_attn_wrapper(x, audio, transformer_options=transformer_options) y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x))) x = torch.addcmul(x, y, repeat_e(e[5], x)) return x class DummyAdapterLayer(nn.Module): def __init__(self, layer): super().__init__() self.layer = layer def forward(self, *args, **kwargs): return self.layer(*args, **kwargs) class AudioProjModel(nn.Module): def __init__( self, seq_len=5, blocks=13, # add a new parameter blocks channels=768, # add a new parameter channels intermediate_dim=512, output_dim=1536, context_tokens=16, device=None, dtype=None, operations=None, ): super().__init__() self.seq_len = seq_len self.blocks = blocks self.channels = channels self.input_dim = seq_len * blocks * channels # update input_dim to be the product of blocks and channels. self.intermediate_dim = intermediate_dim self.context_tokens = context_tokens self.output_dim = output_dim # define multiple linear layers self.audio_proj_glob_1 = DummyAdapterLayer(operations.Linear(self.input_dim, intermediate_dim, dtype=dtype, device=device)) self.audio_proj_glob_2 = DummyAdapterLayer(operations.Linear(intermediate_dim, intermediate_dim, dtype=dtype, device=device)) self.audio_proj_glob_3 = DummyAdapterLayer(operations.Linear(intermediate_dim, context_tokens * output_dim, dtype=dtype, device=device)) self.audio_proj_glob_norm = DummyAdapterLayer(operations.LayerNorm(output_dim, dtype=dtype, device=device)) def forward(self, audio_embeds): video_length = audio_embeds.shape[1] audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c") batch_size, window_size, blocks, channels = audio_embeds.shape audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels) audio_embeds = torch.relu(self.audio_proj_glob_1(audio_embeds)) audio_embeds = torch.relu(self.audio_proj_glob_2(audio_embeds)) context_tokens = self.audio_proj_glob_3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim) context_tokens = self.audio_proj_glob_norm(context_tokens) context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length) return context_tokens class HumoWanModel(WanModel): r""" Wan diffusion backbone supporting both text-to-video and image-to-video. """ def __init__(self, model_type='humo', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, flf_pos_embed_token_number=None, image_model=None, audio_token_num=16, device=None, dtype=None, operations=None, ): super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, wan_attn_block_class=WanAttentionBlockAudio, image_model=image_model, device=device, dtype=dtype, operations=operations) self.audio_proj = AudioProjModel(seq_len=8, blocks=5, channels=1280, intermediate_dim=512, output_dim=1536, context_tokens=audio_token_num, dtype=dtype, device=device, operations=operations) def forward_orig( self, x, t, context, freqs=None, audio_embed=None, reference_latent=None, transformer_options={}, **kwargs, ): bs, _, time, height, width = x.shape # embeddings x = self.patch_embedding(x.float()).to(x.dtype) grid_sizes = x.shape[2:] x = x.flatten(2).transpose(1, 2) # time embeddings e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) e = e.reshape(t.shape[0], -1, e.shape[-1]) e0 = self.time_projection(e).unflatten(2, (6, self.dim)) if reference_latent is not None: ref = self.patch_embedding(reference_latent.float()).to(x.dtype) ref = ref.flatten(2).transpose(1, 2) freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=time, device=x.device, dtype=x.dtype) x = torch.cat([x, ref], dim=1) freqs = torch.cat([freqs, freqs_ref], dim=1) del ref, freqs_ref # context context = self.text_embedding(context) context_img_len = None if audio_embed is not None: if reference_latent is not None: zero_audio_pad = torch.zeros(audio_embed.shape[0], reference_latent.shape[-3], *audio_embed.shape[2:], device=audio_embed.device, dtype=audio_embed.dtype) audio_embed = torch.cat([audio_embed, zero_audio_pad], dim=1) audio = self.audio_proj(audio_embed).permute(0, 3, 1, 2).flatten(2).transpose(1, 2) else: audio = None patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, audio=audio, transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, audio=audio, transformer_options=transformer_options) # head x = self.head(x, e) # unpatchify x = self.unpatchify(x, grid_sizes) return x class SCAILWanModel(WanModel): def __init__(self, model_type="scail", patch_size=(1, 2, 2), in_dim=20, dim=5120, operations=None, device=None, dtype=None, **kwargs): super().__init__(model_type='i2v', patch_size=patch_size, in_dim=in_dim, dim=dim, operations=operations, device=device, dtype=dtype, **kwargs) self.patch_embedding_pose = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32) def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, **kwargs): if reference_latent is not None: x = torch.cat((reference_latent, x), dim=2) # embeddings x = self.patch_embedding(x.float()).to(x.dtype) grid_sizes = x.shape[2:] transformer_options["grid_sizes"] = grid_sizes x = x.flatten(2).transpose(1, 2) scail_pose_seq_len = 0 if pose_latents is not None: scail_x = self.patch_embedding_pose(pose_latents.float()).to(x.dtype) scail_x = scail_x.flatten(2).transpose(1, 2) scail_pose_seq_len = scail_x.shape[1] x = torch.cat([x, scail_x], dim=1) del scail_x # time embeddings e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) e = e.reshape(t.shape[0], -1, e.shape[-1]) e0 = self.time_projection(e).unflatten(2, (6, self.dim)) # context context = self.text_embedding(context) context_img_len = None if clip_fea is not None: if self.img_emb is not None: context_clip = self.img_emb(clip_fea) # bs x 257 x dim context = torch.cat([context_clip, context], dim=1) context_img_len = clip_fea.shape[-2] patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) # head x = self.head(x, e) if scail_pose_seq_len > 0: x = x[:, :-scail_pose_seq_len] # unpatchify x = self.unpatchify(x, grid_sizes) if reference_latent is not None: x = x[:, :, reference_latent.shape[2]:] return x def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, transformer_options={}): main_freqs = super().rope_encode(t, h, w, t_start=t_start, steps_t=steps_t, steps_h=steps_h, steps_w=steps_w, device=device, dtype=dtype, transformer_options=transformer_options) if pose_latents is None: return main_freqs ref_t_patches = 0 if reference_latent is not None: ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0] F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1] # if pose is at half resolution, scale_y/scale_x=2 stretches the position range to cover the same RoPE extent as the main frames h_scale = h / H_pose w_scale = w / W_pose # 120 w-offset and shift 0.5 to place positions at midpoints (0.5, 2.5, ...) to match the original code h_shift = (h_scale - 1) / 2 w_shift = (w_scale - 1) / 2 pose_transformer_options = {"rope_options": {"shift_y": h_shift, "shift_x": 120.0 + w_shift, "scale_y": h_scale, "scale_x": w_scale}} pose_freqs = super().rope_encode(F_pose, H_pose, W_pose, t_start=t_start+ref_t_patches, device=device, dtype=dtype, transformer_options=pose_transformer_options) return torch.cat([main_freqs, pose_freqs], dim=1) def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, **kwargs): bs, c, t, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) if pose_latents is not None: pose_latents = comfy.ldm.common_dit.pad_to_patch_size(pose_latents, self.patch_size) t_len = t if time_dim_concat is not None: time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size) x = torch.cat([x, time_dim_concat], dim=2) t_len = x.shape[2] reference_latent = None if "reference_latent" in kwargs: reference_latent = comfy.ldm.common_dit.pad_to_patch_size(kwargs.pop("reference_latent"), self.patch_size) t_len += reference_latent.shape[2] freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent) return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, **kwargs)[:, :, :t, :h, :w] ================================================ FILE: comfy/ldm/wan/model_animate.py ================================================ from torch import nn import torch from typing import Tuple, Optional from einops import rearrange import torch.nn.functional as F import math from .model import WanModel, sinusoidal_embedding_1d from comfy.ldm.modules.attention import optimized_attention import comfy.model_management class CausalConv1d(nn.Module): def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", operations=None, **kwargs): super().__init__() self.pad_mode = pad_mode padding = (kernel_size - 1, 0) # T self.time_causal_padding = padding self.conv = operations.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs) def forward(self, x): x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) return self.conv(x) class FaceEncoder(nn.Module): def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None, operations=None): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.num_heads = num_heads self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1, operations=operations, **factory_kwargs) self.norm1 = operations.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.act = nn.SiLU() self.conv2 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs) self.conv3 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs) self.out_proj = operations.Linear(1024, hidden_dim, **factory_kwargs) self.norm1 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm2 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.norm3 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs)) def forward(self, x): x = rearrange(x, "b t c -> b c t") b, c, t = x.shape x = self.conv1_local(x) x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads) x = self.norm1(x) x = self.act(x) x = rearrange(x, "b t c -> b c t") x = self.conv2(x) x = rearrange(x, "b c t -> b t c") x = self.norm2(x) x = self.act(x) x = rearrange(x, "b t c -> b c t") x = self.conv3(x) x = rearrange(x, "b c t -> b t c") x = self.norm3(x) x = self.act(x) x = self.out_proj(x) x = rearrange(x, "(b n) t c -> b t n c", b=b) padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1) x = torch.cat([x, padding], dim=-2) x_local = x.clone() return x_local def get_norm_layer(norm_layer, operations=None): """ Get the normalization layer. Args: norm_layer (str): The type of normalization layer. Returns: norm_layer (nn.Module): The normalization layer. """ if norm_layer == "layer": return operations.LayerNorm elif norm_layer == "rms": return operations.RMSNorm else: raise NotImplementedError(f"Norm layer {norm_layer} is not implemented") class FaceAdapter(nn.Module): def __init__( self, hidden_dim: int, heads_num: int, qk_norm: bool = True, qk_norm_type: str = "rms", num_adapter_layers: int = 1, dtype=None, device=None, operations=None ): factory_kwargs = {"dtype": dtype, "device": device} super().__init__() self.hidden_size = hidden_dim self.heads_num = heads_num self.fuser_blocks = nn.ModuleList( [ FaceBlock( self.hidden_size, self.heads_num, qk_norm=qk_norm, qk_norm_type=qk_norm_type, operations=operations, **factory_kwargs, ) for _ in range(num_adapter_layers) ] ) def forward( self, x: torch.Tensor, motion_embed: torch.Tensor, idx: int, freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None, freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None, ) -> torch.Tensor: return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k) class FaceBlock(nn.Module): def __init__( self, hidden_size: int, heads_num: int, qk_norm: bool = True, qk_norm_type: str = "rms", qk_scale: float = None, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, operations=None ): factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.deterministic = False self.hidden_size = hidden_size self.heads_num = heads_num head_dim = hidden_size // heads_num self.scale = qk_scale or head_dim**-0.5 self.linear1_kv = operations.Linear(hidden_size, hidden_size * 2, **factory_kwargs) self.linear1_q = operations.Linear(hidden_size, hidden_size, **factory_kwargs) self.linear2 = operations.Linear(hidden_size, hidden_size, **factory_kwargs) qk_norm_layer = get_norm_layer(qk_norm_type, operations=operations) self.q_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.k_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.pre_norm_feat = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) self.pre_norm_motion = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) def forward( self, x: torch.Tensor, motion_vec: torch.Tensor, motion_mask: Optional[torch.Tensor] = None, # use_context_parallel=False, ) -> torch.Tensor: B, T, N, C = motion_vec.shape T_comp = T x_motion = self.pre_norm_motion(motion_vec) x_feat = self.pre_norm_feat(x) kv = self.linear1_kv(x_motion) q = self.linear1_q(x_feat) k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num) q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num) # Apply QK-Norm if needed. q = self.q_norm(q).to(v) k = self.k_norm(k).to(v) k = rearrange(k, "B L N H D -> (B L) N H D") v = rearrange(v, "B L N H D -> (B L) N H D") q = rearrange(q, "B (L S) H D -> (B L) S (H D)", L=T_comp) attn = optimized_attention(q, k, v, heads=self.heads_num) attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp) output = self.linear2(attn) if motion_mask is not None: output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1) return output # https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162 def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): _, minor, in_h, in_w = input.shape kernel_h, kernel_w = kernel.shape out = input.view(-1, minor, in_h, 1, in_w, 1) out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0]) out = out.view(-1, minor, in_h * up_y, in_w * up_x) out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0)] out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) out = F.conv2d(out, w) out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1) return out[:, :, ::down_y, ::down_x] def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) # https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81 class FusedLeakyReLU(torch.nn.Module): def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=None, device=None): super().__init__() self.bias = torch.nn.Parameter(torch.empty(1, channel, 1, 1, dtype=dtype, device=device)) self.negative_slope = negative_slope self.scale = scale def forward(self, input): return fused_leaky_relu(input, comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype), self.negative_slope, self.scale) def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): return F.leaky_relu(input + bias, negative_slope) * scale class Blur(torch.nn.Module): def __init__(self, kernel, pad, dtype=None, device=None): super().__init__() kernel = torch.tensor(kernel, dtype=dtype, device=device) kernel = kernel[None, :] * kernel[:, None] kernel = kernel / kernel.sum() self.register_buffer('kernel', kernel) self.pad = pad def forward(self, input): return upfirdn2d(input, comfy.model_management.cast_to(self.kernel, dtype=input.dtype, device=input.device), pad=self.pad) #https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590 class ScaledLeakyReLU(torch.nn.Module): def __init__(self, negative_slope=0.2): super().__init__() self.negative_slope = negative_slope def forward(self, input): return F.leaky_relu(input, negative_slope=self.negative_slope) # https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605 class EqualConv2d(torch.nn.Module): def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dtype=None, device=None, operations=None): super().__init__() self.weight = torch.nn.Parameter(torch.empty(out_channel, in_channel, kernel_size, kernel_size, device=device, dtype=dtype)) self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) self.stride = stride self.padding = padding self.bias = torch.nn.Parameter(torch.empty(out_channel, device=device, dtype=dtype)) if bias else None def forward(self, input): if self.bias is None: bias = None else: bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) return F.conv2d(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias, stride=self.stride, padding=self.padding) # https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134 class EqualLinear(torch.nn.Module): def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, dtype=None, device=None, operations=None): super().__init__() self.weight = torch.nn.Parameter(torch.empty(out_dim, in_dim, device=device, dtype=dtype)) self.bias = torch.nn.Parameter(torch.empty(out_dim, device=device, dtype=dtype)) if bias else None self.activation = activation self.scale = (1 / math.sqrt(in_dim)) * lr_mul self.lr_mul = lr_mul def forward(self, input): if self.bias is None: bias = None else: bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) * self.lr_mul if self.activation: out = F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale) return fused_leaky_relu(out, bias) return F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias) # https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654 class ConvLayer(torch.nn.Sequential): def __init__(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, dtype=None, device=None, operations=None): layers = [] if downsample: factor = 2 p = (len(blur_kernel) - factor) + (kernel_size - 1) layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2))) stride, padding = 2, 0 else: stride, padding = 1, kernel_size // 2 layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate, dtype=dtype, device=device, operations=operations)) if activate: layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2)) super().__init__(*layers) # https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704 class ResBlock(torch.nn.Module): def __init__(self, in_channel, out_channel, dtype=None, device=None, operations=None): super().__init__() self.conv1 = ConvLayer(in_channel, in_channel, 3, dtype=dtype, device=device, operations=operations) self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, dtype=dtype, device=device, operations=operations) self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False, dtype=dtype, device=device, operations=operations) def forward(self, input): out = self.conv2(self.conv1(input)) skip = self.skip(input) return (out + skip) / math.sqrt(2) class EncoderApp(torch.nn.Module): def __init__(self, w_dim=512, dtype=None, device=None, operations=None): super().__init__() kwargs = {"device": device, "dtype": dtype, "operations": operations} self.convs = torch.nn.ModuleList([ ConvLayer(3, 32, 1, **kwargs), ResBlock(32, 64, **kwargs), ResBlock(64, 128, **kwargs), ResBlock(128, 256, **kwargs), ResBlock(256, 512, **kwargs), ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs), EqualConv2d(512, w_dim, 4, padding=0, bias=False, **kwargs) ]) def forward(self, x): h = x for conv in self.convs: h = conv(h) return h.squeeze(-1).squeeze(-1) class Encoder(torch.nn.Module): def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, operations=None): super().__init__() self.net_app = EncoderApp(dim, dtype=dtype, device=device, operations=operations) self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim, dtype=dtype, device=device, operations=operations) for _ in range(4)] + [EqualLinear(dim, motion_dim, dtype=dtype, device=device, operations=operations)]) def encode_motion(self, x): return self.fc(self.net_app(x)) class Direction(torch.nn.Module): def __init__(self, motion_dim, dtype=None, device=None, operations=None): super().__init__() self.weight = torch.nn.Parameter(torch.empty(512, motion_dim, device=device, dtype=dtype)) self.motion_dim = motion_dim def forward(self, input): stabilized_weight = comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) + 1e-8 * torch.eye(512, self.motion_dim, device=input.device, dtype=input.dtype) Q, _ = torch.linalg.qr(stabilized_weight.float()) if input is None: return Q return torch.sum(input.unsqueeze(-1) * Q.T.to(input.dtype), dim=1) class Synthesis(torch.nn.Module): def __init__(self, motion_dim, dtype=None, device=None, operations=None): super().__init__() self.direction = Direction(motion_dim, dtype=dtype, device=device, operations=operations) class Generator(torch.nn.Module): def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=None, operations=None): super().__init__() self.enc = Encoder(style_dim, motion_dim, dtype=dtype, device=device, operations=operations) self.dec = Synthesis(motion_dim, dtype=dtype, device=device, operations=operations) def get_motion(self, img): motion_feat = self.enc.encode_motion(img) return self.dec.direction(motion_feat) class AnimateWanModel(WanModel): r""" Wan diffusion backbone supporting both text-to-video and image-to-video. """ def __init__(self, model_type='animate', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, flf_pos_embed_token_number=None, motion_encoder_dim=512, image_model=None, device=None, dtype=None, operations=None, ): super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) self.pose_patch_embedding = operations.Conv3d( 16, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype ) self.motion_encoder = Generator(style_dim=512, motion_dim=20, device=device, dtype=dtype, operations=operations) self.face_adapter = FaceAdapter( heads_num=self.num_heads, hidden_dim=self.dim, num_adapter_layers=self.num_layers // 5, device=device, dtype=dtype, operations=operations ) self.face_encoder = FaceEncoder( in_dim=motion_encoder_dim, hidden_dim=self.dim, num_heads=4, device=device, dtype=dtype, operations=operations ) def after_patch_embedding(self, x, pose_latents, face_pixel_values): if pose_latents is not None: pose_latents = self.pose_patch_embedding(pose_latents) x[:, :, 1:pose_latents.shape[2] + 1] += pose_latents[:, :, :x.shape[2] - 1] if face_pixel_values is None: return x, None b, c, T, h, w = face_pixel_values.shape face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w") encode_bs = 8 face_pixel_values_tmp = [] for i in range(math.ceil(face_pixel_values.shape[0] / encode_bs)): face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i * encode_bs: (i + 1) * encode_bs])) motion_vec = torch.cat(face_pixel_values_tmp) motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T) motion_vec = self.face_encoder(motion_vec) B, L, H, C = motion_vec.shape pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec) motion_vec = torch.cat([pad_face, motion_vec], dim=1) if motion_vec.shape[1] < x.shape[2]: B, L, H, C = motion_vec.shape pad = torch.zeros(B, x.shape[2] - motion_vec.shape[1], H, C).type_as(motion_vec) motion_vec = torch.cat([motion_vec, pad], dim=1) else: motion_vec = motion_vec[:, :x.shape[2]] return x, motion_vec def forward_orig( self, x, t, context, clip_fea=None, pose_latents=None, face_pixel_values=None, freqs=None, transformer_options={}, **kwargs, ): # embeddings x = self.patch_embedding(x.float()).to(x.dtype) x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values) grid_sizes = x.shape[2:] x = x.flatten(2).transpose(1, 2) # time embeddings e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) e = e.reshape(t.shape[0], -1, e.shape[-1]) e0 = self.time_projection(e).unflatten(2, (6, self.dim)) full_ref = None if self.ref_conv is not None: full_ref = kwargs.get("reference_latent", None) if full_ref is not None: full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2) x = torch.concat((full_ref, x), dim=1) # context context = self.text_embedding(context) context_img_len = None if clip_fea is not None: if self.img_emb is not None: context_clip = self.img_emb(clip_fea) # bs x 257 x dim context = torch.concat([context_clip, context], dim=1) context_img_len = clip_fea.shape[-2] patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.blocks): transformer_options["block_index"] = i if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) return out out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) if i % 5 == 0 and motion_vec is not None: x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec) # head x = self.head(x, e) if full_ref is not None: x = x[:, full_ref.shape[1]:] # unpatchify x = self.unpatchify(x, grid_sizes) return x ================================================ FILE: comfy/ldm/wan/model_multitalk.py ================================================ import torch from einops import rearrange, repeat import comfy from comfy.ldm.modules.attention import optimized_attention def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, split_num=8): scale = 1.0 / visual_q.shape[-1] ** 0.5 visual_q = visual_q.transpose(1, 2) * scale B, H, x_seqlens, K = visual_q.shape x_ref_attn_maps = [] for class_idx, ref_target_mask in enumerate(ref_target_masks): ref_target_mask = ref_target_mask.view(1, 1, 1, -1) x_ref_attnmap = torch.zeros(B, H, x_seqlens, device=visual_q.device, dtype=visual_q.dtype) chunk_size = min(max(x_seqlens // split_num, 1), x_seqlens) for i in range(0, x_seqlens, chunk_size): end_i = min(i + chunk_size, x_seqlens) attn_chunk = visual_q[:, :, i:end_i] @ ref_k.permute(0, 2, 3, 1) # B, H, chunk, ref_seqlens # Apply softmax attn_max = attn_chunk.max(dim=-1, keepdim=True).values attn_chunk = (attn_chunk - attn_max).exp() attn_sum = attn_chunk.sum(dim=-1, keepdim=True) attn_chunk = attn_chunk / (attn_sum + 1e-8) # Apply mask and sum masked_attn = attn_chunk * ref_target_mask x_ref_attnmap[:, :, i:end_i] = masked_attn.sum(-1) / (ref_target_mask.sum() + 1e-8) del attn_chunk, masked_attn # Average across heads x_ref_attnmap = x_ref_attnmap.mean(dim=1) # B, x_seqlens x_ref_attn_maps.append(x_ref_attnmap) del visual_q, ref_k return torch.cat(x_ref_attn_maps, dim=0) def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, split_num=2): """Args: query (torch.tensor): B M H K key (torch.tensor): B M H K shape (tuple): (N_t, N_h, N_w) ref_target_masks: [B, N_h * N_w] """ N_t, N_h, N_w = shape x_seqlens = N_h * N_w ref_k = ref_k[:, :x_seqlens] _, seq_lens, heads, _ = visual_q.shape class_num, _ = ref_target_masks.shape x_ref_attn_maps = torch.zeros(class_num, seq_lens).to(visual_q) split_chunk = heads // split_num for i in range(split_num): x_ref_attn_maps_perhead = calculate_x_ref_attn_map( visual_q[:, :, i*split_chunk:(i+1)*split_chunk, :], ref_k[:, :, i*split_chunk:(i+1)*split_chunk, :], ref_target_masks ) x_ref_attn_maps += x_ref_attn_maps_perhead return x_ref_attn_maps / split_num def normalize_and_scale(column, source_range, target_range, epsilon=1e-8): source_min, source_max = source_range new_min, new_max = target_range normalized = (column - source_min) / (source_max - source_min + epsilon) scaled = normalized * (new_max - new_min) + new_min return scaled def rotate_half(x): x = rearrange(x, "... (d r) -> ... d r", r=2) x1, x2 = x.unbind(dim=-1) x = torch.stack((-x2, x1), dim=-1) return rearrange(x, "... d r -> ... (d r)") def get_audio_embeds(encoded_audio, audio_start, audio_end): audio_embs = [] human_num = len(encoded_audio) audio_frames = encoded_audio[0].shape[0] indices = (torch.arange(4 + 1) - 2) * 1 for human_idx in range(human_num): if audio_end > audio_frames: # in case of not enough audio for current window, pad with first audio frame as that's most likely silence pad_len = audio_end - audio_frames pad_shape = list(encoded_audio[human_idx].shape) pad_shape[0] = pad_len pad_tensor = encoded_audio[human_idx][:1].repeat(pad_len, *([1] * (encoded_audio[human_idx].dim() - 1))) encoded_audio_in = torch.cat([encoded_audio[human_idx], pad_tensor], dim=0) else: encoded_audio_in = encoded_audio[human_idx] center_indices = torch.arange(audio_start, audio_end, 1).unsqueeze(1) + indices.unsqueeze(0) center_indices = torch.clamp(center_indices, min=0, max=encoded_audio_in.shape[0] - 1) audio_emb = encoded_audio_in[center_indices].unsqueeze(0) audio_embs.append(audio_emb) return torch.cat(audio_embs, dim=0) def project_audio_features(audio_proj, encoded_audio, audio_start, audio_end): audio_embs = get_audio_embeds(encoded_audio, audio_start, audio_end) first_frame_audio_emb_s = audio_embs[:, :1, ...] latter_frame_audio_emb = audio_embs[:, 1:, ...] latter_frame_audio_emb = rearrange(latter_frame_audio_emb, "b (n_t n) w s c -> b n_t n w s c", n=4) middle_index = audio_proj.seq_len // 2 latter_first_frame_audio_emb = latter_frame_audio_emb[:, :, :1, :middle_index+1, ...] latter_first_frame_audio_emb = rearrange(latter_first_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c") latter_last_frame_audio_emb = latter_frame_audio_emb[:, :, -1:, middle_index:, ...] latter_last_frame_audio_emb = rearrange(latter_last_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c") latter_middle_frame_audio_emb = latter_frame_audio_emb[:, :, 1:-1, middle_index:middle_index+1, ...] latter_middle_frame_audio_emb = rearrange(latter_middle_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c") latter_frame_audio_emb_s = torch.cat([latter_first_frame_audio_emb, latter_middle_frame_audio_emb, latter_last_frame_audio_emb], dim=2) audio_emb = audio_proj(first_frame_audio_emb_s, latter_frame_audio_emb_s) audio_emb = torch.cat(audio_emb.split(1), dim=2) return audio_emb class RotaryPositionalEmbedding1D(torch.nn.Module): def __init__(self, head_dim, ): super().__init__() self.head_dim = head_dim self.base = 10000 def precompute_freqs_cis_1d(self, pos_indices): freqs = 1.0 / (self.base ** (torch.arange(0, self.head_dim, 2)[: (self.head_dim // 2)].float() / self.head_dim)) freqs = freqs.to(pos_indices.device) freqs = torch.einsum("..., f -> ... f", pos_indices.float(), freqs) freqs = repeat(freqs, "... n -> ... (n r)", r=2) return freqs def forward(self, x, pos_indices): freqs_cis = self.precompute_freqs_cis_1d(pos_indices) x_ = x.float() freqs_cis = freqs_cis.float().to(x.device) cos, sin = freqs_cis.cos(), freqs_cis.sin() cos, sin = rearrange(cos, 'n d -> 1 1 n d'), rearrange(sin, 'n d -> 1 1 n d') x_ = (x_ * cos) + (rotate_half(x_) * sin) return x_.type_as(x) class SingleStreamAttention(torch.nn.Module): def __init__( self, dim: int, encoder_hidden_states_dim: int, num_heads: int, qkv_bias: bool, device=None, dtype=None, operations=None ) -> None: super().__init__() self.dim = dim self.encoder_hidden_states_dim = encoder_hidden_states_dim self.num_heads = num_heads self.head_dim = dim // num_heads self.q_linear = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype) self.proj = operations.Linear(dim, dim, device=device, dtype=dtype) self.kv_linear = operations.Linear(encoder_hidden_states_dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype) def forward(self, x: torch.Tensor, encoder_hidden_states: torch.Tensor, shape=None) -> torch.Tensor: N_t, N_h, N_w = shape expected_tokens = N_t * N_h * N_w actual_tokens = x.shape[1] x_extra = None if actual_tokens != expected_tokens: x_extra = x[:, -N_h * N_w:, :] x = x[:, :-N_h * N_w, :] N_t = N_t - 1 B = x.shape[0] S = N_h * N_w x = x.view(B * N_t, S, self.dim) # get q for hidden_state q = self.q_linear(x).view(B * N_t, S, self.num_heads, self.head_dim) # get kv from encoder_hidden_states # shape: (B, N, num_heads, head_dim) kv = self.kv_linear(encoder_hidden_states) encoder_k, encoder_v = kv.view(B * N_t, encoder_hidden_states.shape[1], 2, self.num_heads, self.head_dim).unbind(2) #print("q.shape", q.shape) #torch.Size([21, 1024, 40, 128]) x = optimized_attention( q.transpose(1, 2), encoder_k.transpose(1, 2), encoder_v.transpose(1, 2), heads=self.num_heads, skip_reshape=True, skip_output_reshape=True).transpose(1, 2) # linear transform x = self.proj(x.reshape(B * N_t, S, self.dim)) x = x.view(B, N_t * S, self.dim) if x_extra is not None: x = torch.cat([x, torch.zeros_like(x_extra)], dim=1) return x class SingleStreamMultiAttention(SingleStreamAttention): def __init__( self, dim: int, encoder_hidden_states_dim: int, num_heads: int, qkv_bias: bool, class_range: int = 24, class_interval: int = 4, device=None, dtype=None, operations=None ) -> None: super().__init__( dim=dim, encoder_hidden_states_dim=encoder_hidden_states_dim, num_heads=num_heads, qkv_bias=qkv_bias, device=device, dtype=dtype, operations=operations ) # Rotary-embedding layout parameters self.class_interval = class_interval self.class_range = class_range self.max_humans = self.class_range // self.class_interval # Constant bucket used for background tokens self.rope_bak = int(self.class_range // 2) self.rope_1d = RotaryPositionalEmbedding1D(self.head_dim) def forward( self, x: torch.Tensor, encoder_hidden_states: torch.Tensor, shape=None, x_ref_attn_map=None ) -> torch.Tensor: encoder_hidden_states = encoder_hidden_states.squeeze(0).to(x.device) human_num = x_ref_attn_map.shape[0] if x_ref_attn_map is not None else 1 # Single-speaker fall-through if human_num <= 1: return super().forward(x, encoder_hidden_states, shape) N_t, N_h, N_w = shape x_extra = None if x.shape[0] * N_t != encoder_hidden_states.shape[0]: x_extra = x[:, -N_h * N_w:, :] x = x[:, :-N_h * N_w, :] N_t = N_t - 1 x = rearrange(x, "B (N_t S) C -> (B N_t) S C", N_t=N_t) # Query projection B, N, C = x.shape q = self.q_linear(x) q = q.view(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3) # Use `class_range` logic for 2 speakers rope_h1 = (0, self.class_interval) rope_h2 = (self.class_range - self.class_interval, self.class_range) rope_bak = int(self.class_range // 2) # Normalize and scale attention maps for each speaker max_values = x_ref_attn_map.max(1).values[:, None, None] min_values = x_ref_attn_map.min(1).values[:, None, None] max_min_values = torch.cat([max_values, min_values], dim=2) human1_max_value, human1_min_value = max_min_values[0, :, 0].max(), max_min_values[0, :, 1].min() human2_max_value, human2_min_value = max_min_values[1, :, 0].max(), max_min_values[1, :, 1].min() human1 = normalize_and_scale(x_ref_attn_map[0], (human1_min_value, human1_max_value), rope_h1) human2 = normalize_and_scale(x_ref_attn_map[1], (human2_min_value, human2_max_value), rope_h2) back = torch.full((x_ref_attn_map.size(1),), rope_bak, dtype=human1.dtype, device=human1.device) # Token-wise speaker dominance max_indices = x_ref_attn_map.argmax(dim=0) normalized_map = torch.stack([human1, human2, back], dim=1) normalized_pos = normalized_map[torch.arange(x_ref_attn_map.size(1)), max_indices] # Apply rotary to Q q = rearrange(q, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t) q = self.rope_1d(q, normalized_pos) q = rearrange(q, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t) # Keys / Values _, N_a, _ = encoder_hidden_states.shape encoder_kv = self.kv_linear(encoder_hidden_states) encoder_kv = encoder_kv.view(B, N_a, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) encoder_k, encoder_v = encoder_kv.unbind(0) # Rotary for keys – assign centre of each speaker bucket to its context tokens per_frame = torch.zeros(N_a, dtype=encoder_k.dtype, device=encoder_k.device) per_frame[: per_frame.size(0) // 2] = (rope_h1[0] + rope_h1[1]) / 2 per_frame[per_frame.size(0) // 2 :] = (rope_h2[0] + rope_h2[1]) / 2 encoder_pos = torch.cat([per_frame] * N_t, dim=0) encoder_k = rearrange(encoder_k, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t) encoder_k = self.rope_1d(encoder_k, encoder_pos) encoder_k = rearrange(encoder_k, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t) # Final attention q = rearrange(q, "B H M K -> B M H K") encoder_k = rearrange(encoder_k, "B H M K -> B M H K") encoder_v = rearrange(encoder_v, "B H M K -> B M H K") x = optimized_attention( q.transpose(1, 2), encoder_k.transpose(1, 2), encoder_v.transpose(1, 2), heads=self.num_heads, skip_reshape=True, skip_output_reshape=True).transpose(1, 2) # Linear projection x = x.reshape(B, N, C) x = self.proj(x) # Restore original layout x = rearrange(x, "(B N_t) S C -> B (N_t S) C", N_t=N_t) if x_extra is not None: x = torch.cat([x, torch.zeros_like(x_extra)], dim=1) return x class MultiTalkAudioProjModel(torch.nn.Module): def __init__( self, seq_len: int = 5, seq_len_vf: int = 12, blocks: int = 12, channels: int = 768, intermediate_dim: int = 512, out_dim: int = 768, context_tokens: int = 32, device=None, dtype=None, operations=None ): super().__init__() self.seq_len = seq_len self.blocks = blocks self.channels = channels self.input_dim = seq_len * blocks * channels self.input_dim_vf = seq_len_vf * blocks * channels self.intermediate_dim = intermediate_dim self.context_tokens = context_tokens self.out_dim = out_dim # define multiple linear layers self.proj1 = operations.Linear(self.input_dim, intermediate_dim, device=device, dtype=dtype) self.proj1_vf = operations.Linear(self.input_dim_vf, intermediate_dim, device=device, dtype=dtype) self.proj2 = operations.Linear(intermediate_dim, intermediate_dim, device=device, dtype=dtype) self.proj3 = operations.Linear(intermediate_dim, context_tokens * out_dim, device=device, dtype=dtype) self.norm = operations.LayerNorm(out_dim, device=device, dtype=dtype) def forward(self, audio_embeds, audio_embeds_vf): video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1] B, _, _, S, C = audio_embeds.shape # process audio of first frame audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c") batch_size, window_size, blocks, channels = audio_embeds.shape audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels) # process audio of latter frame audio_embeds_vf = rearrange(audio_embeds_vf, "bz f w b c -> (bz f) w b c") batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_vf) # first projection audio_embeds = torch.relu(self.proj1(audio_embeds)) audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf)) audio_embeds = rearrange(audio_embeds, "(bz f) c -> bz f c", bz=B) audio_embeds_vf = rearrange(audio_embeds_vf, "(bz f) c -> bz f c", bz=B) audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1) batch_size_c, N_t, C_a = audio_embeds_c.shape audio_embeds_c = audio_embeds_c.view(batch_size_c*N_t, C_a) # second projection audio_embeds_c = torch.relu(self.proj2(audio_embeds_c)) context_tokens = self.proj3(audio_embeds_c).reshape(batch_size_c*N_t, self.context_tokens, self.out_dim) # normalization and reshape context_tokens = self.norm(context_tokens) context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length) return context_tokens class WanMultiTalkAttentionBlock(torch.nn.Module): def __init__(self, in_dim=5120, out_dim=768, device=None, dtype=None, operations=None): super().__init__() self.audio_cross_attn = SingleStreamMultiAttention(in_dim, out_dim, num_heads=40, qkv_bias=True, device=device, dtype=dtype, operations=operations) self.norm_x = operations.LayerNorm(in_dim, device=device, dtype=dtype, elementwise_affine=True) class MultiTalkGetAttnMapPatch: def __init__(self, ref_target_masks=None): self.ref_target_masks = ref_target_masks def __call__(self, kwargs): transformer_options = kwargs.get("transformer_options", {}) x = kwargs["x"] if self.ref_target_masks is not None: x_ref_attn_map = get_attn_map_with_target(kwargs["q"], kwargs["k"], transformer_options["grid_sizes"], ref_target_masks=self.ref_target_masks.to(x.device)) transformer_options["x_ref_attn_map"] = x_ref_attn_map return x class MultiTalkCrossAttnPatch: def __init__(self, model_patch, audio_scale=1.0, ref_target_masks=None): self.model_patch = model_patch self.audio_scale = audio_scale self.ref_target_masks = ref_target_masks def __call__(self, kwargs): transformer_options = kwargs.get("transformer_options", {}) block_idx = transformer_options.get("block_index", None) x = kwargs["x"] if block_idx is None: return torch.zeros_like(x) audio_embeds = transformer_options.get("audio_embeds") x_ref_attn_map = transformer_options.pop("x_ref_attn_map", None) norm_x = self.model_patch.model.blocks[block_idx].norm_x(x) x_audio = self.model_patch.model.blocks[block_idx].audio_cross_attn( norm_x, audio_embeds.to(x.dtype), shape=transformer_options["grid_sizes"], x_ref_attn_map=x_ref_attn_map ) x = x + x_audio * self.audio_scale return x def models(self): return [self.model_patch] class MultiTalkApplyModelWrapper: def __init__(self, init_latents): self.init_latents = init_latents def __call__(self, executor, x, *args, **kwargs): x[:, :, :self.init_latents.shape[2]] = self.init_latents.to(x) samples = executor(x, *args, **kwargs) return samples class InfiniteTalkOuterSampleWrapper: def __init__(self, motion_frames_latent, model_patch, is_extend=False): self.motion_frames_latent = motion_frames_latent self.model_patch = model_patch self.is_extend = is_extend def __call__(self, executor, *args, **kwargs): model_patcher = executor.class_obj.model_patcher model_options = executor.class_obj.model_options process_latent_in = model_patcher.model.process_latent_in # for InfiniteTalk, model input first latent(s) need to always be replaced on every step if self.motion_frames_latent is not None: wrappers = model_options["transformer_options"]["wrappers"] w = wrappers.setdefault(comfy.patcher_extension.WrappersMP.APPLY_MODEL, {}) w["MultiTalk_apply_model"] = [MultiTalkApplyModelWrapper(process_latent_in(self.motion_frames_latent))] # run the sampling process result = executor(*args, **kwargs) # insert motion frames before decoding if self.is_extend: overlap = self.motion_frames_latent.shape[2] result = torch.cat([self.motion_frames_latent.to(result), result[:, :, overlap:]], dim=2) return result def to(self, device_or_dtype): if isinstance(device_or_dtype, torch.device): if self.motion_frames_latent is not None: self.motion_frames_latent = self.motion_frames_latent.to(device_or_dtype) return self ================================================ FILE: comfy/ldm/wan/vae.py ================================================ # original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/vae.py # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from comfy.ldm.modules.diffusionmodules.model import vae_attention, torch_cat_if_needed import comfy.ops ops = comfy.ops.disable_weight_init CACHE_T = 2 class CausalConv3d(ops.Conv3d): """ Causal 3d convolusion. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._padding = 2 * self.padding[0] self.padding = (0, self.padding[1], self.padding[2]) def forward(self, x, cache_x=None, cache_list=None, cache_idx=None): if cache_list is not None: cache_x = cache_list[cache_idx] cache_list[cache_idx] = None if cache_x is None and x.shape[2] == 1: #Fast path - the op will pad for use by truncating the weight #and save math on a pile of zeros. return super().forward(x, autopad="causal_zero") if self._padding > 0: padding_needed = self._padding if cache_x is not None: cache_x = cache_x.to(x.device) padding_needed = max(0, padding_needed - cache_x.shape[2]) padding_shape = list(x.shape) padding_shape[2] = padding_needed padding = torch.zeros(padding_shape, device=x.device, dtype=x.dtype) x = torch_cat_if_needed([padding, cache_x, x], dim=2) del cache_x return super().forward(x) class RMS_norm(nn.Module): def __init__(self, dim, channel_first=True, images=True, bias=False): super().__init__() broadcastable_dims = (1, 1, 1) if not images else (1, 1) shape = (dim, *broadcastable_dims) if channel_first else (dim,) self.channel_first = channel_first self.scale = dim**0.5 self.gamma = nn.Parameter(torch.ones(shape)) self.bias = nn.Parameter(torch.zeros(shape)) if bias else None def forward(self, x): return F.normalize( x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0) class Resample(nn.Module): def __init__(self, dim, mode): assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', 'downsample3d') super().__init__() self.dim = dim self.mode = mode # layers if mode == 'upsample2d': self.resample = nn.Sequential( nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), ops.Conv2d(dim, dim // 2, 3, padding=1)) elif mode == 'upsample3d': self.resample = nn.Sequential( nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), ops.Conv2d(dim, dim // 2, 3, padding=1)) self.time_conv = CausalConv3d( dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) elif mode == 'downsample2d': self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), ops.Conv2d(dim, dim, 3, stride=(2, 2))) elif mode == 'downsample3d': self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), ops.Conv2d(dim, dim, 3, stride=(2, 2))) self.time_conv = CausalConv3d( dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) else: self.resample = nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0], final=False): b, c, t, h, w = x.size() if self.mode == 'upsample3d': if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = 'Rep' feat_idx[0] += 1 else: cache_x = x[:, :, -CACHE_T:, :, :] if feat_cache[idx] == 'Rep': x = self.time_conv(x) else: x = self.time_conv(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 x = x.reshape(b, 2, c, t, h, w) x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3) x = x.reshape(b, c, t * 2, h, w) t = x.shape[2] x = rearrange(x, 'b c t h w -> (b t) c h w') x = self.resample(x) x = rearrange(x, '(b t) c h w -> b c t h w', t=t) if self.mode == 'downsample3d': if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = x else: cache_x = x[:, :, -1:, :, :] x = self.time_conv( torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) feat_cache[idx] = cache_x deferred_x = feat_cache[idx + 1] if deferred_x is not None: x = torch.cat([deferred_x, x], 2) feat_cache[idx + 1] = None if x.shape[2] == 1 and not final: feat_cache[idx + 1] = x x = None feat_idx[0] += 2 return x class ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout=0.0): super().__init__() self.in_dim = in_dim self.out_dim = out_dim # layers self.residual = nn.Sequential( RMS_norm(in_dim, images=False), nn.SiLU(), CausalConv3d(in_dim, out_dim, 3, padding=1), RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), CausalConv3d(out_dim, out_dim, 3, padding=1)) self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ if in_dim != out_dim else nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0], final=False): old_x = x for layer in self.residual: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :] x = layer(x, cache_list=feat_cache, cache_idx=idx) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x + self.shortcut(old_x) class AttentionBlock(nn.Module): """ Causal self-attention with a single head. """ def __init__(self, dim): super().__init__() self.dim = dim # layers self.norm = RMS_norm(dim) self.to_qkv = ops.Conv2d(dim, dim * 3, 1) self.proj = ops.Conv2d(dim, dim, 1) self.optimized_attention = vae_attention() def forward(self, x, feat_cache=None, feat_idx=[0], final=False): identity = x b, c, t, h, w = x.size() x = rearrange(x, 'b c t h w -> (b t) c h w') x = self.norm(x) # compute query, key, value q, k, v = self.to_qkv(x).chunk(3, dim=1) x = self.optimized_attention(q, k, v) # output x = self.proj(x) x = rearrange(x, '(b t) c h w-> b c t h w', t=t) return x + identity class Encoder3d(nn.Module): def __init__(self, dim=128, z_dim=4, input_channels=3, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample # dimensions dims = [dim * u for u in [1] + dim_mult] scale = 1.0 # init block self.conv1 = CausalConv3d(input_channels, dims[0], 3, padding=1) # downsample blocks downsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): # residual (+attention) blocks for _ in range(num_res_blocks): downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) if scale in attn_scales: downsamples.append(AttentionBlock(out_dim)) in_dim = out_dim # downsample block if i != len(dim_mult) - 1: mode = 'downsample3d' if temperal_downsample[ i] else 'downsample2d' downsamples.append(Resample(out_dim, mode=mode)) scale /= 2.0 self.downsamples = nn.Sequential(*downsamples) # middle blocks self.middle = nn.Sequential( ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), ResidualBlock(out_dim, out_dim, dropout)) # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, z_dim, 3, padding=1)) def forward(self, x, feat_cache=None, feat_idx=[0], final=False): if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :] x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## downsamples for layer in self.downsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx, final=final) if x is None: return None else: x = layer(x) ## middle for layer in self.middle: if feat_cache is not None: x = layer(x, feat_cache, feat_idx, final=final) else: x = layer(x) ## head for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :] x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x class Decoder3d(nn.Module): def __init__(self, dim=128, z_dim=4, output_channels=3, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_upsample = temperal_upsample # dimensions dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] scale = 1.0 / 2**(len(dim_mult) - 2) # init block self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) # middle blocks self.middle = nn.Sequential( ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), ResidualBlock(dims[0], dims[0], dropout)) # upsample blocks upsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): # residual (+attention) blocks if i == 1 or i == 2 or i == 3: in_dim = in_dim // 2 for _ in range(num_res_blocks + 1): upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) if scale in attn_scales: upsamples.append(AttentionBlock(out_dim)) in_dim = out_dim # upsample block if i != len(dim_mult) - 1: mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' upsamples.append(Resample(out_dim, mode=mode)) scale *= 2.0 self.upsamples = nn.Sequential(*upsamples) # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, output_channels, 3, padding=1)) def run_up(self, layer_idx, x_ref, feat_cache, feat_idx, out_chunks): x = x_ref[0] x_ref[0] = None if layer_idx >= len(self.upsamples): for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: cache_x = x[:, :, -CACHE_T:, :, :] x = layer(x, feat_cache[feat_idx[0]]) feat_cache[feat_idx[0]] = cache_x feat_idx[0] += 1 else: x = layer(x) out_chunks.append(x) return layer = self.upsamples[layer_idx] if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1: for frame_idx in range(x.shape[2]): self.run_up( layer_idx, [x[:, :, frame_idx:frame_idx + 1, :, :]], feat_cache, feat_idx.copy(), out_chunks, ) del x return if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) next_x_ref = [x] del x self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks) def forward(self, x, feat_cache=None, feat_idx=[0]): ## conv1 if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :] x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## middle for layer in self.middle: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) out_chunks = [] self.run_up(0, [x], feat_cache, feat_idx, out_chunks) return out_chunks def count_cache_layers(model): count = 0 for m in model.modules(): if isinstance(m, CausalConv3d) or (isinstance(m, Resample) and m.mode == 'downsample3d'): count += 1 return count class WanVAE(nn.Module): def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], image_channels=3, conv_out_channels=3, dropout=0.0): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample self.temperal_upsample = temperal_downsample[::-1] # modules self.encoder = Encoder3d(dim, z_dim * 2, image_channels, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout) self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) self.conv2 = CausalConv3d(z_dim, z_dim, 1) self.decoder = Decoder3d(dim, z_dim, conv_out_channels, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout) def encode(self, x): conv_idx = [0] ## cache t = x.shape[2] t = 1 + ((t - 1) // 4) * 4 iter_ = 1 + (t - 1) // 2 feat_map = None if iter_ > 1: feat_map = [None] * count_cache_layers(self.encoder) ## 对encode输入的x，按时间拆分为1、2、2、2....(总帧数先按4N+1向下取整) for i in range(iter_): conv_idx = [0] if i == 0: out = self.encoder( x[:, :, :1, :, :], feat_cache=feat_map, feat_idx=conv_idx) else: out_ = self.encoder( x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :], feat_cache=feat_map, feat_idx=conv_idx, final=(i == (iter_ - 1))) if out_ is None: continue out = torch.cat([out, out_], 2) mu, log_var = self.conv1(out).chunk(2, dim=1) return mu def decode(self, z): # z: [b,c,t,h,w] iter_ = 1 + z.shape[2] // 2 feat_map = None if iter_ > 1: feat_map = [None] * count_cache_layers(self.decoder) x = self.conv2(z) for i in range(iter_): conv_idx = [0] if i == 0: out = self.decoder( x[:, :, i:i + 1, :, :], feat_cache=feat_map, feat_idx=conv_idx) else: out_ = self.decoder( x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :], feat_cache=feat_map, feat_idx=conv_idx) out += out_ return torch.cat(out, 2) ================================================ FILE: comfy/ldm/wan/vae2_2.py ================================================ # original version: https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/vae2_2.py # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from .vae import AttentionBlock, CausalConv3d, RMS_norm import comfy.ops ops = comfy.ops.disable_weight_init CACHE_T = 2 class Resample(nn.Module): def __init__(self, dim, mode): assert mode in ( "none", "upsample2d", "upsample3d", "downsample2d", "downsample3d", ) super().__init__() self.dim = dim self.mode = mode # layers if mode == "upsample2d": self.resample = nn.Sequential( nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), ops.Conv2d(dim, dim, 3, padding=1), ) elif mode == "upsample3d": self.resample = nn.Sequential( nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), ops.Conv2d(dim, dim, 3, padding=1), # ops.Conv2d(dim, dim//2, 3, padding=1) ) self.time_conv = CausalConv3d( dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) elif mode == "downsample2d": self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), ops.Conv2d(dim, dim, 3, stride=(2, 2))) elif mode == "downsample3d": self.resample = nn.Sequential( nn.ZeroPad2d((0, 1, 0, 1)), ops.Conv2d(dim, dim, 3, stride=(2, 2))) self.time_conv = CausalConv3d( dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) else: self.resample = nn.Identity() def forward(self, x, feat_cache=None, feat_idx=[0]): b, c, t, h, w = x.size() if self.mode == "upsample3d": if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = "Rep" feat_idx[0] += 1 else: cache_x = x[:, :, -CACHE_T:, :, :].clone() if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep"): # cache last frame of last two chunk cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep"): cache_x = torch.cat( [ torch.zeros_like(cache_x).to(cache_x.device), cache_x ], dim=2, ) if feat_cache[idx] == "Rep": x = self.time_conv(x) else: x = self.time_conv(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 x = x.reshape(b, 2, c, t, h, w) x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3) x = x.reshape(b, c, t * 2, h, w) t = x.shape[2] x = rearrange(x, "b c t h w -> (b t) c h w") x = self.resample(x) x = rearrange(x, "(b t) c h w -> b c t h w", t=t) if self.mode == "downsample3d": if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: feat_cache[idx] = x.clone() feat_idx[0] += 1 else: cache_x = x[:, :, -1:, :, :].clone() x = self.time_conv( torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) feat_cache[idx] = cache_x feat_idx[0] += 1 return x class ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout=0.0): super().__init__() self.in_dim = in_dim self.out_dim = out_dim # layers self.residual = nn.Sequential( RMS_norm(in_dim, images=False), nn.SiLU(), CausalConv3d(in_dim, out_dim, 3, padding=1), RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), CausalConv3d(out_dim, out_dim, 3, padding=1), ) self.shortcut = ( CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()) def forward(self, x, feat_cache=None, feat_idx=[0]): old_x = x for layer in self.residual: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: # cache last frame of last two chunk cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = layer(x, cache_list=feat_cache, cache_idx=idx) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x + self.shortcut(old_x) def patchify(x, patch_size): if patch_size == 1: return x if x.dim() == 4: x = rearrange( x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size) elif x.dim() == 5: x = rearrange( x, "b c f (h q) (w r) -> b (c r q) f h w", q=patch_size, r=patch_size, ) else: raise ValueError(f"Invalid input shape: {x.shape}") return x def unpatchify(x, patch_size): if patch_size == 1: return x if x.dim() == 4: x = rearrange( x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size) elif x.dim() == 5: x = rearrange( x, "b (c r q) f h w -> b c f (h q) (w r)", q=patch_size, r=patch_size, ) return x class AvgDown3D(nn.Module): def __init__( self, in_channels, out_channels, factor_t, factor_s=1, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.factor_t = factor_t self.factor_s = factor_s self.factor = self.factor_t * self.factor_s * self.factor_s assert in_channels * self.factor % out_channels == 0 self.group_size = in_channels * self.factor // out_channels def forward(self, x: torch.Tensor) -> torch.Tensor: pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t pad = (0, 0, 0, 0, pad_t, 0) x = F.pad(x, pad) B, C, T, H, W = x.shape x = x.view( B, C, T // self.factor_t, self.factor_t, H // self.factor_s, self.factor_s, W // self.factor_s, self.factor_s, ) x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() x = x.view( B, C * self.factor, T // self.factor_t, H // self.factor_s, W // self.factor_s, ) x = x.view( B, self.out_channels, self.group_size, T // self.factor_t, H // self.factor_s, W // self.factor_s, ) x = x.mean(dim=2) return x class DupUp3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, factor_t, factor_s=1, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.factor_t = factor_t self.factor_s = factor_s self.factor = self.factor_t * self.factor_s * self.factor_s assert out_channels * self.factor % in_channels == 0 self.repeats = out_channels * self.factor // in_channels def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor: x = x.repeat_interleave(self.repeats, dim=1) x = x.view( x.size(0), self.out_channels, self.factor_t, self.factor_s, self.factor_s, x.size(2), x.size(3), x.size(4), ) x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() x = x.view( x.size(0), self.out_channels, x.size(2) * self.factor_t, x.size(4) * self.factor_s, x.size(6) * self.factor_s, ) if first_chunk: x = x[:, :, self.factor_t - 1:, :, :] return x class Down_ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout, mult, temperal_downsample=False, down_flag=False): super().__init__() # Shortcut path with downsample self.avg_shortcut = AvgDown3D( in_dim, out_dim, factor_t=2 if temperal_downsample else 1, factor_s=2 if down_flag else 1, ) # Main path with residual blocks and downsample downsamples = [] for _ in range(mult): downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) in_dim = out_dim # Add the final downsample block if down_flag: mode = "downsample3d" if temperal_downsample else "downsample2d" downsamples.append(Resample(out_dim, mode=mode)) self.downsamples = nn.Sequential(*downsamples) def forward(self, x, feat_cache=None, feat_idx=[0]): x_copy = x for module in self.downsamples: x = module(x, feat_cache, feat_idx) return x + self.avg_shortcut(x_copy) class Up_ResidualBlock(nn.Module): def __init__(self, in_dim, out_dim, dropout, mult, temperal_upsample=False, up_flag=False): super().__init__() # Shortcut path with upsample if up_flag: self.avg_shortcut = DupUp3D( in_dim, out_dim, factor_t=2 if temperal_upsample else 1, factor_s=2 if up_flag else 1, ) else: self.avg_shortcut = None # Main path with residual blocks and upsample upsamples = [] for _ in range(mult): upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) in_dim = out_dim # Add the final upsample block if up_flag: mode = "upsample3d" if temperal_upsample else "upsample2d" upsamples.append(Resample(out_dim, mode=mode)) self.upsamples = nn.Sequential(*upsamples) def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): x_main = x for module in self.upsamples: x_main = module(x_main, feat_cache, feat_idx) if self.avg_shortcut is not None: x_shortcut = self.avg_shortcut(x, first_chunk) return x_main + x_shortcut else: return x_main class Encoder3d(nn.Module): def __init__( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, ): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample # dimensions dims = [dim * u for u in [1] + dim_mult] scale = 1.0 # init block self.conv1 = CausalConv3d(12, dims[0], 3, padding=1) # downsample blocks downsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): t_down_flag = ( temperal_downsample[i] if i < len(temperal_downsample) else False) downsamples.append( Down_ResidualBlock( in_dim=in_dim, out_dim=out_dim, dropout=dropout, mult=num_res_blocks, temperal_downsample=t_down_flag, down_flag=i != len(dim_mult) - 1, )) scale /= 2.0 self.downsamples = nn.Sequential(*downsamples) # middle blocks self.middle = nn.Sequential( ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), ResidualBlock(out_dim, out_dim, dropout), ) # # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, z_dim, 3, padding=1), ) def forward(self, x, feat_cache=None, feat_idx=[0]): if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) ## downsamples for layer in self.downsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## middle for layer in self.middle: if isinstance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## head for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x class Decoder3d(nn.Module): def __init__( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0, ): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_upsample = temperal_upsample # dimensions dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] # init block self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) # middle blocks self.middle = nn.Sequential( ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), ResidualBlock(dims[0], dims[0], dropout), ) # upsample blocks upsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): t_up_flag = temperal_upsample[i] if i < len( temperal_upsample) else False upsamples.append( Up_ResidualBlock( in_dim=in_dim, out_dim=out_dim, dropout=dropout, mult=num_res_blocks + 1, temperal_upsample=t_up_flag, up_flag=i != len(dim_mult) - 1, )) self.upsamples = nn.Sequential(*upsamples) # output blocks self.head = nn.Sequential( RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, 12, 3, padding=1), ) def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): if feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = self.conv1(x) for layer in self.middle: if isinstance(layer, ResidualBlock) and feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) ## upsamples for layer in self.upsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx, first_chunk) else: x = layer(x) ## head for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:, :, :].clone() if cache_x.shape[2] < 2 and feat_cache[idx] is not None: cache_x = torch.cat( [ feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( cache_x.device), cache_x, ], dim=2, ) x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: x = layer(x) return x def count_conv3d(model): count = 0 for m in model.modules(): if isinstance(m, CausalConv3d): count += 1 return count class WanVAE(nn.Module): def __init__( self, dim=160, dec_dim=256, z_dim=16, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, ): super().__init__() self.dim = dim self.z_dim = z_dim self.dim_mult = dim_mult self.num_res_blocks = num_res_blocks self.attn_scales = attn_scales self.temperal_downsample = temperal_downsample self.temperal_upsample = temperal_downsample[::-1] # modules self.encoder = Encoder3d( dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout, ) self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) self.conv2 = CausalConv3d(z_dim, z_dim, 1) self.decoder = Decoder3d( dec_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout, ) def encode(self, x): conv_idx = [0] feat_map = [None] * count_conv3d(self.encoder) x = patchify(x, patch_size=2) t = x.shape[2] iter_ = 1 + (t - 1) // 4 for i in range(iter_): conv_idx = [0] if i == 0: out = self.encoder( x[:, :, :1, :, :], feat_cache=feat_map, feat_idx=conv_idx, ) else: out_ = self.encoder( x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], feat_cache=feat_map, feat_idx=conv_idx, ) out = torch.cat([out, out_], 2) mu, log_var = self.conv1(out).chunk(2, dim=1) return mu def decode(self, z): conv_idx = [0] feat_map = [None] * count_conv3d(self.decoder) iter_ = z.shape[2] x = self.conv2(z) for i in range(iter_): conv_idx = [0] if i == 0: out = self.decoder( x[:, :, i:i + 1, :, :], feat_cache=feat_map, feat_idx=conv_idx, first_chunk=True, ) else: out_ = self.decoder( x[:, :, i:i + 1, :, :], feat_cache=feat_map, feat_idx=conv_idx, ) out = torch.cat([out, out_], 2) out = unpatchify(out, patch_size=2) return out def reparameterize(self, mu, log_var): std = torch.exp(0.5 * log_var) eps = torch.randn_like(std) return eps * std + mu def sample(self, imgs, deterministic=False): mu, log_var = self.encode(imgs) if deterministic: return mu std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) return mu + std * torch.randn_like(std) ================================================ FILE: comfy/lora.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Comfy This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ from __future__ import annotations import comfy.utils import comfy.model_management import comfy.model_base import comfy.weight_adapter as weight_adapter import logging import torch LORA_CLIP_MAP = { "mlp.fc1": "mlp_fc1", "mlp.fc2": "mlp_fc2", "self_attn.k_proj": "self_attn_k_proj", "self_attn.q_proj": "self_attn_q_proj", "self_attn.v_proj": "self_attn_v_proj", "self_attn.out_proj": "self_attn_out_proj", } def load_lora(lora, to_load, log_missing=True): patch_dict = {} loaded_keys = set() for x in to_load: alpha_name = "{}.alpha".format(x) alpha = None if alpha_name in lora.keys(): alpha = lora[alpha_name].item() loaded_keys.add(alpha_name) dora_scale_name = "{}.dora_scale".format(x) dora_scale = None if dora_scale_name in lora.keys(): dora_scale = lora[dora_scale_name] loaded_keys.add(dora_scale_name) for adapter_cls in weight_adapter.adapters: adapter = adapter_cls.load(x, lora, alpha, dora_scale, loaded_keys) if adapter is not None: patch_dict[to_load[x]] = adapter loaded_keys.update(adapter.loaded_keys) continue w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) w_norm = lora.get(w_norm_name, None) b_norm = lora.get(b_norm_name, None) if w_norm is not None: loaded_keys.add(w_norm_name) patch_dict[to_load[x]] = ("diff", (w_norm,)) if b_norm is not None: loaded_keys.add(b_norm_name) patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) diff_name = "{}.diff".format(x) diff_weight = lora.get(diff_name, None) if diff_weight is not None: patch_dict[to_load[x]] = ("diff", (diff_weight,)) loaded_keys.add(diff_name) diff_bias_name = "{}.diff_b".format(x) diff_bias = lora.get(diff_bias_name, None) if diff_bias is not None: patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) loaded_keys.add(diff_bias_name) set_weight_name = "{}.set_weight".format(x) set_weight = lora.get(set_weight_name, None) if set_weight is not None: patch_dict[to_load[x]] = ("set", (set_weight,)) loaded_keys.add(set_weight_name) if log_missing: for x in lora.keys(): if x not in loaded_keys: logging.warning("lora key not loaded: {}".format(x)) return patch_dict def model_lora_keys_clip(model, key_map={}): sdk = model.state_dict().keys() for k in sdk: if k.endswith(".weight"): key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names tp = k.find(".transformer.") #also map without wrapper prefix for composite text encoder models if tp > 0 and not k.startswith("clip_"): key_map["text_encoders.{}".format(k[tp + 1:-len(".weight")])] = k text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" clip_l_present = False clip_g_present = False for b in range(32): #TODO: clean up for c in LORA_CLIP_MAP: k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base key_map[lora_key] = k clip_l_present = True lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: clip_g_present = True if clip_l_present: lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base key_map[lora_key] = k lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k else: lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner key_map[lora_key] = k lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config key_map[lora_key] = k for k in sdk: if k.endswith(".weight"): if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora l_key = k[len("t5xxl.transformer."):-len(".weight")] t5_index = 1 if clip_g_present: t5_index += 1 if clip_l_present: t5_index += 1 if t5_index == 2: key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux t5_index += 1 key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")] lora_key = "lora_te1_{}".format(l_key.replace(".", "_")) key_map[lora_key] = k k = "clip_g.transformer.text_projection.weight" if k in sdk: key_map["lora_prior_te_text_projection"] = k #cascade lora? # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora k = "clip_l.transformer.text_projection.weight" if k in sdk: key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning return key_map def model_lora_keys_unet(model, key_map={}): sd = model.state_dict() sdk = sd.keys() for k in sdk: if k.startswith("diffusion_model."): if k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = k key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names else: key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) for k in diffusers_keys: if k.endswith(".weight"): unet_key = "diffusion_model.{}".format(diffusers_keys[k]) key_lora = k[:-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = unet_key key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format diffusers_lora_prefix = ["", "unet."] for p in diffusers_lora_prefix: diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) if diffusers_lora_key.endswith(".to_out.0"): diffusers_lora_key = diffusers_lora_key[:-2] key_map[diffusers_lora_key] = unet_key if isinstance(model, comfy.model_base.StableCascade_C): for k in sdk: if k.startswith("diffusion_model."): if k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_prior_unet_{}".format(key_lora)] = k if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): to = diffusers_keys[k] key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format key_map[key_lora] = to key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others? key_map[key_lora] = to key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora key_map[key_lora] = to key_lora = "lycoris_{}".format(k[:-len(".weight")].replace(".", "_")) #simpletuner lycoris format key_map[key_lora] = to if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): to = diffusers_keys[k] key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format key_map[key_lora] = to if isinstance(model, comfy.model_base.PixArt): diffusers_keys = comfy.utils.pixart_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): to = diffusers_keys[k] key_lora = "transformer.{}".format(k[:-len(".weight")]) #default format key_map[key_lora] = to key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #diffusers training script key_map[key_lora] = to key_lora = "unet.base_model.model.{}".format(k[:-len(".weight")]) #old reference peft script key_map[key_lora] = to if isinstance(model, comfy.model_base.HunyuanDiT): for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")] key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): to = diffusers_keys[k] key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer key_map[k[:-len(".weight")]] = to #DiffSynth lora format for k in sdk: hidden_size = model.model_config.unet_config.get("hidden_size", 0) if k.endswith(".weight") and ".linear1." in k: key_map["{}".format(k.replace(".linear1.weight", ".linear1_qkv"))] = (k, (0, 0, hidden_size * 3)) if isinstance(model, comfy.model_base.GenmoMochi): for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official Mochi lora format key_lora = k[len("diffusion_model."):-len(".weight")] key_map["{}".format(key_lora)] = k if isinstance(model, comfy.model_base.HunyuanVideo): for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): # diffusion-pipe lora format key_lora = k key_lora = key_lora.replace("_mod.lin.", "_mod.linear.").replace("_attn.qkv.", "_attn_qkv.").replace("_attn.proj.", "_attn_proj.") key_lora = key_lora.replace("mlp.0.", "mlp.fc1.").replace("mlp.2.", "mlp.fc2.") key_lora = key_lora.replace(".modulation.lin.", ".modulation.linear.") key_lora = key_lora[len("diffusion_model."):-len(".weight")] key_map["transformer.{}".format(key_lora)] = k key_map["diffusion_model.{}".format(key_lora)] = k # Old loras if isinstance(model, comfy.model_base.HiDream): for k in sdk: if k.startswith("diffusion_model."): if k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")] key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format if isinstance(model, comfy.model_base.ACEStep): for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official ACE step lora format key_lora = k[len("diffusion_model."):-len(".weight")] key_map["{}".format(key_lora)] = k if isinstance(model, comfy.model_base.Omnigen2): for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")] key_map["{}".format(key_lora)] = k if isinstance(model, comfy.model_base.QwenImage): for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): #QwenImage lora format key_lora = k[len("diffusion_model."):-len(".weight")] # Direct mapping for transformer_blocks format (QwenImage LoRA format) key_map["{}".format(key_lora)] = k # Support transformer prefix format key_map["transformer.{}".format(key_lora)] = k key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format if isinstance(model, comfy.model_base.Lumina2): diffusers_keys = comfy.utils.z_image_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): to = diffusers_keys[k] key_lora = k[:-len(".weight")] key_map["diffusion_model.{}".format(key_lora)] = to key_map["transformer.{}".format(key_lora)] = to key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to key_map[key_lora] = to if isinstance(model, comfy.model_base.Kandinsky5): for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")] key_map["{}".format(key_lora)] = k key_map["transformer.{}".format(key_lora)] = k if isinstance(model, comfy.model_base.ACEStep15): for k in sdk: if k.startswith("diffusion_model.decoder.") and k.endswith(".weight"): key_lora = k[len("diffusion_model.decoder."):-len(".weight")] key_map["base_model.model.{}".format(key_lora)] = k # Official base model loras key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k # LyCORIS/LoKR format return key_map def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: """ Pad a tensor to a new shape with zeros. Args: tensor (torch.Tensor): The original tensor to be padded. new_shape (List[int]): The desired shape of the padded tensor. Returns: torch.Tensor: A new tensor padded with zeros to the specified shape. Note: If the new shape is smaller than the original tensor in any dimension, the original tensor will be truncated in that dimension. """ if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): raise ValueError("The new shape must be larger than the original tensor in all dimensions") if len(new_shape) != len(tensor.shape): raise ValueError("The new shape must have the same number of dimensions as the original tensor") # Create a new tensor filled with zeros padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) # Create slicing tuples for both tensors orig_slices = tuple(slice(0, dim) for dim in tensor.shape) new_slices = tuple(slice(0, dim) for dim in tensor.shape) # Copy the original tensor into the new tensor padded_tensor[new_slices] = tensor[orig_slices] return padded_tensor def calculate_shape(patches, weight, key, original_weights=None): current_shape = weight.shape for p in patches: v = p[1] offset = p[3] # Offsets restore the old shape; lists force a diff without metadata if offset is not None or isinstance(v, list): continue if isinstance(v, weight_adapter.WeightAdapterBase): adapter_shape = v.calculate_shape(key) if adapter_shape is not None: current_shape = adapter_shape continue # Standard diff logic with padding if len(v) == 2: patch_type, patch_data = v[0], v[1] if patch_type == "diff" and len(patch_data) > 1 and patch_data[1]['pad_weight']: current_shape = patch_data[0].shape return current_shape def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, original_weights=None): for p in patches: strength = p[0] v = p[1] strength_model = p[2] offset = p[3] function = p[4] if function is None: function = lambda a: a old_weight = None if offset is not None: old_weight = weight weight = weight.narrow(offset[0], offset[1], offset[2]) if strength_model != 1.0: weight *= strength_model if isinstance(v, list): v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), ) if isinstance(v, weight_adapter.WeightAdapterBase): output = v.calculate_weight(weight, key, strength, strength_model, offset, function, intermediate_dtype, original_weights) if output is None: logging.warning("Calculate Weight Failed: {} {}".format(v.name, key)) else: weight = output if old_weight is not None: weight = old_weight continue if len(v) == 1: patch_type = "diff" elif len(v) == 2: patch_type = v[0] v = v[1] if patch_type == "diff": diff: torch.Tensor = v[0] # An extra flag to pad the weight if the diff's shape is larger than the weight do_pad_weight = len(v) > 1 and v[1]['pad_weight'] if do_pad_weight and diff.shape != weight.shape: logging.info("Pad weight {} from {} to shape: {}".format(key, weight.shape, diff.shape)) weight = pad_tensor_to_shape(weight, diff.shape) if strength != 0.0: if diff.shape != weight.shape: logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape)) else: weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype)) elif patch_type == "set": weight.copy_(v[0]) elif patch_type == "model_as_lora": target_weight: torch.Tensor = v[0] diff_weight = comfy.model_management.cast_to_device(target_weight, weight.device, intermediate_dtype) - \ comfy.model_management.cast_to_device(original_weights[key][0][0], weight.device, intermediate_dtype) weight += function(strength * comfy.model_management.cast_to_device(diff_weight, weight.device, weight.dtype)) else: logging.warning("patch type not recognized {} {}".format(patch_type, key)) if old_weight is not None: weight = old_weight return weight ================================================ FILE: comfy/lora_convert.py ================================================ import torch import comfy.utils def convert_lora_bfl_control(sd): #BFL loras for Flux sd_out = {} for k in sd: k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.set_weight")) sd_out[k_to] = sd[k] sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]]) return sd_out def convert_lora_wan_fun(sd): #Wan Fun loras return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"}) def convert_uso_lora(sd): sd_out = {} for k in sd: tensor = sd[k] k_to = "diffusion_model.{}".format(k.replace(".down.weight", ".lora_down.weight") .replace(".up.weight", ".lora_up.weight") .replace(".qkv_lora2.", ".txt_attn.qkv.") .replace(".qkv_lora1.", ".img_attn.qkv.") .replace(".proj_lora1.", ".img_attn.proj.") .replace(".proj_lora2.", ".txt_attn.proj.") .replace(".qkv_lora.", ".linear1_qkv.") .replace(".proj_lora.", ".linear2.") .replace(".processor.", ".") ) sd_out[k_to] = tensor return sd_out def convert_lora(sd): if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd: return convert_lora_bfl_control(sd) if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd: return convert_lora_wan_fun(sd) if "single_blocks.37.processor.qkv_lora.up.weight" in sd and "double_blocks.18.processor.qkv_lora2.up.weight" in sd: return convert_uso_lora(sd) return sd ================================================ FILE: comfy/memory_management.py ================================================ import math import ctypes import threading import dataclasses import torch from typing import NamedTuple from comfy.quant_ops import QuantizedTensor class TensorFileSlice(NamedTuple): file_ref: object thread_id: int offset: int size: int def read_tensor_file_slice_into(tensor, destination): if isinstance(tensor, QuantizedTensor): if not isinstance(destination, QuantizedTensor): return False if tensor._layout_cls != destination._layout_cls: return False if not read_tensor_file_slice_into(tensor._qdata, destination._qdata): return False dst_orig_dtype = destination._params.orig_dtype destination._params.copy_from(tensor._params, non_blocking=False) destination._params = dataclasses.replace(destination._params, orig_dtype=dst_orig_dtype) return True info = getattr(tensor.untyped_storage(), "_comfy_tensor_file_slice", None) if info is None: return False file_obj = info.file_ref if (destination.device.type != "cpu" or file_obj is None or threading.get_ident() != info.thread_id or destination.numel() * destination.element_size() < info.size or tensor.numel() * tensor.element_size() != info.size or tensor.storage_offset() != 0 or not tensor.is_contiguous()): return False if info.size == 0: return True buf_type = ctypes.c_ubyte * info.size view = memoryview(buf_type.from_address(destination.data_ptr())) try: file_obj.seek(info.offset) done = 0 while done < info.size: try: n = file_obj.readinto(view[done:]) except OSError: return False if n <= 0: return False done += n return True finally: view.release() class TensorGeometry(NamedTuple): shape: any dtype: torch.dtype def element_size(self): info = torch.finfo(self.dtype) if self.dtype.is_floating_point else torch.iinfo(self.dtype) return info.bits // 8 def numel(self): return math.prod(self.shape) def tensors_to_geometries(tensors, dtype=None): geometries = [] for t in tensors: if t is None or isinstance(t, QuantizedTensor): geometries.append(t) continue tdtype = t.dtype if hasattr(t, "_model_dtype"): tdtype = t._model_dtype if dtype is not None: tdtype = dtype geometries.append(TensorGeometry(shape=t.shape, dtype=tdtype)) return geometries def vram_aligned_size(tensor): if isinstance(tensor, list): return sum([vram_aligned_size(t) for t in tensor]) if isinstance(tensor, QuantizedTensor): inner_tensors, _ = tensor.__tensor_flatten__() return vram_aligned_size([ getattr(tensor, attr) for attr in inner_tensors ]) if tensor is None: return 0 size = tensor.numel() * tensor.element_size() aligment_req = 1024 return (size + aligment_req - 1) // aligment_req * aligment_req def interpret_gathered_like(tensors, gathered): offset = 0 dest_views = [] if gathered.dim() != 1 or gathered.element_size() != 1: raise ValueError(f"Buffer must be 1D and single-byte (got {gathered.dim()}D {gathered.dtype})") for tensor in tensors: if tensor is None: dest_views.append(None) continue if isinstance(tensor, QuantizedTensor): inner_tensors, qt_ctx = tensor.__tensor_flatten__() templates = { attr: getattr(tensor, attr) for attr in inner_tensors } else: templates = { "data": tensor } actuals = {} for attr, template in templates.items(): size = template.numel() * template.element_size() if offset + size > gathered.numel(): raise ValueError(f"Buffer too small: needs {offset + size} bytes, but only has {gathered.numel()}. ") actuals[attr] = gathered[offset:offset+size].view(dtype=template.dtype).view(template.shape) offset += vram_aligned_size(template) if isinstance(tensor, QuantizedTensor): dest_views.append(QuantizedTensor.__tensor_unflatten__(actuals, qt_ctx, 0, 0)) else: dest_views.append(actuals["data"]) return dest_views aimdo_enabled = False ================================================ FILE: comfy/model_base.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Comfy This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import comfy.ldm.hunyuan3dv2_1 import comfy.ldm.hunyuan3dv2_1.hunyuandit import torch import logging import comfy.ldm.lightricks.av_model from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep from comfy.ldm.cascade.stage_c import StageC from comfy.ldm.cascade.stage_b import StageB from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper import comfy.ldm.genmo.joint_model.asymm_models_joint import comfy.ldm.aura.mmdit import comfy.ldm.pixart.pixartms import comfy.ldm.hydit.models import comfy.ldm.audio.dit import comfy.ldm.audio.embedders import comfy.ldm.flux.model import comfy.ldm.lightricks.model import comfy.ldm.hunyuan_video.model import comfy.ldm.cosmos.model import comfy.ldm.cosmos.predict2 import comfy.ldm.lumina.model import comfy.ldm.wan.model import comfy.ldm.wan.model_animate import comfy.ldm.hunyuan3d.model import comfy.ldm.hidream.model import comfy.ldm.chroma.model import comfy.ldm.chroma_radiance.model import comfy.ldm.ace.model import comfy.ldm.omnigen.omnigen2 import comfy.ldm.qwen_image.model import comfy.ldm.kandinsky5.model import comfy.ldm.anima.model import comfy.ldm.ace.ace_step15 import comfy.model_management import comfy.patcher_extension import comfy.conds import comfy.ops from enum import Enum from . import utils import comfy.latent_formats import comfy.model_sampling import math from typing import TYPE_CHECKING if TYPE_CHECKING: from comfy.model_patcher import ModelPatcher class ModelType(Enum): EPS = 1 V_PREDICTION = 2 V_PREDICTION_EDM = 3 STABLE_CASCADE = 4 EDM = 5 FLOW = 6 V_PREDICTION_CONTINUOUS = 7 FLUX = 8 IMG_TO_IMG = 9 FLOW_COSMOS = 10 IMG_TO_IMG_FLOW = 11 def model_sampling(model_config, model_type): s = comfy.model_sampling.ModelSamplingDiscrete if model_type == ModelType.EPS: c = comfy.model_sampling.EPS elif model_type == ModelType.V_PREDICTION: c = comfy.model_sampling.V_PREDICTION elif model_type == ModelType.V_PREDICTION_EDM: c = comfy.model_sampling.V_PREDICTION s = comfy.model_sampling.ModelSamplingContinuousEDM elif model_type == ModelType.FLOW: c = comfy.model_sampling.CONST s = comfy.model_sampling.ModelSamplingDiscreteFlow elif model_type == ModelType.STABLE_CASCADE: c = comfy.model_sampling.EPS s = comfy.model_sampling.StableCascadeSampling elif model_type == ModelType.EDM: c = comfy.model_sampling.EDM s = comfy.model_sampling.ModelSamplingContinuousEDM elif model_type == ModelType.V_PREDICTION_CONTINUOUS: c = comfy.model_sampling.V_PREDICTION s = comfy.model_sampling.ModelSamplingContinuousV elif model_type == ModelType.FLUX: c = comfy.model_sampling.CONST s = comfy.model_sampling.ModelSamplingFlux elif model_type == ModelType.IMG_TO_IMG: c = comfy.model_sampling.IMG_TO_IMG elif model_type == ModelType.FLOW_COSMOS: c = comfy.model_sampling.COSMOS_RFLOW s = comfy.model_sampling.ModelSamplingCosmosRFlow elif model_type == ModelType.IMG_TO_IMG_FLOW: c = comfy.model_sampling.IMG_TO_IMG_FLOW class ModelSampling(s, c): pass return ModelSampling(model_config) def convert_tensor(extra, dtype, device): if hasattr(extra, "dtype"): if extra.dtype != torch.int and extra.dtype != torch.long: extra = comfy.model_management.cast_to_device(extra, device, dtype) else: extra = comfy.model_management.cast_to_device(extra, device, None) return extra class BaseModel(torch.nn.Module): def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): super().__init__() unet_config = model_config.unet_config self.latent_format = model_config.latent_format self.model_config = model_config self.manual_cast_dtype = model_config.manual_cast_dtype self.device = device self.current_patcher: 'ModelPatcher' = None if not unet_config.get("disable_unet_model_creation", False): if model_config.custom_operations is None: fp8 = model_config.optimizations.get("fp8", False) operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, model_config=model_config) else: operations = model_config.custom_operations self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) self.diffusion_model.eval() if comfy.model_management.force_channels_last(): self.diffusion_model.to(memory_format=torch.channels_last) logging.debug("using channels last mode for diffusion model") logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype)) comfy.model_management.archive_model_dtypes(self.diffusion_model) self.model_type = model_type self.model_sampling = model_sampling(model_config, model_type) self.adm_channels = unet_config.get("adm_in_channels", None) if self.adm_channels is None: self.adm_channels = 0 self.concat_keys = () logging.info("model_type {}".format(model_type.name)) logging.debug("adm {}".format(self.adm_channels)) self.memory_usage_factor = model_config.memory_usage_factor self.memory_usage_factor_conds = () self.memory_usage_shape_process = {} def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._apply_model, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, transformer_options) ).execute(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): sigma = t xc = self.model_sampling.calculate_input(sigma, x) if c_concat is not None: xc = torch.cat([xc] + [comfy.model_management.cast_to_device(c_concat, xc.device, xc.dtype)], dim=1) context = c_crossattn dtype = self.get_dtype_inference() xc = xc.to(dtype) device = xc.device t = self.model_sampling.timestep(t).float() if context is not None: context = comfy.model_management.cast_to_device(context, device, dtype) extra_conds = {} for o in kwargs: extra = kwargs[o] if hasattr(extra, "dtype"): extra = convert_tensor(extra, dtype, device) elif isinstance(extra, list): ex = [] for ext in extra: ex.append(convert_tensor(ext, dtype, device)) extra = ex extra_conds[o] = extra t = self.process_timestep(t, x=x, **extra_conds) if "latent_shapes" in extra_conds: xc = utils.unpack_latents(xc, extra_conds.pop("latent_shapes")) model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds) if len(model_output) > 1 and not torch.is_tensor(model_output): model_output, _ = utils.pack_latents(model_output) return self.model_sampling.calculate_denoised(sigma, model_output.float(), x) def process_timestep(self, timestep, **kwargs): return timestep def get_dtype(self): return self.diffusion_model.dtype def get_dtype_inference(self): dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype return dtype def encode_adm(self, **kwargs): return None def concat_cond(self, **kwargs): if len(self.concat_keys) > 0: cond_concat = [] denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) concat_latent_image = kwargs.get("concat_latent_image", None) if concat_latent_image is None: concat_latent_image = kwargs.get("latent_image", None) else: concat_latent_image = self.process_latent_in(concat_latent_image) noise = kwargs.get("noise", None) device = kwargs["device"] if concat_latent_image.shape[1:] != noise.shape[1:]: concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") if noise.ndim == 5: if concat_latent_image.shape[-3] < noise.shape[-3]: concat_latent_image = torch.nn.functional.pad(concat_latent_image, (0, 0, 0, 0, 0, noise.shape[-3] - concat_latent_image.shape[-3]), "constant", 0) else: concat_latent_image = concat_latent_image[:, :, :noise.shape[-3]] concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) if denoise_mask is not None: if len(denoise_mask.shape) == len(noise.shape): denoise_mask = denoise_mask[:, :1] num_dim = noise.ndim - 2 denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:])) if denoise_mask.shape[-2:] != noise.shape[-2:]: denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) for ck in self.concat_keys: if denoise_mask is not None: if ck == "mask": cond_concat.append(denoise_mask.to(device)) elif ck == "masked_image": cond_concat.append(concat_latent_image.to(device)) # NOTE: the latent_image should be masked by the mask in pixel space elif ck == "mask_inverted": cond_concat.append(1.0 - denoise_mask.to(device)) else: if ck == "mask": cond_concat.append(torch.ones_like(noise)[:, :1]) elif ck == "masked_image": cond_concat.append(self.blank_inpaint_image_like(noise)) elif ck == "mask_inverted": cond_concat.append(torch.zeros_like(noise)[:, :1]) if ck == "concat_image": if concat_latent_image is not None: cond_concat.append(concat_latent_image.to(device)) else: cond_concat.append(torch.zeros_like(noise)) data = torch.cat(cond_concat, dim=1) return data return None def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): """Override in subclasses to handle model-specific cond slicing for context windows. Return a sliced cond object, or None to fall through to default handling. Use comfy.context_windows.slice_cond() for common cases.""" return None def extra_conds(self, **kwargs): out = {} concat_cond = self.concat_cond(**kwargs) if concat_cond is not None: out['c_concat'] = comfy.conds.CONDNoiseShape(concat_cond) adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = comfy.conds.CONDRegular(adm) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) cross_attn_cnet = kwargs.get("cross_attn_controlnet", None) if cross_attn_cnet is not None: out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet) c_concat = kwargs.get("noise_concat", None) if c_concat is not None: out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat) return out def load_model_weights(self, sd, unet_prefix="", assign=False): to_load = {} keys = list(sd.keys()) for k in keys: if k.startswith(unet_prefix): to_load[k[len(unet_prefix):]] = sd.pop(k) to_load = self.model_config.process_unet_state_dict(to_load) m, u = self.diffusion_model.load_state_dict(to_load, strict=False, assign=assign) if len(m) > 0: logging.warning("unet missing: {}".format(m)) if len(u) > 0: logging.warning("unet unexpected: {}".format(u)) del to_load return self def process_latent_in(self, latent): return self.latent_format.process_in(latent) def process_latent_out(self, latent): return self.latent_format.process_out(latent) def state_dict_for_saving(self, unet_state_dict, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): extra_sds = [] if clip_state_dict is not None: extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict)) if vae_state_dict is not None: extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict)) if clip_vision_state_dict is not None: extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict)) unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) if self.model_type == ModelType.V_PREDICTION: unet_state_dict["v_pred"] = torch.tensor([]) for sd in extra_sds: unet_state_dict.update(sd) return unet_state_dict def set_inpaint(self): self.concat_keys = ("mask", "masked_image") def blank_inpaint_image_like(latent_image): blank_image = torch.ones_like(latent_image) # these are the values for "zero" in pixel space translated to latent space blank_image[:,0] *= 0.8223 blank_image[:,1] *= -0.6876 blank_image[:,2] *= 0.6364 blank_image[:,3] *= 0.1380 return blank_image self.blank_inpaint_image_like = blank_inpaint_image_like def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image) def memory_required(self, input_shape, cond_shapes={}): input_shapes = [input_shape] for c in self.memory_usage_factor_conds: shape = cond_shapes.get(c, None) if shape is not None: if c in self.memory_usage_shape_process: out = [] for s in shape: out.append(self.memory_usage_shape_process[c](s)) shape = out if len(shape) > 0: input_shapes += shape if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): dtype = self.get_dtype_inference() #TODO: this needs to be tweaked area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024) else: #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024) def extra_conds_shapes(self, **kwargs): return {} def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): adm_inputs = [] weights = [] noise_aug = [] for unclip_cond in unclip_conditioning: for adm_cond in unclip_cond["clip_vision_output"].image_embeds: weight = unclip_cond["strength"] noise_augment = unclip_cond["noise_augmentation"] noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed) adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight weights.append(weight) noise_aug.append(noise_augment) adm_inputs.append(adm_out) if len(noise_aug) > 1: adm_out = torch.stack(adm_inputs).sum(0) noise_augment = noise_augment_merge noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) adm_out = torch.cat((c_adm, noise_level_emb), 1) return adm_out class SD21UNCLIP(BaseModel): def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): super().__init__(model_config, model_type, device=device) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) def encode_adm(self, **kwargs): unclip_conditioning = kwargs.get("unclip_conditioning", None) device = kwargs["device"] if unclip_conditioning is None: return torch.zeros((1, self.adm_channels), device=device) else: return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) def sdxl_pooled(args, noise_augmentor): if "unclip_conditioning" in args: return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280] else: return args["pooled_output"] class SDXLRefiner(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) def encode_adm(self, **kwargs): clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) width = kwargs.get("width", 768) height = kwargs.get("height", 768) crop_w = kwargs.get("crop_w", 0) crop_h = kwargs.get("crop_h", 0) if kwargs.get("prompt_type", "") == "negative": aesthetic_score = kwargs.get("aesthetic_score", 2.5) else: aesthetic_score = kwargs.get("aesthetic_score", 6) out = [] out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([aesthetic_score]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SDXL(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) def encode_adm(self, **kwargs): clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) width = kwargs.get("width", 768) height = kwargs.get("height", 768) crop_w = kwargs.get("crop_w", 0) crop_h = kwargs.get("crop_h", 0) target_width = kwargs.get("target_width", width) target_height = kwargs.get("target_height", height) out = [] out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([target_height]))) out.append(self.embedder(torch.Tensor([target_width]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SVD_img2vid(BaseModel): def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) def encode_adm(self, **kwargs): fps_id = kwargs.get("fps", 6) - 1 motion_bucket_id = kwargs.get("motion_bucket_id", 127) augmentation = kwargs.get("augmentation_level", 0) out = [] out.append(self.embedder(torch.Tensor([fps_id]))) out.append(self.embedder(torch.Tensor([motion_bucket_id]))) out.append(self.embedder(torch.Tensor([augmentation]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) return flat def extra_conds(self, **kwargs): out = {} adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = comfy.conds.CONDRegular(adm) latent_image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) if latent_image is None: latent_image = torch.zeros_like(noise) if latent_image.shape[1:] != noise.shape[1:]: latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) if "time_conditioning" in kwargs: out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"]) out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0]) return out class SV3D_u(SVD_img2vid): def encode_adm(self, **kwargs): augmentation = kwargs.get("augmentation_level", 0) out = [] out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) return flat class SV3D_p(SVD_img2vid): def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): super().__init__(model_config, model_type, device=device) self.embedder_512 = Timestep(512) def encode_adm(self, **kwargs): augmentation = kwargs.get("augmentation_level", 0) elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here azimuth = kwargs.get("azimuth", 0) noise = kwargs.get("noise", None) out = [] out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0)))) out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0)))) out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out)) return torch.cat(out, dim=1) class Stable_Zero123(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): super().__init__(model_config, model_type, device=device) self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) self.cc_projection.weight.copy_(cc_projection_weight) self.cc_projection.bias.copy_(cc_projection_bias) def extra_conds(self, **kwargs): out = {} latent_image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) if latent_image is None: latent_image = torch.zeros_like(noise) if latent_image.shape[1:] != noise.shape[1:]: latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: if cross_attn.shape[-1] != 768: cross_attn = self.cc_projection(cross_attn) out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) return out class SD_X4Upscaler(BaseModel): def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): super().__init__(model_config, model_type, device=device) self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350) def extra_conds(self, **kwargs): out = {} image = kwargs.get("concat_image", None) noise = kwargs.get("noise", None) noise_augment = kwargs.get("noise_augmentation", 0.0) device = kwargs["device"] seed = kwargs["seed"] - 10 noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment) if image is None: image = torch.zeros_like(noise)[:,:3] if image.shape[1:] != noise.shape[1:]: image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") noise_level = torch.tensor([noise_level], device=device) if noise_augment > 0: image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed) image = utils.resize_to_batch_size(image, noise.shape[0]) out['c_concat'] = comfy.conds.CONDNoiseShape(image) out['y'] = comfy.conds.CONDRegular(noise_level) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) return out class IP2P: def concat_cond(self, **kwargs): image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) device = kwargs["device"] if image is None: image = torch.zeros_like(noise) else: image = image.to(device=device) if image.shape[1:] != noise.shape[1:]: image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], "bilinear", "center") image = utils.resize_to_batch_size(image, noise.shape[0]) return self.process_ip2p_image_in(image) class SD15_instructpix2pix(IP2P, BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.process_ip2p_image_in = lambda image: image class SDXL_instructpix2pix(IP2P, SDXL): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) if model_type == ModelType.V_PREDICTION_EDM: self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p else: self.process_ip2p_image_in = lambda image: image #diffusers ip2p class Lotus(BaseModel): def extra_conds(self, **kwargs): out = {} cross_attn = kwargs.get("cross_attn", None) out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) device = kwargs["device"] task_emb = torch.tensor([1, 0]).float().to(device) task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)]).unsqueeze(0) out['y'] = comfy.conds.CONDRegular(task_emb) return out def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG, device=None): super().__init__(model_config, model_type, device=device) class StableCascade_C(BaseModel): def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): super().__init__(model_config, model_type, device=device, unet_model=StageC) def extra_conds(self, **kwargs): out = {} clip_text_pooled = kwargs["pooled_output"] if clip_text_pooled is not None: out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) if "unclip_conditioning" in kwargs: embeds = [] for unclip_cond in kwargs["unclip_conditioning"]: weight = unclip_cond["strength"] embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight) clip_img = torch.cat(embeds, dim=1) else: clip_img = torch.zeros((1, 1, 768)) out["clip_img"] = comfy.conds.CONDRegular(clip_img) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,))) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn) return out class StableCascade_B(BaseModel): def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): super().__init__(model_config, model_type, device=device, unet_model=StageB) def extra_conds(self, **kwargs): out = {} noise = kwargs.get("noise", None) clip_text_pooled = kwargs["pooled_output"] if clip_text_pooled is not None: out['clip'] = comfy.conds.CONDRegular(clip_text_pooled) #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) out["effnet"] = comfy.conds.CONDRegular(prior.to(device=noise.device)) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) return out class SD3(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper) def encode_adm(self, **kwargs): return kwargs["pooled_output"] def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out class AuraFlow(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out class StableAudio1(BaseModel): def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer) self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights) self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights) def extra_conds(self, **kwargs): out = {} noise = kwargs.get("noise", None) device = kwargs["device"] seconds_start = kwargs.get("seconds_start", 0) seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53)) seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device) seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device) global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1)) out['global_embed'] = comfy.conds.CONDRegular(global_embed) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1) out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out def state_dict_for_saving(self, unet_state_dict, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): sd = super().state_dict_for_saving(unet_state_dict, clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict) d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()} for k in d: s = d[k] for l in s: sd["{}{}".format(k, l)] = s[l] return sd class HunyuanDiT(BaseModel): def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask) conditioning_mt5xl = kwargs.get("conditioning_mt5xl", None) if conditioning_mt5xl is not None: out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl) attention_mask_mt5xl = kwargs.get("attention_mask_mt5xl", None) if attention_mask_mt5xl is not None: out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl) width = kwargs.get("width", 768) height = kwargs.get("height", 768) target_width = kwargs.get("target_width", width) target_height = kwargs.get("target_height", height) out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]])) return out class PixArt(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) width = kwargs.get("width", None) height = kwargs.get("height", None) if width is not None and height is not None: out["c_size"] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width]])) out["c_ar"] = comfy.conds.CONDRegular(torch.FloatTensor([[kwargs.get("aspect_ratio", height/width)]])) return out class Flux(BaseModel): def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux): super().__init__(model_config, model_type, device=device, unet_model=unet_model) self.memory_usage_factor_conds = ("ref_latents",) def concat_cond(self, **kwargs): try: #Handle Flux control loras dynamically changing the img_in weight. num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size) except: #Some cases like tensorrt might not have the weights accessible num_channels = self.model_config.unet_config["in_channels"] out_channels = self.model_config.unet_config["out_channels"] if num_channels <= out_channels: return None image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) device = kwargs["device"] if image is None: image = torch.zeros_like(noise) image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") image = utils.resize_to_batch_size(image, noise.shape[0]) image = self.process_latent_in(image) if num_channels <= out_channels * 2: return image #inpaint model mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) if mask is None: mask = torch.ones_like(noise)[:, :1] mask = torch.mean(mask, dim=1, keepdim=True) mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center") mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8) mask = utils.resize_to_batch_size(mask, noise.shape[0]) return torch.cat((image, mask), dim=1) def encode_adm(self, **kwargs): return kwargs["pooled_output"] def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) # upscale the attention mask, since now we attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: shape = kwargs["noise"].shape mask_ref_size = kwargs.get("attention_mask_img_shape", None) if mask_ref_size is not None: # the model will pad to the patch size, and then divide # essentially dividing and rounding up (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) guidance = kwargs.get("guidance", 3.5) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: latents = [] for lat in ref_latents: latents.append(self.process_latent_in(lat)) out['ref_latents'] = comfy.conds.CONDList(latents) ref_latents_method = kwargs.get("reference_latents_method", None) if ref_latents_method is not None: out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method) return out def extra_conds_shapes(self, **kwargs): out = {} ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))]) return out class LongCatImage(Flux): def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): transformer_options = transformer_options.copy() rope_opts = transformer_options.get("rope_options", {}) rope_opts = dict(rope_opts) rope_opts.setdefault("shift_t", 1.0) rope_opts.setdefault("shift_y", 512.0) rope_opts.setdefault("shift_x", 512.0) transformer_options["rope_options"] = rope_opts return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) def encode_adm(self, **kwargs): return None def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) out.pop('guidance', None) return out class Flux2(Flux): def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: target_text_len = 512 if cross_attn.shape[1] < target_text_len: cross_attn = torch.nn.functional.pad(cross_attn, (0, 0, target_text_len - cross_attn.shape[1], 0)) out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out class GenmoMochi(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out class LTXV(BaseModel): def __init__(self, model_config, model_type=ModelType.FLUX, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25)) denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) if denoise_mask is not None: out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) keyframe_idxs = kwargs.get("keyframe_idxs", None) if keyframe_idxs is not None: out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs) guide_attention_entries = kwargs.get("guide_attention_entries", None) if guide_attention_entries is not None: out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries) return out def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): if denoise_mask is None: return timestep return self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0] def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): return latent_image class LTXAV(BaseModel): def __init__(self, model_config, model_type=ModelType.FLUX, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.av_model.LTXAVModel) #TODO def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) device = kwargs["device"] if attention_mask is not None: out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: if hasattr(self.diffusion_model, "preprocess_text_embeds"): cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()), unprocessed=kwargs.get("unprocessed_ltxav_embeds", False)) out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25)) denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) audio_denoise_mask = None if denoise_mask is not None and "latent_shapes" in kwargs: denoise_mask = utils.unpack_latents(denoise_mask, kwargs["latent_shapes"]) if len(denoise_mask) > 1: audio_denoise_mask = denoise_mask[1] denoise_mask = denoise_mask[0] if denoise_mask is not None: out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) if audio_denoise_mask is not None: out["audio_denoise_mask"] = comfy.conds.CONDRegular(audio_denoise_mask) keyframe_idxs = kwargs.get("keyframe_idxs", None) if keyframe_idxs is not None: out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs) latent_shapes = kwargs.get("latent_shapes", None) if latent_shapes is not None: out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes) guide_attention_entries = kwargs.get("guide_attention_entries", None) if guide_attention_entries is not None: out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries) return out def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs): v_timestep = timestep a_timestep = timestep if denoise_mask is not None: v_timestep = self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0] if audio_denoise_mask is not None: a_timestep = self.diffusion_model.a_patchifier.patchify(((audio_denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (audio_denoise_mask.ndim - 1)))[:, :1, :, :1])[0] return v_timestep, a_timestep def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): return latent_image class HunyuanVideo(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) def encode_adm(self, **kwargs): return kwargs["pooled_output"] def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) guidance = kwargs.get("guidance", 6.0) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) guiding_frame_index = kwargs.get("guiding_frame_index", None) if guiding_frame_index is not None: out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index])) ref_latent = kwargs.get("ref_latent", None) if ref_latent is not None: out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent)) return out def scale_latent_inpaint(self, latent_image, **kwargs): return latent_image class HunyuanVideoI2V(HunyuanVideo): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device) self.concat_keys = ("concat_image", "mask_inverted") def scale_latent_inpaint(self, latent_image, **kwargs): return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) class HunyuanVideoSkyreelsI2V(HunyuanVideo): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device) self.concat_keys = ("concat_image",) def scale_latent_inpaint(self, latent_image, **kwargs): return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) class CosmosVideo(BaseModel): def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT) self.image_to_video = image_to_video if self.image_to_video: self.concat_keys = ("mask_inverted",) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) return out def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) sigma_noise_augmentation = 0 #TODO if sigma_noise_augmentation != 0: latent_image = latent_image + noise latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5) class CosmosPredict2(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW_COSMOS, image_to_video=False, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.predict2.MiniTrainDIT) self.image_to_video = image_to_video if self.image_to_video: self.concat_keys = ("mask_inverted",) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) if denoise_mask is not None: out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) return out def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): if denoise_mask is None: return timestep if denoise_mask.ndim <= 4: return timestep condition_video_mask_B_1_T_1_1 = denoise_mask.mean(dim=[1, 3, 4], keepdim=True) c_noise_B_1_T_1_1 = 0.0 * (1.0 - condition_video_mask_B_1_T_1_1) + timestep.reshape(timestep.shape[0], 1, 1, 1, 1) * condition_video_mask_B_1_T_1_1 out = c_noise_B_1_T_1_1.squeeze(dim=[1, 3, 4]) return out def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) sigma_noise_augmentation = 0 #TODO if sigma_noise_augmentation != 0: latent_image = latent_image + noise latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) sigma = (sigma / (sigma + 1)) return latent_image / (1.0 - sigma) class Anima(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.anima.model.Anima) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) t5xxl_ids = kwargs.get("t5xxl_ids", None) t5xxl_weights = kwargs.get("t5xxl_weights", None) device = kwargs["device"] if cross_attn is not None: if t5xxl_ids is not None: if t5xxl_weights is not None: t5xxl_weights = t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(cross_attn) t5xxl_ids = t5xxl_ids.unsqueeze(0) if torch.is_inference_mode_enabled(): # if not we are training cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()), t5xxl_ids.to(device=device), t5xxl_weights=t5xxl_weights.to(device=device, dtype=self.get_dtype_inference())) else: out['t5xxl_ids'] = comfy.conds.CONDRegular(t5xxl_ids) out['t5xxl_weights'] = comfy.conds.CONDRegular(t5xxl_weights) out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out class Lumina2(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT) self.memory_usage_factor_conds = ("ref_latents",) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: if torch.numel(attention_mask) != attention_mask.sum(): out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) if 'num_tokens' not in out: out['num_tokens'] = comfy.conds.CONDConstant(cross_attn.shape[1]) clip_text_pooled = kwargs.get("pooled_output", None) # NewBie if clip_text_pooled is not None: out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) clip_vision_outputs = kwargs.get("clip_vision_outputs", list(map(lambda a: a.get("clip_vision_output"), kwargs.get("unclip_conditioning", [{}])))) # Z Image omni if clip_vision_outputs is not None and len(clip_vision_outputs) > 0: sigfeats = [] for clip_vision_output in clip_vision_outputs: if clip_vision_output is not None: image_size = clip_vision_output.image_sizes[0] shape = clip_vision_output.last_hidden_state.shape sigfeats.append(clip_vision_output.last_hidden_state.reshape(shape[0], image_size[1] // 16, image_size[2] // 16, shape[-1])) if len(sigfeats) > 0: out['siglip_feats'] = comfy.conds.CONDList(sigfeats) ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: latents = [] for lat in ref_latents: latents.append(self.process_latent_in(lat)) out['ref_latents'] = comfy.conds.CONDList(latents) ref_contexts = kwargs.get("reference_latents_text_embeds", None) if ref_contexts is not None: out['ref_contexts'] = comfy.conds.CONDList(ref_contexts) return out def extra_conds_shapes(self, **kwargs): out = {} ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))]) return out class ZImagePixelSpace(Lumina2): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): BaseModel.__init__(self, model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiTPixelSpace) self.memory_usage_factor_conds = ("ref_latents",) class WAN21(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) self.image_to_video = image_to_video def concat_cond(self, **kwargs): noise = kwargs.get("noise", None) extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1] if extra_channels == 0: return None image = kwargs.get("concat_latent_image", None) device = kwargs["device"] if image is None: shape_image = list(noise.shape) shape_image[1] = extra_channels image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) else: latent_dim = self.latent_format.latent_channels image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") for i in range(0, image.shape[1], latent_dim): image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim]) image = utils.resize_to_batch_size(image, noise.shape[0]) if extra_channels != image.shape[1] + 4: if not self.image_to_video or extra_channels == image.shape[1]: return image if image.shape[1] > (extra_channels - 4): image = image[:, :(extra_channels - 4)] mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) if mask is None: mask = torch.zeros_like(noise)[:, :4] else: if mask.shape[1] != 4: mask = torch.mean(mask, dim=1, keepdim=True) mask = 1.0 - mask mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") if mask.shape[-3] < noise.shape[-3]: mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) if mask.shape[1] == 1: mask = mask.repeat(1, 4, 1, 1, 1) mask = utils.resize_to_batch_size(mask, noise.shape[0]) concat_mask_index = kwargs.get("concat_mask_index", 0) if concat_mask_index != 0: return torch.cat((image[:, :concat_mask_index], mask, image[:, concat_mask_index:]), dim=1) else: return torch.cat((mask, image), dim=1) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) clip_vision_output = kwargs.get("clip_vision_output", None) if clip_vision_output is not None: out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states) time_dim_concat = kwargs.get("time_dim_concat", None) if time_dim_concat is not None: out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat)) reference_latents = kwargs.get("reference_latents", None) if reference_latents is not None: out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0]) return out class WAN21_Vace(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.VaceWanModel) self.image_to_video = image_to_video def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) noise = kwargs.get("noise", None) noise_shape = list(noise.shape) vace_frames = kwargs.get("vace_frames", None) if vace_frames is None: noise_shape[1] = 32 vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)] mask = kwargs.get("vace_mask", None) if mask is None: noise_shape[1] = 64 mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames) vace_frames_out = [] for j in range(len(vace_frames)): vf = vace_frames[j].to(device=noise.device, dtype=noise.dtype, copy=True) for i in range(0, vf.shape[1], 16): vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16]) vf = torch.cat([vf, mask[j].to(device=noise.device, dtype=noise.dtype)], dim=1) vace_frames_out.append(vf) vace_frames = torch.stack(vace_frames_out, dim=1) out['vace_context'] = comfy.conds.CONDRegular(vace_frames) vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out)) out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) return out def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): if cond_key == "vace_context": import comfy.context_windows return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=3, retain_index_list=retain_index_list) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) class WAN21_Camera(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel) self.image_to_video = image_to_video def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) camera_conditions = kwargs.get("camera_conditions", None) if camera_conditions is not None: out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions) return out class WAN21_HuMo(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.HumoWanModel) self.image_to_video = image_to_video def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) noise = kwargs.get("noise", None) audio_embed = kwargs.get("audio_embed", None) if audio_embed is not None: out['audio_embed'] = comfy.conds.CONDRegular(audio_embed) if "c_concat" not in out: # 1.7B model reference_latents = kwargs.get("reference_latents", None) if reference_latents is not None: out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])) else: noise_shape = list(noise.shape) noise_shape[1] += 4 concat_latent = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype) zero_vae_values_first = torch.tensor([0.8660, -0.4326, -0.0017, -0.4884, -0.5283, 0.9207, -0.9896, 0.4433, -0.5543, -0.0113, 0.5753, -0.6000, -0.8346, -0.3497, -0.1926, -0.6938]).view(1, 16, 1, 1, 1) zero_vae_values_second = torch.tensor([1.0869, -1.2370, 0.0206, -0.4357, -0.6411, 2.0307, -1.5972, 1.2659, -0.8595, -0.4654, 0.9638, -1.6330, -1.4310, -0.1098, -0.3856, -1.4583]).view(1, 16, 1, 1, 1) zero_vae_values = torch.tensor([0.8642, -1.8583, 0.1577, 0.1350, -0.3641, 2.5863, -1.9670, 1.6065, -1.0475, -0.8678, 1.1734, -1.8138, -1.5933, -0.7721, -0.3289, -1.3745]).view(1, 16, 1, 1, 1) concat_latent[:, 4:] = zero_vae_values concat_latent[:, 4:, :1] = zero_vae_values_first concat_latent[:, 4:, 1:2] = zero_vae_values_second out['c_concat'] = comfy.conds.CONDNoiseShape(concat_latent) reference_latents = kwargs.get("reference_latents", None) if reference_latents is not None: ref_latent = self.process_latent_in(reference_latents[-1]) ref_latent_shape = list(ref_latent.shape) ref_latent_shape[1] += 4 + ref_latent_shape[1] ref_latent_full = torch.zeros(ref_latent_shape, device=ref_latent.device, dtype=ref_latent.dtype) ref_latent_full[:, 20:] = ref_latent ref_latent_full[:, 16:20] = 1.0 out['reference_latent'] = comfy.conds.CONDRegular(ref_latent_full) return out def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): if cond_key == "audio_embed": import comfy.context_windows return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) class WAN22_Animate(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel) self.image_to_video = image_to_video def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) face_video_pixels = kwargs.get("face_video_pixels", None) if face_video_pixels is not None: out['face_pixel_values'] = comfy.conds.CONDRegular(face_video_pixels) pose_latents = kwargs.get("pose_video_latent", None) if pose_latents is not None: out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents)) return out def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): import comfy.context_windows if cond_key == "face_pixel_values": return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_scale=4, temporal_offset=1) if cond_key == "pose_latents": return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) class WAN22_S2V(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V) self.memory_usage_factor_conds = ("reference_latent", "reference_motion") self.memory_usage_shape_process = {"reference_motion": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]} def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) audio_embed = kwargs.get("audio_embed", None) if audio_embed is not None: out['audio_embed'] = comfy.conds.CONDRegular(audio_embed) reference_latents = kwargs.get("reference_latents", None) if reference_latents is not None: out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])) reference_motion = kwargs.get("reference_motion", None) if reference_motion is not None: out['reference_motion'] = comfy.conds.CONDRegular(self.process_latent_in(reference_motion)) control_video = kwargs.get("control_video", None) if control_video is not None: out['control_video'] = comfy.conds.CONDRegular(self.process_latent_in(control_video)) return out def extra_conds_shapes(self, **kwargs): out = {} ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: out['reference_latent'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) reference_motion = kwargs.get("reference_motion", None) if reference_motion is not None: out['reference_motion'] = reference_motion.shape return out def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): if cond_key == "audio_embed": import comfy.context_windows return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) class WAN22(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) self.image_to_video = image_to_video def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) denoise_mask = kwargs.get("denoise_mask", None) if denoise_mask is not None: out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) return out def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): if denoise_mask is None: return timestep temp_ts = (torch.mean(denoise_mask[:, :, :, :, :], dim=(1, 3, 4), keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1) return temp_ts def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): return latent_image class WAN21_FlowRVS(WAN21): def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG_FLOW, image_to_video=False, device=None): model_config.unet_config["model_type"] = "t2v" super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) self.image_to_video = image_to_video class WAN21_SCAIL(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.SCAILWanModel) self.memory_usage_factor_conds = ("reference_latent", "pose_latents") self.memory_usage_shape_process = {"pose_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]} self.image_to_video = image_to_video def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) reference_latents = kwargs.get("reference_latents", None) if reference_latents is not None: ref_latent = self.process_latent_in(reference_latents[-1]) ref_mask = torch.ones_like(ref_latent[:, :4]) ref_latent = torch.cat([ref_latent, ref_mask], dim=1) out['reference_latent'] = comfy.conds.CONDRegular(ref_latent) pose_latents = kwargs.get("pose_video_latent", None) if pose_latents is not None: pose_latents = self.process_latent_in(pose_latents) pose_mask = torch.ones_like(pose_latents[:, :4]) pose_latents = torch.cat([pose_latents, pose_mask], dim=1) out['pose_latents'] = comfy.conds.CONDRegular(pose_latents) return out def extra_conds_shapes(self, **kwargs): out = {} ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: out['reference_latent'] = list([1, 20, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) pose_latents = kwargs.get("pose_video_latent", None) if pose_latents is not None: out['pose_latents'] = [pose_latents.shape[0], 20, *pose_latents.shape[2:]] return out class Hunyuan3Dv2(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) guidance = kwargs.get("guidance", 5.0) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out class Hunyuan3Dv2_1(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3dv2_1.hunyuandit.HunYuanDiTPlain) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) guidance = kwargs.get("guidance", 5.0) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out class HiDream(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel) def encode_adm(self, **kwargs): return kwargs["pooled_output"] def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) conditioning_llama3 = kwargs.get("conditioning_llama3", None) if conditioning_llama3 is not None: out['encoder_hidden_states_llama3'] = comfy.conds.CONDRegular(conditioning_llama3) image_cond = kwargs.get("concat_latent_image", None) if image_cond is not None: out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) return out class Chroma(Flux): def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.chroma.model.Chroma): super().__init__(model_config, model_type, device=device, unet_model=unet_model) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) guidance = kwargs.get("guidance", 0) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out class ChromaRadiance(Chroma): def __init__(self, model_config, model_type=ModelType.FLUX, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma_radiance.model.ChromaRadiance) class ACEStep(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) noise = kwargs.get("noise", None) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) conditioning_lyrics = kwargs.get("conditioning_lyrics", None) if cross_attn is not None: out['lyric_token_idx'] = comfy.conds.CONDRegular(conditioning_lyrics) out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype)) out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get("lyrics_strength", 1.0)) return out class ACEStep15(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.ace_step15.AceStepConditionGenerationModel) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) device = kwargs["device"] noise = kwargs["noise"] cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: if torch.count_nonzero(cross_attn) == 0: out['replace_with_null_embeds'] = comfy.conds.CONDConstant(True) out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) conditioning_lyrics = kwargs.get("conditioning_lyrics", None) if cross_attn is not None: out['lyric_embed'] = comfy.conds.CONDRegular(conditioning_lyrics) refer_audio = kwargs.get("reference_audio_timbre_latents", None) if refer_audio is None or len(refer_audio) == 0: refer_audio = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device) pass_audio_codes = True else: refer_audio = refer_audio[-1][:, :, :noise.shape[2]] out['is_covers'] = comfy.conds.CONDConstant(True) pass_audio_codes = False if pass_audio_codes: audio_codes = kwargs.get("audio_codes", None) if audio_codes is not None: out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device)) refer_audio = refer_audio[:, :, :750] else: out['is_covers'] = comfy.conds.CONDConstant(False) if refer_audio.shape[2] < noise.shape[2]: pad = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device) refer_audio = torch.cat([refer_audio.to(pad), pad[:, :, refer_audio.shape[2]:]], dim=2) out['refer_audio'] = comfy.conds.CONDRegular(refer_audio) return out class Omnigen2(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel) self.memory_usage_factor_conds = ("ref_latents",) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: if torch.numel(attention_mask) != attention_mask.sum(): out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: latents = [] for lat in ref_latents: latents.append(self.process_latent_in(lat)) out['ref_latents'] = comfy.conds.CONDList(latents) return out def extra_conds_shapes(self, **kwargs): out = {} ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) return out class QwenImage(BaseModel): def __init__(self, model_config, model_type=ModelType.FLUX, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.qwen_image.model.QwenImageTransformer2DModel) self.memory_usage_factor_conds = ("ref_latents",) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: latents = [] for lat in ref_latents: latents.append(self.process_latent_in(lat)) out['ref_latents'] = comfy.conds.CONDList(latents) ref_latents_method = kwargs.get("reference_latents_method", None) if ref_latents_method is not None: out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method) return out def extra_conds_shapes(self, **kwargs): out = {} ref_latents = kwargs.get("reference_latents", None) if ref_latents is not None: out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) return out class HunyuanImage21(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: if torch.numel(attention_mask) != attention_mask.sum(): out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) conditioning_byt5small = kwargs.get("conditioning_byt5small", None) if conditioning_byt5small is not None: out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small) guidance = kwargs.get("guidance", 6.0) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out class HunyuanImage21Refiner(HunyuanImage21): def concat_cond(self, **kwargs): noise = kwargs.get("noise", None) image = kwargs.get("concat_latent_image", None) noise_augmentation = kwargs.get("noise_augmentation", 0.0) device = kwargs["device"] if image is None: shape_image = list(noise.shape) image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) else: image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") image = self.process_latent_in(image) image = utils.resize_to_batch_size(image, noise.shape[0]) if noise_augmentation > 0: generator = torch.Generator(device="cpu") generator.manual_seed(kwargs.get("seed", 0) - 10) noise = torch.randn(image.shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device) image = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image else: image = 0.75 * image return image def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) out['disable_time_r'] = comfy.conds.CONDConstant(True) return out class HunyuanVideo15(HunyuanVideo): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device) def concat_cond(self, **kwargs): noise = kwargs.get("noise", None) extra_channels = self.diffusion_model.img_in.proj.weight.shape[1] - noise.shape[1] - 1 #noise 32 img cond 32 + mask 1 if extra_channels == 0: return None image = kwargs.get("concat_latent_image", None) device = kwargs["device"] if image is None: shape_image = list(noise.shape) shape_image[1] = extra_channels image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) else: latent_dim = self.latent_format.latent_channels image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") for i in range(0, image.shape[1], latent_dim): image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim]) image = utils.resize_to_batch_size(image, noise.shape[0]) mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) if mask is None: mask = torch.zeros_like(noise)[:, :1] else: mask = 1.0 - mask mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") if mask.shape[-3] < noise.shape[-3]: mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) mask = utils.resize_to_batch_size(mask, noise.shape[0]) return torch.cat((image, mask), dim=1) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: if torch.numel(attention_mask) != attention_mask.sum(): out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) conditioning_byt5small = kwargs.get("conditioning_byt5small", None) if conditioning_byt5small is not None: out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small) guidance = kwargs.get("guidance", 6.0) if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) clip_vision_output = kwargs.get("clip_vision_output", None) if clip_vision_output is not None: out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.last_hidden_state) return out class HunyuanVideo15_SR_Distilled(HunyuanVideo15): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device) def concat_cond(self, **kwargs): noise = kwargs.get("noise", None) image = kwargs.get("concat_latent_image", None) noise_augmentation = kwargs.get("noise_augmentation", 0.0) device = kwargs["device"] if image is None: image = torch.zeros([noise.shape[0], noise.shape[1] * 2 + 2, noise.shape[-3], noise.shape[-2], noise.shape[-1]], device=comfy.model_management.intermediate_device()) else: image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") #image = self.process_latent_in(image) # scaling wasn't applied in reference code image = utils.resize_to_batch_size(image, noise.shape[0]) lq_image_slice = slice(noise.shape[1] + 1, 2 * noise.shape[1] + 1) if noise_augmentation > 0: generator = torch.Generator(device="cpu") generator.manual_seed(kwargs.get("seed", 0) - 10) noise = torch.randn(image[:, lq_image_slice].shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device) image[:, lq_image_slice] = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image[:, lq_image_slice] else: image[:, lq_image_slice] = 0.75 * image[:, lq_image_slice] return image def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) out['disable_time_r'] = comfy.conds.CONDConstant(False) return out class Kandinsky5(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.kandinsky5.model.Kandinsky5) def encode_adm(self, **kwargs): return kwargs["pooled_output"] def concat_cond(self, **kwargs): noise = kwargs.get("noise", None) device = kwargs["device"] image = torch.zeros_like(noise) mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) if mask is None: mask = torch.zeros_like(noise)[:, :1] else: mask = 1.0 - mask mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") if mask.shape[-3] < noise.shape[-3]: mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) mask = utils.resize_to_batch_size(mask, noise.shape[0]) return torch.cat((image, mask), dim=1) def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None: out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) time_dim_replace = kwargs.get("time_dim_replace", None) if time_dim_replace is not None: out['time_dim_replace'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_replace)) return out class Kandinsky5Image(Kandinsky5): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device) def concat_cond(self, **kwargs): return None ================================================ FILE: comfy/model_detection.py ================================================ import json import comfy.memory_management import comfy.supported_models import comfy.supported_models_base import comfy.utils import math import logging import torch def count_blocks(state_dict_keys, prefix_string): count = 0 while True: c = False for k in state_dict_keys: if k.startswith(prefix_string.format(count)): c = True break if c == False: break count += 1 return count def any_suffix_in(keys, prefix, main, suffix_list=[]): for x in suffix_list: if "{}{}{}".format(prefix, main, x) in keys: return True return False def calculate_transformer_depth(prefix, state_dict_keys, state_dict): context_dim = None use_linear_in_transformer = False transformer_prefix = prefix + "1.transformer_blocks." transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) if len(transformer_keys) > 0: last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross return None def detect_unet_config(state_dict, key_prefix, metadata=None): state_dict_keys = list(state_dict.keys()) if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model unet_config = {} unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1] patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2] unet_config["patch_size"] = patch_size final_layer = '{}final_layer.linear.weight'.format(key_prefix) if final_layer in state_dict: unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size) unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64 unet_config["input_size"] = None y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix) if y_key in state_dict_keys: unet_config["adm_in_channels"] = state_dict[y_key].shape[1] context_key = '{}context_embedder.weight'.format(key_prefix) if context_key in state_dict_keys: in_features = state_dict[context_key].shape[1] out_features = state_dict[context_key].shape[0] unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}} num_patches_key = '{}pos_embed'.format(key_prefix) if num_patches_key in state_dict_keys: num_patches = state_dict[num_patches_key].shape[1] unet_config["num_patches"] = num_patches unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix) if rms_qk in state_dict_keys: unet_config["qk_norm"] = "rms" unet_config["pos_embed_scaling_factor"] = None #unused for inference context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix) if context_processor in state_dict_keys: unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.') unet_config["x_block_self_attn_layers"] = [] for key in state_dict_keys: if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'): layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')] unet_config["x_block_self_attn_layers"].append(int(layer)) return unet_config if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade unet_config = {} text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) if text_mapper_name in state_dict_keys: unet_config['stable_cascade_stage'] = 'c' w = state_dict[text_mapper_name] if w.shape[0] == 1536: #stage c lite unet_config['c_cond'] = 1536 unet_config['c_hidden'] = [1536, 1536] unet_config['nhead'] = [24, 24] unet_config['blocks'] = [[4, 12], [12, 4]] elif w.shape[0] == 2048: #stage c full unet_config['c_cond'] = 2048 elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: unet_config['stable_cascade_stage'] = 'b' w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)] if w.shape[-1] == 640: unet_config['c_hidden'] = [320, 640, 1280, 1280] unet_config['nhead'] = [-1, -1, 20, 20] unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]] unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]] elif w.shape[-1] == 576: #stage b lite unet_config['c_hidden'] = [320, 576, 1152, 1152] unet_config['nhead'] = [-1, 9, 18, 18] unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] return unet_config if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit unet_config = {} unet_config["audio_model"] = "dit1.0" return unet_config if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit unet_config = {} unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1] unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1] double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.') single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.') unet_config["n_double_layers"] = double_layers unet_config["n_layers"] = double_layers + single_layers return unet_config if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT unet_config = {} unet_config["image_model"] = "hydit" unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2 unet_config["mlp_ratio"] = 4.3637 if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968: unet_config["size_cond"] = True unet_config["use_style_cond"] = True unet_config["image_model"] = "hydit1" return unet_config if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video dit_config = {} in_w = state_dict['{}img_in.proj.weight'.format(key_prefix)] out_w = state_dict['{}final_layer.linear.weight'.format(key_prefix)] dit_config["image_model"] = "hunyuan_video" dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels dit_config["patch_size"] = list(in_w.shape[2:]) dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"]) if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys): dit_config["vec_in_dim"] = 768 else: dit_config["vec_in_dim"] = None if len(dit_config["patch_size"]) == 2: dit_config["axes_dim"] = [64, 64] else: dit_config["axes_dim"] = [16, 56, 56] if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys): dit_config["meanflow"] = True else: dit_config["meanflow"] = False dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1] dit_config["hidden_size"] = in_w.shape[0] dit_config["mlp_ratio"] = 4.0 dit_config["num_heads"] = in_w.shape[0] // 128 dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') dit_config["theta"] = 256 dit_config["qkv_bias"] = True if '{}byt5_in.fc1.weight'.format(key_prefix) in state_dict: dit_config["byt5"] = True else: dit_config["byt5"] = False guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys)) dit_config["guidance_embed"] = len(guidance_keys) > 0 # HunyuanVideo 1.5 if '{}cond_type_embedding.weight'.format(key_prefix) in state_dict_keys: dit_config["use_cond_type_embedding"] = True else: dit_config["use_cond_type_embedding"] = False if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys: dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0] dit_config["meanflow_sum"] = True else: dit_config["vision_in_dim"] = None dit_config["meanflow_sum"] = False return dit_config if any_suffix_in(state_dict_keys, key_prefix, 'double_blocks.0.img_attn.norm.key_norm.', ["weight", "scale"]) and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', ["weight", "scale"])): #Flux, Chroma or Chroma Radiance (has no img_in.weight) dit_config = {} if '{}double_stream_modulation_img.lin.weight'.format(key_prefix) in state_dict_keys: dit_config["image_model"] = "flux2" dit_config["axes_dim"] = [32, 32, 32, 32] dit_config["num_heads"] = 48 dit_config["mlp_ratio"] = 3.0 dit_config["theta"] = 2000 dit_config["out_channels"] = 128 dit_config["global_modulation"] = True dit_config["mlp_silu_act"] = True dit_config["qkv_bias"] = False dit_config["ops_bias"] = False dit_config["default_ref_method"] = "index" dit_config["ref_index_scale"] = 10.0 dit_config["txt_ids_dims"] = [3] patch_size = 1 else: dit_config["image_model"] = "flux" dit_config["axes_dim"] = [16, 56, 56] dit_config["num_heads"] = 24 dit_config["mlp_ratio"] = 4.0 dit_config["theta"] = 10000 dit_config["out_channels"] = 16 dit_config["qkv_bias"] = True dit_config["txt_ids_dims"] = [] patch_size = 2 dit_config["in_channels"] = 16 dit_config["hidden_size"] = 3072 dit_config["context_in_dim"] = 4096 dit_config["patch_size"] = patch_size in_key = "{}img_in.weight".format(key_prefix) if in_key in state_dict_keys: w = state_dict[in_key] dit_config["in_channels"] = w.shape[1] // (patch_size * patch_size) dit_config["hidden_size"] = w.shape[0] txt_in_key = "{}txt_in.weight".format(key_prefix) if txt_in_key in state_dict_keys: w = state_dict[txt_in_key] dit_config["context_in_dim"] = w.shape[1] dit_config["hidden_size"] = w.shape[0] vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix) if vec_in_key in state_dict_keys: dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1] else: dit_config["vec_in_dim"] = None dit_config["num_heads"] = dit_config["hidden_size"] // sum(dit_config["axes_dim"]) dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') if any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.0.norms.0.', ["weight", "scale"]) or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', ["weight", "scale"]): #Chroma dit_config["image_model"] = "chroma" dit_config["in_channels"] = 64 dit_config["out_channels"] = 64 dit_config["in_dim"] = 64 dit_config["out_dim"] = 3072 dit_config["hidden_dim"] = 5120 dit_config["n_layers"] = 5 if any_suffix_in(state_dict_keys, key_prefix, 'nerf_blocks.0.norm.', ["weight", "scale"]): #Chroma Radiance dit_config["image_model"] = "chroma_radiance" dit_config["in_channels"] = 3 dit_config["out_channels"] = 3 dit_config["patch_size"] = state_dict.get('{}img_in_patch.weight'.format(key_prefix)).size(dim=-1) dit_config["nerf_hidden_size"] = 64 dit_config["nerf_mlp_ratio"] = 4 dit_config["nerf_depth"] = 4 dit_config["nerf_max_freqs"] = 8 dit_config["nerf_tile_size"] = 512 dit_config["nerf_final_head_type"] = "conv" if any_suffix_in(state_dict_keys, key_prefix, 'nerf_final_layer_conv.norm.', ["weight", "scale"]) else "linear" dit_config["nerf_embedder_dtype"] = torch.float32 if "{}__x0__".format(key_prefix) in state_dict_keys: # x0 pred dit_config["use_x0"] = True else: dit_config["use_x0"] = False else: dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys dit_config["txt_norm"] = any_suffix_in(state_dict_keys, key_prefix, 'txt_norm.', ["weight", "scale"]) if dit_config["yak_mlp"] and dit_config["txt_norm"]: # Ovis model dit_config["txt_ids_dims"] = [1, 2] if dit_config.get("context_in_dim") == 3584 and dit_config["vec_in_dim"] is None: # LongCat-Image dit_config["txt_ids_dims"] = [1, 2] return dit_config if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview dit_config = {} dit_config["image_model"] = "mochi_preview" dit_config["depth"] = 48 dit_config["patch_size"] = 2 dit_config["num_heads"] = 24 dit_config["hidden_size_x"] = 3072 dit_config["hidden_size_y"] = 1536 dit_config["mlp_ratio_x"] = 4.0 dit_config["mlp_ratio_y"] = 4.0 dit_config["learn_sigma"] = False dit_config["in_channels"] = 12 dit_config["qk_norm"] = True dit_config["qkv_bias"] = False dit_config["out_bias"] = True dit_config["attn_drop"] = 0.0 dit_config["patch_embed_bias"] = True dit_config["posenc_preserve_area"] = True dit_config["timestep_mlp_bias"] = True dit_config["attend_to_padding"] = False dit_config["timestep_scale"] = 1000.0 dit_config["use_t5"] = True dit_config["t5_feat_dim"] = 4096 dit_config["t5_token_length"] = 256 dit_config["rope_theta"] = 10000.0 return dit_config if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys and '{}pos_embed.proj.bias'.format(key_prefix) in state_dict_keys: # PixArt diffusers return None if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv dit_config = {} dit_config["image_model"] = "ltxav" if f'{key_prefix}audio_adaln_single.linear.weight' in state_dict_keys else "ltxv" dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape dit_config["attention_head_dim"] = shape[0] // 32 dit_config["cross_attention_dim"] = shape[1] if metadata is not None and "config" in metadata: dit_config.update(json.loads(metadata["config"]).get("transformer", {})) return dit_config if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model dit_config = {} dit_config["audio_model"] = "ace" dit_config["attention_head_dim"] = 128 dit_config["in_channels"] = 8 dit_config["inner_dim"] = 2560 dit_config["max_height"] = 16 dit_config["max_position"] = 32768 dit_config["max_width"] = 32768 dit_config["mlp_ratio"] = 2.5 dit_config["num_attention_heads"] = 20 dit_config["num_layers"] = 24 dit_config["out_channels"] = 8 dit_config["patch_size"] = [16, 1] dit_config["rope_theta"] = 1000000.0 dit_config["speaker_embedding_dim"] = 512 dit_config["text_embedding_dim"] = 768 dit_config["ssl_encoder_depths"] = [8, 8] dit_config["ssl_latent_dims"] = [1024, 768] dit_config["ssl_names"] = ["mert", "m-hubert"] dit_config["lyric_encoder_vocab_size"] = 6693 dit_config["lyric_hidden_size"] = 1024 return dit_config if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt patch_size = 2 dit_config = {} dit_config["num_heads"] = 16 dit_config["patch_size"] = patch_size dit_config["hidden_size"] = 1152 dit_config["in_channels"] = 4 dit_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') y_key = "{}y_embedder.y_embedding".format(key_prefix) if y_key in state_dict_keys: dit_config["model_max_length"] = state_dict[y_key].shape[0] pe_key = "{}pos_embed".format(key_prefix) if pe_key in state_dict_keys: dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix) if ar_key in state_dict_keys: dit_config["image_model"] = "pixart_alpha" dit_config["micro_condition"] = True else: dit_config["image_model"] = "pixart_sigma" dit_config["micro_condition"] = False return dit_config if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys: # Cosmos dit_config = {} dit_config["image_model"] = "cosmos" dit_config["max_img_h"] = 240 dit_config["max_img_w"] = 240 dit_config["max_frames"] = 128 concat_padding_mask = True dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) dit_config["out_channels"] = 16 dit_config["patch_spatial"] = 2 dit_config["patch_temporal"] = 1 dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0] dit_config["block_config"] = "FA-CA-MLP" dit_config["concat_padding_mask"] = concat_padding_mask dit_config["pos_emb_cls"] = "rope3d" dit_config["pos_emb_learnable"] = False dit_config["pos_emb_interpolation"] = "crop" dit_config["block_x_format"] = "THWBD" dit_config["affline_emb_norm"] = True dit_config["use_adaln_lora"] = True dit_config["adaln_lora_dim"] = 256 if dit_config["model_channels"] == 4096: # 7B dit_config["num_blocks"] = 28 dit_config["num_heads"] = 32 dit_config["extra_per_block_abs_pos_emb"] = True dit_config["rope_h_extrapolation_ratio"] = 1.0 dit_config["rope_w_extrapolation_ratio"] = 1.0 dit_config["rope_t_extrapolation_ratio"] = 2.0 dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" else: # 5120 # 14B dit_config["num_blocks"] = 36 dit_config["num_heads"] = 40 dit_config["extra_per_block_abs_pos_emb"] = True dit_config["rope_h_extrapolation_ratio"] = 2.0 dit_config["rope_w_extrapolation_ratio"] = 2.0 dit_config["rope_t_extrapolation_ratio"] = 2.0 dit_config["extra_h_extrapolation_ratio"] = 2.0 dit_config["extra_w_extrapolation_ratio"] = 2.0 dit_config["extra_t_extrapolation_ratio"] = 2.0 dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" return dit_config if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys: # Lumina 2 dit_config = {} dit_config["image_model"] = "lumina2" dit_config["patch_size"] = 2 dit_config["in_channels"] = 16 w = state_dict['{}cap_embedder.1.weight'.format(key_prefix)] dit_config["dim"] = w.shape[0] dit_config["cap_feat_dim"] = w.shape[1] dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.') dit_config["qk_norm"] = True if dit_config["dim"] == 2304: # Original Lumina 2 dit_config["n_heads"] = 24 dit_config["n_kv_heads"] = 8 dit_config["axes_dims"] = [32, 32, 32] dit_config["axes_lens"] = [300, 512, 512] dit_config["rope_theta"] = 10000.0 dit_config["ffn_dim_multiplier"] = 4.0 ctd_weight = state_dict.get('{}clip_text_pooled_proj.0.weight'.format(key_prefix), None) if ctd_weight is not None: # NewBie dit_config["clip_text_dim"] = ctd_weight.shape[0] # NewBie also sets axes_lens = [1024, 512, 512] but it's not used in ComfyUI elif dit_config["dim"] == 3840: # Z image dit_config["n_heads"] = 30 dit_config["n_kv_heads"] = 30 dit_config["axes_dims"] = [32, 48, 48] dit_config["axes_lens"] = [1536, 512, 512] dit_config["rope_theta"] = 256.0 dit_config["ffn_dim_multiplier"] = (8.0 / 3.0) dit_config["z_image_modulation"] = True dit_config["time_scale"] = 1000.0 try: dit_config["allow_fp16"] = torch.std(state_dict['{}layers.{}.ffn_norm1.weight'.format(key_prefix, dit_config["n_layers"] - 2)], unbiased=False).item() < 0.42 except Exception: pass if '{}cap_pad_token'.format(key_prefix) in state_dict_keys: dit_config["pad_tokens_multiple"] = 32 sig_weight = state_dict.get('{}siglip_embedder.0.weight'.format(key_prefix), None) if sig_weight is not None: dit_config["siglip_feat_dim"] = sig_weight.shape[0] dec_cond_key = '{}dec_net.cond_embed.weight'.format(key_prefix) if dec_cond_key in state_dict_keys: # pixel-space variant dit_config["image_model"] = "zimage_pixel" # patch_size and in_channels are derived from x_embedder: # x_embedder: Linear(patch_size * patch_size * in_channels, dim) # The decoder also receives the full flat patch, so decoder_in_channels = x_embedder input dim. x_emb_in = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1] dec_out = state_dict['{}dec_net.final_layer.linear.weight'.format(key_prefix)].shape[0] # patch_size: infer from decoder final layer output matching x_embedder input # in_channels: infer from dec_net input_embedder (in_features = dec_in_ch + max_freqs^2) embedder_w = state_dict['{}dec_net.input_embedder.embedder.0.weight'.format(key_prefix)] dec_in_ch = dec_out # decoder in == decoder out (same pixel space) dit_config["patch_size"] = round((x_emb_in / 3) ** 0.5) # assume RGB (in_channels=3) dit_config["in_channels"] = 3 dit_config["decoder_in_channels"] = dec_in_ch dit_config["decoder_hidden_size"] = state_dict[dec_cond_key].shape[0] dit_config["decoder_num_res_blocks"] = count_blocks( state_dict_keys, '{}dec_net.res_blocks.'.format(key_prefix) + '{}.' ) dit_config["decoder_max_freqs"] = int((embedder_w.shape[1] - dec_in_ch) ** 0.5) if '{}__x0__'.format(key_prefix) in state_dict_keys: dit_config["use_x0"] = True return dit_config if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1 dit_config = {} dit_config["image_model"] = "wan2.1" dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1] out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4 dit_config["dim"] = dim dit_config["out_dim"] = out_dim dit_config["num_heads"] = dim // 128 dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0] dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') dit_config["patch_size"] = (1, 2, 2) dit_config["freq_dim"] = 256 dit_config["window_size"] = (-1, -1) dit_config["qk_norm"] = True dit_config["cross_attn_norm"] = True dit_config["eps"] = 1e-6 dit_config["in_dim"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1] if '{}vace_patch_embedding.weight'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "vace" dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1] dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.') elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys: if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "camera" else: dit_config["model_type"] = "camera_2.2" elif '{}casual_audio_encoder.encoder.final_linear.weight'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "s2v" elif '{}audio_proj.audio_proj_glob_1.layer.bias'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "humo" elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "animate" elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "scail" else: if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "i2v" else: dit_config["model_type"] = "t2v" flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix)) if flf_weight is not None: dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1] ref_conv_weight = state_dict.get('{}ref_conv.weight'.format(key_prefix)) if ref_conv_weight is not None: dit_config["in_dim_ref_conv"] = ref_conv_weight.shape[1] if metadata is not None and "config" in metadata: dit_config.update(json.loads(metadata["config"]).get("transformer", {})) return dit_config if '{}latent_in.weight'.format(key_prefix) in state_dict_keys: # Hunyuan 3D in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape dit_config = {} dit_config["image_model"] = "hunyuan3d2" dit_config["in_channels"] = in_shape[1] dit_config["context_in_dim"] = state_dict['{}cond_in.weight'.format(key_prefix)].shape[1] dit_config["hidden_size"] = in_shape[0] dit_config["mlp_ratio"] = 4.0 dit_config["num_heads"] = 16 dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') dit_config["qkv_bias"] = True dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys return dit_config if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys and f"{key_prefix}blocks.0.attn1.k_norm.weight" in state_dict_keys: # Hunyuan 3D 2.1 dit_config = {} dit_config["image_model"] = "hunyuan3d2_1" dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1] dit_config["context_dim"] = 1024 dit_config["hidden_size"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[0] dit_config["mlp_ratio"] = 4.0 dit_config["num_heads"] = 16 dit_config["depth"] = count_blocks(state_dict_keys, f"{key_prefix}blocks.{{}}") dit_config["qkv_bias"] = False dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys return dit_config if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream dit_config = {} dit_config["image_model"] = "hidream" dit_config["attention_head_dim"] = 128 dit_config["axes_dims_rope"] = [64, 32, 32] dit_config["caption_channels"] = [4096, 4096] dit_config["max_resolution"] = [128, 128] dit_config["in_channels"] = 16 dit_config["llama_layers"] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31] dit_config["num_attention_heads"] = 20 dit_config["num_routed_experts"] = 4 dit_config["num_activated_experts"] = 2 dit_config["num_layers"] = 16 dit_config["num_single_layers"] = 32 dit_config["out_channels"] = 16 dit_config["patch_size"] = 2 dit_config["text_emb_dim"] = 2048 return dit_config if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys: # Cosmos predict2 dit_config = {} dit_config["image_model"] = "cosmos_predict2" if "{}llm_adapter.blocks.0.cross_attn.q_proj.weight".format(key_prefix) in state_dict_keys: dit_config["image_model"] = "anima" dit_config["max_img_h"] = 240 dit_config["max_img_w"] = 240 dit_config["max_frames"] = 128 concat_padding_mask = True dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) dit_config["out_channels"] = 16 dit_config["patch_spatial"] = 2 dit_config["patch_temporal"] = 1 dit_config["model_channels"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0] dit_config["concat_padding_mask"] = concat_padding_mask dit_config["crossattn_emb_channels"] = 1024 dit_config["pos_emb_cls"] = "rope3d" dit_config["pos_emb_learnable"] = True dit_config["pos_emb_interpolation"] = "crop" dit_config["min_fps"] = 1 dit_config["max_fps"] = 30 dit_config["use_adaln_lora"] = True dit_config["adaln_lora_dim"] = 256 if dit_config["model_channels"] == 2048: dit_config["num_blocks"] = 28 dit_config["num_heads"] = 16 elif dit_config["model_channels"] == 5120: dit_config["num_blocks"] = 36 dit_config["num_heads"] = 40 if dit_config["in_channels"] == 16: dit_config["extra_per_block_abs_pos_emb"] = False dit_config["rope_h_extrapolation_ratio"] = 4.0 dit_config["rope_w_extrapolation_ratio"] = 4.0 dit_config["rope_t_extrapolation_ratio"] = 1.0 elif dit_config["in_channels"] == 17: # img to video if dit_config["model_channels"] == 2048: dit_config["extra_per_block_abs_pos_emb"] = False dit_config["rope_h_extrapolation_ratio"] = 3.0 dit_config["rope_w_extrapolation_ratio"] = 3.0 dit_config["rope_t_extrapolation_ratio"] = 1.0 elif dit_config["model_channels"] == 5120: dit_config["rope_h_extrapolation_ratio"] = 2.0 dit_config["rope_w_extrapolation_ratio"] = 2.0 dit_config["rope_t_extrapolation_ratio"] = 0.8333333333333334 dit_config["extra_h_extrapolation_ratio"] = 1.0 dit_config["extra_w_extrapolation_ratio"] = 1.0 dit_config["extra_t_extrapolation_ratio"] = 1.0 dit_config["rope_enable_fps_modulation"] = False return dit_config if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2 dit_config = {} dit_config["image_model"] = "omnigen2" dit_config["axes_dim_rope"] = [40, 40, 40] dit_config["axes_lens"] = [1024, 1664, 1664] dit_config["ffn_dim_multiplier"] = None dit_config["hidden_size"] = 2520 dit_config["in_channels"] = 16 dit_config["multiple_of"] = 256 dit_config["norm_eps"] = 1e-05 dit_config["num_attention_heads"] = 21 dit_config["num_kv_heads"] = 7 dit_config["num_layers"] = 32 dit_config["num_refiner_layers"] = 2 dit_config["out_channels"] = None dit_config["patch_size"] = 2 dit_config["text_feat_dim"] = 2048 dit_config["timestep_scale"] = 1000.0 return dit_config if '{}txt_norm.weight'.format(key_prefix) in state_dict_keys: # Qwen Image dit_config = {} dit_config["image_model"] = "qwen_image" dit_config["in_channels"] = state_dict['{}img_in.weight'.format(key_prefix)].shape[1] dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') if "{}__index_timestep_zero__".format(key_prefix) in state_dict_keys: # 2511 dit_config["default_ref_method"] = "index_timestep_zero" if "{}time_text_embed.addition_t_embedding.weight".format(key_prefix) in state_dict_keys: # Layered dit_config["use_additional_t_cond"] = True dit_config["default_ref_method"] = "negative_index" return dit_config if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5 dit_config = {} model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0] dit_config["model_dim"] = model_dim if model_dim in [4096, 2560]: # pro video and lite image dit_config["axes_dims"] = (32, 48, 48) if model_dim == 2560: # lite image dit_config["rope_scale_factor"] = (1.0, 1.0, 1.0) elif model_dim == 1792: # lite video dit_config["axes_dims"] = (16, 24, 24) dit_config["time_dim"] = state_dict['{}time_embeddings.in_layer.bias'.format(key_prefix)].shape[0] dit_config["image_model"] = "kandinsky5" dit_config["ff_dim"] = state_dict['{}visual_transformer_blocks.0.feed_forward.in_layer.weight'.format(key_prefix)].shape[0] dit_config["visual_embed_dim"] = state_dict['{}visual_embeddings.in_layer.weight'.format(key_prefix)].shape[1] dit_config["num_text_blocks"] = count_blocks(state_dict_keys, '{}text_transformer_blocks.'.format(key_prefix) + '{}.') dit_config["num_visual_blocks"] = count_blocks(state_dict_keys, '{}visual_transformer_blocks.'.format(key_prefix) + '{}.') return dit_config if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys: dit_config = {} dit_config["audio_model"] = "ace1.5" return dit_config if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys: return None unet_config = { "use_checkpoint": False, "image_size": 32, "use_spatial_transformer": True, "legacy": False } y_input = '{}label_emb.0.0.weight'.format(key_prefix) if y_input in state_dict_keys: unet_config["num_classes"] = "sequential" unet_config["adm_in_channels"] = state_dict[y_input].shape[1] else: unet_config["adm_in_channels"] = None model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] out_key = '{}out.2.weight'.format(key_prefix) if out_key in state_dict: out_channels = state_dict[out_key].shape[0] else: out_channels = 4 num_res_blocks = [] channel_mult = [] transformer_depth = [] transformer_depth_output = [] context_dim = None use_linear_in_transformer = False video_model = False video_model_cross = False current_res = 1 count = 0 last_res_blocks = 0 last_channel_mult = 0 input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.') for count in range(input_block_count): prefix = '{}input_blocks.{}.'.format(key_prefix, count) prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1) block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) if len(block_keys) == 0: break block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys))) if "{}0.op.weight".format(prefix) in block_keys: #new layer num_res_blocks.append(last_res_blocks) channel_mult.append(last_channel_mult) current_res *= 2 last_res_blocks = 0 last_channel_mult = 0 out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) if out is not None: transformer_depth_output.append(out[0]) else: transformer_depth_output.append(0) else: res_block_prefix = "{}0.in_layers.0.weight".format(prefix) if res_block_prefix in block_keys: last_res_blocks += 1 last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels out = calculate_transformer_depth(prefix, state_dict_keys, state_dict) if out is not None: transformer_depth.append(out[0]) if context_dim is None: context_dim = out[1] use_linear_in_transformer = out[2] video_model = out[3] video_model_cross = out[4] else: transformer_depth.append(0) res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) if res_block_prefix in block_keys_output: out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) if out is not None: transformer_depth_output.append(out[0]) else: transformer_depth_output.append(0) num_res_blocks.append(last_res_blocks) channel_mult.append(last_channel_mult) if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: transformer_depth_middle = -1 else: transformer_depth_middle = -2 unet_config["in_channels"] = in_channels unet_config["out_channels"] = out_channels unet_config["model_channels"] = model_channels unet_config["num_res_blocks"] = num_res_blocks unet_config["transformer_depth"] = transformer_depth unet_config["transformer_depth_output"] = transformer_depth_output unet_config["channel_mult"] = channel_mult unet_config["transformer_depth_middle"] = transformer_depth_middle unet_config['use_linear_in_transformer'] = use_linear_in_transformer unet_config["context_dim"] = context_dim if video_model: unet_config["extra_ff_mix_layer"] = True unet_config["use_spatial_context"] = True unet_config["merge_strategy"] = "learned_with_images" unet_config["merge_factor"] = 0.0 unet_config["video_kernel_size"] = [3, 1, 1] unet_config["use_temporal_resblock"] = True unet_config["use_temporal_attention"] = True unet_config["disable_temporal_crossattention"] = not video_model_cross else: unet_config["use_temporal_resblock"] = False unet_config["use_temporal_attention"] = False heatmap_key = '{}heatmap_head.conv_layers.0.weight'.format(key_prefix) if heatmap_key in state_dict_keys: unet_config["heatmap_head"] = True return unet_config def model_config_from_unet_config(unet_config, state_dict=None): for model_config in comfy.supported_models.models: if model_config.matches(unet_config, state_dict): return model_config(unet_config) logging.error("no match {}".format(unet_config)) return None def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None): unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata) if unet_config is None: return None model_config = model_config_from_unet_config(unet_config, state_dict) if model_config is None and use_base_if_no_match: model_config = comfy.supported_models_base.BASE(unet_config) # Detect per-layer quantization (mixed precision) quant_config = comfy.utils.detect_layer_quantization(state_dict, unet_key_prefix) if quant_config: model_config.quant_config = quant_config logging.info("Detected mixed precision quantization") return model_config def unet_prefix_from_state_dict(state_dict): candidates = ["model.diffusion_model.", #ldm/sgm models "model.model.", #audio models "net.", #cosmos ] counts = {k: 0 for k in candidates} for k in state_dict: for c in candidates: if k.startswith(c): counts[c] += 1 break top = max(counts, key=counts.get) if counts[top] > 5: return top else: return "model." #aura flow and others def convert_config(unet_config): new_config = unet_config.copy() num_res_blocks = new_config.get("num_res_blocks", None) channel_mult = new_config.get("channel_mult", None) if isinstance(num_res_blocks, int): num_res_blocks = len(channel_mult) * [num_res_blocks] if "attention_resolutions" in new_config: attention_resolutions = new_config.pop("attention_resolutions") transformer_depth = new_config.get("transformer_depth", None) transformer_depth_middle = new_config.get("transformer_depth_middle", None) if isinstance(transformer_depth, int): transformer_depth = len(channel_mult) * [transformer_depth] if transformer_depth_middle is None: transformer_depth_middle = transformer_depth[-1] t_in = [] t_out = [] s = 1 for i in range(len(num_res_blocks)): res = num_res_blocks[i] d = 0 if s in attention_resolutions: d = transformer_depth[i] t_in += [d] * res t_out += [d] * (res + 1) s *= 2 transformer_depth = t_in new_config["transformer_depth"] = t_in new_config["transformer_depth_output"] = t_out new_config["transformer_depth_middle"] = transformer_depth_middle new_config["num_res_blocks"] = num_res_blocks return new_config def unet_config_from_diffusers_unet(state_dict, dtype=None): if "conv_in.weight" not in state_dict: return None match = {} transformer_depth = [] attn_res = 1 down_blocks = count_blocks(state_dict, "down_blocks.{}") for i in range(down_blocks): attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}') for ab in range(attn_blocks): transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') transformer_depth.append(transformer_count) if transformer_count > 0: match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1] attn_res *= 2 if attn_blocks == 0: for i in range(res_blocks): transformer_depth.append(0) match["transformer_depth"] = transformer_depth match["model_channels"] = state_dict["conv_in.weight"].shape[0] match["in_channels"] = state_dict["conv_in.weight"].shape[1] match["adm_in_channels"] = None if "class_embedding.linear_1.weight" in state_dict: match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] elif "add_embedding.linear_1.weight" in state_dict: match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} supported_models = [LotusD, SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint] for unet_config in supported_models: matches = True for k in match: if match[k] != unet_config[k]: matches = False break if matches: return convert_config(unet_config) return None def model_config_from_diffusers_unet(state_dict): unet_config = unet_config_from_diffusers_unet(state_dict) if unet_config is not None: return model_config_from_unet_config(unet_config) return None def convert_diffusers_mmdit(state_dict, output_prefix=""): out_sd = {} if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.') num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.') sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix) elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix) elif 'noise_refiner.0.attention.norm_k.weight' in state_dict: n_layers = count_blocks(state_dict, 'layers.{}.') dim = state_dict['noise_refiner.0.attention.to_k.weight'].shape[0] sd_map = comfy.utils.z_image_to_diffusers({"n_layers": n_layers, "dim": dim}, output_prefix=output_prefix) for k in state_dict: # For zeta chroma if k not in sd_map: sd_map[k] = k elif 'x_embedder.weight' in state_dict: #Flux depth = count_blocks(state_dict, 'transformer_blocks.{}.') depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.') hidden_size = state_dict["x_embedder.bias"].shape[0] sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix) elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict and 'pos_embed.proj.weight' in state_dict: #SD3 num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') depth = state_dict["pos_embed.proj.weight"].shape[0] // 64 sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix) else: return None for k in sd_map: weight = state_dict.get(k, None) if weight is not None: t = sd_map[k] if not isinstance(t, str): if len(t) > 2: fun = t[2] else: fun = lambda a: a offset = t[1] if offset is not None: old_weight = out_sd.get(t[0], None) if old_weight is None: old_weight = torch.empty_like(weight) if old_weight.shape[offset[0]] < offset[1] + offset[2]: exp = list(weight.shape) exp[offset[0]] = offset[1] + offset[2] new = torch.empty(exp, device=weight.device, dtype=weight.dtype) new[:old_weight.shape[0]] = old_weight old_weight = new if old_weight is out_sd.get(t[0], None) and comfy.memory_management.aimdo_enabled: old_weight = old_weight.clone() w = old_weight.narrow(offset[0], offset[1], offset[2]) else: if comfy.memory_management.aimdo_enabled: weight = weight.clone() old_weight = weight w = weight w[:] = fun(weight) t = t[0] out_sd[t] = old_weight else: out_sd[t] = weight state_dict.pop(k) return out_sd ================================================ FILE: comfy/model_management.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Comfy This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import psutil import logging from enum import Enum from comfy.cli_args import args, PerformanceFeature import threading import torch import sys import platform import weakref import gc import os from contextlib import nullcontext import comfy.memory_management import comfy.utils import comfy.quant_ops class VRAMState(Enum): DISABLED = 0 #No vram present: no need to move models to vram NO_VRAM = 1 #Very low vram: enable all the options to save vram LOW_VRAM = 2 NORMAL_VRAM = 3 HIGH_VRAM = 4 SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. class CPUState(Enum): GPU = 0 CPU = 1 MPS = 2 # Determine VRAM State vram_state = VRAMState.NORMAL_VRAM set_vram_to = VRAMState.NORMAL_VRAM cpu_state = CPUState.GPU total_vram = 0 # Training Related State in_training = False def get_supported_float8_types(): float8_types = [] try: float8_types.append(torch.float8_e4m3fn) except: pass try: float8_types.append(torch.float8_e4m3fnuz) except: pass try: float8_types.append(torch.float8_e5m2) except: pass try: float8_types.append(torch.float8_e5m2fnuz) except: pass try: float8_types.append(torch.float8_e8m0fnu) except: pass return float8_types FLOAT8_TYPES = get_supported_float8_types() xpu_available = False torch_version = "" try: torch_version = torch.version.__version__ temp = torch_version.split(".") torch_version_numeric = (int(temp[0]), int(temp[1])) except: pass lowvram_available = True if args.deterministic: logging.info("Using deterministic algorithms for pytorch") torch.use_deterministic_algorithms(True, warn_only=True) directml_enabled = False if args.directml is not None: logging.warning("WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it, there are better options.") import torch_directml directml_enabled = True device_index = args.directml if device_index < 0: directml_device = torch_directml.device() else: directml_device = torch_directml.device(device_index) logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index))) # torch_directml.disable_tiled_resources(True) lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. try: import intel_extension_for_pytorch as ipex # noqa: F401 except: pass try: _ = torch.xpu.device_count() xpu_available = torch.xpu.is_available() except: xpu_available = False try: if torch.backends.mps.is_available(): cpu_state = CPUState.MPS import torch.mps except: pass try: import torch_npu # noqa: F401 _ = torch.npu.device_count() npu_available = torch.npu.is_available() except: npu_available = False try: import torch_mlu # noqa: F401 _ = torch.mlu.device_count() mlu_available = torch.mlu.is_available() except: mlu_available = False try: ixuca_available = hasattr(torch, "corex") except: ixuca_available = False if args.cpu: cpu_state = CPUState.CPU def is_intel_xpu(): global cpu_state global xpu_available if cpu_state == CPUState.GPU: if xpu_available: return True return False def is_ascend_npu(): global npu_available if npu_available: return True return False def is_mlu(): global mlu_available if mlu_available: return True return False def is_ixuca(): global ixuca_available if ixuca_available: return True return False def is_wsl(): version = platform.uname().release if version.endswith("-Microsoft"): return True elif version.endswith("microsoft-standard-WSL2"): return True return False def get_torch_device(): global directml_enabled global cpu_state if directml_enabled: global directml_device return directml_device if cpu_state == CPUState.MPS: return torch.device("mps") if cpu_state == CPUState.CPU: return torch.device("cpu") else: if is_intel_xpu(): return torch.device("xpu", torch.xpu.current_device()) elif is_ascend_npu(): return torch.device("npu", torch.npu.current_device()) elif is_mlu(): return torch.device("mlu", torch.mlu.current_device()) else: return torch.device(torch.cuda.current_device()) def get_total_memory(dev=None, torch_total_too=False): global directml_enabled if dev is None: dev = get_torch_device() if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): mem_total = psutil.virtual_memory().total mem_total_torch = mem_total else: if directml_enabled: mem_total = 1024 * 1024 * 1024 #TODO mem_total_torch = mem_total elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] mem_total_xpu = torch.xpu.get_device_properties(dev).total_memory mem_total_torch = mem_reserved mem_total = mem_total_xpu elif is_ascend_npu(): stats = torch.npu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] _, mem_total_npu = torch.npu.mem_get_info(dev) mem_total_torch = mem_reserved mem_total = mem_total_npu elif is_mlu(): stats = torch.mlu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] _, mem_total_mlu = torch.mlu.mem_get_info(dev) mem_total_torch = mem_reserved mem_total = mem_total_mlu else: stats = torch.cuda.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] _, mem_total_cuda = torch.cuda.mem_get_info(dev) mem_total_torch = mem_reserved mem_total = mem_total_cuda if torch_total_too: return (mem_total, mem_total_torch) else: return mem_total def mac_version(): try: return tuple(int(n) for n in platform.mac_ver()[0].split(".")) except: return None total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) try: logging.info("pytorch version: {}".format(torch_version)) mac_ver = mac_version() if mac_ver is not None: logging.info("Mac Version {}".format(mac_ver)) except: pass try: OOM_EXCEPTION = torch.cuda.OutOfMemoryError except: OOM_EXCEPTION = Exception try: ACCELERATOR_ERROR = torch.AcceleratorError except AttributeError: ACCELERATOR_ERROR = RuntimeError def is_oom(e): if isinstance(e, OOM_EXCEPTION): return True if isinstance(e, ACCELERATOR_ERROR) and (getattr(e, 'error_code', None) == 2 or "out of memory" in str(e).lower()): discard_cuda_async_error() return True return False def raise_non_oom(e): if not is_oom(e): raise e XFORMERS_VERSION = "" XFORMERS_ENABLED_VAE = True if args.disable_xformers: XFORMERS_IS_AVAILABLE = False else: try: import xformers import xformers.ops XFORMERS_IS_AVAILABLE = True try: XFORMERS_IS_AVAILABLE = xformers._has_cpp_library except: pass try: XFORMERS_VERSION = xformers.version.__version__ logging.info("xformers version: {}".format(XFORMERS_VERSION)) if XFORMERS_VERSION.startswith("0.0.18"): logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") logging.warning("Please downgrade or upgrade xformers to a different version.\n") XFORMERS_ENABLED_VAE = False except: pass except: XFORMERS_IS_AVAILABLE = False def is_nvidia(): global cpu_state if cpu_state == CPUState.GPU: if torch.version.cuda: return True return False def is_amd(): global cpu_state if cpu_state == CPUState.GPU: if torch.version.hip: return True return False def amd_min_version(device=None, min_rdna_version=0): if not is_amd(): return False if is_device_cpu(device): return False arch = torch.cuda.get_device_properties(device).gcnArchName if arch.startswith('gfx') and len(arch) == 7: try: cmp_rdna_version = int(arch[4]) + 2 except: cmp_rdna_version = 0 if cmp_rdna_version >= min_rdna_version: return True return False MIN_WEIGHT_MEMORY_RATIO = 0.4 if is_nvidia(): MIN_WEIGHT_MEMORY_RATIO = 0.0 ENABLE_PYTORCH_ATTENTION = False if args.use_pytorch_cross_attention: ENABLE_PYTORCH_ATTENTION = True XFORMERS_IS_AVAILABLE = False try: if is_nvidia(): if torch_version_numeric[0] >= 2: if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True if is_intel_xpu() or is_ascend_npu() or is_mlu() or is_ixuca(): if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True except: pass SUPPORT_FP8_OPS = args.supports_fp8_compute AMD_RDNA2_AND_OLDER_ARCH = ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"] AMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN' try: if is_amd(): arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName.split(':')[0] if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)): if os.getenv(AMD_ENABLE_MIOPEN_ENV) != '1': torch.backends.cudnn.enabled = False # Seems to improve things a lot on AMD logging.info("Set: torch.backends.cudnn.enabled = False for better AMD performance.") try: rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2])) except: rocm_version = (6, -1) def aotriton_supported(gpu_arch): path = torch.__path__[0] path = os.path.join(os.path.join(path, "lib"), "aotriton.images") gfx = set(map(lambda a: a[4:], filter(lambda a: a.startswith("amd-gfx"), os.listdir(path)))) if gpu_arch in gfx: return True if "{}x".format(gpu_arch[:-1]) in gfx: return True if "{}xx".format(gpu_arch[:-2]) in gfx: return True return False logging.info("AMD arch: {}".format(arch)) logging.info("ROCm version: {}".format(rocm_version)) if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: if aotriton_supported(arch): # AMD efficient attention implementation depends on aotriton. if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]): # TODO: more arches, TODO: gfx950 ENABLE_PYTORCH_ATTENTION = True if rocm_version >= (7, 0): if any((a in arch) for a in ["gfx1200", "gfx1201"]): ENABLE_PYTORCH_ATTENTION = True if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4): if any((a in arch) for a in ["gfx1200", "gfx1201", "gfx950"]): # TODO: more arches, "gfx942" gives error on pytorch nightly 2.10 1013 rocm7.0 SUPPORT_FP8_OPS = True except: pass if ENABLE_PYTORCH_ATTENTION: torch.backends.cuda.enable_math_sdp(True) torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) PRIORITIZE_FP16 = False # TODO: remove and replace with something that shows exactly which dtype is faster than the other try: if (is_nvidia() or is_amd()) and PerformanceFeature.Fp16Accumulation in args.fast: torch.backends.cuda.matmul.allow_fp16_accumulation = True PRIORITIZE_FP16 = True # TODO: limit to cards where it actually boosts performance logging.info("Enabled fp16 accumulation.") except: pass if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast: torch.backends.cudnn.benchmark = True try: if torch_version_numeric >= (2, 5): torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True) except: logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp") if args.lowvram: set_vram_to = VRAMState.LOW_VRAM lowvram_available = True elif args.novram: set_vram_to = VRAMState.NO_VRAM elif args.highvram or args.gpu_only: vram_state = VRAMState.HIGH_VRAM FORCE_FP32 = False if args.force_fp32: logging.info("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True if lowvram_available: if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): vram_state = set_vram_to if cpu_state != CPUState.GPU: vram_state = VRAMState.DISABLED if cpu_state == CPUState.MPS: vram_state = VRAMState.SHARED logging.info(f"Set vram state to: {vram_state.name}") DISABLE_SMART_MEMORY = args.disable_smart_memory if DISABLE_SMART_MEMORY: logging.info("Disabling smart memory management") def get_torch_device_name(device): if hasattr(device, 'type'): if device.type == "cuda": try: allocator_backend = torch.cuda.get_allocator_backend() except: allocator_backend = "" return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) elif device.type == "xpu": return "{} {}".format(device, torch.xpu.get_device_name(device)) else: return "{}".format(device.type) elif is_intel_xpu(): return "{} {}".format(device, torch.xpu.get_device_name(device)) elif is_ascend_npu(): return "{} {}".format(device, torch.npu.get_device_name(device)) elif is_mlu(): return "{} {}".format(device, torch.mlu.get_device_name(device)) else: return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) try: logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) except: logging.warning("Could not pick default device.") current_loaded_models = [] def module_size(module): module_mem = 0 sd = module.state_dict() for k in sd: t = sd[k] module_mem += t.nbytes return module_mem def module_mmap_residency(module, free=False): mmap_touched_mem = 0 module_mem = 0 bounced_mmaps = set() sd = module.state_dict() for k in sd: t = sd[k] module_mem += t.nbytes storage = t._qdata.untyped_storage() if isinstance(t, comfy.quant_ops.QuantizedTensor) else t.untyped_storage() if not getattr(storage, "_comfy_tensor_mmap_touched", False): continue mmap_touched_mem += t.nbytes if not free: continue storage._comfy_tensor_mmap_touched = False mmap_obj = storage._comfy_tensor_mmap_refs[0] if mmap_obj in bounced_mmaps: continue mmap_obj.bounce() bounced_mmaps.add(mmap_obj) return mmap_touched_mem, module_mem class LoadedModel: def __init__(self, model): self._set_model(model) self.device = model.load_device self.real_model = None self.currently_used = True self.model_finalizer = None self._patcher_finalizer = None def _set_model(self, model): self._model = weakref.ref(model) if model.parent is not None: self._parent_model = weakref.ref(model.parent) self._patcher_finalizer = weakref.finalize(model, self._switch_parent) self._patcher_finalizer.atexit = False def _switch_parent(self): model = self._parent_model() if model is not None: self._set_model(model) @property def model(self): return self._model() def model_memory(self): return self.model.model_size() def model_mmap_residency(self, free=False): return self.model.model_mmap_residency(free=free) def model_loaded_memory(self): return self.model.loaded_size() def model_offloaded_memory(self): return self.model.model_size() - self.model.loaded_size() def model_memory_required(self, device): if device == self.model.current_loaded_device(): return self.model_offloaded_memory() else: return self.model_memory() def model_load(self, lowvram_model_memory=0, force_patch_weights=False): self.model.model_patches_to(self.device) self.model.model_patches_to(self.model.model_dtype()) # if self.model.loaded_size() > 0: use_more_vram = lowvram_model_memory if use_more_vram == 0: use_more_vram = 1e32 self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights) real_model = self.model.model if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None: with torch.no_grad(): real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True) self.real_model = weakref.ref(real_model) self.model_finalizer = weakref.finalize(real_model, cleanup_models) self.model_finalizer.atexit = False return real_model def should_reload_model(self, force_patch_weights=False): if force_patch_weights and self.model.lowvram_patch_counter() > 0: return True return False def model_unload(self, memory_to_free=None, unpatch_weights=True): if memory_to_free is not None: if memory_to_free < self.model.loaded_size(): freed = self.model.partially_unload(self.model.offload_device, memory_to_free) if freed >= memory_to_free: return False self.model.detach(unpatch_weights) self.model_finalizer.detach() self.model_finalizer = None self.real_model = None return True def model_use_more_vram(self, extra_memory, force_patch_weights=False): return self.model.partially_load(self.device, extra_memory, force_patch_weights=force_patch_weights) def __eq__(self, other): return self.model is other.model def __del__(self): if self._patcher_finalizer is not None: self._patcher_finalizer.detach() def is_dead(self): return self.real_model() is not None and self.model is None def use_more_memory(extra_memory, loaded_models, device): for m in loaded_models: if m.device == device: extra_memory -= m.model_use_more_vram(extra_memory) if extra_memory <= 0: break def offloaded_memory(loaded_models, device): offloaded_mem = 0 for m in loaded_models: if m.device == device: offloaded_mem += m.model_offloaded_memory() return offloaded_mem WINDOWS = any(platform.win32_ver()) EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 if WINDOWS: import comfy.windows EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue if total_vram > (15 * 1024): # more extra reserved vram on 16GB+ cards EXTRA_RESERVED_VRAM += 100 * 1024 * 1024 def get_free_ram(): return comfy.windows.get_free_ram() else: def get_free_ram(): return psutil.virtual_memory().available if args.reserve_vram is not None: EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024 logging.debug("Reserving {}MB vram for other applications.".format(EXTRA_RESERVED_VRAM / (1024 * 1024))) def extra_reserved_memory(): return EXTRA_RESERVED_VRAM def minimum_inference_memory(): return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory() def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0): cleanup_models_gc() unloaded_model = [] can_unload = [] unloaded_models = [] for i in range(len(current_loaded_models) -1, -1, -1): shift_model = current_loaded_models[i] if shift_model.device == device: if shift_model not in keep_loaded and not shift_model.is_dead(): can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) shift_model.currently_used = False can_unload_sorted = sorted(can_unload) for x in can_unload_sorted: i = x[-1] memory_to_free = 1e32 pins_to_free = 1e32 if not DISABLE_SMART_MEMORY: memory_to_free = memory_required - get_free_memory(device) pins_to_free = pins_required - get_free_ram() if current_loaded_models[i].model.is_dynamic() and for_dynamic: #don't actually unload dynamic models for the sake of other dynamic models #as that works on-demand. memory_required -= current_loaded_models[i].model.loaded_size() memory_to_free = 0 if memory_to_free > 0 and current_loaded_models[i].model_unload(memory_to_free): logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}") unloaded_model.append(i) if pins_to_free > 0: logging.debug(f"PIN Unloading {current_loaded_models[i].model.model.__class__.__name__}") current_loaded_models[i].model.partially_unload_ram(pins_to_free) for x in can_unload_sorted: i = x[-1] ram_to_free = ram_required - psutil.virtual_memory().available if ram_to_free <= 0 and i not in unloaded_model: continue resident_memory, _ = current_loaded_models[i].model_mmap_residency(free=True) if resident_memory > 0: logging.debug(f"RAM Unloading {current_loaded_models[i].model.model.__class__.__name__}") for i in sorted(unloaded_model, reverse=True): unloaded_models.append(current_loaded_models.pop(i)) if len(unloaded_model) > 0: soft_empty_cache() else: if vram_state != VRAMState.HIGH_VRAM: mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True) if mem_free_torch > mem_free_total * 0.25: soft_empty_cache() return unloaded_models def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False): cleanup_models_gc() global vram_state inference_memory = minimum_inference_memory() extra_mem = max(inference_memory, memory_required + extra_reserved_memory()) if minimum_memory_required is None: minimum_memory_required = extra_mem else: minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory()) models_temp = set() for m in models: models_temp.add(m) for mm in m.model_patches_models(): models_temp.add(mm) models = models_temp models_to_load = [] free_for_dynamic=True for x in models: if not x.is_dynamic(): free_for_dynamic = False loaded_model = LoadedModel(x) try: loaded_model_index = current_loaded_models.index(loaded_model) except: loaded_model_index = None if loaded_model_index is not None: loaded = current_loaded_models[loaded_model_index] loaded.currently_used = True models_to_load.append(loaded) else: if hasattr(x, "model"): logging.info(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) for loaded_model in models_to_load: to_unload = [] for i in range(len(current_loaded_models)): if loaded_model.model.is_clone(current_loaded_models[i].model): to_unload = [i] + to_unload for i in to_unload: model_to_unload = current_loaded_models.pop(i) model_to_unload.model.detach(unpatch_all=False) model_to_unload.model_finalizer.detach() total_memory_required = {} total_pins_required = {} total_ram_required = {} for loaded_model in models_to_load: device = loaded_model.device total_memory_required[device] = total_memory_required.get(device, 0) + loaded_model.model_memory_required(device) resident_memory, model_memory = loaded_model.model_mmap_residency() pinned_memory = loaded_model.model.pinned_memory_size() #FIXME: This can over-free the pins as it budgets to pin the entire model. We should #make this JIT to keep as much pinned as possible. pins_required = model_memory - pinned_memory ram_required = model_memory - resident_memory total_pins_required[device] = total_pins_required.get(device, 0) + pins_required total_ram_required[device] = total_ram_required.get(device, 0) + ram_required for device in total_memory_required: if device != torch.device("cpu"): free_memory(total_memory_required[device] * 1.1 + extra_mem, device, for_dynamic=free_for_dynamic, pins_required=total_pins_required[device], ram_required=total_ram_required[device]) for device in total_memory_required: if device != torch.device("cpu"): free_mem = get_free_memory(device) if free_mem < minimum_memory_required: models_l = free_memory(minimum_memory_required, device, for_dynamic=free_for_dynamic) logging.info("{} models unloaded.".format(len(models_l))) for loaded_model in models_to_load: model = loaded_model.model torch_dev = model.load_device if is_device_cpu(torch_dev): vram_set_state = VRAMState.DISABLED else: vram_set_state = vram_state lowvram_model_memory = 0 if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load: loaded_memory = loaded_model.model_loaded_memory() current_free_mem = get_free_memory(torch_dev) + loaded_memory lowvram_model_memory = max(0, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) lowvram_model_memory = lowvram_model_memory - loaded_memory if lowvram_model_memory == 0: lowvram_model_memory = 0.1 if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 0.1 loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) current_loaded_models.insert(0, loaded_model) return def load_model_gpu(model): return load_models_gpu([model]) def loaded_models(only_currently_used=False): output = [] for m in current_loaded_models: if only_currently_used: if not m.currently_used: continue output.append(m.model) return output def cleanup_models_gc(): do_gc = False reset_cast_buffers() for i in range(len(current_loaded_models)): cur = current_loaded_models[i] if cur.is_dead(): logging.info("Potential memory leak detected with model {}, doing a full garbage collect, for maximum performance avoid circular references in the model code.".format(cur.real_model().__class__.__name__)) do_gc = True break if do_gc: gc.collect() soft_empty_cache() for i in range(len(current_loaded_models)): cur = current_loaded_models[i] if cur.is_dead(): logging.warning("WARNING, memory leak with model {}. Please make sure it is not being referenced from somewhere.".format(cur.real_model().__class__.__name__)) def archive_model_dtypes(model): for name, module in model.named_modules(): for param_name, param in module.named_parameters(recurse=False): setattr(module, f"{param_name}_comfy_model_dtype", param.dtype) for buf_name, buf in module.named_buffers(recurse=False): setattr(module, f"{buf_name}_comfy_model_dtype", buf.dtype) def cleanup_models(): to_delete = [] for i in range(len(current_loaded_models)): if current_loaded_models[i].real_model() is None: to_delete = [i] + to_delete for i in to_delete: x = current_loaded_models.pop(i) del x def dtype_size(dtype): dtype_size = 4 if dtype == torch.float16 or dtype == torch.bfloat16: dtype_size = 2 elif dtype == torch.float32: dtype_size = 4 else: try: dtype_size = dtype.itemsize except: #Old pytorch doesn't have .itemsize pass return dtype_size def unet_offload_device(): if vram_state == VRAMState.HIGH_VRAM: return get_torch_device() else: return torch.device("cpu") def unet_inital_load_device(parameters, dtype): cpu_dev = torch.device("cpu") if comfy.memory_management.aimdo_enabled: return cpu_dev torch_dev = get_torch_device() if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED: return torch_dev if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM: return cpu_dev model_size = dtype_size(dtype) * parameters mem_dev = get_free_memory(torch_dev) mem_cpu = get_free_memory(cpu_dev) if mem_dev > mem_cpu and model_size < mem_dev: return torch_dev else: return cpu_dev def maximum_vram_for_weights(device=None): return (get_total_memory(device) * 0.88 - minimum_inference_memory()) def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32], weight_dtype=None): if model_params < 0: model_params = 1000000000000000000000 if args.fp32_unet: return torch.float32 if args.fp64_unet: return torch.float64 if args.bf16_unet: return torch.bfloat16 if args.fp16_unet: return torch.float16 if args.fp8_e4m3fn_unet: return torch.float8_e4m3fn if args.fp8_e5m2_unet: return torch.float8_e5m2 if args.fp8_e8m0fnu_unet: return torch.float8_e8m0fnu fp8_dtype = None if weight_dtype in FLOAT8_TYPES: fp8_dtype = weight_dtype if fp8_dtype is not None: if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive return fp8_dtype free_model_memory = maximum_vram_for_weights(device) if model_params * 2 > free_model_memory: return fp8_dtype if PRIORITIZE_FP16 or weight_dtype == torch.float16: if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params): return torch.float16 for dt in supported_dtypes: if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params): if torch.float16 in supported_dtypes: return torch.float16 if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params): if torch.bfloat16 in supported_dtypes: return torch.bfloat16 for dt in supported_dtypes: if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True): if torch.float16 in supported_dtypes: return torch.float16 if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True): if torch.bfloat16 in supported_dtypes: return torch.bfloat16 return torch.float32 # None means no manual cast def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): if weight_dtype == torch.float32 or weight_dtype == torch.float64: return None fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) if fp16_supported and weight_dtype == torch.float16: return None bf16_supported = should_use_bf16(inference_device) if bf16_supported and weight_dtype == torch.bfloat16: return None fp16_supported = should_use_fp16(inference_device, prioritize_performance=True) if PRIORITIZE_FP16 and fp16_supported and torch.float16 in supported_dtypes: return torch.float16 for dt in supported_dtypes: if dt == torch.float16 and fp16_supported: return torch.float16 if dt == torch.bfloat16 and bf16_supported: return torch.bfloat16 return torch.float32 def text_encoder_offload_device(): if args.gpu_only: return get_torch_device() else: return torch.device("cpu") def text_encoder_device(): if args.gpu_only: return get_torch_device() elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled: if should_use_fp16(prioritize_performance=False): return get_torch_device() else: return torch.device("cpu") else: return torch.device("cpu") def text_encoder_initial_device(load_device, offload_device, model_size=0): if comfy.memory_management.aimdo_enabled: return offload_device if load_device == offload_device or model_size <= 1024 * 1024 * 1024: return offload_device if is_device_mps(load_device): return load_device mem_l = get_free_memory(load_device) mem_o = get_free_memory(offload_device) if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l: return load_device else: return offload_device def text_encoder_dtype(device=None): if args.fp8_e4m3fn_text_enc: return torch.float8_e4m3fn elif args.fp8_e5m2_text_enc: return torch.float8_e5m2 elif args.fp16_text_enc: return torch.float16 elif args.bf16_text_enc: return torch.bfloat16 elif args.fp32_text_enc: return torch.float32 if is_device_cpu(device): return torch.float16 return torch.float16 def intermediate_device(): if args.gpu_only: return get_torch_device() else: return torch.device("cpu") def intermediate_dtype(): if args.fp16_intermediates: return torch.float16 else: return torch.float32 def vae_device(): if args.cpu_vae: return torch.device("cpu") return get_torch_device() def vae_offload_device(): if args.gpu_only: return get_torch_device() else: return torch.device("cpu") def vae_dtype(device=None, allowed_dtypes=[]): if args.fp16_vae: return torch.float16 elif args.bf16_vae: return torch.bfloat16 elif args.fp32_vae: return torch.float32 for d in allowed_dtypes: if d == torch.float16 and should_use_fp16(device): return d if d == torch.bfloat16 and should_use_bf16(device): return d return torch.float32 def get_autocast_device(dev): if hasattr(dev, 'type'): return dev.type return "cuda" def supports_dtype(device, dtype): #TODO if dtype == torch.float32: return True if is_device_cpu(device): return False if dtype == torch.float16: return True if dtype == torch.bfloat16: return True return False def supports_cast(device, dtype): #TODO if dtype == torch.float32: return True if dtype == torch.float16: return True if directml_enabled: #TODO: test this return False if dtype == torch.bfloat16: return True if is_device_mps(device): return False if dtype == torch.float8_e4m3fn: return True if dtype == torch.float8_e5m2: return True return False def pick_weight_dtype(dtype, fallback_dtype, device=None): if dtype is None: dtype = fallback_dtype elif dtype_size(dtype) > dtype_size(fallback_dtype): dtype = fallback_dtype if not supports_cast(device, dtype): dtype = fallback_dtype return dtype def device_supports_non_blocking(device): if args.force_non_blocking: return True if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking if is_intel_xpu(): #xpu does support non blocking but it is slower on iGPUs for some reason so disable by default until situation changes return False if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews) return False if directml_enabled: return False return True def force_channels_last(): if args.force_channels_last: return True #TODO return False STREAMS = {} NUM_STREAMS = 0 if args.async_offload is not None: NUM_STREAMS = args.async_offload else: # Enable by default on Nvidia and AMD if is_nvidia() or is_amd(): NUM_STREAMS = 2 if args.disable_async_offload: NUM_STREAMS = 0 if NUM_STREAMS > 0: logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) def current_stream(device): if device is None: return None if is_device_cuda(device): return torch.cuda.current_stream() elif is_device_xpu(device): return torch.xpu.current_stream() else: return None stream_counters = {} STREAM_CAST_BUFFERS = {} LARGEST_CASTED_WEIGHT = (None, 0) def get_cast_buffer(offload_stream, device, size, ref): global LARGEST_CASTED_WEIGHT if offload_stream is not None: wf_context = offload_stream if hasattr(wf_context, "as_context"): wf_context = wf_context.as_context(offload_stream) else: wf_context = nullcontext() cast_buffer = STREAM_CAST_BUFFERS.get(offload_stream, None) if cast_buffer is None or cast_buffer.numel() < size: if ref is LARGEST_CASTED_WEIGHT[0]: #If there is one giant weight we do not want both streams to #allocate a buffer for it. It's up to the caster to get the other #offload stream in this corner case return None if cast_buffer is not None and cast_buffer.numel() > 50 * (1024 ** 2): #I want my wrongly sized 50MB+ of VRAM back from the caching allocator right now synchronize() del STREAM_CAST_BUFFERS[offload_stream] del cast_buffer soft_empty_cache() with wf_context: cast_buffer = torch.empty((size), dtype=torch.int8, device=device) STREAM_CAST_BUFFERS[offload_stream] = cast_buffer if size > LARGEST_CASTED_WEIGHT[1]: LARGEST_CASTED_WEIGHT = (ref, size) return cast_buffer def reset_cast_buffers(): global LARGEST_CASTED_WEIGHT LARGEST_CASTED_WEIGHT = (None, 0) for offload_stream in STREAM_CAST_BUFFERS: offload_stream.synchronize() synchronize() STREAM_CAST_BUFFERS.clear() soft_empty_cache() def get_offload_stream(device): stream_counter = stream_counters.get(device, 0) if NUM_STREAMS == 0: return None if torch.compiler.is_compiling(): return None if device in STREAMS: ss = STREAMS[device] #Sync the oldest stream in the queue with the current ss[stream_counter].wait_stream(current_stream(device)) stream_counter = (stream_counter + 1) % len(ss) stream_counters[device] = stream_counter return ss[stream_counter] elif is_device_cuda(device): ss = [] for k in range(NUM_STREAMS): s1 = torch.cuda.Stream(device=device, priority=0) s1.as_context = torch.cuda.stream ss.append(s1) STREAMS[device] = ss s = ss[stream_counter] stream_counters[device] = stream_counter return s elif is_device_xpu(device): ss = [] for k in range(NUM_STREAMS): s1 = torch.xpu.Stream(device=device, priority=0) s1.as_context = torch.xpu.stream ss.append(s1) STREAMS[device] = ss s = ss[stream_counter] stream_counters[device] = stream_counter return s return None def sync_stream(device, stream): if stream is None or current_stream(device) is None: return current_stream(device).wait_stream(stream) def cast_to_gathered(tensors, r, non_blocking=False, stream=None): wf_context = nullcontext() if stream is not None: wf_context = stream if hasattr(wf_context, "as_context"): wf_context = wf_context.as_context(stream) dest_views = comfy.memory_management.interpret_gathered_like(tensors, r) with wf_context: for tensor in tensors: dest_view = dest_views.pop(0) if tensor is None: continue if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_view): continue storage = tensor._qdata.untyped_storage() if isinstance(tensor, comfy.quant_ops.QuantizedTensor) else tensor.untyped_storage() if hasattr(storage, "_comfy_tensor_mmap_touched"): storage._comfy_tensor_mmap_touched = True dest_view.copy_(tensor, non_blocking=non_blocking) def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None, r=None): if device is None or weight.device == device: if not copy: if dtype is None or weight.dtype == dtype: return weight if stream is not None: wf_context = stream if hasattr(wf_context, "as_context"): wf_context = wf_context.as_context(stream) with wf_context: return weight.to(dtype=dtype, copy=copy) return weight.to(dtype=dtype, copy=copy) if stream is not None: wf_context = stream if hasattr(wf_context, "as_context"): wf_context = wf_context.as_context(stream) with wf_context: if r is None: r = torch.empty_like(weight, dtype=dtype, device=device) r.copy_(weight, non_blocking=non_blocking) else: if r is None: r = torch.empty_like(weight, dtype=dtype, device=device) r.copy_(weight, non_blocking=non_blocking) return r def cast_to_device(tensor, device, dtype, copy=False): non_blocking = device_supports_non_blocking(device) return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy) PINNED_MEMORY = {} TOTAL_PINNED_MEMORY = 0 MAX_PINNED_MEMORY = -1 if not args.disable_pinned_memory: if is_nvidia() or is_amd(): if WINDOWS: MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45 # Windows limit is apparently 50% else: MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95 logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024))) PINNING_ALLOWED_TYPES = set(["Tensor", "Parameter", "QuantizedTensor"]) def discard_cuda_async_error(): try: a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device()) b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device()) _ = a + b synchronize() except RuntimeError: #Dump it! We already know about it from the synchronous return pass def pin_memory(tensor): global TOTAL_PINNED_MEMORY if MAX_PINNED_MEMORY <= 0: return False if type(tensor).__name__ not in PINNING_ALLOWED_TYPES: return False if not is_device_cpu(tensor.device): return False if tensor.is_pinned(): #NOTE: Cuda does detect when a tensor is already pinned and would #error below, but there are proven cases where this also queues an error #on the GPU async. So dont trust the CUDA API and guard here return False if not tensor.is_contiguous(): return False size = tensor.nbytes if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY: return False ptr = tensor.data_ptr() if ptr == 0: return False if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0: PINNED_MEMORY[ptr] = size TOTAL_PINNED_MEMORY += size return True else: logging.warning("Pin error.") discard_cuda_async_error() return False def unpin_memory(tensor): global TOTAL_PINNED_MEMORY if MAX_PINNED_MEMORY <= 0: return False if not is_device_cpu(tensor.device): return False ptr = tensor.data_ptr() size = tensor.nbytes size_stored = PINNED_MEMORY.get(ptr, None) if size_stored is None: logging.warning("Tried to unpin tensor not pinned by ComfyUI") return False if size != size_stored: logging.warning("Size of pinned tensor changed") return False if torch.cuda.cudart().cudaHostUnregister(ptr) == 0: TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr) if len(PINNED_MEMORY) == 0: TOTAL_PINNED_MEMORY = 0 return True else: logging.warning("Unpin error.") discard_cuda_async_error() return False def sage_attention_enabled(): return args.use_sage_attention def flash_attention_enabled(): return args.use_flash_attention def xformers_enabled(): global directml_enabled global cpu_state if cpu_state != CPUState.GPU: return False if is_intel_xpu(): return False if is_ascend_npu(): return False if is_mlu(): return False if is_ixuca(): return False if directml_enabled: return False return XFORMERS_IS_AVAILABLE def xformers_enabled_vae(): enabled = xformers_enabled() if not enabled: return False return XFORMERS_ENABLED_VAE def pytorch_attention_enabled(): global ENABLE_PYTORCH_ATTENTION return ENABLE_PYTORCH_ATTENTION def pytorch_attention_enabled_vae(): if is_amd(): return False # enabling pytorch attention on AMD currently causes crash when doing high res return pytorch_attention_enabled() def pytorch_attention_flash_attention(): global ENABLE_PYTORCH_ATTENTION if ENABLE_PYTORCH_ATTENTION: #TODO: more reliable way of checking for flash attention? if is_nvidia(): return True if is_intel_xpu(): return True if is_ascend_npu(): return True if is_mlu(): return True if is_amd(): return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention if is_ixuca(): return True return False def force_upcast_attention_dtype(): upcast = args.force_upcast_attention macos_version = mac_version() if macos_version is not None and ((14, 5) <= macos_version): # black image bug on recent versions of macOS, I don't think it's ever getting fixed upcast = True if upcast: return {torch.float16: torch.float32} else: return None def get_free_memory(dev=None, torch_free_too=False): global directml_enabled if dev is None: dev = get_torch_device() if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): mem_free_total = psutil.virtual_memory().available mem_free_torch = mem_free_total else: if directml_enabled: mem_free_total = 1024 * 1024 * 1024 #TODO mem_free_torch = mem_free_total elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_xpu + mem_free_torch elif is_ascend_npu(): stats = torch.npu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_npu, _ = torch.npu.mem_get_info(dev) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_npu + mem_free_torch elif is_mlu(): stats = torch.mlu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_mlu, _ = torch.mlu.mem_get_info(dev) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_mlu + mem_free_torch else: stats = torch.cuda.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_cuda, _ = torch.cuda.mem_get_info(dev) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_cuda + mem_free_torch if torch_free_too: return (mem_free_total, mem_free_torch) else: return mem_free_total def cpu_mode(): global cpu_state return cpu_state == CPUState.CPU def mps_mode(): global cpu_state return cpu_state == CPUState.MPS def is_device_type(device, type): if hasattr(device, 'type'): if (device.type == type): return True return False def is_device_cpu(device): return is_device_type(device, 'cpu') def is_device_mps(device): return is_device_type(device, 'mps') def is_device_xpu(device): return is_device_type(device, 'xpu') def is_device_cuda(device): return is_device_type(device, 'cuda') def is_directml_enabled(): global directml_enabled if directml_enabled: return True return False def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): if device is not None: if is_device_cpu(device): return False if args.force_fp16: return True if FORCE_FP32: return False if is_directml_enabled(): return True if (device is not None and is_device_mps(device)) or mps_mode(): return True if cpu_mode(): return False if is_intel_xpu(): if torch_version_numeric < (2, 3): return True else: return torch.xpu.get_device_properties(device).has_fp16 if is_ascend_npu(): return True if is_mlu(): return True if is_ixuca(): return True if torch.version.hip: return True props = torch.cuda.get_device_properties(device) if props.major >= 8: return True if props.major < 6: return False #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32 nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"] for x in nvidia_10_series: if x in props.name.lower(): if WINDOWS or manual_cast: return True else: return False #weird linux behavior where fp32 is faster if manual_cast: free_model_memory = maximum_vram_for_weights(device) if (not prioritize_performance) or model_params * 4 > free_model_memory: return True if props.major < 7: return False #FP16 is just broken on these cards nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"] for x in nvidia_16_series: if x in props.name: return False return True def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): if device is not None: if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow return False if FORCE_FP32: return False if directml_enabled: return False if (device is not None and is_device_mps(device)) or mps_mode(): if mac_version() < (14,): return False return True if cpu_mode(): return False if is_intel_xpu(): if torch_version_numeric < (2, 3): return True else: return torch.xpu.is_bf16_supported() if is_ascend_npu(): return True if is_ixuca(): return True if is_amd(): arch = torch.cuda.get_device_properties(device).gcnArchName if any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH): # RDNA2 and older don't support bf16 if manual_cast: return True return False props = torch.cuda.get_device_properties(device) if is_mlu(): if props.major > 3: return True if props.major >= 8: return True bf16_works = torch.cuda.is_bf16_supported() if bf16_works and manual_cast: free_model_memory = maximum_vram_for_weights(device) if (not prioritize_performance) or model_params * 4 > free_model_memory: return True return False def supports_fp8_compute(device=None): if SUPPORT_FP8_OPS: return True if not is_nvidia(): return False props = torch.cuda.get_device_properties(device) if props.major >= 9: return True if props.major < 8: return False if props.minor < 9: return False if torch_version_numeric < (2, 3): return False if WINDOWS: if torch_version_numeric < (2, 4): return False return True def supports_nvfp4_compute(device=None): if not is_nvidia(): return False props = torch.cuda.get_device_properties(device) if props.major < 10: return False return True def supports_mxfp8_compute(device=None): if not is_nvidia(): return False if torch_version_numeric < (2, 10): return False props = torch.cuda.get_device_properties(device) if props.major < 10: return False return True def extended_fp16_support(): # TODO: check why some models work with fp16 on newer torch versions but not on older if torch_version_numeric < (2, 7): return False return True LORA_COMPUTE_DTYPES = {} def lora_compute_dtype(device): dtype = LORA_COMPUTE_DTYPES.get(device, None) if dtype is not None: return dtype if should_use_fp16(device): dtype = torch.float16 else: dtype = torch.float32 LORA_COMPUTE_DTYPES[device] = dtype return dtype def synchronize(): if cpu_mode(): return if is_intel_xpu(): torch.xpu.synchronize() elif torch.cuda.is_available(): torch.cuda.synchronize() def soft_empty_cache(force=False): if cpu_mode(): return global cpu_state if cpu_state == CPUState.MPS: torch.mps.empty_cache() elif is_intel_xpu(): torch.xpu.empty_cache() elif is_ascend_npu(): torch.npu.empty_cache() elif is_mlu(): torch.mlu.empty_cache() elif torch.cuda.is_available(): torch.cuda.synchronize() torch.cuda.empty_cache() torch.cuda.ipc_collect() def unload_all_models(): free_memory(1e30, get_torch_device()) def debug_memory_summary(): if is_amd() or is_nvidia(): return torch.cuda.memory.memory_summary() return "" class InterruptProcessingException(Exception): pass interrupt_processing_mutex = threading.RLock() interrupt_processing = False def interrupt_current_processing(value=True): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: interrupt_processing = value def processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: return interrupt_processing def throw_exception_if_processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: if interrupt_processing: interrupt_processing = False raise InterruptProcessingException() ================================================ FILE: comfy/model_patcher.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Comfy This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ from __future__ import annotations import collections import inspect import logging import math import uuid from typing import Callable, Optional import torch import comfy.float import comfy.hooks import comfy.lora import comfy.model_management import comfy.patcher_extension import comfy.utils from comfy.comfy_types import UnetWrapperFunction from comfy.quant_ops import QuantizedTensor from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP import comfy_aimdo.model_vbar def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): to = model_options["transformer_options"].copy() if "patches_replace" not in to: to["patches_replace"] = {} else: to["patches_replace"] = to["patches_replace"].copy() if name not in to["patches_replace"]: to["patches_replace"][name] = {} else: to["patches_replace"][name] = to["patches_replace"][name].copy() if transformer_index is not None: block = (block_name, number, transformer_index) else: block = (block_name, number) to["patches_replace"][name][block] = patch model_options["transformer_options"] = to return model_options def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False): model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] if disable_cfg1_optimization: model_options["disable_cfg1_optimization"] = True return model_options def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False): model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function] if disable_cfg1_optimization: model_options["disable_cfg1_optimization"] = True return model_options def create_model_options_clone(orig_model_options: dict): return comfy.patcher_extension.copy_nested_dicts(orig_model_options) def create_hook_patches_clone(orig_hook_patches): new_hook_patches = {} for hook_ref in orig_hook_patches: new_hook_patches[hook_ref] = {} for k in orig_hook_patches[hook_ref]: new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:] return new_hook_patches def wipe_lowvram_weight(m): if hasattr(m, "prev_comfy_cast_weights"): m.comfy_cast_weights = m.prev_comfy_cast_weights del m.prev_comfy_cast_weights if hasattr(m, "weight_function"): m.weight_function = [] if hasattr(m, "bias_function"): m.bias_function = [] def move_weight_functions(m, device): if device is None: return 0 memory = 0 if hasattr(m, "weight_function"): for f in m.weight_function: if hasattr(f, "move_to"): memory += f.move_to(device=device) if hasattr(m, "bias_function"): for f in m.bias_function: if hasattr(f, "move_to"): memory += f.move_to(device=device) return memory def string_to_seed(data): logging.warning("WARNING: string_to_seed has moved from comfy.model_patcher to comfy.utils") return comfy.utils.string_to_seed(data) class LowVramPatch: def __init__(self, key, patches, convert_func=None, set_func=None): self.key = key self.patches = patches self.convert_func = convert_func # TODO: remove self.set_func = set_func def __call__(self, weight): return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype) LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 2 def low_vram_patch_estimate_vram(model, key): weight, set_func, convert_func = get_key_weight(model, key) if weight is None: return 0 model_dtype = getattr(model, "manual_cast_dtype", torch.float32) if model_dtype is None: model_dtype = weight.dtype return weight.numel() * model_dtype.itemsize * LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR def get_key_weight(model, key): set_func = None convert_func = None op_keys = key.rsplit('.', 1) if len(op_keys) < 2: weight = comfy.utils.get_attr(model, key) else: op = comfy.utils.get_attr(model, op_keys[0]) try: set_func = getattr(op, "set_{}".format(op_keys[1])) except AttributeError: pass try: convert_func = getattr(op, "convert_{}".format(op_keys[1])) except AttributeError: pass weight = getattr(op, op_keys[1]) if convert_func is not None: weight = comfy.utils.get_attr(model, key) return weight, set_func, convert_func def key_param_name_to_key(key, param): if len(key) == 0: return param return "{}.{}".format(key, param) class AutoPatcherEjector: def __init__(self, model: 'ModelPatcher', skip_and_inject_on_exit_only=False): self.model = model self.was_injected = False self.prev_skip_injection = False self.skip_and_inject_on_exit_only = skip_and_inject_on_exit_only def __enter__(self): self.was_injected = False self.prev_skip_injection = self.model.skip_injection if self.skip_and_inject_on_exit_only: self.model.skip_injection = True if self.model.is_injected: self.model.eject_model() self.was_injected = True def __exit__(self, *args): if self.skip_and_inject_on_exit_only: self.model.skip_injection = self.prev_skip_injection self.model.inject_model() if self.was_injected and not self.model.skip_injection: self.model.inject_model() self.model.skip_injection = self.prev_skip_injection class MemoryCounter: def __init__(self, initial: int, minimum=0): self.value = initial self.minimum = minimum # TODO: add a safe limit besides 0 def use(self, weight: torch.Tensor): weight_size = weight.nelement() * weight.element_size() if self.is_useable(weight_size): self.decrement(weight_size) return True return False def is_useable(self, used: int): return self.value - used > self.minimum def decrement(self, used: int): self.value -= used CustomTorchDevice = collections.namedtuple("FakeDevice", ["type", "index"])("comfy-lazy-caster", 0) class LazyCastingParam(torch.nn.Parameter): def __new__(cls, model, key, tensor): return super().__new__(cls, tensor) def __init__(self, model, key, tensor): self.model = model self.key = key @property def device(self): return CustomTorchDevice #safetensors will .to() us to the cpu which we catch here to cast on demand. The returned tensor is #then just a short lived thing in the safetensors serialization logic inside its big for loop over #all weights getting garbage collected per-weight def to(self, *args, **kwargs): return self.model.patch_weight_to_device(self.key, device_to=self.model.load_device, return_weight=True).to("cpu") class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False): self.size = size self.model = model if not hasattr(self.model, 'device'): logging.debug("Model doesn't have a device attribute.") self.model.device = offload_device elif self.model.device is None: self.model.device = offload_device self.patches = {} self.backup = {} self.backup_buffers = {} self.object_patches = {} self.object_patches_backup = {} self.weight_wrapper_patches = {} self.model_options = {"transformer_options":{}} self.load_device = load_device self.offload_device = offload_device self.weight_inplace_update = weight_inplace_update self.force_cast_weights = False self.patches_uuid = uuid.uuid4() self.parent = None self.pinned = set() self.attachments: dict[str] = {} self.additional_models: dict[str, list[ModelPatcher]] = {} self.callbacks: dict[str, dict[str, list[Callable]]] = CallbacksMP.init_callbacks() self.wrappers: dict[str, dict[str, list[Callable]]] = WrappersMP.init_wrappers() self.is_injected = False self.skip_injection = False self.injections: dict[str, list[PatcherInjection]] = {} self.hook_patches: dict[comfy.hooks._HookRef] = {} self.hook_patches_backup: dict[comfy.hooks._HookRef] = None self.hook_backup: dict[str, tuple[torch.Tensor, torch.device]] = {} self.cached_hook_patches: dict[comfy.hooks.HookGroup, dict[str, torch.Tensor]] = {} self.current_hooks: Optional[comfy.hooks.HookGroup] = None self.forced_hooks: Optional[comfy.hooks.HookGroup] = None # NOTE: only used for CLIP at this time self.is_clip = False self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed self.cached_patcher_init: tuple[Callable, tuple] | None = None if not hasattr(self.model, 'model_loaded_weight_memory'): self.model.model_loaded_weight_memory = 0 if not hasattr(self.model, 'lowvram_patch_counter'): self.model.lowvram_patch_counter = 0 if not hasattr(self.model, 'model_lowvram'): self.model.model_lowvram = False if not hasattr(self.model, 'current_weight_patches_uuid'): self.model.current_weight_patches_uuid = None if not hasattr(self.model, 'model_offload_buffer_memory'): self.model.model_offload_buffer_memory = 0 def is_dynamic(self): return False def model_size(self): if self.size > 0: return self.size self.size = comfy.model_management.module_size(self.model) return self.size def model_mmap_residency(self, free=False): return comfy.model_management.module_mmap_residency(self.model, free=free) def get_ram_usage(self): return self.model_size() def loaded_size(self): return self.model.model_loaded_weight_memory def lowvram_patch_counter(self): return self.model.lowvram_patch_counter def get_free_memory(self, device): #Prioritize batching (incl. CFG/conds etc) over keeping the model resident. In #the vast majority of setups a little bit of offloading on the giant model more #than pays for CFG. So return everything both torch and Aimdo could give us aimdo_mem = 0 if comfy.memory_management.aimdo_enabled: aimdo_mem = comfy_aimdo.model_vbar.vbars_analyze() return comfy.model_management.get_free_memory(device) + aimdo_mem def get_clone_model_override(self): return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned) def clone(self, disable_dynamic=False, model_override=None): class_ = self.__class__ if self.is_dynamic() and disable_dynamic: class_ = ModelPatcher if model_override is None: if self.cached_patcher_init is None: raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.") temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True) model_override = temp_model_patcher.get_clone_model_override() if model_override is None: model_override = self.get_clone_model_override() n = class_(model_override[0], self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update) n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] n.patches_uuid = self.patches_uuid n.object_patches = self.object_patches.copy() n.weight_wrapper_patches = self.weight_wrapper_patches.copy() n.model_options = comfy.utils.deepcopy_list_dict(self.model_options) n.parent = self n.force_cast_weights = self.force_cast_weights n.backup, n.backup_buffers, n.object_patches_backup, n.pinned = model_override[1] # attachments n.attachments = {} for k in self.attachments: if hasattr(self.attachments[k], "on_model_patcher_clone"): n.attachments[k] = self.attachments[k].on_model_patcher_clone() else: n.attachments[k] = self.attachments[k] # additional models for k, c in self.additional_models.items(): n.additional_models[k] = [x.clone() for x in c] # callbacks for k, c in self.callbacks.items(): n.callbacks[k] = {} for k1, c1 in c.items(): n.callbacks[k][k1] = c1.copy() # sample wrappers for k, w in self.wrappers.items(): n.wrappers[k] = {} for k1, w1 in w.items(): n.wrappers[k][k1] = w1.copy() # injection n.is_injected = self.is_injected n.skip_injection = self.skip_injection for k, i in self.injections.items(): n.injections[k] = i.copy() # hooks n.hook_patches = create_hook_patches_clone(self.hook_patches) n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup for group in self.cached_hook_patches: n.cached_hook_patches[group] = {} for k in self.cached_hook_patches[group]: n.cached_hook_patches[group][k] = self.cached_hook_patches[group][k] n.hook_backup = self.hook_backup n.current_hooks = self.current_hooks.clone() if self.current_hooks else self.current_hooks n.forced_hooks = self.forced_hooks.clone() if self.forced_hooks else self.forced_hooks n.is_clip = self.is_clip n.hook_mode = self.hook_mode n.cached_patcher_init = self.cached_patcher_init for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE): callback(self, n) return n def is_clone(self, other): if hasattr(other, 'model') and self.model is other.model: return True return False def clone_has_same_weights(self, clone: 'ModelPatcher'): if not self.is_clone(clone): return False if self.current_hooks != clone.current_hooks: return False if self.forced_hooks != clone.forced_hooks: return False if self.hook_patches.keys() != clone.hook_patches.keys(): return False if self.attachments.keys() != clone.attachments.keys(): return False if self.additional_models.keys() != clone.additional_models.keys(): return False for key in self.callbacks: if len(self.callbacks[key]) != len(clone.callbacks[key]): return False for key in self.wrappers: if len(self.wrappers[key]) != len(clone.wrappers[key]): return False if self.injections.keys() != clone.injections.keys(): return False if len(self.patches) == 0 and len(clone.patches) == 0: return True if self.patches_uuid == clone.patches_uuid: if len(self.patches) != len(clone.patches): logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.") else: return True def memory_required(self, input_shape): return self.model.memory_required(input_shape=input_shape) def disable_model_cfg1_optimization(self): self.model_options["disable_cfg1_optimization"] = True def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): if len(inspect.signature(sampler_cfg_function).parameters) == 3: self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way else: self.model_options["sampler_cfg_function"] = sampler_cfg_function if disable_cfg1_optimization: self.disable_model_cfg1_optimization() def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization) def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False): self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization) def set_model_sampler_calc_cond_batch_function(self, sampler_calc_cond_batch_function): self.model_options["sampler_calc_cond_batch_function"] = sampler_calc_cond_batch_function def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): self.model_options["model_function_wrapper"] = unet_wrapper_function def set_model_denoise_mask_function(self, denoise_mask_function): self.model_options["denoise_mask_function"] = denoise_mask_function def set_model_patch(self, patch, name): to = self.model_options["transformer_options"] if "patches" not in to: to["patches"] = {} to["patches"][name] = to["patches"].get(name, []) + [patch] def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index) def set_model_attn1_patch(self, patch): self.set_model_patch(patch, "attn1_patch") def set_model_attn2_patch(self, patch): self.set_model_patch(patch, "attn2_patch") def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) def set_model_attn1_output_patch(self, patch): self.set_model_patch(patch, "attn1_output_patch") def set_model_attn2_output_patch(self, patch): self.set_model_patch(patch, "attn2_output_patch") def set_model_input_block_patch(self, patch): self.set_model_patch(patch, "input_block_patch") def set_model_input_block_patch_after_skip(self, patch): self.set_model_patch(patch, "input_block_patch_after_skip") def set_model_output_block_patch(self, patch): self.set_model_patch(patch, "output_block_patch") def set_model_emb_patch(self, patch): self.set_model_patch(patch, "emb_patch") def set_model_forward_timestep_embed_patch(self, patch): self.set_model_patch(patch, "forward_timestep_embed_patch") def set_model_double_block_patch(self, patch): self.set_model_patch(patch, "double_block") def set_model_post_input_patch(self, patch): self.set_model_patch(patch, "post_input") def set_model_noise_refiner_patch(self, patch): self.set_model_patch(patch, "noise_refiner") def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs): rope_options = self.model_options["transformer_options"].get("rope_options", {}) rope_options["scale_x"] = scale_x rope_options["scale_y"] = scale_y rope_options["scale_t"] = scale_t rope_options["shift_x"] = shift_x rope_options["shift_y"] = shift_y rope_options["shift_t"] = shift_t self.model_options["transformer_options"]["rope_options"] = rope_options def add_object_patch(self, name, obj): self.object_patches[name] = obj def set_model_compute_dtype(self, dtype): self.add_object_patch("manual_cast_dtype", dtype) if dtype is not None: self.force_cast_weights = True self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this def add_weight_wrapper(self, name, function): self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function] self.patches_uuid = uuid.uuid4() def get_model_object(self, name: str) -> torch.nn.Module: """Retrieves a nested attribute from an object using dot notation considering object patches. Args: name (str): The attribute path using dot notation (e.g. "model.layer.weight") Returns: The value of the requested attribute Example: patcher = ModelPatcher() weight = patcher.get_model_object("layer1.conv.weight") """ if name in self.object_patches: return self.object_patches[name] else: if name in self.object_patches_backup: return self.object_patches_backup[name] else: return comfy.utils.get_attr(self.model, name) def model_patches_to(self, device): to = self.model_options["transformer_options"] if "patches" in to: patches = to["patches"] for name in patches: patch_list = patches[name] for i in range(len(patch_list)): if hasattr(patch_list[i], "to"): patch_list[i] = patch_list[i].to(device) if "patches_replace" in to: patches = to["patches_replace"] for name in patches: patch_list = patches[name] for k in patch_list: if hasattr(patch_list[k], "to"): patch_list[k] = patch_list[k].to(device) if "model_function_wrapper" in self.model_options: wrap_func = self.model_options["model_function_wrapper"] if hasattr(wrap_func, "to"): self.model_options["model_function_wrapper"] = wrap_func.to(device) def model_patches_models(self): to = self.model_options["transformer_options"] models = [] if "patches" in to: patches = to["patches"] for name in patches: patch_list = patches[name] for i in range(len(patch_list)): if hasattr(patch_list[i], "models"): models += patch_list[i].models() if "patches_replace" in to: patches = to["patches_replace"] for name in patches: patch_list = patches[name] for k in patch_list: if hasattr(patch_list[k], "models"): models += patch_list[k].models() if "model_function_wrapper" in self.model_options: wrap_func = self.model_options["model_function_wrapper"] if hasattr(wrap_func, "models"): models += wrap_func.models() return models def model_patches_call_function(self, function_name="cleanup", arguments={}): to = self.model_options["transformer_options"] if "patches" in to: patches = to["patches"] for name in patches: patch_list = patches[name] for i in range(len(patch_list)): if hasattr(patch_list[i], function_name): getattr(patch_list[i], function_name)(**arguments) if "patches_replace" in to: patches = to["patches_replace"] for name in patches: patch_list = patches[name] for k in patch_list: if hasattr(patch_list[k], function_name): getattr(patch_list[k], function_name)(**arguments) if "model_function_wrapper" in self.model_options: wrap_func = self.model_options["model_function_wrapper"] if hasattr(wrap_func, function_name): getattr(wrap_func, function_name)(**arguments) def model_dtype(self): if hasattr(self.model, "get_dtype"): return self.model.get_dtype() def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): with self.use_ejected(): p = set() model_sd = self.model.state_dict() for k in patches: offset = None function = None if isinstance(k, str): key = k else: offset = k[1] key = k[0] if len(k) > 2: function = k[2] if key in model_sd: p.add(k) current_patches = self.patches.get(key, []) current_patches.append((strength_patch, patches[k], strength_model, offset, function)) self.patches[key] = current_patches self.patches_uuid = uuid.uuid4() return list(p) def get_key_patches(self, filter_prefix=None): model_sd = self.model_state_dict() p = {} for k in model_sd: if filter_prefix is not None: if not k.startswith(filter_prefix): continue bk = self.backup.get(k, None) hbk = self.hook_backup.get(k, None) weight, set_func, convert_func = get_key_weight(self.model, k) if bk is not None: weight = bk.weight if hbk is not None: weight = hbk[0] if convert_func is None: convert_func = lambda a, **kwargs: a if k in self.patches: p[k] = [(weight, convert_func)] + self.patches[k] else: p[k] = [(weight, convert_func)] return p def model_state_dict(self, filter_prefix=None): with self.use_ejected(): sd = self.model.state_dict() keys = list(sd.keys()) if filter_prefix is not None: for k in keys: if not k.startswith(filter_prefix): sd.pop(k) return sd def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False): weight, set_func, convert_func = get_key_weight(self.model, key) if key not in self.patches: return weight inplace_update = self.weight_inplace_update or inplace_update if key not in self.backup and not return_weight: self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update) temp_dtype = comfy.model_management.lora_compute_dtype(device_to) if device_to is not None: temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True) else: temp_weight = weight.to(temp_dtype, copy=True) if convert_func is not None: temp_weight = convert_func(temp_weight, inplace=True) out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) if set_func is None: out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key)) if return_weight: return out_weight elif inplace_update: comfy.utils.copy_to_param(self.model, key, out_weight) else: comfy.utils.set_attr_param(self.model, key, out_weight) else: return set_func(out_weight, inplace_update=inplace_update, seed=comfy.utils.string_to_seed(key), return_weight=return_weight) def pin_weight_to_device(self, key): weight, set_func, convert_func = get_key_weight(self.model, key) if comfy.model_management.pin_memory(weight): self.pinned.add(key) def unpin_weight(self, key): if key in self.pinned: weight, set_func, convert_func = get_key_weight(self.model, key) comfy.model_management.unpin_memory(weight) self.pinned.remove(key) def unpin_all_weights(self): for key in list(self.pinned): self.unpin_weight(key) def _load_list(self, for_dynamic=False, default_device=None): loading = [] for n, m in self.model.named_modules(): default = False params = { name: param for name, param in m.named_parameters(recurse=False) } for name, param in m.named_parameters(recurse=True): if name not in params: default = True # default random weights in non leaf modules break if default and default_device is not None: for param_name, param in params.items(): param.data = param.data.to(device=default_device, dtype=getattr(m, param_name + "_comfy_model_dtype", None)) if not default and (hasattr(m, "comfy_cast_weights") or len(params) > 0): module_mem = comfy.model_management.module_size(m) module_offload_mem = module_mem if hasattr(m, "comfy_cast_weights"): def check_module_offload_mem(key): if key in self.patches: return low_vram_patch_estimate_vram(self.model, key) model_dtype = getattr(self.model, "manual_cast_dtype", None) weight, _, _ = get_key_weight(self.model, key) if model_dtype is None or weight is None: return 0 if (weight.dtype != model_dtype or isinstance(weight, QuantizedTensor)): return weight.numel() * model_dtype.itemsize return 0 module_offload_mem += check_module_offload_mem("{}.weight".format(n)) module_offload_mem += check_module_offload_mem("{}.bias".format(n)) # Dynamic: small weights (<64KB) first, then larger weights prioritized by size. # Non-dynamic: prioritize by module offload cost. if for_dynamic: sort_criteria = (module_offload_mem >= 64 * 1024, -module_offload_mem) else: sort_criteria = (module_offload_mem,) loading.append(sort_criteria + (module_mem, n, m, params)) return loading def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False): with self.use_ejected(): self.unpatch_hooks() mem_counter = 0 patch_counter = 0 lowvram_counter = 0 lowvram_mem_counter = 0 loading = self._load_list() load_completely = [] offloaded = [] offload_buffer = 0 loading.sort(reverse=True) for i, x in enumerate(loading): module_offload_mem, module_mem, n, m, params = x lowvram_weight = False potential_offload = max(offload_buffer, module_offload_mem + sum([ x1[1] for x1 in loading[i+1:i+1+comfy.model_management.NUM_STREAMS]])) lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory weight_key = "{}.weight".format(n) bias_key = "{}.bias".format(n) if not full_load and hasattr(m, "comfy_cast_weights"): if not lowvram_fits: offload_buffer = potential_offload lowvram_weight = True lowvram_counter += 1 lowvram_mem_counter += module_mem if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed continue cast_weight = self.force_cast_weights m.comfy_force_cast_weights = self.force_cast_weights if lowvram_weight: if hasattr(m, "comfy_cast_weights"): m.weight_function = [] m.bias_function = [] if weight_key in self.patches: if force_patch_weights: self.patch_weight_to_device(weight_key) else: _, set_func, convert_func = get_key_weight(self.model, weight_key) m.weight_function = [LowVramPatch(weight_key, self.patches, convert_func, set_func)] patch_counter += 1 if bias_key in self.patches: if force_patch_weights: self.patch_weight_to_device(bias_key) else: _, set_func, convert_func = get_key_weight(self.model, bias_key) m.bias_function = [LowVramPatch(bias_key, self.patches, convert_func, set_func)] patch_counter += 1 cast_weight = True offloaded.append((module_mem, n, m, params)) else: if hasattr(m, "comfy_cast_weights"): wipe_lowvram_weight(m) if full_load or lowvram_fits: mem_counter += module_mem load_completely.append((module_mem, n, m, params)) else: offload_buffer = potential_offload if cast_weight and hasattr(m, "comfy_cast_weights"): m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True if weight_key in self.weight_wrapper_patches: m.weight_function.extend(self.weight_wrapper_patches[weight_key]) if bias_key in self.weight_wrapper_patches: m.bias_function.extend(self.weight_wrapper_patches[bias_key]) mem_counter += move_weight_functions(m, device_to) load_completely.sort(reverse=True) for x in load_completely: n = x[1] m = x[2] params = x[3] if hasattr(m, "comfy_patched_weights"): if m.comfy_patched_weights == True: continue for param in params: key = key_param_name_to_key(n, param) self.unpin_weight(key) self.patch_weight_to_device(key, device_to=device_to) if comfy.model_management.is_device_cuda(device_to): torch.cuda.synchronize() logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) m.comfy_patched_weights = True for x in load_completely: x[2].to(device_to) for x in offloaded: n = x[1] params = x[3] for param in params: self.pin_weight_to_device(key_param_name_to_key(n, param)) usable_stat = "{:.2f} MB usable,".format(lowvram_model_memory / (1024 * 1024)) if lowvram_model_memory < 1e32 else "" if lowvram_counter > 0: logging.info("loaded partially; {} {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(usable_stat, mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter)) self.model.model_lowvram = True else: logging.info("loaded completely; {} {:.2f} MB loaded, full load: {}".format(usable_stat, mem_counter / (1024 * 1024), full_load)) self.model.model_lowvram = False if full_load: self.model.to(device_to) mem_counter = self.model_size() self.model.lowvram_patch_counter += patch_counter self.model.device = device_to self.model.model_loaded_weight_memory = mem_counter self.model.model_offload_buffer_memory = offload_buffer self.model.current_weight_patches_uuid = self.patches_uuid for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD): callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load) self.apply_hooks(self.forced_hooks, force_apply=True) def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False): with self.use_ejected(): for k in self.object_patches: old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) if k not in self.object_patches_backup: self.object_patches_backup[k] = old if lowvram_model_memory == 0: full_load = True else: full_load = False if load_weights: self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load) self.inject_model() return self.model def unpatch_model(self, device_to=None, unpatch_weights=True): self.eject_model() if unpatch_weights: self.unpatch_hooks() self.unpin_all_weights() if self.model.model_lowvram: for m in self.model.modules(): move_weight_functions(m, device_to) wipe_lowvram_weight(m) self.model.model_lowvram = False self.model.lowvram_patch_counter = 0 keys = list(self.backup.keys()) for k in keys: bk = self.backup[k] if bk.inplace_update: comfy.utils.copy_to_param(self.model, k, bk.weight) else: comfy.utils.set_attr_param(self.model, k, bk.weight) self.model.current_weight_patches_uuid = None self.backup.clear() if device_to is not None: self.model.to(device_to) self.model.device = device_to self.model.model_loaded_weight_memory = 0 self.model.model_offload_buffer_memory = 0 for m in self.model.modules(): if hasattr(m, "comfy_patched_weights"): del m.comfy_patched_weights keys = list(self.object_patches_backup.keys()) for k in keys: comfy.utils.set_attr(self.model, k, self.object_patches_backup[k]) self.object_patches_backup.clear() def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=False): with self.use_ejected(): hooks_unpatched = False memory_freed = 0 patch_counter = 0 unload_list = self._load_list() unload_list.sort() offload_buffer = self.model.model_offload_buffer_memory if len(unload_list) > 0: NS = comfy.model_management.NUM_STREAMS offload_weight_factor = [ min(offload_buffer / (NS + 1), unload_list[0][1]) ] * NS for unload in unload_list: if memory_to_free + offload_buffer - self.model.model_offload_buffer_memory < memory_freed: break module_offload_mem, module_mem, n, m, params = unload potential_offload = module_offload_mem + sum(offload_weight_factor) lowvram_possible = hasattr(m, "comfy_cast_weights") if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True: move_weight = True for param in params: key = key_param_name_to_key(n, param) bk = self.backup.get(key, None) if bk is not None: if not lowvram_possible: move_weight = False break if not hooks_unpatched: self.unpatch_hooks() hooks_unpatched = True if bk.inplace_update: comfy.utils.copy_to_param(self.model, key, bk.weight) else: comfy.utils.set_attr_param(self.model, key, bk.weight) self.backup.pop(key) weight_key = "{}.weight".format(n) bias_key = "{}.bias".format(n) if move_weight: cast_weight = self.force_cast_weights m.to(device_to) module_mem += move_weight_functions(m, device_to) if lowvram_possible: if weight_key in self.patches: if force_patch_weights: self.patch_weight_to_device(weight_key) else: _, set_func, convert_func = get_key_weight(self.model, weight_key) m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func)) patch_counter += 1 if bias_key in self.patches: if force_patch_weights: self.patch_weight_to_device(bias_key) else: _, set_func, convert_func = get_key_weight(self.model, bias_key) m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func)) patch_counter += 1 cast_weight = True if cast_weight and hasattr(m, "comfy_cast_weights"): m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True m.comfy_patched_weights = False memory_freed += module_mem offload_buffer = max(offload_buffer, potential_offload) offload_weight_factor.append(module_mem) offload_weight_factor.pop(0) logging.debug("freed {}".format(n)) for param in params: self.pin_weight_to_device(key_param_name_to_key(n, param)) self.model.model_lowvram = True self.model.lowvram_patch_counter += patch_counter self.model.model_loaded_weight_memory -= memory_freed self.model.model_offload_buffer_memory = offload_buffer logging.info("Unloaded partially: {:.2f} MB freed, {:.2f} MB remains loaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(memory_freed / (1024 * 1024), self.model.model_loaded_weight_memory / (1024 * 1024), offload_buffer / (1024 * 1024), self.model.lowvram_patch_counter)) return memory_freed def partially_load(self, device_to, extra_memory=0, force_patch_weights=False): with self.use_ejected(skip_and_inject_on_exit_only=True): unpatch_weights = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid or force_patch_weights) # TODO: force_patch_weights should not unload + reload full model used = self.model.model_loaded_weight_memory self.unpatch_model(self.offload_device, unpatch_weights=unpatch_weights) if unpatch_weights: extra_memory += (used - self.model.model_loaded_weight_memory) self.patch_model(load_weights=False) if extra_memory < 0 and not unpatch_weights: self.partially_unload(self.offload_device, -extra_memory, force_patch_weights=force_patch_weights) return 0 full_load = False if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0: self.apply_hooks(self.forced_hooks, force_apply=True) return 0 if self.model.model_loaded_weight_memory + extra_memory > self.model_size(): full_load = True current_used = self.model.model_loaded_weight_memory try: self.load(device_to, lowvram_model_memory=current_used + extra_memory, force_patch_weights=force_patch_weights, full_load=full_load) except Exception as e: self.detach() raise e return self.model.model_loaded_weight_memory - current_used def pinned_memory_size(self): # Pinned memory pressure tracking is only implemented for DynamicVram loading return 0 def partially_unload_ram(self, ram_to_unload): pass def detach(self, unpatch_all=True): self.eject_model() self.model_patches_to(self.offload_device) if unpatch_all: self.unpatch_model(self.offload_device, unpatch_weights=unpatch_all) for callback in self.get_all_callbacks(CallbacksMP.ON_DETACH): callback(self, unpatch_all) return self.model def current_loaded_device(self): return self.model.device def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32): logging.warning("The ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead") return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype) def cleanup(self): self.model_patches_call_function(function_name="cleanup") self.clean_hooks() if hasattr(self.model, "current_patcher"): self.model.current_patcher = None for callback in self.get_all_callbacks(CallbacksMP.ON_CLEANUP): callback(self) def add_callback(self, call_type: str, callback: Callable): self.add_callback_with_key(call_type, None, callback) def add_callback_with_key(self, call_type: str, key: str, callback: Callable): c = self.callbacks.setdefault(call_type, {}).setdefault(key, []) c.append(callback) def remove_callbacks_with_key(self, call_type: str, key: str): c = self.callbacks.get(call_type, {}) if key in c: c.pop(key) def get_callbacks(self, call_type: str, key: str): return self.callbacks.get(call_type, {}).get(key, []) def get_all_callbacks(self, call_type: str): c_list = [] for c in self.callbacks.get(call_type, {}).values(): c_list.extend(c) return c_list def add_wrapper(self, wrapper_type: str, wrapper: Callable): self.add_wrapper_with_key(wrapper_type, None, wrapper) def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: Callable): w = self.wrappers.setdefault(wrapper_type, {}).setdefault(key, []) w.append(wrapper) def remove_wrappers_with_key(self, wrapper_type: str, key: str): w = self.wrappers.get(wrapper_type, {}) if key in w: w.pop(key) def get_wrappers(self, wrapper_type: str, key: str): return self.wrappers.get(wrapper_type, {}).get(key, []) def get_all_wrappers(self, wrapper_type: str): w_list = [] for w in self.wrappers.get(wrapper_type, {}).values(): w_list.extend(w) return w_list def set_attachments(self, key: str, attachment): self.attachments[key] = attachment def remove_attachments(self, key: str): if key in self.attachments: self.attachments.pop(key) def get_attachment(self, key: str): return self.attachments.get(key, None) def set_injections(self, key: str, injections: list[PatcherInjection]): self.injections[key] = injections def remove_injections(self, key: str): if key in self.injections: self.injections.pop(key) def get_injections(self, key: str): return self.injections.get(key, None) def set_additional_models(self, key: str, models: list['ModelPatcher']): self.additional_models[key] = models def remove_additional_models(self, key: str): if key in self.additional_models: self.additional_models.pop(key) def get_additional_models_with_key(self, key: str): return self.additional_models.get(key, []) def get_additional_models(self): all_models = [] for models in self.additional_models.values(): all_models.extend(models) return all_models def get_nested_additional_models(self): def _evaluate_sub_additional_models(prev_models: list[ModelPatcher], cache_set: set[ModelPatcher]): '''Make sure circular references do not cause infinite recursion.''' next_models = [] for model in prev_models: candidates = model.get_additional_models() for c in candidates: if c not in cache_set: next_models.append(c) cache_set.add(c) if len(next_models) == 0: return prev_models return prev_models + _evaluate_sub_additional_models(next_models, cache_set) all_models = self.get_additional_models() models_set = set(all_models) real_all_models = _evaluate_sub_additional_models(prev_models=all_models, cache_set=models_set) return real_all_models def use_ejected(self, skip_and_inject_on_exit_only=False): return AutoPatcherEjector(self, skip_and_inject_on_exit_only=skip_and_inject_on_exit_only) def inject_model(self): if self.is_injected or self.skip_injection: return for injections in self.injections.values(): for inj in injections: inj.inject(self) self.is_injected = True if self.is_injected: for callback in self.get_all_callbacks(CallbacksMP.ON_INJECT_MODEL): callback(self) def eject_model(self): if not self.is_injected: return for injections in self.injections.values(): for inj in injections: inj.eject(self) self.is_injected = False for callback in self.get_all_callbacks(CallbacksMP.ON_EJECT_MODEL): callback(self) def pre_run(self): if hasattr(self.model, "current_patcher"): self.model.current_patcher = self for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN): callback(self) def prepare_state(self, timestep): for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE): callback(self, timestep) def restore_hook_patches(self): if self.hook_patches_backup is not None: self.hook_patches = self.hook_patches_backup self.hook_patches_backup = None def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode): self.hook_mode = hook_mode def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]): curr_t = t[0] reset_current_hooks = False transformer_options = model_options.get("transformer_options", {}) for hook in hook_group.hooks: changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options) # if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref; # this will cause the weights to be recalculated when sampling if changed: # reset current_hooks if contains hook that changed if self.current_hooks is not None: for current_hook in self.current_hooks.hooks: if current_hook == hook: reset_current_hooks = True break for cached_group in list(self.cached_hook_patches.keys()): if cached_group.contains(hook): self.cached_hook_patches.pop(cached_group) if reset_current_hooks: self.patch_hooks(None) def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None, registered: comfy.hooks.HookGroup = None): self.restore_hook_patches() if registered is None: registered = comfy.hooks.HookGroup() # handle WeightHooks weight_hooks_to_register: list[comfy.hooks.WeightHook] = [] for hook in hooks.get_type(comfy.hooks.EnumHookType.Weight): if hook.hook_ref not in self.hook_patches: weight_hooks_to_register.append(hook) else: registered.add(hook) if len(weight_hooks_to_register) > 0: # clone hook_patches to become backup so that any non-dynamic hooks will return to their original state self.hook_patches_backup = create_hook_patches_clone(self.hook_patches) for hook in weight_hooks_to_register: hook.add_hook_patches(self, model_options, target_dict, registered) for callback in self.get_all_callbacks(CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES): callback(self, hooks, target_dict, model_options, registered) return registered def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0): with self.use_ejected(): # NOTE: this mirrors behavior of add_patches func current_hook_patches: dict[str,list] = self.hook_patches.get(hook.hook_ref, {}) p = set() model_sd = self.model.state_dict() for k in patches: offset = None function = None if isinstance(k, str): key = k else: offset = k[1] key = k[0] if len(k) > 2: function = k[2] if key in model_sd: p.add(k) current_patches: list[tuple] = current_hook_patches.get(key, []) current_patches.append((strength_patch, patches[k], strength_model, offset, function)) current_hook_patches[key] = current_patches self.hook_patches[hook.hook_ref] = current_hook_patches # since should care about these patches too to determine if same model, reroll patches_uuid self.patches_uuid = uuid.uuid4() return list(p) def get_combined_hook_patches(self, hooks: comfy.hooks.HookGroup): # combined_patches will contain weights of all relevant hooks, per key combined_patches = {} if hooks is not None: for hook in hooks.hooks: hook_patches: dict = self.hook_patches.get(hook.hook_ref, {}) for key in hook_patches.keys(): current_patches: list[tuple] = combined_patches.get(key, []) if math.isclose(hook.strength, 1.0): current_patches.extend(hook_patches[key]) else: # patches are stored as tuples: (strength_patch, (tuple_with_weights,), strength_model) for patch in hook_patches[key]: new_patch = list(patch) new_patch[0] *= hook.strength current_patches.append(tuple(new_patch)) combined_patches[key] = current_patches return combined_patches def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_options: dict=None, force_apply=False): # TODO: return transformer_options dict with any additions from hooks if self.current_hooks == hooks and (not force_apply or (not self.is_clip and hooks is None)): return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) self.patch_hooks(hooks=hooks) for callback in self.get_all_callbacks(CallbacksMP.ON_APPLY_HOOKS): callback(self, hooks) return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) def patch_hooks(self, hooks: comfy.hooks.HookGroup): with self.use_ejected(): if hooks is not None: model_sd_keys = list(self.model_state_dict().keys()) memory_counter = None if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: # TODO: minimum_counter should have a minimum that conforms to loaded model requirements memory_counter = MemoryCounter(initial=comfy.model_management.get_free_memory(self.load_device), minimum=comfy.model_management.minimum_inference_memory()*2) # if have cached weights for hooks, use it cached_weights = self.cached_hook_patches.get(hooks, None) if cached_weights is not None: model_sd_keys_set = set(model_sd_keys) for key in cached_weights: if key not in model_sd_keys: logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}") continue self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter) model_sd_keys_set.remove(key) self.unpatch_hooks(model_sd_keys_set) else: self.unpatch_hooks() relevant_patches = self.get_combined_hook_patches(hooks=hooks) original_weights = None if len(relevant_patches) > 0: original_weights = self.get_key_patches() for key in relevant_patches: if key not in model_sd_keys: logging.warning(f"Cached hook would not patch. Key does not exist in model: {key}") continue self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights, memory_counter=memory_counter) else: self.unpatch_hooks() self.current_hooks = hooks def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter): if key not in self.hook_backup: weight: torch.Tensor = comfy.utils.get_attr(self.model, key) target_device = self.offload_device if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: used = memory_counter.use(weight) if used: target_device = weight.device self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device) comfy.utils.copy_to_param(self.model, key, cached_weights[key][0].to(device=cached_weights[key][1])) def clear_cached_hook_weights(self): self.cached_hook_patches.clear() self.patch_hooks(None) def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter): if key not in combined_patches: return weight, set_func, convert_func = get_key_weight(self.model, key) weight: torch.Tensor if key not in self.hook_backup: target_device = self.offload_device if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: used = memory_counter.use(weight) if used: target_device = weight.device self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device) # TODO: properly handle LowVramPatch, if it ends up an issue temp_weight = comfy.model_management.cast_to_device(weight, weight.device, torch.float32, copy=True) if convert_func is not None: temp_weight = convert_func(temp_weight, inplace=True) out_weight = comfy.lora.calculate_weight(combined_patches[key], temp_weight, key, original_weights=original_weights) del original_weights[key] if set_func is None: out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key)) comfy.utils.copy_to_param(self.model, key, out_weight) else: set_func(out_weight, inplace_update=True, seed=comfy.utils.string_to_seed(key)) if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: # TODO: disable caching if not enough system RAM to do so target_device = self.offload_device used = memory_counter.use(weight) if used: target_device = weight.device self.cached_hook_patches.setdefault(hooks, {}) self.cached_hook_patches[hooks][key] = (out_weight.to(device=target_device, copy=False), weight.device) del temp_weight del out_weight del weight def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None: with self.use_ejected(): if len(self.hook_backup) == 0: self.current_hooks = None return keys = list(self.hook_backup.keys()) if whitelist_keys_set: for k in keys: if k in whitelist_keys_set: comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) self.hook_backup.pop(k) else: for k in keys: comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) self.hook_backup.clear() self.current_hooks = None def clean_hooks(self): self.unpatch_hooks() self.clear_cached_hook_weights() def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): unet_state_dict = self.model.diffusion_model.state_dict() for k, v in unet_state_dict.items(): op_keys = k.rsplit('.', 1) if (len(op_keys) < 2) or op_keys[1] not in ["weight", "bias"]: continue try: op = comfy.utils.get_attr(self.model.diffusion_model, op_keys[0]) except: continue if not op or not hasattr(op, "comfy_cast_weights") or \ (hasattr(op, "comfy_patched_weights") and op.comfy_patched_weights == True): continue key = "diffusion_model." + k unet_state_dict[k] = LazyCastingParam(self, key, comfy.utils.get_attr(self.model, key)) return self.model.state_dict_for_saving(unet_state_dict, clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict) def __del__(self): self.unpin_all_weights() self.detach(unpatch_all=False) class ModelPatcherDynamic(ModelPatcher): def __new__(cls, model=None, load_device=None, offload_device=None, size=0, weight_inplace_update=False): if load_device is not None and comfy.model_management.is_device_cpu(load_device): #reroute to default MP for CPUs return ModelPatcher(model, load_device, offload_device, size, weight_inplace_update) return super().__new__(cls) def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False): super().__init__(model, load_device, offload_device, size, weight_inplace_update) if not hasattr(self.model, "dynamic_vbars"): self.model.dynamic_vbars = {} self.non_dynamic_delegate_model = None assert load_device is not None def is_dynamic(self): return True def _vbar_get(self, create=False): if self.load_device == torch.device("cpu"): return None vbar = self.model.dynamic_vbars.get(self.load_device, None) if create and vbar is None: # x10. We dont know what model defined type casts we have in the vbar, but virtual address # space is pretty free. This will cover someone casting an entire model from FP4 to FP32 # with some left over. vbar = comfy_aimdo.model_vbar.ModelVBAR(self.model_size() * 10, self.load_device.index) self.model.dynamic_vbars[self.load_device] = vbar return vbar def loaded_size(self): vbar = self._vbar_get() return (vbar.loaded_size() if vbar is not None else 0) + self.model.model_loaded_weight_memory #Pinning is deferred to ops time. Assert against this API to avoid pin leaks. def pin_weight_to_device(self, key): raise RuntimeError("pin_weight_to_device invalid for dymamic weight loading") def unpin_weight(self, key): raise RuntimeError("unpin_weight invalid for dymamic weight loading") def unpin_all_weights(self): self.partially_unload_ram(1e32) def memory_required(self, input_shape): #Pad this significantly. We are trying to get away from precise estimates. This #estimate is only used when using the ModelPatcherDynamic after ModelPatcher. If you #use all ModelPatcherDynamic this is ignored and its all done dynamically. return super().memory_required(input_shape=input_shape) * 1.3 + (1024 ** 3) def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False, dirty=False): #Force patching doesn't make sense in Dynamic loading, as you dont know what does and #doesn't need to be forced at this stage. The only thing you could do would be patch #it all on CPU which consumes huge RAM. assert not force_patch_weights #Full load doesn't make sense as we dont actually have any loader capability here and #now. assert not full_load assert device_to == self.load_device num_patches = 0 allocated_size = 0 self.model.model_loaded_weight_memory = 0 with self.use_ejected(): self.unpatch_hooks() vbar = self._vbar_get(create=True) if vbar is not None: vbar.prioritize() loading = self._load_list(for_dynamic=True, default_device=device_to) loading.sort() for x in loading: *_, module_mem, n, m, params = x def set_dirty(item, dirty): if dirty or not hasattr(item, "_v_signature"): item._v_signature = None def setup_param(self, m, n, param_key): nonlocal num_patches key = key_param_name_to_key(n, param_key) weight_function = [] weight, _, _ = get_key_weight(self.model, key) if weight is None: return (False, 0) if key in self.patches: if comfy.lora.calculate_shape(self.patches[key], weight, key) != weight.shape: return (True, 0) setattr(m, param_key + "_lowvram_function", LowVramPatch(key, self.patches)) num_patches += 1 else: setattr(m, param_key + "_lowvram_function", None) if key in self.weight_wrapper_patches: weight_function.extend(self.weight_wrapper_patches[key]) setattr(m, param_key + "_function", weight_function) geometry = weight if not isinstance(weight, QuantizedTensor): model_dtype = getattr(m, param_key + "_comfy_model_dtype", None) or weight.dtype weight._model_dtype = model_dtype geometry = comfy.memory_management.TensorGeometry(shape=weight.shape, dtype=model_dtype) return (False, comfy.memory_management.vram_aligned_size(geometry)) def force_load_param(self, param_key, device_to): key = key_param_name_to_key(n, param_key) if key in self.backup: comfy.utils.set_attr_param(self.model, key, self.backup[key].weight) self.patch_weight_to_device(key, device_to=device_to) weight, _, _ = get_key_weight(self.model, key) if weight is not None: self.model.model_loaded_weight_memory += weight.numel() * weight.element_size() if hasattr(m, "comfy_cast_weights"): m.comfy_cast_weights = True m.pin_failed = False m.seed_key = n set_dirty(m, dirty) force_load, v_weight_size = setup_param(self, m, n, "weight") force_load_bias, v_weight_bias = setup_param(self, m, n, "bias") force_load = force_load or force_load_bias v_weight_size += v_weight_bias if force_load: logging.info(f"Module {n} has resizing Lora - force loading") force_load_param(self, "weight", device_to) force_load_param(self, "bias", device_to) else: if vbar is not None and not hasattr(m, "_v"): m._v = vbar.alloc(v_weight_size) allocated_size += v_weight_size else: for param in params: key = key_param_name_to_key(n, param) weight, _, _ = get_key_weight(self.model, key) if key not in self.backup: self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight, False) model_dtype = getattr(m, param + "_comfy_model_dtype", None) casted_weight = weight.to(dtype=model_dtype, device=device_to) comfy.utils.set_attr_param(self.model, key, casted_weight) self.model.model_loaded_weight_memory += casted_weight.numel() * casted_weight.element_size() move_weight_functions(m, device_to) for key, buf in self.model.named_buffers(recurse=True): if key not in self.backup_buffers: self.backup_buffers[key] = buf module, buf_name = comfy.utils.resolve_attr(self.model, key) model_dtype = getattr(module, buf_name + "_comfy_model_dtype", None) casted_buf = buf.to(dtype=model_dtype, device=device_to) comfy.utils.set_attr_buffer(self.model, key, casted_buf) self.model.model_loaded_weight_memory += casted_buf.numel() * casted_buf.element_size() force_load_stat = f" Force pre-loaded {len(self.backup)} weights: {self.model.model_loaded_weight_memory // 1024} KB." if len(self.backup) > 0 else "" logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.{force_load_stat}") self.model.device = device_to self.model.current_weight_patches_uuid = self.patches_uuid for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD): #These are all super dangerous. Who knows what the custom nodes actually do here... callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load) self.apply_hooks(self.forced_hooks, force_apply=True) def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=False): assert not force_patch_weights #See above assert self.load_device != torch.device("cpu") vbar = self._vbar_get() freed = 0 if vbar is None else vbar.free_memory(memory_to_free) if freed < memory_to_free: for key in list(self.backup.keys()): bk = self.backup.pop(key) comfy.utils.set_attr_param(self.model, key, bk.weight) for key in list(self.backup_buffers.keys()): comfy.utils.set_attr_buffer(self.model, key, self.backup_buffers.pop(key)) freed += self.model.model_loaded_weight_memory self.model.model_loaded_weight_memory = 0 return freed def pinned_memory_size(self): total = 0 loading = self._load_list(for_dynamic=True) for x in loading: _, _, _, _, m, _ = x pin = comfy.pinned_memory.get_pin(m) if pin is not None: total += pin.numel() * pin.element_size() return total def partially_unload_ram(self, ram_to_unload): loading = self._load_list(for_dynamic=True, default_device=self.offload_device) for x in loading: *_, m, _ = x ram_to_unload -= comfy.pinned_memory.unpin_memory(m) if ram_to_unload <= 0: return def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False): #This isn't used by the core at all and can only be to load a model out of #the control of proper model_managment. If you are a custom node author reading #this, the correct pattern is to call load_models_gpu() to get a proper #managed load of your model. assert not load_weights return super().patch_model(load_weights=load_weights, force_patch_weights=force_patch_weights) def unpatch_model(self, device_to=None, unpatch_weights=True): super().unpatch_model(device_to=None, unpatch_weights=False) if unpatch_weights: self.partially_unload_ram(1e32) self.partially_unload(None, 1e32) for m in self.model.modules(): move_weight_functions(m, device_to) def partially_load(self, device_to, extra_memory=0, force_patch_weights=False): assert not force_patch_weights #See above with self.use_ejected(skip_and_inject_on_exit_only=True): dirty = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid) self.unpatch_model(self.offload_device, unpatch_weights=False) self.patch_model(load_weights=False) try: self.load(device_to, dirty=dirty) except Exception as e: self.detach() raise e #ModelPatcher::partially_load returns a number on what got loaded but #nothing in core uses this and we have no data in the Dynamic world. Hit #the custom node devs with a None rather than a 0 that would mislead any #logic they might have. return None def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter): assert False #Should be unreachable - we dont ever cache in the new implementation def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter): if key not in combined_patches: return raise RuntimeError("Hooks not implemented in ModelPatcherDynamic. Please remove --fast arguments form ComfyUI startup") def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None: pass def get_non_dynamic_delegate(self): model_patcher = self.clone(disable_dynamic=True, model_override=self.non_dynamic_delegate_model) self.non_dynamic_delegate_model = model_patcher.get_clone_model_override() return model_patcher CoreModelPatcher = ModelPatcher ================================================ FILE: comfy/model_sampling.py ================================================ import torch from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule import math def rescale_zero_terminal_snr_sigmas(sigmas): alphas_cumprod = 1 / ((sigmas * sigmas) + 1) alphas_bar_sqrt = alphas_cumprod.sqrt() # Store old values. alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() # Shift so the last timestep is zero. alphas_bar_sqrt -= (alphas_bar_sqrt_T) # Scale so the first timestep is back to the old value. alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) # Convert alphas_bar_sqrt to betas alphas_bar = alphas_bar_sqrt**2 # Revert sqrt alphas_bar[-1] = 4.8973451890853435e-08 return ((1 - alphas_bar) / alphas_bar) ** 0.5 def reshape_sigma(sigma, noise_dim): if sigma.nelement() == 1: return sigma.view(()) else: return sigma.view(sigma.shape[:1] + (1,) * (noise_dim - 1)) class EPS: def calculate_input(self, sigma, noise): sigma = reshape_sigma(sigma, noise.ndim) return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 def calculate_denoised(self, sigma, model_output, model_input): sigma = reshape_sigma(sigma, model_output.ndim) return model_input - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): sigma = reshape_sigma(sigma, noise.ndim) if max_denoise: noise = noise * torch.sqrt(1.0 + sigma ** 2.0) else: noise = noise * sigma noise += latent_image return noise def inverse_noise_scaling(self, sigma, latent): return latent class V_PREDICTION(EPS): def calculate_denoised(self, sigma, model_output, model_input): sigma = reshape_sigma(sigma, model_output.ndim) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class EDM(V_PREDICTION): def calculate_denoised(self, sigma, model_output, model_input): sigma = reshape_sigma(sigma, model_output.ndim) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class CONST: def calculate_input(self, sigma, noise): return noise def calculate_denoised(self, sigma, model_output, model_input): sigma = reshape_sigma(sigma, model_output.ndim) return model_input - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): sigma = reshape_sigma(sigma, noise.ndim) return sigma * noise + (1.0 - sigma) * latent_image def inverse_noise_scaling(self, sigma, latent): sigma = reshape_sigma(sigma, latent.ndim) return latent / (1.0 - sigma) class X0(EPS): def calculate_denoised(self, sigma, model_output, model_input): return model_output class IMG_TO_IMG(X0): def calculate_input(self, sigma, noise): return noise class IMG_TO_IMG_FLOW(CONST): def calculate_denoised(self, sigma, model_output, model_input): return model_output def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): return latent_image def inverse_noise_scaling(self, sigma, latent): return 1.0 - latent class COSMOS_RFLOW: def calculate_input(self, sigma, noise): sigma = (sigma / (sigma + 1)) sigma = reshape_sigma(sigma, noise.ndim) return noise * (1.0 - sigma) def calculate_denoised(self, sigma, model_output, model_input): sigma = (sigma / (sigma + 1)) sigma = reshape_sigma(sigma, model_output.ndim) return model_input * (1.0 - sigma) - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): sigma = reshape_sigma(sigma, noise.ndim) noise = noise * sigma noise += latent_image return noise def inverse_noise_scaling(self, sigma, latent): return latent class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None, zsnr=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} beta_schedule = sampling_settings.get("beta_schedule", "linear") linear_start = sampling_settings.get("linear_start", 0.00085) linear_end = sampling_settings.get("linear_end", 0.012) timesteps = sampling_settings.get("timesteps", 1000) if zsnr is None: zsnr = sampling_settings.get("zsnr", False) self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr) self.sigma_data = 1.0 def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, zsnr=False): if given_betas is not None: betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = torch.cumprod(alphas, dim=0) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end self.zsnr = zsnr # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 if self.zsnr: sigmas = rescale_zero_terminal_snr_sigmas(sigmas) self.set_sigmas(sigmas) def set_sigmas(self, sigmas): self.register_buffer('sigmas', sigmas.float()) self.register_buffer('log_sigmas', sigmas.log().float()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) def sigma(self, timestep): t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp().to(timestep.device) def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent * 999.0)).item() class ModelSamplingDiscreteEDM(ModelSamplingDiscrete): def timestep(self, sigma): return 0.25 * sigma.log() def sigma(self, timestep): return (timestep / 0.25).exp() class ModelSamplingContinuousEDM(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} sigma_min = sampling_settings.get("sigma_min", 0.002) sigma_max = sampling_settings.get("sigma_max", 120.0) sigma_data = sampling_settings.get("sigma_data", 1.0) self.set_parameters(sigma_min, sigma_max, sigma_data) def set_parameters(self, sigma_min, sigma_max, sigma_data): self.sigma_data = sigma_data sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers self.register_buffer('log_sigmas', sigmas.log()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): return 0.25 * sigma.log() def sigma(self, timestep): return (timestep / 0.25).exp() def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent log_sigma_min = math.log(self.sigma_min) return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) class ModelSamplingContinuousV(ModelSamplingContinuousEDM): def timestep(self, sigma): return sigma.atan() / math.pi * 2 def sigma(self, timestep): return (timestep * math.pi / 2).tan() def time_snr_shift(alpha, t): if alpha == 1.0: return t return alpha * t / (1 + (alpha - 1) * t) class ModelSamplingDiscreteFlow(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000)) def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000): self.shift = shift self.multiplier = multiplier ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier) self.register_buffer('sigmas', ts) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): return sigma * self.multiplier def sigma(self, timestep): return time_snr_shift(self.shift, timestep / self.multiplier) def percent_to_sigma(self, percent): if percent <= 0.0: return 1.0 if percent >= 1.0: return 0.0 return time_snr_shift(self.shift, 1.0 - percent) class StableCascadeSampling(ModelSamplingDiscrete): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} self.set_parameters(sampling_settings.get("shift", 1.0)) def set_parameters(self, shift=1.0, cosine_s=8e-3): self.shift = shift self.cosine_s = torch.tensor(cosine_s) self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 #This part is just for compatibility with some schedulers in the codebase self.num_timesteps = 10000 sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) for x in range(self.num_timesteps): t = (x + 1) / self.num_timesteps sigmas[x] = self.sigma(t) self.set_sigmas(sigmas) def sigma(self, timestep): alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod) if self.shift != 1.0: var = alpha_cumprod logSNR = (var/(1-var)).log() logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift)) alpha_cumprod = logSNR.sigmoid() alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999) return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 def timestep(self, sigma): var = 1 / ((sigma * sigma) + 1) var = var.clamp(0, 1.0) s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device) t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s return t def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent)) def flux_time_shift(mu: float, sigma: float, t): return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) class ModelSamplingFlux(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} self.set_parameters(shift=sampling_settings.get("shift", 1.15)) def set_parameters(self, shift=1.15, timesteps=10000): self.shift = shift ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps)) self.register_buffer('sigmas', ts) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): return sigma def sigma(self, timestep): return flux_time_shift(self.shift, 1.0, timestep) def percent_to_sigma(self, percent): if percent <= 0.0: return 1.0 if percent >= 1.0: return 0.0 return flux_time_shift(self.shift, 1.0, 1.0 - percent) class ModelSamplingCosmosRFlow(ModelSamplingContinuousEDM): def timestep(self, sigma): return sigma / (sigma + 1) def sigma(self, timestep): sigma_max = self.sigma_max if timestep >= (sigma_max / (sigma_max + 1)): return sigma_max return timestep / (1 - timestep) ================================================ FILE: comfy/nested_tensor.py ================================================ import torch class NestedTensor: def __init__(self, tensors): self.tensors = list(tensors) self.is_nested = True def _copy(self): return NestedTensor(self.tensors) def apply_operation(self, other, operation): o = self._copy() if isinstance(other, NestedTensor): for i, t in enumerate(o.tensors): o.tensors[i] = operation(t, other.tensors[i]) else: for i, t in enumerate(o.tensors): o.tensors[i] = operation(t, other) return o def __add__(self, b): return self.apply_operation(b, lambda x, y: x + y) def __sub__(self, b): return self.apply_operation(b, lambda x, y: x - y) def __mul__(self, b): return self.apply_operation(b, lambda x, y: x * y) # def __itruediv__(self, b): # return self.apply_operation(b, lambda x, y: x / y) def __truediv__(self, b): return self.apply_operation(b, lambda x, y: x / y) def __getitem__(self, *args, **kwargs): return self.apply_operation(None, lambda x, y: x.__getitem__(*args, **kwargs)) def unbind(self): return self.tensors def to(self, *args, **kwargs): o = self._copy() for i, t in enumerate(o.tensors): o.tensors[i] = t.to(*args, **kwargs) return o def new_ones(self, *args, **kwargs): return self.tensors[0].new_ones(*args, **kwargs) def float(self): return self.to(dtype=torch.float) def chunk(self, *args, **kwargs): return self.apply_operation(None, lambda x, y: x.chunk(*args, **kwargs)) def size(self): return self.tensors[0].size() @property def shape(self): return self.tensors[0].shape @property def ndim(self): dims = 0 for t in self.tensors: dims = max(t.ndim, dims) return dims @property def device(self): return self.tensors[0].device @property def dtype(self): return self.tensors[0].dtype @property def layout(self): return self.tensors[0].layout def cat_nested(tensors, *args, **kwargs): cated_tensors = [] for i in range(len(tensors[0].tensors)): tens = [] for j in range(len(tensors)): tens.append(tensors[j].tensors[i]) cated_tensors.append(torch.cat(tens, *args, **kwargs)) return NestedTensor(cated_tensors) ================================================ FILE: comfy/ops.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch import logging import comfy.model_management from comfy.cli_args import args, PerformanceFeature import comfy.float import json import comfy.memory_management import comfy.pinned_memory import comfy.utils import comfy_aimdo.model_vbar import comfy_aimdo.torch def run_every_op(): if torch.compiler.is_compiling(): return comfy.model_management.throw_exception_if_processing_interrupted() def scaled_dot_product_attention(q, k, v, *args, **kwargs): return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs) try: if torch.cuda.is_available() and comfy.model_management.WINDOWS: from torch.nn.attention import SDPBackend, sdpa_kernel import inspect if "set_priority" in inspect.signature(sdpa_kernel).parameters: SDPA_BACKEND_PRIORITY = [ SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION, SDPBackend.MATH, ] SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION) def scaled_dot_product_attention(q, k, v, *args, **kwargs): if q.nelement() < 1024 * 128: # arbitrary number, for small inputs cudnn attention seems slower return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs) with sdpa_kernel(SDPA_BACKEND_PRIORITY, set_priority=True): return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs) else: logging.warning("Torch version too old to set sdpa backend priority.") except (ModuleNotFoundError, TypeError): logging.warning("Could not set sdpa backend priority.") NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False try: if comfy.model_management.is_nvidia(): cudnn_version = torch.backends.cudnn.version() if (cudnn_version >= 91002 and cudnn_version < 91500) and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10): #TODO: change upper bound version once it's fixed' NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True logging.info("working around nvidia conv3d memory bug.") except: pass cast_to = comfy.model_management.cast_to #TODO: remove once no more references def cast_to_input(weight, input, non_blocking=False, copy=True): return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant): #vbar doesn't support CPU weights, but some custom nodes have weird paths #that might switch the layer to the CPU and expect it to work. We have to take #a clone conservatively as we are mmapped and some SFT files are packed misaligned #If you are a custom node author reading this, please move your layer to the GPU #or declare your ModelPatcher as CPU in the first place. if comfy.model_management.is_device_cpu(device): weight = s.weight.to(dtype=dtype, copy=True) if isinstance(weight, QuantizedTensor): weight = weight.dequantize() bias = None if s.bias is not None: bias = s.bias.to(dtype=bias_dtype, copy=True) return weight, bias, (None, None, None) offload_stream = None xfer_dest = None signature = comfy_aimdo.model_vbar.vbar_fault(s._v) resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature) if signature is not None: if resident: weight = s._v_weight bias = s._v_bias else: xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device) if not resident: cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ]) cast_dest = None xfer_source = [ s.weight, s.bias ] pin = comfy.pinned_memory.get_pin(s) if pin is not None: xfer_source = [ pin ] for data, geometry in zip([ s.weight, s.bias ], cast_geometry): if data is None: continue if data.dtype != geometry.dtype: cast_dest = xfer_dest if cast_dest is None: cast_dest = torch.empty((comfy.memory_management.vram_aligned_size(cast_geometry),), dtype=torch.uint8, device=device) xfer_dest = None break dest_size = comfy.memory_management.vram_aligned_size(xfer_source) offload_stream = comfy.model_management.get_offload_stream(device) if xfer_dest is None and offload_stream is not None: xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s) if xfer_dest is None: offload_stream = comfy.model_management.get_offload_stream(device) xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s) if xfer_dest is None: xfer_dest = torch.empty((dest_size,), dtype=torch.uint8, device=device) offload_stream = None if signature is None and pin is None: comfy.pinned_memory.pin_memory(s) pin = comfy.pinned_memory.get_pin(s) else: pin = None if pin is not None: comfy.model_management.cast_to_gathered(xfer_source, pin) xfer_source = [ pin ] #send it over comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=offload_stream) comfy.model_management.sync_stream(device, offload_stream) if cast_dest is not None: for pre_cast, post_cast in zip(comfy.memory_management.interpret_gathered_like([s.weight, s.bias ], xfer_dest), comfy.memory_management.interpret_gathered_like(cast_geometry, cast_dest)): if post_cast is not None: post_cast.copy_(pre_cast) xfer_dest = cast_dest params = comfy.memory_management.interpret_gathered_like(cast_geometry, xfer_dest) weight = params[0] bias = params[1] if signature is not None: s._v_weight = weight s._v_bias = bias s._v_signature=signature def post_cast(s, param_key, x, dtype, resident, update_weight): lowvram_fn = getattr(s, param_key + "_lowvram_function", None) fns = getattr(s, param_key + "_function", []) orig = x def to_dequant(tensor, dtype): tensor = tensor.to(dtype=dtype) if isinstance(tensor, QuantizedTensor): tensor = tensor.dequantize() return tensor if orig.dtype != dtype or len(fns) > 0: x = to_dequant(x, dtype) if not resident and lowvram_fn is not None: x = to_dequant(x, dtype if compute_dtype is None else compute_dtype) x = lowvram_fn(x) if (want_requant and len(fns) == 0 or update_weight): seed = comfy.utils.string_to_seed(s.seed_key) if isinstance(orig, QuantizedTensor): y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed) else: y = comfy.float.stochastic_rounding(x, orig.dtype, seed=seed) if want_requant and len(fns) == 0: x = y if update_weight: orig.copy_(y) for f in fns: x = f(x) return x update_weight = signature is not None weight = post_cast(s, "weight", weight, dtype, resident, update_weight) if s.bias is not None: bias = post_cast(s, "bias", bias, bias_dtype, resident, update_weight) #FIXME: weird offload return protocol return weight, bias, (offload_stream, device if signature is not None else None, None) def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False, compute_dtype=None, want_requant=False): # NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass # offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This # will add async-offload support to your cast and improve performance. if input is not None: if dtype is None: if isinstance(input, QuantizedTensor): dtype = input.params.orig_dtype else: dtype = input.dtype if bias_dtype is None: bias_dtype = dtype if device is None: device = input.device non_blocking = comfy.model_management.device_supports_non_blocking(device) if hasattr(s, "_v"): return cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant) if offloadable and (device != s.weight.device or (s.bias is not None and device != s.bias.device)): offload_stream = comfy.model_management.get_offload_stream(device) else: offload_stream = None bias = None weight = None if offload_stream is not None and not args.cuda_malloc: cast_buffer_size = comfy.memory_management.vram_aligned_size([ s.weight, s.bias ]) cast_buffer = comfy.model_management.get_cast_buffer(offload_stream, device, cast_buffer_size, s) #The streams can be uneven in buffer capability and reject us. Retry to get the other stream if cast_buffer is None: offload_stream = comfy.model_management.get_offload_stream(device) cast_buffer = comfy.model_management.get_cast_buffer(offload_stream, device, cast_buffer_size, s) params = comfy.memory_management.interpret_gathered_like([ s.weight, s.bias ], cast_buffer) weight = params[0] bias = params[1] weight_has_function = len(s.weight_function) > 0 bias_has_function = len(s.bias_function) > 0 weight = comfy.model_management.cast_to(s.weight, None, device, non_blocking=non_blocking, copy=weight_has_function, stream=offload_stream, r=weight) if s.bias is not None: bias = comfy.model_management.cast_to(s.bias, None, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream, r=bias) comfy.model_management.sync_stream(device, offload_stream) bias_a = bias weight_a = weight if s.bias is not None: bias = bias.to(dtype=bias_dtype) for f in s.bias_function: bias = f(bias) if weight_has_function or weight.dtype != dtype: weight = weight.to(dtype=dtype) if isinstance(weight, QuantizedTensor): weight = weight.dequantize() for f in s.weight_function: weight = f(weight) if offloadable: return weight, bias, (offload_stream, weight_a, bias_a) else: #Legacy function signature return weight, bias def uncast_bias_weight(s, weight, bias, offload_stream): if offload_stream is None: return os, weight_a, bias_a = offload_stream device=None #FIXME: This is really bad RTTI if weight_a is not None and not isinstance(weight_a, torch.Tensor): comfy_aimdo.model_vbar.vbar_unpin(s._v) device = weight_a if os is None: return if device is None: if weight_a is not None: device = weight_a.device else: if bias_a is None: return device = bias_a.device os.wait_stream(comfy.model_management.current_stream(device)) class CastWeightBiasOp: comfy_cast_weights = False weight_function = [] bias_function = [] class disable_weight_init: @staticmethod def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata, missing_keys, unexpected_keys, weight_shape, bias_shape=None): assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False) prefix_len = len(prefix) for k, v in state_dict.items(): key = k[prefix_len:] if key == "weight": if not assign_to_params_buffers: v = v.clone() module.weight = torch.nn.Parameter(v, requires_grad=False) elif bias_shape is not None and key == "bias" and v is not None: if not assign_to_params_buffers: v = v.clone() module.bias = torch.nn.Parameter(v, requires_grad=False) else: unexpected_keys.append(k) if module.weight is None: module.weight = torch.nn.Parameter(torch.zeros(weight_shape), requires_grad=False) missing_keys.append(prefix + "weight") if bias_shape is not None and module.bias is None and getattr(module, "comfy_need_lazy_init_bias", False): module.bias = torch.nn.Parameter(torch.zeros(bias_shape), requires_grad=False) missing_keys.append(prefix + "bias") class Linear(torch.nn.Linear, CastWeightBiasOp): def __init__(self, in_features, out_features, bias=True, device=None, dtype=None): # don't trust subclasses that BYO state dict loader to call us. if (not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict): super().__init__(in_features, out_features, bias, device, dtype) return # Issue is with `torch.empty` still reserving the full memory for the layer. # Windows doesn't over-commit memory so without this, We are momentarily commit # charged for the weight even though we might zero-copy it when we load the # state dict. If the commit charge exceeds the ceiling we can destabilize the # system. torch.nn.Module.__init__(self) self.in_features = in_features self.out_features = out_features self.weight = None self.bias = None self.comfy_need_lazy_init_bias=bias self.weight_comfy_model_dtype = dtype self.bias_comfy_model_dtype = dtype def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): if (not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict): return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) disable_weight_init._lazy_load_from_state_dict( self, state_dict, prefix, local_metadata, missing_keys, unexpected_keys, weight_shape=(self.in_features, self.out_features), bias_shape=(self.out_features,), ) def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = torch.nn.functional.linear(input, weight, bias) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = self._conv_forward(input, weight, bias) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = self._conv_forward(input, weight, bias) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): def reset_parameters(self): return None def _conv_forward(self, input, weight, bias, autopad=None, *args, **kwargs): if autopad == "causal_zero": weight = weight[:, :, -input.shape[2]:, :, :] if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16): out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True) if bias is not None: out += bias.reshape((1, -1) + (1,) * (out.ndim - 2)) return out else: return super()._conv_forward(input, weight, bias, *args, **kwargs) def forward_comfy_cast_weights(self, input, autopad=None): weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = self._conv_forward(input, weight, bias, autopad=autopad) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0 or "autopad" in kwargs: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): if self.weight is not None: weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) else: weight = None bias = None offload_stream = None x = torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class RMSNorm(torch.nn.RMSNorm, CastWeightBiasOp): def reset_parameters(self): self.bias = None return None def forward_comfy_cast_weights(self, input): if self.weight is not None: weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) else: weight = None bias = None offload_stream = None x = torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input, output_size=None): num_spatial_dims = 2 output_padding = self._output_padding( input, output_size, self.stride, self.padding, self.kernel_size, num_spatial_dims, self.dilation) weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = torch.nn.functional.conv_transpose2d( input, weight, bias, self.stride, self.padding, output_padding, self.groups, self.dilation) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input, output_size=None): num_spatial_dims = 1 output_padding = self._output_padding( input, output_size, self.stride, self.padding, self.kernel_size, num_spatial_dims, self.dilation) weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = torch.nn.functional.conv_transpose1d( input, weight, bias, self.stride, self.padding, output_padding, self.groups, self.dilation) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Embedding(torch.nn.Embedding, CastWeightBiasOp): def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, _freeze=False, device=None, dtype=None): # don't trust subclasses that BYO state dict loader to call us. if (not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict): super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse, _weight, _freeze, device, dtype) return torch.nn.Module.__init__(self) self.num_embeddings = num_embeddings self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.max_norm = max_norm self.norm_type = norm_type self.scale_grad_by_freq = scale_grad_by_freq self.sparse = sparse # Keep shape/dtype visible for module introspection without reserving storage. embedding_dtype = dtype if dtype is not None else torch.get_default_dtype() self.weight = torch.nn.Parameter( torch.empty((num_embeddings, embedding_dim), device="meta", dtype=embedding_dtype), requires_grad=False, ) self.bias = None self.weight_comfy_model_dtype = dtype def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): if (not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict): return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) disable_weight_init._lazy_load_from_state_dict( self, state_dict, prefix, local_metadata, missing_keys, unexpected_keys, weight_shape=(self.num_embeddings, self.embedding_dim), ) def reset_parameters(self): self.bias = None return None def forward_comfy_cast_weights(self, input, out_dtype=None): output_dtype = out_dtype if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16: out_dtype = None weight, bias, offload_stream = cast_bias_weight(self, device=input.device, dtype=out_dtype, offloadable=True) x = torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: if "out_dtype" in kwargs: kwargs.pop("out_dtype") return super().forward(*args, **kwargs) @classmethod def conv_nd(s, dims, *args, **kwargs): if dims == 2: return s.Conv2d(*args, **kwargs) elif dims == 3: return s.Conv3d(*args, **kwargs) else: raise ValueError(f"unsupported dimensions: {dims}") class manual_cast(disable_weight_init): class Linear(disable_weight_init.Linear): comfy_cast_weights = True class Conv1d(disable_weight_init.Conv1d): comfy_cast_weights = True class Conv2d(disable_weight_init.Conv2d): comfy_cast_weights = True class Conv3d(disable_weight_init.Conv3d): comfy_cast_weights = True class GroupNorm(disable_weight_init.GroupNorm): comfy_cast_weights = True class LayerNorm(disable_weight_init.LayerNorm): comfy_cast_weights = True class ConvTranspose2d(disable_weight_init.ConvTranspose2d): comfy_cast_weights = True class ConvTranspose1d(disable_weight_init.ConvTranspose1d): comfy_cast_weights = True class RMSNorm(disable_weight_init.RMSNorm): comfy_cast_weights = True class Embedding(disable_weight_init.Embedding): comfy_cast_weights = True def fp8_linear(self, input): """ Legacy FP8 linear function for backward compatibility. Uses QuantizedTensor subclass for dispatch. """ dtype = self.weight.dtype if dtype not in [torch.float8_e4m3fn]: return None input_dtype = input.dtype input_shape = input.shape tensor_3d = input.ndim == 3 if tensor_3d: input = input.reshape(-1, input_shape[2]) if input.ndim != 2: return None lora_compute_dtype=comfy.model_management.lora_compute_dtype(input.device) w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True, compute_dtype=lora_compute_dtype, want_requant=True) scale_weight = torch.ones((), device=input.device, dtype=torch.float32) scale_input = torch.ones((), device=input.device, dtype=torch.float32) input = torch.clamp(input, min=-448, max=448, out=input) input_fp8 = input.to(dtype).contiguous() layout_params_input = TensorCoreFP8Layout.Params(scale=scale_input, orig_dtype=input_dtype, orig_shape=tuple(input_fp8.shape)) quantized_input = QuantizedTensor(input_fp8, "TensorCoreFP8Layout", layout_params_input) # Wrap weight in QuantizedTensor - this enables unified dispatch # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py! layout_params_weight = TensorCoreFP8Layout.Params(scale=scale_weight, orig_dtype=input_dtype, orig_shape=tuple(w.shape)) quantized_weight = QuantizedTensor(w, "TensorCoreFP8Layout", layout_params_weight) o = torch.nn.functional.linear(quantized_input, quantized_weight, bias) uncast_bias_weight(self, w, bias, offload_stream) if tensor_3d: o = o.reshape((input_shape[0], input_shape[1], w.shape[0])) return o class fp8_ops(manual_cast): class Linear(manual_cast.Linear): def reset_parameters(self): self.scale_weight = None self.scale_input = None return None def forward_comfy_cast_weights(self, input): if len(self.weight_function) == 0 and len(self.bias_function) == 0: try: out = fp8_linear(self, input) if out is not None: return out except Exception as e: logging.info("Exception during fp8 op: {}".format(e)) weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = torch.nn.functional.linear(input, weight, bias) uncast_bias_weight(self, weight, bias, offload_stream) return x CUBLAS_IS_AVAILABLE = False try: from cublas_ops import CublasLinear, cublas_half_matmul CUBLAS_IS_AVAILABLE = True except ImportError: pass if CUBLAS_IS_AVAILABLE: class cublas_ops(manual_cast): class Linear(CublasLinear, manual_cast.Linear): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) x = cublas_half_matmul(input, weight, bias, self._epilogue_str, self.has_bias) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, *args, **kwargs): run_every_op() if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) # ============================================================================== # Mixed Precision Operations # ============================================================================== from .quant_ops import ( QuantizedTensor, QUANT_ALGOS, TensorCoreFP8Layout, get_layout_class, ) class QuantLinearFunc(torch.autograd.Function): """Custom autograd function for quantized linear: quantized forward, compute_dtype backward. Handles any input rank by flattening to 2D for matmul and restoring shape after. """ @staticmethod def forward(ctx, input_float, weight, bias, layout_type, input_scale, compute_dtype): input_shape = input_float.shape inp = input_float.detach().flatten(0, -2) # zero-cost view to 2D # Quantize input (same as inference path) if layout_type is not None: q_input = QuantizedTensor.from_float(inp, layout_type, scale=input_scale) else: q_input = inp w = weight.detach() if weight.requires_grad else weight b = bias.detach() if bias is not None and bias.requires_grad else bias output = torch.nn.functional.linear(q_input, w, b) # Restore original input shape if len(input_shape) > 2: output = output.unflatten(0, input_shape[:-1]) ctx.save_for_backward(input_float, weight) ctx.input_shape = input_shape ctx.has_bias = bias is not None ctx.compute_dtype = compute_dtype ctx.weight_requires_grad = weight.requires_grad return output @staticmethod @torch.autograd.function.once_differentiable def backward(ctx, grad_output): input_float, weight = ctx.saved_tensors compute_dtype = ctx.compute_dtype grad_2d = grad_output.flatten(0, -2).to(compute_dtype) # Dequantize weight to compute dtype for backward matmul if isinstance(weight, QuantizedTensor): weight_f = weight.dequantize().to(compute_dtype) else: weight_f = weight.to(compute_dtype) # grad_input = grad_output @ weight grad_input = torch.mm(grad_2d, weight_f) if len(ctx.input_shape) > 2: grad_input = grad_input.unflatten(0, ctx.input_shape[:-1]) # grad_weight (only if weight requires grad, typically frozen for quantized training) grad_weight = None if ctx.weight_requires_grad: input_f = input_float.flatten(0, -2).to(compute_dtype) grad_weight = torch.mm(grad_2d.t(), input_f) # grad_bias grad_bias = None if ctx.has_bias: grad_bias = grad_2d.sum(dim=0) return grad_input, grad_weight, grad_bias, None, None, None def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]): class MixedPrecisionOps(manual_cast): _quant_config = quant_config _compute_dtype = compute_dtype _full_precision_mm = full_precision_mm _disabled = disabled class Linear(torch.nn.Module, CastWeightBiasOp): def __init__( self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None, ) -> None: super().__init__() self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype} # self.factory_kwargs = {"device": device, "dtype": dtype} self.in_features = in_features self.out_features = out_features if bias: self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs)) else: self.register_parameter("bias", None) self.tensor_class = None self._full_precision_mm = MixedPrecisionOps._full_precision_mm self._full_precision_mm_config = False def reset_parameters(self): return None def _load_scale_param(self, state_dict, prefix, param_name, device, manually_loaded_keys, dtype=None): key = f"{prefix}{param_name}" value = state_dict.pop(key, None) if value is not None: value = value.to(device=device) if dtype is not None: value = value.view(dtype=dtype) manually_loaded_keys.append(key) return value def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): device = self.factory_kwargs["device"] layer_name = prefix.rstrip('.') weight_key = f"{prefix}weight" weight = state_dict.pop(weight_key, None) if weight is None: logging.warning(f"Missing weight for layer {layer_name}") return manually_loaded_keys = [weight_key] layer_conf = state_dict.pop(f"{prefix}comfy_quant", None) if layer_conf is not None: layer_conf = json.loads(layer_conf.numpy().tobytes()) if layer_conf is None: self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False) else: self.quant_format = layer_conf.get("format", None) self._full_precision_mm_config = layer_conf.get("full_precision_matrix_mult", False) if not self._full_precision_mm: self._full_precision_mm = self._full_precision_mm_config if self.quant_format in MixedPrecisionOps._disabled: self._full_precision_mm = True if self.quant_format is None: raise ValueError(f"Unknown quantization format for layer {layer_name}") qconfig = QUANT_ALGOS[self.quant_format] self.layout_type = qconfig["comfy_tensor_layout"] layout_cls = get_layout_class(self.layout_type) # Load format-specific parameters if self.quant_format in ["float8_e4m3fn", "float8_e5m2"]: # FP8: single tensor scale scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys) params = layout_cls.Params( scale=scale, orig_dtype=MixedPrecisionOps._compute_dtype, orig_shape=(self.out_features, self.in_features), ) elif self.quant_format == "mxfp8": # MXFP8: E8M0 block scales stored as uint8 in safetensors block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys, dtype=torch.uint8) if block_scale is None: raise ValueError(f"Missing MXFP8 block scales for layer {layer_name}") block_scale = block_scale.view(torch.float8_e8m0fnu) params = layout_cls.Params( scale=block_scale, orig_dtype=MixedPrecisionOps._compute_dtype, orig_shape=(self.out_features, self.in_features), ) elif self.quant_format == "nvfp4": # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale) tensor_scale = self._load_scale_param(state_dict, prefix, "weight_scale_2", device, manually_loaded_keys) block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys, dtype=torch.float8_e4m3fn) if tensor_scale is None or block_scale is None: raise ValueError(f"Missing NVFP4 scales for layer {layer_name}") params = layout_cls.Params( scale=tensor_scale, block_scale=block_scale, orig_dtype=MixedPrecisionOps._compute_dtype, orig_shape=(self.out_features, self.in_features), ) else: raise ValueError(f"Unsupported quantization format: {self.quant_format}") self.weight = torch.nn.Parameter( QuantizedTensor(weight.to(device=device, dtype=qconfig["storage_t"]), self.layout_type, params), requires_grad=False ) for param_name in qconfig["parameters"]: if param_name in {"weight_scale", "weight_scale_2"}: continue # Already handled above param_key = f"{prefix}{param_name}" _v = state_dict.pop(param_key, None) if _v is None: continue self.register_parameter(param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False)) manually_loaded_keys.append(param_key) super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) for key in manually_loaded_keys: if key in missing_keys: missing_keys.remove(key) def state_dict(self, *args, destination=None, prefix="", **kwargs): if destination is not None: sd = destination else: sd = {} if not hasattr(self, 'weight'): logging.warning("Warning: state dict on uninitialized op {}".format(prefix)) return sd if self.bias is not None: sd["{}bias".format(prefix)] = self.bias if isinstance(self.weight, QuantizedTensor): sd_out = self.weight.state_dict("{}weight".format(prefix)) for k in sd_out: sd[k] = sd_out[k] quant_conf = {"format": self.quant_format} if self._full_precision_mm_config: quant_conf["full_precision_matrix_mult"] = True sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8) input_scale = getattr(self, 'input_scale', None) if input_scale is not None: sd["{}input_scale".format(prefix)] = input_scale else: sd["{}weight".format(prefix)] = self.weight return sd def _forward(self, input, weight, bias): return torch.nn.functional.linear(input, weight, bias) def forward_comfy_cast_weights(self, input, compute_dtype=None, want_requant=False): weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True, compute_dtype=compute_dtype, want_requant=want_requant) x = self._forward(input, weight, bias) uncast_bias_weight(self, weight, bias, offload_stream) return x def forward(self, input, *args, **kwargs): run_every_op() input_shape = input.shape reshaped_3d = False #If cast needs to apply lora, it should be done in the compute dtype compute_dtype = input.dtype _use_quantized = ( getattr(self, 'layout_type', None) is not None and not isinstance(input, QuantizedTensor) and not self._full_precision_mm and not getattr(self, 'comfy_force_cast_weights', False) and len(self.weight_function) == 0 and len(self.bias_function) == 0 ) # Training path: quantized forward with compute_dtype backward via autograd function if (input.requires_grad and _use_quantized): weight, bias, offload_stream = cast_bias_weight( self, input, offloadable=True, compute_dtype=compute_dtype, want_requant=True ) scale = getattr(self, 'input_scale', None) if scale is not None: scale = comfy.model_management.cast_to_device(scale, input.device, None) output = QuantLinearFunc.apply( input, weight, bias, self.layout_type, scale, compute_dtype ) uncast_bias_weight(self, weight, bias, offload_stream) return output # Inference path (unchanged) if _use_quantized: # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others) input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input # Fall back to non-quantized for non-2D tensors if input_reshaped.ndim == 2: reshaped_3d = input.ndim == 3 # dtype is now implicit in the layout class scale = getattr(self, 'input_scale', None) if scale is not None: scale = comfy.model_management.cast_to_device(scale, input.device, None) input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale) output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor)) # Reshape output back to 3D if input was 3D if reshaped_3d: output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0])) return output def convert_weight(self, weight, inplace=False, **kwargs): if isinstance(weight, QuantizedTensor): return weight.dequantize() else: return weight def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs): if getattr(self, 'layout_type', None) is not None: # dtype is now implicit in the layout class weight = QuantizedTensor.from_float(weight, self.layout_type, scale="recalculate", stochastic_rounding=seed, inplace_ops=True).to(self.weight.dtype) else: weight = weight.to(self.weight.dtype) if return_weight: return weight assert inplace_update is False # TODO: eventually remove the inplace_update stuff self.weight = torch.nn.Parameter(weight, requires_grad=False) def _apply(self, fn, recurse=True): # This is to get torch.compile + moving weights to another device working if recurse: for module in self.children(): module._apply(fn) for key, param in self._parameters.items(): if param is None: continue p = fn(param) if p.is_inference(): p = p.clone() self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False)) for key, buf in self._buffers.items(): if buf is not None: self._buffers[key] = fn(buf) return self return MixedPrecisionOps def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None): fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device) mxfp8_compute = comfy.model_management.supports_mxfp8_compute(load_device) if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config: logging.info("Using mixed precision operations") disabled = set() if not nvfp4_compute: disabled.add("nvfp4") if not mxfp8_compute: disabled.add("mxfp8") if not fp8_compute: disabled.add("float8_e4m3fn") disabled.add("float8_e5m2") return mixed_precision_ops(model_config.quant_config, compute_dtype, disabled=disabled) if ( fp8_compute and (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and not disable_fast_fp8 ): return fp8_ops if ( PerformanceFeature.CublasOps in args.fast and CUBLAS_IS_AVAILABLE and weight_dtype == torch.float16 and (compute_dtype == torch.float16 or compute_dtype is None) ): logging.info("Using cublas ops") return cublas_ops if compute_dtype is None or weight_dtype == compute_dtype: return disable_weight_init return manual_cast ================================================ FILE: comfy/options.py ================================================ args_parsing = False def enable_args_parsing(enable=True): global args_parsing args_parsing = enable ================================================ FILE: comfy/patcher_extension.py ================================================ from __future__ import annotations from typing import Callable class CallbacksMP: ON_CLONE = "on_clone" ON_LOAD = "on_load_after" ON_DETACH = "on_detach_after" ON_CLEANUP = "on_cleanup" ON_PRE_RUN = "on_pre_run" ON_PREPARE_STATE = "on_prepare_state" ON_APPLY_HOOKS = "on_apply_hooks" ON_REGISTER_ALL_HOOK_PATCHES = "on_register_all_hook_patches" ON_INJECT_MODEL = "on_inject_model" ON_EJECT_MODEL = "on_eject_model" # callbacks dict is in the format: # {"call_type": {"key": [Callable1, Callable2, ...]} } @classmethod def init_callbacks(cls) -> dict[str, dict[str, list[Callable]]]: return {} def add_callback(call_type: str, callback: Callable, transformer_options: dict, is_model_options=False): add_callback_with_key(call_type, None, callback, transformer_options, is_model_options) def add_callback_with_key(call_type: str, key: str, callback: Callable, transformer_options: dict, is_model_options=False): if is_model_options: transformer_options = transformer_options.setdefault("transformer_options", {}) callbacks: dict[str, dict[str, list]] = transformer_options.setdefault("callbacks", {}) c = callbacks.setdefault(call_type, {}).setdefault(key, []) c.append(callback) def get_callbacks_with_key(call_type: str, key: str, transformer_options: dict, is_model_options=False): if is_model_options: transformer_options = transformer_options.get("transformer_options", {}) c_list = [] callbacks: dict[str, list] = transformer_options.get("callbacks", {}) c_list.extend(callbacks.get(call_type, {}).get(key, [])) return c_list def get_all_callbacks(call_type: str, transformer_options: dict, is_model_options=False): if is_model_options: transformer_options = transformer_options.get("transformer_options", {}) c_list = [] callbacks: dict[str, list] = transformer_options.get("callbacks", {}) for c in callbacks.get(call_type, {}).values(): c_list.extend(c) return c_list class WrappersMP: OUTER_SAMPLE = "outer_sample" PREPARE_SAMPLING = "prepare_sampling" SAMPLER_SAMPLE = "sampler_sample" PREDICT_NOISE = "predict_noise" CALC_COND_BATCH = "calc_cond_batch" APPLY_MODEL = "apply_model" DIFFUSION_MODEL = "diffusion_model" # wrappers dict is in the format: # {"wrapper_type": {"key": [Callable1, Callable2, ...]} } @classmethod def init_wrappers(cls) -> dict[str, dict[str, list[Callable]]]: return {} def add_wrapper(wrapper_type: str, wrapper: Callable, transformer_options: dict, is_model_options=False): add_wrapper_with_key(wrapper_type, None, wrapper, transformer_options, is_model_options) def add_wrapper_with_key(wrapper_type: str, key: str, wrapper: Callable, transformer_options: dict, is_model_options=False): if is_model_options: transformer_options = transformer_options.setdefault("transformer_options", {}) wrappers: dict[str, dict[str, list]] = transformer_options.setdefault("wrappers", {}) w = wrappers.setdefault(wrapper_type, {}).setdefault(key, []) w.append(wrapper) def get_wrappers_with_key(wrapper_type: str, key: str, transformer_options: dict, is_model_options=False): if is_model_options: transformer_options = transformer_options.get("transformer_options", {}) w_list = [] wrappers: dict[str, list] = transformer_options.get("wrappers", {}) w_list.extend(wrappers.get(wrapper_type, {}).get(key, [])) return w_list def get_all_wrappers(wrapper_type: str, transformer_options: dict, is_model_options=False): if is_model_options: transformer_options = transformer_options.get("transformer_options", {}) w_list = [] wrappers: dict[str, list] = transformer_options.get("wrappers", {}) for w in wrappers.get(wrapper_type, {}).values(): w_list.extend(w) return w_list class WrapperExecutor: """Handles call stack of wrappers around a function in an ordered manner.""" def __init__(self, original: Callable, class_obj: object, wrappers: list[Callable], idx: int): # NOTE: class_obj exists so that wrappers surrounding a class method can access # the class instance at runtime via executor.class_obj self.original = original self.class_obj = class_obj self.wrappers = wrappers.copy() self.idx = idx self.is_last = idx == len(wrappers) def __call__(self, *args, **kwargs): """Calls the next wrapper or original function, whichever is appropriate.""" new_executor = self._create_next_executor() return new_executor.execute(*args, **kwargs) def execute(self, *args, **kwargs): """Used to initiate executor internally - DO NOT use this if you received executor in wrapper.""" args = list(args) kwargs = dict(kwargs) if self.is_last: return self.original(*args, **kwargs) return self.wrappers[self.idx](self, *args, **kwargs) def _create_next_executor(self) -> 'WrapperExecutor': new_idx = self.idx + 1 if new_idx > len(self.wrappers): raise Exception("Wrapper idx exceeded available wrappers; something went very wrong.") if self.class_obj is None: return WrapperExecutor.new_executor(self.original, self.wrappers, new_idx) return WrapperExecutor.new_class_executor(self.original, self.class_obj, self.wrappers, new_idx) @classmethod def new_executor(cls, original: Callable, wrappers: list[Callable], idx=0): return cls(original, class_obj=None, wrappers=wrappers, idx=idx) @classmethod def new_class_executor(cls, original: Callable, class_obj: object, wrappers: list[Callable], idx=0): return cls(original, class_obj, wrappers, idx=idx) class PatcherInjection: def __init__(self, inject: Callable, eject: Callable): self.inject = inject self.eject = eject def copy_nested_dicts(input_dict: dict): new_dict = input_dict.copy() for key, value in input_dict.items(): if isinstance(value, dict): new_dict[key] = copy_nested_dicts(value) elif isinstance(value, list): new_dict[key] = value.copy() return new_dict def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True): if copy_dict1: merged_dict = copy_nested_dicts(dict1) else: merged_dict = dict1 for key, value in dict2.items(): if isinstance(value, dict): curr_value = merged_dict.setdefault(key, {}) merged_dict[key] = merge_nested_dicts(curr_value, value) elif isinstance(value, list): merged_dict.setdefault(key, []).extend(value) else: merged_dict[key] = value return merged_dict ================================================ FILE: comfy/pinned_memory.py ================================================ import comfy.model_management import comfy.memory_management import comfy_aimdo.host_buffer import comfy_aimdo.torch from comfy.cli_args import args def get_pin(module): return getattr(module, "_pin", None) def pin_memory(module): if module.pin_failed or args.disable_pinned_memory or get_pin(module) is not None: return #FIXME: This is a RAM cache trigger event size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ]) if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY: module.pin_failed = True return False try: hostbuf = comfy_aimdo.host_buffer.HostBuffer(size) except RuntimeError: module.pin_failed = True return False module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf) module._pin_hostbuf = hostbuf comfy.model_management.TOTAL_PINNED_MEMORY += size return True def unpin_memory(module): if get_pin(module) is None: return 0 size = module._pin.numel() * module._pin.element_size() comfy.model_management.TOTAL_PINNED_MEMORY -= size if comfy.model_management.TOTAL_PINNED_MEMORY < 0: comfy.model_management.TOTAL_PINNED_MEMORY = 0 del module._pin del module._pin_hostbuf return size ================================================ FILE: comfy/pixel_space_convert.py ================================================ import torch # "Fake" VAE that converts from IMAGE B, H, W, C and values on the scale of 0..1 # to LATENT B, C, H, W and values on the scale of -1..1. class PixelspaceConversionVAE(torch.nn.Module): def __init__(self): super().__init__() self.pixel_space_vae = torch.nn.Parameter(torch.tensor(1.0)) def encode(self, pixels: torch.Tensor, *_args, **_kwargs) -> torch.Tensor: return pixels def decode(self, samples: torch.Tensor, *_args, **_kwargs) -> torch.Tensor: return samples ================================================ FILE: comfy/quant_ops.py ================================================ import torch import logging try: import comfy_kitchen as ck from comfy_kitchen.tensor import ( QuantizedTensor, QuantizedLayout, TensorCoreFP8Layout as _CKFp8Layout, TensorCoreNVFP4Layout as _CKNvfp4Layout, register_layout_op, register_layout_class, get_layout_class, ) _CK_AVAILABLE = True if torch.version.cuda is None: ck.registry.disable("cuda") else: cuda_version = tuple(map(int, str(torch.version.cuda).split('.'))) if cuda_version < (13,): ck.registry.disable("cuda") logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.") ck.registry.disable("triton") for k, v in ck.list_backends().items(): logging.info(f"Found comfy_kitchen backend {k}: {v}") except ImportError as e: logging.error(f"Failed to import comfy_kitchen, Error: {e}, fp8 and fp4 support will not be available.") _CK_AVAILABLE = False class QuantizedTensor: pass class _CKFp8Layout: pass class _CKNvfp4Layout: pass def register_layout_class(name, cls): pass def get_layout_class(name): return None _CK_MXFP8_AVAILABLE = False if _CK_AVAILABLE: try: from comfy_kitchen.tensor import TensorCoreMXFP8Layout as _CKMxfp8Layout _CK_MXFP8_AVAILABLE = True except ImportError: logging.warning("comfy_kitchen does not support MXFP8, please update comfy_kitchen.") if not _CK_MXFP8_AVAILABLE: class _CKMxfp8Layout: pass import comfy.float # ============================================================================== # FP8 Layouts with Comfy-Specific Extensions # ============================================================================== class _TensorCoreFP8LayoutBase(_CKFp8Layout): FP8_DTYPE = None # Must be overridden in subclass @classmethod def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False): if cls.FP8_DTYPE is None: raise NotImplementedError(f"{cls.__name__} must define FP8_DTYPE") orig_dtype = tensor.dtype orig_shape = tuple(tensor.shape) if isinstance(scale, str) and scale == "recalculate": scale = torch.amax(tensor.abs()).to(dtype=torch.float32) / torch.finfo(cls.FP8_DTYPE).max if tensor.dtype not in [torch.float32, torch.bfloat16]: # Prevent scale from being too small tensor_info = torch.finfo(tensor.dtype) scale = (1.0 / torch.clamp((1.0 / scale), min=tensor_info.min, max=tensor_info.max)) if scale is None: scale = torch.ones((), device=tensor.device, dtype=torch.float32) if not isinstance(scale, torch.Tensor): scale = torch.tensor(scale, device=tensor.device, dtype=torch.float32) if stochastic_rounding > 0: if inplace_ops: tensor *= (1.0 / scale).to(tensor.dtype) else: tensor = tensor * (1.0 / scale).to(tensor.dtype) qdata = comfy.float.stochastic_rounding(tensor, dtype=cls.FP8_DTYPE, seed=stochastic_rounding) else: qdata = ck.quantize_per_tensor_fp8(tensor, scale, cls.FP8_DTYPE) params = cls.Params(scale=scale.float(), orig_dtype=orig_dtype, orig_shape=orig_shape) return qdata, params class TensorCoreMXFP8Layout(_CKMxfp8Layout): @classmethod def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False): if tensor.dim() != 2: raise ValueError(f"MXFP8 requires 2D tensor, got {tensor.dim()}D") orig_dtype = tensor.dtype orig_shape = tuple(tensor.shape) padded_shape = cls.get_padded_shape(orig_shape) needs_padding = padded_shape != orig_shape if stochastic_rounding > 0: qdata, block_scale = comfy.float.stochastic_round_quantize_mxfp8_by_block(tensor, pad_32x=needs_padding, seed=stochastic_rounding) else: qdata, block_scale = ck.quantize_mxfp8(tensor, pad_32x=needs_padding) params = cls.Params( scale=block_scale, orig_dtype=orig_dtype, orig_shape=orig_shape, ) return qdata, params class TensorCoreNVFP4Layout(_CKNvfp4Layout): @classmethod def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False): if tensor.dim() != 2: raise ValueError(f"NVFP4 requires 2D tensor, got {tensor.dim()}D") orig_dtype = tensor.dtype orig_shape = tuple(tensor.shape) if scale is None or (isinstance(scale, str) and scale == "recalculate"): scale = torch.amax(tensor.abs()) / (ck.float_utils.F8_E4M3_MAX * ck.float_utils.F4_E2M1_MAX) if not isinstance(scale, torch.Tensor): scale = torch.tensor(scale) scale = scale.to(device=tensor.device, dtype=torch.float32) padded_shape = cls.get_padded_shape(orig_shape) needs_padding = padded_shape != orig_shape if stochastic_rounding > 0: qdata, block_scale = comfy.float.stochastic_round_quantize_nvfp4_by_block(tensor, scale, pad_16x=needs_padding, seed=stochastic_rounding) else: qdata, block_scale = ck.quantize_nvfp4(tensor, scale, pad_16x=needs_padding) params = cls.Params( scale=scale, orig_dtype=orig_dtype, orig_shape=orig_shape, block_scale=block_scale, ) return qdata, params class TensorCoreFP8E4M3Layout(_TensorCoreFP8LayoutBase): FP8_DTYPE = torch.float8_e4m3fn class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase): FP8_DTYPE = torch.float8_e5m2 # Backward compatibility alias - default to E4M3 TensorCoreFP8Layout = TensorCoreFP8E4M3Layout # ============================================================================== # Registry # ============================================================================== register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout) register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout) register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout) register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout) if _CK_MXFP8_AVAILABLE: register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout) QUANT_ALGOS = { "float8_e4m3fn": { "storage_t": torch.float8_e4m3fn, "parameters": {"weight_scale", "input_scale"}, "comfy_tensor_layout": "TensorCoreFP8E4M3Layout", }, "float8_e5m2": { "storage_t": torch.float8_e5m2, "parameters": {"weight_scale", "input_scale"}, "comfy_tensor_layout": "TensorCoreFP8E5M2Layout", }, "nvfp4": { "storage_t": torch.uint8, "parameters": {"weight_scale", "weight_scale_2", "input_scale"}, "comfy_tensor_layout": "TensorCoreNVFP4Layout", "group_size": 16, }, } if _CK_MXFP8_AVAILABLE: QUANT_ALGOS["mxfp8"] = { "storage_t": torch.float8_e4m3fn, "parameters": {"weight_scale", "input_scale"}, "comfy_tensor_layout": "TensorCoreMXFP8Layout", "group_size": 32, } # ============================================================================== # Re-exports for backward compatibility # ============================================================================== __all__ = [ "QuantizedTensor", "QuantizedLayout", "TensorCoreFP8Layout", "TensorCoreFP8E4M3Layout", "TensorCoreFP8E5M2Layout", "TensorCoreNVFP4Layout", "QUANT_ALGOS", "register_layout_op", ] ================================================ FILE: comfy/rmsnorm.py ================================================ import torch import comfy.model_management RMSNorm = torch.nn.RMSNorm def rms_norm(x, weight=None, eps=1e-6): if weight is None: return torch.nn.functional.rms_norm(x, (x.shape[-1],), eps=eps) else: return torch.nn.functional.rms_norm(x, weight.shape, weight=comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps) ================================================ FILE: comfy/sample.py ================================================ import torch import comfy.model_management import comfy.samplers import comfy.utils import numpy as np import logging import comfy.nested_tensor def prepare_noise_inner(latent_image, generator, noise_inds=None): if noise_inds is None: return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") unique_inds, inverse = np.unique(noise_inds, return_inverse=True) noises = [] for i in range(unique_inds[-1]+1): noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") if i in unique_inds: noises.append(noise) noises = [noises[i] for i in inverse] return torch.cat(noises, axis=0) def prepare_noise(latent_image, seed, noise_inds=None): """ creates random noise given a latent image and a seed. optional arg skip can be used to skip and discard x number of noise generations for a given seed """ generator = torch.manual_seed(seed) if latent_image.is_nested: tensors = latent_image.unbind() noises = [] for t in tensors: noises.append(prepare_noise_inner(t, generator, noise_inds)) noises = comfy.nested_tensor.NestedTensor(noises) else: noises = prepare_noise_inner(latent_image, generator, noise_inds) return noises def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None): if latent_image.is_nested: return latent_image latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels if torch.count_nonzero(latent_image) == 0: if latent_format.latent_channels != latent_image.shape[1]: latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1) if downscale_ratio_spacial is not None: if downscale_ratio_spacial != latent_format.spacial_downscale_ratio: ratio = downscale_ratio_spacial / latent_format.spacial_downscale_ratio latent_image = comfy.utils.common_upscale(latent_image, round(latent_image.shape[-1] * ratio), round(latent_image.shape[-2] * ratio), "nearest-exact", crop="disabled") if latent_format.latent_dimensions == 3 and latent_image.ndim == 4: latent_image = latent_image.unsqueeze(2) return latent_image def prepare_sampling(model, noise_shape, positive, negative, noise_mask): logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") return model, positive, negative, noise_mask, [] def cleanup_additional_models(models): logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed") def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return samples ================================================ FILE: comfy/sampler_helpers.py ================================================ from __future__ import annotations import uuid import math import collections import comfy.model_management import comfy.conds import comfy.utils import comfy.hooks import comfy.patcher_extension from typing import TYPE_CHECKING if TYPE_CHECKING: from comfy.model_patcher import ModelPatcher from comfy.model_base import BaseModel from comfy.controlnet import ControlBase def prepare_mask(noise_mask, shape, device): return comfy.utils.reshape_mask(noise_mask, shape).to(device) def get_models_from_cond(cond, model_type): models = [] for c in cond: if model_type in c: if isinstance(c[model_type], list): models += c[model_type] else: models += [c[model_type]] return models def get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup): # get hooks from conds, and collect cnets so they can be checked for extra_hooks cnets: list[ControlBase] = [] for c in cond: if 'hooks' in c: for hook in c['hooks'].hooks: full_hooks.add(hook) if 'control' in c: cnets.append(c['control']) def get_extra_hooks_from_cnet(cnet: ControlBase, _list: list): if cnet.extra_hooks is not None: _list.append(cnet.extra_hooks) if cnet.previous_controlnet is None: return _list return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list) hooks_list = [] cnets = set(cnets) for base_cnet in cnets: get_extra_hooks_from_cnet(base_cnet, hooks_list) extra_hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks_list) if extra_hooks is not None: for hook in extra_hooks.hooks: full_hooks.add(hook) return full_hooks def convert_cond(cond): out = [] for c in cond: temp = c[1].copy() model_conds = temp.get("model_conds", {}) if c[0] is not None: temp["cross_attn"] = c[0] temp["model_conds"] = model_conds temp["uuid"] = uuid.uuid4() out.append(temp) return out def cond_has_hooks(cond): for c in cond: temp = c[1] if "hooks" in temp: return True if "control" in temp: control = temp["control"] extra_hooks = control.get_extra_hooks() if len(extra_hooks) > 0: return True return False def get_additional_models(conds, dtype): """loads additional models in conditioning""" cnets: list[ControlBase] = [] gligen = [] add_models = [] for k in conds: cnets += get_models_from_cond(conds[k], "control") gligen += get_models_from_cond(conds[k], "gligen") add_models += get_models_from_cond(conds[k], "additional_models") control_nets = set(cnets) inference_memory = 0 control_models = [] for m in control_nets: control_models += m.get_models() inference_memory += m.inference_memory_requirements(dtype) gligen = [x[1] for x in gligen] models = control_models + gligen + add_models return models, inference_memory def get_additional_models_from_model_options(model_options: dict[str]=None): """loads additional models from registered AddModels hooks""" models = [] if model_options is not None and "registered_hooks" in model_options: registered: comfy.hooks.HookGroup = model_options["registered_hooks"] for hook in registered.get_type(comfy.hooks.EnumHookType.AdditionalModels): hook: comfy.hooks.AdditionalModelsHook models.extend(hook.models) return models def cleanup_additional_models(models): """cleanup additional models that were loaded""" for m in models: if hasattr(m, 'cleanup'): m.cleanup() def estimate_memory(model, noise_shape, conds): cond_shapes = collections.defaultdict(list) cond_shapes_min = {} for _, cs in conds.items(): for cond in cs: for k, v in model.model.extra_conds_shapes(**cond).items(): cond_shapes[k].append(v) if cond_shapes_min.get(k, None) is None: cond_shapes_min[k] = [v] elif math.prod(v) > math.prod(cond_shapes_min[k][0]): cond_shapes_min[k] = [v] memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes) minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min) return memory_required, minimum_memory_required def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False): executor = comfy.patcher_extension.WrapperExecutor.new_executor( _prepare_sampling, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True) ) return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load, force_offload=force_offload) def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False): real_model: BaseModel = None models, inference_memory = get_additional_models(conds, model.model_dtype()) models += get_additional_models_from_model_options(model_options) models += model.get_nested_additional_models() # TODO: does this require inference_memory update? if force_offload: # In training + offload enabled, we want to force prepare sampling to trigger partial load memory_required = 1e20 minimum_memory_required = None else: memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds) memory_required += inference_memory minimum_memory_required += inference_memory comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required, force_full_load=force_full_load) real_model = model.model return real_model, conds, models def cleanup_models(conds, models): cleanup_additional_models(models) control_cleanup = [] for k in conds: control_cleanup += get_models_from_cond(conds[k], "control") cleanup_additional_models(set(control_cleanup)) def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict): ''' Registers hooks from conds. ''' # check for hooks in conds - if not registered, see if can be applied hooks = comfy.hooks.HookGroup() for k in conds: get_hooks_from_cond(conds[k], hooks) # add wrappers and callbacks from ModelPatcher to transformer_options comfy.patcher_extension.merge_nested_dicts(model_options["transformer_options"].setdefault("wrappers", {}), model.wrappers, copy_dict1=False) comfy.patcher_extension.merge_nested_dicts(model_options["transformer_options"].setdefault("callbacks", {}), model.callbacks, copy_dict1=False) # begin registering hooks registered = comfy.hooks.HookGroup() target_dict = comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Model) # handle all TransformerOptionsHooks for hook in hooks.get_type(comfy.hooks.EnumHookType.TransformerOptions): hook: comfy.hooks.TransformerOptionsHook hook.add_hook_patches(model, model_options, target_dict, registered) # handle all AddModelsHooks for hook in hooks.get_type(comfy.hooks.EnumHookType.AdditionalModels): hook: comfy.hooks.AdditionalModelsHook hook.add_hook_patches(model, model_options, target_dict, registered) # handle all WeightHooks by registering on ModelPatcher model.register_all_hook_patches(hooks, target_dict, model_options, registered) # add registered_hooks onto model_options for further reference if len(registered) > 0: model_options["registered_hooks"] = registered # merge original wrappers and callbacks with hooked wrappers and callbacks to_load_options: dict[str] = model_options.setdefault("to_load_options", {}) for wc_name in ["wrappers", "callbacks"]: comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name], copy_dict1=False) return to_load_options ================================================ FILE: comfy/samplers.py ================================================ from __future__ import annotations from .k_diffusion import sampling as k_diffusion_sampling from .extra_samplers import uni_pc from typing import TYPE_CHECKING, Callable, NamedTuple if TYPE_CHECKING: from comfy.model_patcher import ModelPatcher from comfy.model_base import BaseModel from comfy.controlnet import ControlBase import torch from functools import partial import collections import math import logging import comfy.sampler_helpers import comfy.model_patcher import comfy.patcher_extension import comfy.hooks import comfy.context_windows import comfy.utils import scipy.stats import numpy def add_area_dims(area, num_dims): while (len(area) // 2) < num_dims: area = [2147483648] + area[:len(area) // 2] + [0] + area[len(area) // 2:] return area def get_area_and_mult(conds, x_in, timestep_in): dims = tuple(x_in.shape[2:]) area = None strength = 1.0 if 'timestep_start' in conds: timestep_start = conds['timestep_start'] if timestep_in[0] > timestep_start: return None if 'timestep_end' in conds: timestep_end = conds['timestep_end'] if timestep_in[0] < timestep_end: return None if 'area' in conds: area = list(conds['area']) area = add_area_dims(area, len(dims)) if (len(area) // 2) > len(dims): area = area[:len(dims)] + area[len(area) // 2:(len(area) // 2) + len(dims)] if 'strength' in conds: strength = conds['strength'] input_x = x_in if area is not None: for i in range(len(dims)): area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i]) input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i]) if 'mask' in conds: # Scale the mask to the size of the input # The mask should have been resized as we began the sampling process mask_strength = 1.0 if "mask_strength" in conds: mask_strength = conds["mask_strength"] mask = conds['mask'] # assert (mask.shape[1:] == x_in.shape[2:]) mask = mask[:input_x.shape[0]] if area is not None: for i in range(len(dims)): mask = mask.narrow(i + 1, area[len(dims) + i], area[i]) mask = mask * mask_strength mask = mask.unsqueeze(1).repeat((input_x.shape[0] // mask.shape[0], input_x.shape[1]) + (1, ) * (mask.ndim - 1)) else: mask = torch.ones_like(input_x) mult = mask * strength if 'mask' not in conds and area is not None: fuzz = 8 for i in range(len(dims)): rr = min(fuzz, mult.shape[2 + i] // 4) if area[len(dims) + i] != 0: for t in range(rr): m = mult.narrow(i + 2, t, 1) m *= ((1.0 / rr) * (t + 1)) if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]: for t in range(rr): m = mult.narrow(i + 2, area[i] - 1 - t, 1) m *= ((1.0 / rr) * (t + 1)) conditioning = {} model_conds = conds["model_conds"] for c in model_conds: conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], area=area) hooks = conds.get('hooks', None) control = conds.get('control', None) patches = None if 'gligen' in conds: gligen = conds['gligen'] patches = {} gligen_type = gligen[0] gligen_model = gligen[1] if gligen_type == "position": gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) else: gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) patches['middle_patch'] = [gligen_patch] cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches', 'uuid', 'hooks']) return cond_obj(input_x, mult, conditioning, area, control, patches, conds['uuid'], hooks) def cond_equal_size(c1, c2): if c1 is c2: return True if c1.keys() != c2.keys(): return False for k in c1: if not c1[k].can_concat(c2[k]): return False return True def can_concat_cond(c1, c2): if c1.input_x.shape != c2.input_x.shape: return False def objects_concatable(obj1, obj2): if (obj1 is None) != (obj2 is None): return False if obj1 is not None: if obj1 is not obj2: return False return True if not objects_concatable(c1.control, c2.control): return False if not objects_concatable(c1.patches, c2.patches): return False return cond_equal_size(c1.conditioning, c2.conditioning) def cond_cat(c_list): temp = {} for x in c_list: for k in x: cur = temp.get(k, []) cur.append(x[k]) temp[k] = cur out = {} for k in temp: conds = temp[k] out[k] = conds[0].concat(conds[1:]) return out def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]], default_conds: list[list[dict]], x_in, timestep, model_options): # need to figure out remaining unmasked area for conds default_mults = [] for _ in default_conds: default_mults.append(torch.ones_like(x_in)) # look through each finalized cond in hooked_to_run for 'mult' and subtract it from each cond for lora_hooks, to_run in hooked_to_run.items(): for cond_obj, i in to_run: # if no default_cond for cond_type, do nothing if len(default_conds[i]) == 0: continue area: list[int] = cond_obj.area if area is not None: curr_default_mult: torch.Tensor = default_mults[i] dims = len(area) // 2 for i in range(dims): curr_default_mult = curr_default_mult.narrow(i + 2, area[i + dims], area[i]) curr_default_mult -= cond_obj.mult else: default_mults[i] -= cond_obj.mult # for each default_mult, ReLU to make negatives=0, and then check for any nonzeros for i, mult in enumerate(default_mults): # if no default_cond for cond type, do nothing if len(default_conds[i]) == 0: continue torch.nn.functional.relu(mult, inplace=True) # if mult is all zeros, then don't add default_cond if torch.max(mult) == 0.0: continue cond = default_conds[i] for x in cond: # do get_area_and_mult to get all the expected values p = get_area_and_mult(x, x_in, timestep) if p is None: continue # replace p's mult with calculated mult p = p._replace(mult=mult) if p.hooks is not None: model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options) hooked_to_run.setdefault(p.hooks, list()) hooked_to_run[p.hooks] += [(p, i)] def calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options: dict[str]): handler: comfy.context_windows.ContextHandlerABC = model_options.get("context_handler", None) if handler is None or not handler.should_use_context(model, conds, x_in, timestep, model_options): return _calc_cond_batch_outer(model, conds, x_in, timestep, model_options) return handler.execute(_calc_cond_batch_outer, model, conds, x_in, timestep, model_options) def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): executor = comfy.patcher_extension.WrapperExecutor.new_executor( _calc_cond_batch, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, model_options, is_model_options=True) ) return executor.execute(model, conds, x_in, timestep, model_options) def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): out_conds = [] out_counts = [] # separate conds by matching hooks hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {} default_conds = [] has_default_conds = False for i in range(len(conds)): out_conds.append(torch.zeros_like(x_in)) out_counts.append(torch.ones_like(x_in) * 1e-37) cond = conds[i] default_c = [] if cond is not None: for x in cond: if 'default' in x: default_c.append(x) has_default_conds = True continue p = get_area_and_mult(x, x_in, timestep) if p is None: continue if p.hooks is not None: model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options) hooked_to_run.setdefault(p.hooks, list()) hooked_to_run[p.hooks] += [(p, i)] default_conds.append(default_c) if has_default_conds: finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options) model.current_patcher.prepare_state(timestep) # run every hooked_to_run separately for hooks, to_run in hooked_to_run.items(): while len(to_run) > 0: first = to_run[0] first_shape = first[0][0].shape to_batch_temp = [] for x in range(len(to_run)): if can_concat_cond(to_run[x][0], first[0]): to_batch_temp += [x] to_batch_temp.reverse() to_batch = to_batch_temp[:1] free_memory = model.current_patcher.get_free_memory(x_in.device) for i in range(1, len(to_batch_temp) + 1): batch_amount = to_batch_temp[:len(to_batch_temp)//i] input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] cond_shapes = collections.defaultdict(list) for tt in batch_amount: cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()} for k, v in to_run[tt][0].conditioning.items(): cond_shapes[k].append(v.size()) if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory: to_batch = batch_amount break input_x = [] mult = [] c = [] cond_or_uncond = [] uuids = [] area = [] control = None patches = None for x in to_batch: o = to_run.pop(x) p = o[0] input_x.append(p.input_x) mult.append(p.mult) c.append(p.conditioning) area.append(p.area) cond_or_uncond.append(o[1]) uuids.append(p.uuid) control = p.control patches = p.patches batch_chunks = len(cond_or_uncond) input_x = torch.cat(input_x) c = cond_cat(c) timestep_ = torch.cat([timestep] * batch_chunks) transformer_options = model.current_patcher.apply_hooks(hooks=hooks) if 'transformer_options' in model_options: transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options, model_options['transformer_options'], copy_dict1=False) if patches is not None: transformer_options["patches"] = comfy.patcher_extension.merge_nested_dicts( transformer_options.get("patches", {}), patches ) transformer_options["cond_or_uncond"] = cond_or_uncond[:] transformer_options["uuids"] = uuids[:] transformer_options["sigmas"] = timestep c['transformer_options'] = transformer_options if control is not None: c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options) if 'model_function_wrapper' in model_options: output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) else: output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) for o in range(batch_chunks): cond_index = cond_or_uncond[o] a = area[o] if a is None: out_conds[cond_index] += output[o] * mult[o] out_counts[cond_index] += mult[o] else: out_c = out_conds[cond_index] out_cts = out_counts[cond_index] dims = len(a) // 2 for i in range(dims): out_c = out_c.narrow(i + 2, a[i + dims], a[i]) out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) out_c += output[o] * mult[o] out_cts += mult[o] for i in range(len(out_conds)): out_conds[i] /= out_counts[i] return out_conds def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None): if "sampler_cfg_function" in model_options: args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options, "input_cond": cond, "input_uncond": uncond} cfg_result = x - model_options["sampler_cfg_function"](args) else: cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale for fn in model_options.get("sampler_post_cfg_function", []): args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "cond_scale": cond_scale, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, "sigma": timestep, "model_options": model_options, "input": x} cfg_result = fn(args) return cfg_result #The main sampling function shared by all the samplers #Returns denoised def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: uncond_ = None else: uncond_ = uncond conds = [cond, uncond_] if "sampler_calc_cond_batch_function" in model_options: args = {"conds": conds, "input": x, "sigma": timestep, "model": model, "model_options": model_options} out = model_options["sampler_calc_cond_batch_function"](args) else: out = calc_cond_batch(model, conds, x, timestep, model_options) for fn in model_options.get("sampler_pre_cfg_function", []): args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "model": model, "model_options": model_options} out = fn(args) return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) class KSamplerX0Inpaint: def __init__(self, model, sigmas): self.inner_model = model self.sigmas = sigmas def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: if "denoise_mask_function" in model_options: denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) latent_mask = 1. - denoise_mask x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask out = self.inner_model(x, sigma, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask return out def simple_scheduler(model_sampling, steps): s = model_sampling sigs = [] ss = len(s.sigmas) / steps for x in range(steps): sigs += [float(s.sigmas[-(1 + int(x * ss))])] sigs += [0.0] return torch.FloatTensor(sigs) def ddim_scheduler(model_sampling, steps): s = model_sampling sigs = [] x = 1 if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001): steps += 1 sigs = [] else: sigs = [0.0] ss = max(len(s.sigmas) // steps, 1) while x < len(s.sigmas): sigs += [float(s.sigmas[x])] x += ss sigs = sigs[::-1] return torch.FloatTensor(sigs) def normal_scheduler(model_sampling, steps, sgm=False, floor=False): s = model_sampling start = s.timestep(s.sigma_max) end = s.timestep(s.sigma_min) append_zero = True if sgm: timesteps = torch.linspace(start, end, steps + 1)[:-1] else: if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001): steps += 1 append_zero = False timesteps = torch.linspace(start, end, steps) sigs = [] for x in range(len(timesteps)): ts = timesteps[x] sigs.append(float(s.sigma(ts))) if append_zero: sigs += [0.0] return torch.FloatTensor(sigs) # Implemented based on: https://arxiv.org/abs/2407.12173 def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6): total_timesteps = (len(model_sampling.sigmas) - 1) ts = 1 - numpy.linspace(0, 1, steps, endpoint=False) ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) sigs = [] last_t = -1 for t in ts: if t != last_t: sigs += [float(model_sampling.sigmas[int(t)])] last_t = t sigs += [0.0] return torch.FloatTensor(sigs) # from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41 def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None): if steps == 1: sigma_schedule = [1.0, 0.0] else: if linear_steps is None: linear_steps = steps // 2 linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)] threshold_noise_step_diff = linear_steps - threshold_noise * steps quadratic_steps = steps - linear_steps quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2) linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2) const = quadratic_coef * (linear_steps ** 2) quadratic_sigma_schedule = [ quadratic_coef * (i ** 2) + linear_coef * i + const for i in range(linear_steps, steps) ] sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0] sigma_schedule = [1.0 - x for x in sigma_schedule] return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu() # Referenced from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608 def kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> torch.Tensor: adj_idxs = torch.arange(n, dtype=torch.float).div_(n - 1) sigmas = adj_idxs.new_zeros(n + 1) sigmas[:-1] = (adj_idxs * math.atan(sigma_min) + (1 - adj_idxs) * math.atan(sigma_max)).tan_() return sigmas def get_mask_aabb(masks): if masks.numel() == 0: return torch.zeros((0, 4), device=masks.device, dtype=torch.int) b = masks.shape[0] bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) for i in range(b): mask = masks[i] if mask.numel() == 0: continue if torch.max(mask != 0) == False: is_empty[i] = True continue y, x = torch.where(mask) bounding_boxes[i, 0] = torch.min(x) bounding_boxes[i, 1] = torch.min(y) bounding_boxes[i, 2] = torch.max(x) bounding_boxes[i, 3] = torch.max(y) return bounding_boxes, is_empty def resolve_areas_and_cond_masks_multidim(conditions, dims, device): # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. # While we're doing this, we can also resolve the mask device and scaling for performance reasons for i in range(len(conditions)): c = conditions[i] if 'area' in c: area = c['area'] if area[0] == "percentage": modified = c.copy() a = area[1:] a_len = len(a) // 2 area = () for d in range(len(dims)): area += (max(1, round(a[d] * dims[d])),) for d in range(len(dims)): area += (round(a[d + a_len] * dims[d]),) modified['area'] = area c = modified conditions[i] = c if 'mask' in c: mask = c['mask'] mask = mask.to(device=device) modified = c.copy() if len(mask.shape) == len(dims): mask = mask.unsqueeze(0) if mask.shape[1:] != dims: if mask.ndim < 4: mask = comfy.utils.common_upscale(mask.unsqueeze(1), dims[-1], dims[-2], 'bilinear', 'none').squeeze(1) else: mask = comfy.utils.common_upscale(mask, dims[-1], dims[-2], 'bilinear', 'none') if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2 bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) boxes, is_empty = get_mask_aabb(bounds) if is_empty[0]: # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) modified['area'] = (8, 8, 0, 0) else: box = boxes[0] H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) H = max(8, H) W = max(8, W) area = (int(H), int(W), int(Y), int(X)) modified['area'] = area modified['mask'] = mask conditions[i] = modified def resolve_areas_and_cond_masks(conditions, h, w, device): logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.") return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device) def create_cond_with_same_area_if_none(conds, c): if 'area' not in c: return def area_inside(a, area_cmp): a = add_area_dims(a, len(area_cmp) // 2) area_cmp = add_area_dims(area_cmp, len(a) // 2) a_l = len(a) // 2 area_cmp_l = len(area_cmp) // 2 for i in range(min(a_l, area_cmp_l)): if a[a_l + i] < area_cmp[area_cmp_l + i]: return False for i in range(min(a_l, area_cmp_l)): if (a[i] + a[a_l + i]) > (area_cmp[i] + area_cmp[area_cmp_l + i]): return False return True c_area = c['area'] smallest = None for x in conds: if 'area' in x: a = x['area'] if area_inside(c_area, a): if smallest is None: smallest = x elif 'area' not in smallest: smallest = x else: if math.prod(smallest['area'][:len(smallest['area']) // 2]) > math.prod(a[:len(a) // 2]): smallest = x else: if smallest is None: smallest = x if smallest is None: return if 'area' in smallest: if smallest['area'] == c_area: return out = c.copy() out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied? conds += [out] def calculate_start_end_timesteps(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] timestep_start = None timestep_end = None # handle clip hook schedule, if needed if 'clip_start_percent' in x: timestep_start = s.percent_to_sigma(max(x['clip_start_percent'], x.get('start_percent', 0.0))) timestep_end = s.percent_to_sigma(min(x['clip_end_percent'], x.get('end_percent', 1.0))) else: if 'start_percent' in x: timestep_start = s.percent_to_sigma(x['start_percent']) if 'end_percent' in x: timestep_end = s.percent_to_sigma(x['end_percent']) if (timestep_start is not None) or (timestep_end is not None): n = x.copy() if (timestep_start is not None): n['timestep_start'] = timestep_start if (timestep_end is not None): n['timestep_end'] = timestep_end conds[t] = n def pre_run_control(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] percent_to_timestep_function = lambda a: s.percent_to_sigma(a) if 'control' in x: x['control'].pre_run(model, percent_to_timestep_function) def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): cond_cnets = [] cond_other = [] uncond_cnets = [] uncond_other = [] for t in range(len(conds)): x = conds[t] if 'area' not in x: if name in x and x[name] is not None: cond_cnets.append(x[name]) else: cond_other.append((x, t)) for t in range(len(uncond)): x = uncond[t] if 'area' not in x: if name in x and x[name] is not None: uncond_cnets.append(x[name]) else: uncond_other.append((x, t)) if len(uncond_cnets) > 0: return for x in range(len(cond_cnets)): temp = uncond_other[x % len(uncond_other)] o = temp[0] if name in o and o[name] is not None: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond += [n] else: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond[temp[1]] = n def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): for t in range(len(conds)): x = conds[t] params = x.copy() params["device"] = device params["noise"] = noise default_width = None if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model default_width = noise.shape[3] * 8 params["width"] = params.get("width", default_width) params["height"] = params.get("height", noise.shape[2] * 8) params["prompt_type"] = params.get("prompt_type", prompt_type) for k in kwargs: if k not in params: params[k] = kwargs[k] out = model_function(**params) x = x.copy() model_conds = x['model_conds'].copy() for k in out: model_conds[k] = out[k] x['model_conds'] = model_conds conds[t] = x return conds class Sampler: def sample(self): pass def max_denoise(self, model_wrap, sigmas): max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) sigma = float(sigmas[0]) return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2", "exp_heun_2_x0", "exp_heun_2_x0_sde", "dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp", "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3", "sa_solver", "sa_solver_pece"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): self.sampler_function = sampler_function self.extra_options = extra_options self.inpaint_options = inpaint_options def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): extra_args["denoise_mask"] = denoise_mask model_k = KSamplerX0Inpaint(model_wrap, sigmas) model_k.latent_image = latent_image if self.inpaint_options.get("random", False): #TODO: Should this be the default? generator = torch.manual_seed(extra_args.get("seed", 41) + 1) model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) else: model_k.noise = noise noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) k_callback = None total_steps = len(sigmas) - 1 if callback is not None: k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) return samples def ksampler(sampler_name, extra_options={}, inpaint_options={}): if sampler_name == "dpm_fast": def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): if len(sigmas) <= 1: return noise sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] total_steps = len(sigmas) - 1 return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) sampler_function = dpm_fast_function elif sampler_name == "dpm_adaptive": def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): if len(sigmas) <= 1: return noise sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options) sampler_function = dpm_adaptive_function else: sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) return KSAMPLER(sampler_function, extra_options, inpaint_options) def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None, latent_shapes=None): for k in conds: conds[k] = conds[k][:] resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) for k in conds: calculate_start_end_timesteps(model, conds[k]) if hasattr(model, 'extra_conds'): for k in conds: conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed, latent_shapes=latent_shapes) #make sure each cond area has an opposite one with the same area for k in conds: for c in conds[k]: for kk in conds: if k != kk: create_cond_with_same_area_if_none(conds[kk], c) for k in conds: for c in conds[k]: if 'hooks' in c: for hook in c['hooks'].hooks: hook.initialize_timesteps(model) for k in conds: pre_run_control(model, conds[k]) if "positive" in conds: positive = conds["positive"] for k in conds: if k != "positive": apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x]) apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x]) return conds def preprocess_conds_hooks(conds: dict[str, list[dict[str]]]): # determine which ControlNets have extra_hooks that should be combined with normal hooks hook_replacement: dict[tuple[ControlBase, comfy.hooks.HookGroup], list[dict]] = {} for k in conds: for kk in conds[k]: if 'control' in kk: control: 'ControlBase' = kk['control'] extra_hooks = control.get_extra_hooks() if len(extra_hooks) > 0: hooks: comfy.hooks.HookGroup = kk.get('hooks', None) to_replace = hook_replacement.setdefault((control, hooks), []) to_replace.append(kk) # if nothing to replace, do nothing if len(hook_replacement) == 0: return # for optimal sampling performance, common ControlNets + hook combos should have identical hooks # on the cond dicts for key, conds_to_modify in hook_replacement.items(): control = key[0] hooks = key[1] hooks = comfy.hooks.HookGroup.combine_all_hooks(control.get_extra_hooks() + [hooks]) # if combined hooks are not None, set as new hooks for all relevant conds if hooks is not None: for cond in conds_to_modify: cond['hooks'] = hooks def filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], model_options: dict[str]): '''Modify 'hooks' on conds so that only hooks that were registered remain. Properly accounts for HookGroups that have the same reference.''' registered: comfy.hooks.HookGroup = model_options.get('registered_hooks', None) # if None were registered, make sure all hooks are cleaned from conds if registered is None: for k in conds: for kk in conds[k]: kk.pop('hooks', None) return # find conds that contain hooks to be replaced - group by common HookGroup refs hook_replacement: dict[comfy.hooks.HookGroup, list[dict]] = {} for k in conds: for kk in conds[k]: hooks: comfy.hooks.HookGroup = kk.get('hooks', None) if hooks is not None: if not hooks.is_subset_of(registered): to_replace = hook_replacement.setdefault(hooks, []) to_replace.append(kk) # for each hook to replace, create a new proper HookGroup and assign to all common conds for hooks, conds_to_modify in hook_replacement.items(): new_hooks = hooks.new_with_common_hooks(registered) if len(new_hooks) == 0: new_hooks = None for kk in conds_to_modify: kk['hooks'] = new_hooks def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]): hooks_set = set() for k in conds: for kk in conds[k]: hooks_set.add(kk.get('hooks', None)) return len(hooks_set) def cast_to_load_options(model_options: dict[str], device=None, dtype=None): ''' If any patches from hooks, wrappers, or callbacks have .to to be called, call it. ''' if model_options is None: return to_load_options = model_options.get("to_load_options", None) if to_load_options is None: return casts = [] if device is not None: casts.append(device) if dtype is not None: casts.append(dtype) # if nothing to apply, do nothing if len(casts) == 0: return # try to call .to on patches if "patches" in to_load_options: patches = to_load_options["patches"] for name in patches: patch_list = patches[name] for i in range(len(patch_list)): if hasattr(patch_list[i], "to"): for cast in casts: patch_list[i] = patch_list[i].to(cast) if "patches_replace" in to_load_options: patches = to_load_options["patches_replace"] for name in patches: patch_list = patches[name] for k in patch_list: if hasattr(patch_list[k], "to"): for cast in casts: patch_list[k] = patch_list[k].to(cast) # try to call .to on any wrappers/callbacks wrappers_and_callbacks = ["wrappers", "callbacks"] for wc_name in wrappers_and_callbacks: if wc_name in to_load_options: wc: dict[str, list] = to_load_options[wc_name] for wc_dict in wc.values(): for wc_list in wc_dict.values(): for i in range(len(wc_list)): if hasattr(wc_list[i], "to"): for cast in casts: wc_list[i] = wc_list[i].to(cast) class CFGGuider: def __init__(self, model_patcher: ModelPatcher): self.model_patcher = model_patcher self.model_options = model_patcher.model_options self.original_conds = {} self.cfg = 1.0 def set_conds(self, positive, negative): self.inner_set_conds({"positive": positive, "negative": negative}) def set_cfg(self, cfg): self.cfg = cfg def inner_set_conds(self, conds): for k in conds: if self.model_patcher.is_dynamic() and comfy.sampler_helpers.cond_has_hooks(conds[k]): self.model_patcher = self.model_patcher.get_non_dynamic_delegate() self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) def __call__(self, *args, **kwargs): return self.outer_predict_noise(*args, **kwargs) def outer_predict_noise(self, x, timestep, model_options={}, seed=None): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self.predict_noise, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, self.model_options, is_model_options=True) ).execute(x, timestep, model_options, seed) def predict_noise(self, x, timestep, model_options={}, seed=None): return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None): if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. latent_image = self.inner_model.process_latent_in(latent_image) self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed, latent_shapes=latent_shapes) extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options) extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas extra_args = {"model_options": extra_model_options, "seed": seed} executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( sampler.sample, sampler, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, extra_args["model_options"], is_model_options=True) ) samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) return self.inner_model.process_latent_out(samples.to(torch.float32)) def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None): self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options) device = self.model_patcher.load_device noise = noise.to(device) latent_image = latent_image.to(device) sigmas = sigmas.to(device) cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) try: self.model_patcher.pre_run() output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) finally: self.model_patcher.cleanup() comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) del self.inner_model del self.loaded_models return output def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): if sigmas.shape[-1] == 0: return latent_image if latent_image.is_nested: latent_image, latent_shapes = comfy.utils.pack_latents(latent_image.unbind()) noise, _ = comfy.utils.pack_latents(noise.unbind()) else: latent_shapes = [latent_image.shape] if denoise_mask is not None: if denoise_mask.is_nested: denoise_masks = denoise_mask.unbind() denoise_masks = denoise_masks[:len(latent_shapes)] else: denoise_masks = [denoise_mask] for i in range(len(denoise_masks), len(latent_shapes)): denoise_masks.append(torch.ones(latent_shapes[i])) for i in range(len(denoise_masks)): denoise_masks[i] = comfy.sampler_helpers.prepare_mask(denoise_masks[i], latent_shapes[i], self.model_patcher.load_device) if len(denoise_masks) > 1: denoise_mask, _ = comfy.utils.pack_latents(denoise_masks) else: denoise_mask = denoise_masks[0] self.conds = {} for k in self.original_conds: self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) preprocess_conds_hooks(self.conds) try: orig_model_options = self.model_options self.model_options = comfy.model_patcher.create_model_options_clone(self.model_options) # if one hook type (or just None), then don't bother caching weights for hooks (will never change after first step) orig_hook_mode = self.model_patcher.hook_mode if get_total_hook_groups_in_conds(self.conds) <= 1: self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options) filter_registered_hooks_on_conds(self.conds, self.model_options) executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( self.outer_sample, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True) ) output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) finally: cast_to_load_options(self.model_options, device=self.model_patcher.offload_device) self.model_options = orig_model_options self.model_patcher.hook_mode = orig_hook_mode self.model_patcher.restore_hook_patches() del self.conds if len(latent_shapes) > 1: output = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(output, latent_shapes)) return output def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): cfg_guider = CFGGuider(model) cfg_guider.set_conds(positive, negative) cfg_guider.set_cfg(cfg) return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] class SchedulerHandler(NamedTuple): handler: Callable[..., torch.Tensor] # Boolean indicates whether to call the handler like: # scheduler_function(model_sampling, steps) or # scheduler_function(n, sigma_min: float, sigma_max: float) use_ms: bool = True SCHEDULER_HANDLERS = { "simple": SchedulerHandler(simple_scheduler), "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)), "karras": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False), "exponential": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False), "ddim_uniform": SchedulerHandler(ddim_scheduler), "beta": SchedulerHandler(beta_scheduler), "normal": SchedulerHandler(normal_scheduler), "linear_quadratic": SchedulerHandler(linear_quadratic_schedule), "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False), } SCHEDULER_NAMES = list(SCHEDULER_HANDLERS) def calculate_sigmas(model_sampling: object, scheduler_name: str, steps: int) -> torch.Tensor: handler = SCHEDULER_HANDLERS.get(scheduler_name) if handler is None: err = f"error invalid scheduler {scheduler_name}" logging.error(err) raise ValueError(err) if handler.use_ms: return handler.handler(model_sampling, steps) return handler.handler(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) def sampler_object(name): if name == "uni_pc": sampler = KSAMPLER(uni_pc.sample_unipc) elif name == "uni_pc_bh2": sampler = KSAMPLER(uni_pc.sample_unipc_bh2) elif name == "ddim": sampler = ksampler("euler", inpaint_options={"random": True}) else: sampler = ksampler(name) return sampler class KSampler: SCHEDULERS = SCHEDULER_NAMES SAMPLERS = SAMPLER_NAMES DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2')) def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): self.model = model self.device = device if scheduler not in self.SCHEDULERS: scheduler = self.SCHEDULERS[0] if sampler not in self.SAMPLERS: sampler = self.SAMPLERS[0] self.scheduler = scheduler self.sampler = sampler self.set_steps(steps, denoise) self.denoise = denoise self.model_options = model_options def calculate_sigmas(self, steps): sigmas = None discard_penultimate_sigma = False if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS: steps += 1 discard_penultimate_sigma = True sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps) if discard_penultimate_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas def set_steps(self, steps, denoise=None): self.steps = steps if denoise is None or denoise > 0.9999: self.sigmas = self.calculate_sigmas(steps).to(self.device) else: if denoise <= 0.0: self.sigmas = torch.FloatTensor([]) else: new_steps = int(steps/denoise) sigmas = self.calculate_sigmas(new_steps).to(self.device) self.sigmas = sigmas[-(steps + 1):] def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): if sigmas is None: sigmas = self.sigmas if last_step is not None and last_step < (len(sigmas) - 1): sigmas = sigmas[:last_step + 1] if force_full_denoise: sigmas[-1] = 0 if start_step is not None: if start_step < (len(sigmas) - 1): sigmas = sigmas[start_step:] else: if latent_image is not None: return latent_image else: return torch.zeros_like(noise) sampler = sampler_object(self.sampler) return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) ================================================ FILE: comfy/sd.py ================================================ from __future__ import annotations import json import torch from enum import Enum import logging from comfy import model_management from comfy.utils import ProgressBar from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine from .ldm.cascade.stage_a import StageA from .ldm.cascade.stage_c_coder import StageC_coder from .ldm.audio.autoencoder import AudioOobleckVAE import comfy.ldm.genmo.vae.model import comfy.ldm.lightricks.vae.causal_video_autoencoder import comfy.ldm.cosmos.vae import comfy.ldm.wan.vae import comfy.ldm.wan.vae2_2 import comfy.ldm.hunyuan3d.vae import comfy.ldm.ace.vae.music_dcae_pipeline import comfy.ldm.hunyuan_video.vae import comfy.ldm.mmaudio.vae.autoencoder import comfy.pixel_space_convert import comfy.weight_adapter import yaml import math import os import comfy.utils from . import clip_vision from . import gligen from . import diffusers_convert from . import model_detection from . import sd1_clip from . import sdxl_clip import comfy.text_encoders.sd2_clip import comfy.text_encoders.sd3_clip import comfy.text_encoders.sa_t5 import comfy.text_encoders.aura_t5 import comfy.text_encoders.pixart_t5 import comfy.text_encoders.hydit import comfy.text_encoders.flux import comfy.text_encoders.long_clipl import comfy.text_encoders.genmo import comfy.text_encoders.lt import comfy.text_encoders.hunyuan_video import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan import comfy.text_encoders.hidream import comfy.text_encoders.ace import comfy.text_encoders.omnigen2 import comfy.text_encoders.qwen_image import comfy.text_encoders.hunyuan_image import comfy.text_encoders.z_image import comfy.text_encoders.ovis import comfy.text_encoders.kandinsky5 import comfy.text_encoders.jina_clip_2 import comfy.text_encoders.newbie import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image import comfy.model_patcher import comfy.lora import comfy.lora_convert import comfy.hooks import comfy.t2i_adapter.adapter import comfy.taesd.taesd import comfy.taesd.taehv import comfy.latent_formats import comfy.ldm.flux.redux def load_lora_for_models(model, clip, lora, strength_model, strength_clip): key_map = {} if model is not None: key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) if clip is not None: key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) lora = comfy.lora_convert.convert_lora(lora) loaded = comfy.lora.load_lora(lora, key_map) if model is not None: new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) else: k = () new_modelpatcher = None if clip is not None: new_clip = clip.clone() k1 = new_clip.add_patches(loaded, strength_clip) else: k1 = () new_clip = None k = set(k) k1 = set(k1) for x in loaded: if (x not in k) and (x not in k1): logging.warning("NOT LOADED {}".format(x)) return (new_modelpatcher, new_clip) def load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip): """ Load LoRA in bypass mode without modifying base model weights. Instead of patching weights, this injects the LoRA computation into the forward pass: output = base_forward(x) + lora_path(x) Non-adapter patches (bias diff, weight diff, etc.) are applied as regular patches. This is useful for training and when model weights are offloaded. """ key_map = {} if model is not None: key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) if clip is not None: key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) logging.debug(f"[BypassLoRA] key_map has {len(key_map)} entries") lora = comfy.lora_convert.convert_lora(lora) loaded = comfy.lora.load_lora(lora, key_map) logging.debug(f"[BypassLoRA] loaded has {len(loaded)} entries") # Separate adapters (for bypass) from other patches (for regular patching) bypass_patches = {} # WeightAdapterBase instances -> bypass mode regular_patches = {} # diff, set, bias patches -> regular weight patching for key, patch_data in loaded.items(): if isinstance(patch_data, comfy.weight_adapter.WeightAdapterBase): bypass_patches[key] = patch_data else: regular_patches[key] = patch_data logging.debug(f"[BypassLoRA] {len(bypass_patches)} bypass adapters, {len(regular_patches)} regular patches") k = set() k1 = set() if model is not None: new_modelpatcher = model.clone() # Apply regular patches (bias diff, weight diff, etc.) via normal patching if regular_patches: patched_keys = new_modelpatcher.add_patches(regular_patches, strength_model) k.update(patched_keys) # Apply adapter patches via bypass injection manager = comfy.weight_adapter.BypassInjectionManager() model_sd_keys = set(new_modelpatcher.model.state_dict().keys()) for key, adapter in bypass_patches.items(): if key in model_sd_keys: manager.add_adapter(key, adapter, strength=strength_model) k.add(key) else: logging.warning(f"[BypassLoRA] Adapter key not in model state_dict: {key}") injections = manager.create_injections(new_modelpatcher.model) if manager.get_hook_count() > 0: new_modelpatcher.set_injections("bypass_lora", injections) else: new_modelpatcher = None if clip is not None: new_clip = clip.clone() # Apply regular patches to clip if regular_patches: patched_keys = new_clip.add_patches(regular_patches, strength_clip) k1.update(patched_keys) # Apply adapter patches via bypass injection clip_manager = comfy.weight_adapter.BypassInjectionManager() clip_sd_keys = set(new_clip.cond_stage_model.state_dict().keys()) for key, adapter in bypass_patches.items(): if key in clip_sd_keys: clip_manager.add_adapter(key, adapter, strength=strength_clip) k1.add(key) clip_injections = clip_manager.create_injections(new_clip.cond_stage_model) if clip_manager.get_hook_count() > 0: new_clip.patcher.set_injections("bypass_lora", clip_injections) else: new_clip = None for x in loaded: if (x not in k) and (x not in k1): patch_data = loaded[x] patch_type = type(patch_data).__name__ if isinstance(patch_data, tuple): patch_type = f"tuple({patch_data[0]})" logging.warning(f"NOT LOADED: {x} (type={patch_type})") return (new_modelpatcher, new_clip) class CLIP: def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}, disable_dynamic=False): if no_init: return params = target.params.copy() clip = target.clip tokenizer = target.tokenizer load_device = model_options.get("load_device", model_management.text_encoder_device()) offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device()) dtype = model_options.get("dtype", None) if dtype is None: dtype = model_management.text_encoder_dtype(load_device) params['dtype'] = dtype params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))) params['model_options'] = model_options self.cond_stage_model = clip(**(params)) for dt in self.cond_stage_model.dtypes: if not model_management.supports_cast(load_device, dt): load_device = offload_device if params['device'] != offload_device: self.cond_stage_model.to(offload_device) logging.warning("Had to shift TE back.") model_management.archive_model_dtypes(self.cond_stage_model) self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) #Match torch.float32 hardcode upcast in TE implemention self.patcher.set_model_compute_dtype(torch.float32) self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram self.patcher.is_clip = True self.apply_hooks_to_conds = None if len(state_dict) > 0: if isinstance(state_dict, list): for c in state_dict: m, u = self.load_sd(c) if len(m) > 0: logging.warning("clip missing: {}".format(m)) if len(u) > 0: logging.debug("clip unexpected: {}".format(u)) else: m, u = self.load_sd(state_dict, full_model=True) if len(m) > 0: m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) if len(m_filter) > 0: logging.warning("clip missing: {}".format(m)) else: logging.debug("clip missing: {}".format(m)) if len(u) > 0: logging.debug("clip unexpected {}:".format(u)) if params['device'] == load_device: model_management.load_models_gpu([self.patcher], force_full_load=True) self.layer_idx = None self.use_clip_schedule = False logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype)) self.tokenizer_options = {} def clone(self, disable_dynamic=False): n = CLIP(no_init=True) n.patcher = self.patcher.clone(disable_dynamic=disable_dynamic) n.cond_stage_model = self.cond_stage_model n.tokenizer = self.tokenizer n.layer_idx = self.layer_idx n.tokenizer_options = self.tokenizer_options.copy() n.use_clip_schedule = self.use_clip_schedule n.apply_hooks_to_conds = self.apply_hooks_to_conds return n def get_ram_usage(self): return self.patcher.get_ram_usage() def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): return self.patcher.add_patches(patches, strength_patch, strength_model) def set_tokenizer_option(self, option_name, value): self.tokenizer_options[option_name] = value def clip_layer(self, layer_idx): self.layer_idx = layer_idx def tokenize(self, text, return_word_ids=False, **kwargs): tokenizer_options = kwargs.get("tokenizer_options", {}) if len(self.tokenizer_options) > 0: tokenizer_options = {**self.tokenizer_options, **tokenizer_options} if len(tokenizer_options) > 0: kwargs["tokenizer_options"] = tokenizer_options return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs) def add_hooks_to_dict(self, pooled_dict: dict[str]): if self.apply_hooks_to_conds: pooled_dict["hooks"] = self.apply_hooks_to_conds return pooled_dict def encode_from_tokens_scheduled(self, tokens, unprojected=False, add_dict: dict[str]={}, show_pbar=True): all_cond_pooled: list[tuple[torch.Tensor, dict[str]]] = [] all_hooks = self.patcher.forced_hooks if all_hooks is None or not self.use_clip_schedule: # if no hooks or shouldn't use clip schedule, do unscheduled encode_from_tokens and perform add_dict return_pooled = "unprojected" if unprojected else True pooled_dict = self.encode_from_tokens(tokens, return_pooled=return_pooled, return_dict=True) cond = pooled_dict.pop("cond") # add/update any keys with the provided add_dict pooled_dict.update(add_dict) all_cond_pooled.append([cond, pooled_dict]) else: scheduled_keyframes = all_hooks.get_hooks_for_clip_schedule() self.cond_stage_model.reset_clip_options() if self.layer_idx is not None: self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) if unprojected: self.cond_stage_model.set_clip_options({"projected_pooled": False}) self.load_model(tokens) self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device}) all_hooks.reset() self.patcher.patch_hooks(None) if show_pbar: pbar = ProgressBar(len(scheduled_keyframes)) for scheduled_opts in scheduled_keyframes: t_range = scheduled_opts[0] # don't bother encoding any conds outside of start_percent and end_percent bounds if "start_percent" in add_dict: if t_range[1] < add_dict["start_percent"]: continue if "end_percent" in add_dict: if t_range[0] > add_dict["end_percent"]: continue hooks_keyframes = scheduled_opts[1] for hook, keyframe in hooks_keyframes: hook.hook_keyframe._current_keyframe = keyframe # apply appropriate hooks with values that match new hook_keyframe self.patcher.patch_hooks(all_hooks) # perform encoding as normal o = self.cond_stage_model.encode_token_weights(tokens) cond, pooled = o[:2] pooled_dict = {"pooled_output": pooled} # add clip_start_percent and clip_end_percent in pooled pooled_dict["clip_start_percent"] = t_range[0] pooled_dict["clip_end_percent"] = t_range[1] # add/update any keys with the provided add_dict pooled_dict.update(add_dict) # add hooks stored on clip self.add_hooks_to_dict(pooled_dict) all_cond_pooled.append([cond, pooled_dict]) if show_pbar: pbar.update(1) model_management.throw_exception_if_processing_interrupted() all_hooks.reset() return all_cond_pooled def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False): self.cond_stage_model.reset_clip_options() if self.layer_idx is not None: self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) if return_pooled == "unprojected": self.cond_stage_model.set_clip_options({"projected_pooled": False}) self.load_model(tokens) self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device}) o = self.cond_stage_model.encode_token_weights(tokens) cond, pooled = o[:2] if return_dict: out = {"cond": cond, "pooled_output": pooled} if len(o) > 2: for k in o[2]: out[k] = o[2][k] self.add_hooks_to_dict(out) return out if return_pooled: return cond, pooled return cond def encode(self, text): tokens = self.tokenize(text) return self.encode_from_tokens(tokens) def load_sd(self, sd, full_model=False): if full_model: return self.cond_stage_model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic()) else: can_assign = self.patcher.is_dynamic() self.cond_stage_model.can_assign_sd = can_assign # The CLIP models are a pretty complex web of wrappers and its # a bit of an API change to plumb this all the way through. # So spray paint the model with this flag that the loading # nn.Module can then inspect for itself. for m in self.cond_stage_model.modules(): m.can_assign_sd = can_assign return self.cond_stage_model.load_sd(sd) def get_sd(self): sd_clip = self.cond_stage_model.state_dict() sd_tokenizer = self.tokenizer.state_dict() for k in sd_tokenizer: sd_clip[k] = sd_tokenizer[k] return sd_clip def load_model(self, tokens={}): memory_used = 0 if hasattr(self.cond_stage_model, "memory_estimation_function"): memory_used = self.cond_stage_model.memory_estimation_function(tokens, device=self.patcher.load_device) model_management.load_models_gpu([self.patcher], memory_required=memory_used) return self.patcher def get_key_patches(self): return self.patcher.get_key_patches() def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None): self.cond_stage_model.reset_clip_options() self.load_model(tokens) self.cond_stage_model.set_clip_options({"layer": None}) self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device}) return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed) def decode(self, token_ids, skip_special_tokens=True): return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens) class VAE: def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) if model_management.is_amd(): VAE_KL_MEM_RATIO = 2.73 else: VAE_KL_MEM_RATIO = 1.0 self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO #These are for AutoencoderKL and need tweaking (should be lower) self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO self.downscale_ratio = 8 self.upscale_ratio = 8 self.latent_channels = 4 self.latent_dim = 2 self.output_channels = 3 self.pad_channel_value = None self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: image.add_(1.0).div_(2.0).clamp_(0.0, 1.0) self.working_dtypes = [torch.bfloat16, torch.float32] self.disable_offload = False self.not_video = False self.size = None self.downscale_index_formula = None self.upscale_index_formula = None self.extra_1d_channel = None self.crop_input = True self.audio_sample_rate = 44100 if config is None: if "decoder.mid.block_1.mix_factor" in sd: encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} decoder_config = encoder_config.copy() decoder_config["video_kernel_size"] = [3, 1, 1] decoder_config["alpha"] = 0.0 self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade self.first_stage_model = StageA() self.downscale_ratio = 4 self.upscale_ratio = 4 #TODO #self.memory_used_encode #self.memory_used_decode self.process_input = lambda image: image self.process_output = lambda image: image elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade self.first_stage_model = StageC_coder() self.downscale_ratio = 32 self.latent_channels = 16 new_sd = {} for k in sd: new_sd["encoder.{}".format(k)] = sd[k] sd = new_sd elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade self.first_stage_model = StageC_coder() self.latent_channels = 16 new_sd = {} for k in sd: new_sd["previewer.{}".format(k)] = sd[k] sd = new_sd elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade self.first_stage_model = StageC_coder() self.downscale_ratio = 32 self.latent_channels = 16 elif "decoder.conv_in.weight" in sd: if sd['decoder.conv_in.weight'].shape[1] == 64: ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True} self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] self.downscale_ratio = 32 self.upscale_ratio = 32 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.hunyuan_video.vae.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.hunyuan_video.vae.Decoder", 'params': ddconfig}) self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype) elif sd['decoder.conv_in.weight'].shape[1] == 32 and sd['decoder.conv_in.weight'].ndim == 5: ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True, "refiner_vae": False} self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) self.upscale_index_formula = (4, 16, 16) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) self.downscale_index_formula = (4, 16, 16) self.latent_dim = 3 self.not_video = True self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig}) self.memory_used_encode = lambda shape, dtype: (2800 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (2800 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) else: #default SD1.x/SD2.x VAE parameters ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE ddconfig['ch_mult'] = [1, 2, 4] self.downscale_ratio = 4 self.upscale_ratio = 4 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] if 'decoder.post_quant_conv.weight' in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"decoder.post_quant_conv.": "post_quant_conv.", "encoder.quant_conv.": "quant_conv."}) if 'bn.running_mean' in sd: ddconfig["batch_norm_latent"] = True self.downscale_ratio *= 2 self.upscale_ratio *= 2 self.latent_channels *= 4 old_memory_used_decode = self.memory_used_decode self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0 if 'post_quant_conv.weight' in sd: self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) else: self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) elif "decoder.layers.1.layers.0.beta" in sd: config = {} param_key = None self.upscale_ratio = 2048 self.downscale_ratio = 2048 if "decoder.layers.2.layers.1.weight_v" in sd: param_key = "decoder.layers.2.layers.1.weight_v" if "decoder.layers.2.layers.1.parametrizations.weight.original1" in sd: param_key = "decoder.layers.2.layers.1.parametrizations.weight.original1" if param_key is not None: if sd[param_key].shape[-1] == 12: config["strides"] = [2, 4, 4, 6, 10] self.audio_sample_rate = 48000 self.upscale_ratio = 1920 self.downscale_ratio = 1920 self.first_stage_model = AudioOobleckVAE(**config) self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) self.latent_channels = 64 self.output_channels = 2 self.pad_channel_value = "replicate" self.latent_dim = 1 self.process_output = lambda audio: audio self.process_input = lambda audio: audio self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] self.disable_offload = True elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae if "blocks.2.blocks.3.stack.5.weight" in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."}) if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."}) self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE() self.latent_channels = 12 self.latent_dim = 3 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8) self.upscale_index_formula = (6, 8, 8) self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8) self.downscale_index_formula = (6, 8, 8) self.working_dtypes = [torch.float16, torch.float32] elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv tensor_conv1 = sd["decoder.up_blocks.0.res_blocks.0.conv1.conv.weight"] version = 0 if tensor_conv1.shape[0] == 512: version = 0 elif tensor_conv1.shape[0] == 1024: version = 1 if "encoder.down_blocks.1.conv.conv.bias" in sd: version = 2 vae_config = None if metadata is not None and "config" in metadata: vae_config = json.loads(metadata["config"]).get("vae", None) self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config) self.latent_channels = 128 self.latent_dim = 3 self.memory_used_decode = lambda shape, dtype: (1200 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) self.memory_used_encode = lambda shape, dtype: (80 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype) self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32) self.upscale_index_formula = (8, 32, 32) self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32) self.downscale_index_formula = (8, 32, 32) self.working_dtypes = [torch.bfloat16, torch.float32] elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32: ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True} ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] self.latent_channels = 32 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) self.upscale_index_formula = (4, 16, 16) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) self.downscale_index_formula = (4, 16, 16) self.latent_dim = 3 self.not_video = False self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.EmptyRegularizer"}, encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig}) self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (3600 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) elif "decoder.conv_in.conv.weight" in sd: ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} ddconfig["conv3d"] = True ddconfig["time_compress"] = 4 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) self.upscale_index_formula = (4, 8, 8) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) self.downscale_index_formula = (4, 8, 8) self.latent_dim = 3 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) #This is likely to significantly over-estimate with single image or low frame counts as the #implementation is able to completely skip caching. Rework if used as an image only VAE self.memory_used_decode = lambda shape, dtype: (2800 * min(8, ((shape[2] - 1) * 4) + 1) * shape[3] * shape[4] * (8 * 8)) * model_management.dtype_size(dtype) self.memory_used_encode = lambda shape, dtype: (1400 * min(9, shape[2]) * shape[3] * shape[4]) * model_management.dtype_size(dtype) self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] elif "decoder.unpatcher3d.wavelets" in sd: self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8) self.upscale_index_formula = (8, 8, 8) self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 8, 8) self.downscale_index_formula = (8, 8, 8) self.latent_dim = 3 self.latent_channels = 16 ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8} self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig) #TODO: these values are a bit off because this is not a standard VAE self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype) self.working_dtypes = [torch.bfloat16, torch.float32] elif "decoder.middle.0.residual.0.gamma" in sd: if "decoder.upsamples.0.upsamples.0.residual.2.weight" in sd: # Wan 2.2 VAE self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) self.upscale_index_formula = (4, 16, 16) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) self.downscale_index_formula = (4, 16, 16) self.latent_dim = 3 self.latent_channels = 48 ddconfig = {"dim": 160, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0} self.first_stage_model = comfy.ldm.wan.vae2_2.WanVAE(**ddconfig) self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype) else: # Wan 2.1 VAE dim = sd["decoder.head.0.gamma"].shape[0] self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) self.upscale_index_formula = (4, 8, 8) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) self.downscale_index_formula = (4, 8, 8) self.latent_dim = 3 self.latent_channels = 16 self.output_channels = sd["encoder.conv1.weight"].shape[1] self.conv_out_channels = sd["decoder.head.2.weight"].shape[0] self.pad_channel_value = 1.0 ddconfig = {"dim": dim, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "image_channels": self.output_channels, "conv_out_channels": self.conv_out_channels, "dropout": 0.0} self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig) self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] self.memory_used_encode = lambda shape, dtype: (1500 if shape[2]<=4 else 6000) * shape[3] * shape[4] * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (2200 if shape[2]<=4 else 7000) * shape[3] * shape[4] * (8*8) * model_management.dtype_size(dtype) # Hunyuan 3d v2 2.0 & 2.1 elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd: self.latent_dim = 1 def estimate_memory(shape, dtype, num_layers = 16, kv_cache_multiplier = 2): batch, num_tokens, hidden_dim = shape dtype_size = model_management.dtype_size(dtype) total_mem = batch * num_tokens * hidden_dim * dtype_size * (1 + kv_cache_multiplier * num_layers) return total_mem # better memory estimations self.memory_used_encode = lambda shape, dtype, num_layers = 8, kv_cache_multiplier = 0:\ estimate_memory(shape, dtype, num_layers, kv_cache_multiplier) self.memory_used_decode = lambda shape, dtype, num_layers = 16, kv_cache_multiplier = 2: \ estimate_memory(shape, dtype, num_layers, kv_cache_multiplier) self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE() self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100) self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) self.latent_channels = 8 self.output_channels = 2 self.pad_channel_value = "replicate" self.upscale_ratio = 4096 self.downscale_ratio = 4096 self.latent_dim = 2 self.process_output = lambda audio: audio self.process_input = lambda audio: audio self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] self.disable_offload = True self.extra_1d_channel = 16 elif "pixel_space_vae" in sd: self.first_stage_model = comfy.pixel_space_convert.PixelspaceConversionVAE() self.memory_used_encode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype) self.downscale_ratio = 1 self.upscale_ratio = 1 self.latent_channels = 3 self.latent_dim = 2 self.output_channels = 3 elif "vocoder.activation_post.downsample.lowpass.filter" in sd: #MMAudio VAE sample_rate = 16000 if sample_rate == 16000: mode = '16k' else: mode = '44k' self.first_stage_model = comfy.ldm.mmaudio.vae.autoencoder.AudioAutoencoder(mode=mode) self.memory_used_encode = lambda shape, dtype: (30 * shape[2]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (90 * shape[2] * 1411.2) * model_management.dtype_size(dtype) self.latent_channels = 20 self.output_channels = 2 self.upscale_ratio = 512 * (44100 / sample_rate) self.downscale_ratio = 512 * (44100 / sample_rate) self.latent_dim = 1 self.process_output = lambda audio: audio self.process_input = lambda audio: audio self.working_dtypes = [torch.float32] self.crop_input = False elif "decoder.22.bias" in sd: # taehv, taew and lighttae self.latent_channels = sd["decoder.1.weight"].shape[1] self.latent_dim = 3 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) self.upscale_index_formula = (4, 16, 16) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) self.downscale_index_formula = (4, 16, 16) if self.latent_channels in [48, 128]: # Wan 2.2 and LTX2 self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=None) # taehv doesn't need scaling self.process_input = self.process_output = lambda image: image self.process_output = lambda image: image self.memory_used_decode = lambda shape, dtype: (1800 * (max(1, (shape[-3] ** 0.7 * 0.1)) * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)) elif self.latent_channels == 32 and sd["decoder.22.bias"].shape[0] == 12: # lighttae_hv15 self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=comfy.latent_formats.HunyuanVideo15) self.memory_used_decode = lambda shape, dtype: (1200 * (max(1, (shape[-3] ** 0.7 * 0.05)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype)) else: if sd["decoder.1.weight"].dtype == torch.float16: # taehv currently only available in float16, so assume it's not lighttaew2_1 as otherwise state dicts are identical latent_format=comfy.latent_formats.HunyuanVideo else: latent_format=None # lighttaew2_1 doesn't need scaling self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=latent_format) self.process_input = self.process_output = lambda image: image self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) self.upscale_index_formula = (4, 8, 8) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) self.downscale_index_formula = (4, 8, 8) self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)) self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype)) else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None return else: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() if device is None: device = model_management.vae_device() self.device = device offload_device = model_management.vae_offload_device() if dtype is None: dtype = model_management.vae_dtype(self.device, self.working_dtypes) self.vae_dtype = dtype self.first_stage_model.to(self.vae_dtype) model_management.archive_model_dtypes(self.first_stage_model) self.output_device = model_management.intermediate_device() mp = comfy.model_patcher.CoreModelPatcher if self.disable_offload: mp = comfy.model_patcher.ModelPatcher self.patcher = mp(self.first_stage_model, load_device=self.device, offload_device=offload_device) m, u = self.first_stage_model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic()) if len(m) > 0: logging.warning("Missing VAE keys {}".format(m)) if len(u) > 0: logging.debug("Leftover VAE keys {}".format(u)) logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) self.model_size() def model_size(self): if self.size is not None: return self.size self.size = comfy.model_management.module_size(self.first_stage_model) return self.size def get_ram_usage(self): return self.model_size() def throw_exception_if_invalid(self): if self.first_stage_model is None: raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.") def vae_encode_crop_pixels(self, pixels): if self.crop_input: downscale_ratio = self.spacial_compression_encode() dims = pixels.shape[1:-1] for d in range(len(dims)): x = (dims[d] // downscale_ratio) * downscale_ratio x_offset = (dims[d] % downscale_ratio) // 2 if x != dims[d]: pixels = pixels.narrow(d + 1, x_offset, x) if pixels.shape[-1] > self.output_channels: pixels = pixels[..., :self.output_channels] elif pixels.shape[-1] < self.output_channels: if self.pad_channel_value is not None: if isinstance(self.pad_channel_value, str): mode = self.pad_channel_value value = None else: mode = "constant" value = self.pad_channel_value pixels = torch.nn.functional.pad(pixels, (0, self.output_channels - pixels.shape[-1]), mode=mode, value=value) return pixels def vae_output_dtype(self): return model_management.intermediate_dtype() def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = comfy.utils.ProgressBar(steps) decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) output = self.process_output( (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar)) / 3.0) return output def decode_tiled_1d(self, samples, tile_x=256, overlap=32): if samples.ndim == 3: decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) else: og_shape = samples.shape samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1)) decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device)) def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = comfy.utils.ProgressBar(steps) encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples /= 3.0 return samples def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048): if self.latent_dim == 1: encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) out_channels = self.latent_channels upscale_amount = 1 / self.downscale_ratio else: extra_channel_size = self.extra_1d_channel out_channels = self.latent_channels * extra_channel_size tile_x = tile_x // extra_channel_size overlap = overlap // extra_channel_size upscale_amount = 1 / self.downscale_ratio encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).to(dtype=self.vae_output_dtype()) out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device) if self.latent_dim == 1: return out else: return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1) def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)): encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device) def decode(self, samples_in, vae_options={}): self.throw_exception_if_invalid() pixel_samples = None do_tile = False if self.latent_dim == 2 and samples_in.ndim == 5: samples_in = samples_in[:, :, 0] try: memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) free_memory = self.patcher.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) # Pre-allocate output for VAEs that support direct buffer writes preallocated = False if getattr(self.first_stage_model, 'comfy_has_chunked_io', False): pixel_samples = torch.empty(self.first_stage_model.decode_output_shape(samples_in.shape), device=self.output_device, dtype=self.vae_output_dtype()) preallocated = True for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype) if preallocated: self.first_stage_model.decode(samples, output_buffer=pixel_samples[x:x+batch_number], **vae_options) else: out = self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True) if pixel_samples is None: pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) pixel_samples[x:x+batch_number].copy_(out) del out self.process_output(pixel_samples[x:x+batch_number]) except Exception as e: model_management.raise_non_oom(e) logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") #NOTE: We don't know what tensors were allocated to stack variables at the time of the #exception and the exception itself refs them all until we get out of this except block. #So we just set a flag for tiler fallback so that tensor gc can happen once the #exception is fully off the books. do_tile = True if do_tile: comfy.model_management.soft_empty_cache() dims = samples_in.ndim - 2 if dims == 1 or self.extra_1d_channel is not None: pixel_samples = self.decode_tiled_1d(samples_in) elif dims == 2: pixel_samples = self.decode_tiled_(samples_in) elif dims == 3: tile = 256 // self.spacial_compression_decode() overlap = tile // 4 pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) return pixel_samples def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): self.throw_exception_if_invalid() memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) dims = samples.ndim - 2 args = {} if tile_x is not None: args["tile_x"] = tile_x if tile_y is not None: args["tile_y"] = tile_y if overlap is not None: args["overlap"] = overlap if dims == 1 or self.extra_1d_channel is not None: args.pop("tile_y") output = self.decode_tiled_1d(samples, **args) elif dims == 2: output = self.decode_tiled_(samples, **args) elif dims == 3: if overlap_t is None: args["overlap"] = (1, overlap, overlap) else: args["overlap"] = (max(1, overlap_t), overlap, overlap) if tile_t is not None: args["tile_t"] = max(2, tile_t) output = self.decode_tiled_3d(samples, **args) return output.movedim(1, -1) def encode(self, pixel_samples): self.throw_exception_if_invalid() pixel_samples = self.vae_encode_crop_pixels(pixel_samples) pixel_samples = pixel_samples.movedim(-1, 1) do_tile = False if self.latent_dim == 3 and pixel_samples.ndim < 5: if not self.not_video: pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) else: pixel_samples = pixel_samples.unsqueeze(2) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) free_memory = self.patcher.get_free_memory(self.device) batch_number = int(free_memory / max(1, memory_used)) batch_number = max(1, batch_number) samples = None for x in range(0, pixel_samples.shape[0], batch_number): pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype) if getattr(self.first_stage_model, 'comfy_has_chunked_io', False): out = self.first_stage_model.encode(pixels_in, device=self.device) else: pixels_in = pixels_in.to(self.device) out = self.first_stage_model.encode(pixels_in) out = out.to(self.output_device).to(dtype=self.vae_output_dtype()) if samples is None: samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) samples[x:x + batch_number] = out except Exception as e: model_management.raise_non_oom(e) logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") #NOTE: We don't know what tensors were allocated to stack variables at the time of the #exception and the exception itself refs them all until we get out of this except block. #So we just set a flag for tiler fallback so that tensor gc can happen once the #exception is fully off the books. do_tile = True if do_tile: comfy.model_management.soft_empty_cache() if self.latent_dim == 3: tile = 256 overlap = tile // 4 samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) elif self.latent_dim == 1 or self.extra_1d_channel is not None: samples = self.encode_tiled_1d(pixel_samples) else: samples = self.encode_tiled_(pixel_samples) return samples def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): self.throw_exception_if_invalid() pixel_samples = self.vae_encode_crop_pixels(pixel_samples) dims = self.latent_dim pixel_samples = pixel_samples.movedim(-1, 1) if dims == 3: if not self.not_video: pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) else: pixel_samples = pixel_samples.unsqueeze(2) memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) # TODO: calculate mem required for tile model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) args = {} if tile_x is not None: args["tile_x"] = tile_x if tile_y is not None: args["tile_y"] = tile_y if overlap is not None: args["overlap"] = overlap if dims == 1: args.pop("tile_y") samples = self.encode_tiled_1d(pixel_samples, **args) elif dims == 2: samples = self.encode_tiled_(pixel_samples, **args) elif dims == 3: if tile_t is not None: tile_t_latent = max(2, self.downscale_ratio[0](tile_t)) else: tile_t_latent = 9999 args["tile_t"] = self.upscale_ratio[0](tile_t_latent) if overlap_t is None: args["overlap"] = (1, overlap, overlap) else: args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap) maximum = pixel_samples.shape[2] maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum)) samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args) return samples def get_sd(self): return self.first_stage_model.state_dict() def spacial_compression_decode(self): try: return self.upscale_ratio[-1] except: return self.upscale_ratio def spacial_compression_encode(self): try: return self.downscale_ratio[-1] except: return self.downscale_ratio def temporal_compression_decode(self): try: return round(self.upscale_ratio[0](8192) / 8192) except: return None class StyleModel: def __init__(self, model, device="cpu"): self.model = model def get_cond(self, input): return self.model(input.last_hidden_state) def load_style_model(ckpt_path): model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) keys = model_data.keys() if "style_embedding" in keys: model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) elif "redux_down.weight" in keys: model = comfy.ldm.flux.redux.ReduxImageEncoder() else: raise Exception("invalid style model {}".format(ckpt_path)) model.load_state_dict(model_data) return StyleModel(model) class CLIPType(Enum): STABLE_DIFFUSION = 1 STABLE_CASCADE = 2 SD3 = 3 STABLE_AUDIO = 4 HUNYUAN_DIT = 5 FLUX = 6 MOCHI = 7 LTXV = 8 HUNYUAN_VIDEO = 9 PIXART = 10 COSMOS = 11 LUMINA2 = 12 WAN = 13 HIDREAM = 14 CHROMA = 15 ACE = 16 OMNIGEN2 = 17 QWEN_IMAGE = 18 HUNYUAN_IMAGE = 19 HUNYUAN_VIDEO_15 = 20 OVIS = 21 KANDINSKY5 = 22 KANDINSKY5_IMAGE = 23 NEWBIE = 24 FLUX2 = 25 LONGCAT_IMAGE = 26 def load_clip_model_patcher(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False): clip = load_clip(ckpt_paths, embedding_directory, clip_type, model_options, disable_dynamic) return clip.patcher def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False): clip_data = [] for p in ckpt_paths: sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True) if model_options.get("custom_operations", None) is None: sd, metadata = comfy.utils.convert_old_quants(sd, model_prefix="", metadata=metadata) clip_data.append(sd) clip = load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options, disable_dynamic=disable_dynamic) clip.patcher.cached_patcher_init = (load_clip_model_patcher, (ckpt_paths, embedding_directory, clip_type, model_options)) return clip class TEModel(Enum): CLIP_L = 1 CLIP_H = 2 CLIP_G = 3 T5_XXL = 4 T5_XL = 5 T5_BASE = 6 LLAMA3_8 = 7 T5_XXL_OLD = 8 GEMMA_2_2B = 9 QWEN25_3B = 10 QWEN25_7B = 11 BYT5_SMALL_GLYPH = 12 GEMMA_3_4B = 13 MISTRAL3_24B = 14 MISTRAL3_24B_PRUNED_FLUX2 = 15 QWEN3_4B = 16 QWEN3_2B = 17 GEMMA_3_12B = 18 JINA_CLIP_2 = 19 QWEN3_8B = 20 QWEN3_06B = 21 GEMMA_3_4B_VISION = 22 def detect_te_model(sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: return TEModel.CLIP_G if "text_model.encoder.layers.22.mlp.fc1.weight" in sd: return TEModel.CLIP_H if "text_model.encoder.layers.0.mlp.fc1.weight" in sd: return TEModel.CLIP_L if "model.encoder.layers.0.mixer.Wqkv.weight" in sd: return TEModel.JINA_CLIP_2 if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"] if weight.shape[0] == 10240: return TEModel.T5_XXL elif weight.shape[0] == 5120: return TEModel.T5_XL if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd: return TEModel.T5_XXL_OLD if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd: weight = sd['encoder.block.0.layer.0.SelfAttention.k.weight'] if weight.shape[0] == 384: return TEModel.BYT5_SMALL_GLYPH return TEModel.T5_BASE if 'model.layers.0.post_feedforward_layernorm.weight' in sd: if 'model.layers.47.self_attn.q_norm.weight' in sd: return TEModel.GEMMA_3_12B if 'model.layers.0.self_attn.q_norm.weight' in sd: if 'vision_model.embeddings.patch_embedding.weight' in sd: return TEModel.GEMMA_3_4B_VISION else: return TEModel.GEMMA_3_4B return TEModel.GEMMA_2_2B if 'model.layers.0.self_attn.k_proj.bias' in sd: weight = sd['model.layers.0.self_attn.k_proj.bias'] if weight.shape[0] == 256: return TEModel.QWEN25_3B if weight.shape[0] == 512: return TEModel.QWEN25_7B if "model.layers.0.post_attention_layernorm.weight" in sd: weight = sd['model.layers.0.post_attention_layernorm.weight'] if 'model.layers.0.self_attn.q_norm.weight' in sd: if weight.shape[0] == 2560: return TEModel.QWEN3_4B elif weight.shape[0] == 2048: return TEModel.QWEN3_2B elif weight.shape[0] == 4096: return TEModel.QWEN3_8B elif weight.shape[0] == 1024: return TEModel.QWEN3_06B if weight.shape[0] == 5120: if "model.layers.39.post_attention_layernorm.weight" in sd: return TEModel.MISTRAL3_24B else: return TEModel.MISTRAL3_24B_PRUNED_FLUX2 return TEModel.LLAMA3_8 return None def t5xxl_detect(clip_data): weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" weight_name_old = "encoder.block.23.layer.1.DenseReluDense.wi.weight" for sd in clip_data: if weight_name in sd or weight_name_old in sd: return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd) return {} def llama_detect(clip_data): weight_name = "model.layers.0.self_attn.k_proj.weight" for sd in clip_data: if weight_name in sd: return comfy.text_encoders.hunyuan_video.llama_detect(sd) return {} def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False): clip_data = state_dicts class EmptyClass: pass for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") else: if "text_projection" in clip_data[i]: clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node if "lm_head.weight" in clip_data[i]: clip_data[i]["model.lm_head.weight"] = clip_data[i].pop("lm_head.weight") # prefix missing in some models tokenizer_data = {} clip_target = EmptyClass() clip_target.params = {} if len(clip_data) == 1: te_model = detect_te_model(clip_data[0]) if te_model == TEModel.CLIP_G: if clip_type == CLIPType.STABLE_CASCADE: clip_target.clip = sdxl_clip.StableCascadeClipModel clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer elif clip_type == CLIPType.SD3: clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer elif clip_type == CLIPType.HIDREAM: clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer else: clip_target.clip = sdxl_clip.SDXLRefinerClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif te_model == TEModel.CLIP_H: clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer elif te_model == TEModel.T5_XXL: if clip_type == CLIPType.SD3: clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer elif clip_type == CLIPType.LTXV: clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA: clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer elif clip_type == CLIPType.WAN: clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) elif clip_type == CLIPType.HIDREAM: clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer else: #CLIPType.MOCHI clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer elif te_model == TEModel.T5_XXL_OLD: clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer elif te_model == TEModel.T5_XL: clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer elif te_model == TEModel.T5_BASE: if clip_type == CLIPType.ACE or "spiece_model" in clip_data[0]: clip_target.clip = comfy.text_encoders.ace.AceT5Model clip_target.tokenizer = comfy.text_encoders.ace.AceT5Tokenizer tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) else: clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer elif te_model == TEModel.GEMMA_2_2B: clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) elif te_model == TEModel.GEMMA_3_4B: clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b") clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) elif te_model == TEModel.GEMMA_3_4B_VISION: clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b_vision") clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) elif te_model == TEModel.GEMMA_3_12B: clip_target.clip = comfy.text_encoders.lt.gemma3_te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lt.Gemma3_12BTokenizer tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) elif te_model == TEModel.LLAMA3_8: clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data), clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer elif te_model == TEModel.QWEN25_3B: clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer elif te_model == TEModel.QWEN25_7B: if clip_type == CLIPType.HUNYUAN_IMAGE: clip_target.clip = comfy.text_encoders.hunyuan_image.te(byt5=False, **llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer elif clip_type == CLIPType.LONGCAT_IMAGE: clip_target.clip = comfy.text_encoders.longcat_image.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.longcat_image.LongCatImageTokenizer else: clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer elif te_model == TEModel.MISTRAL3_24B or te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2: clip_target.clip = comfy.text_encoders.flux.flux2_te(**llama_detect(clip_data), pruned=te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2) clip_target.tokenizer = comfy.text_encoders.flux.Flux2Tokenizer tokenizer_data["tekken_model"] = clip_data[0].get("tekken_model", None) elif te_model == TEModel.QWEN3_4B: if clip_type == CLIPType.FLUX or clip_type == CLIPType.FLUX2: clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_4b") clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer else: clip_target.clip = comfy.text_encoders.z_image.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.z_image.ZImageTokenizer elif te_model == TEModel.QWEN3_2B: clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer elif te_model == TEModel.QWEN3_8B: clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_8b") clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B elif te_model == TEModel.JINA_CLIP_2: clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper elif te_model == TEModel.QWEN3_06B: clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer else: # clip_l if clip_type == CLIPType.SD3: clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer elif clip_type == CLIPType.HIDREAM: clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer elif len(clip_data) == 2: if clip_type == CLIPType.SD3: te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])] clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer elif clip_type == CLIPType.HUNYUAN_DIT: clip_target.clip = comfy.text_encoders.hydit.HyditModel clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer elif clip_type == CLIPType.FLUX: clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer elif clip_type == CLIPType.HUNYUAN_VIDEO: clip_target.clip = comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer elif clip_type == CLIPType.HIDREAM: # Detect hidream_dualclip_classes = [] for hidream_te in clip_data: te_model = detect_te_model(hidream_te) hidream_dualclip_classes.append(te_model) clip_l = TEModel.CLIP_L in hidream_dualclip_classes clip_g = TEModel.CLIP_G in hidream_dualclip_classes t5 = TEModel.T5_XXL in hidream_dualclip_classes llama = TEModel.LLAMA3_8 in hidream_dualclip_classes # Initialize t5xxl_detect and llama_detect kwargs if needed t5_kwargs = t5xxl_detect(clip_data) if t5 else {} llama_kwargs = llama_detect(clip_data) if llama else {} clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, **t5_kwargs, **llama_kwargs) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer elif clip_type == CLIPType.HUNYUAN_IMAGE: clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer elif clip_type == CLIPType.HUNYUAN_VIDEO_15: clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer elif clip_type == CLIPType.KANDINSKY5: clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer elif clip_type == CLIPType.KANDINSKY5_IMAGE: clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage elif clip_type == CLIPType.LTXV: clip_target.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data), **comfy.text_encoders.lt.sd_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lt.LTXAVGemmaTokenizer tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) elif clip_type == CLIPType.NEWBIE: clip_target.clip = comfy.text_encoders.newbie.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.newbie.NewBieTokenizer if "model.layers.0.self_attn.q_norm.weight" in clip_data[0]: clip_data_gemma = clip_data[0] clip_data_jina = clip_data[1] else: clip_data_gemma = clip_data[1] clip_data_jina = clip_data[0] tokenizer_data["gemma_spiece_model"] = clip_data_gemma.get("spiece_model", None) tokenizer_data["jina_spiece_model"] = clip_data_jina.get("spiece_model", None) elif clip_type == CLIPType.ACE: te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])] if TEModel.QWEN3_4B in te_models: model_type = "qwen3_4b" else: model_type = "qwen3_2b" clip_target.clip = comfy.text_encoders.ace15.te(lm_model=model_type, **llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.ace15.ACE15Tokenizer else: clip_target.clip = sdxl_clip.SDXLClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif len(clip_data) == 3: clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer elif len(clip_data) == 4: clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), **llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer parameters = 0 for c in clip_data: parameters += comfy.utils.calculate_parameters(c) tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options, disable_dynamic=disable_dynamic) return clip def load_gligen(ckpt_path): data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() return comfy.model_patcher.CoreModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) def model_detection_error_hint(path, state_dict): filename = os.path.basename(path) if 'lora' in filename.lower(): return "\nHINT: This seems to be a Lora file and Lora files should be put in the lora folder and loaded with a lora loader node.." return "" def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) #TODO: this function is a mess and should be removed eventually if config is None: with open(config_path, 'r') as stream: config = yaml.safe_load(stream) model_config_params = config['model']['params'] clip_config = model_config_params['cond_stage_config'] if "parameterization" in model_config_params: if model_config_params["parameterization"] == "v": m = model.clone() class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION): pass m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config)) model = m layer_idx = clip_config.get("params", {}).get("layer_idx", None) if layer_idx is not None: clip.clip_layer(layer_idx) return (model, clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, disable_dynamic=False): sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic) if out is None: raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd))) if output_model and out[0] is not None: out[0].cached_patcher_init = (load_checkpoint_guess_config_model_only, (ckpt_path, embedding_directory, model_options, te_model_options)) if output_clip and out[1] is not None: out[1].patcher.cached_patcher_init = (load_checkpoint_guess_config_clip_only, (ckpt_path, embedding_directory, model_options, te_model_options)) return out def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False): model, *_ = load_checkpoint_guess_config(ckpt_path, False, False, False, embedding_directory=embedding_directory, model_options=model_options, te_model_options=te_model_options, disable_dynamic=disable_dynamic) return model def load_checkpoint_guess_config_clip_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False): _, clip, *_ = load_checkpoint_guess_config(ckpt_path, False, True, False, embedding_directory=embedding_directory, output_model=False, model_options=model_options, te_model_options=te_model_options, disable_dynamic=disable_dynamic) return clip.patcher def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None, disable_dynamic=False): clip = None clipvision = None vae = None model = None model_patcher = None diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) load_device = model_management.get_torch_device() custom_operations = model_options.get("custom_operations", None) if custom_operations is None: sd, metadata = comfy.utils.convert_old_quants(sd, diffusion_model_prefix, metadata=metadata) model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata) if model_config is None: logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.") diffusion_model = load_diffusion_model_state_dict(sd, model_options={}) if diffusion_model is None: return None return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used' unet_weight_dtype = list(model_config.supported_inference_dtypes) if model_config.quant_config is not None: weight_dtype = None if custom_operations is not None: model_config.custom_operations = custom_operations unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) if unet_dtype is None: unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) if model_config.quant_config is not None: manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes) else: manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) if model_config.clip_vision_prefix is not None: if output_clipvision: clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) if output_model: inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher model_patcher = ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device()) model.load_model_weights(sd, diffusion_model_prefix, assign=model_patcher.is_dynamic()) if output_vae: vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) vae_sd = model_config.process_vae_state_dict(vae_sd) vae = VAE(sd=vae_sd, metadata=metadata) if output_clip: if te_model_options.get("custom_operations", None) is None: scaled_fp8_list = [] for k in list(sd.keys()): # Convert scaled fp8 to mixed ops if k.endswith(".scaled_fp8"): scaled_fp8_list.append(k[:-len("scaled_fp8")]) if len(scaled_fp8_list) > 0: out_sd = {} for k in sd: skip = False for pref in scaled_fp8_list: skip = skip or k.startswith(pref) if not skip: out_sd[k] = sd[k] for pref in scaled_fp8_list: quant_sd, qmetadata = comfy.utils.convert_old_quants(sd, pref, metadata={}) for k in quant_sd: out_sd[k] = quant_sd[k] sd = out_sd clip_target = model_config.clip_target(state_dict=sd) if clip_target is not None: clip_sd = model_config.process_clip_state_dict(sd) if len(clip_sd) > 0: parameters = comfy.utils.calculate_parameters(clip_sd) clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options, disable_dynamic=disable_dynamic) else: logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") left_over = sd.keys() if len(left_over) > 0: logging.debug("left over keys: {}".format(left_over)) if output_model: if inital_load_device != torch.device("cpu"): logging.info("loaded diffusion model directly to GPU") model_management.load_models_gpu([model_patcher], force_full_load=True) return (model_patcher, clip, vae, clipvision) def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable_dynamic=False): """ Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats. Args: sd (dict): State dictionary containing model weights and configuration model_options (dict, optional): Additional options for model loading. Supports: - dtype: Override model data type - custom_operations: Custom model operations - fp8_optimizations: Enable FP8 optimizations Returns: ModelPatcher: A wrapped model instance that handles device management and weight loading. Returns None if the model configuration cannot be detected. The function: 1. Detects and handles different model formats (regular, diffusers, mmdit) 2. Configures model dtype based on parameters and device capabilities 3. Handles weight conversion and device placement 4. Manages model optimization settings 5. Loads weights and returns a device-managed model instance """ dtype = model_options.get("dtype", None) #Allow loading unets from checkpoint files diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) if len(temp_sd) > 0: sd = temp_sd custom_operations = model_options.get("custom_operations", None) if custom_operations is None: sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata) parameters = comfy.utils.calculate_parameters(sd) weight_dtype = comfy.utils.weight_dtype(sd) load_device = model_management.get_torch_device() model_config = model_detection.model_config_from_unet(sd, "", metadata=metadata) if model_config is not None: new_sd = sd else: new_sd = model_detection.convert_diffusers_mmdit(sd, "") if new_sd is not None: #diffusers mmdit model_config = model_detection.model_config_from_unet(new_sd, "") if model_config is None: return None else: #diffusers unet model_config = model_detection.model_config_from_diffusers_unet(sd) if model_config is None: return None diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config) new_sd = {} for k in diffusers_keys: if k in sd: new_sd[diffusers_keys[k]] = sd.pop(k) else: logging.warning("{} {}".format(diffusers_keys[k], k)) offload_device = model_management.unet_offload_device() unet_weight_dtype = list(model_config.supported_inference_dtypes) if model_config.quant_config is not None: weight_dtype = None if dtype is None: unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) else: unet_dtype = dtype if model_config.quant_config is not None: manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes) else: manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) if custom_operations is not None: model_config.custom_operations = custom_operations if model_options.get("fp8_optimizations", False): model_config.optimizations["fp8"] = True model = model_config.get_model(new_sd, "") ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher model_patcher = ModelPatcher(model, load_device=load_device, offload_device=offload_device) if not model_management.is_device_cpu(offload_device): model.to(offload_device) model.load_model_weights(new_sd, "", assign=model_patcher.is_dynamic()) left_over = sd.keys() if len(left_over) > 0: logging.info("left over keys in diffusion model: {}".format(left_over)) return model_patcher def load_diffusion_model(unet_path, model_options={}, disable_dynamic=False): sd, metadata = comfy.utils.load_torch_file(unet_path, return_metadata=True) model = load_diffusion_model_state_dict(sd, model_options=model_options, metadata=metadata, disable_dynamic=disable_dynamic) if model is None: logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path)) raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, sd))) model.cached_patcher_init = (load_diffusion_model, (unet_path, model_options)) return model def load_unet(unet_path, dtype=None): logging.warning("The load_unet function has been deprecated and will be removed please switch to: load_diffusion_model") return load_diffusion_model(unet_path, model_options={"dtype": dtype}) def load_unet_state_dict(sd, dtype=None): logging.warning("The load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict") return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype}) def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}): clip_sd = None load_models = [model] if clip is not None: load_models.append(clip.load_model()) clip_sd = clip.get_sd() vae_sd = None if vae is not None: vae_sd = vae.get_sd() if metadata is None: metadata = {} model_management.load_models_gpu(load_models) clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None sd = model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd) for k in extra_keys: sd[k] = extra_keys[k] for k in sd: t = sd[k] if not t.is_contiguous(): sd[k] = t.contiguous() comfy.utils.save_torch_file(sd, output_path, metadata=metadata) ================================================ FILE: comfy/sd1_clip.py ================================================ import os from transformers import CLIPTokenizer import comfy.ops import torch import traceback import zipfile from . import model_management import comfy.clip_model import json import logging import numbers import re def gen_empty_tokens(special_tokens, length): start_token = special_tokens.get("start", None) end_token = special_tokens.get("end", None) pad_token = special_tokens.get("pad") output = [] if start_token is not None: output.append(start_token) if end_token is not None: output.append(end_token) output += [pad_token] * (length - len(output)) return output class ClipTokenWeightEncoder: def encode_token_weights(self, token_weight_pairs): to_encode = list() max_token_len = 0 has_weights = False for x in token_weight_pairs: tokens = list(map(lambda a: a[0], x)) max_token_len = max(len(tokens), max_token_len) has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) to_encode.append(tokens) sections = len(to_encode) if has_weights or sections == 0: if hasattr(self, "gen_empty_tokens"): to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len)) else: to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) o = self.encode(to_encode) out, pooled = o[:2] if pooled is not None: first_pooled = pooled[0:1].to(device=model_management.intermediate_device()) else: first_pooled = pooled output = [] for k in range(0, sections): z = out[k:k+1] if has_weights: z_empty = out[-1] for i in range(len(z)): for j in range(len(z[i])): weight = token_weight_pairs[k][j][1] if weight != 1.0: z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] output.append(z) if (len(output) == 0): r = (out[-1:].to(device=model_management.intermediate_device()), first_pooled) else: r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device()), first_pooled) if len(o) > 2: extra = {} for k in o[2]: v = o[2][k] if k == "attention_mask": v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device()) extra[k] = v r = r + (extra,) return r class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): LAYERS = [ "last", "pooled", "hidden", "all" ] def __init__(self, device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32 super().__init__() if textmodel_json_config is None: textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") if "model_name" not in model_options: model_options = {**model_options, "model_name": "clip_l"} if isinstance(textmodel_json_config, dict): config = textmodel_json_config else: with open(textmodel_json_config) as f: config = json.load(f) te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {}) for k, v in te_model_options.items(): config[k] = v operations = model_options.get("custom_operations", None) quant_config = model_options.get("quantization_metadata", None) if operations is None: if quant_config is not None: operations = comfy.ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True) logging.info("Using MixedPrecisionOps for text encoder") else: operations = comfy.ops.manual_cast self.operations = operations self.transformer = model_class(config, dtype, device, self.operations) self.num_layers = self.transformer.num_layers self.max_length = max_length if freeze: self.freeze() self.layer = layer self.layer_idx = None self.special_tokens = special_tokens self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.enable_attention_masks = enable_attention_masks self.zero_out_masked = zero_out_masked self.layer_norm_hidden_state = layer_norm_hidden_state self.return_projected_pooled = return_projected_pooled self.return_attention_masks = return_attention_masks self.execution_device = None if layer == "hidden": assert layer_idx is not None assert abs(layer_idx) < self.num_layers self.set_clip_options({"layer": layer_idx}) self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled) def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False def set_clip_options(self, options): layer_idx = options.get("layer", self.layer_idx) self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) self.execution_device = options.get("execution_device", self.execution_device) if isinstance(self.layer, list) or self.layer == "all": pass elif isinstance(layer_idx, list): self.layer = layer_idx elif layer_idx is None or abs(layer_idx) > self.num_layers: self.layer = "last" else: self.layer = "hidden" self.layer_idx = layer_idx def reset_clip_options(self): self.layer = self.options_default[0] self.layer_idx = self.options_default[1] self.return_projected_pooled = self.options_default[2] self.execution_device = None def process_tokens(self, tokens, device): end_token = self.special_tokens.get("end", None) pad_token = self.special_tokens.get("pad", -1) if end_token is None: cmp_token = pad_token else: cmp_token = end_token embeds_out = [] attention_masks = [] num_tokens = [] for x in tokens: attention_mask = [] tokens_temp = [] other_embeds = [] eos = False index = 0 left_pad = False for y in x: if isinstance(y, numbers.Integral): token = int(y) if index == 0 and token == pad_token: left_pad = True if eos or (left_pad and token == pad_token): attention_mask.append(0) else: attention_mask.append(1) left_pad = False tokens_temp += [token] if not eos and token == cmp_token and not left_pad: if end_token is None: attention_mask[-1] = 0 eos = True else: other_embeds.append((index, y)) index += 1 tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long) tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32) index = 0 pad_extra = 0 embeds_info = [] for o in other_embeds: emb = o[1] if torch.is_tensor(emb): emb = {"type": "embedding", "data": emb} extra = None emb_type = emb.get("type", None) if emb_type == "embedding": emb = emb.get("data", None) else: if hasattr(self.transformer, "preprocess_embed"): emb, extra = self.transformer.preprocess_embed(emb, device=device) else: emb = None if emb is None: index += -1 continue ind = index + o[0] emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32) emb_shape = emb.shape[1] if emb.shape[-1] == tokens_embed.shape[-1]: tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1) attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:] index += emb_shape - 1 embeds_info.append({"type": emb_type, "index": ind, "size": emb_shape, "extra": extra}) else: index += -1 pad_extra += emb_shape logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1])) if pad_extra > 0: padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32) tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1) attention_mask = attention_mask + [0] * pad_extra embeds_out.append(tokens_embed) attention_masks.append(attention_mask) num_tokens.append(sum(attention_mask)) return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info def forward(self, tokens): if self.execution_device is None: device = self.transformer.get_input_embeddings().weight.device else: device = self.execution_device embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device) attention_mask_model = None if self.enable_attention_masks: attention_mask_model = attention_mask if isinstance(self.layer, list): intermediate_output = self.layer elif self.layer == "all": intermediate_output = "all" else: intermediate_output = self.layer_idx outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32, embeds_info=embeds_info) if self.layer == "last": z = outputs[0].float() else: z = outputs[1].float() if self.zero_out_masked: z *= attention_mask.unsqueeze(-1).float() pooled_output = None if len(outputs) >= 3: if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None: pooled_output = outputs[3].float() elif outputs[2] is not None: pooled_output = outputs[2].float() extra = {} if self.return_attention_masks: extra["attention_mask"] = attention_mask if len(extra) > 0: return z, pooled_output, extra return z, pooled_output def encode(self, tokens): return self(tokens) def load_sd(self, sd): return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, "can_assign_sd", False)) def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed): if isinstance(tokens, dict): tokens_only = next(iter(tokens.values())) # todo: get this better? else: tokens_only = tokens tokens_only = [[t[0] for t in b] for b in tokens_only] embeds = self.process_tokens(tokens_only, device=self.execution_device)[0] return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed) def parse_parentheses(string): result = [] current_item = "" nesting_level = 0 for char in string: if char == "(": if nesting_level == 0: if current_item: result.append(current_item) current_item = "(" else: current_item = "(" else: current_item += char nesting_level += 1 elif char == ")": nesting_level -= 1 if nesting_level == 0: result.append(current_item + ")") current_item = "" else: current_item += char else: current_item += char if current_item: result.append(current_item) return result def token_weights(string, current_weight): a = parse_parentheses(string) out = [] for x in a: weight = current_weight if len(x) >= 2 and x[-1] == ')' and x[0] == '(': x = x[1:-1] xx = x.rfind(":") weight *= 1.1 if xx > 0: try: weight = float(x[xx+1:]) x = x[:xx] except: pass out += token_weights(x, weight) else: out += [(x, current_weight)] return out def escape_important(text): text = text.replace("\\)", "\0\1") text = text.replace("\\(", "\0\2") return text def unescape_important(text): text = text.replace("\0\1", ")") text = text.replace("\0\2", "(") return text def safe_load_embed_zip(embed_path): with zipfile.ZipFile(embed_path) as myzip: names = list(filter(lambda a: "data/" in a, myzip.namelist())) names.reverse() for n in names: with myzip.open(n) as myfile: data = myfile.read() number = len(data) // 4 length_embed = 1024 #sd2.x if number < 768: continue if number % 768 == 0: length_embed = 768 #sd1.x num_embeds = number // length_embed embed = torch.frombuffer(data, dtype=torch.float) out = embed.reshape((num_embeds, length_embed)).clone() del embed return out def expand_directory_list(directories): dirs = set() for x in directories: dirs.add(x) for root, subdir, file in os.walk(x, followlinks=True): dirs.add(root) return list(dirs) def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format out_list = [] for k in embed: if k.startswith(prefix) and k.endswith(suffix): out_list.append(embed[k]) if len(out_list) == 0: return None return torch.cat(out_list, dim=0) def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): if isinstance(embedding_directory, str): embedding_directory = [embedding_directory] embedding_directory = expand_directory_list(embedding_directory) valid_file = None for embed_dir in embedding_directory: embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) embed_dir = os.path.abspath(embed_dir) try: if os.path.commonpath((embed_dir, embed_path)) != embed_dir: continue except: continue if not os.path.isfile(embed_path): extensions = ['.safetensors', '.pt', '.bin'] for x in extensions: t = embed_path + x if os.path.isfile(t): valid_file = t break else: valid_file = embed_path if valid_file is not None: break if valid_file is None: return None embed_path = valid_file embed_out = None try: if embed_path.lower().endswith(".safetensors"): import safetensors.torch embed = safetensors.torch.load_file(embed_path, device="cpu") else: try: embed = torch.load(embed_path, weights_only=True, map_location="cpu") except: embed_out = safe_load_embed_zip(embed_path) except Exception: logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) return None if embed_out is None: if 'string_to_param' in embed: values = embed['string_to_param'].values() embed_out = next(iter(values)) elif isinstance(embed, list): out_list = [] for x in range(len(embed)): for k in embed[x]: t = embed[x][k] if t.shape[-1] != embedding_size: continue out_list.append(t.reshape(-1, t.shape[-1])) embed_out = torch.cat(out_list, dim=0) elif embed_key is not None and embed_key in embed: embed_out = embed[embed_key] else: embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*') if embed_out is None: embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key)) if embed_out is None: values = embed.values() embed_out = next(iter(values)) return embed_out class SDTokenizer: def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, start_token=None, min_padding=None, pad_left=False, disable_weights=False, tokenizer_data={}, tokenizer_args={}): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args) self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length) self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length) self.end_token = None self.min_padding = min_padding self.pad_left = pad_left empty = self.tokenizer('')["input_ids"] self.tokenizer_adds_end_token = has_end_token if has_start_token: if len(empty) > 0: self.tokens_start = 1 self.start_token = empty[0] else: self.tokens_start = 0 self.start_token = start_token if start_token is None: logging.warning("WARNING: There's something wrong with your tokenizers.'") if end_token is not None: self.end_token = end_token else: if has_end_token: self.end_token = empty[1] else: self.tokens_start = 0 self.start_token = start_token if end_token is not None: self.end_token = end_token else: if has_end_token: self.end_token = empty[0] if pad_token is not None: self.pad_token = pad_token elif pad_with_end: self.pad_token = self.end_token else: self.pad_token = 0 self.pad_with_end = pad_with_end self.pad_to_max_length = pad_to_max_length vocab = self.tokenizer.get_vocab() self.inv_vocab = {v: k for k, v in vocab.items()} self.embedding_directory = embedding_directory self.max_word_length = 8 self.embedding_identifier = "embedding:" self.embedding_size = embedding_size self.embedding_key = embedding_key self.disable_weights = disable_weights def _try_get_embedding(self, embedding_name:str): ''' Takes a potential embedding name and tries to retrieve it. Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. ''' split_embed = embedding_name.split() embedding_name = split_embed[0] leftover = ' '.join(split_embed[1:]) embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) if embed is None: stripped = embedding_name.strip(',') if len(stripped) < len(embedding_name): embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) return (embed, "{} {}".format(embedding_name[len(stripped):], leftover)) return (embed, leftover) def pad_tokens(self, tokens, amount): if self.pad_left: for i in range(amount): tokens.insert(0, (self.pad_token, 1.0, 0)) else: tokens.extend([(self.pad_token, 1.0, 0)] * amount) def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs): ''' Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLIP tensors. Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. Returned list has the dimensions NxM where M is the input size of CLIP ''' min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length) min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding) min_length = kwargs.get("min_length", min_length) text = escape_important(text) if kwargs.get("disable_weights", self.disable_weights): parsed_weights = [(text, 1.0)] else: parsed_weights = token_weights(text, 1.0) # tokenize words tokens = [] for weighted_segment, weight in parsed_weights: to_tokenize = unescape_important(weighted_segment) split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize) to_tokenize = [split[0]] for i in range(1, len(split)): to_tokenize.append("{}{}".format(self.embedding_identifier, split[i])) to_tokenize = [x for x in to_tokenize if x != ""] for word in to_tokenize: # if we find an embedding, deal with the embedding if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: embedding_name = word[len(self.embedding_identifier):].strip('\n') embed, leftover = self._try_get_embedding(embedding_name) if embed is None: logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring") else: if len(embed.shape) == 1: tokens.append([(embed, weight)]) else: tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) #if we accidentally have leftover text, continue parsing using leftover, else move on to next word if leftover != "": word = leftover else: continue end = 999999999999 if self.tokenizer_adds_end_token: end = -1 #parse word tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:end]]) #reshape token array to CLIP input size batched_tokens = [] batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) for i, t_group in enumerate(tokens): #determine if we're going to try and keep the tokens in a single batch is_large = len(t_group) >= self.max_word_length if self.end_token is not None: has_end_token = 1 else: has_end_token = 0 while len(t_group) > 0: if len(t_group) + len(batch) > self.max_length - has_end_token: remaining_length = self.max_length - len(batch) - has_end_token #break word in two and add end token if is_large: batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) t_group = t_group[remaining_length:] #add end token and pad else: if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: self.pad_tokens(batch, remaining_length) #start new batch batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) else: batch.extend([(t,w,i+1) for t,w in t_group]) t_group = [] #fill last batch if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) if min_padding is not None: self.pad_tokens(batch, min_padding) if self.pad_to_max_length and len(batch) < self.max_length: self.pad_tokens(batch, self.max_length - len(batch)) if min_length is not None and len(batch) < min_length: self.pad_tokens(batch, min_length - len(batch)) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] return batched_tokens def untokenize(self, token_weight_pair): return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) def state_dict(self): return {} def decode(self, token_ids, skip_special_tokens=True): return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens) class SD1Tokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None): if name is not None: self.clip_name = name self.clip = "{}".format(self.clip_name) else: self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer) setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return getattr(self, self.clip).untokenize(token_weight_pair) def state_dict(self): return getattr(self, self.clip).state_dict() def decode(self, token_ids, skip_special_tokens=True): return getattr(self, self.clip).decode(token_ids, skip_special_tokens=skip_special_tokens) class SD1CheckpointClipModel(SDClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options) class SD1ClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs): super().__init__() if name is not None: self.clip_name = name self.clip = "{}".format(self.clip_name) else: self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) clip_model = model_options.get("{}_class".format(self.clip), clip_model) model_options = {**model_options, "model_name": self.clip} setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs)) self.dtypes = set() if dtype is not None: self.dtypes.add(dtype) def set_clip_options(self, options): getattr(self, self.clip).set_clip_options(options) def reset_clip_options(self): getattr(self, self.clip).reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs = token_weight_pairs[self.clip_name] out = getattr(self, self.clip).encode_token_weights(token_weight_pairs) return out def load_sd(self, sd): return getattr(self, self.clip).load_sd(sd) def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None): return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed) ================================================ FILE: comfy/sd1_clip_config.json ================================================ { "_name_or_path": "openai/clip-vit-large-patch14", "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 49407, "hidden_act": "quick_gelu", "hidden_size": 768, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "projection_dim": 768, "torch_dtype": "float32", "transformers_version": "4.24.0", "vocab_size": 49408 } ================================================ FILE: comfy/sd1_tokenizer/merges.txt ================================================ #version: 0.2 i n t h a n r e a r e r th e in g o u o n s t o r e n o n a l a t e r i t i n t o r o i s l e i c a t an d e d o f c h o r e s i l e l s t a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al h el r ic b ir vi e w ay t ri d a p le b ro st o oo l ni ght tr u b a re ad re s ye ar f r t or al s c oun c la t ure v el at ed le c en d th ing v o ic i be st c an wor k la st af ter en ce p ri p e e s i l âĢ ¦ d re y s o ver i es ðŁ ij com m t w in k s un c l li fe t t a ch l and s y t re t al p ol s m du c s al f t ' re ch e w ar t ur ati ons ac h m s il e p m ou gh at e st ar wee k ! !! c lu th ere n er t om s el ï¸ ı wor ld v es c am go t in ter of f u m ton ight o ther h ou loo k j e i d si on be au at t el i or t re c f f st er su pp g en be en il y te am m m i c pe op it t at s on ly mb er en g b ri m p k now b ur b ar in s lo w sh e ro w â Ŀ t ro peop le vi a lo w ag a be t x t f ac ch ar e ar w al s en f am b le n ati is h n or g ame li ve s co le y d on ic k b all ver y the se p an i a at ing c r a re g ir ma ke st re sho w . " f l u p d r than ks il li w om st s i g s ur ever y c ur vie w le t in to mo st n a in di g ar ha d s ou v ed an t iti on ma de f ol un i it ed ðŁ ı ic al th r read y ch ec d ra k es boo k e p si c mor ning ne ws c au c t w ell an c pho to th an or s bir th g g ou t ne xt som e en ing stor y ch ri do wn hom e f fe fre e d a b or f il ci al than k si de le ar qu e l ine t en at es ye ars m y pho to beau ti ri ght n u for m shi p b an th er d ays g am as on g y ðŁ İ birth day se t ic k e t st ill com ing ta ke ðŁ ĩ b b s ol s on d en e p mu sic the m de n wh y f oo c ra am az w n h ol t ting w r u e ma g c ro l an c lo b ra a k s ing c al re ad ' ve jo h b ab d ri b lo bi g er ic in t t or tr y l a le g hou se m ic v al beauti ful l itt chec k ne w ver s s w ar i pla y h er âĢ ĵ w in m a con gr sch ool f un . @ he al ic h d el wh ere l on ke t tw o mu ch wat ch v en d ed a st k ed b as go ing m p e ver w ays ro o de sig l y s ed to p l in ch an to o it ing d ent gh ts t y sp o ne ed b lu in st be ing âĿ ¤ w el l s hi m m ay st ing n a el y litt le g a n at tom or m c h on w ant a ir pi c am eric p er le ss wee k ve l a h c ap ch am g er ti m tomor row ne ss st ate h al ser v z e o s p at v is ex c s in f f c ity c en an y b el su mm t in w ould loo king k o ce le fam ily m er po w hel p bu s c o c le sel f en s ic s th o an i ch o le ad b s t wee th ink for e ch il vi de di d al e ch i v il en ds w ing p as ' ll v ol s a g s man y j ec be fore gra ph n y ur ing w il d d bu il f av st ed tr an l ing ou d d ge fi el nati onal st a c er w ere in a se ason c ou n ed amaz ing ti ons cele br n s a th he ad s day d ar lo c v in an other g oo s at n y jo in pre s s es s ing an a in ing .. .. c our ï¸ ı ac t cau se li ght am s t a b al f c hi gh off ici t t chri st d ic d ay ra l h or : ) vi si n am o b ma s gh t re ally t un fin d thr ough por t u t ti ve st y n e or e ðŁĺ Ĥ supp ort ne ver ev en ðŁ Ķ h a y a l d u k r an j am wi th me di d es ne y ch ing al e h y k in ! ! 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"ðŁĻĮðŁĻĮ": 30430, "ðŁĻĮðŁĻĮðŁĻĮ": 37127, "ðŁĻı": 4260, "ðŁĻı": 5503, "ðŁĻıðŁı»": 25100, "ðŁĻıðŁı»": 16650, "ðŁĻıðŁı¼": 31163, "ðŁĻıðŁı¼": 18952, "ðŁĻıðŁı½": 34103, "ðŁĻıðŁı½": 21540, "ðŁĻıðŁı¾": 34277, "ðŁĻıðŁı¾": 21979, "ðŁĻıðŁĻı": 18227, "ðŁĻıðŁĻı": 26510, "ðŁĻıðŁĻıðŁĻı": 31702, "ðŁļ": 2730, "ðŁļ¨": 12198, "ðŁļ¨": 6056, "ðŁļ¨ðŁļ¨": 36487, "ðŁļ¨ðŁļ¨": 21440, "ðŁļ¨ðŁļ¨ðŁļ¨": 41515, "ðŁļ©": 44514, "ðŁļ«": 35291, "ðŁļ²": 37085, "ðŁļ´": 30825, "ðŁļ¶": 46060, "ðŁļĢ": 22400, "ðŁļĢ": 13542, "ðŁļĢðŁļĢ": 49033, "ðŁļĤ": 38949, "ðŁļĮ": 46891, "ðŁļĹ": 33054, "ðŁļĹ": 22783, "ðŁļĺ": 35825, "ðŁļĻ": 48487, "ðŁĽ": 11306, "ñ": 173, "ñ": 429, "ò": 174, "ò": 430, "ó": 175, "ó": 431, "ô": 176, "ô": 432, "õ": 177, "õ": 433, "ö": 178, "ö": 434, "÷": 179, "÷": 435, "ø": 180, "ø": 436, "ù": 181, "ù": 437, "ú": 182, "ú": 438, "û": 183, "û": 439, "ü": 184, "ü": 440, "ý": 185, "ý": 441, "þ": 186, "þ": 442, "ÿ": 187, "ÿ": 443, "Ā": 188, "Ā": 444, "ā": 189, "ā": 445, "Ă": 190, "Ă": 446, "ă": 191, "ă": 447, "Ą": 192, "Ą": 448, "ą": 193, "ą": 449, "Ć": 194, "Ć": 450, "ć": 195, "ć": 451, "Ĉ": 196, "Ĉ": 452, "ĉ": 197, "ĉ": 453, "Ċ": 198, "Ċ": 454, "ċ": 199, "ċ": 455, "Č": 200, "Č": 456, "č": 201, "č": 457, "Ď": 202, "Ď": 458, "ď": 203, "ď": 459, "Đ": 204, "Đ": 460, "đ": 205, "đ": 461, "Ē": 206, "Ē": 462, "ē": 207, "ē": 463, "Ĕ": 208, "Ĕ": 464, "ĕ": 209, "ĕ": 465, "Ė": 210, "Ė": 466, "ė": 211, "ė": 467, "Ę": 212, "Ę": 468, "ę": 213, "ę": 469, "Ě": 214, "Ě": 470, "ě": 215, "ě": 471, "Ĝ": 216, "Ĝ": 472, "ĝ": 217, "ĝ": 473, "Ğ": 218, "Ğ": 474, "ğ": 219, "ğ": 475, "Ġ": 220, "Ġ": 476, "ġ": 221, "ġ": 477, "Ģ": 222, "Ģ": 478, "Ģï¸ı": 9668, "Ģï¸ı": 5511, "ģ": 223, "ģ": 479, "ģà¸": 15016, "Ĥ": 224, "Ĥ": 480, "Ĥâĸ": 29036, "ĤâĸĤâĸ": 30832, "ĥ": 225, "ĥ": 481, "Ħ": 226, "Ħ": 482, "Ħà¸": 20537, "Ħë": 34462, "Ħëĭ": 25170, "ħ": 227, "ħ": 483, "ħï¸ı": 33950, "Ĩ": 228, "Ĩ": 484, "ĩ": 229, "ĩ": 485, "Ī": 230, "Ī": 486, "ī": 231, "ī": 487, "īï¸ı": 37463, "Ĭ": 232, "Ĭ": 488, "Ĭãģ": 30294, "ĭ": 233, "ĭ": 489, "ĭãģ": 36218, "ĭãĤ": 45737, "Į": 234, "Į": 490, "ĮãĤĬãģ": 45969, "ĮãĤĬãģŁãģĦ": 47021, "Įë": 17003, "į": 235, "į": 491, "İ": 236, "İ": 492, "ı": 237, "ı": 493, "IJ": 238, "IJ": 494, "ij": 239, "ij": 495, "Ĵ": 240, "Ĵ": 496, "ĵ": 241, "ĵ": 497, "Ķ": 242, "Ķ": 498, "Ķë": 37978, "Ķï¸ı": 24395, "Ķï¸ı": 7443, "ķ": 243, "ķ": 499, "ķãĤ": 26609, "ķï¸ı": 44853, "ĸ": 244, "ĸ": 500, "ĸï¸ı": 28877, "Ĺ": 245, "Ĺ": 501, "ĺ": 246, "ĺ": 502, "Ļ": 247, "Ļ": 503, "ļ": 248, "ļ": 504, "Ľ": 249, "Ľ": 505, "ľ": 250, "ľ": 506, "ľë": 39810, "Ŀ": 251, "Ŀ": 507, "ŀ": 252, "ŀ": 508, "Ł": 253, "Ł": 509, "ŁãģĦ": 46023, "ł": 254, "ł": 510, "łï¸ı": 27899, "łï¸ı": 12715, "łĪ": 43364, "Ń": 255, "Ń": 511 } ================================================ FILE: comfy/sdxl_clip.py ================================================ from comfy import sd1_clip import torch import os class SDXLClipG(sd1_clip.SDClipModel): def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): if layer == "penultimate": layer="hidden" layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") model_options = {**model_options, "model_name": "clip_g"} super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False, return_projected_pooled=True, model_options=model_options) def load_sd(self, sd): return super().load_sd(sd) class SDXLClipGTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) class SDXLTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.clip_g.untokenize(token_weight_pair) def state_dict(self): return {} class SDXLClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__() self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options) self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options) self.dtypes = set([dtype]) def set_clip_options(self, options): self.clip_l.set_clip_options(options) self.clip_g.set_clip_options(options) def reset_clip_options(self): self.clip_g.reset_clip_options() self.clip_l.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_g = token_weight_pairs["g"] token_weight_pairs_l = token_weight_pairs["l"] g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) cut_to = min(l_out.shape[1], g_out.shape[1]) return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], dim=-1), g_pooled def load_sd(self, sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: return self.clip_g.load_sd(sd) else: return self.clip_l.load_sd(sd) class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG, model_options=model_options) class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer) class StableCascadeClipG(sd1_clip.SDClipModel): def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") model_options = {**model_options, "model_name": "clip_g"} super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options) def load_sd(self, sd): return super().load_sd(sd) class StableCascadeClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG, model_options=model_options) ================================================ FILE: comfy/supported_models.py ================================================ import torch from . import model_base from . import utils from . import sd1_clip from . import sdxl_clip import comfy.text_encoders.sd2_clip import comfy.text_encoders.sd3_clip import comfy.text_encoders.sa_t5 import comfy.text_encoders.aura_t5 import comfy.text_encoders.pixart_t5 import comfy.text_encoders.hydit import comfy.text_encoders.flux import comfy.text_encoders.genmo import comfy.text_encoders.lt import comfy.text_encoders.hunyuan_video import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan import comfy.text_encoders.ace import comfy.text_encoders.omnigen2 import comfy.text_encoders.qwen_image import comfy.text_encoders.hunyuan_image import comfy.text_encoders.kandinsky5 import comfy.text_encoders.z_image import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image from . import supported_models_base from . import latent_formats from . import diffusers_convert import comfy.model_management class SD15(supported_models_base.BASE): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "num_heads": 8, "num_head_channels": -1, } latent_format = latent_formats.SD15 memory_usage_factor = 1.0 def process_clip_state_dict(self, state_dict): k = list(state_dict.keys()) for x in k: if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") state_dict[y] = state_dict.pop(x) if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] if ids.dtype == torch.float32: state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() replace_prefix = {} replace_prefix["cond_stage_model."] = "clip_l." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) return state_dict def process_clip_state_dict_for_saving(self, state_dict): pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] for p in pop_keys: if p in state_dict: state_dict.pop(p) replace_prefix = {"clip_l.": "cond_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) class SD20(supported_models_base.BASE): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "num_heads": -1, "num_head_channels": 64, "attn_precision": torch.float32, } latent_format = latent_formats.SD15 memory_usage_factor = 1.0 def model_type(self, state_dict, prefix=""): if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) out = state_dict.get(k, None) if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. return model_base.ModelType.V_PREDICTION return model_base.ModelType.EPS def process_clip_state_dict(self, state_dict): replace_prefix = {} replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format replace_prefix["cond_stage_model.model."] = "clip_h." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.") return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} replace_prefix["clip_h"] = "cond_stage_model.model" state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) return state_dict def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel) class SD21UnclipL(SD20): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 1536, "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} class SD21UnclipH(SD20): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 2048, "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} class SDXLRefiner(supported_models_base.BASE): unet_config = { "model_channels": 384, "use_linear_in_transformer": True, "context_dim": 1280, "adm_in_channels": 2560, "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], "use_temporal_attention": False, } latent_format = latent_formats.SDXL memory_usage_factor = 1.0 def get_model(self, state_dict, prefix="", device=None): return model_base.SDXLRefiner(self, device=device) def process_clip_state_dict(self, state_dict): keys_to_replace = {} replace_prefix = {} replace_prefix["conditioner.embedders.0.model."] = "clip_g." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") replace_prefix["clip_g"] = "conditioner.embedders.0.model" state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) class SDXL(supported_models_base.BASE): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 10, 10], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } latent_format = latent_formats.SDXL memory_usage_factor = 0.8 def model_type(self, state_dict, prefix=""): if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5 self.latent_format = latent_formats.SDXL_Playground_2_5() self.sampling_settings["sigma_data"] = 0.5 self.sampling_settings["sigma_max"] = 80.0 self.sampling_settings["sigma_min"] = 0.002 return model_base.ModelType.EDM elif "edm_vpred.sigma_max" in state_dict: self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item()) if "edm_vpred.sigma_min" in state_dict: self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item()) return model_base.ModelType.V_PREDICTION_EDM elif "v_pred" in state_dict: if "ztsnr" in state_dict: #Some zsnr anime checkpoints self.sampling_settings["zsnr"] = True return model_base.ModelType.V_PREDICTION else: return model_base.ModelType.EPS def get_model(self, state_dict, prefix="", device=None): out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) if self.inpaint_model(): out.set_inpaint() return out def process_clip_state_dict(self, state_dict): keys_to_replace = {} replace_prefix = {} replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model" replace_prefix["conditioner.embedders.1.model."] = "clip_g." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") for k in state_dict: if k.startswith("clip_l"): state_dict_g[k] = state_dict[k] state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1)) pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] for p in pop_keys: if p in state_dict_g: state_dict_g.pop(p) replace_prefix["clip_g"] = "conditioner.embedders.1.model" replace_prefix["clip_l"] = "conditioner.embedders.0" state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) class SSD1B(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 4, 4], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class Segmind_Vega(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 1, 1, 2, 2], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class KOALA_700M(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 2, 5], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class KOALA_1B(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 2, 6], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class SVD_img2vid(supported_models_base.BASE): unet_config = { "model_channels": 320, "in_channels": 8, "use_linear_in_transformer": True, "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], "context_dim": 1024, "adm_in_channels": 768, "use_temporal_attention": True, "use_temporal_resblock": True } unet_extra_config = { "num_heads": -1, "num_head_channels": 64, "attn_precision": torch.float32, } clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." latent_format = latent_formats.SD15 sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} def get_model(self, state_dict, prefix="", device=None): out = model_base.SVD_img2vid(self, device=device) return out def clip_target(self, state_dict={}): return None class SV3D_u(SVD_img2vid): unet_config = { "model_channels": 320, "in_channels": 8, "use_linear_in_transformer": True, "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], "context_dim": 1024, "adm_in_channels": 256, "use_temporal_attention": True, "use_temporal_resblock": True } vae_key_prefix = ["conditioner.embedders.1.encoder."] def get_model(self, state_dict, prefix="", device=None): out = model_base.SV3D_u(self, device=device) return out class SV3D_p(SV3D_u): unet_config = { "model_channels": 320, "in_channels": 8, "use_linear_in_transformer": True, "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], "context_dim": 1024, "adm_in_channels": 1280, "use_temporal_attention": True, "use_temporal_resblock": True } def get_model(self, state_dict, prefix="", device=None): out = model_base.SV3D_p(self, device=device) return out class Stable_Zero123(supported_models_base.BASE): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, "in_channels": 8, } unet_extra_config = { "num_heads": 8, "num_head_channels": -1, } required_keys = { "cc_projection.weight": None, "cc_projection.bias": None, } clip_vision_prefix = "cond_stage_model.model.visual." latent_format = latent_formats.SD15 def get_model(self, state_dict, prefix="", device=None): out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) return out def clip_target(self, state_dict={}): return None class SD_X4Upscaler(SD20): unet_config = { "context_dim": 1024, "model_channels": 256, 'in_channels': 7, "use_linear_in_transformer": True, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "disable_self_attentions": [True, True, True, False], "num_classes": 1000, "num_heads": 8, "num_head_channels": -1, } latent_format = latent_formats.SD_X4 sampling_settings = { "linear_start": 0.0001, "linear_end": 0.02, } def get_model(self, state_dict, prefix="", device=None): out = model_base.SD_X4Upscaler(self, device=device) return out class Stable_Cascade_C(supported_models_base.BASE): unet_config = { "stable_cascade_stage": 'c', } unet_extra_config = {} latent_format = latent_formats.SC_Prior supported_inference_dtypes = [torch.bfloat16, torch.float32] sampling_settings = { "shift": 2.0, } vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoder."] clip_vision_prefix = "clip_l_vision." def process_unet_state_dict(self, state_dict): key_list = list(state_dict.keys()) for y in ["weight", "bias"]: suffix = "in_proj_{}".format(y) keys = filter(lambda a: a.endswith(suffix), key_list) for k_from in keys: weights = state_dict.pop(k_from) prefix = k_from[:-(len(suffix) + 1)] shape_from = weights.shape[0] // 3 for x in range(3): p = ["to_q", "to_k", "to_v"] k_to = "{}.{}.{}".format(prefix, p[x], y) state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] return state_dict def process_clip_state_dict(self, state_dict): state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) if "clip_g.text_projection" in state_dict: state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1) return state_dict def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_C(self, device=device) return out def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) class Stable_Cascade_B(Stable_Cascade_C): unet_config = { "stable_cascade_stage": 'b', } unet_extra_config = {} latent_format = latent_formats.SC_B supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] sampling_settings = { "shift": 1.0, } clip_vision_prefix = None def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_B(self, device=device) return out class SD15_instructpix2pix(SD15): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, "in_channels": 8, } def get_model(self, state_dict, prefix="", device=None): return model_base.SD15_instructpix2pix(self, device=device) class SDXL_instructpix2pix(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 10, 10], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, "in_channels": 8, } def get_model(self, state_dict, prefix="", device=None): return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) class LotusD(SD20): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "use_temporal_attention": False, "adm_in_channels": 4, "in_channels": 4, } unet_extra_config = { "num_classes": 'sequential', "num_head_channels": 64, } def get_model(self, state_dict, prefix="", device=None): return model_base.Lotus(self, device=device) class SD3(supported_models_base.BASE): unet_config = { "in_channels": 16, "pos_embed_scaling_factor": None, } sampling_settings = { "shift": 3.0, } unet_extra_config = {} latent_format = latent_formats.SD3 memory_usage_factor = 1.6 text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.SD3(self, device=device) return out def clip_target(self, state_dict={}): clip_l = False clip_g = False t5 = False pref = self.text_encoder_key_prefix[0] if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: clip_l = True if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: clip_g = True t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) if "dtype_t5" in t5_detect: t5 = True return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect)) class StableAudio(supported_models_base.BASE): unet_config = { "audio_model": "dit1.0", } sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03} unet_extra_config = {} latent_format = latent_formats.StableAudio1 text_encoder_key_prefix = ["text_encoders."] vae_key_prefix = ["pretransform.model."] def get_model(self, state_dict, prefix="", device=None): seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True) seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True) return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device) def process_unet_state_dict(self, state_dict): for k in list(state_dict.keys()): if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero state_dict.pop(k) return state_dict def process_unet_state_dict_for_saving(self, state_dict): replace_prefix = {"": "model.model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model) class AuraFlow(supported_models_base.BASE): unet_config = { "cond_seq_dim": 2048, } sampling_settings = { "multiplier": 1.0, "shift": 1.73, } unet_extra_config = {} latent_format = latent_formats.SDXL vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.AuraFlow(self, device=device) return out def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model) class PixArtAlpha(supported_models_base.BASE): unet_config = { "image_model": "pixart_alpha", } sampling_settings = { "beta_schedule" : "sqrt_linear", "linear_start" : 0.0001, "linear_end" : 0.02, "timesteps" : 1000, } unet_extra_config = {} latent_format = latent_formats.SD15 memory_usage_factor = 0.5 vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.PixArt(self, device=device) return out.eval() def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.PixArtT5XXL) class PixArtSigma(PixArtAlpha): unet_config = { "image_model": "pixart_sigma", } latent_format = latent_formats.SDXL class HunyuanDiT(supported_models_base.BASE): unet_config = { "image_model": "hydit", } unet_extra_config = { "attn_precision": torch.float32, } sampling_settings = { "linear_start": 0.00085, "linear_end": 0.018, } latent_format = latent_formats.SDXL memory_usage_factor = 1.3 vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanDiT(self, device=device) return out def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel) class HunyuanDiT1(HunyuanDiT): unet_config = { "image_model": "hydit1", } unet_extra_config = {} sampling_settings = { "linear_start" : 0.00085, "linear_end" : 0.03, } class Flux(supported_models_base.BASE): unet_config = { "image_model": "flux", "guidance_embed": True, } sampling_settings = { } unet_extra_config = {} latent_format = latent_formats.Flux memory_usage_factor = 3.1 # TODO: debug why flux mem usage is so weird on windows. supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] def process_unet_state_dict(self, state_dict): out_sd = {} for k in list(state_dict.keys()): key_out = k if key_out.endswith("_norm.scale"): key_out = "{}.weight".format(key_out[:-len(".scale")]) out_sd[key_out] = state_dict[k] return out_sd vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.Flux(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect)) class FluxInpaint(Flux): unet_config = { "image_model": "flux", "guidance_embed": True, "in_channels": 96, } supported_inference_dtypes = [torch.bfloat16, torch.float32] class FluxSchnell(Flux): unet_config = { "image_model": "flux", "guidance_embed": False, } sampling_settings = { "multiplier": 1.0, "shift": 1.0, } def get_model(self, state_dict, prefix="", device=None): out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device) return out class Flux2(Flux): unet_config = { "image_model": "flux2", } sampling_settings = { "shift": 2.02, } unet_extra_config = {} latent_format = latent_formats.Flux2 supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def __init__(self, unet_config): super().__init__(unet_config) self.memory_usage_factor = self.memory_usage_factor * (2.0 * 2.0) * (unet_config['hidden_size'] / 2604) def get_model(self, state_dict, prefix="", device=None): out = model_base.Flux2(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref)) if len(detect) > 0: detect["model_type"] = "qwen3_4b" return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer, comfy.text_encoders.flux.klein_te(**detect)) detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_8b.transformer.".format(pref)) if len(detect) > 0: detect["model_type"] = "qwen3_8b" return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer8B, comfy.text_encoders.flux.klein_te(**detect)) detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}mistral3_24b.transformer.".format(pref)) if len(detect) > 0: if "{}mistral3_24b.transformer.model.layers.39.post_attention_layernorm.weight".format(pref) not in state_dict: detect["pruned"] = True return supported_models_base.ClipTarget(comfy.text_encoders.flux.Flux2Tokenizer, comfy.text_encoders.flux.flux2_te(**detect)) return None class GenmoMochi(supported_models_base.BASE): unet_config = { "image_model": "mochi_preview", } sampling_settings = { "multiplier": 1.0, "shift": 6.0, } unet_extra_config = {} latent_format = latent_formats.Mochi memory_usage_factor = 2.0 #TODO supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.GenmoMochi(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect)) class LTXV(supported_models_base.BASE): unet_config = { "image_model": "ltxv", } sampling_settings = { "shift": 2.37, } unet_extra_config = {} latent_format = latent_formats.LTXV memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def __init__(self, unet_config): super().__init__(unet_config) self.memory_usage_factor = (unet_config.get("cross_attention_dim", 2048) / 2048) * 5.5 def get_model(self, state_dict, prefix="", device=None): out = model_base.LTXV(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect)) class LTXAV(LTXV): unet_config = { "image_model": "ltxav", } latent_format = latent_formats.LTXAV def __init__(self, unet_config): super().__init__(unet_config) self.memory_usage_factor = 0.077 # TODO def get_model(self, state_dict, prefix="", device=None): out = model_base.LTXAV(self, device=device) return out class HunyuanVideo(supported_models_base.BASE): unet_config = { "image_model": "hunyuan_video", } sampling_settings = { "shift": 7.0, } unet_extra_config = {} latent_format = latent_formats.HunyuanVideo memory_usage_factor = 1.8 #TODO supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanVideo(self, device=device) return out def process_unet_state_dict(self, state_dict): out_sd = {} for k in list(state_dict.keys()): key_out = k key_out = key_out.replace("txt_in.t_embedder.mlp.0.", "txt_in.t_embedder.in_layer.").replace("txt_in.t_embedder.mlp.2.", "txt_in.t_embedder.out_layer.") key_out = key_out.replace("txt_in.c_embedder.linear_1.", "txt_in.c_embedder.in_layer.").replace("txt_in.c_embedder.linear_2.", "txt_in.c_embedder.out_layer.") key_out = key_out.replace("_mod.linear.", "_mod.lin.").replace("_attn_qkv.", "_attn.qkv.") key_out = key_out.replace("mlp.fc1.", "mlp.0.").replace("mlp.fc2.", "mlp.2.") key_out = key_out.replace("_attn_q_norm.weight", "_attn.norm.query_norm.weight").replace("_attn_k_norm.weight", "_attn.norm.key_norm.weight") key_out = key_out.replace(".q_norm.weight", ".norm.query_norm.weight").replace(".k_norm.weight", ".norm.key_norm.weight") key_out = key_out.replace("_attn_proj.", "_attn.proj.") key_out = key_out.replace(".modulation.linear.", ".modulation.lin.") key_out = key_out.replace("_in.mlp.2.", "_in.out_layer.").replace("_in.mlp.0.", "_in.in_layer.") if key_out.endswith(".scale"): key_out = "{}.weight".format(key_out[:-len(".scale")]) out_sd[key_out] = state_dict[k] return out_sd def process_unet_state_dict_for_saving(self, state_dict): replace_prefix = {"": "model.model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect)) class HunyuanVideoI2V(HunyuanVideo): unet_config = { "image_model": "hunyuan_video", "in_channels": 33, } def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanVideoI2V(self, device=device) return out class HunyuanVideoSkyreelsI2V(HunyuanVideo): unet_config = { "image_model": "hunyuan_video", "in_channels": 32, } def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanVideoSkyreelsI2V(self, device=device) return out class CosmosT2V(supported_models_base.BASE): unet_config = { "image_model": "cosmos", "in_channels": 16, } sampling_settings = { "sigma_data": 0.5, "sigma_max": 80.0, "sigma_min": 0.002, } unet_extra_config = {} latent_format = latent_formats.Cosmos1CV8x8x8 memory_usage_factor = 1.6 #TODO supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.CosmosVideo(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) class CosmosI2V(CosmosT2V): unet_config = { "image_model": "cosmos", "in_channels": 17, } def get_model(self, state_dict, prefix="", device=None): out = model_base.CosmosVideo(self, image_to_video=True, device=device) return out class CosmosT2IPredict2(supported_models_base.BASE): unet_config = { "image_model": "cosmos_predict2", "in_channels": 16, } sampling_settings = { "sigma_data": 1.0, "sigma_max": 80.0, "sigma_min": 0.002, } unet_extra_config = {} latent_format = latent_formats.Wan21 memory_usage_factor = 1.0 supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] def __init__(self, unet_config): super().__init__(unet_config) self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95 def get_model(self, state_dict, prefix="", device=None): out = model_base.CosmosPredict2(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) class Anima(supported_models_base.BASE): unet_config = { "image_model": "anima", } sampling_settings = { "multiplier": 1.0, "shift": 3.0, } unet_extra_config = {} latent_format = latent_formats.Wan21 memory_usage_factor = 1.0 supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] def get_model(self, state_dict, prefix="", device=None): out = model_base.Anima(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_06b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.anima.AnimaTokenizer, comfy.text_encoders.anima.te(**detect)) def set_inference_dtype(self, dtype, manual_cast_dtype, **kwargs): self.memory_usage_factor = (self.unet_config.get("model_channels", 2048) / 2048) * 0.95 if dtype is torch.float16: self.memory_usage_factor *= 1.4 return super().set_inference_dtype(dtype, manual_cast_dtype, **kwargs) class CosmosI2VPredict2(CosmosT2IPredict2): unet_config = { "image_model": "cosmos_predict2", "in_channels": 17, } def get_model(self, state_dict, prefix="", device=None): out = model_base.CosmosPredict2(self, image_to_video=True, device=device) return out class Lumina2(supported_models_base.BASE): unet_config = { "image_model": "lumina2", } sampling_settings = { "multiplier": 1.0, "shift": 6.0, } memory_usage_factor = 1.4 unet_extra_config = {} latent_format = latent_formats.Flux supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.Lumina2(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect)) class ZImage(Lumina2): unet_config = { "image_model": "lumina2", "dim": 3840, } sampling_settings = { "multiplier": 1.0, "shift": 3.0, } memory_usage_factor = 2.8 supported_inference_dtypes = [torch.bfloat16, torch.float32] def __init__(self, unet_config): super().__init__(unet_config) if comfy.model_management.extended_fp16_support() and unet_config.get("allow_fp16", False): self.supported_inference_dtypes = self.supported_inference_dtypes.copy() self.supported_inference_dtypes.insert(1, torch.float16) def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect)) class ZImagePixelSpace(ZImage): unet_config = { "image_model": "zimage_pixel", } # Pixel-space model: no spatial compression, operates on raw RGB patches. latent_format = latent_formats.ZImagePixelSpace # Much lower memory than latent-space models (no VAE, small patches). memory_usage_factor = 0.03 # TODO: figure out the optimal value for this. def get_model(self, state_dict, prefix="", device=None): return model_base.ZImagePixelSpace(self, device=device) class WAN21_T2V(supported_models_base.BASE): unet_config = { "image_model": "wan2.1", "model_type": "t2v", } sampling_settings = { "shift": 8.0, } unet_extra_config = {} latent_format = latent_formats.Wan21 memory_usage_factor = 0.9 supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def __init__(self, unet_config): super().__init__(unet_config) self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2222 def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}umt5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.wan.WanT5Tokenizer, comfy.text_encoders.wan.te(**t5_detect)) class WAN21_I2V(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "i2v", "in_dim": 36, } def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21(self, image_to_video=True, device=device) return out class WAN21_FunControl2V(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "i2v", "in_dim": 48, } def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21(self, image_to_video=False, device=device) return out class WAN21_Camera(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "camera", "in_dim": 32, } def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21_Camera(self, image_to_video=False, device=device) return out class WAN22_Camera(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "camera_2.2", "in_dim": 36, } def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21_Camera(self, image_to_video=False, device=device) return out class WAN21_Vace(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "vace", } def __init__(self, unet_config): super().__init__(unet_config) self.memory_usage_factor = 1.2 * self.memory_usage_factor def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21_Vace(self, image_to_video=False, device=device) return out class WAN21_HuMo(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "humo", } def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21_HuMo(self, image_to_video=False, device=device) return out class WAN22_S2V(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "s2v", } def __init__(self, unet_config): super().__init__(unet_config) def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN22_S2V(self, device=device) return out class WAN22_Animate(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "animate", } def __init__(self, unet_config): super().__init__(unet_config) def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN22_Animate(self, device=device) return out class WAN22_T2V(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "t2v", "out_dim": 48, } latent_format = latent_formats.Wan22 def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN22(self, image_to_video=True, device=device) return out class WAN21_FlowRVS(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "flow_rvs", } def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21_FlowRVS(self, image_to_video=True, device=device) return out class WAN21_SCAIL(WAN21_T2V): unet_config = { "image_model": "wan2.1", "model_type": "scail", } def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device) return out class Hunyuan3Dv2(supported_models_base.BASE): unet_config = { "image_model": "hunyuan3d2", } unet_extra_config = {} sampling_settings = { "multiplier": 1.0, "shift": 1.0, } memory_usage_factor = 3.5 clip_vision_prefix = "conditioner.main_image_encoder.model." vae_key_prefix = ["vae."] latent_format = latent_formats.Hunyuan3Dv2 def process_unet_state_dict(self, state_dict): out_sd = {} for k in list(state_dict.keys()): key_out = k if key_out.endswith(".scale"): key_out = "{}.weight".format(key_out[:-len(".scale")]) out_sd[key_out] = state_dict[k] return out_sd def process_unet_state_dict_for_saving(self, state_dict): replace_prefix = {"": "model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def get_model(self, state_dict, prefix="", device=None): out = model_base.Hunyuan3Dv2(self, device=device) return out def clip_target(self, state_dict={}): return None class Hunyuan3Dv2_1(Hunyuan3Dv2): unet_config = { "image_model": "hunyuan3d2_1", } latent_format = latent_formats.Hunyuan3Dv2_1 def get_model(self, state_dict, prefix="", device=None): out = model_base.Hunyuan3Dv2_1(self, device = device) return out class Hunyuan3Dv2mini(Hunyuan3Dv2): unet_config = { "image_model": "hunyuan3d2", "depth": 8, } latent_format = latent_formats.Hunyuan3Dv2mini class HiDream(supported_models_base.BASE): unet_config = { "image_model": "hidream", } sampling_settings = { "shift": 3.0, } sampling_settings = { } # memory_usage_factor = 1.2 # TODO unet_extra_config = {} latent_format = latent_formats.Flux supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.HiDream(self, device=device) return out def clip_target(self, state_dict={}): return None # TODO class Chroma(supported_models_base.BASE): unet_config = { "image_model": "chroma", } unet_extra_config = { } sampling_settings = { "multiplier": 1.0, } latent_format = comfy.latent_formats.Flux memory_usage_factor = 3.2 supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] def process_unet_state_dict(self, state_dict): out_sd = {} for k in list(state_dict.keys()): key_out = k if key_out.endswith(".scale"): key_out = "{}.weight".format(key_out[:-len(".scale")]) out_sd[key_out] = state_dict[k] return out_sd def get_model(self, state_dict, prefix="", device=None): out = model_base.Chroma(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect)) class ChromaRadiance(Chroma): unet_config = { "image_model": "chroma_radiance", } latent_format = comfy.latent_formats.ChromaRadiance # Pixel-space model, no spatial compression for model input. memory_usage_factor = 0.044 def get_model(self, state_dict, prefix="", device=None): return model_base.ChromaRadiance(self, device=device) class ACEStep(supported_models_base.BASE): unet_config = { "audio_model": "ace", } unet_extra_config = { } sampling_settings = { "shift": 3.0, } latent_format = comfy.latent_formats.ACEAudio memory_usage_factor = 0.5 supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.ACEStep(self, device=device) return out def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model) class Omnigen2(supported_models_base.BASE): unet_config = { "image_model": "omnigen2", } sampling_settings = { "multiplier": 1.0, "shift": 2.6, } memory_usage_factor = 1.95 #TODO unet_extra_config = {} latent_format = latent_formats.Flux supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def __init__(self, unet_config): super().__init__(unet_config) if comfy.model_management.extended_fp16_support(): self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes def get_model(self, state_dict, prefix="", device=None): out = model_base.Omnigen2(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect)) class QwenImage(supported_models_base.BASE): unet_config = { "image_model": "qwen_image", } sampling_settings = { "multiplier": 1.0, "shift": 1.15, } memory_usage_factor = 1.8 #TODO unet_extra_config = {} latent_format = latent_formats.Wan21 supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.QwenImage(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.qwen_image.QwenImageTokenizer, comfy.text_encoders.qwen_image.te(**hunyuan_detect)) class HunyuanImage21(HunyuanVideo): unet_config = { "image_model": "hunyuan_video", "vec_in_dim": None, } sampling_settings = { "shift": 5.0, } latent_format = latent_formats.HunyuanImage21 memory_usage_factor = 8.7 supported_inference_dtypes = [torch.bfloat16, torch.float32] def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanImage21(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect)) class HunyuanImage21Refiner(HunyuanVideo): unet_config = { "image_model": "hunyuan_video", "patch_size": [1, 1, 1], "vec_in_dim": None, } sampling_settings = { "shift": 4.0, } latent_format = latent_formats.HunyuanImage21Refiner def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanImage21Refiner(self, device=device) return out class HunyuanVideo15(HunyuanVideo): unet_config = { "image_model": "hunyuan_video", "vision_in_dim": 1152, } sampling_settings = { "shift": 7.0, } memory_usage_factor = 4.0 #TODO supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] latent_format = latent_formats.HunyuanVideo15 def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanVideo15(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect)) class HunyuanVideo15_SR_Distilled(HunyuanVideo): unet_config = { "image_model": "hunyuan_video", "vision_in_dim": 1152, "in_channels": 98, } sampling_settings = { "shift": 2.0, } memory_usage_factor = 4.0 #TODO supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] latent_format = latent_formats.HunyuanVideo15 def get_model(self, state_dict, prefix="", device=None): out = model_base.HunyuanVideo15_SR_Distilled(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect)) class Kandinsky5(supported_models_base.BASE): unet_config = { "image_model": "kandinsky5", } sampling_settings = { "shift": 10.0, } unet_extra_config = {} latent_format = latent_formats.HunyuanVideo memory_usage_factor = 1.25 #TODO supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.Kandinsky5(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer, comfy.text_encoders.kandinsky5.te(**hunyuan_detect)) class Kandinsky5Image(Kandinsky5): unet_config = { "image_model": "kandinsky5", "model_dim": 2560, "visual_embed_dim": 64, } sampling_settings = { "shift": 3.0, } latent_format = latent_formats.Flux memory_usage_factor = 1.25 #TODO def get_model(self, state_dict, prefix="", device=None): out = model_base.Kandinsky5Image(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect)) class ACEStep15(supported_models_base.BASE): unet_config = { "audio_model": "ace1.5", } unet_extra_config = { } sampling_settings = { "multiplier": 1.0, "shift": 3.0, } latent_format = comfy.latent_formats.ACEAudio15 memory_usage_factor = 4.7 supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.ACEStep15(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] detect_2b = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_2b.transformer.".format(pref)) detect_4b = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref)) if "dtype_llama" in detect_2b: detect = detect_2b detect["lm_model"] = "qwen3_2b" elif "dtype_llama" in detect_4b: detect = detect_4b detect["lm_model"] = "qwen3_4b" return supported_models_base.ClipTarget(comfy.text_encoders.ace15.ACE15Tokenizer, comfy.text_encoders.ace15.te(**detect)) class LongCatImage(supported_models_base.BASE): unet_config = { "image_model": "flux", "guidance_embed": False, "vec_in_dim": None, "context_in_dim": 3584, "txt_ids_dims": [1, 2], } sampling_settings = { } unet_extra_config = {} latent_format = latent_formats.Flux memory_usage_factor = 2.5 supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.LongCatImage(self, device=device) return out def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect)) models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima] models += [SVD_img2vid] ================================================ FILE: comfy/supported_models_base.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Comfy This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch import logging from . import model_base from . import utils from . import latent_formats class ClipTarget: def __init__(self, tokenizer, clip): self.clip = clip self.tokenizer = tokenizer self.params = {} class BASE: unet_config = {} unet_extra_config = { "num_heads": -1, "num_head_channels": 64, } required_keys = {} clip_prefix = [] clip_vision_prefix = None noise_aug_config = None sampling_settings = {} latent_format = latent_formats.LatentFormat vae_key_prefix = ["first_stage_model."] text_encoder_key_prefix = ["cond_stage_model."] supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] memory_usage_factor = 2.0 manual_cast_dtype = None custom_operations = None quant_config = None # quantization configuration for mixed precision optimizations = {"fp8": False} @classmethod def matches(s, unet_config, state_dict=None): for k in s.unet_config: if k not in unet_config or s.unet_config[k] != unet_config[k]: return False if state_dict is not None: for k in s.required_keys: if k not in state_dict: return False return True def model_type(self, state_dict, prefix=""): return model_base.ModelType.EPS def inpaint_model(self): return self.unet_config["in_channels"] > 4 def __init__(self, unet_config): self.unet_config = unet_config.copy() self.sampling_settings = self.sampling_settings.copy() self.latent_format = self.latent_format() self.optimizations = self.optimizations.copy() for x in self.unet_extra_config: self.unet_config[x] = self.unet_extra_config[x] def get_model(self, state_dict, prefix="", device=None): if self.noise_aug_config is not None: out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) else: out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) if self.inpaint_model(): out.set_inpaint() return out def process_clip_state_dict(self, state_dict): state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) return state_dict def process_unet_state_dict(self, state_dict): return state_dict def process_vae_state_dict(self, state_dict): return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {"": self.text_encoder_key_prefix[0]} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_clip_vision_state_dict_for_saving(self, state_dict): replace_prefix = {} if self.clip_vision_prefix is not None: replace_prefix[""] = self.clip_vision_prefix return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_unet_state_dict_for_saving(self, state_dict): replace_prefix = {"": "model.diffusion_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_vae_state_dict_for_saving(self, state_dict): replace_prefix = {"": self.vae_key_prefix[0]} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def set_inference_dtype(self, dtype, manual_cast_dtype): self.unet_config['dtype'] = dtype self.manual_cast_dtype = manual_cast_dtype def __getattr__(self, name): logging.warning("\nWARNING, you accessed {} from the model config object which doesn't exist. Please fix your code.\n".format(name)) return None ================================================ FILE: comfy/t2i_adapter/adapter.py ================================================ #taken from https://github.com/TencentARC/T2I-Adapter import torch import torch.nn as nn from collections import OrderedDict def conv_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D convolution module. """ if dims == 1: return nn.Conv1d(*args, **kwargs) elif dims == 2: return nn.Conv2d(*args, **kwargs) elif dims == 3: return nn.Conv3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels if not self.use_conv: padding = [x.shape[2] % 2, x.shape[3] % 2] self.op.padding = padding x = self.op(x) return x class ResnetBlock(nn.Module): def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): super().__init__() ps = ksize // 2 if in_c != out_c or sk == False: self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) else: # print('n_in') self.in_conv = None self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) self.act = nn.ReLU() self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) if sk == False: self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) else: self.skep = None self.down = down if self.down == True: self.down_opt = Downsample(in_c, use_conv=use_conv) def forward(self, x): if self.down == True: x = self.down_opt(x) if self.in_conv is not None: # edit x = self.in_conv(x) h = self.block1(x) h = self.act(h) h = self.block2(h) if self.skep is not None: return h + self.skep(x) else: return h + x class Adapter(nn.Module): def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): super(Adapter, self).__init__() self.unshuffle_amount = 8 resblock_no_downsample = [] resblock_downsample = [3, 2, 1] self.xl = xl if self.xl: self.unshuffle_amount = 16 resblock_no_downsample = [1] resblock_downsample = [2] self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) self.channels = channels self.nums_rb = nums_rb self.body = [] for i in range(len(channels)): for j in range(nums_rb): if (i in resblock_downsample) and (j == 0): self.body.append( ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) elif (i in resblock_no_downsample) and (j == 0): self.body.append( ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) else: self.body.append( ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) self.body = nn.ModuleList(self.body) self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) def forward(self, x): # unshuffle x = self.unshuffle(x) # extract features features = [] x = self.conv_in(x) for i in range(len(self.channels)): for j in range(self.nums_rb): idx = i * self.nums_rb + j x = self.body[idx](x) if self.xl: features.append(None) if i == 0: features.append(None) features.append(None) if i == 2: features.append(None) else: features.append(None) features.append(None) features.append(x) features = features[::-1] if self.xl: return {"input": features[1:], "middle": features[:1]} else: return {"input": features} class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): orig_type = x.dtype ret = super().forward(x.type(torch.float32)) return ret.type(orig_type) class QuickGELU(nn.Module): def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class ResidualAttentionBlock(nn.Module): def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): super().__init__() self.attn = nn.MultiheadAttention(d_model, n_head) self.ln_1 = LayerNorm(d_model) self.mlp = nn.Sequential( OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), ("c_proj", nn.Linear(d_model * 4, d_model))])) self.ln_2 = LayerNorm(d_model) self.attn_mask = attn_mask def attention(self, x: torch.Tensor): self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] def forward(self, x: torch.Tensor): x = x + self.attention(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class StyleAdapter(nn.Module): def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): super().__init__() scale = width ** -0.5 self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) self.num_token = num_token self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) self.ln_post = LayerNorm(width) self.ln_pre = LayerNorm(width) self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) def forward(self, x): # x shape [N, HW+1, C] style_embedding = self.style_embedding + torch.zeros( (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) x = torch.cat([x, style_embedding], dim=1) x = self.ln_pre(x) x = x.permute(1, 0, 2) # NLD -> LND x = self.transformer_layes(x) x = x.permute(1, 0, 2) # LND -> NLD x = self.ln_post(x[:, -self.num_token:, :]) x = x @ self.proj return x class ResnetBlock_light(nn.Module): def __init__(self, in_c): super().__init__() self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) self.act = nn.ReLU() self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) def forward(self, x): h = self.block1(x) h = self.act(h) h = self.block2(h) return h + x class extractor(nn.Module): def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): super().__init__() self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) self.body = [] for _ in range(nums_rb): self.body.append(ResnetBlock_light(inter_c)) self.body = nn.Sequential(*self.body) self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) self.down = down if self.down == True: self.down_opt = Downsample(in_c, use_conv=False) def forward(self, x): if self.down == True: x = self.down_opt(x) x = self.in_conv(x) x = self.body(x) x = self.out_conv(x) return x class Adapter_light(nn.Module): def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): super(Adapter_light, self).__init__() self.unshuffle_amount = 8 self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) self.channels = channels self.nums_rb = nums_rb self.body = [] self.xl = False for i in range(len(channels)): if i == 0: self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) else: self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) self.body = nn.ModuleList(self.body) def forward(self, x): # unshuffle x = self.unshuffle(x) # extract features features = [] for i in range(len(self.channels)): x = self.body[i](x) features.append(None) features.append(None) features.append(x) return {"input": features[::-1]} ================================================ FILE: comfy/taesd/taehv.py ================================================ # Tiny AutoEncoder for HunyuanVideo and WanVideo https://github.com/madebyollin/taehv import torch import torch.nn as nn import torch.nn.functional as F from tqdm.auto import tqdm from collections import namedtuple, deque import comfy.ops operations=comfy.ops.disable_weight_init DecoderResult = namedtuple("DecoderResult", ("frame", "memory")) TWorkItem = namedtuple("TWorkItem", ("input_tensor", "block_index")) def conv(n_in, n_out, **kwargs): return operations.Conv2d(n_in, n_out, 3, padding=1, **kwargs) class Clamp(nn.Module): def forward(self, x): return torch.tanh(x / 3) * 3 class MemBlock(nn.Module): def __init__(self, n_in, n_out, act_func): super().__init__() self.conv = nn.Sequential(conv(n_in * 2, n_out), act_func, conv(n_out, n_out), act_func, conv(n_out, n_out)) self.skip = operations.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() self.act = act_func def forward(self, x, past): return self.act(self.conv(torch.cat([x, past], 1)) + self.skip(x)) class TPool(nn.Module): def __init__(self, n_f, stride): super().__init__() self.stride = stride self.conv = operations.Conv2d(n_f*stride,n_f, 1, bias=False) def forward(self, x): _NT, C, H, W = x.shape return self.conv(x.reshape(-1, self.stride * C, H, W)) class TGrow(nn.Module): def __init__(self, n_f, stride): super().__init__() self.stride = stride self.conv = operations.Conv2d(n_f, n_f*stride, 1, bias=False) def forward(self, x): _NT, C, H, W = x.shape x = self.conv(x) return x.reshape(-1, C, H, W) def apply_model_with_memblocks(model, x, parallel, show_progress_bar): B, T, C, H, W = x.shape if parallel: x = x.reshape(B*T, C, H, W) # parallel over input timesteps, iterate over blocks for b in tqdm(model, disable=not show_progress_bar): if isinstance(b, MemBlock): BT, C, H, W = x.shape T = BT // B _x = x.reshape(B, T, C, H, W) mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape) x = b(x, mem) else: x = b(x) BT, C, H, W = x.shape T = BT // B x = x.view(B, T, C, H, W) else: out = [] work_queue = deque([TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(B, T * C, H, W).chunk(T, dim=1))]) progress_bar = tqdm(range(T), disable=not show_progress_bar) mem = [None] * len(model) while work_queue: xt, i = work_queue.popleft() if i == 0: progress_bar.update(1) if i == len(model): out.append(xt) del xt else: b = model[i] if isinstance(b, MemBlock): if mem[i] is None: xt_new = b(xt, xt * 0) mem[i] = xt.detach().clone() else: xt_new = b(xt, mem[i]) mem[i] = xt.detach().clone() del xt work_queue.appendleft(TWorkItem(xt_new, i+1)) elif isinstance(b, TPool): if mem[i] is None: mem[i] = [] mem[i].append(xt.detach().clone()) if len(mem[i]) == b.stride: B, C, H, W = xt.shape xt = b(torch.cat(mem[i], 1).view(B*b.stride, C, H, W)) mem[i] = [] work_queue.appendleft(TWorkItem(xt, i+1)) elif isinstance(b, TGrow): xt = b(xt) NT, C, H, W = xt.shape for xt_next in reversed(xt.view(B, b.stride*C, H, W).chunk(b.stride, 1)): work_queue.appendleft(TWorkItem(xt_next, i+1)) del xt else: xt = b(xt) work_queue.appendleft(TWorkItem(xt, i+1)) progress_bar.close() x = torch.stack(out, 1) return x class TAEHV(nn.Module): def __init__(self, latent_channels, parallel=False, encoder_time_downscale=(True, True, False), decoder_time_upscale=(False, True, True), decoder_space_upscale=(True, True, True), latent_format=None, show_progress_bar=False): super().__init__() self.image_channels = 3 self.patch_size = 1 self.latent_channels = latent_channels self.parallel = parallel self.latent_format = latent_format self.show_progress_bar = show_progress_bar self.process_in = latent_format().process_in if latent_format is not None else (lambda x: x) self.process_out = latent_format().process_out if latent_format is not None else (lambda x: x) if self.latent_channels in [48, 32]: # Wan 2.2 and HunyuanVideo1.5 self.patch_size = 2 elif self.latent_channels == 128: # LTX2 self.patch_size, self.latent_channels, encoder_time_downscale, decoder_time_upscale = 4, 128, (True, True, True), (True, True, True) if self.latent_channels == 32: # HunyuanVideo1.5 act_func = nn.LeakyReLU(0.2, inplace=True) else: # HunyuanVideo, Wan 2.1 act_func = nn.ReLU(inplace=True) self.encoder = nn.Sequential( conv(self.image_channels*self.patch_size**2, 64), act_func, TPool(64, 2 if encoder_time_downscale[0] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), TPool(64, 2 if encoder_time_downscale[1] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), TPool(64, 2 if encoder_time_downscale[2] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), conv(64, self.latent_channels), ) n_f = [256, 128, 64, 64] self.decoder = nn.Sequential( Clamp(), conv(self.latent_channels, n_f[0]), act_func, MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 2 if decoder_time_upscale[0] else 1), conv(n_f[0], n_f[1], bias=False), MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[1] else 1), conv(n_f[1], n_f[2], bias=False), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[2] else 1), conv(n_f[2], n_f[3], bias=False), act_func, conv(n_f[3], self.image_channels*self.patch_size**2), ) self.t_downscale = 2**sum(t.stride == 2 for t in self.encoder if isinstance(t, TPool)) self.t_upscale = 2**sum(t.stride == 2 for t in self.decoder if isinstance(t, TGrow)) self.frames_to_trim = self.t_upscale - 1 self._show_progress_bar = show_progress_bar @property def show_progress_bar(self): return self._show_progress_bar @show_progress_bar.setter def show_progress_bar(self, value): self._show_progress_bar = value def encode(self, x, **kwargs): x = x.movedim(2, 1) # [B, C, T, H, W] -> [B, T, C, H, W] if self.patch_size > 1: B, T, C, H, W = x.shape x = x.reshape(B * T, C, H, W) x = F.pixel_unshuffle(x, self.patch_size) x = x.reshape(B, T, C * self.patch_size ** 2, H // self.patch_size, W // self.patch_size) if x.shape[1] % self.t_downscale != 0: # pad at end to multiple of t_downscale n_pad = self.t_downscale - x.shape[1] % self.t_downscale padding = x[:, -1:].repeat_interleave(n_pad, dim=1) x = torch.cat([x, padding], 1) x = apply_model_with_memblocks(self.encoder, x, self.parallel, self.show_progress_bar).movedim(2, 1) return self.process_out(x) def decode(self, x, **kwargs): x = x.unsqueeze(0) if x.ndim == 4 else x # [T, C, H, W] -> [1, T, C, H, W] x = x.movedim(1, 2) if x.shape[1] != self.latent_channels else x # [B, T, C, H, W] or [B, C, T, H, W] x = self.process_in(x).movedim(2, 1) # [B, C, T, H, W] -> [B, T, C, H, W] x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar) if self.patch_size > 1: x = F.pixel_shuffle(x, self.patch_size) return x[:, self.frames_to_trim:].movedim(2, 1) ================================================ FILE: comfy/taesd/taesd.py ================================================ #!/usr/bin/env python3 """ Tiny AutoEncoder for Stable Diffusion (DNN for encoding / decoding SD's latent space) """ import torch import torch.nn as nn import comfy.utils import comfy.ops def conv(n_in, n_out, **kwargs): return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) class Clamp(nn.Module): def forward(self, x): return torch.tanh(x / 3) * 3 class Block(nn.Module): def __init__(self, n_in, n_out): super().__init__() self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() self.fuse = nn.ReLU() def forward(self, x): return self.fuse(self.conv(x) + self.skip(x)) def Encoder(latent_channels=4): return nn.Sequential( conv(3, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, latent_channels), ) def Decoder(latent_channels=4): return nn.Sequential( Clamp(), conv(latent_channels, 64), nn.ReLU(), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), conv(64, 3), ) class TAESD(nn.Module): latent_magnitude = 3 latent_shift = 0.5 def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): """Initialize pretrained TAESD on the given device from the given checkpoints.""" super().__init__() self.taesd_encoder = Encoder(latent_channels=latent_channels) self.taesd_decoder = Decoder(latent_channels=latent_channels) self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) if encoder_path is not None: self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) if decoder_path is not None: self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True)) @staticmethod def scale_latents(x): """raw latents -> [0, 1]""" return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) @staticmethod def unscale_latents(x): """[0, 1] -> raw latents""" return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) def decode(self, x): x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) x_sample = x_sample.sub(0.5).mul(2) return x_sample def encode(self, x): return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift ================================================ FILE: comfy/text_encoders/ace.py ================================================ from comfy import sd1_clip from .spiece_tokenizer import SPieceTokenizer import comfy.text_encoders.t5 import os import re import torch import logging from tokenizers import Tokenizer from .ace_text_cleaners import multilingual_cleaners, japanese_to_romaji SUPPORT_LANGUAGES = { "en": 259, "de": 260, "fr": 262, "es": 284, "it": 285, "pt": 286, "pl": 294, "tr": 295, "ru": 267, "cs": 293, "nl": 297, "ar": 5022, "zh": 5023, "ja": 5412, "hu": 5753, "ko": 6152, "hi": 6680 } structure_pattern = re.compile(r"\[.*?\]") DEFAULT_VOCAB_FILE = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") class VoiceBpeTokenizer: def __init__(self, vocab_file=DEFAULT_VOCAB_FILE): self.tokenizer = None if vocab_file is not None: self.tokenizer = Tokenizer.from_file(vocab_file) def preprocess_text(self, txt, lang): txt = multilingual_cleaners(txt, lang) return txt def encode(self, txt, lang='en'): # lang = lang.split("-")[0] # remove the region # self.check_input_length(txt, lang) txt = self.preprocess_text(txt, lang) lang = "zh-cn" if lang == "zh" else lang txt = f"[{lang}]{txt}" txt = txt.replace(" ", "[SPACE]") return self.tokenizer.encode(txt).ids def get_lang(self, line): if line.startswith("[") and line[3:4] == ']': lang = line[1:3].lower() if lang in SUPPORT_LANGUAGES: return lang, line[4:] return "en", line def __call__(self, string): lines = string.split("\n") lyric_token_idx = [261] for line in lines: line = line.strip() if not line: lyric_token_idx += [2] continue lang, line = self.get_lang(line) if lang not in SUPPORT_LANGUAGES: lang = "en" if "zh" in lang: lang = "zh" if "spa" in lang: lang = "es" try: line_out = japanese_to_romaji(line) if line_out != line: lang = "ja" line = line_out except: pass try: if structure_pattern.match(line): token_idx = self.encode(line, "en") else: token_idx = self.encode(line, lang) lyric_token_idx = lyric_token_idx + token_idx + [2] except Exception as e: logging.warning("tokenize error {} for line {} major_language {}".format(e, line, lang)) return {"input_ids": lyric_token_idx} @staticmethod def from_pretrained(path, **kwargs): return VoiceBpeTokenizer(path, **kwargs) def get_vocab(self): return {} class UMT5BaseModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_base.json") super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=False, model_options=model_options) class UMT5BaseTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer = tokenizer_data.get("spiece_model", None) super().__init__(tokenizer, pad_with_end=False, embedding_size=768, embedding_key='umt5base', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0, tokenizer_data=tokenizer_data) def state_dict(self): return {"spiece_model": self.tokenizer.serialize_model()} class LyricsTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='lyrics', tokenizer_class=VoiceBpeTokenizer, has_start_token=True, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=2, has_end_token=False, tokenizer_data=tokenizer_data) class AceT5Tokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.voicebpe = LyricsTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.umt5base = UMT5BaseTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["lyrics"] = self.voicebpe.tokenize_with_weights(kwargs.get("lyrics", ""), return_word_ids, **kwargs) out["umt5base"] = self.umt5base.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.umt5base.untokenize(token_weight_pair) def state_dict(self): return self.umt5base.state_dict() class AceT5Model(torch.nn.Module): def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): super().__init__() self.umt5base = UMT5BaseModel(device=device, dtype=dtype, model_options=model_options) self.dtypes = set() if dtype is not None: self.dtypes.add(dtype) def set_clip_options(self, options): self.umt5base.set_clip_options(options) def reset_clip_options(self): self.umt5base.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_umt5base = token_weight_pairs["umt5base"] token_weight_pairs_lyrics = token_weight_pairs["lyrics"] t5_out, t5_pooled = self.umt5base.encode_token_weights(token_weight_pairs_umt5base) lyrics_embeds = torch.tensor(list(map(lambda a: a[0], token_weight_pairs_lyrics[0]))).unsqueeze(0) return t5_out, None, {"conditioning_lyrics": lyrics_embeds} def load_sd(self, sd): return self.umt5base.load_sd(sd) ================================================ FILE: comfy/text_encoders/ace15.py ================================================ from .anima import Qwen3Tokenizer import comfy.text_encoders.llama from comfy import sd1_clip import torch import math import yaml import comfy.utils def sample_manual_loop_no_classes( model, ids=None, execution_dtype=None, cfg_scale: float = 2.0, temperature: float = 0.85, top_p: float = 0.9, top_k: int = None, min_p: float = 0.000, seed: int = 1, min_tokens: int = 1, max_new_tokens: int = 2048, audio_start_id: int = 151669, # The cutoff ID for audio codes audio_end_id: int = 215669, eos_token_id: int = 151645, ): if ids is None: return [] device = model.execution_device if execution_dtype is None: if comfy.model_management.should_use_bf16(device): execution_dtype = torch.bfloat16 else: execution_dtype = torch.float32 embeds, attention_mask, num_tokens, embeds_info = model.process_tokens(ids, device) embeds_batch = embeds.shape[0] output_audio_codes = [] past_key_values = [] generator = torch.Generator(device=device) generator.manual_seed(seed) model_config = model.transformer.model.config past_kv_shape = [embeds_batch, model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim] for x in range(model_config.num_hidden_layers): past_key_values.append((torch.empty(past_kv_shape, device=device, dtype=execution_dtype), torch.empty(past_kv_shape, device=device, dtype=execution_dtype), 0)) progress_bar = comfy.utils.ProgressBar(max_new_tokens) for step in comfy.utils.model_trange(max_new_tokens, desc="LM sampling"): outputs = model.transformer(None, attention_mask, embeds=embeds.to(execution_dtype), num_tokens=num_tokens, intermediate_output=None, dtype=execution_dtype, embeds_info=embeds_info, past_key_values=past_key_values) next_token_logits = model.transformer.logits(outputs[0])[:, -1] past_key_values = outputs[2] if cfg_scale != 1.0: cond_logits = next_token_logits[0:1] uncond_logits = next_token_logits[1:2] cfg_logits = uncond_logits + cfg_scale * (cond_logits - uncond_logits) else: cfg_logits = next_token_logits[0:1] use_eos_score = eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step if use_eos_score: eos_score = cfg_logits[:, eos_token_id].clone() remove_logit_value = torch.finfo(cfg_logits.dtype).min # Only generate audio tokens cfg_logits[:, :audio_start_id] = remove_logit_value cfg_logits[:, audio_end_id:] = remove_logit_value if use_eos_score: cfg_logits[:, eos_token_id] = eos_score if top_k is not None and top_k > 0: top_k_vals, _ = torch.topk(cfg_logits, top_k) min_val = top_k_vals[..., -1, None] cfg_logits[cfg_logits < min_val] = remove_logit_value if min_p is not None and min_p > 0: probs = torch.softmax(cfg_logits, dim=-1) p_max = probs.max(dim=-1, keepdim=True).values indices_to_remove = probs < (min_p * p_max) cfg_logits[indices_to_remove] = remove_logit_value if top_p is not None and top_p < 1.0: sorted_logits, sorted_indices = torch.sort(cfg_logits, descending=True) cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) cfg_logits[indices_to_remove] = remove_logit_value if temperature > 0: cfg_logits = cfg_logits / temperature next_token = torch.multinomial(torch.softmax(cfg_logits, dim=-1), num_samples=1, generator=generator).squeeze(1) else: next_token = torch.argmax(cfg_logits, dim=-1) token = next_token.item() if token == eos_token_id: break embed, _, _, _ = model.process_tokens([[token]], device) embeds = embed.repeat(embeds_batch, 1, 1) attention_mask = torch.cat([attention_mask, torch.ones((embeds_batch, 1), device=device, dtype=attention_mask.dtype)], dim=1) output_audio_codes.append(token - audio_start_id) progress_bar.update_absolute(step) return output_audio_codes def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0, cfg_scale=2.0, temperature=0.85, top_p=0.9, top_k=0, min_p=0.000): positive = [[token for token, _ in inner_list] for inner_list in positive] positive = positive[0] if cfg_scale != 1.0: negative = [[token for token, _ in inner_list] for inner_list in negative] negative = negative[0] neg_pad = 0 if len(negative) < len(positive): neg_pad = (len(positive) - len(negative)) negative = [model.special_tokens["pad"]] * neg_pad + negative pos_pad = 0 if len(negative) > len(positive): pos_pad = (len(negative) - len(positive)) positive = [model.special_tokens["pad"]] * pos_pad + positive ids = [positive, negative] else: ids = [positive] return sample_manual_loop_no_classes(model, ids, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens) class ACE15Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_06b", tokenizer=Qwen3Tokenizer) def _metas_to_cot(self, *, return_yaml: bool = False, **kwargs) -> str: user_metas = { k: kwargs.pop(k) for k in ("bpm", "duration", "keyscale", "timesignature") if k in kwargs } timesignature = user_metas.get("timesignature") if isinstance(timesignature, str) and timesignature.endswith("/4"): user_metas["timesignature"] = timesignature[:-2] user_metas = { k: v if not isinstance(v, str) or not v.isdigit() else int(v) for k, v in user_metas.items() if v not in {"unspecified", None} } if len(user_metas): meta_yaml = yaml.dump(user_metas, allow_unicode=True, sort_keys=True).strip() else: meta_yaml = "" return f"\n{meta_yaml}\n" if not return_yaml else meta_yaml def _metas_to_cap(self, **kwargs) -> str: use_keys = ("bpm", "timesignature", "keyscale", "duration") user_metas = { k: kwargs.pop(k, "N/A") for k in use_keys } timesignature = user_metas.get("timesignature") if isinstance(timesignature, str) and timesignature.endswith("/4"): user_metas["timesignature"] = timesignature[:-2] duration = user_metas["duration"] if duration == "N/A": user_metas["duration"] = "30 seconds" elif isinstance(duration, (str, int, float)): user_metas["duration"] = f"{math.ceil(float(duration))} seconds" else: raise TypeError("Unexpected type for duration key, must be str, int or float") return "\n".join(f"- {k}: {user_metas[k]}" for k in use_keys) def tokenize_with_weights(self, text, return_word_ids=False, **kwargs): text = text.strip() text_negative = kwargs.get("caption_negative", text).strip() lyrics = kwargs.get("lyrics", "") lyrics_negative = kwargs.get("lyrics_negative", lyrics) duration = kwargs.get("duration", 120) if isinstance(duration, str): duration = float(duration.split(None, 1)[0]) language = kwargs.get("language") seed = kwargs.get("seed", 0) generate_audio_codes = kwargs.get("generate_audio_codes", True) cfg_scale = kwargs.get("cfg_scale", 2.0) temperature = kwargs.get("temperature", 0.85) top_p = kwargs.get("top_p", 0.9) top_k = kwargs.get("top_k", 0.0) min_p = kwargs.get("min_p", 0.000) duration = math.ceil(duration) kwargs["duration"] = duration tokens_duration = duration * 5 min_tokens = int(kwargs.get("min_tokens", tokens_duration)) max_tokens = int(kwargs.get("max_tokens", tokens_duration)) metas_negative = { k.rsplit("_", 1)[0]: kwargs.pop(k) for k in ("bpm_negative", "duration_negative", "keyscale_negative", "timesignature_negative", "language_negative", "caption_negative") if k in kwargs } if not kwargs.get("use_negative_caption"): _ = metas_negative.pop("caption", None) cot_text = self._metas_to_cot(caption=text, **kwargs) cot_text_negative = "\n\n" if not metas_negative else self._metas_to_cot(**metas_negative) meta_cap = self._metas_to_cap(**kwargs) lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n\n# Lyric\n{}\n<|im_end|>\n<|im_start|>assistant\n{}\n\n<|im_end|>\n" lyrics_template = "# Languages\n{}\n\n# Lyric\n{}<|endoftext|><|endoftext|>" qwen3_06b_template = "# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}\n\n# Metas\n{}\n<|endoftext|>\n<|endoftext|>" llm_prompts = { "lm_prompt": lm_template.format(text, lyrics.strip(), cot_text), "lm_prompt_negative": lm_template.format(text_negative, lyrics_negative.strip(), cot_text_negative), "lyrics": lyrics_template.format(language if language is not None else "", lyrics), "qwen3_06b": qwen3_06b_template.format(text, meta_cap), } out = { prompt_key: self.qwen3_06b.tokenize_with_weights( prompt, prompt_key == "qwen3_06b" and return_word_ids, disable_weights = True, **kwargs, ) for prompt_key, prompt in llm_prompts.items() } out["lm_metadata"] = {"min_tokens": min_tokens, "max_tokens": max_tokens, "seed": seed, "generate_audio_codes": generate_audio_codes, "cfg_scale": cfg_scale, "temperature": temperature, "top_p": top_p, "top_k": top_k, "min_p": min_p, } return out class Qwen3_06BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_06B_ACE15, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Qwen3_2B_ACE15(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_2B_ACE15_lm, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Qwen3_4B_ACE15(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B_ACE15_lm, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class ACE15TEModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, dtype_llama=None, lm_model=None, model_options={}): super().__init__() if dtype_llama is None: dtype_llama = dtype model = None self.constant = 0.4375 if lm_model == "qwen3_4b": model = Qwen3_4B_ACE15 self.constant = 0.5625 elif lm_model == "qwen3_2b": model = Qwen3_2B_ACE15 self.lm_model = lm_model self.qwen3_06b = Qwen3_06BModel(device=device, dtype=dtype, model_options=model_options) if model is not None: setattr(self, self.lm_model, model(device=device, dtype=dtype_llama, model_options=model_options)) self.dtypes = set([dtype, dtype_llama]) def encode_token_weights(self, token_weight_pairs): token_weight_pairs_base = token_weight_pairs["qwen3_06b"] token_weight_pairs_lyrics = token_weight_pairs["lyrics"] self.qwen3_06b.set_clip_options({"layer": None}) base_out, _, extra = self.qwen3_06b.encode_token_weights(token_weight_pairs_base) self.qwen3_06b.set_clip_options({"layer": [0]}) lyrics_embeds, _, extra_l = self.qwen3_06b.encode_token_weights(token_weight_pairs_lyrics) out = {"conditioning_lyrics": lyrics_embeds[:, 0]} lm_metadata = token_weight_pairs["lm_metadata"] if lm_metadata["generate_audio_codes"]: audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"], cfg_scale=lm_metadata["cfg_scale"], temperature=lm_metadata["temperature"], top_p=lm_metadata["top_p"], top_k=lm_metadata["top_k"], min_p=lm_metadata["min_p"]) out["audio_codes"] = [audio_codes] return base_out, None, out def set_clip_options(self, options): self.qwen3_06b.set_clip_options(options) lm_model = getattr(self, self.lm_model, None) if lm_model is not None: lm_model.set_clip_options(options) def reset_clip_options(self): self.qwen3_06b.reset_clip_options() lm_model = getattr(self, self.lm_model, None) if lm_model is not None: lm_model.reset_clip_options() def load_sd(self, sd): if "model.layers.0.post_attention_layernorm.weight" in sd: shape = sd["model.layers.0.post_attention_layernorm.weight"].shape if shape[0] == 1024: return self.qwen3_06b.load_sd(sd) else: return getattr(self, self.lm_model).load_sd(sd) def memory_estimation_function(self, token_weight_pairs, device=None): lm_metadata = token_weight_pairs.get("lm_metadata", {}) constant = self.constant if comfy.model_management.should_use_bf16(device): constant *= 0.5 token_weight_pairs = token_weight_pairs.get("lm_prompt", []) num_tokens = sum(map(lambda a: len(a), token_weight_pairs)) num_tokens += lm_metadata.get("min_tokens", 0) return num_tokens * constant * 1024 * 1024 def te(dtype_llama=None, llama_quantization_metadata=None, lm_model="qwen3_2b"): class ACE15TEModel_(ACE15TEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, dtype_llama=dtype_llama, lm_model=lm_model, dtype=dtype, model_options=model_options) return ACE15TEModel_ ================================================ FILE: comfy/text_encoders/ace_lyrics_tokenizer/vocab.json ================================================ { "version": "1.0", "truncation": null, "padding": null, "added_tokens": [ { "id": 0, "special": true, "content": "[STOP]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false }, { "id": 1, "special": true, "content": "[UNK]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false }, { "id": 2, "special": true, "content": "[SPACE]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false }, { "id": 259, "special": true, "content": "[en]", "single_word": false, 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uo2", "e r", "b i", "a 3", "h e2", "e 3", "y i2", "d i4", "zh ong1", "b u4", "g u", "a i2", "n 2", "z ai4", "sh i2", "e ng1", "r en2", "o ng2", "xi an4", "y i", "x u", "n 4", "l i4", "en 4", "y u2", "e i2", "yi2 ge4", "o u4", "e i3", "d i", "u i4", "a 2", "yo u3", "ao 1", "d a4", "ch eng2", "en 1", "e ng4", "y i4", "s i1", "zh i4", "ji a1", "yu an2", "n i", "t a1", "de5 yi2ge4", "k e1", "sh u3", "x i1", "j i2", "ao 2", "t i", "o u3", "o ng4", "xi a4", "a i1", "g ong1", "zh i1", "en 3", "w ei2", "j u", "xu e2", "q u1", "zho u1", "er 3", "mi ng2", "zho ng3", "l i3", "w u4", "y i3", "uo 1", "e 5", "j i4", "xi ng2", "ji an4", "hu a4", "y u3", "uo 3", "j i1", "a i3", "z uo4", "h ou4", "hu i4", "e i1", "ni an2", "q i2", "p i", "d ao4", "sh eng1", "de 2", "d ai4", "u an2", "zh e4", "zh eng4", "b en3", "sh ang4", "zh u3", "b ei4", "y e4", "ch u1", "zh an4", "l e5", "l ai2", "sh i3", "n an2", "r en4", "yo u2", "k e4", "b a1", "f u4", "d ui4", "y a4", "m ei3", "z i4", "xi n1", "ji ng1", "zh u", "n 3", "yo ng4", "m u4", "ji ao4", "y e3", "ji n4", "bi an4", "l u4", "q i1", "sh e4", "xi ang1", "o ng3", "sh u4", "d ong4", "s uo3", "gu an1", "s an1", "b o", "t e4", "d uo1", "f u2", "mi n2", "l a1", "zh i2", "zh en4", "o u1", "w u3", "m a3", "i 5", "z i5", "j u4", "er 4", "y ao4", "xia4 de5yi2ge4", "s i4", "t u2", "sh an1", "z ui4", "ch u", "yi n1", "er 2", "t ong2", "d ong1", "y u4", "y an2", "qi an2", "shu3 xia4de5yi2ge4", "ju n1", "k e3", "w en2", "f a3", "l uo2", "zh u4", "x i4", "k ou3", "b ei3", "ji an1", "f a1", "di an4", "ji ang1", "wei4 yu2", "xi ang4", "zh i3", "e ng3", "f ang1", "l an2", "sh u", "r i4", "li an2", "sh ou3", "m o", "qi u2", "ji n1", "h uo4", "shu3xia4de5yi2ge4 zhong3", "f en1", "n ei4", "g ai1", "mei3 guo2", "u n2", "g e2", "b ao3", "qi ng1", "g ao1", "t ai2", "d u", "xi ao3", "ji e2", "ti an1", "ch ang2", "q uan2", "li e4", "h ai3", "f ei1", "t i3", "ju e2", "o u2", "c i3", "z u2", "n i2", "bi ao3", "zhong1 guo2", "d u4", "yu e4", "xi ng4", "sh eng4", "ch e1", "d an1", "ji e1", "li n2", "pi ng2", "f u3", "g u3", "ji e4", "w o", "v 3", "sh eng3", "n a4", "yu an4", "zh ang3", "gu an3", "d ao3", "z u3", "di ng4", "di an3", "c eng2", "ren2 kou3", "t ai4", "t ong1", "g uo4", "n eng2", "ch ang3", "hu a2", "li u2", "yi ng1", "xi ao4", "c i4", "bian4 hua4", "li ang3", "g ong4", "zho ng4", "de5 yi1", "s e4", "k ai1", "w ang2", "ji u4", "sh i1", "sh ou4", "m ei2", "k u", "s u", "f eng1", "z e2", "tu2 shi4", "t i2", "q i4", "ji u3", "sh en1", "zh e3", "ren2kou3 bian4hua4", "ren2kou3bian4hua4 tu2shi4", "di4 qu1", "y ang2", "m en", "men 5", "l ong2", "bi ng4", "ch an3", "zh u1", "w ei3", "w ai4", "xi ng1", "bo 1", "b i3", "t ang2", "hu a1", "bo 2", "shu i3", "sh u1", "d ou1", "s ai4", "ch ao2", "b i4", "li ng2", "l ei4", "da4 xue2", "f en4", "shu3 de5", "m u3", "ji ao1", "d ang1", "ch eng1", "t ong3", "n v3", "q i3", "y an3", "mi an4", "l uo4", "ji ng4", "g e1", "r u4", "d an4", "ri4 ben3", "p u3", "yu n4", "hu ang2", "wo 3", "l v", "h ai2", "shi4 yi1", "xi e1", "yi ng3", "w u2", "sh en2", "w ang3", "gu ang3", "li u4", "s u4", "shi4 zhen4", "c an1", "c ao3", "xi a2", "k a3", "d a2", "h u4", "b an4", "d ang3", "h u2", "z ong3", "de ng3", "de5yi2ge4 shi4zhen4", "ch uan2", "mo 4", "zh ang1", "b an1", "mo 2", "ch a2", "c e4", "zhu3 yao4", "t ou2", "j u2", "shi4 wei4yu2", "s a4", "u n1", "ke3 yi3", "d u1", "h an4", "li ang4", "sh a1", "ji a3", "z i1", "lv 4", "f u1", "xi an1", "x u4", "gu ang1", "m eng2", "b ao4", "yo u4", "r ong2", "zhi1 yi1", "w ei1", "m ao2", "guo2 jia1", "c ong2", "g ou4", "ti e3", "zh en1", "d u2", "bi an1", "c i2", "q u3", "f an4", "xi ang3", "m en2", "j u1", "h ong2", "z i3", "ta1 men5", "ji 3", "z ong1", "zhou1 de5yi2ge4shi4zhen4", "t uan2", "ji ng3", "gong1 si1", "xi e4", "l i2", "li4 shi3", "b ao1", "g ang3", "gu i1", "zh eng1", "zhi2 wu4", "ta1 de5", "pi n3", "zhu an1", "ch ong2", "shi3 yong4", "w a3", "sh uo1", "chu an1", "l ei2", "w an1", "h uo2", "q u", "s u1", "z ao3", "g ai3", "q u4", "g u4", "l u", "x i2", "h ang2", "yi ng4", "c un1", "g en1", "yi ng2", "ti ng2", "cheng2 shi4", "ji ang3", "li ng3", "l un2", "bu4 fen4", "de ng1", "xu an3", "dong4 wu4", "de2 guo2", "xi an3", "f an3", "zh e5", "h an2", "h ao4", "m i4", "r an2", "qi n1", "ti ao2", "zh an3", "h i", "k a", "n o", "t e", "s u", "s hi", "t a", "t o", "n a", "w a", "o u", "r u", "n i", "k u", "k i", "g a", "d e", "k o", "m a", "r e", "r a", "m o", "t su", "w o", "e n", "r i", "s a", "d a", "s e", "j i", "h a", "c hi", "k e", "te ki", "m i", "y ou", "s h", "s o", "y o", "y a", "na i", "t te", "a ru", "b a", "u u", "t ta", "ka i", "ka n", "shi te", "m e", "d o", "mo no", "se i", "r o", "ko to", "ka ra", "shi ta", "b u", "m u", "c h", "su ru", "k ou", "g o", "ma su", "ta i", "f u", "k en", "i u", "g en", "wa re", "shi n", "z u", "a i", "o n", "o ku", "g i", "d ou", "n e", "y uu", "i ru", "i te", "ji ko", "de su", "j u", "ra re", "sh u", "b e", "sh ou", "s ha", "se kai", "s ou", "k you", "ma shita", "s en", "na ra", "sa n", "ke i", "i ta", "a ri", "i tsu", "ko no", "j ou", "na ka", "ch ou", "so re", "g u", "na ru", "ga ku", "re ba", "g e", "h o", "i n", "hi to", "sa i", "na n", "da i", "tsu ku", "shi ki", "sa re", "na ku", "p p", "bu n", "ju n", "so no", "ka ku", "z ai", "b i", "to u", "wa ta", "sh uu", "i i", "te i", "ka re", "y u", "shi i", "ma de", "sh o", "a n", "ke reba", "shi ka", "i chi", "ha n", "de ki", "ni n", "ware ware", "na kereba", "o ite", "h ou", "ya ku", "ra i", "mu jun", "l e", "yo ku", "bu tsu", "o o", "ko n", "o mo", "ga e", "nara nai", "ta chi", "z en", "ch uu", "kan gae", "ta ra", "to ki", "ko ro", "mujun teki", "z e", "na ga", "ji n", "shi ma", "te n", "i ki", "i ku", "no u", "i masu", "r ou", "h on", "ka e", "t to", "ko re", "ta n", "ki ta", "i s", "da tta", "ji tsu", "ma e", "i e", "me i", "da n", "h e", "to ku", "dou itsu", "ri tsu", "k yuu", "h you", "rare ta", "kei sei", "k kan", "rare ru", "m ou", "do ko", "r you", "da ke", "naka tta", "so ko", "ta be", "e r", "ha na", "c o", "fu ku", "p a", "so n", "ya su", "ch o", "wata ku", "ya ma", "z a", "k yo", "gen zai", "b oku", "a ta", "j a", "ka wa", "ma sen", "j uu", "ro n", "b o", "na tte", "wataku shi", "yo tte", "ma i", "g ou", "ha i", "mo n", "ba n", "ji shin", "c a", "re te", "n en", "o ka", "ka gaku", "na tta", "p o", "ka ru", "na ri", "m en", "ma ta", "e i", "ku ru", "ga i", "ka ri", "sha kai", "kou i", "yo ri", "se tsu", "j o", "re ru", "to koro", "ju tsu", "i on", "sa ku", "tta i", "c ha", "nin gen", "n u", "c e", "ta me", "kan kyou", "de n", "o oku", "i ma", "wata shi", "tsuku ru", "su gi", "b en", "ji bun", "shi tsu", "ke ru", "ki n", "ki shi", "shika shi", "mo to", "ma ri", "i tte", "de shita", "n de", "ari masu", "te r", "z ou", "ko e", "ze ttai", "kkan teki", "h en", "re kishi", "deki ru", "tsu ka", "l a", "i tta", "o i", "ko butsu", "mi ru", "sh oku", "shi masu", "gi jutsu", "g you", "jou shiki", "a tta", "ho do", "ko ko", "tsuku rareta", "z oku", "hi tei", "ko ku", "rekishi teki", "ke te", "o ri", "i mi", "ka ko", "naga ra", "ka karu", "shu tai", "ha ji", "ma n", "ta ku", "ra n", "douitsu teki", "z o", "me te", "re i", "tsu u", "sare te", "gen jitsu", "p e", "s t", "ba i", "na wa", "ji kan", "wa ru", "r t", "a tsu", "so ku", "koui teki", "a ra", "u ma", "a no", "i de", "ka ta", "te tsu", "ga wa", "ke do", "re ta", "mi n", "sa you", "tte ru", "to ri", "p u", "ki mi", "b ou", "mu ra", "sare ru", "ma chi", "k ya", "o sa", "kon na", "a ku", "a l", "sare ta", "i pp", "shi ku", "u chi", "hito tsu", "ha tara", "tachi ba", "shi ro", "ka tachi", "to mo", "e te", "me ru", "ni chi", "da re", "ka tta", "e ru", "su ki", "a ge", "oo ki", "ma ru", "mo ku", "o ko", "kangae rareru", "o to", "tan ni", "ta da", "tai teki", "mo tte", "ki nou", "shi nai", "k ki", "u e", "ta ri", "l i", "ra nai", "k kou", "mi rai", "pp on", "go to", "hi n", "hi tsu", "te ru", "mo chi", "ka tsu", "re n", "n yuu", "su i", "zu ka", "tsu ite", "no mi", "su gu", "ku da", "tetsu gaku", "i ka", "ron ri", "o ki", "ni ppon", "p er", "shi mashita", "chi shiki", "cho kkanteki", "su ko", "t ion", "ku u", "a na", "a rou", "ka tte", "ku ri", "i nai", "hyou gen", "i shiki", "do ku", "a tte", "a tara", "to n", "wa ri", "ka o", "sei san", "hana shi", "s i", "ka ke", "na ji", "su nawa", "sunawa chi", "u go", "su u", "ba ra", "le v", "hi ro", "i wa", "be tsu", "yo i", "se ru", "shite ru", "rare te", "to shi", "se ki", "tai ritsu", "wa kara", "to kyo", "k ka", "k yoku", "u n", "i ro", "mi te", "sa ki", "kan ji", "mi ta", "su be", "r yoku", "ma tta", "kuda sai", "omo i", "ta no", "ware ru", "co m", "hitsu you", "ka shi", "re nai", "kan kei", "a to", "ga tte", "o chi", "mo tsu", "in g", "son zai", "l l", "o re", "tai shite", "a me", "sei mei", "ka no", "gi ri", "kangae ru", "yu e", "a sa", "o naji", "yo ru", "ni ku", "osa ka", "suko shi", "c k", "ta ma", "kano jo", "ki te", "mon dai", "a mari", "e ki", "ko jin", "ha ya", "i t", "de te", "atara shii", "a wa", "ga kkou", "tsu zu", "shu kan", "i mashita", "mi na", "ata e", "da rou", "hatara ku", "ga ta", "da chi", "ma tsu", "ari masen", "sei butsu", "mi tsu", "he ya", "yasu i", "d i", "de ni", "no ko", "ha ha", "do mo", "ka mi", "su deni", "na o", "ra ku", "i ke", "a ki", "me ta", "l o", "ko domo", "so shite", "ga me", "ba kari", "to te", "ha tsu", "mi se", "moku teki", "da kara", "s z", "e l", "g y", "e n", "t t", "e m", "a n", "a k", "e r", "a z", "a l", "e t", "o l", "e g", "e k", "m i", "o n", "é s", "c s", "a t", "á r", "h o", "e z", "á l", "i s", "á n", "o r", "a r", "e gy", "e s", "é r", "á t", "o tt", "e tt", "m eg", "t a", "o k", "o s", "ho gy", "n em", "é g", "n y", "k i", "é l", "h a", "á s", "ü l", "i n", "mi n", "n a", "e d", "o m", "i k", "k ö", "m a", "n i", "v a", "v ol", "é t", "b b", "f el", "i g", "l e", "r a", "é n", "t e", "d e", "a d", "ó l", "b e", "on d", "j a", "r e", "u l", "b en", "n ek", "u t", "vol t", "b an", "ö r", "o g", "a p", "o d", "á g", "n k", "é k", "v al", "k or", "a m", "i l", "í t", "á k", "b a", "u d", "sz er", "min d", "o z", "é p", "el l", "ér t", "m ond", "i t", "sz t", "n ak", "a mi", "n e", "ő l", "cs ak", "n é", "ma g", "ol y", "m er", "ál l", "án y", "ö n", "ö l", "min t", "m ár", "ö tt", "na gy", "é sz", "az t", "el ő", "t ud", "o t", "é ny", "á z", "m ég", "kö z", "el y", "s ég", "en t", "s em", "ta m", "h et", "h al", "f i", "a s", "v an", "ho z", "v e", "u k", "k ez", "á m", "v el", "b er", "a j", "u nk", "i z", "va gy", "m os", "sz em", "em ber", "f og", "mer t", "ü k", "l en", "ö s", "e j", "t al", "h at", "t ak", "h i", "m ás", "s ág", "ett e", "l eg", "ü nk", "h át", "sz a", "on y", "ez t", "mind en", "en d", "ül t", "h an", "j ó", "k is", "á j", "in t", "ú gy", "i d", "mos t", "ar t", "í r", "k er", "i tt", "a tt", "el t", "mond ta", "k ell", "l á", "ak i", "ál t", "ér d", "t ö", "l an", "v ár", "h ol", "t el", "l át", "ő k", "v et", "s e", "ut án", "k ét", "na p", "í v", "ál y", "v ég", "ö k", "i r", "d ul", "v is", "né z", "t er", "á ban", "k ül", "ak kor", "k ap", "sz él", "y en", "ú j", "i m", "oly an", "es en", "k ed", "h ely", "t ör", "b ól", "el m", "r á", "ár a", "r ó", "l ó", "vol na", "t an", "le het", "e bb", "t en", "t ek", "s ok", "k al", "f or", "u g", "ol t", "k a", "ek et", "b or", "f ej", "g ond", "a g", "ak ar", "f él", "ú l", "b el", "ott a", "mi t", "val ami", "j el", "é d", "ar c", "u r", "hal l", "t i", "f öl", "á ba", "ol g", "ki r", "ol d", "m ar", "k érd", "j ár", "ú r", "sz e", "z s", "él et", "j át", "o v", "u s", "é z", "v il", "v er", "ő r", "á d", "ö g", "le sz", "on t", "b iz", "k oz", "á bb", "kir ály", "es t", "a b", "en g", "ig az", "b ar", "ha j", "d i", "o b", "k od", "r ól", "v ez", "tö bb", "sz ó", "é ben", "ö t", "ny i", "t á", "sz ól", "gond ol", "eg ész", "í gy", "ő s", "o bb", "os an", "b ől", "a bb", "c i", "ő t", "n ál", "k ép", "azt án", "v i", "t art", "be szél", "m en", "elő tt", "a szt", "ma j", "kö r", "han g", "í z", "in cs", "a i", "é v", "ó d", "ó k", "hoz z", "t em", "ok at", "an y", "nagy on", "h áz", "p er", "p ed", "ez te", "et len", "nek i", "maj d", "sz ony", "án ak", "fel é", "egy szer", "j e", "ad t", "gy er", "ami kor", "f oly", "sz ak", "ő d", "h ú", "á sz", "am ely", "h ar", "ér e", "il yen", "od a", "j ák", "t ár", "á val", "l ak", "t ó", "m ent", "gy an", "él y", "ú t", "v ar", "kez d", "m ell", "mi kor", "h ez", "val ó", "k o", "m es", "szer et", "r end", "l et", "vis sza", "ig en", "f ő", "va s", "as szony", "r ől", "ped ig", "p i", "sz ép", "t ák", "ö v", "an i", "vil ág", "p en", "mag a", "t et", "sz ik", "é j", "én t", "j ött", "s an", "sz í", "i de", "g at", "ett em", "ul t", "h ány", "ás t", "a hol", "ők et", "h ár", "k el", "n ő", "cs i", "tal ál", "el te", "lá tt", "tör t", "ha gy", "e sz", "s en", "n él", "p ar", "v ál", "k ut", "l ány", "ami t", "s ő", "ell en", "mag át", "in k", "u gyan", "kül ön", "a sz", "mind ig", "l ép", "tal án", "u n", "sz or", "k e", "il lan", "n incs", "z et", "vagy ok", "tel en", "is mer", "s or", "is ten", "ít ott", "j obb", "v es", "dul t", "j uk", "sz en", "r o", "ö m", "l ett", "k ar", "egy ik", "b ár", "sz i", "sz ív", "az on", "e szt", "föl d", "kut y", "p illan", "f ér", "k om", "t ől", "t ű", "é be", "t ött", "bar át", "í g", "a hogy", "e h", "e p", "s o", "v en", "jel ent", "t at", "sz eg", "mint ha", "f al", "egy en", "mi l", "sza b", "r i", "é m", "biz ony", "j on", "ör eg", "d olg", "cs ap", "ti szt", "áll t", "an cs", "id ő", "k at", "ü gy", "mi ért", "ó t", "ü r", "cs in", "h az", "b et", "én ek", "v ér", "j ól", "al att", "m ely", "l o", "sem mi", "ny ug", "v ág", "kö vet", "ös sze", "ma d", "l i", "a cs", "fi ú", "kö n", "más ik", "j ön", "sz ám", "g er", "s ó", "r ész", "k ér", "z el", "é vel", "e o", "e u", "a n", "eu l", "eu n", "eo n", "a e", "d a", "a l", "s s", "i n", "i l", "a g", "an g", "y eon", "y eo", "d o", "c h", "n g", "j i", "h an", "g a", "g o", "u i", "h ae", "a m", "u l", "u n", "g eo", "s i", "n eun", "ss da", "s eo", "eon g", "y o", "i da", "t t", "k k", "j eo", "d eul", "w a", "eu m", "g e", "o n", "o g", "s al", "m an", "yeon g", "geo s", "h ag", "an eun", "j a", "g i", "s u", "i ss", "o l", "d ae", "eo b", "h a", "j u", "eo l", "g eu", "j eong", "s ae", "do e", "g eul", "s eu", "s in", "eul o", "b n", "s ang", "bn ida", "h al", "b o", "han eun", "m al", "i m", "m o", "b u", "jeo g", "sae ng", "in eun", "an h", "m a", "sal am", "j o", "s a", "eo m", "n ae", "w i", "l o", "g wa", "yeo l", "n a", "e seo", "y e", "m yeon", "tt ae", "h w", "j e", "eob s", "j ang", "g u", "g w", "il eul", "yeo g", "j eon", "si g", "j ag", "j in", "y u", "o e", "s e", "hag o", "d eun", "y a", "m un", "s eong", "g ag", "h am", "d ang", "b a", "l eul", "s il", "do ng", "kk a", "b al", "da l", "han da", "eo ssda", "ae g", "l i", "ha ji", "s eon", "o ng", "hae ssda", "d e", "i ssda", "e ge", "b un", "m ul", "ju ng", "ji g", "m u", "iss neun", "b i", "g eun", "seu bnida", "w on", "p p", "d aneun", "eo h", "d eo", "ga m", "j al", "hae ng", "ag o", "y ang", "b ul", "b ang", "u m", "s o", "h i", "j ae", "si m", "saeng gag", "hag e", "s og", "eo ss", "d an", "ja sin", "j il", "eo g", "g yeong", "doe n", "go ng", "m i", "ch i", "d eu", "d eon", "hae ss", "d u", "n am", "eun g", "jo h", "n al", "m yeong", "w o", "eon a", "i go", "g yeol", "y ag", "gw an", "ul i", "yo ng", "n o", "l yeo", "j og", "eoh ge", "ga t", "b og", "mo s", "t ong", "ch a", "man h", "jeo l", "geo l", "h oe", "ag a", "n aneun", "g an", "un eun", "ch eol", "ch e", "do l", "b on", "b an", "ba d", "ch u", "ham yeon", "yeo ssda", "i bnida", "g ye", "eo s", "hw al", "salam deul", "ji man", "dang sin", "ji b", "ttae mun", "m ae", "i b", "e neun", "eu g", "jeo m", "geul eon", "h wa", "a ssda", "b eob", "bu t", "b ae", "yeo ss", "ch in", "ch aeg", "g eon", "g ae", "nae ga", "i ga", "m og", "sig an", "g il", "h yeon", "l yeog", "gu g", "p yeon", "s an", "w ae", "j ul", "s eul", "deun g", "haji man", "eum yeon", "p il", "m ol", "n eu", "a ss", "n yeon", "t ae", "h u", "p yo", "s ul", "g ang", "j ineun", "b eon", "ha da", "seo l", "si p", "dal eun", "a p", "sal m", "g yo", "ch eon", "hag i", "in a", "cheol eom", "g al", "il a", "kka ji", "anh neun", "ha bnida", "tt eon", "n u", "hae seo", "doen da", "s ol", "tt al", "l a", "il o", "seu b", "b yeon", "m yeo", "b eol", "s on", "n un", "j un", "j am", "j eung", "tt o", "e n", "mo m", "h o", "ch im", "hw ang", "eun eun", "jo ng", "bo da", "n ol", "n eom", "but eo", "jig eum", "eobs da", "dae lo", "i g", "y ul", "p yeong", "seon eun", "sal ang", "seu t", "h im", "n an", "h eom", "h yang", "p i", "gw ang", "eobs neun", "hw ag", "ge ss", "jag i", "il eon", "wi hae", "dae han", "ga ji", "m eog", "j yeo", "cha j", "b yeong", "eo d", "g yeo", "do n", "eo ji", "g ul", "mo deun", "j on", "in saeng", "geul ae", "h ang", "sa sil", "si b", "ch al", "il ago", "doe l", "g eum", "doe neun", "b ol", "ga jang", "geul igo", "e l", "h yeong", "haeng bog", "ch ul", "h on", "ch ae", "s am", "m ang", "in da", "da m", "w ol", "ch oe", "d ul", "si jag", "ch eong", "il aneun", "ul ineun", "ae n", "kk e", "mun je", "a do", "t eu", "g un", "geun eun", "b ge", "ch eo", "b aeg", "ju g", "t a", "sang dae", "geu geos", "do g", "eu s", "deu s", "ja b", "h yeo", "tt eohge", "u g", "ma j", "ch il", "s wi", "j ileul", "ch ang", "g aneun", "m ag", "i ji", "da go", "m in", "yo han", "t eug", "pp un", "al eul", "haeng dong", "p o", "m il", "ch am", "se sang", "e do", "p an", "man deul", "am yeon", "a b", "kk ae", "b ag", "i deul", "p um", "m eol", "s un", "n eul", "ham kke", "chu ng", "da b", "yu g", "s ag", "gwang ye", "il eohge", "bal o", "neun de", "ham yeo", "go s", "geul eoh", "an ila", "bang beob", "da si", "b yeol", "g yeon", "gam jeong", "on eul", "j aneun", "yeo m", "l ago", "i gi", "hw an", "t eul", "eo seo", "si k", "ch o", "jag a", "geul eom", "geul eona", "jeong do", "g yeog", "geul eohge", "geu deul", "eu t", "im yeon", "j jae", "k eun", "i sang", "mal haessda", "eu ge", "no p", "in gan", "bo myeon", "t aeg", "seu s", "d wi", "s aneun", "w an", "anh go", "t an", "nu gu", "su ng", "da myeon", "a deul", "p eul", "ttal a", "d i", "geos do", "a ji", "m eon", "eum yeo", "dol og", "neun g", "mo du", "क े", "ह ै", "े ं", "् र", "ा र", "न े", "य ा", "म ें", "स े", "क ी", "क ा", "ो ं", "त ा", "क र", "स ्", "क ि", "क ो", "र ्", "न ा", "क ्", "ह ी", "औ र", "प र", "त े", "ह ो", "प ्र", "ा न", "् य", "ल ा", "व ा", "ल े", "स ा", "है ं", "ल ि", "ज ा", "ह ा", "भ ी", "व ि", "इ स", "त ी", "न ्", "र ा", "म ा", "द े", "द ि", "ब ा", "त ि", "थ ा", "न ि", "क ार", "ए क", "ही ं", "ह ु", "ं ग", "ै ं", "न ी", "स ी", "अ प", "त ्", "न हीं", "र ी", "म े", "म ु", "ि त", "त ो", "प ा", "ल ी", "लि ए", "ग ा", "ल ्", "र ह", "र े", "क् ष", "म ैं", "स म", "उ स", "ज ि", "त ्र", "म ि", "च ा", "ो ग", "स ं", "द ्", "स ि", "आ प", "त ु", "द ा", "क ु", "य ों", "व े", "ज ी", "् या", "उ न", "ि क", "य े", "भ ा", "् ट", "ह म", "स् ट", "श ा", "ड ़", "ं द", "ख ा", "म ्", "श ्", "य ह", "स क", "प ू", "कि या", "अप ने", "र ू", "स ु", "म ी", "ह ि", "ज ो", "थ े", "र ि", "द ी", "थ ी", "ग ी", "ल ोग", "ग या", "त र", "न् ह", "च ्", "व ार", "ब ी", "प ्", "द ो", "ट ी", "श ि", "कर ने", "ग े", "ै से", "इ न", "ं ड", "सा थ", "प ु", "ब े", "ब ार", "व ी", "अ न", "ह र", "उ न्ह", "हो ता", "ज ब", "कु छ", "म ान", "क ्र", "ब ि", "प ह", "फ ि", "स र", "ार ी", "र ो", "द ू", "क हा", "त क", "श न", "ब ्", "स् थ", "व ह", "बा द", "ओ ं", "ग ु", "ज ्", "्र े", "ग र", "रह े", "व र्", "ह ू", "ार ्", "प ी", "ब हु", "मु झ", "्र ा", "दि या", "स ब", "कर ते", "अप नी", "बहु त", "क ह", "ट े", "हु ए", "कि सी", "र हा", "ष ्ट", "ज ़", "ब ना", "स ो", "ड ि", "को ई", "व ्य", "बा त", "र ु", "व ो", "मुझ े", "द् ध", "च ार", "मे रे", "व र", "्र ी", "जा ता", "न ों", "प्र ा", "दे ख", "ट ा", "क् या", "अ ध", "ल ग", "ल ो", "प ि", "य ु", "च े", "जि स", "ं त", "ान ी", "प ै", "ज न", "ार े", "च ी", "मि ल", "द ु", "दे श", "च् छ", "ष ्", "स ू", "ख े", "च ु", "ि या", "ल गा", "ब ु", "उन के", "ज् ञ", "क्ष ा", "त रह", "्या दा", "वा ले", "पू र्", "मैं ने", "का म", "रू प", "हो ती", "उ प", "ज ान", "प्र कार", "भ ार", "म न", "हु आ", "ट र", "हू ँ", "पर ि", "पा स", "अन ु", "रा ज", "लोग ों", "अ ब", "सम झ", "ड ी", "म ौ", "श ु", "च ि", "प े", "क ृ", "सक ते", "म ह", "य ोग", "द र्", "उ से", "ं ध", "ड ा", "जा ए", "ब ो", "ू ल", "म ो", "ों ने", "ं स", "तु म", "पह ले", "ब ता", "त था", "य ो", "ग ई", "उ त्", "सक ता", "क म", "ज ्यादा", "र ख", "सम य", "ार ा", "अ गर", "स् त", "च ल", "फि र", "वार ा", "कर ना", "श ी", "ग ए", "ब न", "ौ र", "हो ने", "चा ह", "ख ु", "हा ँ", "उन्ह ें", "उन्ह ोंने", "छ ो", "म् ह", "प्र ति", "नि क", "व न", "्य ू", "र ही", "तु म्ह", "ज ैसे", "ि यों", "क् यों", "ल ों", "फ ़", "ं त्र", "हो ते", "क् ति", "त ्य", "कर ्", "क ई", "व ं", "कि न", "प ो", "कार ण", "ड़ ी", "भ ि", "इस के", "ब र", "उस के", "द् वारा", "श े", "क ॉ", "दि न", "न् न", "ड़ ा", "स् व", "नि र्", "मु ख", "लि या", "ट ि", "ज्ञ ान", "क् त", "द ्र", "ग ्", "क् स", "म ै", "ग ो", "ज े", "ट ्र", "म ार", "त् व", "ध ार", "भा व", "कर ता", "ख ि", "क ं", "चा हि", "य र", "प् त", "क ों", "ं च", "ज ु", "म त", "अ च्छ", "हु ई", "क भी", "ले किन", "भ ू", "अप ना", "दू स", "चाहि ए", "य ू", "घ र", "सब से", "मे री", "ना म", "ढ ़", "ं ट", "ें गे", "ब ै", "फ ा", "ए वं", "य ी", "ग ्र", "क्ष े", "आ ज", "आप को", "भा ग", "ठ ा", "क ै", "भार त", "उन की", "प हु", "स भी", "ध ा", "ण ा", "स ान", "हो गा", "त ब", "स ंग", "प र्", "अ व", "त ना", "ग ि", "य न", "स् था", "च ित", "ट ्", "छ ा", "जा ने", "क्षे त्र", "वा ली", "पूर् ण", "स मा", "कार ी" ] } } ================================================ FILE: comfy/text_encoders/ace_text_cleaners.py ================================================ # basic text cleaners for the ACE step model # I didn't copy the ones from the reference code because I didn't want to deal with the dependencies # TODO: more languages than english? import re def japanese_to_romaji(japanese_text): """ Convert Japanese hiragana and katakana to romaji (Latin alphabet representation). Args: japanese_text (str): Text containing hiragana and/or katakana characters Returns: str: The romaji (Latin alphabet) equivalent """ # Dictionary mapping kana characters to their romaji equivalents kana_map = { # Katakana characters 'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o', 'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko', 'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so', 'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to', 'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no', 'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho', 'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo', 'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo', 'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro', 'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n', # Katakana voiced consonants 'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go', 'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo', 'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do', 'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo', 'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po', # Katakana combinations 'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo', 'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho', 'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho', 'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo', 'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo', 'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo', 'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo', 'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo', 'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo', 'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo', 'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo', # Katakana small characters and special cases 'ッ': '', # Small tsu (doubles the following consonant) 'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo', # Katakana extras 'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo', 'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo', # Hiragana characters 'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o', 'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko', 'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so', 'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to', 'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no', 'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho', 'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo', 'や': 'ya', 'ゆ': 'yu', 'よ': 'yo', 'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro', 'わ': 'wa', 'を': 'wo', 'ん': 'n', # Hiragana voiced consonants 'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go', 'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo', 'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do', 'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo', 'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po', # Hiragana combinations 'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo', 'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho', 'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho', 'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo', 'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo', 'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo', 'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo', 'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo', 'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo', 'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo', 'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo', # Hiragana small characters and special cases 'っ': '', # Small tsu (doubles the following consonant) 'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo', # Common punctuation and spaces '　': ' ', # Japanese space '、': ', ', '。': '. ', } result = [] i = 0 while i < len(japanese_text): # Check for small tsu (doubling the following consonant) if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'): if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map: next_romaji = kana_map[japanese_text[i+1]] if next_romaji and next_romaji[0] not in 'aiueon': result.append(next_romaji[0]) # Double the consonant i += 1 continue # Check for combinations with small ya, yu, yo if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'): combo = japanese_text[i:i+2] if combo in kana_map: result.append(kana_map[combo]) i += 2 continue # Regular character if japanese_text[i] in kana_map: result.append(kana_map[japanese_text[i]]) else: # If it's not in our map, keep it as is (might be kanji, romaji, etc.) result.append(japanese_text[i]) i += 1 return ''.join(result) def number_to_text(num, ordinal=False): """ Convert a number (int or float) to its text representation. Args: num: The number to convert Returns: str: Text representation of the number """ if not isinstance(num, (int, float)): return "Input must be a number" # Handle special case of zero if num == 0: return "zero" # Handle negative numbers negative = num < 0 num = abs(num) # Handle floats if isinstance(num, float): # Split into integer and decimal parts int_part = int(num) # Convert both parts int_text = _int_to_text(int_part) # Handle decimal part (convert to string and remove '0.') decimal_str = str(num).split('.')[1] decimal_text = " point " + " ".join(_digit_to_text(int(digit)) for digit in decimal_str) result = int_text + decimal_text else: # Handle integers result = _int_to_text(num) # Add 'negative' prefix for negative numbers if negative: result = "negative " + result return result def _int_to_text(num): """Helper function to convert an integer to text""" ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen"] tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"] if num < 20: return ones[num] if num < 100: return tens[num // 10] + (" " + ones[num % 10] if num % 10 != 0 else "") if num < 1000: return ones[num // 100] + " hundred" + (" " + _int_to_text(num % 100) if num % 100 != 0 else "") if num < 1000000: return _int_to_text(num // 1000) + " thousand" + (" " + _int_to_text(num % 1000) if num % 1000 != 0 else "") if num < 1000000000: return _int_to_text(num // 1000000) + " million" + (" " + _int_to_text(num % 1000000) if num % 1000000 != 0 else "") return _int_to_text(num // 1000000000) + " billion" + (" " + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else "") def _digit_to_text(digit): """Convert a single digit to text""" digits = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] return digits[digit] _whitespace_re = re.compile(r"\s+") # List of (regular expression, replacement) pairs for abbreviations: _abbreviations = { "en": [ (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) for x in [ ("mrs", "misess"), ("mr", "mister"), ("dr", "doctor"), ("st", "saint"), ("co", "company"), ("jr", "junior"), ("maj", "major"), ("gen", "general"), ("drs", "doctors"), ("rev", "reverend"), ("lt", "lieutenant"), ("hon", "honorable"), ("sgt", "sergeant"), ("capt", "captain"), ("esq", "esquire"), ("ltd", "limited"), ("col", "colonel"), ("ft", "fort"), ] ], } def expand_abbreviations_multilingual(text, lang="en"): for regex, replacement in _abbreviations[lang]: text = re.sub(regex, replacement, text) return text _symbols_multilingual = { "en": [ (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) for x in [ ("&", " and "), ("@", " at "), ("%", " percent "), ("#", " hash "), ("$", " dollar "), ("£", " pound "), ("°", " degree "), ] ], } def expand_symbols_multilingual(text, lang="en"): for regex, replacement in _symbols_multilingual[lang]: text = re.sub(regex, replacement, text) text = text.replace(" ", " ") # Ensure there are no double spaces return text.strip() _ordinal_re = { "en": re.compile(r"([0-9]+)(st|nd|rd|th)"), } _number_re = re.compile(r"[0-9]+") _currency_re = { "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"), "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"), "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"), } _comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b") _dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b") _decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)") def _remove_commas(m): text = m.group(0) if "," in text: text = text.replace(",", "") return text def _remove_dots(m): text = m.group(0) if "." in text: text = text.replace(".", "") return text def _expand_decimal_point(m, lang="en"): amount = m.group(1).replace(",", ".") return number_to_text(float(amount)) def _expand_currency(m, lang="en", currency="USD"): amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", ".")))) full_amount = number_to_text(amount) and_equivalents = { "en": ", ", "es": " con ", "fr": " et ", "de": " und ", "pt": " e ", "it": " e ", "pl": ", ", "cs": ", ", "ru": ", ", "nl": ", ", "ar": ", ", "tr": ", ", "hu": ", ", "ko": ", ", } if amount.is_integer(): last_and = full_amount.rfind(and_equivalents[lang]) if last_and != -1: full_amount = full_amount[:last_and] return full_amount def _expand_ordinal(m, lang="en"): return number_to_text(int(m.group(1)), ordinal=True) def _expand_number(m, lang="en"): return number_to_text(int(m.group(0))) def expand_numbers_multilingual(text, lang="en"): if lang in ["en", "ru"]: text = re.sub(_comma_number_re, _remove_commas, text) else: text = re.sub(_dot_number_re, _remove_dots, text) try: text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text) text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text) text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text) except: pass text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text) text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text) text = re.sub(_number_re, lambda m: _expand_number(m, lang), text) return text def lowercase(text): return text.lower() def collapse_whitespace(text): return re.sub(_whitespace_re, " ", text) def multilingual_cleaners(text, lang): text = text.replace('"', "") if lang == "tr": text = text.replace("İ", "i") text = text.replace("Ö", "ö") text = text.replace("Ü", "ü") text = lowercase(text) try: text = expand_numbers_multilingual(text, lang) except: pass try: text = expand_abbreviations_multilingual(text, lang) except: pass try: text = expand_symbols_multilingual(text, lang=lang) except: pass text = collapse_whitespace(text) return text def basic_cleaners(text): """Basic pipeline that lowercases and collapses whitespace without transliteration.""" text = lowercase(text) text = collapse_whitespace(text) return text ================================================ FILE: comfy/text_encoders/anima.py ================================================ from transformers import Qwen2Tokenizer, T5TokenizerFast import comfy.text_encoders.llama from comfy import sd1_clip import os import torch class Qwen3Tokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='qwen3_06b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) class AnimaTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.qwen3_06b = Qwen3Tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} qwen_ids = self.qwen3_06b.tokenize_with_weights(text, return_word_ids, **kwargs) out["qwen3_06b"] = [[(k[0], 1.0, k[2]) if return_word_ids else (k[0], 1.0) for k in inner_list] for inner_list in qwen_ids] # Set weights to 1.0 out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.t5xxl.untokenize(token_weight_pair) def state_dict(self): return {} def decode(self, token_ids, **kwargs): return self.qwen3_06b.decode(token_ids, **kwargs) class Qwen3_06BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_06B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class AnimaTEModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="qwen3_06b", clip_model=Qwen3_06BModel, model_options=model_options) def encode_token_weights(self, token_weight_pairs): out = super().encode_token_weights(token_weight_pairs) out[2]["t5xxl_ids"] = torch.tensor(list(map(lambda a: a[0], token_weight_pairs["t5xxl"][0])), dtype=torch.int) out[2]["t5xxl_weights"] = torch.tensor(list(map(lambda a: a[1], token_weight_pairs["t5xxl"][0]))) return out def te(dtype_llama=None, llama_quantization_metadata=None): class AnimaTEModel_(AnimaTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if dtype_llama is not None: dtype = dtype_llama if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, dtype=dtype, model_options=model_options) return AnimaTEModel_ ================================================ FILE: comfy/text_encoders/aura_t5.py ================================================ from comfy import sd1_clip from .spiece_tokenizer import SPieceTokenizer import comfy.text_encoders.t5 import os class PT5XlModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json") super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) class PT5XlTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1, tokenizer_data=tokenizer_data) class AuraT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer) class AuraT5Model(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): super().__init__(device=device, dtype=dtype, model_options=model_options, name="pile_t5xl", clip_model=PT5XlModel, **kwargs) ================================================ FILE: comfy/text_encoders/bert.py ================================================ import torch from comfy.ldm.modules.attention import optimized_attention_for_device import comfy.ops class BertAttention(torch.nn.Module): def __init__(self, embed_dim, heads, dtype, device, operations): super().__init__() self.heads = heads self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x, mask=None, optimized_attention=None): q = self.query(x) k = self.key(x) v = self.value(x) out = optimized_attention(q, k, v, self.heads, mask) return out class BertOutput(torch.nn.Module): def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): super().__init__() self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device) # self.dropout = nn.Dropout(0.0) def forward(self, x, y): x = self.dense(x) # hidden_states = self.dropout(hidden_states) x = self.LayerNorm(x + y) return x class BertAttentionBlock(torch.nn.Module): def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): super().__init__() self.self = BertAttention(embed_dim, heads, dtype, device, operations) self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) def forward(self, x, mask, optimized_attention): y = self.self(x, mask, optimized_attention) return self.output(y, x) class BertIntermediate(torch.nn.Module): def __init__(self, embed_dim, intermediate_dim, dtype, device, operations): super().__init__() self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device) def forward(self, x): x = self.dense(x) return torch.nn.functional.gelu(x) class BertBlock(torch.nn.Module): def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): super().__init__() self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations) self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations) self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations) def forward(self, x, mask, optimized_attention): x = self.attention(x, mask, optimized_attention) y = self.intermediate(x) return self.output(y, x) class BertEncoder(torch.nn.Module): def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): super().__init__() self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)]) def forward(self, x, mask=None, intermediate_output=None): optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) if intermediate_output is not None: if intermediate_output < 0: intermediate_output = len(self.layer) + intermediate_output intermediate = None for i, l in enumerate(self.layer): x = l(x, mask, optimized_attention) if i == intermediate_output: intermediate = x.clone() return x, intermediate class BertEmbeddings(torch.nn.Module): def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations): super().__init__() self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device) self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device) self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device) self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device) def forward(self, input_tokens, embeds=None, token_type_ids=None, dtype=None): if embeds is not None: x = embeds else: x = self.word_embeddings(input_tokens, out_dtype=dtype) x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x) if token_type_ids is not None: x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype) else: x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x) x = self.LayerNorm(x) return x class BertModel_(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() embed_dim = config_dict["hidden_size"] layer_norm_eps = config_dict["layer_norm_eps"] self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations) self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations) def forward(self, input_tokens, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): x = self.embeddings(input_tokens, embeds=embeds, dtype=dtype) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) x, i = self.encoder(x, mask, intermediate_output) return x, i class BertModel(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.bert = BertModel_(config_dict, dtype, device, operations) self.num_layers = config_dict["num_hidden_layers"] def get_input_embeddings(self): return self.bert.embeddings.word_embeddings def set_input_embeddings(self, embeddings): self.bert.embeddings.word_embeddings = embeddings def forward(self, *args, **kwargs): return self.bert(*args, **kwargs) ================================================ FILE: comfy/text_encoders/byt5_config_small_glyph.json ================================================ { "d_ff": 3584, "d_kv": 64, "d_model": 1472, 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"tokenizer_class": "ByT5Tokenizer", "unk_token": "" } ================================================ FILE: comfy/text_encoders/cosmos.py ================================================ from comfy import sd1_clip import comfy.text_encoders.t5 import os from transformers import T5TokenizerFast class T5XXLModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json") t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None) if t5xxl_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = t5xxl_quantization_metadata super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options) class CosmosT5XXL(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, tokenizer_data=tokenizer_data) class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) def te(dtype_t5=None, t5_quantization_metadata=None): class CosmosTEModel_(CosmosT5XXL): def __init__(self, device="cpu", dtype=None, model_options={}): if t5_quantization_metadata is not None: model_options = model_options.copy() model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata if dtype_t5 is not None: dtype = dtype_t5 super().__init__(device=device, dtype=dtype, model_options=model_options) return CosmosTEModel_ ================================================ FILE: comfy/text_encoders/flux.py ================================================ from comfy import sd1_clip import comfy.text_encoders.t5 import comfy.text_encoders.sd3_clip import comfy.text_encoders.llama import comfy.model_management from transformers import T5TokenizerFast, LlamaTokenizerFast, Qwen2Tokenizer import torch import os import json import base64 class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) class FluxTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.clip_l.untokenize(token_weight_pair) def state_dict(self): return {} class FluxClipModel(torch.nn.Module): def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): super().__init__() dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options) self.dtypes = set([dtype, dtype_t5]) def set_clip_options(self, options): self.clip_l.set_clip_options(options) self.t5xxl.set_clip_options(options) def reset_clip_options(self): self.clip_l.reset_clip_options() self.t5xxl.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_l = token_weight_pairs["l"] token_weight_pairs_t5 = token_weight_pairs["t5xxl"] t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5) l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) return t5_out, l_pooled def load_sd(self, sd): if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: return self.clip_l.load_sd(sd) else: return self.t5xxl.load_sd(sd) def flux_clip(dtype_t5=None, t5_quantization_metadata=None): class FluxClipModel_(FluxClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): if t5_quantization_metadata is not None: model_options = model_options.copy() model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options) return FluxClipModel_ def load_mistral_tokenizer(data): if torch.is_tensor(data): data = data.numpy().tobytes() try: from transformers.integrations.mistral import MistralConverter except ModuleNotFoundError: from transformers.models.pixtral.convert_pixtral_weights_to_hf import MistralConverter mistral_vocab = json.loads(data) special_tokens = {} vocab = {} max_vocab = mistral_vocab["config"]["default_vocab_size"] max_vocab -= len(mistral_vocab["special_tokens"]) for w in mistral_vocab["vocab"]: r = w["rank"] if r >= max_vocab: continue vocab[base64.b64decode(w["token_bytes"])] = r for w in mistral_vocab["special_tokens"]: if "token_bytes" in w: special_tokens[base64.b64decode(w["token_bytes"])] = w["rank"] else: special_tokens[w["token_str"]] = w["rank"] all_special = [] for v in special_tokens: all_special.append(v) special_tokens.update(vocab) vocab = special_tokens return {"tokenizer_object": MistralConverter(vocab=vocab, additional_special_tokens=all_special).converted(), "legacy": False} class MistralTokenizerClass: @staticmethod def from_pretrained(path, **kwargs): return LlamaTokenizerFast(**kwargs) class Mistral3Tokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): self.tekken_data = tokenizer_data.get("tekken_model", None) super().__init__("", pad_with_end=False, embedding_directory=embedding_directory, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_class=MistralTokenizerClass, has_end_token=False, pad_to_max_length=False, pad_token=11, start_token=1, max_length=99999999, min_length=1, pad_left=True, tokenizer_args=load_mistral_tokenizer(self.tekken_data), tokenizer_data=tokenizer_data) def state_dict(self): return {"tekken_model": self.tekken_data} class Flux2Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="mistral3_24b", tokenizer=Mistral3Tokenizer) self.llama_template = '[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]{}[/INST]' def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): if llama_template is None: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) return tokens class Mistral3_24BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer=[10, 20, 30], layer_idx=None, dtype=None, attention_mask=True, model_options={}): textmodel_json_config = {} num_layers = model_options.get("num_layers", None) if num_layers is not None: textmodel_json_config["num_hidden_layers"] = num_layers if num_layers < 40: textmodel_json_config["final_norm"] = False super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 1, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Mistral3Small24B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Flux2TEModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}, name="mistral3_24b", clip_model=Mistral3_24BModel): super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options) def encode_token_weights(self, token_weight_pairs): out, pooled, extra = super().encode_token_weights(token_weight_pairs) out = torch.stack((out[:, 0], out[:, 1], out[:, 2]), dim=1) out = out.movedim(1, 2) out = out.reshape(out.shape[0], out.shape[1], -1) return out, pooled, extra def flux2_te(dtype_llama=None, llama_quantization_metadata=None, pruned=False): class Flux2TEModel_(Flux2TEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if dtype_llama is not None: dtype = dtype_llama if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata if pruned: model_options = model_options.copy() model_options["num_layers"] = 30 super().__init__(device=device, dtype=dtype, model_options=model_options) return Flux2TEModel_ class Qwen3Tokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=2560, embedding_key='qwen3_4b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=151643, tokenizer_data=tokenizer_data) class Qwen3Tokenizer8B(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=4096, embedding_key='qwen3_8b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=151643, tokenizer_data=tokenizer_data) class KleinTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}, name="qwen3_4b"): if name == "qwen3_4b": tokenizer = Qwen3Tokenizer elif name == "qwen3_8b": tokenizer = Qwen3Tokenizer8B super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=name, tokenizer=tokenizer) self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): if llama_template is None: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) return tokens class KleinTokenizer8B(KleinTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}, name="qwen3_8b"): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=name) class Qwen3_4BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer=[9, 18, 27], layer_idx=None, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Qwen3_8BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer=[9, 18, 27], layer_idx=None, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_8B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) def klein_te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen3_4b"): if model_type == "qwen3_4b": model = Qwen3_4BModel elif model_type == "qwen3_8b": model = Qwen3_8BModel class Flux2TEModel_(Flux2TEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model) return Flux2TEModel_ ================================================ FILE: comfy/text_encoders/genmo.py ================================================ from comfy import sd1_clip import comfy.text_encoders.sd3_clip import os from transformers import T5TokenizerFast class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): def __init__(self, **kwargs): kwargs["attention_mask"] = True super().__init__(**kwargs) class MochiT5XXL(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) class MochiT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) def mochi_te(dtype_t5=None, t5_quantization_metadata=None): class MochiTEModel_(MochiT5XXL): def __init__(self, device="cpu", dtype=None, model_options={}): if t5_quantization_metadata is not None: model_options = model_options.copy() model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata if dtype_t5 is not None: dtype = dtype_t5 super().__init__(device=device, dtype=dtype, model_options=model_options) return MochiTEModel_ ================================================ FILE: comfy/text_encoders/hidream.py ================================================ from . import hunyuan_video from . import sd3_clip from comfy import sd1_clip from comfy import sdxl_clip import comfy.model_management import torch import logging class HiDreamTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data) self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) out["t5xxl"] = [t5xxl[0]] # Use only first 128 tokens out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.clip_g.untokenize(token_weight_pair) def state_dict(self): return {} class HiDreamTEModel(torch.nn.Module): def __init__(self, clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, device="cpu", dtype=None, model_options={}): super().__init__() self.dtypes = set() if clip_l: self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=True, model_options=model_options) self.dtypes.add(dtype) else: self.clip_l = None if clip_g: self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) self.dtypes.add(dtype) else: self.clip_g = None if t5: dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) self.t5xxl = sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=True) self.dtypes.add(dtype_t5) else: self.t5xxl = None if llama: dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) if "vocab_size" not in model_options: model_options["vocab_size"] = 128256 self.llama = hunyuan_video.LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None, special_tokens={"start": 128000, "pad": 128009}) self.dtypes.add(dtype_llama) else: self.llama = None logging.debug("Created HiDream text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}, llama {}:{}".format(clip_l, clip_g, t5, dtype_t5, llama, dtype_llama)) def set_clip_options(self, options): if self.clip_l is not None: self.clip_l.set_clip_options(options) if self.clip_g is not None: self.clip_g.set_clip_options(options) if self.t5xxl is not None: self.t5xxl.set_clip_options(options) if self.llama is not None: self.llama.set_clip_options(options) def reset_clip_options(self): if self.clip_l is not None: self.clip_l.reset_clip_options() if self.clip_g is not None: self.clip_g.reset_clip_options() if self.t5xxl is not None: self.t5xxl.reset_clip_options() if self.llama is not None: self.llama.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_l = token_weight_pairs["l"] token_weight_pairs_g = token_weight_pairs["g"] token_weight_pairs_t5 = token_weight_pairs["t5xxl"] token_weight_pairs_llama = token_weight_pairs["llama"] lg_out = None pooled = None extra = {} if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: if self.clip_l is not None: lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) else: l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) if self.clip_g is not None: g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) else: g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) pooled = torch.cat((l_pooled, g_pooled), dim=-1) if self.t5xxl is not None: t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) t5_out, t5_pooled = t5_output[:2] else: t5_out = None if self.llama is not None: ll_output = self.llama.encode_token_weights(token_weight_pairs_llama) ll_out, ll_pooled = ll_output[:2] ll_out = ll_out[:, 1:] else: ll_out = None if t5_out is None: t5_out = torch.zeros((1, 128, 4096), device=comfy.model_management.intermediate_device()) if ll_out is None: ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device()) if pooled is None: pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) extra["conditioning_llama3"] = ll_out return t5_out, pooled, extra def load_sd(self, sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: return self.clip_g.load_sd(sd) elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: return self.clip_l.load_sd(sd) elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: return self.t5xxl.load_sd(sd) else: return self.llama.load_sd(sd) def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5_quantization_metadata=None, llama_quantization_metadata=None): class HiDreamTEModel_(HiDreamTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if t5_quantization_metadata is not None: model_options = model_options.copy() model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options) return HiDreamTEModel_ ================================================ FILE: comfy/text_encoders/hunyuan_image.py ================================================ from comfy import sd1_clip import comfy.text_encoders.llama from .qwen_image import QwenImageTokenizer, QwenImageTEModel from transformers import ByT5Tokenizer import os import re class ByT5SmallTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "byt5_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1472, embedding_key='byt5_small', tokenizer_class=ByT5Tokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) class HunyuanImageTokenizer(QwenImageTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>" # self.llama_template_images = "{}" self.byt5 = ByT5SmallTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = super().tokenize_with_weights(text, return_word_ids, **kwargs) # ByT5 processing for HunyuanImage text_prompt_texts = [] pattern_quote_double = r'\"(.*?)\"' pattern_quote_chinese_single = r'‘(.*?)’' pattern_quote_chinese_double = r'“(.*?)”' matches_quote_double = re.findall(pattern_quote_double, text) matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, text) matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, text) text_prompt_texts.extend(matches_quote_double) text_prompt_texts.extend(matches_quote_chinese_single) text_prompt_texts.extend(matches_quote_chinese_double) if len(text_prompt_texts) > 0: out['byt5'] = self.byt5.tokenize_with_weights(''.join(map(lambda a: 'Text "{}". '.format(a), text_prompt_texts)), return_word_ids, **kwargs) return out class Qwen25_7BVLIModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class ByT5SmallModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "byt5_config_small_glyph.json") super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True) class HunyuanImageTEModel(QwenImageTEModel): def __init__(self, byt5=True, device="cpu", dtype=None, model_options={}): super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) if byt5: self.byt5_small = ByT5SmallModel(device=device, dtype=dtype, model_options=model_options) else: self.byt5_small = None def encode_token_weights(self, token_weight_pairs): tok_pairs = token_weight_pairs["qwen25_7b"][0] template_end = -1 if tok_pairs[0][0] == 27: if len(tok_pairs) > 36: # refiner prompt uses a fixed 36 template_end template_end = 36 cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=template_end) if self.byt5_small is not None and "byt5" in token_weight_pairs: out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"]) extra["conditioning_byt5small"] = out[0] return cond, p, extra def set_clip_options(self, options): super().set_clip_options(options) if self.byt5_small is not None: self.byt5_small.set_clip_options(options) def reset_clip_options(self): super().reset_clip_options() if self.byt5_small is not None: self.byt5_small.reset_clip_options() def load_sd(self, sd): if "encoder.block.0.layer.0.SelfAttention.o.weight" in sd: return self.byt5_small.load_sd(sd) else: return super().load_sd(sd) def te(byt5=True, dtype_llama=None, llama_quantization_metadata=None): class QwenImageTEModel_(HunyuanImageTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options) return QwenImageTEModel_ ================================================ FILE: comfy/text_encoders/hunyuan_video.py ================================================ from comfy import sd1_clip import comfy.model_management import comfy.text_encoders.llama from .hunyuan_image import HunyuanImageTokenizer from transformers import LlamaTokenizerFast import torch import os import numbers import comfy.utils def llama_detect(state_dict, prefix=""): out = {} norm_keys = ["{}model.norm.weight".format(prefix), "{}model.layers.0.input_layernorm.weight".format(prefix)] for norm_key in norm_keys: if norm_key in state_dict: out["dtype_llama"] = state_dict[norm_key].dtype break quant = comfy.utils.detect_layer_quantization(state_dict, prefix) if quant is not None: out["llama_quantization_metadata"] = quant return out class LLAMA3Tokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256, pad_token=128258): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "llama_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=pad_token, min_length=min_length, tokenizer_data=tokenizer_data) class LLAMAModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata textmodel_json_config = {} vocab_size = model_options.get("vocab_size", None) if vocab_size is not None: textmodel_json_config["vocab_size"] = vocab_size model_options = {**model_options, "model_name": "llama"} super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Llama2, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class HunyuanVideoTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>""" # 95 tokens self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs): out = {} out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) if llama_template is None: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs) embed_count = 0 for r in llama_text_tokens: for i in range(len(r)): if r[i][0] == 128257: if image_embeds is not None and embed_count < image_embeds.shape[0]: r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image", "image_interleave": image_interleave},) + r[i][1:] embed_count += 1 out["llama"] = llama_text_tokens return out def untokenize(self, token_weight_pair): return self.clip_l.untokenize(token_weight_pair) def state_dict(self): return {} class HunyuanVideo15Tokenizer(HunyuanImageTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.llama_template = "<|im_start|>system\nYou are a helpful assistant. Describe the video by detailing the following aspects:\n1. The main content and theme of the video.\n2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.\n3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.\n4. background environment, light, style and atmosphere.\n5. camera angles, movements, and transitions used in the video.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): return super().tokenize_with_weights(text, return_word_ids, prevent_empty_text=True, **kwargs) class HunyuanVideoClipModel(torch.nn.Module): def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}): super().__init__() dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) self.llama = LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options) self.dtypes = set([dtype, dtype_llama]) def set_clip_options(self, options): self.clip_l.set_clip_options(options) self.llama.set_clip_options(options) def reset_clip_options(self): self.clip_l.reset_clip_options() self.llama.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_l = token_weight_pairs["l"] token_weight_pairs_llama = token_weight_pairs["llama"] llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama) template_end = 0 extra_template_end = 0 extra_sizes = 0 user_end = 9999999999999 images = [] tok_pairs = token_weight_pairs_llama[0] for i, v in enumerate(tok_pairs): elem = v[0] if not torch.is_tensor(elem): if isinstance(elem, numbers.Integral): if elem == 128006: if tok_pairs[i + 1][0] == 882: if tok_pairs[i + 2][0] == 128007: template_end = i + 2 user_end = -1 if elem == 128009 and user_end == -1: user_end = i + 1 else: if elem.get("original_type") == "image": elem_size = elem.get("data").shape[0] if template_end > 0: if user_end == -1: extra_template_end += elem_size - 1 else: image_start = i + extra_sizes image_end = i + elem_size + extra_sizes images.append((image_start, image_end, elem.get("image_interleave", 1))) extra_sizes += elem_size - 1 if llama_out.shape[1] > (template_end + 2): if tok_pairs[template_end + 1][0] == 271: template_end += 2 llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]): llama_extra_out.pop("attention_mask") # attention mask is useless if no masked elements if len(images) > 0: out = [] for i in images: out.append(llama_out[:, i[0]: i[1]: i[2]]) llama_output = torch.cat(out + [llama_output], dim=1) l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) return llama_output, l_pooled, llama_extra_out def load_sd(self, sd): if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: return self.clip_l.load_sd(sd) else: return self.llama.load_sd(sd) def hunyuan_video_clip(dtype_llama=None, llama_quantization_metadata=None): class HunyuanVideoClipModel_(HunyuanVideoClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options) return HunyuanVideoClipModel_ ================================================ FILE: comfy/text_encoders/hydit.py ================================================ from comfy import sd1_clip from transformers import BertTokenizer from .spiece_tokenizer import SPieceTokenizer from .bert import BertModel import comfy.text_encoders.t5 import os import torch class HyditBertModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json") model_options = {**model_options, "model_name": "hydit_clip"} super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) class HyditBertTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77, tokenizer_data=tokenizer_data) class MT5XLModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json") model_options = {**model_options, "model_name": "mt5xl"} super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) class MT5XLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model") tokenizer = tokenizer_data.get("spiece_model", None) super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) def state_dict(self): return {"spiece_model": self.tokenizer.serialize_model()} class HyditTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None) self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory) self.mt5xl = MT5XLTokenizer(tokenizer_data={**tokenizer_data, "spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs) out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.hydit_clip.untokenize(token_weight_pair) def state_dict(self): return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]} class HyditModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__() self.hydit_clip = HyditBertModel(dtype=dtype, model_options=model_options) self.mt5xl = MT5XLModel(dtype=dtype, model_options=model_options) self.dtypes = set() if dtype is not None: self.dtypes.add(dtype) def encode_token_weights(self, token_weight_pairs): hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"]) mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"]) return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]} def load_sd(self, sd): if "bert.encoder.layer.0.attention.self.query.weight" in sd: return self.hydit_clip.load_sd(sd) else: return self.mt5xl.load_sd(sd) def set_clip_options(self, options): self.hydit_clip.set_clip_options(options) self.mt5xl.set_clip_options(options) def reset_clip_options(self): self.hydit_clip.reset_clip_options() self.mt5xl.reset_clip_options() ================================================ FILE: comfy/text_encoders/hydit_clip.json ================================================ { "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", "architectures": [ "BertModel" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 0, "classifier_dropout": null, "directionality": "bidi", "eos_token_id": 2, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 16, "num_hidden_layers": 24, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc_layers": 3, "pooler_size_per_head": 128, "pooler_type": "first_token_transform", "position_embedding_type": "absolute", "torch_dtype": "float32", "transformers_version": "4.22.1", "type_vocab_size": 2, "use_cache": true, "vocab_size": 47020 } ================================================ FILE: comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json ================================================ { "cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]" } ================================================ FILE: comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json ================================================ { "cls_token": "[CLS]", "do_basic_tokenize": true, "do_lower_case": true, "mask_token": "[MASK]", "name_or_path": "hfl/chinese-roberta-wwm-ext", "never_split": null, "pad_token": "[PAD]", "sep_token": "[SEP]", "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json", "strip_accents": null, "tokenize_chinese_chars": true, "tokenizer_class": "BertTokenizer", "unk_token": "[UNK]", "model_max_length": 77 } ================================================ FILE: comfy/text_encoders/hydit_clip_tokenizer/vocab.txt ================================================ [PAD] [unused1] [unused2] [unused3] [unused4] [unused5] [unused6] [unused7] [unused8] [unused9] [unused10] [unused11] [unused12] [unused13] [unused14] [unused15] [unused16] [unused17] [unused18] [unused19] [unused20] [unused21] [unused22] [unused23] [unused24] [unused25] [unused26] [unused27] [unused28] [unused29] [unused30] [unused31] [unused32] [unused33] [unused34] [unused35] [unused36] [unused37] [unused38] [unused39] [unused40] [unused41] [unused42] [unused43] [unused44] [unused45] [unused46] [unused47] [unused48] [unused49] [unused50] [unused51] [unused52] [unused53] [unused54] [unused55] [unused56] [unused57] [unused58] [unused59] [unused60] [unused61] [unused62] [unused63] [unused64] [unused65] [unused66] [unused67] [unused68] [unused69] 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##騁 ##騎 ##騏 ##験 ##騙 ##騨 ##騰 ##騷 ##驀 ##驅 ##驊 ##驍 ##驒 ##驕 ##驗 ##驚 ##驛 ##驟 ##驢 ##驥 ##马 ##驭 ##驮 ##驯 ##驰 ##驱 ##驳 ##驴 ##驶 ##驷 ##驸 ##驹 ##驻 ##驼 ##驾 ##驿 ##骁 ##骂 ##骄 ##骅 ##骆 ##骇 ##骈 ##骊 ##骋 ##验 ##骏 ##骐 ##骑 ##骗 ##骚 ##骛 ##骜 ##骞 ##骠 ##骡 ##骤 ##骥 ##骧 ##骨 ##骯 ##骰 ##骶 ##骷 ##骸 ##骼 ##髂 ##髅 ##髋 ##髏 ##髒 ##髓 ##體 ##髖 ##高 ##髦 ##髪 ##髮 ##髯 ##髻 ##鬃 ##鬆 ##鬍 ##鬓 ##鬚 ##鬟 ##鬢 ##鬣 ##鬥 ##鬧 ##鬱 ##鬼 ##魁 ##魂 ##魄 ##魅 ##魇 ##魍 ##魏 ##魔 ##魘 ##魚 ##魯 ##魷 ##鮑 ##鮨 ##鮪 ##鮭 ##鮮 ##鯉 ##鯊 ##鯖 ##鯛 ##鯨 ##鯰 ##鯽 ##鰍 ##鰓 ##鰭 ##鰲 ##鰻 ##鰾 ##鱈 ##鱉 ##鱔 ##鱗 ##鱷 ##鱸 ##鱼 ##鱿 ##鲁 ##鲈 ##鲍 ##鲑 ##鲛 ##鲜 ##鲟 ##鲢 ##鲤 ##鲨 ##鲫 ##鲱 ##鲲 ##鲶 ##鲷 ##鲸 ##鳃 ##鳄 ##鳅 ##鳌 ##鳍 ##鳕 ##鳖 ##鳗 ##鳝 ##鳞 ##鳥 ##鳩 ##鳳 ##鳴 ##鳶 ##鴉 ##鴕 ##鴛 ##鴦 ##鴨 ##鴻 ##鴿 ##鵑 ##鵜 ##鵝 ##鵡 ##鵬 ##鵰 ##鵲 ##鶘 ##鶩 ##鶯 ##鶴 ##鷗 ##鷲 ##鷹 ##鷺 ##鸚 ##鸞 ##鸟 ##鸠 ##鸡 ##鸢 ##鸣 ##鸥 ##鸦 ##鸨 ##鸪 ##鸭 ##鸯 ##鸳 ##鸵 ##鸽 ##鸾 ##鸿 ##鹂 ##鹃 ##鹄 ##鹅 ##鹈 ##鹉 ##鹊 ##鹌 ##鹏 ##鹑 ##鹕 ##鹘 ##鹜 ##鹞 ##鹤 ##鹦 ##鹧 ##鹫 ##鹭 ##鹰 ##鹳 ##鹵 ##鹹 ##鹼 ##鹽 ##鹿 ##麂 ##麋 ##麒 ##麓 ##麗 ##麝 ##麟 ##麥 ##麦 ##麩 ##麴 ##麵 ##麸 ##麺 ##麻 ##麼 ##麽 ##麾 ##黃 ##黄 ##黍 ##黎 ##黏 ##黑 ##黒 ##黔 ##默 ##黛 ##黜 ##黝 ##點 ##黠 ##黨 ##黯 ##黴 ##鼋 ##鼎 ##鼐 ##鼓 ##鼠 ##鼬 ##鼹 ##鼻 ##鼾 ##齁 ##齊 ##齋 ##齐 ##齒 ##齡 ##齢 ##齣 ##齦 ##齿 ##龄 ##龅 ##龈 ##龊 ##龋 ##龌 ##龍 ##龐 ##龔 ##龕 ##龙 ##龚 ##龛 ##龜 ##龟 ##︰ ##︱ ##︶ ##︿ ##﹁ ##﹂ ##﹍ ##﹏ ##﹐ ##﹑ ##﹒ ##﹔ ##﹕ ##﹖ ##﹗ ##﹙ ##﹚ ##﹝ ##﹞ ##﹡ ##﹣ ##！ ##＂ ##＃ ##＄ ##％ ##＆ ##＇ ##（ ##） ##＊ ##， ##－ ##． ##／ ##： ##； ##＜ ##？ ##＠ ##［ ##＼ ##］ ##＾ ##＿ ##｀ ##ｆ ##ｈ ##ｊ ##ｕ ##ｗ ##ｚ ##｛ ##｝ ##｡ ##｢ ##｣ ##､ ##･ ##ｯ ##ｰ ##ｲ ##ｸ ##ｼ ##ｽ ##ﾄ ##ﾉ ##ﾌ ##ﾗ ##ﾙ ##ﾝ ##ﾞ ##ﾟ ##￣ ##￥ ##👍 ##🔥 ##😂 ##😎 [unused0] [unused100] [unused101] [unused102] [unused103] [unused104] [unused105] [unused106] [unused107] [unused108] [unused109] [unused110] [unused111] [unused112] [unused113] [unused114] [unused115] [unused116] [unused117] [unused118] [unused119] [unused120] [unused121] [unused122] [unused123] [unused124] [unused125] [unused126] [unused127] [unused128] [unused129] [unused130] [unused131] [unused132] [unused133] [unused134] [unused135] [unused136] [unused137] [unused138] [unused139] [unused140] [unused141] [unused142] [unused143] [unused144] [unused145] [unused146] [unused147] [unused148] [unused149] [unused150] [unused151] [unused152] [unused153] [unused154] [unused155] [unused156] [unused157] [unused158] [unused159] [unused160] [unused161] [unused162] [unused163] [unused164] [unused165] [unused166] [unused167] [unused168] [unused169] [unused170] [unused171] [unused172] [unused173] [unused174] [unused175] [unused176] [unused177] [unused178] [unused179] [unused180] [unused181] [unused182] [unused183] [unused184] [unused185] [unused186] [unused187] [unused188] [unused189] [unused190] [unused191] [unused192] [unused193] [unused194] [unused195] [unused196] [unused197] [unused198] [unused199] [unused200] [unused201] [unused202] [unused203] [unused204] [unused205] [unused206] [unused207] [unused208] [unused209] [unused210] [unused211] [unused212] [unused213] [unused214] [unused215] [unused216] [unused217] [unused218] [unused219] [unused220] [unused221] [unused222] [unused223] [unused224] [unused225] [unused226] [unused227] [unused228] [unused229] [unused230] [unused231] [unused232] [unused233] [unused234] [unused235] [unused236] [unused237] [unused238] [unused239] [unused240] [unused241] [unused242] [unused243] [unused244] [unused245] [unused246] [unused247] [unused248] [unused249] [unused250] [unused251] [unused252] [unused253] [unused254] [unused255] [unused256] [unused257] [unused258] [unused259] [unused260] [unused261] [unused262] [unused263] [unused264] [unused265] [unused266] [unused267] [unused268] [unused269] [unused270] [unused271] [unused272] [unused273] [unused274] [unused275] [unused276] [unused277] [unused278] [unused279] [unused280] [unused281] [unused282] [unused283] [unused284] [unused285] [unused286] [unused287] [unused288] [unused289] [unused290] [unused291] [unused292] [unused293] [unused294] [unused295] [unused296] [unused297] [unused298] [unused299] [unused300] [unused301] [unused302] [unused303] [unused304] [unused305] [unused306] [unused307] [unused308] [unused309] [unused310] [unused311] [unused312] [unused313] [unused314] [unused315] [unused316] [unused317] [unused318] [unused319] [unused320] [unused321] [unused322] [unused323] [unused324] [unused325] [unused326] [unused327] [unused328] [unused329] [unused330] [unused331] [unused332] [unused333] [unused334] [unused335] [unused336] [unused337] [unused338] [unused339] [unused340] [unused341] [unused342] [unused343] [unused344] [unused345] [unused346] [unused347] [unused348] [unused349] [unused350] [unused351] [unused352] [unused353] [unused354] [unused355] [unused356] [unused357] [unused358] [unused359] [unused360] [unused361] [unused362] [unused363] [unused364] [unused365] [unused366] [unused367] [unused368] [unused369] [unused370] [unused371] [unused372] [unused373] [unused374] [unused375] [unused376] [unused377] [unused378] [unused379] [unused380] [unused381] [unused382] [unused383] [unused384] [unused385] [unused386] [unused387] [unused388] [unused389] [unused390] [unused391] [unused392] [unused393] [unused394] [unused395] [unused396] [unused397] [unused398] [unused399] [unused400] [unused401] [unused402] [unused403] [unused404] [unused405] [unused406] [unused407] [unused408] [unused409] [unused410] [unused411] [unused412] [unused413] [unused414] [unused415] [unused416] [unused417] [unused418] [unused419] [unused420] [unused421] [unused422] [unused423] [unused424] [unused425] [unused426] [unused427] [unused428] [unused429] [unused430] [unused431] [unused432] [unused433] [unused434] [unused435] [unused436] [unused437] [unused438] [unused439] [unused440] [unused441] [unused442] [unused443] [unused444] [unused445] [unused446] [unused447] [unused448] [unused449] [unused450] [unused451] [unused452] [unused453] [unused454] [unused455] [unused456] [unused457] [unused458] [unused459] [unused460] [unused461] [unused462] [unused463] [unused464] [unused465] [unused466] [unused467] [unused468] [unused469] [unused470] [unused471] [unused472] [unused473] [unused474] [unused475] [unused476] [unused477] [unused478] [unused479] [unused480] [unused481] [unused482] [unused483] [unused484] [unused485] [unused486] [unused487] [unused488] [unused489] [unused490] [unused491] [unused492] [unused493] [unused494] [unused495] [unused496] [unused497] [unused498] [unused499] [unused500] [unused501] [unused502] [unused503] [unused504] [unused505] [unused506] [unused507] [unused508] [unused509] [unused510] [unused511] [unused512] [unused513] [unused514] [unused515] [unused516] [unused517] [unused518] [unused519] [unused520] [unused521] [unused522] [unused523] [unused524] [unused525] [unused526] [unused527] [unused528] [unused529] [unused530] [unused531] [unused532] [unused533] [unused534] [unused535] [unused536] [unused537] [unused538] [unused539] [unused540] [unused541] [unused542] [unused543] [unused544] [unused545] [unused546] [unused547] [unused548] [unused549] [unused550] [unused551] [unused552] [unused553] [unused554] [unused555] [unused556] [unused557] [unused558] [unused559] [unused560] [unused561] [unused562] [unused563] [unused564] [unused565] [unused566] [unused567] [unused568] [unused569] [unused570] [unused571] [unused572] [unused573] [unused574] [unused575] [unused576] [unused577] [unused578] [unused579] [unused580] [unused581] [unused582] [unused583] [unused584] [unused585] [unused586] [unused587] [unused588] [unused589] [unused590] [unused591] [unused592] [unused593] [unused594] [unused595] [unused596] [unused597] [unused598] [unused599] [unused600] [unused601] [unused602] [unused603] [unused604] [unused605] [unused606] [unused607] [unused608] [unused609] [unused610] [unused611] [unused612] [unused613] [unused614] [unused615] [unused616] [unused617] [unused618] [unused619] [unused620] [unused621] [unused622] [unused623] [unused624] [unused625] [unused626] [unused627] [unused628] [unused629] [unused630] [unused631] [unused632] [unused633] [unused634] [unused635] [unused636] [unused637] [unused638] [unused639] [unused640] [unused641] [unused642] [unused643] [unused644] [unused645] [unused646] [unused647] [unused648] [unused649] [unused650] [unused651] [unused652] [unused653] [unused654] [unused655] [unused656] [unused657] [unused658] [unused659] [unused660] [unused661] [unused662] [unused663] [unused664] [unused665] [unused666] [unused667] [unused668] [unused669] [unused670] [unused671] [unused672] [unused673] [unused674] [unused675] [unused676] [unused677] [unused678] [unused679] [unused680] [unused681] [unused682] [unused683] [unused684] [unused685] [unused686] [unused687] [unused688] [unused689] [unused690] [unused691] [unused692] [unused693] [unused694] [unused695] [unused696] [unused697] [unused698] [unused699] [unused700] [unused701] [unused702] [unused703] [unused704] [unused705] [unused706] [unused707] [unused708] [unused709] [unused710] [unused711] [unused712] [unused713] [unused714] [unused715] [unused716] [unused717] [unused718] [unused719] [unused720] 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court short round seen always become sure almost director council career things using couldn better students married nothing worked others record anything continued give military established returned does written thing feet far already championship western department role various production television produced working region present period looking least total england wife per brother soon political taken created further able reached joined upon done important either appeared position ground lead election arms police instead words moment someone announced less wrote past followed founded finally india taking records considered northern toward european outside described track playing heard professional australia miles yet trying blood southern maybe everything mouth race recorded above daughter points middle move tried elected closed ten minister chief person similar brought rest formed floor doing killed training needed turn finished railway rather sent example ran term coming currently forces despite areas fact dead originally germany probably developed pulled stood signed songs child eventually met average teams minutes current kind decided usually eastern seemed episode bed added indian route available throughout addition appointed eight construction mean remained schools sometimes events possible australian forward debut seat performance committee features character herself lot russian range hours sold quickly directed guitar performed players smile myself placed province towards wouldn leading whole designed census europe attack japanese getting alone lower wide hospital believe changed sister gone hadn ship studies academy shot below involved kept largest especially beginning movement section female professor lord longer walked actually civil families thus aircraft completed includes captain fight vocals featured fourth officer hear means medical groups lips competition entire lived leaving federal tournament passed independent kingdom spent fine doesn reported fall raised itself replaced leader theatre whose parents spanish canadian degree writing awarded higher coast provided senior organization stopped onto countries parts conference interest saying allowed earlier matter winning try happened moving los breath nearly mid certain italian african standing fell artist shows deal mine industry everyone republic provide student primary owned older heavy 1st makes attention anyone africa stated length ended fingers command staff foreign opening governor okay medal kill introduced chest hell feeling success meet reason meeting novel trade buildings guy goal native husband previously entered producer operations takes covered forced roman complete successful texas cold traditional films clear approximately nine prince question tracks ireland regional personal operation economic holding twenty additional hour regular historic places whom shook km² secretary prior scored units ask property ready immediately month listed contract themselves lines navy writer meant runs practice championships singer commission required starting generally giving attended couple stand catholic caught executive thinking chair quite shoulder hope decision plays defeated municipality whether offered slowly pain direction mission mostly noted individual managed lives plant helped except studied computer figure relationship issue significant loss smiled gun highest male bring goals mexico problem distance commercial completely location annual famous neck caused italy understand greek highway wrong comes appearance issues musical companies castle income assembly bass initially parliament artists experience particular walk foot engineering talking dropped boys stars remember carried train stadium angeles evidence becoming assistant soviet upper youth reach actor numerous nodded arrived minute believed complex victory associated temple chance perhaps bishop launched particularly retired subject prize contains yeah theory empire suddenly waiting trust recording terms champion religious zealand names 2nd ancient corner represented legal justice cause watched brothers material changes simply response answer historical stories straight feature increased administration virginia activities cultural overall winner programs basketball legs guard cast doctor flight results remains cost effect winter larger islands problems chairman grew commander isn failed selected hurt fort regiment majority plans shown pretty irish characters directly scene likely operated allow matches looks houses fellow marriage rules florida expected nearby congress peace recent wait subsequently variety serving agreed poor attempt wood democratic rural mile appears township soldiers ##ized pennsylvania closer fighting claimed score physical filled genus specific sitting mom therefore supported status fear cases meaning wales minor spain vice parish separate horse fifth remaining branch presented stared uses forms baseball exactly choice discovered composed truth russia dad ring referred numbers greater metres slightly direct increase responsible crew rule trees troops broke goes individuals hundred weight creek sleep defense provides ordered jewish safe judge whatever corps realized growing cities gaze lies spread letter showed situation mayor transport watching workers extended expression normal chart multiple border mrs walls piano heat cannot earned products drama era authority seasons join grade difficult territory mainly stations squadron stepped iron 19th serve appear speak broken charge knowledge kilometres removed ships campus pushed britain leaves recently boston latter acquired poland quality officers presence planned nations mass broadcast influence wild emperor electric headed ability promoted yellow ministry throat smaller politician latin spoke cars males lack acting seeing consists estate pressure newspaper olympics conditions beat elements walking vote needs carolina featuring levels francisco purpose females dutch duke ahead gas safety serious turning highly lieutenant firm amount mixed proposed perfect agreement affairs 3rd seconds contemporary paid prison label administrative intended constructed academic teacher races formerly nation issued shut drums housing seems graduated mentioned picked recognized shortly protection picture notable elections 1980s loved percent racing elizabeth volume hockey beside settled competed replied drew actress marine scotland steel glanced farm risk tonight positive singles effects gray screen residents sides none secondary literature polish destroyed flying founder households lay reserve industrial younger approach appearances ones finish powerful fully growth honor jersey projects revealed infantry pair equipment visit evening grant effort treatment buried republican primarily bottom owner 1970s israel gives remain spot produce champions accepted ways ##ally losing split capacity basis trial questions 20th guess officially memorial naval initial ##ization whispered median engineer sydney columbia strength tears senate asian draw warm supposed transferred leaned candidate escape mountains potential activity seem traffic murder slow orchestra haven agency taught website comedy unable storm planning albums rugby environment scientific grabbed protect boat typically damage principal divided dedicated ohio pick fought driver empty shoulders sort thank berlin prominent account freedom necessary efforts headquarters follows alongside suggested operating steps technical begin easily teeth speaking settlement scale renamed enemy semi joint compared scottish leadership analysis offers georgia pieces captured animal deputy organized combined method challenge 1960s huge wants battalion sons rise crime types facilities telling platform sit 1990s tells assigned pull commonly alive letters concept conducted wearing happen bought becomes holy gets defeat languages purchased occurred titled declared applied sciences concert sounds jazz brain painting fleet tax michigan animals leaders episodes birth clubs palace critical refused fair leg laughed returning surrounding participated formation lifted pointed connected rome medicine laid powers tall shared focused knowing yards entrance falls calling sources chosen beneath resources yard nominated silence defined gained thirty bodies adopted christmas widely register apart iran premier serves unknown parties generation continues fields brigade quiet teaching clothes impact weapons partner flat theater relations plants suffered begins seats armed models worth laws communities classes background knows thanks quarter reaching humans carry killing format setting architecture disease railroad possibly arthur thoughts doors density crowd illinois stomach tone unique reports anyway liberal vehicle thick dry drug faced largely facility theme holds creation strange colonel revolution politics turns silent rail relief independence combat shape determined sales learned 4th finger providing heritage fiction situated designated allowing hosted sight interview estimated reduced toronto footballer keeping guys damn claim motion sixth stayed rear receive handed twelve dress audience granted brazil spirit ##ated noticed olympic representative tight trouble reviews drink vampire missing roles ranked newly household finals critics phase massachusetts pilot unlike philadelphia bright guns crown organizations roof respectively clearly tongue marked circle bronze expanded sexual supply yourself inspired labour reference draft connection reasons driving jesus cells entry neither trail claims atlantic orders labor nose afraid identified intelligence calls cancer attacked passing positions imperial grey swedish avoid extra uncle covers allows surprise materials fame hunter citizens figures environmental confirmed shit titles performing difference acts attacks existing votes opportunity nor entirely trains opposite pakistan develop resulted representatives actions reality pressed barely conversation faculty northwest ends documentary nuclear stock sets eat alternative resulting creating surprised cemetery drop finding cricket streets tradition ride ear explained composer injury apartment municipal educational occupied netherlands clean billion constitution learn maximum classical lose opposition ontario hills rolled ending drawn permanent lewis sites chamber scoring height lyrics staring officials snow oldest qualified interior apparently succeeded thousand dinner lights existence heavily greatest conservative send bowl catch duty speech authorities princess performances versions shall graduate pictures effective remembered poetry desk crossed starring starts passenger sharp acres ass weather falling rank fund supporting adult heads southeast lane condition transfer prevent regions earl federation relatively answered besides obtained portion reaction liked peak counter religion chain rare convention aid lie vehicles perform squad wonder lying crazy sword attempted centuries weren philosophy interested sweden wolf frequently abandoned literary alliance task entitled threw promotion tiny soccer visited achieved defence internal persian methods arrested otherwise programming villages elementary districts rooms criminal conflict worry trained attempts waited signal truck subsequent programme communist faith sector carrying laugh controlled korean showing origin fuel evil brief identity darkness pool missed publication wings invited briefly standards kissed ideas climate causing walter worse albert winners desire aged northeast dangerous gate doubt wooden poet rising funding communications communication violence copies prepared investigation skills pulling containing ultimately offices singing understanding tomorrow christ ward pope stands 5th flow studios aired commissioned contained exist americans wrestling approved kid employed respect suit asking increasing frame angry selling 1950s thin finds temperature statement ali explain inhabitants towns extensive narrow flowers promise somewhere closely bureau cape weekly presidential legislative launch founding artillery strike un institutions roll writers landing chose anymore attorney billboard receiving agricultural breaking sought dave admitted lands mexican ##bury specifically hole moscow roads accident proved struck guards stuff slid expansion melbourne opposed sub southwest architect failure plane tank listen regarding wet introduction metropolitan fighter inch grown gene anger fixed khan domestic worldwide chapel mill functions examples developing turkey hits pocket antonio papers grow unless circuit 18th concerned attached journalist selection journey converted provincial painted hearing aren bands negative aside wondered knight lap noise billy shooting bedroom priest resistance motor homes sounded giant scenes equal comic patients hidden solid actual bringing afternoon touched funds consisted marie canal treaty turkish recognition residence cathedral broad knees incident shaped fired norwegian handle cheek contest represent representing birds advantage emergency wrapped drawing notice broadcasting somehow bachelor seventh collected registered establishment assumed chemical personnel retirement portuguese wore tied device threat progress advance ##ised banks hired manchester nfl teachers structures forever tennis helping saturday applications junction incorporated neighborhood dressed ceremony influenced hers stairs decades inner kansas hung hoped gain scheduled downtown engaged austria clock norway certainly pale victor employees plate putting surrounded ##ists finishing blues tropical minnesota consider philippines accept retrieved concern anderson properties institution gordon successfully vietnam backing outstanding muslim crossing folk producing usual demand occurs observed lawyer educated pleasure budget items quietly colorado philip typical ##worth derived survived asks mental jake jews distinguished sri extremely athletic loud thousands worried transportation horses weapon arena importance users objects contributed douglas aware senator johnny sisters engines flag investment samuel shock capable clark row wheel refers familiar biggest wins hate maintained drove hamilton expressed injured underground churches wars tunnel passes stupid agriculture softly cabinet regarded joining indiana dates spend behavior woods protein gently chase morgan mention burning wake combination occur mirror leads indeed impossible paintings covering soldier locations attendance sell historian wisconsin invasion argued painter diego changing egypt experienced inches missouri grounds spoken switzerland reform rolling forget massive resigned burned tennessee locked values improved wounded universe sick dating facing purchase ##pur moments merged anniversary coal brick understood causes dynasty queensland establish stores crisis promote hoping cards referee extension raise arizona improve colonial formal charged palm hide rescue faces feelings candidates juan 6th courses weekend luke cash fallen delivered affected installed carefully tries hollywood costs lincoln responsibility shore proper normally maryland assistance constant offering friendly waters persons realize contain trophy partnership factor musicians bound oregon indicated houston medium consisting somewhat cycle beer moore frederick gotten worst weak approached arranged chin loan bond fifteen pattern disappeared translated ##zed lip arab capture interests insurance shifted cave prix warning sections courts coat plot smell golf favorite maintain knife voted degrees finance quebec opinion translation manner ruled operate productions choose musician confused tired separated stream techniques committed attend ranking kings throw passengers measure horror mining sand danger salt calm decade dam require runner rush associate greece rivers consecutive matthew ##ski sighed sq documents closing tie accused islamic distributed directors organisation 7th breathing mad lit arrival concrete taste composition shaking faster amateur adjacent stating twin flew publications obviously ridge storage carl pages concluded desert driven universities ages terminal sequence borough constituency cousin economics dreams margaret notably reduce montreal 17th ears saved vocal riding roughly threatened meters meanwhile landed compete repeated grass czech regularly charges sudden appeal solution describes classification glad parking belt physics rachel hungarian participate expedition damaged gift childhood fifty mathematics jumped letting defensive mph testing hundreds shoot owners matters smoke israeli kentucky dancing mounted grandfather designs profit argentina truly lawrence cole begun detroit willing branches smiling decide miami enjoyed recordings ##dale poverty ethnic arabic accompanied fishing determine residential acid returns starred strategy forty businesses equivalent commonwealth distinct ill seriously ##ped harris replace rio imagine formula ensure additionally scheme conservation occasionally purposes feels favor 1930s contrast hanging hunt movies instruments victims danish christopher busy demon sugar earliest colony studying duties belgium slipped carter visible stages iraq commune forming continuing talked counties legend bathroom option tail clay daughters afterwards severe jaw visitors devices aviation entering subjects temporary swimming forth smooth bush operates rocks movements signs eddie voices honorary memories dallas measures racial promised harvard 16th parliamentary indicate benefit flesh dublin louisiana patient sleeping membership coastal medieval wanting element scholars rice limit survive makeup rating definitely collaboration obvious baron birthday linked soil diocese ncaa offensive shouldn waist plain ross organ resolution manufacturing adding relative kennedy whilst moth gardens crash heading partners credited carlos moves cable marshall depending bottle represents rejected responded existed denmark ##ating treated graham routes talent commissioner drugs secure tests reign restored photography contributions oklahoma designer disc grin seattle robin paused atlanta unusual praised las laughing satellite hungary visiting interesting factors deck poems norman ##water stuck speaker rifle premiered comics actors reputation eliminated 8th ceiling prisoners leather austin mississippi rapidly admiral parallel charlotte guilty tools gender divisions fruit laboratory nelson marry rapid aunt tribe requirements aspects suicide amongst adams bone ukraine kick sees edinburgh clothing column rough gods hunting broadway gathered concerns spending ty 12th snapped requires solar bones cavalry iowa drinking waste franklin charity thompson stewart tip landscape enjoy singh poem listening eighth fred differences adapted bomb ukrainian surgery corporate masters anywhere waves odd portugal orleans dick debate kent eating puerto cleared expect cinema guitarist blocks electrical agree involving depth dying panel struggle peninsula adults novels emerged vienna debuted shoes tamil songwriter meets prove beating instance heaven scared sending marks artistic passage superior significantly retained ##izing technique cheeks warren maintenance destroy extreme allied appearing fill advice alabama qualifying policies cleveland hat battery authors 10th soundtrack acted dated lb glance equipped coalition funny outer ambassador roy possibility couples campbell loose ethan supplies gonna monster shake agents frequency springs dogs practices gang plastic easier suggests gulf blade exposed colors industries markets nervous electoral charts legislation ownership ##idae appointment shield assault socialist abbey monument license throne employment replacement charter suffering accounts oak connecticut strongly wright colour 13th context welsh networks voiced gabriel forehead manage schedule totally remix forests occupation print nicholas brazilian strategic vampires engineers roots seek correct instrumental und alfred backed stanley robinson traveled wayne austrian achieve exit rates strip whereas sing deeply adventure bobby jamie careful components cap useful personality knee pushing hosts protest ottoman symphony boundary processes considering considerable tons cooper trading conduct illegal revolutionary definition harder jacob circumstances destruction popularity grip classified liverpool baltimore flows seeking honour approval mechanical till happening statue critic increasingly immediate describe commerce stare indonesia meat rounds boats baker orthodox depression formally worn naked muttered sentence 11th document criticism wished vessel spiritual bent virgin minimum murray lunch danny printed compilation keyboards blow belonged raising cutting pittsburgh 9th shadows hated indigenous jon 15th barry scholar oliver stick susan meetings attracted spell romantic ye demanded customers logan revival keys modified commanded jeans upset phil detective hiding resident ##bly experiences diamond defeating coverage lucas external parks franchise helen bible successor percussion celebrated lift clan romania ##ied mills nobody achievement shrugged fault rhythm initiative breakfast carbon lasted violent wound killer gradually filmed °c processing remove criticized guests sang chemistry legislature ##bridge uniform escaped integrated proposal purple denied liquid influential morris nights stones intense experimental twisted pace nazi mitchell ny blind reporter newspapers 14th centers burn basin forgotten surviving filed collections monastery losses manual couch description appropriate merely missions sebastian restoration replacing triple elder julia warriors benjamin julian convinced stronger amazing declined versus merchant happens output finland bare barbara absence ignored dawn injuries producers luis ##ities kw admit expensive electricity exception symbol ladies shower sheriff characteristics ##je aimed button ratio effectively summit angle jury bears foster vessels pants executed evans dozen advertising kicked patrol competitions lifetime principles athletics birmingham sponsored rob nomination acoustic creature longest credits harbor dust josh territories milk infrastructure completion thailand indians leon archbishop assist pitch blake arrangement girlfriend serbian operational hence sad scent fur sessions refer rarely exists 1892 scientists dirty penalty burst portrait seed pole limits rival stable grave constitutional alcohol arrest flower mystery devil architectural relationships greatly habitat ##istic larry progressive remote cotton preserved reaches cited vast scholarship decisions teach editions knocked eve searching partly participation animated fate excellent alternate saints youngest climbed suggest discussion staying choir lakes jacket revenue nevertheless peaked instrument wondering annually managing neil 1891 signing terry apply clinical brooklyn aim catherine fuck farmers figured ninth pride hugh ordinary involvement comfortable shouted encouraged representation sharing panic exact cargo competing fat cried 1920s occasions cabin borders utah marcus ##isation badly muscles victorian transition warner bet permission slave terrible similarly shares seth uefa possession medals benefits colleges lowered perfectly transit ##kar publisher ##ened harrison deaths elevation asleep machines sigh ash hardly argument occasion parent decline contribution concentration opportunities hispanic guardian extent emotions hips mason volumes bloody controversy diameter steady mistake phoenix identify violin departure richmond spin funeral enemies 1864 literally connor random sergeant grab confusion 1865 transmission informed leaning sacred suspended thinks gates portland luck agencies yours hull expert muscle layer practical sculpture jerusalem latest lloyd statistics deeper recommended warrior arkansas mess supports greg eagle recovered rated concerts rushed stops eggs premiere keith delhi turner pit affair belief paint ##zing victim withdrew bonus styles fled glasgow technologies funded adaptation portrayed cooperation supporters judges bernard hallway ralph graduating controversial distant continental spider bite recognize intention mixing egyptian bow tourism suppose claiming dominated participants nurse partially tape psychology essential touring duo voting civilian emotional channels apparent hebrew 1887 tommy carrier intersection beast hudson bench discuss costa ##ered detailed behalf drivers unfortunately obtain rocky ##dae siege friendship 1861 hang governments collins respond wildlife preferred operator laura pregnant videos dennis suspected boots instantly weird automatic businessman alleged placing throwing mood 1862 perry venue jet remainder passion biological boyfriend 1863 dirt buffalo ron segment abuse genre thrown stroke colored stress exercise displayed struggled abroad dramatic wonderful thereafter madrid component widespread ##sed tale citizen todd vancouver overseas forcing crying descent discussed substantial ranks regime provinces drum zane tribes proof researchers volunteer manor silk milan donated allies venture principle delivery enterprise bars traditionally witch reminded copper pete inter colin grinned elsewhere competitive frequent scream tension texts submarine finnish defending defend pat detail affiliated stuart themes periods tool belgian ruling crimes answers folded licensed demolished hans lucy 1881 lion traded photographs writes craig trials generated beth noble debt percentage yorkshire erected viewed grades confidence ceased islam telephone retail chile m² roberts sixteen commented hampshire innocent dual pounds checked regulations afghanistan sung rico liberty assets bigger options angels relegated tribute wells attending leaf romanian monthly patterns gmina madison hurricane rev ##ians bristol elite valuable disaster democracy awareness germans freyja loop absolutely paying populations maine sole prayer spencer releases doorway bull lover midnight conclusion thirteen mediterranean nhl proud sample ##hill drummer guinea murphy climb instant attributed horn ain railways autumn ferry opponent traveling secured corridor stretched tales sheet trinity cattle helps indicates manhattan murdered fitted gentle grandmother mines shocked vegas produces caribbean belong continuous desperate drunk historically trio waved raf dealing nathan murmured interrupted residing scientist pioneer harold aaron delta attempting minority believes chorus tend lots eyed indoor load shots updated jail concerning connecting wealth slaves arrive rangers sufficient rebuilt ##wick cardinal flood muhammad whenever relation runners moral repair viewers arriving revenge punk assisted bath fairly breathe lists innings illustrated whisper nearest voters clinton ties ultimate screamed beijing lions andre fictional gathering comfort radar suitable dismissed hms ban pine wrist atmosphere voivodeship bid timber ##ned giants cameron recovery uss identical categories switched serbia laughter noah ensemble therapy peoples touching ##off locally pearl platforms everywhere ballet tables lanka herbert outdoor toured derek 1883 spaces contested swept 1878 exclusive slight connections winds prisoner collective bangladesh tube publicly wealthy isolated insisted fortune ticket spotted reportedly animation enforcement tanks decides wider lowest owen nod hitting gregory furthermore magazines fighters solutions pointing requested peru reed chancellor knights mask worker eldest flames reduction volunteers reporting wire advisory endemic origins settlers pursue knock consumer 1876 eu compound creatures mansion sentenced ivan deployed guitars frowned involves mechanism kilometers perspective shops terminus duncan alien fist bridges ##pers heroes derby swallowed patent sara illness characterized adventures slide hawaii jurisdiction organised adelaide walks biology rogers swing tightly boundaries prepare implementation stolen certified colombia edwards garage recalled rage harm nigeria breast furniture pupils settle cuba balls alaska 21st linear thrust celebration latino genetic terror ##ening lightning fee witness lodge establishing skull earning hood rebellion sporting warned missile devoted activist porch worship fourteen package decorated ##shire housed chess sailed doctors oscar joan treat garcia harbour jeremy traditions dominant jacques ##gon relocated 1879 amendment sized companion simultaneously volleyball spun acre increases stopping loves belongs affect drafted tossed scout battles 1875 filming shoved munich tenure vertical romance argue craft ranging opens honest tyler yesterday muslims reveal snake immigrants radical screaming speakers firing saving belonging ease lighting prefecture blame farmer hungry grows rubbed beam sur subsidiary armenian dropping conventional qualify spots sweat festivals immigration physician discover exposure sandy explanation isaac implemented ##fish hart initiated stakes presents heights householder pleased tourist regardless slip closest surely sultan brings riley preparation aboard slammed baptist experiment ongoing interstate organic playoffs 1877 hindu tours tier plenty arrangements talks trapped excited sank athens 1872 denver welfare suburb athletes trick diverse belly exclusively yelled conversion 1874 internationally computers conductor abilities sensitive dispute measured globe rocket prices amsterdam flights tigers municipalities emotion references explains airlines manufactured archaeological 1873 interpretation devon ##ites settlements kissing absolute improvement impressed barcelona sullivan jefferson towers jesse julie grandson gauge regard rings interviews trace raymond thumb departments burns serial bulgarian scores demonstrated 1866 kyle alberta underneath romanized relieved acquisition phrase cliff reveals cuts merger custom nee gilbert graduation assessment difficulty demands swung democrat commons 1940s grove completing focuses sum substitute bearing stretch reception reflected essentially destination pairs ##ched survival resource ##bach promoting doubles messages tear ##fully parade florence harvey incumbent partial pedro frozen procedure olivia controls shelter personally temperatures brisbane tested sits marble comprehensive oxygen leonard ##kov inaugural iranian referring quarters attitude mainstream lined mars dakota norfolk unsuccessful explosion helicopter congressional ##sing inspector bitch seal departed divine coaching examination punishment manufacturer sink columns unincorporated signals nevada squeezed dylan dining martial manuel eighteen elevator brushed plates ministers congregation slept specialized taxes restricted negotiations likes statistical arnold inspiration execution bold intermediate significance margin ruler wheels gothic intellectual dependent listened eligible buses widow syria earn cincinnati collapsed recipient secrets accessible philippine maritime goddess clerk surrender breaks playoff ideal beetle aspect soap regulation strings expand anglo shorter crosses retreat tough coins wallace directions pressing shipping locomotives comparison topics nephew distinction honors travelled sierra ibn fortress recognised carved 1869 clients intent coaches describing bread ##ington beaten northwestern merit collapse challenges historians objective submitted virus attacking drake assume diseases stem leeds farming glasses visits nowhere fellowship relevant carries restaurants experiments constantly bases targets shah tenth opponents verse territorial writings corruption instruction inherited reverse emphasis employee arch keeps rabbi watson payment uh nancy ##tre venice fastest sexy banned adrian properly ruth touchdown dollar boards metre circles edges favour travels liberation scattered firmly holland permitted diesel kenya den originated demons resumed dragged rider servant blinked extend torn ##sey input meal everybody cylinder kinds camps bullet logic croatian evolved healthy fool wise preserve pradesh respective artificial gross corresponding convicted cage caroline dialogue ##dor narrative stranger mario christianity failing trent commanding buddhist 1848 maurice focusing yale bike altitude mouse revised ##sley veteran pulls theology crashed campaigns legion ##ability drag excellence customer cancelled intensity excuse liga participating contributing printing ##burn variable curious legacy renaissance symptoms binding vocalist dancer grammar gospel democrats enters diplomatic hitler clouds mathematical quit defended oriented ##heim fundamental hardware impressive equally convince confederate guilt chuck sliding magnetic narrowed petersburg bulgaria otto phd skill hopes pitcher reservoir hearts automatically expecting mysterious bennett extensively imagined seeds monitor fix ##ative journalism struggling signature ranch encounter photographer observation protests influences calendar cruz croatia locomotive hughes naturally shakespeare basement hook uncredited faded theories approaches dare phillips filling fury obama efficient arc deliver breeding inducted leagues efficiency axis montana eagles ##ked supplied instructions karen picking indicating trap anchor practically christians tomb vary occasional electronics lords readers newcastle faint innovation collect situations engagement claude mixture ##feld peer tissue lean °f floors architects reducing rope 1859 ottawa ##har samples banking declaration proteins resignation francois saudi advocate exhibited armor twins divorce ##ras abraham reviewed temporarily matrix physically pulse curled difficulties bengal usage ##ban riders certificate holes warsaw distinctive mutual 1857 customs circular eugene removal loaded mere vulnerable depicted generations dame heir enormous lightly climbing pitched lessons pilots nepal preparing brad louise renowned liam ##ably shaw brilliant bills ##nik fucking mainland pleasant seized veterans jerked fail brush radiation stored warmth southeastern nate sin raced berkeley joke athlete designation trunk roland qualification heels artwork receives judicial reserves ##bed woke installation abu floating fake lesser excitement interface concentrated addressed characteristic amanda saxophone monk releasing egg dies interaction defender outbreak glory loving sequel consciousness awake ski enrolled handling rookie brow somebody biography warfare amounts contracts presentation fabric dissolved challenged meter psychological elevated rally accurate ##tha hospitals undergraduate specialist venezuela exhibit shed nursing protestant fluid structural footage jared consistent prey ##ska succession reflect exile lebanon wiped suspect shanghai resting integration preservation marvel variant pirates sheep rounded capita sailing colonies manuscript deemed variations clarke functional emerging boxing relaxed curse azerbaijan heavyweight nickname editorial rang grid tightened earthquake flashed miguel rushing ##ches improvements boxes brooks consumption molecular felix societies repeatedly variation aids civic graphics professionals realm autonomous receiver delayed workshop militia chairs canyon harsh extending lovely happiness ##jan stake eyebrows embassy wellington hannah corners bishops swear cloth contents namely commenced 1854 stanford nashville courage graphic commitment garrison hamlet clearing rebels attraction literacy cooking ruins temples jenny humanity celebrate hasn freight sixty rebel bastard newton deer ##ges ##ching smiles delaware singers approaching assists flame boulevard barrel planted pursuit consequences shallow invitation rode depot ernest kane rod concepts preston topic chambers striking blast arrives descendants montgomery ranges worlds chaos praise fewer 1855 sanctuary mud programmes maintaining harper bore handsome closure tournaments nebraska linda facade puts satisfied argentine dale cork dome panama ##yl 1858 tasks experts ##ates feeding equation engage bryan um quartet disbanded sheffield blocked gasped delay kisses connects ##non sts poured creator publishers guided ellis extinct hug gaining ##ord complicated poll clenched investigate thereby quantum spine cdp humor kills administered semifinals encountered ignore commentary ##maker bother roosevelt plains halfway flowing cultures crack imprisoned neighboring airline gather wolves marathon transformed cruise organisations punch exhibitions numbered alarm ratings daddy silently ##stein queens colours impression guidance tactical ##rat marshal della arrow rested feared tender owns bitter advisor escort ##ides spare farms grants dragons encourage colleagues cameras sucked pile spirits prague statements suspension landmark fence torture recreation bags permanently survivors pond spy predecessor bombing coup protecting transformation glow ##lands dug priests andrea feat barn jumping ##ologist casualties stern auckland pipe serie revealing trevor mercy spectrum consist governing collaborated possessed epic comprises blew shane lopez honored magical sacrifice judgment perceived hammer baronet tune das missionary sheets neutral oral threatening attractive shade aims seminary estates 1856 michel wounds refugees manufacturers mercury syndrome porter ##iya ##din hamburg identification upstairs purse widened pause cared breathed affiliate santiago prevented celtic fisher recruited byzantine reconstruction farther diet sake spite sensation blank separation ##hon vladimir armies anime accommodate orbit cult sofia ##ify founders sustained disorder honours northeastern mia crops violet threats blanket fires canton followers southwestern prototype voyage assignment altered moderate protocol pistol questioned brass lifting 1852 math authored doug dimensional dynamic 1851 pronounced grateful quest uncomfortable boom presidency stevens relating politicians barrier quinn diana mosque tribal palmer portions sometime chester treasure bend millions reforms registration consequently monitoring ate preliminary brandon invented eaten exterior intervention ports documented displays lecture sally favourite vermont invisible isle breed journalists relay speaks backward explore midfielder actively stefan procedures cannon blond kenneth centered servants chains libraries malcolm essex henri slavery ##hal facts fairy coached cassie cats washed cop announcement 2000s vinyl activated marco frontier growled curriculum ##das loyal accomplished leslie ritual kenny vii napoleon hollow hybrid jungle stationed friedrich counted ##ulated platinum theatrical seated col rubber glen diversity healing extends provisions administrator columbus tributary assured ##uous prestigious examined lectures grammy ronald associations bailey allan essays flute believing consultant proceedings travelling 1853 kerala yugoslavia buddy methodist burial centres batman discontinued dock stockholm lungs severely citing manga steal mumbai iraqi robot celebrity bride broadcasts abolished pot joel overhead franz packed reconnaissance johann acknowledged introduce handled doctorate developments drinks alley palestine ##aki proceeded recover bradley grain patch afford infection nationalist legendary interchange virtually gen gravity exploration amber vital wishes powell doctrine elbow screenplay ##bird contribute indonesian creates enzyme kylie discipline drops manila hunger layers suffer fever bits monica keyboard manages ##hood searched appeals ##bad testament grande reid ##war beliefs congo requiring casey 1849 regret streak rape depends syrian sprint pound tourists upcoming pub tense ##els practiced nationwide guild motorcycle liz ##zar chiefs desired elena precious absorbed relatives booth pianist ##mal citizenship exhausted wilhelm ##ceae ##hed noting quarterback urge hectares ##gue holly blonde davies parked sustainable stepping twentieth airfield nest chip ##nell shaft paulo requirement paradise tobacco trans renewed vietnamese suggesting catching holmes enjoying trips colt holder butterfly nerve reformed cherry bowling trailer carriage goodbye appreciate toy joshua interactive enabled involve ##kan collar determination bunch recall shorts superintendent episcopal frustration giovanni nineteenth laser privately array circulation ##ovic armstrong deals painful permit discrimination aires retiring cottage horizon ellen jamaica ripped fernando chapters patron lecturer behaviour genes georgian export solomon rivals seventeen rodriguez princeton independently sox 1847 arguing entity casting hank criteria oakland geographic milwaukee reflection expanding conquest dubbed halt brave brunswick arched curtis divorced predominantly somerset streams ugly zoo horrible curved buenos fierce dictionary vector theological unions handful stability punjab segments altar ignoring gesture monsters pastor thighs unexpected operators abruptly coin compiled associates improving migration compact collegiate quarterfinals roster restore assembled hurry oval ##cies 1846 flags martha victories sharply ##rated argues deadly drawings symbols performer griffin restrictions editing andrews journals arabia compositions dee pierce removing hindi casino runway civilians minds ##zation refuge rent retain potentially conferences suburban conducting descended massacre ammunition terrain fork souls counts chelsea durham drives cab perth realizing palestinian finn simpson ##dal betty moreover particles cardinals tent evaluation extraordinary inscription wednesday chloe maintains panels ashley trucks ##nation cluster sunlight strikes zhang dialect tucked collecting ##mas ##sville quoted evan franco aria buying cleaning closet provision apollo clinic rat necessarily ##ising venues flipped cent spreading trustees checking authorized disappointed ##ado notion duration trumpet hesitated topped brussels rolls theoretical hint define aggressive repeat wash peaceful optical width allegedly mcdonald strict ##illa investors jam witnesses sounding miranda michelle hugo harmony valid lynn glared nina headquartered diving boarding gibson albanian marsh routine dealt enhanced intelligent substance targeted enlisted discovers spinning observations pissed smoking capitol varied costume seemingly indies compensation surgeon thursday arsenal westminster suburbs rid anglican ##ridge knots foods alumni lighter fraser whoever portal scandal gavin advised instructor flooding terrorist teenage interim senses duck teen thesis abby eager overcome newport glenn rises shame prompted priority forgot bomber nicolas protective cartoon katherine breeze lonely trusted henderson richardson relax palms remarkable legends cricketer essay ordained edmund rifles trigger ##uri ##away sail alert 1830 audiences penn sussex siblings pursued indianapolis resist rosa consequence succeed avoided 1845 ##ulation inland ##tie ##nna counsel profession chronicle hurried ##una eyebrow eventual bleeding innovative cure committees accounting scope hardy heather tenor gut herald codes tore scales wagon luxury tin prefer fountain triangle bonds darling convoy dried traced beings troy accidentally slam findings smelled joey lawyers outcome steep bosnia configuration shifting toll brook performers lobby philosophical construct shrine aggregate cox phenomenon savage insane solely reynolds nationally holdings consideration enable edgar fights relegation chances atomic hub conjunction awkward reactions currency finale kumar underwent steering elaborate gifts comprising melissa veins reasonable sunshine solve trails inhabited elimination ethics huh ana molly consent apartments layout marines hunters bulk ##oma hometown ##wall ##mont cracked reads neighbouring withdrawn admission wingspan damned anthology lancashire brands batting forgive cuban awful ##lyn dimensions imagination dante tracking desperately goalkeeper ##yne groaned workshops confident burton gerald milton circus uncertain slope copenhagen sophia fog philosopher portraits accent cycling varying gripped larvae garrett specified scotia mature luther kurt rap ##kes aerial ferdinand heated transported ##shan safely nonetheless ##orn ##gal motors demanding ##sburg startled ##brook ally generate caps ghana stained mentions beds afterward ##bling utility ##iro richards 1837 conspiracy conscious shining footsteps observer cyprus urged loyalty developer probability olive upgraded gym miracle insects graves 1844 ourselves hydrogen katie tickets poets planes prevention witnessed dense jin randy tang warehouse monroe archived elderly investigations alec granite mineral conflicts controlling aboriginal mechanics stan stark rhode skirt est bombs respected ##horn imposed limestone deny nominee memphis grabbing disabled amusement frankfurt corn referendum varies slowed disk firms unconscious incredible clue sue ##zhou twist ##cio joins idaho chad developers computing destroyer mortal tucker kingston choices carson whitney geneva pretend dimension staged plateau maya ##une freestyle rovers ##ids tristan classroom prospect ##hus honestly diploma lied thermal auxiliary feast unlikely iata morocco pounding treasury lithuania considerably 1841 dish 1812 geological matching stumbled destroying marched brien advances nicole settling measuring directing ##mie tuesday bassist capabilities stunned fraud torpedo ##phone anton wisdom surveillance ruined ##ulate lawsuit healthcare theorem halls trend aka horizontal dozens acquire lasting swim hawk gorgeous fees vicinity decrease adoption tactics ##ography pakistani ##ole draws ##hall willie burke heath algorithm integral powder elliott brigadier jackie tate varieties darker ##cho lately cigarette specimens adds ##ensis ##inger exploded finalist murders wilderness arguments nicknamed acceptance onwards manufacture robertson jets tampa enterprises loudly composers nominations 1838 malta inquiry automobile hosting viii rays tilted grief museums strategies furious euro equality cohen poison surrey wireless governed ridiculous moses ##esh vanished barnes attract morrison istanbul ##iness absent rotation petition janet ##logical satisfaction custody deliberately observatory comedian surfaces pinyin novelist strictly canterbury oslo monks embrace jealous photograph continent dorothy marina excess holden allegations explaining stack avoiding lance storyline majesty poorly spike bradford raven travis classics proven voltage pillow fists butt 1842 interpreted 1839 gage telegraph lens promising expelled casual collector zones silly nintendo ##kh downstairs chef suspicious afl flies vacant uganda pregnancy condemned lutheran estimates cheap decree saxon proximity stripped idiot deposits contrary presenter magnus glacier offense edwin ##ori upright ##long bolt ##ois toss geographical ##izes environments delicate marking abstract xavier nails windsor plantation occurring equity saskatchewan fears drifted sequences vegetation revolt ##stic 1843 sooner fusion opposing nato skating 1836 secretly ruin lease flora anxiety ##ological ##mia bout taxi emmy frost rainbow compounds foundations rainfall assassination nightmare dominican achievements deserve orlando intact armenia ##nte calgary valentine marion proclaimed theodore bells courtyard thigh gonzalez console troop minimal everyday supporter terrorism buck openly presbyterian activists carpet ##iers rubbing uprising cute conceived legally ##cht millennium cello velocity rescued cardiff 1835 rex concentrate senators beard rendered glowing battalions scouts competitors sculptor catalogue arctic ion raja bicycle glancing lawn ##woman gentleman lighthouse publish predicted calculated variants ##gne strain winston deceased touchdowns brady caleb sinking echoed crush hon blessed protagonist hayes endangered magnitude editors ##tine estimate responsibilities ##mel backup laying consumed sealed zurich lovers frustrated ##eau ahmed kicking treasurer 1832 biblical refuse terrified pump agrees genuine imprisonment refuses plymouth lou ##nen tara trembling antarctic ton learns ##tas crap crucial faction atop ##borough wrap lancaster odds hopkins erik lyon ##eon bros snap locality empress crowned cal acclaimed chuckled clara sends mild towel wishing assuming interviewed ##bal interactions eden cups helena indie beck ##fire batteries filipino wizard parted traces ##born rows idol albany delegates ##ees ##sar discussions notre instructed belgrade highways suggestion lauren possess orientation alexandria abdul beats salary reunion ludwig alright wagner intimate pockets slovenia hugged brighton merchants cruel stole trek slopes repairs enrollment politically underlying promotional counting boeing isabella naming keen bacteria listing separately belfast ussr lithuanian anybody ribs sphere martinez cock embarrassed proposals fragments nationals ##wski premises fin alpine matched freely bounded jace sleeve pier populated evident ##like frances flooded ##dle frightened pour trainer framed visitor challenging pig wickets ##fold infected ##pes arose reward ecuador oblast vale shuttle ##usa bach rankings forbidden cornwall accordance salem consumers bruno fantastic toes machinery resolved julius remembering propaganda iceland bombardment tide contacts wives ##rah concerto macdonald albania implement daisy tapped sudan helmet mistress crop sunk finest ##craft hostile boxer fr paths adjusted habit ballot supervision soprano bullets wicked sunset regiments disappear lamp performs ##gia rabbit digging incidents entries ##cion dishes introducing ##ati ##fied freshman slot jill tackles baroque backs ##iest lone sponsor destiny altogether convert ##aro consensus shapes demonstration basically feminist auction artifacts ##bing strongest halifax allmusic mighty smallest precise alexandra viola ##los ##ille manuscripts ##illo dancers ari managers monuments blades barracks springfield maiden consolidated electron berry airing wheat nobel inclusion blair payments geography bee eleanor react ##hurst afc manitoba lineup fitness recreational investments airborne disappointment ##dis edmonton viewing renovation infant bankruptcy roses aftermath pavilion carpenter withdrawal ladder discussing popped reliable agreements rochester ##abad curves bombers rao reverend decreased choosing stiff consulting naples crawford tracy ribbon cops crushed deciding unified teenager accepting flagship poles sanchez inspection revived skilled induced exchanged flee locals tragedy swallow hanna demonstrate ##ela salvador flown contestants civilization ##ines wanna rhodes fletcher hector knocking considers nash mechanisms sensed mentally walt unclear ##eus renovated madame crews governmental undertaken monkey ##ben ##ato fatal armored copa caves governance grasp perception certification froze damp tugged wyoming ##rg ##ero newman nerves curiosity graph ##ami withdraw tunnels dull meredith moss exhibits neighbors communicate accuracy explored raiders republicans secular kat superman penny criticised freed conviction ham likewise delegation gotta doll promises technological myth nationality resolve convent sharon dig sip coordinator entrepreneur fold ##dine capability councillor synonym blown swan cursed 1815 jonas haired sofa canvas keeper rivalry ##hart rapper speedway swords postal maxwell estonia potter recurring errors ##oni cognitive 1834 claws nadu roberto bce wrestler ellie infinite ink ##tia presumably finite staircase noel patricia nacional chill eternal tu preventing prussia fossil limbs ##logist ernst frog perez rene prussian ##ios molecules regulatory answering opinions sworn lengths supposedly hypothesis upward habitats seating ancestors drank yield synthesis researcher modest ##var mothers peered voluntary homeland acclaim ##igan static valve luxembourg alto carroll receptor norton ambulance ##tian johnston catholics depicting jointly elephant gloria mentor badge ahmad distinguish remarked councils precisely allison advancing detection crowded cooperative ankle mercedes dagger surrendered pollution commit subway jeffrey lesson sculptures provider ##fication membrane timothy rectangular fiscal heating teammate basket particle anonymous deployment missiles courthouse proportion shoe sec complaints forbes blacks abandon remind sizes overwhelming autobiography natalie ##awa risks contestant countryside babies scorer invaded enclosed proceed hurling disorders ##cu reflecting continuously cruiser graduates freeway investigated ore deserved maid blocking phillip jorge shakes dove mann variables lacked burden accompanying que consistently organizing provisional complained endless tubes juice georges krishna mick thriller laps arcade sage snail shannon laurence seoul vacation presenting hire churchill surprisingly prohibited savannah technically ##oli ##lessly testimony suited speeds toys romans flowering measurement talented kay settings charleston expectations shattered achieving triumph ceremonies portsmouth lanes mandatory loser stretching cologne realizes seventy cornell careers webb ##ulating americas budapest ava suspicion yo conrad sterling jessie rector ##az 1831 transform organize loans christine volcanic warrant slender summers subfamily newer danced dynamics rhine proceeds heinrich gastropod commands sings facilitate easter positioned responses expense fruits yanked imported 25th velvet vic primitive tribune baldwin neighbourhood donna rip hay ##uro 1814 espn welcomed ##aria qualifier glare highland timing ##cted shells eased geometry louder exciting slovakia ##iz savings prairie marching rafael tonnes ##lled curtain preceding shy heal greene worthy ##pot detachment bury sherman ##eck reinforced seeks bottles contracted duchess outfit walsh mickey geoffrey archer squeeze dawson eliminate invention ##enberg neal ##eth stance dealer coral maple retire simplified 1833 hid watts backwards jules ##oke genesis frames rebounds burma woodland moist santos whispers drained subspecies streaming ulster burnt correspondence maternal gerard denis stealing genius duchy ##oria inaugurated momentum suits placement sovereign clause thames ##hara confederation reservation sketch yankees lets rotten charm hal verses commercially dot salon citation adopt winnipeg mist allocated cairo jenkins interference objectives ##wind 1820 portfolio armoured sectors initiatives integrity exercises robe tap gazed ##tones distracted rulers favorable jerome tended cart factories ##eri diplomat valued gravel charitable calvin exploring shepherd terrace pupil ##ural reflects ##rch governors shelf depths ##nberg trailed crest tackle ##nian hatred ##kai clare makers ethiopia longtime detected embedded lacking slapped rely thomson anticipation morton successive agnes screenwriter straightened philippe playwright haunted licence iris intentions sutton logical correctly ##weight branded licked tipped silva ricky narrator requests ##ents greeted supernatural cow ##wald lung refusing employer strait gaelic liner ##piece zoe sabha ##mba driveway harvest prints bates reluctantly threshold algebra ira wherever coupled assumption picks designers raids gentlemen roller blowing leipzig locks screw dressing strand ##lings scar dwarf depicts ##nu nods differ boris ##eur yuan flip ##gie mob invested questioning applying shout ##sel gameplay blamed illustrations bothered weakness rehabilitation ##zes envelope rumors miners leicester subtle kerry ferguson premiership bengali prof catches remnants dana ##rily shouting presidents baltic ought ghosts dances sailors shirley fancy dominic ##bie madonna ##rick bark buttons gymnasium ashes liver toby oath providence doyle evangelical nixon cement carnegie embarked hatch surroundings guarantee needing pirate essence filter crane hammond projected immune percy twelfth regent doctoral damon mikhail ##ichi critically elect realised abortion acute screening mythology steadily frown nottingham kirk wa minneapolis ##rra module algeria nautical encounters surprising statues availability shirts pie alma brows munster mack soup crater tornado sanskrit cedar explosive bordered dixon planets stamp exam happily ##bble carriers kidnapped accommodation emigrated ##met knockout correspondent violation profits peaks lang specimen agenda ancestry pottery spelling equations obtaining ki linking 1825 debris asylum buddhism ##ants gazette dental eligibility fathers averaged zimbabwe francesco coloured hissed translator lynch mandate humanities mackenzie uniforms ##iana asset fitting samantha genera rim beloved shark riot entities expressions indo carmen slipping owing abbot neighbor sidney rats recommendations encouraging squadrons anticipated commanders conquered donations diagnosed divide ##iva guessed decoration vernon auditorium revelation conversations ##kers ##power herzegovina dash alike protested lateral herman accredited ##gent freeman mel fiji crow crimson ##rine livestock ##pped humanitarian bored oz whip ##lene ##ali legitimate alter grinning spelled anxious oriental wesley ##nin ##hole carnival controller detect ##ssa bowed educator kosovo macedonia ##sin occupy mastering stephanie janeiro para unaware nurses noon hopefully ranger combine sociology polar rica ##eer neill ##sman holocaust doubled lust 1828 decent cooling unveiled 1829 nsw homer chapman meyer dive mae reagan expertise ##gled darwin brooke sided prosecution investigating comprised petroleum genres reluctant differently trilogy johns vegetables corpse highlighted lounge pension unsuccessfully elegant aided ivory beatles amelia cain dubai immigrant babe underwater combining mumbled atlas horns accessed ballad physicians homeless gestured rpm freak louisville corporations patriots prizes rational warn modes decorative overnight din troubled phantom monarch sheer ##dorf generals guidelines organs addresses enhance curling parishes cord ##kie caesar deutsche bavaria coleman cyclone ##eria bacon petty ##yama ##old hampton diagnosis 1824 throws complexity rita disputed pablo marketed trafficking ##ulus examine plague formats vault faithful ##bourne webster highlights ##ient phones vacuum sandwich modeling ##gated bolivia clergy qualities isabel ##nas ##ars wears screams reunited annoyed bra ##ancy ##rate differential transmitter tattoo container poker ##och excessive resides cowboys ##tum augustus trash providers statute retreated balcony reversed void storey preceded masses leap laughs neighborhoods wards schemes falcon santo battlefield ronnie lesbian venus ##dian beg sandstone daylight punched gwen analog stroked wwe acceptable measurements toxic ##kel adequate surgical economist parameters varsity ##sberg quantity ##chy ##rton countess generating precision diamonds expressway ##ı 1821 uruguay talents galleries expenses scanned colleague outlets ryder lucien ##ila paramount syracuse dim fangs gown sweep ##sie missionaries websites sentences adviser val trademark spells ##plane patience starter slim ##borg toe incredibly shoots elliot nobility ##wyn cowboy endorsed gardner tendency persuaded organisms emissions kazakhstan amused boring chips themed ##hand constantinople chasing systematic guatemala borrowed erin carey ##hard highlands struggles 1810 ##ifying ##ced exceptions develops enlarged kindergarten castro ##rina leigh zombie juvenile ##most consul sailor hyde clarence intensive pinned nasty useless jung clayton stuffed exceptional ix apostolic transactions exempt swinging cove religions shields dairy bypass pursuing joyce bombay chassis southampton chat interact redesignated ##pen nascar pray salmon rigid regained malaysian grim publicity constituted capturing toilet delegate purely tray drift loosely striker weakened trinidad mitch itv defines transmitted scarlet nodding fitzgerald narrowly tooth standings virtue ##wara ##cting chateau gloves lid hurting conservatory ##pel sinclair reopened sympathy nigerian strode advocated optional chronic discharge suck compatible laurel stella fails wage dodge informal sorts levi buddha villagers chronicles heavier summoned gateway eleventh jewelry translations accordingly seas ##ency fiber pyramid cubic dragging ##ista caring ##ops contacted lunar lisbon patted 1826 sacramento theft madagascar subtropical disputes holidays piper willow mare cane newfoundland benny companions dong raj observe roar charming plaque tibetan fossils enacted manning bubble tanzania ##eda ##hir funk swamp deputies cloak ufc scenario par scratch metals anthem guru engaging specially ##boat dialects nineteen cecil duet disability unofficial ##lies defunct moonlight drainage surname puzzle switching conservatives mammals knox broadcaster sidewalk cope ##ried benson princes peterson ##sal bedford sharks eli wreck alberto gasp archaeology lgbt teaches securities madness compromise waving coordination davidson visions leased possibilities eighty fernandez enthusiasm assassin sponsorship reviewer kingdoms estonian laboratories ##fy ##nal applies verb celebrations ##zzo rowing lightweight sadness submit balanced dude explicitly metric magnificent mound brett mohammad mistakes irregular sanders betrayed shipped surge ##enburg reporters termed georg pity verbal bulls abbreviated enabling appealed sicily sting heel sweetheart bart spacecraft brutal monarchy aberdeen cameo diane survivor clyde ##aries complaint ##makers clarinet delicious chilean karnataka coordinates 1818 panties ##rst pretending dramatically kiev tends distances catalog launching instances telecommunications portable lindsay vatican ##eim angles aliens marker stint screens bolton ##rne judy wool benedict plasma europa imaging filmmaker swiftly contributor opted stamps apologize financing butter gideon sophisticated alignment avery chemicals yearly speculation prominence professionally immortal institutional inception wrists identifying tribunal derives gains papal preference linguistic vince operative brewery ##ont unemployment boyd ##ured ##outs albeit prophet 1813 ##rad quarterly asteroid cleaned radius temper ##llen telugu jerk viscount ##ote glimpse ##aya yacht hawaiian baden laptop readily ##gu monetary offshore scots watches ##yang ##arian upgrade needle lea encyclopedia flank fingertips delight teachings confirm roth beaches midway winters ##iah teasing daytime beverly gambling ##backs regulated clement hermann tricks knot ##shing ##uring ##vre detached ecological owed specialty byron inventor bats stays screened unesco midland trim affection ##ander jess thoroughly feedback chennai strained heartbeat wrapping overtime pleaded ##sworth leisure oclc ##tate ##ele feathers angelo thirds nuts surveys clever gill commentator ##dos darren rides gibraltar dissolution dedication shin meals saddle elvis reds chaired taller appreciation functioning niece favored advocacy robbie criminals suffolk yugoslav passport constable congressman hastings ##rov consecrated sparks ecclesiastical confined ##ovich muller floyd nora 1822 paved 1827 cumberland ned saga spiral appreciated collaborative treating similarities feminine finishes ##ib jade import ##hot champagne mice securing celebrities helsinki attributes ##gos cousins phases ache lucia gandhi submission vicar spear shine tasmania biting detention constitute tighter seasonal ##gus terrestrial matthews effectiveness parody philharmonic ##onic 1816 strangers encoded consortium guaranteed regards shifts tortured collision supervisor inform broader insight theaters armour emeritus blink incorporates mapping handball flexible ##nta substantially generous thief carr loses 1793 prose ucla romeo generic metallic realization damages commissioners zach default helicopters lengthy stems partnered spectators rogue indication penalties teresa 1801 sen ##tric dalton ##wich irving photographic ##vey deaf peters excluded unsure ##vable patterson crawled ##zio resided whipped latvia slower ecole pipes employers maharashtra comparable textile pageant ##gel alphabet binary irrigation chartered choked antoine offs waking supplement quantities demolition regain locate urdu folks scary andreas whites ##ava classrooms mw aesthetic publishes valleys guides cubs johannes bryant conventions affecting ##itt drain awesome isolation prosecutor ambitious apology captive downs atmospheric lorenzo aisle beef foul ##onia kidding composite disturbed illusion natives ##ffer rockets riverside wartime painters adolf melted uncertainty simulation hawks progressed meantime builder spray breach unhappy regina russians determining tram 1806 ##quin aging 1823 garion rented mister diaz terminated clip 1817 depend nervously disco owe defenders shiva notorious disbelief shiny worcester ##gation ##yr trailing undertook islander belarus limitations watershed fuller overlooking utilized raphael 1819 synthetic breakdown klein ##nate moaned memoir lamb practicing ##erly cellular arrows exotic witches charted rey hut hierarchy subdivision freshwater giuseppe aloud reyes qatar marty sideways utterly sexually jude prayers mccarthy softball blend damien ##gging ##metric wholly erupted lebanese negro revenues tasted comparative teamed transaction labeled maori sovereignty parkway trauma gran malay advancement descendant buzz salvation inventory symbolic ##making antarctica mps ##bro mohammed myanmar holt submarines tones ##lman locker patriarch bangkok emerson remarks predators kin afghan confession norwich rental emerge advantages ##zel rca ##hold shortened storms aidan ##matic autonomy compliance ##quet dudley ##osis 1803 motto documentation summary professors spectacular christina archdiocese flashing innocence remake ##dell psychic reef scare employ sticks meg gus leans accompany bergen tomas doom wages pools ##bes breasts scholarly alison outline brittany breakthrough willis realistic ##cut ##boro competitor ##stan pike picnic designing commercials washing villain skiing costumes auburn halted executives logistics cycles vowel applicable barrett exclaimed eurovision eternity ramon ##umi modifications sweeping disgust torch aviv ensuring rude dusty sonic donovan outskirts cu pathway ##band ##gun disciplines acids cadet paired sketches ##sive marriages folding peers slovak implies admired ##beck 1880s leopold instinct attained weston megan horace ##ination dorsal ingredients evolutionary complications deity lethal brushing levy deserted institutes posthumously delivering telescope coronation motivated rapids luc flicked pays volcano tanner weighed ##nica crowds frankie gifted addressing granddaughter winding ##rna constantine gomez ##front landscapes rudolf anthropology slate werewolf astronomy circa rouge dreaming sack knelt drowned naomi prolific tracked freezing herb agony randall twisting wendy deposit touches vein wheeler ##bbled batted retaining tire presently compare specification daemon nigel ##grave merry recommendation czechoslovakia sandra roma ##sts lambert inheritance sheikh winchester cries examining ##yle comeback cuisine nave ##iv retrieve tomatoes barker polished defining irene lantern personalities begging tract swore 1809 ##gic omaha brotherhood haiti ##ots exeter ##ete ##zia steele dumb pearson surveyed elisabeth trends fritz bugs fraction calmly viking ##birds tug inserted unusually ##ield confronted distress crashing brent turks resign ##olo cambodia gabe sauce ##kal evelyn extant clusters quarry teenagers luna ##lers ##ister affiliation drill ##ashi panthers scenic libya anita strengthen inscriptions ##cated lace sued judith riots ##uted mint ##eta preparations midst dub challenger ##vich mock displaced wicket breaths enables schmidt analyst ##lum highlight automotive axe josef newark sufficiently resembles 50th ##pal flushed mum traits ##ante commodore incomplete warming titular ceremonial ethical celebrating eighteenth cao lima medalist mobility strips snakes miniature zagreb barton escapes umbrella automated doubted differs cooled georgetown dresden cooked fade wyatt jacobs carlton abundant stereo madras inning spur malayalam begged osaka groan escaping charging dose ##aj bud papa communists advocates edged tri resemble peaking necklace fried montenegro saxony goose glances stuttgart curator recruit grocery sympathetic ##tting ##fort lotus randolph ancestor ##rand succeeding jupiter 1798 macedonian ##heads hiking 1808 handing fischer ##itive garbage ##pies prone singular papua inclined attractions italia pouring motioned grandma garnered jacksonville corp ego ringing aluminum ##hausen ordering ##foot drawer traders synagogue ##kawa resistant wandering fragile fiona teased hardcore soaked jubilee decisive exposition mercer poster valencia hale kuwait 1811 ##ises ##wr ##eed tavern gamma johan ##uer airways amino gil vocational domains torres generator folklore outcomes ##keeper canberra shooter fl beams confrontation ##gram aligned forestry pipeline jax motorway conception decay coffin ##cott stalin 1805 escorted minded ##nam sitcom purchasing twilight veronica additions passive tensions straw frequencies 1804 refugee cultivation ##iate christie clary bulletin crept disposal ##rich ##zong processor crescent ##rol emphasized whale nazis aurora dwelling hauled sponsors toledo ideology theatres tessa cerambycidae saves turtle cone suspects kara rusty yelling greeks mozart shades cocked participant shire spit freeze necessity ##cos inmates nielsen councillors loaned uncommon omar peasants botanical offspring daniels formations jokes 1794 pioneers sigma licensing ##sus wheelchair polite 1807 liquor pratt trustee ##uta forewings balloon kilometre camping explicit casually shawn foolish teammates nm hassan carrie judged satisfy vanessa knives selective flowed ##lice stressed eliza mathematician cease cultivated ##roy commissions browns ##ania destroyers sheridan meadow ##rius minerals ##cial downstream clash gram memoirs ventures baha seymour archie midlands edith fare flynn invite canceled tiles stabbed boulder incorporate amended camden facial mollusk unreleased descriptions grabs raises ramp shiver ##rose coined pioneering tunes qing warwick tops melanie giles ##rous wandered ##inal annexed 30th unnamed ##ished organizational airplane normandy stoke whistle blessing violations chased holders shotgun ##ctic reactor ##vik tires tearing shores fortified mascot constituencies columnist productive tibet ##rta lineage hooked tapes judging cody ##gger hansen kashmir triggered ##eva solved cliffs ##tree resisted anatomy protesters transparent implied ##iga injection mattress excluding ##mbo defenses helpless devotion ##elli growl liberals weber phenomena atoms plug ##iff mortality apprentice howe convincing swimmer barber leone promptly sodium def nowadays arise ##oning gloucester corrected dignity norm erie ##ders elders evacuated compression ##yar hartford backpack reasoning accepts 24th wipe millimetres marcel ##oda dodgers albion 1790 overwhelmed aerospace oaks 1795 showcase acknowledge recovering nolan ashe hurts geology fashioned disappearance farewell swollen shrug marquis wimbledon rue 1792 commemorate reduces experiencing inevitable calcutta ##court murderer sticking fisheries imagery bloom ##inus gustav hesitation memorable viral beans accidents tunisia antenna spilled consort treatments aye perimeter ##gard donation hostage migrated banker addiction apex lil trout ##ously conscience ##nova rams sands genome passionate troubles ##lets amid ##ibility ##ret higgins exceed vikings ##vie payne ##zan muscular defendant sucking ##wal ibrahim fuselage claudia vfl europeans snails interval ##garh preparatory statewide tasked lacrosse viktor ##lation angola ##hra flint implications employs teens patrons stall weekends barriers scrambled nucleus tehran jenna parsons lifelong robots displacement ##bles precipitation knuckles clutched 1802 marrying ecology marx accusations declare scars kolkata mat meadows bermuda skeleton finalists vintage crawl coordinate affects subjected orchestral mistaken mirrors dipped relied arches candle ##nick incorporating wildly fond basilica owl fringe rituals whispering stirred feud tertiary slick goat honorable whereby ricardo stripes parachute adjoining submerged synthesizer ##gren intend positively ninety phi beaver partition fellows alexis prohibition carlisle bizarre fraternity doubts icy aquatic sneak sonny combines airports crude supervised spatial merge alfonso ##bic corrupt scan undergo ##ams disabilities colombian comparing dolphins perkins reprinted unanimous bounced hairs underworld midwest semester bucket paperback miniseries coventry demise ##leigh demonstrations sensor rotating yan ##hler arrange soils ##idge hyderabad labs brakes grandchildren ##nde negotiated rover ferrari continuation directorate augusta stevenson counterpart gore ##rda nursery rican ave collectively broadly pastoral repertoire asserted discovering nordic styled fiba cunningham harley middlesex survives tumor tempo zack aiming lok urgent ##nto devils contractor turin ##wl bliss repaired simmons moan astronomical negotiate lyric 1890s lara bred clad angus pbs engineered posed hernandez possessions elbows psychiatric strokes confluence electorate lifts campuses lava alps ##ution ##date physicist woody ##ographic ##itis juliet reformation sparhawk complement suppressed jewel ##½ floated ##kas continuity sadly ##ische inability melting scanning paula flour judaism safer vague solving curb ##stown financially gable bees expired miserable cassidy dominion 1789 cupped robbery facto amos warden resume tallest marvin pounded declaring gasoline ##aux darkened sophomore ##mere erection gossip televised risen dial ##eu pillars passages profound arabian ashton silicon nail ##lated ##hardt fleming firearms ducked circuits blows waterloo titans fireplace cheshire financed activation algorithms constituent catcher cherokee partnerships sexuality platoon tragic vivian guarded whiskey meditation poetic ##nga porto listeners dominance kendra mona chandler factions 22nd salisbury attitudes derivative ##ido ##haus intake paced javier illustrator barrels bias cockpit burnett dreamed ensuing receptors someday hawkins mattered ##lal slavic 1799 jesuit cameroon wasted wax lowering victorious freaking outright hancock librarian sensing bald calcium myers tablet announcing barack shipyard pharmaceutical greenwich flush medley patches wolfgang speeches acquiring exams nikolai hayden kannada reilly waitress abdomen devastated capped pseudonym pharmacy fulfill paraguay 1796 clicked ##trom archipelago syndicated ##hman lumber orgasm rejection clifford lorraine advent mafia rodney brock ##used ##elia cassette chamberlain despair mongolia sensors developmental upstream ##alis spanning trombone basque seeded interred renewable rhys leapt revision molecule ##ages chord vicious nord shivered 23rd arlington debts corpus sunrise bays blackburn centimetres ##uded shuddered strangely gripping cartoons isabelle orbital ##ppa seals proving refusal strengthened bust assisting baghdad batsman portrayal mara pushes spears og ##cock reside nathaniel brennan 1776 confirmation caucus ##worthy markings yemen nobles ku lazy viewer catalan encompasses sawyer ##fall sparked substances patents braves arranger evacuation sergio persuade dover tolerance penguin cum jockey insufficient townships occupying declining plural processed projection puppet flanders introduces liability ##yon gymnastics antwerp hobart candles jeep wes observers chaplain bundle glorious ##hine hazel flung sol excavations dumped stares bangalore triangular icelandic intervals expressing turbine ##vers songwriting crafts ##igo jasmine ditch rite entertaining comply sorrow wrestlers basel emirates marian rivera helpful ##some caution downward networking ##atory ##tered darted genocide emergence replies specializing spokesman convenient unlocked fading augustine concentrations resemblance elijah investigator andhra ##uda promotes ##rrell fleeing simone announcer lydia weaver residency modification ##fest stretches alternatively nat lowe lacks ##ented pam tile concealed inferior abdullah residences tissues vengeance ##ided moisture peculiar groove bologna jennings ninja oversaw zombies pumping batch livingston emerald installations 1797 peel nitrogen rama ##fying schooling strands responding werner lime casa accurately targeting ##rod underway ##uru hemisphere lester ##yard occupies griffith angrily reorganized ##owing courtney deposited estadio ##ifies dunn exiled ##ying checks ##combe successes unexpectedly blu assessed ##flower observing sacked spiders kn nodes prosperity audrey divisional broncos tangled adjust feeds erosion paolo surf directory snatched humid admiralty screwed reddish ##nese modules trench lamps bind leah bucks competes ##nz transcription isles violently clutching pga cyclist inflation flats ragged unnecessary ##hian stubborn coordinated harriet baba disqualified insect wolfe ##fies reinforcements rocked duel winked embraced bricks ##raj hiatus defeats pending brightly jealousy ##xton ##uki lena colorful ##dley stein kidney ##shu underwear wanderers ##haw ##icus guardians m³ roared habits ##wise permits uranium punished disguise bundesliga elise dundee erotic partisan collectors float individually rendering behavioral bucharest ser hare valerie corporal nutrition proportional immense ##kis pavement ##zie ##eld sutherland crouched 1775 suzuki trades endurance operas crosby prayed priory rory socially gujarat walton cube pasha privilege lennon floods thorne waterfall nipple scouting approve ##lov minorities voter dwight extensions assure ballroom slap dripping privileges rejoined confessed demonstrating patriotic yell investor ##uth pagan slumped squares confront bert embarrassment aston urging sweater starr yuri brains williamson commuter mortar structured selfish exports ##jon cds ##him unfinished ##rre mortgage destinations ##nagar canoe solitary buchanan delays magistrate fk ##pling motivation ##lier ##vier recruiting assess ##mouth malik antique 1791 pius rahman reich tub zhou smashed airs galway xii conditioning honduras discharged dexter ##pf lionel debates lemon volunteered dioxide procession devi sic tremendous advertisements colts transferring verdict hanover decommissioned utter relate pac racism beacon limp similarity terra occurrence ant becky capt updates armament richie pal ##graph halloween mayo ##ssen ##bone cara serena fcc dolls obligations ##dling violated lafayette jakarta exploitation infamous iconic ##lah ##park moody reginald dread spill crystals olivier modeled bluff equilibrium separating notices ordnance extinction onset cosmic attachment sammy expose privy anchored ##bil abbott admits bending baritone emmanuel policeman vaughan winged climax dresses denny polytechnic mohamed burmese authentic nikki genetics grandparents homestead gaza postponed metacritic una ##sby unstable dissertation ##cian curls obscure uncovered bronx praying disappearing ##hoe prehistoric coke turret mutations nonprofit pits monaco ##usion prominently dispatched podium ##mir uci ##uation fortifications birthplace kendall ##lby ##oll preacher rack goodman persistent ##ott countless jaime recorder lexington persecution jumps renewal wagons crushing ##holder decorations ##lake abundance wrath laundry £1 garde jeanne beetles peasant splitting caste sergei ##rer ##ema scripts ##ively rub satellites ##vor inscribed verlag scrapped gale packages chick potato slogan kathleen arabs ##culture counterparts reminiscent choral ##tead rand retains bushes dane accomplish courtesy closes ##oth slaughter hague krakow lawson tailed elias ginger ##ttes canopy betrayal rebuilding turf ##hof frowning allegiance brigades kicks rebuild polls alias nationalism rowan audition bowie fortunately recognizes harp dillon horrified ##oro renault ropes presumed rewarded infrared wiping accelerated illustration presses practitioners badminton ##iard detained ##tera recognizing relates misery ##sies ##tly reproduction piercing potatoes thornton esther manners hbo ##aan ours bullshit ernie perennial sensitivity illuminated rupert ##iss rfc nassau ##dock staggered socialism ##haven appointments nonsense prestige sharma haul solidarity ##rata igor pedestrian ##uit baxter tenants wires medication unlimited guiding impacts diabetes ##rama sasha pas clive extraction continually constraints ##bilities sonata hunted sixteenth chu planting quote mayer pretended spat ceramic ##cci curtains pigs pitching ##dad latvian sore dayton ##sted patrols slice playground ##nted shone stool apparatus inadequate mates treason ##ija desires ##liga ##croft somalia laurent mir grape obliged chevrolet thirteenth stunning enthusiastic ##ede accounted concludes currents basil ##kovic drought ##rica mai ##aire shove posting ##shed pilgrimage humorous packing fry pencil wines smells marilyn aching newest clung bon neighbours sanctioned ##pie mug ##stock drowning hydraulic ##vil hiring reminder lilly investigators ##ncies sour ##eous compulsory packet ##rion ##graphic ##elle cannes ##inate depressed ##rit heroic importantly theresa ##tled conway saturn marginal rae ##xia corresponds royce pact jasper explosives packaging aluminium ##ttered denotes rhythmic spans assignments hereditary outlined originating sundays lad reissued greeting beatrice ##dic pillar marcos plots handbook alcoholic judiciary avant slides extract masculine blur ##eum homage trembled owens hymn trey signaling socks accumulated reacted attic theo lining angie distraction primera talbot creativity billed ##hey deacon eduardo identifies proposition dizzy gunner hogan ##yam ##pping ##hol ja ##chan jensen reconstructed ##berger clearance darius ##nier abe harlem plea dei circled emotionally notation fascist neville exceeded upwards viable ducks workforce racer limiting shri ##lson possesses kerr moths devastating laden disturbing locking gal fearing accreditation flavor aide 1870s mountainous ##baum melt ##ures texture servers soda herd ##nium erect puzzled hum peggy examinations gould testified geoff ren devised sacks ##law denial posters grunted cesar tutor gerry offerings byrne falcons combinations incoming pardon rocking 26th avengers flared mankind seller uttar loch nadia stroking exposing fertile ancestral instituted ##has noises prophecy taxation eminent vivid pol ##bol dart indirect multimedia notebook upside displaying adrenaline referenced geometric ##iving progression ##ddy blunt announce ##far implementing ##lav aggression liaison cooler cares headache plantations gorge dots impulse thickness ashamed averaging kathy obligation precursor fowler symmetry thee hears ##rai undergoing butcher bowler ##lip cigarettes subscription goodness ##ically browne ##hos kyoto donor ##erty damaging friction drifting expeditions hardened prostitution fauna blankets claw tossing snarled butterflies recruits investigative coated healed communal hai xiii academics boone psychologist restless lahore stephens brendan foreigners printer ached explode 27th deed scratched dared ##pole cardiac 1780 okinawa proto commando compelled oddly electrons replica thanksgiving ##rist sheila deliberate stafford tidal representations hercules ou ##path ##iated kidnapping lenses ##tling deficit samoa mouths consuming computational maze granting smirk razor fixture ideals inviting aiden nominal issuing julio pitt ramsey docks ##oss exhaust ##owed bavarian draped anterior mating ethiopian explores noticing ##nton discarded convenience hoffman endowment beasts cartridge mormon paternal probe sleeves interfere lump deadline jenks bulldogs scrap alternating justified reproductive nam seize descending secretariat kirby grouped smash panther sedan tapping lola cheer germanic unfortunate ##eter unrelated ##fan subordinate ##sdale suzanne advertisement ##ility horsepower ##lda cautiously discourse luigi ##mans ##fields noun prevalent mao schneider everett surround governorate kira ##avia westward ##take misty rails sustainability unused ##rating packs toast unwilling regulate thy suffrage nile awe assam definitions travelers affordable ##rb conferred sells undefeated beneficial torso basal repeating remixes bahrain cables fang ##itated excavated numbering statutory deluxe ##lian forested ramirez derbyshire zeus slamming transfers astronomer banana lottery berg histories bamboo ##uchi resurrection posterior bowls vaguely ##thi thou preserving tensed offence ##inas meyrick callum ridden watt langdon tying lowland snorted daring truman ##hale ##girl aura overly filing weighing goa infections philanthropist saunders eponymous ##owski latitude perspectives reviewing mets commandant radial ##kha flashlight reliability koch vowels amazed ada elaine supper ##encies predator debated soviets cola ##boards ##nah compartment crooked arbitrary fourteenth havana majors steelers clips profitable ambush exited packers ##tile nude cracks fungi limb trousers josie shelby tens frederic ##ος definite smoothly constellation insult baton discs lingering ##nco conclusions lent staging becker grandpa shaky ##tron einstein obstacles adverse economically ##moto mccartney thor dismissal motions readings nostrils treatise ##pace squeezing evidently prolonged 1783 venezuelan je marguerite beirut takeover shareholders ##vent denise digit airplay norse ##bbling imaginary pills hubert blaze vacated eliminating vine mansfield retrospective barrow borne clutch bail forensic weaving ##nett ##witz desktop citadel promotions worrying dorset subdivided ##iating manned expeditionary pickup synod chuckle barney ##rz ##ffin functionality karachi litigation meanings lick anders ##ffed execute curl oppose ankles typhoon ##ache linguistics compassion pressures grazing perfection ##iting immunity monopoly muddy backgrounds namibia francesca monitors attracting stunt tuition ##ии vegetable ##mates ##quent mgm jen complexes forts cellar bites seventeenth royals flemish failures mast charities ##cular peruvian capitals macmillan ipswich outward frigate postgraduate folds employing ##ouse concurrently fiery ##tai contingent nightmares monumental nicaragua ##kowski lizard mal fielding gig reject harding ##ipe coastline ##cin beethoven humphrey innovations ##tam norris doris solicitor obey niagara shelves bourbon nightclub specifications hilton ##ndo centennial dispersed worm neglected briggs kuala uneasy ##nstein ##bound ##aking ##burgh awaiting pronunciation ##bbed ##quest eh optimal zhu raped greens presided brenda worries venetian marxist turnout ##lius refined braced sins grasped sunderland nickel speculated lowell cyrillic communism fundraising resembling colonists mutant freddie usc ##mos gratitude ##run mural ##lous chemist reminds 28th steals tess pietro ##ingen promoter ri microphone honoured rai sant ##qui feather ##nson burlington kurdish terrorists deborah sickness ##wed hazard irritated desperation veil clarity ##rik jewels xv ##gged ##ows ##cup berkshire unfair mysteries orchid winced exhaustion renovations stranded obe infinity ##nies adapt redevelopment thanked registry olga domingo noir tudor ole commenting behaviors ##ais crisp pauline probable stirling wigan paralympics panting surpassed ##rew luca barred famed ##sters cassandra waiter carolyn exported ##orted andres destructive deeds jonah castles vacancy ##glass 1788 orchard yep famine belarusian sprang ##forth skinny ##mis administrators rotterdam zambia zhao boiler discoveries ##ride ##physics lucius disappointing outreach spoon ##frame qualifications unanimously enjoys regency ##iidae stade realism veterinary rodgers dump alain chestnut castile censorship rumble gibbs communion reggae inactivated logs loads ##houses homosexual ##iano ale informs ##cas phrases plaster linebacker ambrose kaiser fascinated limerick recruitment forge mastered ##nding leinster rooted threaten ##strom borneo ##hes suggestions scholarships propeller documentaries patronage coats constructing invest neurons comet entirety shouts identities annoying unchanged wary ##antly ##ogy neat oversight ##kos phillies replay constance ##kka incarnation humble skies minus ##acy smithsonian guerrilla jar cadets ##plate surplus audit ##aru cracking joanna louisa pacing ##lights intentionally ##iri diner nwa imprint australians tong unprecedented bunker naive specialists ark nichols railing leaked pedal ##uka shrub longing roofs captains neural tuned ##ntal ##jet emission medina frantic codex definitive sid abolition intensified stocks enrique sustain genoa oxide ##written clues cha ##gers tributaries fragment venom ##ente ##sca muffled vain sire laos ##ingly ##hana hastily snapping surfaced sentiment motive ##oft contests approximate mesa luckily dinosaur exchanges propelled accord bourne relieve tow masks offended ##ues cynthia ##mmer rains bartender zinc reviewers lois ##sai legged arrogant rafe comprise handicap blockade inlet lagoon copied drilling shelley petals ##inian mandarin obsolete ##inated onward arguably productivity praising seldom busch discusses raleigh shortage ranged stanton encouragement firstly conceded overs temporal ##uke cbe ##bos woo certainty pumps ##pton stalked ##uli lizzie periodic thieves weaker gases shoving chooses wc ##chemical prompting weights ##kill robust flanked sticky tuberculosis ##eb ##eal christchurch resembled wallet reese inappropriate pictured distract fixing fiddle giggled burger heirs hairy mechanic torque obsessed chiefly cheng logging extracted meaningful numb ##vsky gloucestershire reminding unite ##lit breeds diminished clown glove 1860s archibald focal freelance sliced depiction ##yk organism switches sights stray crawling ##ril lever leningrad interpretations loops anytime reel alicia delighted ##ech inhaled xiv suitcase bernie vega licenses northampton exclusion induction monasteries racecourse homosexuality ##sfield ##rky dimitri michele alternatives ions commentators genuinely objected pork hospitality fencing stephan warships peripheral wit drunken wrinkled quentin spends departing chung numerical spokesperson johannesburg caliber killers ##udge assumes neatly demographic abigail bloc mounting ##lain bentley slightest xu recipients ##jk merlin ##writer seniors prisons blinking hindwings flickered kappa ##hel 80s strengthening appealing brewing gypsy mali lashes hulk unpleasant harassment bio treaties predict instrumentation pulp troupe boiling mantle ##ffe ##vn dividing handles verbs ##onal coconut senegal thorough gum momentarily ##sto cocaine panicked destined ##turing teatro denying weary captained mans ##hawks wakefield bollywood thankfully cyril amendments ##bahn consultation stud reflections kindness 1787 internally ##ovo tex mosaic distribute paddy seeming ##hic piers ##mura popularly winger kang sentinel mccoy ##anza covenant ##bag verge fireworks suppress thrilled dominate ##jar swansea reconciliation stiffened cue dorian ##uf damascus amor ida foremost ##aga porsche unseen dir ##had ##azi stony lexi melodies ##nko angular integer podcast ants inherent jaws justify persona ##olved josephine ##nr ##ressed customary flashes gala cyrus glaring backyard ariel physiology greenland stir avon atletico finch methodology ked mas catholicism townsend branding quincy fits containers 1777 ashore aragon forearm poisoning adopting conquer grinding amnesty keller finances evaluate forged lankan instincts ##uto guam bosnian photographed workplace desirable protector allocation intently encourages willy ##sten bodyguard electro brighter bihar ##chev lasts opener amphibious sal verde arte ##cope captivity vocabulary yields ##tted agreeing desmond pioneered ##chus strap campaigned railroads ##ович emblem ##dre stormed ##ulous marijuana northumberland ##nath bowen landmarks beaumont ##qua danube ##bler attorneys th flyers critique villains cass mutation acc ##0s colombo mckay motif sampling concluding syndicate ##rell neon stables warnings clint mourning wilkinson ##tated merrill leopard evenings exhaled emil sonia ezra discrete stove farrell fifteenth prescribed superhero ##rier worms helm wren ##duction expo ##rator hq unfamiliar antony prevents acceleration fiercely mari painfully calculations cheaper ign clifton irvine davenport mozambique pierced ##evich wonders ##wig ##cate ##iling crusade ware enzymes reasonably mls ##coe mater ambition bunny eliot kernel ##fin asphalt headmaster torah aden lush pins waived ##yas joao substrate enforce ##grad ##ules alvarez selections epidemic tempted bremen translates ensured waterfront 29th forrest manny malone kramer reigning simpler absorption engraved ##ffy evaluated 1778 haze comforting crossover ##abe thorn ##rift ##imo suppression fatigue cutter wurttemberg ##orf enforced hovering proprietary samurai syllable ascent lacey tick lars tractor merchandise rep bouncing defendants ##yre huntington ##oko standardized ##hor ##hima assassinated predecessors rainy liar assurance lyrical ##uga secondly flattened parameter undercover ##mity bordeaux punish ridges markers exodus inactive hesitate debbie nyc pledge savoy nagar offset organist ##tium hesse marin converting ##iver diagram propulsion validity reverted supportive ministries clans responds proclamation ##inae ein pleading patriot birch islanders strauss hates ##dh brandenburg concession 1900s killings textbook antiquity cinematography wharf embarrassing setup creed farmland inequality centred signatures fallon ##ingham ##uts ceylon gazing directive laurie ##tern globally ##uated ##dent allah excavation threads ##cross frantically icc utilize determines respiratory thoughtful receptions ##dicate merging chandra seine builders builds diagnostic dev visibility goddamn analyses dhaka proves chancel concurrent curiously canadians pumped restoring 1850s turtles jaguar sinister spinal declan vows 1784 glowed capitalism swirling universidad ##lder ##oat soloist ##genic ##oor coincidence beginnings nissan dip resorts caucasus combustion infectious ##eno pigeon serpent ##itating conclude masked salad jew ##gr surreal toni ##wc harmonica ##gins ##etic ##coat fishermen intending bravery ##wave klaus titan wembley taiwanese ransom 40th incorrect hussein eyelids cooke dramas utilities ##etta ##print 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favoured appoint transgender elephants poked greenwood defences fulfilled militant somali 1758 chalk potent ##ucci migrants wink assistants nos restriction activism niger ##ario colon shaun ##sat daphne ##erated swam congregations reprise considerations magnet playable xvi overthrow tobias knob chavez coding ##mers propped katrina orient newcomer ##suke temperate ##pool farmhouse interrogation committing ##vert forthcoming strawberry joaquin macau ponds shocking siberia ##cellular chant contributors ##nant ##ologists sped absorb hail 1782 spared ##hore barbados karate opus originates saul ##xie evergreen leaped ##rock correlation exaggerated weekday unification bump tracing brig afb pathways utilizing disturbance kneeling ##stad ##guchi 100th pune ##thy decreasing manipulation miriam academia ecosystem occupational rbi ##lem rift rotary stacked incorporation awakening generators guerrero racist ##omy cyber derivatives culminated allie annals panzer sainte pops zu austro ##vate algerian politely nicholson mornings educate tastes thrill dartmouth ##gating ##jee regan differing concentrating choreography divinity pledged alexandre routing gregor madeline ##idal apocalypse ##hora gunfire culminating elves fined liang lam programmed tar guessing transparency gabrielle ##gna cancellation flexibility ##lining accession shea stronghold nets specializes ##rgan abused hasan sgt exceeding admiration supermarket photographers specialised tilt resonance hmm perfume sami threatens garland botany guarding boiled greet puppy russo supplier wilmington vibrant vijay ##bius paralympic grumbled paige faa licking margins hurricanes ##gong fest grenade ripping ##uz counseling weigh ##sian needles wiltshire edison costly ##not fulton tramway redesigned staffordshire gasping watkins sleepy candidacy monkeys timeline throbbing ##bid ##sos berth uzbekistan vanderbilt bothering overturned ballots gem ##iger sunglasses subscribers hooker compelling ang exceptionally saloon stab ##rdi carla terrifying ##vision coil ##oids satisfying vendors 31st mackay deities overlooked ambient bahamas felipe olympia whirled botanist advertised tugging disciples morales unionist rites foley morse motives creepy ##₀ soo ##sz bargain highness frightening turnpike tory reorganization depict biographer unopposed manifesto ##gles institut emile accidental kapoor ##dam kilkenny cortex lively romanesque jain shan cannons ##ske petrol echoing amalgamated disappears cautious proposes sanctions trenton flotilla aus contempt tor canary cote theirs ##hun conceptual deleted fascinating paso blazing elf honourable hutchinson ##eiro ##outh ##zin surveyor amidst wooded reissue intro ##ono cobb shelters newsletter hanson brace encoding confiscated dem caravan marino scroll melodic cows imam ##adi ##aneous northward searches biodiversity cora roaring ##bers connell theologian halo compose pathetic unmarried dynamo az calculation toulouse deserves humour nr forgiveness tam undergone martyr pamela myths whore counselor hicks heavens battleship electromagnetic stellar establishments presley hopped ##chin temptation 90s wills ##yuan nhs ##nya seminars ##yev adaptations gong asher lex indicator sikh tobago cites goin ##yte satirical ##gies characterised correspond bubbles lure participates ##vid eruption skate therapeutic 1785 canals wholesale defaulted sac petit ##zzled virgil leak ravens portraying ##yx ghetto creators dams portray vicente ##rington fae namesake bounty ##arium joachim ##ota ##iser aforementioned axle snout depended dismantled reuben ##ibly gallagher ##lau earnest ##ieu ##iary inflicted objections ##llar asa gritted ##athy jericho ##sea ##was flick underside ceramics undead substituted eastward undoubtedly wheeled chimney ##iche guinness siding traitor baptiste disguised inauguration tipperary choreographer perched warmed stationary ##ntes bacterial ##aurus flores phosphate attacker invaders alvin intersects indirectly immigrated businessmen cornelius valves narrated pill sober nationale monastic applicants scenery ##jack motifs constitutes ##osh jurisdictions tuning irritation woven ##uddin fertility gao ##erie antagonist impatient glacial hides boarded denominations interception ##jas nicola algebraic marquess bahn parole buyers bait turbines paperwork bestowed natasha renee oceans purchases vaccine ##tock fixtures playhouse integrate jai oswald intellectuals booked nests mortimer ##isi obsession sept ##gler ##sum scrutiny simultaneous squinted ##shin collects oven shankar penned remarkably slips luggage spectral 1786 collaborations louie consolidation ##ailed ##ivating hoover blackpool harness ignition vest tails belmont mongol skinner ##nae visually mage derry ##tism ##unce stevie transitional ##rdy redskins drying prep prospective annoyance oversee ##loaded fills ##books announces scowled respects prasad mystic tucson ##vale revue springer bankrupt 1772 aristotle habsburg ##geny dal natal nut pod chewing darts moroccan walkover rosario lenin punjabi ##ße grossed scattering wired invasive hui polynomial corridors wakes gina portrays ##cratic arid retreating erich irwin sniper ##dha linen lindsey maneuver butch shutting socio bounce commemorative postseason jeremiah pines mystical beads abbas furnace bidding consulted assaulted empirical rubble enclosure sob weakly cancel polly yielded ##emann curly prediction battered 70s vhs jacqueline render sails barked detailing grayson riga sloane raging ##yah herbs bravo ##athlon alloy giggle imminent suffers assumptions waltz ##itate accomplishments ##ited bathing remixed deception ##emia deepest ##eis balkan frogs ##rong slab ##pate philosophers peterborough grains imports dickinson rwanda ##atics 1774 dirk tablets ##rove clone ##rice caretaker hostilities mclean ##gre regimental treasures norms impose tsar tango diplomacy variously complain recognise arrests 1779 celestial pulitzer ##dus libretto ##moor adele splash expectation lds confronts ##izer spontaneous harmful wedge entrepreneurs buyer bilingual translate rugged conner circulated uae eaton ##gra ##zzle lingered lockheed vishnu reelection alonso ##oom joints yankee headline cooperate heinz laureate invading ##sford echoes scandinavian ##dham hugging vitamin salute micah hind trader ##sper radioactive ##ndra militants poisoned ratified remark campeonato deprived wander prop ##dong ##tani ##eye chiang darcy ##oping mandolin spice statesman babylon walled forgetting afro ##cap giorgio buffer ##polis planetary ##gis overlap terminals kinda centenary ##bir arising manipulate elm ke 1770 ##tad chrysler mapped moose pomeranian quad macarthur assemblies shoreline recalls stratford ##rted noticeable ##evic imp ##rita ##sque accustomed supplying tents disgusted sipped filters khz reno selecting luftwaffe mcmahon tyne masterpiece carriages collided dunes exercised flare remembers muzzle heck ##rson burgess lunged middleton boycott bilateral ##sity hazardous lumpur multiplayer spotlight jackets goldman liege porcelain rag waterford attracts hopeful battling ottomans kensington baked hymns cheyenne lattice levine borrow polymer clashes michaels monitored commitments denounced ##von cavity ##oney hobby akin ##holders futures intricate cornish patty ##oned illegally dolphin ##lag barlow yellowish maddie apologized luton plagued ##puram ##rds sway fanny łodz ##rino psi suspicions hanged ##eding initiate charlton ##por nak competent analytical annex wardrobe reservations sect fairfax hedge piled buckingham uneven bauer simplicity snyder interpret accountability donors moderately byrd continents ##cite disciple jamaican nominees ##uss mongolian diver attackers eagerly ideological pillows miracles apartheid revolver sulfur clinics moran ##enko ile katy rhetoric ##icated chronology recycling ##hrer elongated mughal pascal profiles vibration databases domination ##fare matthias digest rehearsal polling weiss initiation reeves clinging flourished impress ##hoff buckley symposium rhythms weed emphasize transforming ##taking ##yman accountant analyze flicker foil priesthood voluntarily decreases ##hya slater sv charting mcgill ##lde moreno besieged zur robes ##phic admitting deported turmoil peyton earthquakes ##ares nationalists beau clair brethren interrupt welch curated galerie requesting ##ested impending steward viper ##vina complaining beautifully brandy foam nl 1660 alessandro punches laced explanations ##lim attribute clit reggie discomfort ##cards smoothed whales ##cene adler countered duffy disciplinary widening recipe reliance conducts goats gradient preaching ##shaw matilda quasi striped meridian cannabis cordoba certificates ##agh ##tering graffiti hangs pilgrims repeats ##ych revive urine etat ##hawk fueled belts fuzzy susceptible mauritius salle sincere beers hooks ##cki arbitration entrusted advise sniffed seminar junk donnell processors principality strapped celia mendoza everton fortunes prejudice starving reassigned steamer ##lund tuck evenly foreman ##ffen dans envisioned slit baseman liberia rosemary ##weed electrified periodically potassium stride contexts sperm slade mariners influx bianca subcommittee ##rane spilling icao estuary ##nock delivers ##ulata isa mira bohemian dessert ##sbury welcoming proudly slowing ##chs musee ascension russ ##vian waits ##psy africans exploit ##morphic eccentric crab peck entrances formidable marketplace groom bolted metabolism patton robbins courier payload endure ##ifier andes refrigerator ornate ##uca ruthless illegitimate masonry strasbourg bikes apples quintet willingly niche bakery corpses energetic ##cliffe ##sser ##ards centimeters centro fuscous cretaceous rancho ##yde andrei telecom tottenham oasis ordination vulnerability presiding corey penguins sims ##pis malawi piss correction ##cked ##ffle ##ryn countdown detectives psychiatrist psychedelic dinosaurs blouse choi vowed randomly ##pol 49ers scrub blanche bruins dusseldorf ##using unwanted ##ums dominique elevations headlights om laguna ##oga 1750 famously ignorance shrewsbury breuning che confederacy greco overhaul ##screen paz skirts disagreement cruelty jagged phoebe shifter hovered viruses ##wes ##lined landlord squirrel dashed ornamental gag wally grange literal spurs undisclosed proceeding billie orphan spanned humidity indy weighted presentations explosions lucian ##tary vaughn hindus ##anga ##hell psycho daytona protects efficiently rematch sly tandem ##oya rebranded impaired hee metropolis peach godfrey diaspora ethnicity prosperous gleaming dar grossing playback ##rden stripe pistols ##tain births labelled ##cating rudy alba ##onne aquarium hostility ##tase shudder sumatra hardest lakers consonant creeping demos homicide capsule zeke liberties expulsion pueblo ##comb trait transporting ##ddin ##neck ##yna depart gregg mold ledge hangar oldham playboy termination analysts gmbh romero ##itic insist cradle filthy brightness slash shootout deposed bordering ##truct microwave tumbled sheltered cathy 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statutes wilde gail kung sabine comfortably motorcycles ##rgo pneumonia fetch ##sonic axel faintly parallels ##oop mclaren spouse compton interdisciplinary miner ##eni clamped ##chal ##llah separates versa ##mler scarborough labrador ##lity ##osing rutgers hurdles como burt divers wichita cade coincided bruised mla vineyard ##ili ##brush notch mentioning jase hearted kits doe ##acle pomerania ##ady ronan seizure pavel problematic ##zaki domenico ##ulin catering penelope dependence parental emilio ministerial atkinson ##bolic clarkson chargers colby grill peeked arises summon ##aged fools ##grapher faculties qaeda ##vial garner refurbished ##hwa geelong disasters nudged bs shareholder lori algae reinstated rot ##ades ##nous invites stainless inclusive ##itude diocesan til ##icz denomination ##xa benton floral registers ##erman ##kell absurd brunei guangzhou hitter retaliation ##uled ##eve blanc nh consistency contamination ##eres dire palermo broadcasters diaries inspire vols brewer 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armistice ##estinal elton aj encompassing camel commemorated malaria woodward calf cigar penetrate ##oso willard ##rno ##uche illustrate amusing convergence noteworthy ##lma ##rva journeys realise manfred ##sable ##vocation hearings fiance ##posed educators provoked adjusting ##cturing modular stockton paterson vlad rejects electors selena maureen ##tres ##rce swirled ##num proportions nanny pawn naturalist parma apostles awoke ethel wen ##bey monsoon overview ##inating mccain rendition risky adorned ##ih equestrian germain nj conspicuous confirming ##yoshi shivering ##imeter milestone rumours flinched bounds smacked token ##bei lectured automobiles ##shore impacted ##iable nouns nero ##leaf ismail prostitute trams bridget sud stimulus impressions reins revolves ##gned giro honeymoon ##swell criterion ##sms ##uil libyan prefers ##osition preview sucks accusation bursts metaphor diffusion tolerate faye betting cinematographer liturgical specials bitterly humboldt ##ckle flux rattled ##itzer archaeologists odor authorised marshes discretion ##ов alarmed archaic inverse ##leton explorers ##pine drummond tsunami woodlands ##minate ##tland booklet insanity owning insert crafted calculus receivers stung ##eca ##nched prevailing travellers eyeing lila graphs ##borne julien ##won morale adaptive therapist erica cw libertarian bowman pitches vita ##ional crook ##entation caledonia mutiny ##sible 1840s automation flock ##pia ironic pathology ##imus remarried joker withstand energies ##att shropshire hostages madeleine tentatively conflicting mateo recipes euros mercenaries nico ##ndon albuquerque augmented mythical bel freud ##child cough ##lica freddy lillian genetically nuremberg calder bonn outdoors paste suns urgency vin restraint tyson ##cera ##selle barrage bethlehem kahn ##par mounts nippon barony happier ryu makeshift sheldon blushed castillo barking listener taped bethel fluent headlines pornography rum disclosure sighing mace doubling gunther manly ##plex interventions physiological forwards emerges ##tooth ##gny compliment rib recession visibly barge faults connector exquisite prefect ##rlin patio ##cured elevators italics pena wasp satin botswana graceful respectable ##jima ##rter ##oic franciscan generates ##dl alfredo disgusting ##olate ##iously sherwood warns cod promo cheryl sino ##escu twitch ##zhi brownish thom ortiz ##dron densely ##beat carmel reinforce ##bana anastasia downhill vertex contaminated remembrance harmonic homework fiancee gears olds angelica ramsay quiz colliery sevens ##cape autism ##hil walkway ##boats ruben abnormal ounce khmer ##bbe zachary bedside morphology punching ##olar sparrow convinces hewitt queer remastered rods mabel solemn notified lyricist symmetric ##xide encore passports wildcats ##uni baja ##pac mildly ##ease bleed commodity mounds glossy orchestras ##omo damian prelude ambitions ##vet awhile remotely ##aud asserts imply ##iques distinctly modelling remedy ##dded windshield dani xiao ##endra 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unto ##yuki localities ##cko ##ln darlington slain academie lobbying sediment puzzles ##grass defiance dickens manifest tongues alumnus arbor coincide appalachian mustafa examiner cabaret traumatic yves bracelet draining heroin magnum baths odessa consonants mitsubishi ##gua kellan vaudeville joked straps probation ##ław ceded interfaces ##pas ##zawa blinding viet rothschild museo huddersfield tactic ##storm brackets dazed incorrectly ##vu reg glazed fearful manifold benefited irony stumbling ##rte willingness balkans mei wraps ##aba injected ##lea gu syed harmless ##hammer bray takeoff poppy timor cardboard astronaut purdue weeping southbound cursing stalls diagonal ##neer lamar bryce comte weekdays harrington ##uba negatively ##see lays grouping ##cken ##henko affirmed halle modernist ##lai hodges smelling aristocratic baptized dismiss justification oilers coupling qin snack healer ##qing gardener layla battled formulated stephenson gravitational ##gill 1768 granny coordinating 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marries squared tentative ##chman boer ##isch bolts swap fisherman assyrian impatiently guthrie martins murdoch tanya nicely dolly lacy med syn decks fashionable millionaire surfing heaved tammy consulate attendees routinely fuse saxophonist backseat malaya ##lord scowl tau ##ishly sighted steaming ##rks ##holes ##hong ching ##wife bless conserved jurassic stacey zion chunk rigorous blaine peabody slayer dismay brewers nz ##jer det ##glia glover postwar penetration sylvester imitation vertically airlift heiress knoxville viva ##uin macon ##rim ##fighter ##gonal janice ##orescence ##wari marius belongings leicestershire blanco inverted preseason sanity sobbing ##due ##elt ##dled collingwood regeneration flickering shortest ##mount ##osi feminism ##lat sherlock cabinets fumbled northbound precedent snaps ##mme researching ##akes guillaume insights manipulated vapor neighbour gangster frey stalking scarcely callie barnett tendencies doomed assessing slung panchayat ambiguous bartlett 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memorandum armenians ##ddle popularized rhodesia 60s lame ##illon sans bikini header orbits ##finger ##ulator sharif spines biotechnology strolled naughty yates ##wire fremantle milo ##mour abducted removes ##atin humming ##chrome ##ester hume pivotal ##rates armand grams believers elector rte apron bis scraped ##yria endorsement initials ##llation dotted hints buzzing emigration nearer indicators ##ulu coarse neutron protectorate ##uze directional exploits pains loire 1830s proponents guggenheim rabbits ritchie hectare inputs hutton ##raz verify ##ako boilers longitude ##lev skeletal yer emilia citrus compromised ##gau prescription paragraph eduard cadillac attire categorized kenyan weddings charley ##bourg entertain monmouth ##lles nutrients davey mesh incentive practised ecosystems kemp subdued overheard ##rya bodily maxim ##nius apprenticeship ursula ##fight lodged rug silesian unconstitutional patel inspected coyote unbeaten ##hak 34th disruption convict parcel ##nham collier implicated mallory ##iac susannah winkler ##rber shia phelps sediments graphical robotic ##sner adulthood mart smoked ##isto kathryn clarified ##aran divides convictions oppression pausing burying ##mt federico mathias eileen ##tana kite hunched ##acies ##atz disadvantage liza kinetic greedy paradox yokohama dowager trunks ventured ##gement gupta vilnius olaf ##thest crimean hopper ##ej progressively arturo mouthed arrondissement ##fusion rubin simulcast oceania ##orum ##stra ##rred busiest intensely navigator cary ##vine ##hini ##bies fife rowe rowland posing insurgents shafts lawsuits activate conor inward culturally garlic ##eering eclectic ##hui ##kee ##nl furrowed vargas meteorological rendezvous ##aus culinary commencement ##dition quota ##notes mommy salaries overlapping mule ##iology ##mology sums wentworth ##isk ##zione mainline subgroup ##illy hack plaintiff verdi bulb differentiation engagements multinational supplemented bertrand caller regis ##naire ##sler ##arts ##imated 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akbar migrant syllables indefinitely ##ogical destroys replaces applause ##phine pest ##fide articulated bertie ##cars ##ptic courtroom crowley aesthetics cummings tehsil hormones titanic dangerously ##ibe stadion jaenelle auguste ciudad ##chu mysore partisans lucan philipp ##aly debating henley interiors ##rano ##tious homecoming beyonce usher henrietta prepares weeds ely plucked ##pire ##dable luxurious ##aq artifact password pasture juno maddy minsk ##dder ##ologies ##rone assessments martian royalist 1765 examines ##mani nino parry scooped relativity ##eli ##uting ##cao congregational noisy traverse ##agawa strikeouts nickelodeon obituary transylvania binds depictions polk trolley ##yed ##lard breeders ##under dryly hokkaido 1762 strengths stacks bonaparte neared prostitutes stamped anaheim gutierrez sinai ##zzling bram fresno madhya proton ##lena ##llum ##phon reelected wanda ##anus ##lb ample distinguishing ##yler grasping sermons tomato bland stimulation avenues ##eux spreads 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colton ##founded outsider espionage kelsey edible ##ulf dora establishes ##sham ##tries contracting ##tania cinematic costello nesting ##uron connolly duff ##nology mma ##mata fergus sexes optics spectator woodstock banning ##hee ##fle differentiate outfielder refinery gerhard horde lair drastically ##udi landfall ##cheng motorsport odi ##achi predominant quay skins ##ental edna harshly complementary murdering ##aves wreckage ono outstretched lennox munitions galen reconcile scalp bicycles gillespie questionable rosenberg guillermo jarvis kabul opium yd ##twined abuses decca outpost ##cino sensible neutrality ponce anchorage atkins turrets inadvertently disagree libre vodka reassuring weighs ##yal glide jumper ceilings repertory outs stain ##bial envy ##ucible smashing heightened policing hyun mixes lai prima ##ples celeste ##bina lucrative intervened kc manually ##rned stature staffed bun bastards nairobi priced ##auer thatcher ##kia tripped comune ##ogan ##pled brasil incentives 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revelations jolly gibbons paw ##dro ##quel freeing shack fries palatine ##hiko accompaniment cruising recycled ##aver erwin sorting synthesizers dyke realities strides enslaved wetland ##ghan competence gunpowder grassy maroon reactors objection ##oms carlson gearbox macintosh radios shelton ##sho clergyman prakash mongols trophies oricon stimuli twenty20 cantonese cortes mirrored ##saurus bhp cristina melancholy ##lating enjoyable nuevo ##wny downfall schumacher ##ind banging lausanne rumbled paramilitary reflex ax amplitude migratory ##gall ##ups midi barnard lastly sherry ##nall keystone ##kra carleton slippery coloring foe socket otter ##rgos mats ##tose consultants bafta bison topping primal abandonment transplant atoll hideous mort pained reproduced tae howling ##turn unlawful billionaire hotter poised lansing ##chang dinamo retro messing domesday ##mina blitz timed ##athing ##kley ascending gesturing ##izations signaled tis chinatown mermaid savanna jameson ##aint catalina ##pet ##hers cochrane cy chatting ##kus alerted computation mused noelle majestic mohawk campo octagonal ##sant ##hend aspiring ##mart comprehend iona paralyzed shimmering swindon rhone ##eley reputed configurations pitchfork agitation francais gillian lipstick ##ilo outsiders pontifical resisting bitterness sewer rockies ##edd ##ucher misleading 1756 exiting galloway ##nging risked ##heart commemoration schultz ##rka integrating ##rsa poses shrieked ##weiler guineas gladys jerking owls goldsmith nightly penetrating ##unced lia ignited betsy ##aring ##thorpe follower vigorously ##rave coded kiran knit zoology tbilisi ##bered repository govt deciduous dino growling ##bba enhancement unleashed chanting pussy biochemistry ##eric kettle repression toxicity nrhp ##arth ##kko ##bush ernesto commended outspoken mca parchment kristen ##aton bisexual raked glamour navajo conditioned showcased ##hma spacious youthful ##esa usl appliances junta brest layne conglomerate enchanted chao loosened picasso circulating inspect montevideo ##centric ##kti piazza spurred ##aith bari freedoms poultry stamford lieu indigo sarcastic bahia stump attach dvds frankenstein lille approx scriptures pollen ##script nmi overseen ##ivism tides proponent newmarket inherit milling ##erland centralized ##rou distributors credentials drawers abbreviation ##lco downing uncomfortably ripe ##oes erase franchises populace ##bery ##khar decomposition pleas ##tet daryl sabah ##wide fearless genie lesions annette ##ogist oboe appendix nair dripped petitioned maclean mosquito parrot hampered 1648 operatic reservoirs ##tham irrelevant jolt summarized ##fp medallion ##taff clawed harlow narrower goddard marcia bodied fremont suarez altering tempest mussolini porn ##isms sweetly oversees walkers solitude grimly shrines ich supervisors hostess dietrich legitimacy brushes expressive ##yp dissipated ##rse localized systemic ##nikov gettysburg ##uaries dialogues muttering housekeeper sicilian discouraged ##frey beamed kaladin halftime kidnap ##amo ##llet 1754 synonymous depleted instituto insulin reprised ##opsis clashed ##ctric interrupting radcliffe insisting medici 1715 ejected playfully turbulent starvation ##rini shipment rebellious petersen verification merits ##rified cakes ##charged 1757 milford shortages spying fidelity ##aker emitted storylines harvested seismic ##iform cheung kilda theoretically barbie lynx ##rgy ##tius goblin mata poisonous ##nburg reactive residues obedience ##евич conjecture ##rac hating sixties kicker moaning motown ##bha emancipation neoclassical ##hering consoles ebert professorship ##tures sustaining assaults obeyed affluent incurred tornadoes ##eber ##zow emphasizing highlanders cheated helmets ##ctus internship terence bony executions legislators berries peninsular tinged ##aco 1689 amplifier corvette ribbons lavish pennant ##lander worthless ##chfield ##forms mariano pyrenees expenditures ##icides chesterfield mandir tailor 39th sergey nestled willed aristocracy 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rollins atheist ominous ##ault herr chariot martina strung ##fell ##farlane horrific sahib gazes saetan erased ptolemy ##olic flushing lauderdale analytic ##ices navarro beak gorilla herrera broom guadalupe raiding sykes bsc deliveries 1720 invasions carmichael tajikistan thematic ecumenical sentiments onstage ##rians ##brand ##sume catastrophic flanks molten ##arns waller aimee terminating ##icing alternately ##oche nehru printers outraged ##eving empires template banners repetitive za ##oise vegetarian ##tell guiana opt cavendish lucknow synthesized ##hani ##mada finalized ##ctable fictitious mayoral unreliable ##enham embracing peppers rbis ##chio ##neo inhibition slashed togo orderly embroidered salty barron benito totaled ##dak pubs simulated caden devin tolkien momma welding sesame ##ept gottingen hardness shaman temeraire adequately pediatric assertion radicals composure cadence seafood beaufort lazarus mani warily cunning kurdistan cantata ##kir ares ##clusive nape townland 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integers wallis yamaha ##isha hushed oblivion aviator evangelist friars ##eller monograph ode ##nary airplanes labourers charms ##nee 1661 hagen tnt rudder fiesta transcript dorothea ska inhibitor maccabi retorted raining encompassed clauses menacing 1642 lineman ##gist vamps ##dick gloom ##rera dealings easing seekers ##nut ##pment helens unmanned ##anu ##isson basics ##amy ##ckman adjustments 1688 brutality horne ##zell ##mable aggregator ##thal rhino ##drick ##vira counters ##rting mn montenegrin packard ##unciation ##♭ ##kki reclaim scholastic thugs pulsed ##icia syriac quan saddam banda kobe blaming buddies dissent ##lusion ##usia corbett jaya delle erratic lexie ##hesis amiga hermes ##pressing ##leen chapels gospels jamal ##uating compute revolving warp ##sso ##thes armory ##eras ##gol antrim loki ##kow ##asian ##good ##zano braid handwriting subdistrict funky pantheon ##iculate concurrency estimation improper juliana ##his newcomers johnstone staten communicated ##oco ##alle 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mcintyre repay tarzan darting harrisburg margarita repulsed ##lding belinda hamburger novo compliant runways bingham registrar skyscraper cuthbert improvisation livelihood ##corp ##elial admiring ##dened sporadic believer casablanca popcorn asha shovel ##bek ##dice coiled tangible ##dez casper elsie resin tenderness rectory ##ivision avail sonar ##mori boutique ##dier guerre bathed upbringing vaulted sandals blessings ##naut ##utnant 1680 foxes pia corrosion hesitantly confederates crystalline footprints shapiro tirana valentin drones 45th microscope shipments texted inquisition wry guernsey unauthorized resigning ripple schubert stu reassure felony ##ardo brittle koreans ##havan ##ives dun implicit tyres ##aldi ##lth magnolia ##ehan ##puri ##poulos aggressively fei gr familiarity ##poo indicative ##trust fundamentally jimmie overrun anchors moans ##opus britannia armagh purposely seizing ##vao bewildered mundane avoidance cosmopolitan geometridae quartermaster caf chatter engulfed gleam purge ##icate juliette jurisprudence guerra revisions ##bn casimir brew ##jm 1749 clapton cloudy conde hermitage simulations torches vincenzo matteo ##rill hidalgo booming westbound accomplishment tentacles unaffected ##sius annabelle flopped sloping ##litz dreamer interceptor vu ##loh consecration copying messaging breaker climates hospitalized 1752 torino afternoons winfield witnessing ##teacher breakers choirs sawmill coldly ##ege sipping haste uninhabited conical bibliography pamphlets severn edict ##oca deux illnesses grips rehearsals sis thinkers tame ##keepers 1690 acacia reformer ##osed ##rys shuffling ##iring ##shima eastbound ionic rhea flees littered ##oum rocker vomiting groaning champ overwhelmingly civilizations paces sloop adoptive ##tish skaters ##vres aiding nikola shriek ##ignon pharmaceuticals tuna calvert gustavo stocked yearbook ##urai ##mana computed subsp riff hanoi kelvin hamid moors pastures summons jihad nectar ##ctors bayou untitled pleasing vastly republics intellect ##ulio ##tou crumbling stylistic ##ی consolation frequented h₂o walden widows ##iens ##ignment chunks improves grit recited ##dev snarl sociological ##arte ##gul inquired ##held bruise clube consultancy homogeneous hornets multiplication pasta prick savior ##grin ##kou ##phile yoon ##gara grimes vanishing cheering reacting bn distillery ##quisite ##vity coe dockyard massif ##jord escorts voss ##valent byte chopped hawke illusions workings floats ##koto ##vac kv annapolis madden ##onus alvaro noctuidae ##cum ##scopic avenge steamboat forte illustrates erika ##trip dew nationalities bran manifested thirsty diversified muscled reborn ##standing arson ##lessness ##dran ##logram ##boys ##kushima ##vious willoughby ##phobia alsace dashboard yuki ##chai granville myspace publicized tricked ##gang adjective ##ater relic reorganisation enthusiastically indications saxe ##lassified consolidate iec padua helplessly ramps renaming regulars pedestrians accents convicts 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swearing ##nob ##tically fleeting prostate amulet educating ##mined ##tler 75th jens respondents cavaliers papacy raju ##iente ##ulum ##tip funnel disneyland ##lley sociologist ##iam faulkner louvre menon ##dson ##ower afterlife mannheim peptide referees comedians meaningless ##anger ##laise fabrics hurley renal sleeps ##bour ##icle breakout kristin roadside animator clover disdain unsafe redesign ##urity firth barnsley portage reset narrows commandos expansive speechless tubular essendon eyelashes smashwords ##yad ##bang ##claim craved sprinted chet somme astor wrocław orton bane ##erving ##uing mischief ##amps ##sund scaling terre ##xious impairment offenses undermine moi soy contiguous arcadia inuit seam ##tops macbeth rebelled ##icative ##iot elaborated frs uniformed ##dberg powerless priscilla stimulated qc arboretum frustrating trieste bullock ##nified enriched glistening intern ##adia locus nouvelle ollie ike lash starboard tapestry headlined hove rigged ##vite pollock ##yme thrive clustered cas roi gleamed olympiad ##lino pressured regimes ##hosis ##lick ripley ##ophone kickoff gallon rockwell ##arable crusader glue revolutions scrambling 1714 grover ##jure englishman aztec contemplating coven preach triumphant tufts ##esian rotational ##phus falkland ##brates strewn clarissa rejoin environmentally glint banded drenched moat albanians johor rr maestro malley nouveau shaded taxonomy adhere bunk airfields ##ritan 1741 encompass remington tran ##erative amelie mazda friar morals passions ##zai breadth vis ##hae argus burnham caressing insider rudd ##imov ##rso italianate murderous textual wainwright armada bam weave timer ##taken ##nh fra ##crest ardent salazar taps tunis ##ntino allegro gland philanthropic ##chester implication ##optera esq judas noticeably wynn ##dara inched indexed crises villiers bandit royalties patterned cupboard interspersed accessory isla kendrick entourage stitches ##esthesia headwaters ##ior interlude distraught draught 1727 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rounding sired ##emon ##nist remade uncover ##mack complied lei newsweek ##jured ##parts ##enting ##pg finer guerrillas athenian deng disused stepmother accuse gingerly seduction confronting ##going gora nostalgia sabres virginity wrenched ##minated syndication wielding eyre ##gnon ##igny behaved taxpayer sweeps ##growth childless gallant ##ywood amplified geraldine scrape ##ffi babylonian fresco ##rdan ##kney ##position 1718 restricting tack fukuoka osborn selector partnering ##dlow kia tak whitley gables ##mania mri softness immersion ##bots ##evsky 1713 chilling insignificant pcs ##uis elites lina purported supplemental teaming ##americana ##dding ##inton proficient rouen ##nage ##rret niccolo selects ##bread fluffy 1621 gruff knotted mukherjee polgara thrash nicholls secluded smoothing thru corsica loaf whitaker inquiries ##rrier ##kam indochina marlins myles peking ##tea extracts pastry superhuman connacht vogel ##ditional ##het ##udged ##lash gloss quarries refit teaser ##alic 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chimed incline zoning archduke hellenistic ##oses ##sions candi thong ##ople magnate rustic ##rsk projective slant ##offs danes hollis vocalists ##ammed congenital contend gesellschaft ##ocating ##pressive douglass quieter ##kshi howled salim spontaneously townsville buena southport ##bold kato 1638 faerie stiffly ##vus ##rled flawless realising taboo ##7th straightening jena ##hid cartwright berber bertram soloists noses coping fission hardin inca ##cen 1717 mobilized vhf ##raf biscuits curate ##anial gaunt neighbourhoods 1540 ##abas blanca bypassed sockets behold coincidentally ##bane nara shave splinter terrific ##arion ##erian commonplace juris redwood waistband boxed caitlin fingerprints jennie naturalized ##ired balfour craters jody bungalow hugely quilt glitter pigeons undertaker bulging constrained ##sil ##akh assimilation reworked ##person persuasion ##pants felicia ##cliff ##ulent 1732 explodes ##dun ##inium ##zic lyman vulture hog overlook begs northwards ow spoil ##urer fatima favorably accumulate sargent sorority corresponded dispersal kochi toned ##imi ##lita internacional newfound ##agger ##lynn ##rigue booths peanuts ##eborg medicare muriel nur ##uram crates millennia pajamas worsened ##breakers jimi vanuatu yawned ##udeau carousel ##hony hurdle ##ccus ##mounted ##pod rv ##eche airship ambiguity compulsion recapture ##claiming arthritis ##osomal 1667 asserting ngc sniffing dade discontent glendale ported ##amina defamation rammed ##scent fling livingstone ##fleet 875 apocalyptic comrade ##lowe cessna eine persecuted subsistence demi hoop reliefs coptic progressing stemmed perpetrators 1665 priestess ##nio dobson ebony rooster itf tortricidae ##bbon ##jian cleanup ##jean ##øy 1721 eighties taxonomic holiness ##hearted ##spar antilles showcasing stabilized ##nb gia mascara michelangelo dawned ##uria ##vinsky extinguished fitz grotesque £100 ##fera ##loid ##mous barges neue throbbed cipher johnnie ##mpt outburst ##swick spearheaded administrations heartbreak pixels pleasantly ##enay lombardy plush ##nsed bobbie ##hly reapers tremor xiang minogue substantive hitch barak ##wyl kwan ##encia 910 obscene elegance indus surfer bribery conserve ##hyllum ##masters horatio ##fat apes rebound psychotic ##pour iteration ##mium ##vani botanic horribly antiques dispose paxton ##hli ##wg timeless 1704 disregard engraver hounds ##bau ##version looted uno facilitates groans masjid rutland antibody disqualification decatur footballers quake slacks 48th rein scribe stabilize commits exemplary tho ##hort ##chison pantry traversed ##hiti disrepair identifiable vibrated baccalaureate csa interviewing ##iensis ##raße greaves wealthiest classed jogged £5 ##atal illuminating knicks respecting ##uno scrubbed ##iji ##dles kruger moods growls raider silvia chefs kam cree percival ##terol gunter counterattack defiant henan ze ##rasia ##riety equivalence submissions ##fra ##thor bautista mechanically ##heater cornice herbal templar ##mering outputs ruining ligand renumbered extravagant mika blockbuster eta insurrection ##ilia darkening ferocious pianos strife kinship ##aer melee ##anor ##iste ##oue decidedly weep ##jad ##missive ##ppel puget unease ##gnant 1629 hammering kassel wessex ##lga bromwich egan paranoia utilization ##atable ##idad contradictory provoke ##ols ##ouring ##tangled knesset ##very ##lette plumbing ##sden greensboro occult sniff zev beaming gamer haggard mahal ##olt ##pins mendes utmost briefing gunnery ##gut ##pher ##zh ##rok 1679 khalifa sonya ##boot principals urbana wiring ##liffe ##minating ##rrado dahl nyu skepticism townspeople ithaca lobster somethin ##fur ##arina ##−1 freighter zimmerman biceps contractual ##herton amend hurrying subconscious ##anal meng clermont spawning ##eia ##lub dignitaries impetus snacks spotting twigs ##bilis ##cz ##ouk libertadores nic skylar ##aina gustave asean ##anum dieter legislatures flirt bromley trolls umar ##bbies ##tyle blah parc bridgeport crank negligence ##nction 46th constantin molded bandages seriousness 00pm siegel carpets compartments upbeat statehood ##dner ##edging marko platt ##hane paving ##iy 1738 abbess impatience limousine nbl lucille mojo nightfall robbers ##nais karel brisk calves replicate ascribed telescopes ##olf intimidated ballast specialization aerodynamic caliphate visionary ##arded epsilon ##aday ##onte aggregation auditory boosted reunification kathmandu loco robyn acknowledges appointing humanoid newell redeveloped restraints ##tained barbarians chopper 1609 italiana ##lez ##lho investigates wrestlemania ##anies ##bib ##falls creaked dragoons gravely minions stupidity volley ##harat ##week musik ##eries ##uously fungal massimo semantics malvern ##ahl ##pee discourage embryo imperialism 1910s profoundly ##ddled jiangsu sparkled stat ##holz sweatshirt tobin ##iction sneered ##cheon ##oit brit causal smyth ##neuve diffuse perrin silvio ##ipes ##recht detonated iqbal selma ##nism ##zumi roasted ##riders tay ##ados ##mament ##mut ##rud completes nipples flavour hirsch ##laus calderon sneakers moravian ##ksha 1622 ##imeters bodo ##isance ##pre ##ronia anatomical excerpt ##lke dh kunst ##tablished ##scoe biomass panted unharmed gael housemates montpellier coa rodents tonic hickory singleton ##taro 1719 aldo breaststroke dempsey och rocco ##cuit merton dissemination midsummer serials ##idi haji polynomials enoch prematurely shutter taunton £3 ##grating ##inates archangel harassed ##asco archway dazzling ##ecin 1736 sumo wat ##kovich 1086 honneur ##ently ##nostic ##ttal ##idon 1605 1716 rents ##gnan hires ##ikh ##dant howie ##rons handler retracted shocks 1632 arun duluth kepler trumpeter ##lary peeking seasoned trooper ##mara laszlo ##iciencies ##rti heterosexual ##inatory indira jogging ##inga ##lism beit dissatisfaction malice ##ately nedra peeling ##rgeon 47th stadiums vertigo ##ains iced restroom ##plify ##tub illustrating pear ##chner ##sibility inorganic rappers receipts watery ##kura lucinda ##oulos reintroduced ##8th ##tched gracefully saxons nutritional wastewater rained favourites bedrock fisted hallways likeness upscale ##lateral 1580 blinds prequel ##pps ##tama deter humiliating restraining tn vents 1659 laundering recess rosary tractors coulter federer ##ifiers ##plin persistence ##quitable geschichte pendulum quakers ##beam bassett pictorial koln ##sitor drills reciprocal shooters ##cton ##tees converge pip dmitri donnelly yamamoto aqua azores demographics hypnotic spitfire suspend wryly roderick ##rran sebastien ##asurable mavericks ##fles himalayan prodigy ##iance transvaal demonstrators handcuffs dodged mcnamara sublime 1726 crazed ##efined ##till ivo pondered reconciled shrill sava ##duk bal heresy jaipur goran ##nished lux shelly whitehall ##hre israelis peacekeeping ##wled 1703 demetrius ousted ##arians ##zos beale anwar backstroke raged shrinking cremated ##yck benign towing wadi darmstadt landfill parana soothe colleen sidewalks mayfair tumble hepatitis ferrer superstructure ##gingly ##urse ##wee anthropological translators ##mies closeness hooves ##pw mondays ##roll ##vita landscaping ##urized purification sock thorns thwarted jalan tiberius ##taka saline ##rito confidently khyber sculptors ##ij brahms hammersmith inspectors battista fivb fragmentation hackney ##uls arresting exercising antoinette bedfordshire ##zily dyed ##hema 1656 racetrack variability ##tique 1655 austrians deteriorating madman theorists aix lehman weathered 1731 decreed eruptions 1729 flaw quinlan sorbonne flutes nunez 1711 adored downwards fable rasped 1712 moritz mouthful renegade shivers stunts dysfunction restrain translit pancakes ##avio ##cision ##tray vial ##lden bain ##maid ##oxide chihuahua malacca vimes ##rba ##rnier 1664 donnie plaques ##ually bangs floppy huntsville loretta nikolay ##otte eater handgun ubiquitous ##hett eras zodiac 1634 ##omorphic 1820s ##zog cochran ##bula ##lithic warring ##rada dalai excused blazers mcconnell reeling este ##abi geese hoax taxon ##bla guitarists condemning hunts inversion moffat taekwondo ##lvis 1624 stammered ##rest ##rzy sousa fundraiser marylebone navigable uptown cabbage daniela salman shitty whimper ##kian ##utive programmers protections ##rmi ##rued forceful ##enes fuss ##tao ##wash brat oppressive reykjavik spartak ticking ##inkles ##kiewicz adolph horst maui protege straighten cpc landau concourse clements resultant ##ando imaginative joo reactivated ##rem ##ffled ##uising consultative ##guide flop kaitlyn mergers parenting somber ##vron supervise vidhan ##imum courtship exemplified harmonies medallist refining ##rrow ##ка amara ##hum goalscorer sited overshadowed rohan displeasure secretive multiplied osman ##orth engravings padre ##kali ##veda miniatures mis ##yala clap pali rook ##cana 1692 57th antennae astro oskar 1628 bulldog crotch hackett yucatan ##sure amplifiers brno ferrara migrating ##gree thanking turing ##eza mccann ting andersson onslaught gaines ganga incense standardization 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idiots shamrock 1646 dutton malabar mustache ##endez ##ocytes referencing terminates marche yarmouth ##sop acton mated seton subtly baptised beige extremes jolted kristina telecast ##actic safeguard waldo ##baldi ##bular endeavors sloppy subterranean ##ensburg ##itung delicately pigment tq ##scu 1626 collisions coveted herds ##personal ##meister ##nberger chopra ##ricting abnormalities defective galician lucie ##dilly alligator likened ##genase burundi clears complexion derelict deafening diablo fingered champaign dogg enlist isotope labeling mrna ##erre brilliance marvelous ##ayo 1652 crawley ether footed dwellers deserts hamish rubs warlock skimmed ##lizer buick embark heraldic irregularities ##ajan kiara ##kulam ##ieg antigen kowalski ##lge oakley visitation ##mbit vt ##suit 1570 murderers ##miento ##rites chimneys ##sling condemn custer exchequer havre ##ghi fluctuations ##rations dfb hendricks vaccines ##tarian nietzsche biking juicy ##duced brooding scrolling selangor ##ragan annum boomed seminole sugarcane ##dna departmental dismissing innsbruck arteries ashok batavia daze kun overtook ##rga ##tlan beheaded gaddafi holm electronically faulty galilee fractures kobayashi ##lized gunmen magma aramaic mala eastenders inference messengers bf ##qu bathrooms ##vere 1658 flashbacks ideally misunderstood ##jali ##weather mendez ##grounds uncanny ##iii 1709 friendships ##nbc sacrament accommodated reiterated logistical pebbles thumped ##escence administering decrees drafts ##flight ##cased ##tula futuristic picket intimidation winthrop ##fahan interfered afar francoise morally uta cochin croft dwarfs ##bruck ##dents ##nami biker ##hner ##meral ##isen ##ometric ##pres ##ан brightened meek parcels securely gunners ##jhl ##zko agile hysteria ##lten ##rcus bukit champs chevy cuckoo leith sadler theologians welded ##section 1663 plurality xander ##rooms ##formed shredded temps intimately pau tormented ##lok ##stellar 1618 charred essen ##mmel alarms spraying ascot blooms twinkle ##abia ##apes internment obsidian ##chaft snoop ##dav ##ooping malibu ##tension quiver ##itia hays mcintosh travers walsall ##ffie 1623 beverley schwarz plunging structurally rosenthal vikram ##tsk ghz ##onda ##tiv chalmers groningen pew reckon unicef ##rvis 55th ##gni 1651 sulawesi avila cai metaphysical screwing turbulence ##mberg augusto samba 56th baffled momentary toxin ##urian ##wani aachen condoms dali steppe ##oed ##year adolescence dauphin electrically inaccessible microscopy nikita ##ega atv ##enter ##oles ##oteric accountants punishments wrongly bribes adventurous clinch flinders southland ##hem ##kata gough ##ciency lads soared ##ה undergoes deformation outlawed rubbish ##arus ##mussen ##nidae ##rzburg arcs ##ingdon ##tituted 1695 wheelbase wheeling bombardier campground zebra ##lices ##oj ##bain lullaby ##ecure donetsk wylie grenada ##arding ##ης squinting eireann opposes ##andra maximal runes ##broken ##cuting ##iface ##ror ##rosis additive britney adultery triggering ##drome detrimental aarhus containment jc swapped vichy ##ioms madly ##oric ##rag brant ##ckey 1560 1612 broughton rustling ##stems ##uder asbestos mentoring ##nivorous finley leaps ##isan apical pry slits substitutes ##dict intuitive fantasia insistent unreasonable ##igen ##vna domed hannover margot ponder ##zziness impromptu jian rampage stemming ##eft andrey gerais whichever amnesia appropriated anzac clicks modifying ultimatum cambrian maids verve yellowstone ##mbs conservatoire ##scribe adherence dinners spectra imperfect mysteriously sidekick tatar tuba ##aks ##ifolia distrust ##athan ##zle ronin zac ##pse celaena instrumentalist scents skopje ##mbling comical compensated vidal condor intersect jingle wavelengths ##urrent mcqueen ##izzly carp weasel militias postdoctoral eugen gunslinger ##ɛ faux hospice ##for appalled derivation dwarves ##elis dilapidated ##folk astoria philology ##lwyn ##otho ##saka inducing philanthropy ##bf ##itative geek markedly ##yce bessie indices ##flict frowns resolving weightlifting tugs cleric contentious 1653 mania rms ##miya ##reate ##ruck ##tucket bien eels marek ##ayton ##cence discreet unofficially ##ife leaks ##bber 1705 dung compressor hillsborough pandit shillings distal ##skin ##tat nosed ##nir mangrove undeveloped ##idia textures ##inho ##rise irritating nay amazingly bancroft apologetic compassionate kata symphonies ##lovic airspace ##lch gifford precautions fulfillment sevilla vulgar martinique ##urities looting piccolo tidy ##dermott quadrant armchair incomes mathematicians stampede nilsson ##inking ##scan foo quarterfinal ##ostal shang shouldered squirrels ##owe vinegar ##bner ##rchy ##systems delaying ##trics ars dwyer rhapsody sponsoring ##gration bipolar cinder starters ##olio ##urst signage ##nty aground figurative mons acquaintances duets erroneously soyuz elliptic recreated ##cultural ##quette ##ssed ##tma ##zcz moderator scares ##itaire ##stones ##udence juniper sighting ##just ##nsen britten calabria ry bop cramer forsyth stillness airmen gathers unfit ##umber ##upt taunting seeker streamlined ##bution holster schumann tread vox ##gano ##onzo strive dil reforming covent newbury predicting ##orro decorate tre ##puted andover asahi dept dunkirk gills ##tori buren huskies ##stis ##stov abstracts bets loosen ##opa 1682 yearning ##glio ##sir berman effortlessly enamel napoli persist ##peration ##uez attache elisa invitations ##kic accelerating reindeer boardwalk clutches nelly polka ##kei adamant huey lough unbroken adventurer embroidery inspecting stanza ##ducted naia taluka ##pone ##roids chases deprivation florian ##ppet earthly ##lib ##ssee colossal foreigner vet freaks patrice rosewood triassic upstate ##pkins dominates ata chants ks vo ##bley ##raya ##rmed agra infiltrate ##ailing ##ilation ##tzer ##uppe ##werk binoculars enthusiast fujian squeak ##avs abolitionist almeida boredom hampstead marsden rations ##ands inflated bonuses rosalie patna ##rco detachments penitentiary 54th flourishing woolf ##dion ##etched papyrus ##lster ##nsor ##toy bobbed dismounted endelle inhuman motorola wince wreath ##ticus hideout inspections sanjay disgrace infused pudding stalks ##urbed arsenic leases ##hyl ##rrard collarbone ##waite ##wil dowry ##bant ##edance genealogical nitrate salamanca scandals thyroid necessitated ##` ##¡ ##¢ ##¦ ##¨ ##ª ##¬ ##´ ##¶ ##¾ ##¿ ##ð ##þ ##ħ ##œ ##ƒ ##ɐ ##ɑ ##ɒ ##ɕ ##ɣ ##ɨ ##ɪ ##ɫ ##ɬ ##ɯ ##ɲ ##ɴ ##ɹ ##ɾ ##ʀ ##ʁ ##ʂ ##ʃ ##ʉ ##ʊ ##ʋ ##ʌ ##ʎ ##ʐ ##ʑ ##ʒ ##ʔ ##ʲ ##ʳ ##ʷ ##ʸ ##ʻ ##ʼ ##ʾ ##ʿ ##ˡ ##ˣ ##ˤ ##ζ ##ξ ##щ ##ъ ##э ##ю ##ђ ##є ##ј ##љ ##њ ##ћ ##ӏ ##ա ##բ ##գ ##դ ##ե ##թ ##ի ##լ ##կ ##հ ##մ ##յ ##ն ##ո ##պ ##ս ##վ ##տ ##ր ##ւ ##ք ##־ ##א ##ב ##ג ##ד ##ו ##ז ##ח ##ט ##י ##ך ##כ ##ל ##ם ##מ ##ן ##נ ##ס ##ע ##ף ##פ ##ץ ##צ ##ק ##ר ##ש ##ת ##، ##ء ##ث ##ج ##ح ##خ ##ذ ##ز ##ش ##ص ##ض ##ط ##ظ ##غ ##ـ ##ف ##ق ##ك ##ى ##ٹ ##پ ##چ ##ک ##گ ##ں ##ھ ##ہ ##ے ##अ ##आ ##उ ##ए ##क ##ख ##ग ##च ##ज ##ट ##ड ##ण ##त ##थ ##द ##ध ##न ##प ##ब ##भ ##म ##य ##र ##ल ##व ##श ##ष ##स ##ह ##ा ##ि ##ी ##ो ##। ##॥ ##ং ##অ ##আ ##ই ##উ ##এ ##ও ##ক ##খ ##গ ##চ ##ছ ##জ ##ট ##ড ##ণ ##ত ##থ ##দ ##ধ ##ন ##প ##ব ##ভ ##ম ##য ##র ##ল ##শ ##ষ ##স ##হ ##া ##ি ##ী ##ে ##க ##ச ##ட ##த ##ந ##ன ##ப ##ம ##ய ##ர ##ல ##ள ##வ ##ா ##ி ##ு ##ே ##ை ##ನ ##ರ ##ಾ ##ක ##ය ##ර ##ල ##ව ##ා ##ต ##ท ##พ ##ล ##ว ##ส ##། ##ག ##ང ##ད ##ན ##པ ##བ ##མ ##འ ##ར ##ལ ##ས ##မ ##ა ##ბ ##გ ##დ ##ე ##ვ ##თ ##ი ##კ ##ლ ##მ ##ნ ##ო ##რ ##ს ##ტ ##უ ##ᄊ ##ᴬ ##ᴮ ##ᴰ ##ᴵ ##ᴺ ##ᵀ ##ᵇ ##ᵈ ##ᵖ ##ᵗ ##ᵣ ##ᵤ ##ᵥ ##ᶜ ##ᶠ ##‐ ##‑ ##‒ ##– ##— ##― ##‘ ##’ ##‚ ##“ ##” ##‡ ##… ##⁰ ##⁴ ##⁵ ##⁶ ##⁷ ##⁸ ##⁹ ##⁻ ##₅ ##₆ ##₇ ##₈ ##₉ ##₊ ##₍ ##₎ ##ₐ ##ₑ ##ₒ ##ₓ ##ₕ ##ₖ ##ₗ ##ₘ ##ₚ ##ₛ ##ₜ ##₤ ##₩ ##₱ ##₹ ##ℓ ##ℝ ##⅓ ##⅔ ##↦ ##⇄ ##⇌ ##∂ ##∅ ##∆ ##∇ ##∈ ##∗ ##∘ ##∧ ##∨ ##∪ ##⊂ ##⊆ ##⊕ ##⊗ ##☉ ##♯ ##⟨ ##⟩ ##ⱼ ##⺩ ##⺼ ##⽥ ##亻 ##宀 ##彳 ##忄 ##扌 ##氵 ##疒 ##糹 ##訁 ##辶 ##阝 ##龸 ##fi ##fl ================================================ FILE: comfy/text_encoders/jina_clip_2.py ================================================ # Jina CLIP v2 and Jina Embeddings v3 both use their modified XLM-RoBERTa architecture. Reference implementation: # Jina CLIP v2 (both text and vision): https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/modeling_clip.py # Jina XLM-RoBERTa (text only): http://huggingface.co/jinaai/xlm-roberta-flash-implementation/blob/2b6bc3f30750b3a9648fe9b63448c09920efe9be/modeling_xlm_roberta.py from dataclasses import dataclass import torch from torch import nn as nn from torch.nn import functional as F import comfy.model_management import comfy.ops from comfy import sd1_clip from .spiece_tokenizer import SPieceTokenizer class JinaClip2Tokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer = tokenizer_data.get("spiece_model", None) # The official NewBie uses max_length=8000, but Jina Embeddings v3 actually supports 8192 super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='jina_clip_2', tokenizer_class=SPieceTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=False, max_length=8192, min_length=1, pad_token=1, end_token=2, tokenizer_args={"add_bos": True, "add_eos": True}, tokenizer_data=tokenizer_data) def state_dict(self): return {"spiece_model": self.tokenizer.serialize_model()} class JinaClip2TokenizerWrapper(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=JinaClip2Tokenizer, name="jina_clip_2") # https://huggingface.co/jinaai/jina-embeddings-v3/blob/343dbf534c76fe845f304fa5c2d1fd87e1e78918/config.json @dataclass class XLMRobertaConfig: vocab_size: int = 250002 type_vocab_size: int = 1 hidden_size: int = 1024 num_hidden_layers: int = 24 num_attention_heads: int = 16 rotary_emb_base: float = 20000.0 intermediate_size: int = 4096 hidden_act: str = "gelu" hidden_dropout_prob: float = 0.1 attention_probs_dropout_prob: float = 0.1 layer_norm_eps: float = 1e-05 bos_token_id: int = 0 eos_token_id: int = 2 pad_token_id: int = 1 class XLMRobertaEmbeddings(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() embed_dim = config.hidden_size self.word_embeddings = ops.Embedding(config.vocab_size, embed_dim, padding_idx=config.pad_token_id, device=device, dtype=dtype) self.token_type_embeddings = ops.Embedding(config.type_vocab_size, embed_dim, device=device, dtype=dtype) def forward(self, input_ids=None, embeddings=None): if input_ids is not None and embeddings is None: embeddings = self.word_embeddings(input_ids) if embeddings is not None: token_type_ids = torch.zeros(embeddings.shape[1], device=embeddings.device, dtype=torch.int32) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = embeddings + token_type_embeddings return embeddings class RotaryEmbedding(nn.Module): def __init__(self, dim, base, device=None): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._cos_cached = None self._sin_cached = None def _update_cos_sin_cache(self, seqlen, device=None, dtype=None): if seqlen > self._seq_len_cached or self._cos_cached is None or self._cos_cached.device != device or self._cos_cached.dtype != dtype: self._seq_len_cached = seqlen t = torch.arange(seqlen, device=device, dtype=torch.float32) freqs = torch.outer(t, self.inv_freq.to(device=t.device)) emb = torch.cat((freqs, freqs), dim=-1) self._cos_cached = emb.cos().to(dtype) self._sin_cached = emb.sin().to(dtype) def forward(self, q, k): batch, seqlen, heads, head_dim = q.shape self._update_cos_sin_cache(seqlen, device=q.device, dtype=q.dtype) cos = self._cos_cached[:seqlen].view(1, seqlen, 1, head_dim) sin = self._sin_cached[:seqlen].view(1, seqlen, 1, head_dim) def rotate_half(x): size = x.shape[-1] // 2 x1, x2 = x[..., :size], x[..., size:] return torch.cat((-x2, x1), dim=-1) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class MHA(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = embed_dim // config.num_attention_heads self.rotary_emb = RotaryEmbedding(self.head_dim, config.rotary_emb_base, device=device) self.Wqkv = ops.Linear(embed_dim, 3 * embed_dim, device=device, dtype=dtype) self.out_proj = ops.Linear(embed_dim, embed_dim, device=device, dtype=dtype) def forward(self, x, mask=None, optimized_attention=None): qkv = self.Wqkv(x) batch_size, seq_len, _ = qkv.shape qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim) q, k, v = qkv.unbind(2) q, k = self.rotary_emb(q, k) # NHD -> HND q = q.transpose(1, 2) k = k.transpose(1, 2) v = v.transpose(1, 2) out = optimized_attention(q, k, v, heads=self.num_heads, mask=mask, skip_reshape=True) return self.out_proj(out) class MLP(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() self.fc1 = ops.Linear(config.hidden_size, config.intermediate_size, device=device, dtype=dtype) self.activation = F.gelu self.fc2 = ops.Linear(config.intermediate_size, config.hidden_size, device=device, dtype=dtype) def forward(self, x): x = self.fc1(x) x = self.activation(x) x = self.fc2(x) return x class Block(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() self.mixer = MHA(config, device=device, dtype=dtype, ops=ops) self.dropout1 = nn.Dropout(config.hidden_dropout_prob) self.norm1 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype) self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) self.dropout2 = nn.Dropout(config.hidden_dropout_prob) self.norm2 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype) def forward(self, hidden_states, mask=None, optimized_attention=None): mixer_out = self.mixer(hidden_states, mask=mask, optimized_attention=optimized_attention) hidden_states = self.norm1(self.dropout1(mixer_out) + hidden_states) mlp_out = self.mlp(hidden_states) hidden_states = self.norm2(self.dropout2(mlp_out) + hidden_states) return hidden_states class XLMRobertaEncoder(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() self.layers = nn.ModuleList([Block(config, device=device, dtype=dtype, ops=ops) for _ in range(config.num_hidden_layers)]) def forward(self, hidden_states, attention_mask=None): optimized_attention = comfy.ldm.modules.attention.optimized_attention_for_device(hidden_states.device, mask=attention_mask is not None, small_input=True) for layer in self.layers: hidden_states = layer(hidden_states, mask=attention_mask, optimized_attention=optimized_attention) return hidden_states class XLMRobertaModel_(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() self.embeddings = XLMRobertaEmbeddings(config, device=device, dtype=dtype, ops=ops) self.emb_ln = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype) self.emb_drop = nn.Dropout(config.hidden_dropout_prob) self.encoder = XLMRobertaEncoder(config, device=device, dtype=dtype, ops=ops) def forward(self, input_ids, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): x = self.embeddings(input_ids=input_ids, embeddings=embeds) x = self.emb_ln(x) x = self.emb_drop(x) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, 1, attention_mask.shape[-1])) mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) sequence_output = self.encoder(x, attention_mask=mask) # Mean pool, see https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/hf_model.py pooled_output = None if attention_mask is None: pooled_output = sequence_output.mean(dim=1) else: attention_mask = attention_mask.to(sequence_output.dtype) pooled_output = (sequence_output * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=-1, keepdim=True) # Intermediate output is not yet implemented, use None for placeholder return sequence_output, None, pooled_output class XLMRobertaModel(nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.config = XLMRobertaConfig(**config_dict) self.model = XLMRobertaModel_(self.config, device=device, dtype=dtype, ops=operations) self.num_layers = self.config.num_hidden_layers def get_input_embeddings(self): return self.model.embeddings.word_embeddings def set_input_embeddings(self, embeddings): self.model.embeddings.word_embeddings = embeddings def forward(self, *args, **kwargs): return self.model(*args, **kwargs) class JinaClip2TextModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, textmodel_json_config={}, model_class=XLMRobertaModel, special_tokens={"start": 0, "end": 2, "pad": 1}, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) class JinaClip2TextModelWrapper(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, clip_model=JinaClip2TextModel, name="jina_clip_2", model_options=model_options) ================================================ FILE: comfy/text_encoders/kandinsky5.py ================================================ from comfy import sd1_clip from .qwen_image import QwenImageTokenizer, QwenImageTEModel from .llama import Qwen25_7BVLI class Kandinsky5Tokenizer(QwenImageTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.llama_template = "<|im_start|>system\nYou are a prompt engineer. Describe the video in detail.\nDescribe how the camera moves or shakes, describe the zoom and view angle, whether it follows the objects.\nDescribe the location of the video, main characters or objects and their action.\nDescribe the dynamism of the video and presented actions.\nName the visual style of the video: whether it is a professional footage, user generated content, some kind of animation, video game or screen content.\nDescribe the visual effects, postprocessing and transitions if they are presented in the video.\nPay attention to the order of key actions shown in the scene.<|im_end|>\n<|im_start|>user\n{}<|im_end|>" self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = super().tokenize_with_weights(text, return_word_ids, **kwargs) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) return out class Kandinsky5TokenizerImage(Kandinsky5Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.llama_template = "<|im_start|>system\nYou are a promt engineer. Describe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>" class Qwen25_7BVLIModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Kandinsky5TEModel(QwenImageTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) def encode_token_weights(self, token_weight_pairs): cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=-1) l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs["l"]) return cond, l_pooled, extra def set_clip_options(self, options): super().set_clip_options(options) self.clip_l.set_clip_options(options) def reset_clip_options(self): super().reset_clip_options() self.clip_l.reset_clip_options() def load_sd(self, sd): if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: return self.clip_l.load_sd(sd) else: return super().load_sd(sd) def te(dtype_llama=None, llama_quantization_metadata=None): class Kandinsky5TEModel_(Kandinsky5TEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(device=device, dtype=dtype, model_options=model_options) return Kandinsky5TEModel_ ================================================ FILE: comfy/text_encoders/llama.py ================================================ import torch import torch.nn as nn from dataclasses import dataclass from typing import Optional, Any, Tuple import math from tqdm import tqdm import comfy.utils from comfy.ldm.modules.attention import optimized_attention_for_device import comfy.model_management import comfy.ops import comfy.ldm.common_dit import comfy.clip_model from . import qwen_vl @dataclass class Llama2Config: vocab_size: int = 128320 hidden_size: int = 4096 intermediate_size: int = 14336 num_hidden_layers: int = 32 num_attention_heads: int = 32 num_key_value_heads: int = 8 max_position_embeddings: int = 8192 rms_norm_eps: float = 1e-5 rope_theta: float = 500000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = None k_norm = None rope_scale = None final_norm: bool = True lm_head: bool = False @dataclass class Mistral3Small24BConfig: vocab_size: int = 131072 hidden_size: int = 5120 intermediate_size: int = 32768 num_hidden_layers: int = 40 num_attention_heads: int = 32 num_key_value_heads: int = 8 max_position_embeddings: int = 8192 rms_norm_eps: float = 1e-5 rope_theta: float = 1000000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = None k_norm = None rope_scale = None final_norm: bool = True lm_head: bool = False @dataclass class Qwen25_3BConfig: vocab_size: int = 151936 hidden_size: int = 2048 intermediate_size: int = 11008 num_hidden_layers: int = 36 num_attention_heads: int = 16 num_key_value_heads: int = 2 max_position_embeddings: int = 128000 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = True rope_dims = None q_norm = None k_norm = None rope_scale = None final_norm: bool = True lm_head: bool = False @dataclass class Qwen3_06BConfig: vocab_size: int = 151936 hidden_size: int = 1024 intermediate_size: int = 3072 num_hidden_layers: int = 28 num_attention_heads: int = 16 num_key_value_heads: int = 8 max_position_embeddings: int = 32768 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" rope_scale = None final_norm: bool = True lm_head: bool = False stop_tokens = [151643, 151645] @dataclass class Qwen3_06B_ACE15_Config: vocab_size: int = 151669 hidden_size: int = 1024 intermediate_size: int = 3072 num_hidden_layers: int = 28 num_attention_heads: int = 16 num_key_value_heads: int = 8 max_position_embeddings: int = 32768 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" rope_scale = None final_norm: bool = True lm_head: bool = False stop_tokens = [151643, 151645] @dataclass class Qwen3_2B_ACE15_lm_Config: vocab_size: int = 217204 hidden_size: int = 2048 intermediate_size: int = 6144 num_hidden_layers: int = 28 num_attention_heads: int = 16 num_key_value_heads: int = 8 max_position_embeddings: int = 40960 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" rope_scale = None final_norm: bool = True lm_head: bool = False stop_tokens = [151643, 151645] @dataclass class Qwen3_4B_ACE15_lm_Config: vocab_size: int = 217204 hidden_size: int = 2560 intermediate_size: int = 9728 num_hidden_layers: int = 36 num_attention_heads: int = 32 num_key_value_heads: int = 8 max_position_embeddings: int = 40960 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" rope_scale = None final_norm: bool = True lm_head: bool = False stop_tokens = [151643, 151645] @dataclass class Qwen3_4BConfig: vocab_size: int = 151936 hidden_size: int = 2560 intermediate_size: int = 9728 num_hidden_layers: int = 36 num_attention_heads: int = 32 num_key_value_heads: int = 8 max_position_embeddings: int = 40960 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" rope_scale = None final_norm: bool = True lm_head: bool = False stop_tokens = [151643, 151645] @dataclass class Qwen3_8BConfig: vocab_size: int = 151936 hidden_size: int = 4096 intermediate_size: int = 12288 num_hidden_layers: int = 36 num_attention_heads: int = 32 num_key_value_heads: int = 8 max_position_embeddings: int = 40960 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" rope_scale = None final_norm: bool = True lm_head: bool = False stop_tokens = [151643, 151645] @dataclass class Ovis25_2BConfig: vocab_size: int = 151936 hidden_size: int = 2048 intermediate_size: int = 6144 num_hidden_layers: int = 28 num_attention_heads: int = 16 num_key_value_heads: int = 8 max_position_embeddings: int = 40960 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" rope_scale = None final_norm: bool = True lm_head: bool = False @dataclass class Qwen25_7BVLI_Config: vocab_size: int = 152064 hidden_size: int = 3584 intermediate_size: int = 18944 num_hidden_layers: int = 28 num_attention_heads: int = 28 num_key_value_heads: int = 4 max_position_embeddings: int = 128000 rms_norm_eps: float = 1e-6 rope_theta: float = 1000000.0 transformer_type: str = "llama" head_dim = 128 rms_norm_add = False mlp_activation = "silu" qkv_bias = True rope_dims = [16, 24, 24] q_norm = None k_norm = None rope_scale = None final_norm: bool = True lm_head: bool = False @dataclass class Gemma2_2B_Config: vocab_size: int = 256000 hidden_size: int = 2304 intermediate_size: int = 9216 num_hidden_layers: int = 26 num_attention_heads: int = 8 num_key_value_heads: int = 4 max_position_embeddings: int = 8192 rms_norm_eps: float = 1e-6 rope_theta: float = 10000.0 transformer_type: str = "gemma2" head_dim = 256 rms_norm_add = True mlp_activation = "gelu_pytorch_tanh" qkv_bias = False rope_dims = None q_norm = None k_norm = None sliding_attention = None rope_scale = None final_norm: bool = True lm_head: bool = False stop_tokens = [1] @dataclass class Gemma3_4B_Config: vocab_size: int = 262208 hidden_size: int = 2560 intermediate_size: int = 10240 num_hidden_layers: int = 34 num_attention_heads: int = 8 num_key_value_heads: int = 4 max_position_embeddings: int = 131072 rms_norm_eps: float = 1e-6 rope_theta = [1000000.0, 10000.0] transformer_type: str = "gemma3" head_dim = 256 rms_norm_add = True mlp_activation = "gelu_pytorch_tanh" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" sliding_attention = [1024, 1024, 1024, 1024, 1024, False] rope_scale = [8.0, 1.0] final_norm: bool = True lm_head: bool = False stop_tokens = [1, 106] GEMMA3_VISION_CONFIG = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14} @dataclass class Gemma3_4B_Vision_Config(Gemma3_4B_Config): vision_config = GEMMA3_VISION_CONFIG mm_tokens_per_image = 256 @dataclass class Gemma3_12B_Config: vocab_size: int = 262208 hidden_size: int = 3840 intermediate_size: int = 15360 num_hidden_layers: int = 48 num_attention_heads: int = 16 num_key_value_heads: int = 8 max_position_embeddings: int = 131072 rms_norm_eps: float = 1e-6 rope_theta = [1000000.0, 10000.0] transformer_type: str = "gemma3" head_dim = 256 rms_norm_add = True mlp_activation = "gelu_pytorch_tanh" qkv_bias = False rope_dims = None q_norm = "gemma3" k_norm = "gemma3" sliding_attention = [1024, 1024, 1024, 1024, 1024, False] rope_scale = [8.0, 1.0] final_norm: bool = True lm_head: bool = False vision_config = GEMMA3_VISION_CONFIG mm_tokens_per_image = 256 stop_tokens = [1, 106] class RMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) self.add = add def forward(self, x: torch.Tensor): w = self.weight if self.add: w = w + 1.0 return comfy.ldm.common_dit.rms_norm(x, w, self.eps) def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None): if not isinstance(theta, list): theta = [theta] out = [] for index, t in enumerate(theta): theta_numerator = torch.arange(0, head_dim, 2, device=device).float() inv_freq = 1.0 / (t ** (theta_numerator / head_dim)) if rope_scale is not None: if isinstance(rope_scale, list): inv_freq /= rope_scale[index] else: inv_freq /= rope_scale inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) position_ids_expanded = position_ids[:, None, :].float() freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() sin = emb.sin() if rope_dims is not None and position_ids.shape[0] > 1: mrope_section = rope_dims * 2 cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) else: cos = cos.unsqueeze(1) sin = sin.unsqueeze(1) sin_split = sin.shape[-1] // 2 out.append((cos, sin[..., : sin_split], -sin[..., sin_split :])) if len(out) == 1: return out[0] return out def apply_rope(xq, xk, freqs_cis): org_dtype = xq.dtype cos = freqs_cis[0] sin = freqs_cis[1] nsin = freqs_cis[2] q_embed = (xq * cos) q_split = q_embed.shape[-1] // 2 q_embed[..., : q_split].addcmul_(xq[..., q_split :], nsin) q_embed[..., q_split :].addcmul_(xq[..., : q_split], sin) k_embed = (xk * cos) k_split = k_embed.shape[-1] // 2 k_embed[..., : k_split].addcmul_(xk[..., k_split :], nsin) k_embed[..., k_split :].addcmul_(xk[..., : k_split], sin) return q_embed.to(org_dtype), k_embed.to(org_dtype) class Attention(nn.Module): def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): super().__init__() self.num_heads = config.num_attention_heads self.num_kv_heads = config.num_key_value_heads self.hidden_size = config.hidden_size self.head_dim = config.head_dim self.inner_size = self.num_heads * self.head_dim ops = ops or nn self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype) self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype) self.q_norm = None self.k_norm = None if config.q_norm == "gemma3": self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) if config.k_norm == "gemma3": self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, freqs_cis: Optional[torch.Tensor] = None, optimized_attention=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, sliding_window: Optional[int] = None, ): batch_size, seq_length, _ = hidden_states.shape xq = self.q_proj(hidden_states) xk = self.k_proj(hidden_states) xv = self.v_proj(hidden_states) xq = xq.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) if self.q_norm is not None: xq = self.q_norm(xq) if self.k_norm is not None: xk = self.k_norm(xk) xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis) present_key_value = None if past_key_value is not None: index = 0 num_tokens = xk.shape[2] if len(past_key_value) > 0: past_key, past_value, index = past_key_value if past_key.shape[2] >= (index + num_tokens): past_key[:, :, index:index + xk.shape[2]] = xk past_value[:, :, index:index + xv.shape[2]] = xv xk = past_key[:, :, :index + xk.shape[2]] xv = past_value[:, :, :index + xv.shape[2]] present_key_value = (past_key, past_value, index + num_tokens) else: xk = torch.cat((past_key[:, :, :index], xk), dim=2) xv = torch.cat((past_value[:, :, :index], xv), dim=2) present_key_value = (xk, xv, index + num_tokens) else: present_key_value = (xk, xv, index + num_tokens) if sliding_window is not None and xk.shape[2] > sliding_window: xk = xk[:, :, -sliding_window:] xv = xv[:, :, -sliding_window:] attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True) return self.o_proj(output), present_key_value class MLP(nn.Module): def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): super().__init__() ops = ops or nn self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype) if config.mlp_activation == "silu": self.activation = torch.nn.functional.silu elif config.mlp_activation == "gelu_pytorch_tanh": self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh") def forward(self, x): return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x)) class TransformerBlock(nn.Module): def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None): super().__init__() self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) def forward( self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, freqs_cis: Optional[torch.Tensor] = None, optimized_attention=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ): # Self Attention residual = x x = self.input_layernorm(x) x, present_key_value = self.self_attn( hidden_states=x, attention_mask=attention_mask, freqs_cis=freqs_cis, optimized_attention=optimized_attention, past_key_value=past_key_value, ) x = residual + x # MLP residual = x x = self.post_attention_layernorm(x) x = self.mlp(x) x = residual + x return x, present_key_value class TransformerBlockGemma2(nn.Module): def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None): super().__init__() self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) if config.sliding_attention is not None: self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)] else: self.sliding_attention = False self.transformer_type = config.transformer_type def forward( self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, freqs_cis: Optional[torch.Tensor] = None, optimized_attention=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ): sliding_window = None if self.transformer_type == 'gemma3': if self.sliding_attention: sliding_window = self.sliding_attention if x.shape[1] > self.sliding_attention: sliding_mask = torch.full((x.shape[1], x.shape[1]), torch.finfo(x.dtype).min, device=x.device, dtype=x.dtype) sliding_mask.tril_(diagonal=-self.sliding_attention) if attention_mask is not None: attention_mask = attention_mask + sliding_mask else: attention_mask = sliding_mask freqs_cis = freqs_cis[1] else: freqs_cis = freqs_cis[0] # Self Attention residual = x x = self.input_layernorm(x) x, present_key_value = self.self_attn( hidden_states=x, attention_mask=attention_mask, freqs_cis=freqs_cis, optimized_attention=optimized_attention, past_key_value=past_key_value, sliding_window=sliding_window, ) x = self.post_attention_layernorm(x) x = residual + x # MLP residual = x x = self.pre_feedforward_layernorm(x) x = self.mlp(x) x = self.post_feedforward_layernorm(x) x = residual + x return x, present_key_value class Llama2_(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() self.config = config self.vocab_size = config.vocab_size self.embed_tokens = ops.Embedding( config.vocab_size, config.hidden_size, device=device, dtype=dtype ) if self.config.transformer_type == "gemma2" or self.config.transformer_type == "gemma3": transformer = TransformerBlockGemma2 self.normalize_in = True else: transformer = TransformerBlock self.normalize_in = False self.layers = nn.ModuleList([ transformer(config, index=i, device=device, dtype=dtype, ops=ops) for i in range(config.num_hidden_layers) ]) if config.final_norm: self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) else: self.norm = None if config.lm_head: self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype) def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None): if embeds is not None: x = embeds else: x = self.embed_tokens(x, out_dtype=dtype) if self.normalize_in: x *= self.config.hidden_size ** 0.5 seq_len = x.shape[1] past_len = 0 if past_key_values is not None and len(past_key_values) > 0: past_len = past_key_values[0][2] if position_ids is None: position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0) freqs_cis = precompute_freqs_cis(self.config.head_dim, position_ids, self.config.rope_theta, self.config.rope_scale, self.config.rope_dims, device=x.device) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), torch.finfo(x.dtype).min / 4) if seq_len > 1: causal_mask = torch.empty(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device).fill_(torch.finfo(x.dtype).min / 4).triu_(1) if mask is not None: mask += causal_mask else: mask = causal_mask optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) intermediate = None all_intermediate = None only_layers = None if intermediate_output is not None: if isinstance(intermediate_output, list): all_intermediate = [] only_layers = set(intermediate_output) elif intermediate_output == "all": all_intermediate = [] intermediate_output = None elif intermediate_output < 0: intermediate_output = len(self.layers) + intermediate_output next_key_values = [] for i, layer in enumerate(self.layers): if all_intermediate is not None: if only_layers is None or (i in only_layers): all_intermediate.append(x.unsqueeze(1).clone()) past_kv = None if past_key_values is not None: past_kv = past_key_values[i] if len(past_key_values) > 0 else [] x, current_kv = layer( x=x, attention_mask=mask, freqs_cis=freqs_cis, optimized_attention=optimized_attention, past_key_value=past_kv, ) if current_kv is not None: next_key_values.append(current_kv) if i == intermediate_output: intermediate = x.clone() if self.norm is not None: x = self.norm(x) if all_intermediate is not None: if only_layers is None or ((i + 1) in only_layers): all_intermediate.append(x.unsqueeze(1).clone()) if all_intermediate is not None: intermediate = torch.cat(all_intermediate, dim=1) if intermediate is not None and final_layer_norm_intermediate and self.norm is not None: intermediate = self.norm(intermediate) if len(next_key_values) > 0: return x, intermediate, next_key_values else: return x, intermediate class Gemma3MultiModalProjector(torch.nn.Module): def __init__(self, config, dtype, device, operations): super().__init__() self.mm_input_projection_weight = nn.Parameter( torch.empty(config.vision_config["hidden_size"], config.hidden_size, device=device, dtype=dtype) ) self.mm_soft_emb_norm = RMSNorm(config.vision_config["hidden_size"], eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) self.patches_per_image = int(config.vision_config["image_size"] // config.vision_config["patch_size"]) self.tokens_per_side = int(config.mm_tokens_per_image**0.5) self.kernel_size = self.patches_per_image // self.tokens_per_side self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size) def forward(self, vision_outputs: torch.Tensor): batch_size, _, seq_length = vision_outputs.shape reshaped_vision_outputs = vision_outputs.transpose(1, 2) reshaped_vision_outputs = reshaped_vision_outputs.reshape( batch_size, seq_length, self.patches_per_image, self.patches_per_image ) reshaped_vision_outputs = reshaped_vision_outputs.contiguous() pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs) pooled_vision_outputs = pooled_vision_outputs.flatten(2) pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2) normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs) projected_vision_outputs = torch.matmul(normed_vision_outputs, comfy.model_management.cast_to_device(self.mm_input_projection_weight, device=normed_vision_outputs.device, dtype=normed_vision_outputs.dtype)) return projected_vision_outputs.type_as(vision_outputs) class BaseLlama: def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, embeddings): self.model.embed_tokens = embeddings def forward(self, input_ids, *args, **kwargs): return self.model(input_ids, *args, **kwargs) class BaseGenerate: def logits(self, x): input = x[:, -1:] if hasattr(self.model, "lm_head"): module = self.model.lm_head else: module = self.model.embed_tokens offload_stream = None if module.comfy_cast_weights: weight, _, offload_stream = comfy.ops.cast_bias_weight(module, input, offloadable=True) else: weight = self.model.embed_tokens.weight.to(x) x = torch.nn.functional.linear(input, weight, None) comfy.ops.uncast_bias_weight(module, weight, None, offload_stream) return x def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0): device = embeds.device model_config = self.model.config if stop_tokens is None: stop_tokens = self.model.config.stop_tokens if execution_dtype is None: if comfy.model_management.should_use_bf16(device): execution_dtype = torch.bfloat16 else: execution_dtype = torch.float32 embeds = embeds.to(execution_dtype) if embeds.ndim == 2: embeds = embeds.unsqueeze(0) past_key_values = [] #kv_cache init max_cache_len = embeds.shape[1] + max_length for x in range(model_config.num_hidden_layers): past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0)) generator = torch.Generator(device=device).manual_seed(seed) if do_sample else None generated_token_ids = [] pbar = comfy.utils.ProgressBar(max_length) # Generation loop for step in tqdm(range(max_length), desc="Generating tokens"): x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values) logits = self.logits(x)[:, -1] next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample) token_id = next_token[0].item() generated_token_ids.append(token_id) embeds = self.model.embed_tokens(next_token).to(execution_dtype) pbar.update(1) if token_id in stop_tokens: break return generated_token_ids def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True): if not do_sample or temperature == 0.0: return torch.argmax(logits, dim=-1, keepdim=True) # Sampling mode if repetition_penalty != 1.0: for i in range(logits.shape[0]): for token_id in set(token_history): logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty if temperature != 1.0: logits = logits / temperature if top_k > 0: indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = torch.finfo(logits.dtype).min if min_p > 0.0: probs_before_filter = torch.nn.functional.softmax(logits, dim=-1) top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True) min_threshold = min_p * top_probs indices_to_remove = probs_before_filter < min_threshold logits[indices_to_remove] = torch.finfo(logits.dtype).min if top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 0] = False indices_to_remove = torch.zeros_like(logits, dtype=torch.bool) indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove) logits[indices_to_remove] = torch.finfo(logits.dtype).min probs = torch.nn.functional.softmax(logits, dim=-1) return torch.multinomial(probs, num_samples=1, generator=generator) class BaseQwen3: def logits(self, x): input = x[:, -1:] module = self.model.embed_tokens offload_stream = None if module.comfy_cast_weights: weight, _, offload_stream = comfy.ops.cast_bias_weight(module, input, offloadable=True) else: weight = self.model.embed_tokens.weight.to(x) x = torch.nn.functional.linear(input, weight, None) comfy.ops.uncast_bias_weight(module, weight, None, offload_stream) return x class Llama2(BaseLlama, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Llama2Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Mistral3Small24B(BaseLlama, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Mistral3Small24BConfig(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen25_3B(BaseLlama, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen25_3BConfig(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen3_06B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen3_06BConfig(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen3_06B_ACE15(BaseLlama, BaseQwen3, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen3_06B_ACE15_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen3_2B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen3_2B_ACE15_lm_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen3_4B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen3_4BConfig(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen3_4B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen3_4B_ACE15_lm_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen3_8B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen3_8BConfig(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Ovis25_2B(BaseLlama, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Ovis25_2BConfig(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Qwen25_7BVLI_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.visual = qwen_vl.Qwen2VLVisionTransformer(hidden_size=1280, output_hidden_size=config.hidden_size, device=device, dtype=dtype, ops=operations) self.dtype = dtype # todo: should this be tied or not? #self.lm_head = operations.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype) def preprocess_embed(self, embed, device): if embed["type"] == "image": image, grid = qwen_vl.process_qwen2vl_images(embed["data"]) return self.visual(image.to(device, dtype=torch.float32), grid), grid return None, None def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): grid = None position_ids = None offset = 0 for e in embeds_info: if e.get("type") == "image": grid = e.get("extra", None) start = e.get("index") if position_ids is None: position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device) position_ids[:, :start] = torch.arange(0, start, device=embeds.device) end = e.get("size") + start len_max = int(grid.max()) // 2 start_next = len_max + start position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device) position_ids[0, start:end] = start + offset max_d = int(grid[0][1]) // 2 position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] max_d = int(grid[0][2]) // 2 position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start] offset += len_max - (end - start) if grid is None: position_ids = None return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids) class Gemma2_2B(BaseLlama, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Gemma2_2B_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Gemma3_4B(BaseLlama, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Gemma3_4B_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype class Gemma3_4B_Vision(BaseLlama, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Gemma3_4B_Vision_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations) self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations) self.image_size = config.vision_config["image_size"] def preprocess_embed(self, embed, device): if embed["type"] == "image": image = comfy.clip_model.clip_preprocess(embed["data"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True) return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None return None, None class Gemma3_12B(BaseLlama, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Gemma3_12B_Config(**config_dict) self.num_layers = config.num_hidden_layers self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations) self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations) self.dtype = dtype self.image_size = config.vision_config["image_size"] def preprocess_embed(self, embed, device): if embed["type"] == "image": image = comfy.clip_model.clip_preprocess(embed["data"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True) return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None return None, None ================================================ FILE: comfy/text_encoders/llama_tokenizer/tokenizer.json ================================================ { "version": "1.0", "truncation": null, "padding": null, "added_tokens": [ { "id": 128000, "content": "<|begin_of_text|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true }, { "id": 128001, "content": "<|end_of_text|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true }, { "id": 128002, "content": "<|reserved_special_token_0|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true }, { "id": 128003, "content": "<|reserved_special_token_1|>", "single_word": false, 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"Ġapproximately": 13489, "Ġlosing": 13490, "thesis": 13491, "ĠFund": 13492, "Ġdiagn": 13493, "Autowired": 13494, "properties": 13495, "Ġ_.": 13496, "Ġcnt": 13497, "cedure": 13498, "Ġyy": 13499, "Ġgrant": 13500, "sock": 13501, ".innerHTML": 13502, "Ġ]);Ċ": 13503, "ĠCONFIG": 13504, "='$": 13505, "550": 13506, "]];Ċ": 13507, "UND": 13508, "Ġglob": 13509, "Ġdire": 13510, "uffle": 13511, "_MEM": 13512, "Ġauthentic": 13513, ">(\"": 13514, "Ġdecade": 13515, "ĠImport": 13516, "Ġoriginally": 13517, "ĠjQuery": 13518, "Ġindicate": 13519, "Ġourselves": 13520, "Sw": 13521, ".lbl": 13522, "enerate": 13523, "Ġbasically": 13524, "ĠHom": 13525, "Ġ+#+": 13526, "ĠBritain": 13527, "ĠKar": 13528, "toEqual": 13529, ".stop": 13530, "Ġmodal": 13531, "isi": 13532, "Ġsuggests": 13533, "Ġdtype": 13534, "Ġtur": 13535, "bf": 13536, "Ġconnections": 13537, "ĠBefore": 13538, "isted": 13539, "mouse": 13540, "Ġpulled": 13541, ".build": 13542, "Ġlegislation": 13543, "Ġforth": 13544, "pad": 13545, "ego": 13546, ".Now": 13547, "Ġexciting": 13548, "}ĊĊĊĊ": 13549, "Ġcompr": 13550, "Ġshares": 13551, "Ġrig": 13552, "green": 13553, "_vec": 13554, "Ġenumerate": 13555, "Auto": 13556, "icator": 13557, "ĠRay": 13558, "asse": 13559, "Ġholiday": 13560, "Ġnullable": 13561, "gun": 13562, "_details": 13563, "Ġwrapper": 13564, "seq": 13565, "ĠYoung": 13566, "juana": 13567, "Ġ\"__": 13568, "license": 13569, "serve": 13570, "^(": 13571, "iders": 13572, ".Remove": 13573, "ropdown": 13574, "'S": 13575, "pin": 13576, "(token": 13577, ".Default": 13578, "Ġreasonable": 13579, "ampion": 13580, "ĠSociety": 13581, "Ġbei": 13582, "erves": 13583, "rad": 13584, "ĠFox": 13585, "_images": 13586, "Ġwheel": 13587, "')[": 13588, "Ġcfg": 13589, "(By": 13590, "Constructor": 13591, "Ġvary": 13592, ".swift": 13593, "Ġproxy": 13594, "ĉH": 13595, "ĠAnother": 13596, "ĠPen": 13597, "Ġchecking": 13598, "Ġjest": 13599, "manager": 13600, "Origin": 13601, "ugs": 13602, "oir": 13603, ">čĊ": 16336, "Ġrelief": 16337, "lap": 16338, "quer": 16339, "_parent": 16340, "heap": 16341, "LOSE": 16342, "Ġcombine": 16343, "ĠRose": 16344, "owers": 16345, "Ġprocedures": 16346, "ĠSort": 16347, "anim": 16348, "variant": 16349, "ehicle": 16350, "Ġsigning": 16351, "Primary": 16352, "currency": 16353, "Ġsexe": 16354, "oen": 16355, "theta": 16356, "eman": 16357, "Ġimpressive": 16358, "('_": 16359, "ĉU": 16360, "ĠTextStyle": 16361, "_cnt": 16362, "Ġslice": 16363, "(':": 16364, "Ġunderstood": 16365, "His": 16366, "277": 16367, "013": 16368, "Ġinformed": 16369, "Ġnick": 16370, "429": 16371, "(TAG": 16372, "hd": 16373, "Ġelections": 16374, "esture": 16375, "ĠSanta": 16376, "ĠCoast": 16377, ".pdf": 16378, "inciple": 16379, ".clone": 16380, "born": 16381, "uta": 16382, "Ġlicensed": 16383, "Cr": 16384, "Ġbread": 16385, "ĠHouston": 16386, "Ġnod": 16387, "Ġhopes": 16388, "ĠCGRect": 16389, "Ġguilty": 16390, ".gif": 16391, "Ġrose": 16392, ".Common": 16393, "Tip": 16394, "ANK": 16395, "ĠFC": 16396, "During": 16397, "ĠSymfony": 16398, "Ġdefensive": 16399, "km": 16400, ")>": 16401, "archive": 16402, "ĠURI": 16403, "ycling": 16404, "-o": 16405, "ĠWebsite": 16406, "AMP": 16407, "405": 16408, "ishment": 16409, "Ġdoctors": 16410, "Direct": 16411, "ARI": 16412, "ĠRedirect": 16413, "ieren": 16414, "960": 16415, "_dist": 16416, "yo": 16417, "ĠProgress": 16418, "Ġzum": 16419, "Ġmemor": 16420, "ĠED": 16421, "Ġjur": 16422, "æį®": 16423, "_TABLE": 16424, "Ġuuid": 16425, "Expr": 16426, ".head": 16427, "('%": 16428, "pointer": 16429, "Ġestimate": 16430, "ĠGreg": 16431, "Ġloader": 16432, "ĠiOS": 16433, "Ġmens": 16434, "[y": 16435, "Ġrefused": 16436, "Ġprecision": 16437, "isch": 16438, "ĠACTION": 16439, "Cloud": 16440, "sWith": 16441, "(ret": 16442, "292": 16443, "_ADDR": 16444, "_conf": 16445, "(df": 16446, "Ġlocked": 16447, "Ġrising": 16448, "ãĥ»ãĥ»": 16449, "ĠMs": 16450, "Ġscenes": 16451, "_EXT": 16452, "_raw": 16453, "_the": 16454, "people": 16455, "Ġrecon": 16456, "ĠFun": 16457, "Ġbless": 16458, "ĠUpdated": 16459, "422": 16460, "ün": 16461, "ĠĠĠĠĠĠĠĠĠĠĠĠčĊ": 16462, "pection": 16463, "Release": 16464, ".logger": 16465, "ĠSY": 16466, "Ġcounsel": 16467, "urd": 16468, "_true": 16469, "Ġeverybody": 16470, "ivot": 16471, "Ġhence": 16472, "ĠNAS": 16473, "789": 16474, "Ġopposed": 16475, "unknown": 16476, "ĠDESC": 16477, "ĠChair": 16478, "failed": 16479, "ĠINCLUDING": 16480, "386": 16481, "352": 16482, "Ġwriters": 16483, "{}Ċ": 16484, "ÃŃt": 16485, "_copy": 16486, "}:": 16487, "ĠBat": 16488, "Ġconverted": 16489, "eding": 16490, "placement": 16491, "ĠHost": 16492, "Sound": 16493, "им": 16494, "Ġsought": 16495, "402": 16496, "mid": 16497, "Ġsalary": 16498, "ogg": 16499, "âĦ¢": 16500, "bul": 16501, "Ġwir": 16502, "validator": 16503, "_STAT": 16504, ".store": 16505, "ĠBattle": 16506, "ın": 16507, "Ġ-->ĊĊ": 16508, "Trump": 16509, "dot": 16510, "ĠCONT": 16511, ".fetch": 16512, "Ġcontinu": 16513, "was": 16514, "Ġfraud": 16515, "_tmp": 16516, "mitter": 16517, ".pictureBox": 16518, "GA": 16519, "Ġtournament": 16520, ".Input": 16521, "343": 16522, "[r": 16523, "exion": 16524, "centage": 16525, "ĠKorean": 16526, "undef": 16527, "ĠAvailable": 16528, "reshape": 16529, "Ġkit": 16530, "ĠStruct": 16531, "ĠSUB": 16532, "Answer": 16533, "_lib": 16534, ".twitter": 16535, "Ġore": 16536, "ĠDragon": 16537, ".Ext": 16538, ",k": 16539, "Ġexplanation": 16540, "refs": 16541, "ĠDrive": 16542, "ĠTraining": 16543, "282": 16544, ".Has": 16545, "341": 16546, "intage": 16547, "big": 16548, "ologist": 16549, "ennis": 16550, "460": 16551, "Ùĩ": 16552, "Ġchicken": 16553, "ĠĠĠĠĠĠĠĠĠĠĊ": 16554, "çĽ": 16555, "ãģ§": 16556, "Ġpeak": 16557, "Ġdrinking": 16558, "Ġencode": 16559, "ĠNEW": 16560, "malloc": 16561, "ĉfprintf": 16562, "Ġ=================================================================": 16563, "including": 16564, "Ġprinciples": 16565, "ĠMah": 16566, "267": 16567, "storage": 16568, "-key": 16569, "Ġkeyword": 16570, "%;": 16571, "Ġtrained": 16572, ".contrib": 16573, "Ġkv": 16574, "__':Ċ": 16575, "ĠBoy": 16576, "parameter": 16577, "Ġsuite": 16578, "Ġthousand": 16579, "Ġcoordinate": 16580, "-generated": 16581, "íķĺ": 16582, "generated": 16583, "Ġadmitted": 16584, "Ġpussy": 16585, "#w": 16586, "Ġswim": 16587, "union": 16588, "Na": 16589, "274": 16590, "ĠRoyal": 16591, ".channel": 16592, "Updated": 16593, "_ROOT": 16594, "Ġvital": 16595, "335": 16596, "raction": 16597, "ĠCrusher": 16598, "Ġpreced": 16599, "Ġhorizontal": 16600, "Blueprint": 16601, "Ġattrs": 16602, "Ġsmoke": 16603, "ÐĴ": 16604, ".Equals": 16605, "FB": 16606, "ĠResources": 16607, "rolling": 16608, "Ġpasses": 16609, "ĠNum": 16610, "rotate": 16611, "etype": 16612, "\\\",": 16613, "Ġsensitive": 16614, "Ġtall": 16615, "?âĢĿĊĊ": 16616, "Proxy": 16617, "iy": 16618, "_section": 16619, "âĢĶâĢĶâĢĶâĢĶ": 16620, "brid": 16621, "Ġcircuit": 16622, "atan": 16623, "ENC": 16624, "Ġdriven": 16625, "Ġvoted": 16626, "Ġeducational": 16627, "Ġinteraction": 16628, "abetes": 16629, "Ġtone": 16630, "ĠInitializeComponent": 16631, "Ġmerely": 16632, "Ġìŀ": 16633, "cookie": 16634, "_div": 16635, "ĠUILabel": 16636, "vely": 16637, "});čĊ": 16638, "_ENT": 16639, "#+#+": 16640, "articles": 16641, "ĠSouthern": 16642, "Ġstronger": 16643, "ĠGiven": 16644, "ĠEric": 16645, "ĠIR": 16646, "abstract": 16647, "Under": 16648, "nable": 16649, "Ġincrement": 16650, "oven": 16651, "Ġcoin": 16652, "_timer": 16653, "Ġsuffered": 16654, "ĠFREE": 16655, "'].\"": 16656, "ĠQueen": 16657, "stats": 16658, "Ġmeetings": 16659, "276": 16660, "Ġentering": 16661, "Ġalongside": 16662, "(session": 16663, "itals": 16664, "Ġfoundation": 16665, "ĠCredit": 16666, ".div": 16667, "_ALL": 16668, "pcion": 16669, "_stat": 16670, "icking": 16671, "Defaults": 16672, "_src": 16673, "Ġoutputs": 16674, "/B": 16675, "Ġenthus": 16676, "-bl": 16677, ".ForeColor": 16678, "ĉtemp": 16679, "Face": 16680, "Ġinteract": 16681, "Ġweird": 16682, "Mount": 16683, "rell": 16684, "udents": 16685, "Ġrequirement": 16686, "ĠSus": 16687, "IER": 16688, "Ġelected": 16689, "reference": 16690, "ĠME": 16691, "Ġservers": 16692, ".wait": 16693, "Ġsnapshot": 16694, "ilton": 16695, "Ġtries": 16696, "Ġtipo": 16697, ".Time": 16698, ">w": 16699, "Ġmountain": 16700, "Ġpounds": 16701, "Ġ[...": 16702, "exists": 16703, "ĠngOn": 16704, "_MAP": 16705, "Ġflying": 16706, "331": 16707, "xiety": 16708, "ĉvalue": 16709, "_DB": 16710, "uno": 16711, "Ġseats": 16712, "TURN": 16713, ".author": 16714, "!)": 16715, "orce": 16716, "Ġindicated": 16717, "317": 16718, ".sin": 16719, "Ġassignment": 16720, "imiento": 16721, "ĠFrame": 16722, "324": 16723, "_gen": 16724, "inery": 16725, "_)": 16726, "messages": 16727, ".settings": 16728, "ĠMean": 16729, "ĠMuseum": 16730, "irq": 16731, "attach": 16732, "ĠPalestin": 16733, "_QU": 16734, "_tags": 16735, "Ġcasual": 16736, "emen": 16737, "ASSWORD": 16738, "432": 16739, "$s": 16740, "ĠCirc": 16741, "ой": 16742, "etric": 16743, "/P": 16744, "018": 16745, "Ġepoch": 16746, "The": 16761, "ĠAk": 16762, "Ġgrass": 16763, "/*čĊ": 16764, "(dis": 16765, "Ġguns": 16766, "Ġtb": 16767, "ĠKevin": 16768, ".args": 16769, "ĠAh": 16770, "oped": 16771, "(J": 16772, "columns": 16773, "arguments": 16774, "ĠWithEvents": 16775, "_full": 16776, "ĠDefense": 16777, "Simple": 16778, "Ġdeaths": 16779, "295": 16780, "Ġextensive": 16781, "ĠStill": 16782, "ĠExpression": 16783, "ĠAgency": 16784, "Ġperforming": 16785, "FX": 16786, "Ġusuario": 16787, "UAL": 16788, "Side": 16789, "odos": 16790, "aptop": 16791, "Ġcredentials": 16792, "_cap": 16793, "atient": 16794, "ĠDisney": 16795, "Ġai": 16796, "Ġchip": 16797, "Ġvolt": 16798, ".makeText": 16799, "%%%%%%%%%%%%%%%%": 16800, "Ġbelief": 16801, "_LOC": 16802, "ĠCivil": 16803, "Navigation": 16804, "Ġreveal": 16805, "Ġviolent": 16806, "ĠFil": 16807, "Ġcatalog": 16808, "emed": 16809, "scan": 16810, ".control": 16811, "Ġconstitution": 16812, "Country": 16813, "Separator": 16814, "_APP": 16815, "topic": 16816, "uetooth": 16817, "MIN": 16818, "Ġdescriptor": 16819, "yt": 16820, "ETHER": 16821, "Ġdistribute": 16822, "'}Ċ": 16823, ".trim": 16824, ".Line": 16825, "Ġlbl": 16826, "assertEquals": 16827, "ĠDet": 16828, "ombok": 16829, "(width": 16830, "Ġtort": 16831, "ĠEXPRESS": 16832, "aco": 16833, "Using": 16834, "ĠBrand": 16835, "wall": 16836, "EMENT": 16837, "ĠCommunic": 16838, "(Ċ": 17492, "?>\"": 17493, "Ġ///Ċ": 17494, "Ġeiner": 17495, "Ġweekly": 17496, "ĉlogger": 17497, "_pop": 17498, "_man": 17499, "Ġmigrations": 17500, "Ġasks": 17501, "Ġbs": 17502, "Ġfalls": 17503, ".Where": 17504, "-height": 17505, "_feature": 17506, ".Min": 17507, "Ġhyper": 17508, "Ġvolatile": 17509, "Ġtwenty": 17510, "Typography": 17511, "Unable": 17512, "Det": 17513, ",f": 17514, "-mod": 17515, "Ġsettlement": 17516, "Ġcontracts": 17517, "nome": 17518, "Bad": 17519, "ĠBrian": 17520, "768": 17521, "(username": 17522, "!!!!": 17523, "Ġhack": 17524, ".Field": 17525, "HR": 17526, "ĠJordan": 17527, "iza": 17528, "ĠÂł": 17529, "ĠSher": 17530, ".header": 17531, "(other": 17532, "ĠDub": 17533, "(op": 17534, "ĠRound": 17535, "Ġvie": 17536, "Ġappl": 17537, "ĉJ": 17538, "ĠInsert": 17539, "ĠLP": 17540, "regon": 17541, "ĠMPI": 17542, "Ġanchor": 17543, "aca": 17544, "ør": 17545, "Ġade": 17546, "anchor": 17547, "quee": 17548, "ĠTreeNode": 17549, "Ġtargeted": 17550, "Ġlaid": 17551, "ABEL": 17552, "vet": 17553, "ĠOrigin": 17554, "Ant": 17555, ".');Ċ": 17556, "expect": 17557, "edReader": 17558, "ĠMajor": 17559, "Ġinch": 17560, "Compar": 17561, "Ġpreview": 17562, "Ġillness": 17563, "ĠCONTRACT": 17564, "ĠIndepend": 17565, "uuid": 17566, "Ġnome": 17567, "Ġtc": 17568, "ĠAvenue": 17569, "isan": 17570, "Ġphrase": 17571, "_move": 17572, "\")[": 17573, "412": 17574, "Ġprovision": 17575, "Ġconcentr": 17576, "_IR": 17577, "ĠUt": 17578, "()+": 17579, "Ġnas": 17580, "!,": 17581, "ĠRobin": 17582, "iations": 17583, "atitude": 17584, "Ġpx": 17585, "ĠWithout": 17586, "/bash": 17587, "ekt": 17588, "reement": 17589, "342": 17590, "Observer": 17591, "318": 17592, "ĠRegion": 17593, "UBLIC": 17594, "Ġ{//": 17595, "KN": 17596, "å·": 17597, "GameObject": 17598, "å¾": 17599, "encoding": 17600, "Ġ***": 17601, "projects": 17602, "Ġtk": 17603, "Ġcheese": 17604, "EMPL": 17605, "aro": 17606, "ĠاÙĦ": 17607, "610": 17608, "337": 17609, "Ġconsists": 17610, "refresh": 17611, "ureau": 17612, "ĠScanner": 17613, "Ġsoil": 17614, "Ġflavor": 17615, "DataSource": 17616, "Execute": 17617, "ение": 17618, "Ġshit": 17619, "åĪĨ": 17620, "Ċ": 17875, "Ġsubsequent": 17876, "posable": 17877, "-fluid": 17878, "Ġthorough": 17879, "Ġpublicly": 17880, "apters": 17881, "ĠWilson": 17882, "_PRE": 17883, "yard": 17884, "ä¼": 17885, "ĉin": 17886, "339": 17887, "Ġrevers": 17888, "Ġbullet": 17889, "cribed": 17890, "nesota": 17891, "Ġ($_": 17892, "annon": 17893, "cursor": 17894, "Ġclothing": 17895, "ĠMulti": 17896, "287": 17897, ":',": 17898, "Ġvess": 17899, "ordinator": 17900, "Ġeinem": 17901, "Cannot": 17902, "Ġarmed": 17903, "ĉV": 17904, "ä¸Ĭ": 17905, ".Flat": 17906, "ĠSep": 17907, "ĠSubject": 17908, "_font": 17909, "Ġcharacteristics": 17910, "Done": 17911, "eln": 17912, "############": 17913, "POS": 17914, "Ġdensity": 17915, "ĠPlatform": 17916, "-items": 17917, "Ġovers": 17918, "Ġpushing": 17919, "ç¤": 17920, ".Connection": 17921, "_term": 17922, "Ġinitialization": 17923, "________________________________": 17924, "ç¬": 17925, ".document": 17926, "lesh": 17927, "ĉdocument": 17928, "ĠPin": 17929, "ça": 17930, "Ġdefinitions": 17931, ".Path": 17932, "_WRITE": 17933, "ĠĉĊ": 17934, "?>ĊĊ": 17935, "Ġterrible": 17936, "bean": 17937, "ickets": 17938, "ĠSV": 17939, "Buy": 17940, "(task": 17941, "Ġregime": 17942, "google": 17943, "Ġcrack": 17944, ".visit": 17945, "NUM": 17946, "energy": 17947, "Ġstruck": 17948, "_sample": 17949, ".payload": 17950, "Ġrevis": 17951, "ĠScene": 17952, "Ġpg": 17953, "Ġbreakfast": 17954, "URRENT": 17955, ".charAt": 17956, "_exception": 17957, "ĠAnton": 17958, "Ġguidelines": 17959, "Ġexhaust": 17960, "ĠFinancial": 17961, "Ġindent": 17962, "Ġdesktop": 17963, "Hidden": 17964, "Failure": 17965, "Ġprinciple": 17966, "Ġiv": 17967, "Ġseks": 17968, "network": 17969, "ĠnumberOf": 17970, "ĠAlbert": 17971, "ĉlong": 17972, "801": 17973, ",.": 17974, "Ġzeros": 17975, "fade": 17976, "ĠTyp": 17977, "ĠTerm": 17978, "ĠArts": 17979, ".Application": 17980, "Ġbehalf": 17981, "æĪ·": 17982, "Ġmere": 17983, "(`${": 17984, "Ġawareness": 17985, "elpers": 17986, "flix": 17987, "Ġweigh": 17988, "Ġestimates": 17989, ".child": 17990, "/O": 17991, "ĠBitmap": 17992, ".bottom": 17993, "Ġ**************************************************************************": 17994, "Expect": 17995, "ento": 17996, "ĠForum": 17997, "veral": 17998, "Ġjail": 17999, "Ġabilities": 18000, "ĠHOLD": 18001, "ĠCit": 18002, "Ġdynam": 18003, "Ġgray": 18004, "ĉĉĉĉĉĉĉĉĉĉĉĉĉ": 18005, ".nextInt": 18006, "antly": 18007, "ĠARISING": 18008, "(private": 18009, "Ġrejected": 18010, "ĠNic": 18011, "Ġleather": 18012, "={Ċ": 18013, "alytics": 18014, "thetic": 18015, ".Top": 18016, "373": 18017, ".Page": 18018, "={`": 18019, "Ġ;čĊ": 18020, "depth": 18021, "mann": 18022, "WD": 18023, "ĠSom": 18024, ".Right": 18025, "Ġ)}Ċ": 18026, "Ġtrait": 18027, "ÃĹ": 18028, "iac": 18029, "Ġrv": 18030, "Sample": 18031, ".Xml": 18032, "opped": 18033, "ĠÑĦ": 18034, "lists": 18035, "Ġtear": 18036, "iversary": 18037, ".collection": 18038, "ĠConstitution": 18039, "ĠHttpResponse": 18040, "Ġbrill": 18041, "ĠProm": 18042, "hover": 18043, "366": 18044, "ĠMiami": 18045, "Ġargue": 18046, "_float": 18047, "504": 18048, "ĠãĤ": 18049, "Ġnat": 18050, "ĠTal": 18051, "Ġintegration": 18052, "(cur": 18053, "Ġremoving": 18054, "Ġcoeff": 18055, "ĠThough": 18056, "Ġforecast": 18057, "408": 18058, "ĠVegas": 18059, "Site": 18060, "346": 18061, "Ġtrab": 18062, "ĠHenry": 18063, "-i": 18064, "Ġinvolves": 18065, "BT": 18066, "Ġslo": 18067, "Invoke": 18068, "Ġlucky": 18069, "025": 18070, "rat": 18071, "Ġ?Ċ": 18072, "Ġhandled": 18073, "(fd": 18074, "contents": 18075, "ĠOFF": 18076, "RF": 18077, "Ġsty": 18078, "ĠMotor": 18079, "tery": 18080, "tax": 18081, "MAP": 18082, "ĠMrs": 18083, "Ġphones": 18084, "ĠUIView": 18085, "\")));Ċ": 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18325, "LA": 18326, "Ġmirror": 18327, "Ġassistant": 18328, "(device": 18329, "Ġspiritual": 18330, "builder": 18331, "§": 18332, "Ġoutr": 18333, "Ġtt": 18334, "ĠPER": 18335, "Ġradical": 18336, "Methods": 18337, "Ġpace": 18338, "udy": 18339, "Ġgut": 18340, "ĠGreek": 18341, "Ġnonatomic": 18342, "ĠPaper": 18343, "_GPIO": 18344, "Ġobst": 18345, ".Ad": 18346, "vironments": 18347, "ĠSov": 18348, "356": 18349, "(con": 18350, "ĠTransaction": 18351, ".assign": 18352, "ĉcatch": 18353, "elter": 18354, "Ġbitcoin": 18355, "_GR": 18356, "ĠčĊ": 18473, "metic": 18474, "Ġtransformation": 18475, "åı·": 18476, "Ġrgb": 18477, "istributions": 18478, "Ġimplicit": 18479, "/in": 18480, "destination": 18481, "аÑĤÑĮ": 18482, "Zero": 18483, "Ġunset": 18484, "920": 18485, ".where": 18486, ".go": 18487, "Ġformation": 18488, "Ġdeclaration": 18489, "()čĊčĊ": 18490, "ĠExpl": 18491, "ĉĉĉĠĠ": 18492, "/pro": 18493, ".JSON": 18494, "441": 18495, "Ġdesk": 18496, ".substr": 18497, 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18728, "ĠMarketing": 18729, "@Test": 18730, "untu": 18731, "ĠVen": 18732, "ĠCou": 18733, "ivals": 18734, "Donald": 18735, "limited": 18736, "ĉĉĉĉĉĉĊ": 18737, "Ġanalyst": 18738, "(entry": 18739, "Ġrepresentative": 18740, "_attributes": 18741, "Ġfur": 18742, ".hide": 18743, "resp": 18744, "adores": 18745, "rides": 18746, "ĠJosh": 18747, "robot": 18748, "ĠNAT": 18749, "Ġsesso": 18750, "Ġintegrated": 18751, ":true": 18752, "parts": 18753, "Ġstupid": 18754, ":event": 18755, "@endsection": 18756, "Ġpu": 18757, ".Table": 18758, "ĠYii": 18759, "`;ĊĊ": 18760, "Ġclang": 18761, "=\"\">": 18762, "engan": 18763, "_parameters": 18764, ".internal": 18765, "ĠModern": 18766, "Ġmetric": 18767, "Ġsemi": 18768, "={{Ċ": 18769, "707": 18770, ".amazon": 18771, "ĠBB": 18772, "ainty": 18773, "viewport": 18774, "367": 18775, "ĠstartActivity": 18776, "dispatch": 18777, "*****": 18778, "Ġflav": 18779, "ifferent": 18780, "382": 18781, "[this": 18782, "Ġstake": 18783, "Ġargued": 18784, "viously": 18785, ".work": 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".createParallelGroup": 18946, "Rotation": 18947, ".err": 18948, "Ġpleased": 18949, "itage": 18950, ".Wh": 18951, "ĉĉĠĠĠĠ": 18952, "MR": 18953, "ĠMORE": 18954, "ĠNatural": 18955, "_transform": 18956, "BASE": 18957, "eneral": 18958, "utdown": 18959, ".commons": 18960, "WT": 18961, "Ġaan": 18962, ".Result": 18963, "dog": 18964, "Ġclicking": 18965, "),ĊĊ": 18966, "#line": 18967, "Operator": 18968, "Ġciv": 18969, "Ġmerg": 18970, "obuf": 18971, "ngthen": 18972, "Ġ[{": 18973, "Ġcancell": 18974, "trigger": 18975, ".:": 18976, "WORK": 18977, "declare": 18978, "Ġdecrease": 18979, "ÅĽci": 18980, "loom": 18981, ".None": 18982, "ĠMI": 18983, "ĠJason": 18984, "Ġhealthcare": 18985, "iamond": 18986, "sylvania": 18987, "*x": 18988, "ĠRa": 18989, "[b": 18990, "Ġprinting": 18991, "phabet": 18992, "ĠLabour": 18993, "opper": 18994, "Ġzijn": 18995, "-target": 18996, "_FUNCTION": 18997, "Ġoct": 18998, "ениÑı": 18999, "åľ¨": 19000, "Ġwestern": 19001, "Ġcomputers": 19002, "ĠRET": 19003, "HashMap": 19004, 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"Ġpresum": 23073, "ĠWalker": 23074, "Ġovercome": 23075, "etection": 23076, "Ġembarrass": 23077, "China": 23078, "639": 23079, "Include": 23080, "ROLL": 23081, "ĠdataType": 23082, "David": 23083, "ร": 23084, "lop": 23085, "-month": 23086, "Ġscar": 23087, "ĠSafe": 23088, "Ġ****************************************************************": 23089, "Ġaccessories": 23090, "Ġramp": 23091, "_USE": 23092, "Ġcontrad": 23093, "))]Ċ": 23094, "Ġprest": 23095, "ĠHR": 23096, "ĠRap": 23097, "Ġusize": 23098, "Ġcapability": 23099, "Ġcort": 23100, "-next": 23101, "077": 23102, "627": 23103, "Ġburden": 23104, "822": 23105, "_reader": 23106, "Ġ@@": 23107, "regular": 23108, "ĠKa": 23109, "036": 23110, "MAN": 23111, "Ġastr": 23112, "Ġ'')Ċ": 23113, "Ġfed": 23114, "Ġparsing": 23115, "ĠYears": 23116, "Ġbroker": 23117, "\":{\"": 23118, "Ġakt": 23119, "Inventory": 23120, "abeled": 23121, "Ġargparse": 23122, "*******Ċ": 23123, "versation": 23124, "Ġcord": 23125, "ĠTi": 23126, "Ġhopefully": 23127, "Ġah": 23128, "verb": 23129, "Ġstolen": 23130, ".Entry": 23131, "Ġexpecting": 23132, "Orientation": 23133, "Ġpowered": 23134, "Ġpersist": 23135, "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 23136, "']);": 23137, "')),Ċ": 23138, "ĠCash": 23139, "ĉitem": 23140, "818": 23141, "grades": 23142, "ropol": 23143, "basic": 23144, "Ġ\");čĊ": 23145, "Ġawards": 23146, "(range": 23147, "-all": 23148, "ĠIBOutlet": 23149, "ĠIndeed": 23150, "----------------------------------------------------------------------------": 23151, "Ġstomach": 23152, "Ġflower": 23153, "Ġsew": 23154, "_times": 23155, "avis": 23156, "QString": 23157, "ĠRoutes": 23158, "_prot": 23159, "Ġcomedy": 23160, "Ġlogout": 23161, "Ġwooden": 23162, "Ġposter": 23163, "piece": 23164, ".Join": 23165, "ĠPok": 23166, "celona": 23167, "mutex": 23168, ";čĊčĊčĊ": 23169, "Ġstrikes": 23170, "787": 23171, "Loaded": 23172, ")arg": 23173, "esa": 23174, "United": 23175, "Ep": 23176, "PELL": 23177, "807": 23178, "ĠAtlantic": 23179, 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23301, "/view": 23302, "gree": 23303, "-get": 23304, "903": 23305, "hit": 23306, "Jo": 23307, ".getC": 23308, "725": 23309, "Initialized": 23310, "ÑĤи": 23311, "cuts": 23312, "(Type": 23313, "ĠAgreement": 23314, "ĠVietnam": 23315, "Ġ/*!": 23316, "Ġpizza": 23317, "-view": 23318, "_em": 23319, "Ġlhs": 23320, "Ġmuy": 23321, "ĠIdent": 23322, "ĠFriends": 23323, "061": 23324, "Ġabund": 23325, "_AD": 23326, ".timestamp": 23327, "-'": 23328, "Ġduplicate": 23329, "Ġhunting": 23330, "Ġregulatory": 23331, "iao": 23332, "amous": 23333, "ĠEntertainment": 23334, "[A": 23335, "iatric": 23336, "_CLIENT": 23337, "ĠKids": 23338, "/pkg": 23339, "Break": 23340, ")));ĊĊ": 23341, "ĠShape": 23342, "Ġrelating": 23343, "Interrupt": 23344, "ableOpacity": 23345, "embre": 23346, "Ġmystery": 23347, "Ġjournalists": 23348, "ritable": 23349, ".Link": 23350, "Ġstopping": 23351, "CRET": 23352, ".DB": 23353, "Ġpopularity": 23354, "Ġgew": 23355, "Ġimpr": 23356, "setValue": 23357, "FLAG": 23358, "ĉmax": 23359, "Ġbake": 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33453, "_opts": 33454, "Roll": 33455, "(round": 33456, "Ġcoment": 33457, "_UART": 33458, "Ġextending": 33459, "RG": 33460, "resultado": 33461, "itu": 33462, ".getSession": 33463, "Ġattraction": 33464, "&D": 33465, "$html": 33466, "ĠJessica": 33467, "ĠAssociate": 33468, "añ": 33469, "_ed": 33470, "ĠLag": 33471, "Ġorigins": 33472, "())->": 33473, "addEventListener": 33474, "IALOG": 33475, "åIJ¦": 33476, ".Compare": 33477, "Album": 33478, "ĠKu": 33479, "\";ĊĊ": 33523, "quisite": 33524, "channels": 33525, "/res": 33526, "ĠAnalytics": 33527, ".appcompat": 33528, "/to": 33529, "ĠonError": 33530, "(attr": 33531, "IRM": 33532, "Ġragaz": 33533, "-as": 33534, ".Second": 33535, "oriented": 33536, "Ġdonn": 33537, "Ġlightning": 33538, "fid": 33539, "ĠPle": 33540, "ãģ¾ãģĻ": 33541, "tro": 33542, ".True": 33543, "Observable": 33544, "×Ļ": 33545, "umbing": 33546, "Ġprospective": 33547, "-filter": 33548, "Ġpursuant": 33549, "(points": 33550, ".Bind": 33551, "Ġpalm": 33552, "clearfix": 33553, "ös": 33554, "ĠGonz": 33555, "Ġweaken": 33556, "Drive": 33557, "enido": 33558, "lld": 33559, "obox": 33560, "anean": 33561, "Got": 33562, "ä¿Ŀ": 33563, "Regex": 33564, "æĥ": 33565, "Ġsalad": 33566, "assis": 33567, "\"net": 33568, "inheritDoc": 33569, "ĠRV": 33570, "quier": 33571, "Ġclazz": 33572, "Ä±ÅŁ": 33573, "osterone": 33574, "Ġairline": 33575, ".listdir": 33576, "Ġdownloading": 33577, "ĠPalm": 33578, "waukee": 33579, "<": 33580, ".BL": 33581, "_INLINE": 33582, "offs": 33583, "<<(": 33584, "_news": 33585, "Ġchase": 33586, "/><": 33587, "Ġeuros": 33588, "ĠEgyptian": 33589, "ĠStainless": 33590, "_BOOL": 33591, "ĠGuild": 33592, "ĠDynam": 33593, "[indexPath": 33594, "Ġï": 33595, "Ġmemorable": 33596, "ĠChampion": 33597, "ResourceManager": 33598, ".Login": 33599, "ĠFormer": 33600, "yped": 33601, "Ġlleg": 33602, ";\",": 33603, "DWORD": 33604, "Ġtaxi": 33605, "Ġbombs": 33606, "rah": 33607, ".tags": 33608, "_tests": 33609, "stones": 33610, "âĢĿ)": 33611, "[g": 33612, "rtype": 33613, "Ġvu": 33614, 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"Ġposes": 34103, "Ġvulnerability": 34104, "instanc": 34105, "orum": 34106, "dess": 34107, "Ġxl": 34108, "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%": 34109, "(fig": 34110, "Ġdeleting": 34111, ".del": 34112, ")')Ċ": 34113, "ĠWeekly": 34114, "???": 34115, "(strcmp": 34116, "smith": 34117, "Ġpursuing": 34118, "-so": 34119, "ĠApps": 34120, "/'Ċ": 34121, "Ġdecis": 34122, "FORE": 34123, "Everyone": 34124, "Ġlanes": 34125, "Virtual": 34126, ".attach": 34127, "(Log": 34128, "ĠMedicaid": 34129, "(Path": 34130, "ĠTurner": 34131, "/application": 34132, "Ġportrait": 34133, "Ġoppose": 34134, "checkout": 34135, "Ġfinishes": 34136, "_ME": 34137, "Barrier": 34138, "Song": 34139, "VAR": 34140, "Earlier": 34141, "rella": 34142, "Ġhast": 34143, "azar": 34144, "Ġpulls": 34145, "ngx": 34146, "Ġinspiring": 34147, "ÑĥÑİ": 34148, "-direction": 34149, "Ġexplosive": 34150, "ĠcreatedAt": 34151, "sto": 34152, "Ġwheat": 34153, "ĠBuilt": 34154, "'ai": 34155, "Ġtracked": 34156, "hammad": 34157, "RowAtIndexPath": 34158, "_heap": 34159, "Due": 34160, "Ġconnects": 34161, ".publish": 34162, "emu": 34163, "Ġbullets": 34164, "BAR": 34165, "olate": 34166, "Ġinternally": 34167, "Ġcatching": 34168, "-password": 34169, "ouched": 34170, "æĢ§": 34171, "eous": 34172, "Ġxrange": 34173, "Quality": 34174, "vv": 34175, "Manage": 34176, "(($": 34177, "acements": 34178, "ĠBrothers": 34179, "ĠHEAD": 34180, "ĠUnsupported": 34181, "san": 34182, "esi": 34183, "***Ċ": 34184, "Ġadaptation": 34185, "ĠWorker": 34186, "']/": 34187, ".savefig": 34188, "(trans": 34189, "ج": 34190, "nee": 34191, "Correct": 34192, "...\")Ċ": 34193, "Ġsubmitting": 34194, "-path": 34195, "ĉlast": 34196, "issan": 34197, ".xlabel": 34198, "ĠSepar": 34199, "/no": 34200, "_best": 34201, "ĠMills": 34202, "_sock": 34203, "(flag": 34204, "Ġdestinations": 34205, "emption": 34206, "ĠFAIL": 34207, "åĴĮ": 34208, "Ġrp": 34209, "fact": 34210, "ĉlen": 34211, "DAY": 34212, "Ġseiz": 34213, "_dst": 34214, "lip": 34215, ".Linear": 34216, "ĠBasket": 34217, "$t": 34218, "$i": 34219, "-brand": 34220, "ĠNeil": 34221, "ĠEq": 34222, "Ġthou": 34223, "ogene": 34224, "Ġscholarship": 34225, "æĽ´": 34226, "Ġswo": 34227, "aginator": 34228, "eni": 34229, "(book": 34230, "Ġblink": 34231, "thus": 34232, "ĠcancellationToken": 34233, "ĠPalestinians": 34234, "Ġprofitable": 34235, "Ġbackpack": 34236, "enson": 34237, "true": 34384, "ĠNYC": 34385, "Ġbored": 34386, "ĠDetect": 34387, "Ġappar": 34388, "Ġjeans": 34389, "ĠTak": 34390, "IOD": 34391, "ĠHorse": 34392, "(FILE": 34393, "(?": 34394, "rique": 34395, "optimizer": 34396, "nat": 34397, "loys": 34398, "ĉToken": 34399, "oubted": 34400, "uess": 34401, "ocoa": 34402, "DataMember": 34403, "_POWER": 34404, "classList": 34405, "PushButton": 34406, "ĠWiFi": 34407, ".Stream": 34408, ".guild": 34409, "Ġnog": 34410, "ĠPortugal": 34411, "ĠUnter": 34412, "Primitive": 34413, "boss": 34414, "ĠDeutsch": 34415, "Ġerotic": 34416, "Ġstrconv": 34417, ".TryParse": 34418, "Ġgrams": 34419, ".Success": 34420, "_pk": 34421, "ĠHarvey": 34422, "-minded": 34423, ".country": 34424, "[]\"": 34425, "Ġangel": 34426, "Ġbeats": 34427, "ĠVor": 34428, "ilio": 34429, ".master": 34430, "something": 34431, "ĠPACK": 34432, "(if": 34433, "RequestBody": 34434, "Ġantes": 34435, "/widget": 34436, "Ġmodo": 34437, "ĠAW": 34438, "finder": 34439, "Ġoptimized": 34440, "Ġmissiles": 34441, "NB": 34442, "ĉinternal": 34443, "tex": 34444, "ĠSri": 34445, "Ġdamaging": 34446, "ĠMais": 34447, "-Allow": 34448, "ĠZh": 34449, "-alt": 34450, "Ġ));ĊĊ": 34451, "èī": 34452, "Ġinfluences": 34453, "Ġcatal": 34454, "_REGISTER": 34455, "ĠAPIs": 34456, "-century": 34457, "Ġbiology": 34458, "ĠActual": 34459, "Ġheels": 34460, "TRACE": 34461, "_DIG": 34462, "Dataset": 34463, "ĠMatter": 34464, "Ġclassifier": 34465, ".wikipedia": 34466, "ĠRogers": 34467, "Ġdonated": 34468, "rawler": 34469, "enen": 34470, "Ġcasinos": 34471, "ortal": 34472, "Ġprive": 34473, "spe": 34474, "ducers": 34475, ".ep": 34476, "Ġgrasp": 34477, "acji": 34478, "Ġdairy": 34479, "Ġbuses": 34480, ".comm": 34481, ".ins": 34482, "ĠIRS": 34483, "ĠBeer": 34484, "adc": 34485, "oard": 34486, "_MET": 34487, "Ġ'+'": 34488, "rans": 34489, "Ġkinda": 34490, "ĠâĶĤ": 34491, "ĠMaur": 34492, "аг": 34493, "Ġbandwidth": 34494, "ibus": 34495, "ĠDifferent": 34496, "(mat": 34497, "ĠResume": 34498, "_UNS": 34499, "establish": 34500, "Ġfonction": 34501, "Subscription": 34502, "_company": 34503, "Ġlightly": 34504, ".confirm": 34505, ".yaml": 34506, "ĠBoost": 34507, "Commerce": 34508, "-template": 34509, "_DELAY": 34510, "ĠHI": 34511, "Ġnavig": 34512, "(Sender": 34513, "ĠHS": 34514, "_\"+": 34515, "ĠREQUEST": 34516, "Ġwifi": 34517, "=\"\"Ċ": 34518, "])->": 34519, "Ġrope": 34520, "Ġviolated": 34521, "Ġglance": 34522, "ĠKurd": 34523, "Ġè®": 34524, "deck": 34525, "ĠISBN": 34526, "Ġinfect": 34527, "ĠFoo": 34528, "Ġgetter": 34529, "Ġtener": 34530, "appe": 34531, ".hh": 34532, "_hot": 34533, "\".$": 34743, "Ġrelies": 34744, "(Console": 34745, "International": 34746, "->{$": 34747, "Mid": 34748, "Ġdissert": 34749, "dds": 34750, "Ġdeposits": 34751, "ĉdriver": 34752, "#ga": 34753, "prising": 34754, "println": 34755, "Ġpresenter": 34756, "Ġmines": 34757, "CSS": 34758, "ĠDual": 34759, "(!(": 34760, "Ġkam": 34761, "ĠisLoading": 34762, "ĠProtect": 34763, ".upper": 34764, "arium": 34765, "]:ĊĊĊ": 34766, "Yii": 34767, "-shirt": 34768, "ĠIMAGE": 34769, "_colors": 34770, "Ġurgent": 34771, ".Container": 34772, "!(Ċ": 34773, "Saturday": 34774, "Ġsocieties": 34775, "ĠThan": 34776, "ĠCod": 34777, "=@": 34778, "Ġattachments": 34779, ".mobile": 34780, "Ġspite": 34781, "Ġbounce": 34782, "rawl": 34783, "instancetype": 34784, "ĠTruck": 34785, "Ġmanipulation": 34786, "(Config": 34787, "-inst": 34788, "Ġstor": 34789, "itution": 34790, "PreferredGap": 34791, "ĠmainAxisAlignment": 34792, "Ġlistened": 34793, "'''ĊĊ": 34794, "ottage": 34795, "-project": 34796, ".APPLICATION": 34797, "ĉroot": 34798, "Ġwhit": 34799, "Ġbilder": 34800, "Ġker": 34801, "Ġappliances": 34802, "rowave": 34803, "ìĿĢ": 34804, "ematics": 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34863, "ĠElite": 34864, "Ġhelm": 34865, "(nonatomic": 34866, "asher": 34867, "Community": 34868, "alleng": 34869, "iry": 34870, "ĠGrowth": 34871, "Ġsue": 34872, "Ġfrequencies": 34873, "_descriptor": 34874, ".Attribute": 34875, "Ġrecipients": 34876, "_NS": 34877, "/\"+": 34878, "iban": 34879, "Ġathlete": 34880, "ĠIgn": 34881, "_DMA": 34882, "(ds": 34883, "ĠRequirements": 34884, "ADI": 34885, "erez": 34886, "\\Admin": 34887, "braska": 34888, "ĠRust": 34889, "Relation": 34890, "COD": 34891, "ĠVERSION": 34892, "emma": 34893, ")){": 34894, ".Duration": 34895, "ĠCamb": 34896, "-logo": 34897, "Ġreadable": 34898, "Ġcreators": 34899, "()];Ċ": 34900, "UpDown": 34901, "-half": 34902, ".getMonth": 34903, "(sf": 34904, "Pic": 34905, "Ġhunger": 34906, ".tx": 34907, "Ġexceeded": 34908, "_seed": 34909, "(^": 34910, "_sk": 34911, ".perform": 34912, "Ġ>::": 34913, "Ġmongo": 34914, "=float": 34915, "bindParam": 34916, "Smart": 34917, "ifa": 34918, "Ġsecurities": 34919, "Ġprejud": 34920, "Ġ,\"": 34921, 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ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "t h", ") Ċ", "Ġ {Ċ", "Ġ{ Ċ", "Ġ g", "i g", "i v", ", Ċ", "c e", "o d", "Ġ v", "a te", "at e", "Ġ T", "a g", "a y", "Ġ *", "o t", "u s", "Ġ C", "Ġ st", "Ġs t", "Ġ I", "u n", "u l", "u e", "Ġ A", "o w", "Ġ '", "e w", "Ġ <", "a tion", "at ion", "atio n", "ati on", "( )", "Ġ for", "Ġf or", "Ġfo r", "a b", "o rt", "or t", "u m", "a me", "am e", "Ġ is", "Ġi s", "p e", "t r", "c k", "â Ģ", "Ġ y", "i st", "is t", "- ---", "-- --", "--- -", ". ĊĊ", ".Ċ Ċ", "h e", "Ġ e", "l o", "Ġ M", "Ġ be", "Ġb e", "e rs", "er s", "Ġ on", "Ġo n", "Ġ con", "Ġc on", "Ġco n", "a p", "u b", "Ġ P", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "a ss", "as s", "i nt", "in t", "> Ċ", "l y", "u rn", "ur n", "Ġ $", "; ĊĊ", ";Ċ Ċ", "a v", "p ort", "por t", "po rt", "i r", "- >", "n t", "c tion", "ct ion", "e nd", "en d", "Ġ de", "Ġd e", "0 0", "i th", "it h", "o ut", "ou t", "t urn", "tu rn", "tur n", "o ur", "ou r", "Ġ ĠĠĠĠ", "ĠĠ ĠĠĠ", "ĠĠĠĠ Ġ", "ĠĠĠ ĠĠ", "l ic", "li c", "r es", "re s", "p t", "= =", "Ġ this", "Ġt his", "Ġth is", "Ġthi s", "Ġ wh", "Ġw h", "Ġ if", "Ġi f", "Ġ D", "v er", "ve r", "a ge", "ag e", "Ġ B", "h t", "e xt", "ex t", "= \"", "Ġ that", "Ġt hat", "Ġth at", "Ġtha t", "* ***", "** **", "*** *", "Ġ R", "Ġ it", "Ġi t", "e ss", "es s", "Ġ F", "Ġ r", "o s", "a nd", "an d", "Ġ as", "Ġa s", "e ct", "ec t", "k e", "r om", "ro m", "Ġ //", "Ġ/ /", "c on", "co n", "Ġ L", "( \"", "q u", "l ass", "la ss", "las s", "Ġ with", "Ġw ith", "Ġwi th", "Ġwit h", "i z", "d e", "Ġ N", "Ġ al", "Ġa l", "o p", "u p", "g et", "ge t", "Ġ }Ċ", "Ġ} Ċ", "i le", "il e", "Ġ an", "Ġa n", "a ta", "at a", "o re", "or e", "r i", "Ġ pro", "Ġp ro", "Ġpr o", "; čĊ", "ĉ ĉĉĉ", "ĉĉ ĉĉ", "ĉĉĉ ĉ", "t er", "te r", "a in", "ai n", "Ġ W", "Ġ E", "Ġ com", "Ġc om", "Ġco m", "Ġ return", "Ġre turn", "Ġr eturn", "Ġret urn", "a rt", "ar t", "Ġ H", "a ck", "ac k", "im port", "imp ort", "ub lic", "ubl ic", "Ġ or", "Ġo r", "e st", "es t", "m ent", "me nt", "men t", "Ġ G", "a ble", "ab le", "abl e", "Ġ -", "i ne", "in e", "i ll", "il l", "i nd", "in d", "e re", "er e", ": :", "i ty", "it y", "Ġ +", "Ġ tr", "Ġt r", "e lf", "el f", "i ght", "ig ht", "igh t", "( '", "o rm", "or m", "u lt", "ul t", "s tr", "st r", ". .", "\" ,", "Ġ you", "Ġy ou", "Ġyo u", "y pe", "yp e", "p l", "Ġ new", "Ġn ew", "Ġne w", "Ġ j", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġ from", "Ġf rom", "Ġfr om", "Ġfro m", "Ġ ex", "Ġe x", "Ġ O", "2 0", "l d", "Ġ [", "o c", ": Ċ", "Ġ se", "Ġs e", "Ġ le", "Ġl e", "- -------", "-- ------", "---- ----", "--- -----", "----- ---", "------ --", "------- -", ". s", "{ Ċ", "' ,", "a nt", "an t", "Ġ at", "Ġa t", "a se", "as e", ". c", "Ġ ch", "Ġc h", "< /", "a ve", "av e", "a ng", "an g", "Ġ are", "Ġa re", "Ġar e", "Ġ int", "Ġin t", "Ġi nt", "âĢ Ļ", "_ t", "e rt", "er t", "i al", "ia l", "a ct", "ac t", "} Ċ", "i ve", "iv e", "o de", "od e", "o st", "os t", "Ġ class", "Ġc lass", "Ġcl ass", "Ġclas s", "Ġcla ss", "Ġ not", "Ġn ot", "Ġno t", "o g", "o rd", "or d", "a lue", "al ue", "alu e", "a ll", "al l", "f f", "( );Ċ", "() ;Ċ", "(); Ċ", "o nt", "on t", "i me", "im e", "a re", "ar e", "Ġ U", "Ġ pr", "Ġp r", "Ġ :", "i es", "ie s", "i ze", "iz e", "u re", "ur e", "Ġ by", "Ġb y", "i re", "ir e", "Ġ }ĊĊ", "Ġ} ĊĊ", "Ġ}Ċ Ċ", ". p", "Ġ sh", "Ġs h", "i ce", "ic e", "a st", "as t", "p tion", "pt ion", "t ring", "tr ing", "tri ng", "o k", "_ _", "c l", "# #", "Ġ he", "Ġh e", "a rd", "ar d", ") .", "Ġ @", "i ew", "ie w", "ĉ ĉĉ", "ĉĉ ĉ", "Ġ was", "Ġw as", "Ġwa s", "i p", "t his", "th is", "Ġ u", "Ġ The", "ĠT he", "ĠTh e", "i de", "id e", "a ce", "ac e", "i b", "a c", "r ou", "ro u", "Ġ we", "Ġw e", "j ect", "je ct", "jec t", "Ġ public", "Ġp ublic", "Ġpub lic", "Ġpubli c", "a k", "v e", "a th", "at h", "o id", "oi d", "Ġ =>", "Ġ= >", "u st", "us t", "q ue", "qu e", "Ġ res", "Ġre s", "Ġr es", ") )", "' s", "Ġ k", "a ns", "an s", "y st", "ys t", "un ction", "unc tion", "unct ion", "* *******", "** ******", "**** ****", "****** **", "*** *****", "***** ***", "******* *", "Ġ i", "Ġ us", "Ġu s", "p p", "1 0", "o ne", "on e", "a il", "ai l", "= ===", "== ==", "=== =", "n ame", "na me", "nam e", "Ġ str", "Ġs tr", "Ġst r", "Ġ /", "Ġ &", "a ch", "ac h", "d iv", "di v", "y stem", "yst em", "ys tem", "e ll", "el l", "Ġ have", "Ġh ave", "Ġha ve", "Ġhav e", "e rr", "er r", "o uld", "ou ld", "oul d", "u ll", "ul l", "p on", "po n", "Ġ J", "_ p", "Ġ ==", "Ġ= =", "i gn", "ig n", "S t", ". Ċ", "Ġ pl", "Ġp l", ") ;ĊĊ", ");Ċ Ċ", "); ĊĊ", "f orm", "fo rm", "for m", "p ut", "pu t", "o unt", "ou nt", "oun t", "} ĊĊ", "}Ċ Ċ", "d d", "i te", "it e", "Ġ get", "Ġg et", "Ġge t", "r r", "o me", "om e", "Ġ âĢ", "Ġâ Ģ", "a ram", "ar am", "ara m", "c c", "Ġ */", "Ġ* /", "E R", "I n", "l es", "le s", "_ s", "o ng", "on g", "i e", "Ġ can", "Ġc an", "Ġca n", "Ġ V", "e rv", "er v", "p r", "Ġ un", "Ġu n", "r ow", "ro w", "b er", "be r", "Ġ do", "Ġd o", "l l", "Ġ el", "Ġe l", "Ġ self", "Ġs elf", "Ġse lf", "Ġsel f", "a ted", "at ed", "ate d", "a ry", "ar y", "Ġ .", "' ]", "u d", "Ġ en", "Ġe n", "Ġ Th", "ĠT h", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "t e", "_ c", "u ct", "uc t", "Ġ ab", "Ġa b", "o rk", "or k", ". get", ".g et", ".ge t", "Ġ #", "a w", "r ess", "re ss", "res s", "o b", "N ame", "Na me", "Nam e", "2 01", "20 1", "a pp", "ap p", "[ '", "Ġ all", "Ġa ll", "Ġal l", "o ry", "or y", "i tion", "it ion", "iti on", "a nce", "an ce", "anc e", "e ar", "ea r", "Ġ cont", "Ġc ont", "Ġcon t", "Ġco nt", "v ent", "ve nt", "ven t", "i a", "Ġ will", "Ġw ill", "Ġwi ll", "Ġwil l", "I N", "Ġ ĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠ", "r eturn", "re turn", "ret urn", "Ġ ", "\" ,Ċ", "\", Ċ", "e c", "Ġ In", "ĠI n", "p h", "Ġ |", "_ f", "Ġ var", "Ġv ar", "Ġva r", "e nce", "en ce", "enc e", "I d", "r ee", "re e", "i nk", "in k", "l ect", "le ct", "lec t", "u g", "e th", "et h", "Ġ else", "Ġe lse", "Ġel se", "Ġels e", "- ---------------", "-- --------------", "---- ------------", "-------- --------", "--- -------------", "------------ ----", "----- -----------", "---------- ------", "------ ----------", "----------- -----", "------------- ---", "------- ---------", "--------- -------", "--------------- -", "-------------- --", "1 9", "c ont", "con t", "co nt", "Ġ so", "Ġs o", "a tic", "at ic", "ati c", "Ġ lo", "Ġl o", "p ro", "pr o", "t on", "to n", "s s", "o wn", "ow n", "a bel", "ab el", "abe l", "o int", "oin t", "oi nt", "o us", "ou s", "e ld", "el d", "S T", "T he", "Th e", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "R E", "\" :", "o lor", "ol or", "olo r", "t p", "e g", "k ey", "ke y", "u de", "ud e", "Ġ St", "ĠS t", "o und", "ou nd", "oun d", "Ġ ar", "Ġa r", "\" );Ċ", "\") ;Ċ", "\"); Ċ", "e ner", "en er", "ene r", "s er", "se r", "1 1", "b ject", "bj ect", "es sage", "ess age", "essa ge", "f er", "fe r", "Ġ more", "Ġm ore", "Ġmor e", "Ġmo re", "at ions", "ation s", "atio ns", "ati ons", "e nts", "en ts", "ent s", "Ġ his", "Ġh is", "Ġhi s", "Ġ they", "Ġt hey", "Ġth ey", "Ġthe y", ". S", "Ġ Y", "u se", "us e", "n e", "i sh", "is h", "o ld", "ol d", "_ d", "i o", "i eld", "ie ld", "iel d", "Ġ per", "Ġp er", "Ġpe r", "C ont", "Con t", "Co nt", "in gs", "ing s", "# ###", "## ##", "### #", "Ġ data", "Ġd ata", "Ġda ta", "Ġdat a", "Ġ sa", "Ġs a", "e f", "f o", "Ġ one", "Ġo ne", "Ġon e", "e ng", "en g", "Ġ dis", "Ġd is", "Ġdi s", "A T", "Ġ name", "Ġn ame", "Ġna me", "Ġnam e", "Ġ true", "Ġtr ue", "v al", "va l", "l ed", "le d", ". f", "Ġ ne", "Ġn e", "Ġ end", "Ġe nd", "Ġen d", "3 2", ". T", "1 6", "c re", "cr e", "a rk", "ar k", "l og", "lo g", "E x", "e rror", "er ror", "err or", "erro r", "_ id", "_i d", "ur re", "urr e", "a nge", "an ge", "ang e", "Ġ null", "Ġn ull", "Ġnu ll", "r ray", "rr ay", "Ġ my", "Ġm y", "p an", "pa n", "i ct", "ic t", "a tor", "at or", "ato r", "V iew", "Vi ew", "L ist", "Li st", "ĉ return", "ĉr eturn", "ĉret urn", "ĉre turn", "âĢ Ŀ", "Ġ pre", "Ġp re", "Ġpr e", "Ġ x", "c lude", "cl ude", "clud e", "a rg", "ar g", "1 5", "o v", ". h", "Ġ >", "Ġ their", "Ġthe ir", "' )", "i rst", "ir st", "irs t", "i ck", "ic k", "g h", "L E", "O R", "Ġ private", "Ġpr ivate", "Ġpriv ate", "Ġprivat e", "t em", "te m", "čĊ čĊ", "u ser", "us er", "use r", "Ġ )", "c om", "co m", ". A", "\" ;Ċ", "\"; Ċ", "Ġ id", "Ġi d", "r ead", "re ad", "rea d", "Ġ who", "Ġw ho", "Ġwh o", "_ b", "\" >Ċ", "\"> Ċ", "Ġ time", "Ġt ime", "Ġtim e", "Ġti me", "Ġ man", "Ġm an", "Ġma n", "r y", "= =======", "== ======", "==== ====", "=== =====", "====== ==", "===== ===", "======= =", "r oup", "ro up", "rou p", "r op", "ro p", "p ublic", "pub lic", "v el", "ve l", "um ber", "umb er", "b le", "bl e", "Ġ which", "Ġwh ich", "** **************", "**** ************", "******** ********", "************ ****", "************** **", "Ġ any", "Ġa ny", "Ġan y", "Ġ false", "Ġf alse", "Ġfa lse", "Ġfal se", "Ġfals e", "w e", "Ġ value", "Ġv alue", "Ġval ue", "Ġva lue", "Ġvalu e", "Ġ li", "Ġl i", "\" )", "n der", "nd er", "nde r", "g r", "Ġ no", "Ġn o", "p aram", "par am", "pa ram", "para m", "2 5", "f ig", "fi g", ". com", ".c om", ".co m", "Ġ app", "Ġa pp", "Ġap p", "_ l", "i ons", "ion s", "io ns", ". D", "Ġ Ch", "ĠC h", "Ġ about", "Ġa bout", "Ġab out", "Ġ add", "Ġa dd", "Ġad d", "Ġ su", "Ġs u", "Ġ string", "Ġs tring", "Ġst ring", "Ġstr ing", "Ġstri ng", "I D", "Ġ over", "Ġo ver", "Ġov er", "s tring", "st ring", "str ing", "stri ng", ". l", "our ce", "0 00", "00 0", "_ C", "] Ċ", "Ġ qu", "Ġq u", "Ġ String", "ĠS tring", "ĠSt ring", "ĠStr ing", "c a", "S E", "Ġ ro", "Ġr o", "s h", "u al", "ua l", "T ype", "Typ e", "Ty pe", "s on", "so n", "n ew", "ne w", "e rn", "er n", "Ġ ag", "Ġa g", "A R", "] ;Ċ", "]; Ċ", "] .", "Ġ ?", "i cal", "ic al", "ica l", "Ġ des", "Ġd es", "Ġde s", "u th", "ut h", "i x", "a ys", "ay s", "Ġ type", "Ġt ype", "Ġtyp e", "Ġty pe", "' t", "a ult", "au lt", "aul t", "Ġ inter", "Ġin ter", "Ġint er", "Ġinte r", "v ar", "va r", ". b", "Ġ part", "Ġp art", "Ġpar t", "Ġpa rt", ". d", "ur rent", "urre nt", "urr ent", "I T", "E N", "3 0", "e nc", "en c", "( f", "r a", "v alue", "val ue", "va lue", "valu e", "c ho", "ch o", "1 8", "ut ton", "utt on", "utto n", "o se", "os e", "1 4", "Ġ !=", "Ġ! =", "a ter", "at er", "ate r", "à ©", "re ate", "reat e", "rea te", "o ll", "ol l", "p os", "po s", "y le", "yl e", "n g", "A L", "u sing", "us ing", "usi ng", "a mes", "am es", "ame s", "Ġ {čĊ", "Ġ{ čĊ", "a tes", "at es", "ate s", "e ly", "el y", "Ġ work", "Ġw ork", "Ġwor k", "Ġwo rk", "Ġ em", "Ġe m", "i nal", "in al", "ina l", "Ġ sp", "Ġs p", "Ġ when", "Ġw hen", "Ġwh en", "Ġwhe n", ". set", ".s et", ".se t", "Ġ ĠĠĠĠĠ", "ĠĠ ĠĠĠĠ", "ĠĠĠĠ ĠĠ", "ĠĠĠ ĠĠĠ", "ĠĠĠĠĠ Ġ", ") :Ċ", "): Ċ", "t o", "q uire", "qu ire", "quir e", "qui re", "ind ow", "indo w", "l ement", "le ment", "lem ent", "lemen t", "leme nt", "p ect", "pe ct", "pec t", "a sh", "as h", "[ i", "Ġ use", "Ġu se", "Ġus e", ". F", "p ec", "pe c", "Ġ ad", "Ġa d", "o ve", "ov e", "ce ption", "cept ion", "cep tion", "e ngth", "en gth", "eng th", "in clude", "inc lude", "incl ude", "inclu de", "a der", "ad er", "ade r", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "at us", "atu s", "T h", "i tle", "it le", "r it", "ri t", "v oid", "vo id", "( ).", "() .", "( Ċ", "Ġ off", "Ġo ff", "Ġof f", "Ġ other", "Ġo ther", "Ġot her", "Ġ &&", "Ġ& &", "' ;Ċ", "'; Ċ", "m s", "Ġ been", "Ġb een", "Ġbe en", "Ġbee n", "Ġ te", "Ġt e", "m l", "c o", "n c", "1 3", "er vice", "erv ice", "Ġ %", "* *Ċ", "** Ċ", "a nn", "an n", "a de", "ad e", "Ċ ĊĊĊ", "ĊĊ ĊĊ", "ĊĊĊ Ċ", "l ock", "lo ck", "loc k", "con st", "co nst", "cons t", "1 00", "10 0", "p onse", "pon se", "pons e", "Ġ sup", "Ġs up", "Ġsu p", "+ +", "d ate", "da te", "dat e", "Ġ acc", "Ġa cc", "Ġac c", "Ġ had", "Ġh ad", "Ġha d", "Ġ bu", "Ġb u", "2 00", "20 0", "Ġ Re", "ĠR e", "Ġ were", "Ġw ere", "Ġwe re", "Ġwer e", "Ġ file", "Ġf ile", "Ġfil e", "Ġfi le", "Ġ would", "Ġw ould", "Ġwo uld", "Ġ âĢľ", "ĠâĢ ľ", "v en", "ve n", "i ss", "is s", "Ġ our", "Ġo ur", "Ġou r", "c lass", "cl ass", "cla ss", "clas s", "r aw", "ra w", "Ġ year", "Ġy ear", "Ġye ar", "D ata", "Da ta", "Dat a", "Ġ val", "Ġv al", "Ġva l", "Ġ some", "Ġs ome", "Ġso me", "Ġsom e", "f ter", "ft er", "fte r", "y s", "Ġ ///", "Ġ// /", "Ġ/ //", "r ound", "ro und", "rou nd", "v iew", "vi ew", "vie w", "Ġ pe", "Ġp e", "Ġ there", "Ġt here", "Ġth ere", "Ġthe re", "Ġther e", "Ġ said", "Ġs aid", "Ġsa id", "Ġsai d", "d u", "o f", "l ine", "li ne", "lin e", "/ *", "d uct", "du ct", "duc t", "Ġ her", "Ġh er", "Ġhe r", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "R es", "Re s", "Ġ co", "Ġc o", "Ġ comm", "Ġc omm", "Ġcom m", "Ġco mm", "i se", "is e", "m in", "mi n", "Ġ ĠĠĠĊ", "ĠĠ ĠĠĊ", "ĠĠĠĠ Ċ", "ĠĠĠ ĠĊ", "# include", "et hod", "eth od", ". P", "u te", "ut e", "Ġ ass", "Ġa ss", "Ġas s", "I nt", "In t", "a sk", "as k", "l oc", "lo c", "Ġ like", "Ġl ike", "Ġli ke", "Ġlik e", "o dy", "od y", "Ġ let", "Ġl et", "Ġle t", "l oad", "lo ad", "Ġ am", "Ġa m", "r ol", "ro l", "Ġ gr", "Ġg r", "y p", "Ġ also", "Ġal so", "Ġals o", "Ġ It", "ĠI t", "u rl", "ur l", "i fic", "if ic", "ifi c", "o rs", "or s", "_ P", "_ n", "i gh", "ig h", "Ġ than", "Ġt han", "Ġth an", "Ġtha n", "C om", "Co m", "A N", "U L", "a ting", "at ing", "ati ng", "atin g", "1 7", "Ġ This", "ĠT his", "ĠTh is", "ĠThi s", "r ef", "re f", "_ S", "Ġ static", "Ġst atic", "Ġstat ic", "Ġsta tic", "Ġstati c", "r oll", "ro ll", "rol l", "Ġ just", "Ġj ust", "Ġju st", "Ġjus t", "Ġ result", "Ġres ult", "i an", "ia n", "id th", "Ġ them", "Ġt hem", "Ġth em", "Ġthe m", ") );Ċ", ")) ;Ċ", ")); Ċ", "d er", "de r", "re ak", "rea k", "C on", "Co n", ": //", ":/ /", "u le", "ul e", ". ..", ".. .", "a rch", "ar ch", "arc h", "e ment", "em ent", "eme nt", "emen t", "Ġ <<", "Ġ< <", "5 0", "u sh", "us h", "en se", "ens e", "a rr", "ar r", "Ġ into", "Ġin to", "Ġint o", "c ess", "ce ss", "ces s", "a mp", "am p", "i ed", "ie d", "u ment", "um ent", "ume nt", "umen t", "Ġ \\", "] ,", "w o", "a ls", "al s", "Ġ what", "Ġw hat", "Ġwh at", "a nc", "an c", "V alue", "Val ue", "Va lue", "= '", "o lum", "ol um", "olu m", "Ġ pos", "Ġp os", "Ġpo s", "a ges", "ag es", "age s", "a yer", "ay er", "aye r", "Ġ sc", "Ġs c", "u es", "ue s", "\" )Ċ", "\") Ċ", "_ T", "Ġ list", "Ġl ist", "Ġli st", "Ġlis t", "( s", "Ġ case", "Ġc ase", "Ġca se", "Ġcas e", "C h", "ĉ ĉĉĉĉ", "ĉĉ ĉĉĉ", "ĉĉĉĉ ĉ", "ĉĉĉ ĉĉ", "//// ////", "/// /////", "///// ///", "p onent", "pon ent", "pone nt", "Ġ z", "Ġ kn", "Ġk n", "l et", "le t", "D E", "r ed", "re d", "Ġ fe", "Ġf e", "Ġ },Ċ", "Ġ} ,Ċ", "Ġ}, Ċ", "Ġ ,", "( t", "Ġ first", "Ġf irst", "Ġfi rst", "Ġfir st", "' );Ċ", "') ;Ċ", "'); Ċ", "w ord", "wo rd", "wor d", "Ġ import", "Ġim port", "Ġimp ort", "Ġ act", "Ġa ct", "Ġac t", "Ġ char", "Ġc har", "Ġch ar", "Ġcha r", "C T", "Ġ Tr", "ĠT r", "o ple", "op le", "opl e", "= {", "ĉ f", "2 4", "i ent", "ie nt", "ien t", "c ent", "ce nt", "cen t", ". j", "l ection", "le ction", "lect ion", "lec tion", ") )Ċ", ")) Ċ", "Ġ only", "Ġon ly", "Ġ print", "Ġp rint", "Ġpr int", "Ġpri nt", "Ġprin t", "m er", "me r", ". W", "o ck", "oc k", "Ġ --", "Ġ- -", "T ext", "Te xt", "Tex t", "Ġ op", "Ġo p", "a nk", "an k", "Ġ its", "Ġit s", "Ġi ts", "Ġ back", "Ġb ack", "Ġba ck", "Ġbac k", "[ \"", "Ġ need", "Ġn eed", "Ġne ed", "Ġ cl", "Ġc l", "Ġ sub", "Ġs ub", "Ġsu b", "Ġ la", "Ġl a", "( (", ". \"", "O bject", "Ob ject", "Obj ect", "Ġ start", "Ġst art", "Ġstar t", "Ġsta rt", "f ile", "fi le", "fil e", "( self", "(s elf", "(se lf", "(sel f", "n er", "ne r", "e y", "Ġ user", "Ġu ser", "Ġus er", "Ġuse r", "Ġ ent", "Ġe nt", "Ġen t", "Ġ Com", "ĠC om", "ĠCo m", "i ts", "it s", "Ġ Con", "ĠC on", "ĠCo n", "o uble", "ou ble", "oub le", "o wer", "ow er", "owe r", "i tem", "it em", "ite m", "v ery", "ver y", "ve ry", "Ġ We", "ĠW e", "6 4", "l ick", "lic k", "li ck", "Ġ Q", "p hp", "ph p", "t tp", "tt p", "' :", "i cs", "ic s", "Ġ under", "Ġu nder", "Ġun der", "Ġund er", "Ġunde r", "Ġ *Ċ", "Ġ* Ċ", ". L", ") ;", "i ces", "ic es", "ice s", "Ġ reg", "Ġre g", "Ġr eg", ") čĊ", "ĉ public", "ĉp ublic", "ĉpub lic", "S S", "Ġ then", "Ġt hen", "Ġth en", "Ġthe n", "r eat", "re at", "rea t", "i ous", "io us", "iou s", ". G", "e k", "i rect", "ir ect", "ire ct", "h eck", "he ck", "hec k", "cri pt", "cr ipt", "n ing", "ni ng", "nin g", "Ġ Un", "ĠU n", "Ġ may", "Ġm ay", "Ġma y", "Ġ Wh", "ĠW h", "B o", "I tem", "It em", "str uct", "stru ct", ". st", ".s t", "r eam", "re am", "rea m", "i ble", "ib le", "lo at", "Ġ org", "Ġo rg", "Ġor g", "u nd", "un d", "s um", "su m", "_ in", "_i n", ". ./", ".. /", "_ M", "Ġ how", "Ġh ow", "Ġho w", "r ite", "ri te", "rit e", "' Ċ", "T o", "4 0", "w w", "Ġ people", "Ġpe ople", "in dex", "ind ex", "inde x", ". n", "h ttp", "ht tp", "htt p", "( m", "e ctor", "ect or", "ec tor", "Ġ ind", "Ġin d", "Ġi nd", "Ġ jav", "Ġj av", "Ġja v", "] ,Ċ", "], Ċ", "Ġ He", "ĠH e", "_ st", "_s t", "f ul", "fu l", "o le", "ol e", ") {Ċ", "){ Ċ", "Ġ should", "Ġsh ould", "Ġsho uld", "o py", "op y", "e lp", "el p", "i er", "ie r", "_ name", "_n ame", "_na me", "er son", "ers on", "I ON", "IO N", "o te", "ot e", "Ġ test", "Ġt est", "Ġte st", "Ġtes t", "Ġ bet", "Ġb et", "Ġbe t", "r ror", "rr or", "u lar", "ul ar", "ula r", "ã Ģ", "Ġ Ð", "b s", "t ing", "ti ng", "tin g", "Ġ make", "Ġm ake", "Ġma ke", "Ġmak e", "T r", "Ġ after", "Ġa fter", "Ġaf ter", "Ġaft er", "ar get", "arg et", "arge t", "R O", "ol umn", "olum n", "olu mn", "r c", "_ re", "_r e", "de fine", "def ine", "2 2", "Ġ right", "Ġr ight", "Ġrig ht", "Ġri ght", "r ight", "ri ght", "rig ht", "d ay", "da y", "Ġ long", "Ġl ong", "Ġlo ng", "Ġlon g", "[ ]", "( p", "t d", "c ond", "con d", "co nd", "Ġ Pro", "ĠP ro", "ĠPr o", "Ġ rem", "Ġre m", "Ġr em", "pt ions", "ption s", "v id", "vi d", ". g", "Ġ ext", "Ġe xt", "Ġex t", "Ġ __", "Ġ_ _", "' )Ċ", "') Ċ", "p ace", "pa ce", "pac e", "m p", "Ġ min", "Ġm in", "Ġmi n", "st ance", "sta nce", "stan ce", "a ir", "ai r", "a ction", "act ion", "ac tion", "w h", "t ype", "ty pe", "typ e", "u til", "ut il", "uti l", "a it", "ai t", "< ?", "I C", "t ext", "te xt", "tex t", "Ġ ph", "Ġp h", "Ġ fl", "Ġf l", ". M", "c cess", "cc ess", "cce ss", "b r", "f ore", "fo re", "for e", "ers ion", ") ,Ċ", "), Ċ", ". re", ".r e", "a teg", "at eg", "ate g", "Ġ loc", "Ġl oc", "Ġlo c", "i ns", "in s", "- s", "t rib", "tr ib", "tri b", "Ġ Int", "ĠI nt", "ĠIn t", "Ġ array", "Ġa rray", "Ġar ray", "Ġarr ay", ", \"", "P ro", "Pr o", "( c", "ess ion", "> ĊĊ", ">Ċ Ċ", "Ġ she", "Ġs he", "Ġsh e", "\" ]", "a ph", "ap h", "Ġ exp", "Ġe xp", "Ġex p", "er ty", "ert y", "Ġ Se", "ĠS e", "Ġ par", "Ġp ar", "Ġpa r", "u nc", "un c", "E T", "Ġ read", "Ġre ad", "Ġr ead", "p rint", "pr int", "pri nt", "Ġ rel", "Ġre l", "Ġr el", "Ġ form", "Ġf orm", "Ġfor m", "Ġfo rm", "Ġ dr", "Ġd r", "Ex ception", "Except ion", "in put", "inp ut", "Ġ trans", "Ġt rans", "Ġtr ans", "Ġtra ns", "Ġtran s", "# #######", "## ######", "#### ####", "### #####", "##### ###", "###### ##", "####### #", "or der", "ord er", "orde r", "B y", "Ġ aw", "Ġa w", "i ties", "it ies", "iti es", "u ff", "uf f", "p lay", "pl ay", "pla y", ". add", ".a dd", ".ad d", "Ġ âĢĵ", "ĠâĢ ĵ", "Ġ want", "Ġw ant", "Ġwa nt", "Ġwan t", "Ġ comp", "Ġc omp", "Ġcom p", "Ġco mp", "m ents", "ment s", "me nts", "men ts", "Ġ ||", "Ġ| |", "a z", "b e", "Ġ number", "Ġn umber", "Ġnum ber", "Ġnumb er", "Ġ require", "Ġre quire", "Ġreq uire", "Ġrequ ire", "Ġ Ex", "ĠE x", "6 0", "Ġ col", "Ġc ol", "Ġco l", "Ġ key", "Ġk ey", "Ġke y", "em ber", "emb er", "Ġ two", "Ġt wo", "Ġtw o", "Ġ size", "Ġs ize", "Ġsi ze", "Ġsiz e", "Ġ where", "Ġw here", "Ġwh ere", "Ġwhe re", "U T", "res ult", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "o ugh", "ou gh", "oug h", "or ld", "o od", "oo d", "u ch", "uc h", "at ive", "ati ve", "ativ e", "g er", "ge r", "a rent", "ar ent", "are nt", "aren t", "Ġ /*", "Ġ/ *", "Ġ arg", "Ġa rg", "Ġar g", "Ġ while", "Ġwh ile", "2 3", "( this", "(t his", "(th is", "Ġ rec", "Ġre c", "Ġr ec", "Ġ dif", "Ġd if", "Ġdi f", "St ate", "Stat e", "Ġ spec", "Ġs pec", "Ġsp ec", "Ġspe c", "r ide", "ri de", "rid e", "_ F", "Ġ look", "Ġl ook", "Ġlo ok", "A M", "il ity", "ilit y", "ili ty", "e ter", "et er", "ete r", "âĢĻ t", "Ċ ĊĊ", "ĊĊ Ċ", "ay out", "ayo ut", "-- ------------------------------", "---- ----------------------------", "---------------- ----------------", "------------ --------------------", "---------------------------- ----", "------------------------------ --", "-------------------- ------------", "a ger", "ag er", "age r", "Ġ could", "Ġc ould", "Ġco uld", "Ġcou ld", "Ġ br", "Ġb r", "e nds", "en ds", "end s", "u res", "ur es", "ure s", "Ġ know", "Ġk now", "Ġkn ow", "e ts", "et s", "Ġ If", "ĠI f", "Ġ Sh", "ĠS h", ". w", "b ack", "ba ck", "bac k", "Ġ ser", "Ġs er", "Ġse r", "Ġ +=", "Ġ+ =", "Ġ fr", "Ġf r", "( ));Ċ", "() );Ċ", "()) ;Ċ", "()); Ċ", "Ġ hand", "Ġh and", "Ġha nd", "Ġhan d", "I nd", "In d", "U LL", "UL L", "I m", "( );ĊĊ", "() ;ĊĊ", "();Ċ Ċ", "(); ĊĊ", "Ġ most", "Ġm ost", "Ġmo st", "Ġmos t", "Ġ try", "Ġt ry", "Ġtr y", "Ġ now", "Ġn ow", "Ġno w", "r ough", "ro ugh", "rou gh", "> čĊ", "ack age", "Ġ him", "Ġh im", "Ġhi m", ". _", "i fy", "if y", "Ġ break", "Ġb reak", "Ġbre ak", "Ġ );Ċ", "Ġ) ;Ċ", "Ġ); Ċ", "r en", "re n", "# define", "i tt", "it t", "Ġ ap", "Ġa p", "ĉ c", "( n", "Ġ You", "ĠY ou", "ĠYo u", ": ĊĊ", ":Ċ Ċ", "- m", "Ġ every", "Ġe very", "Ġever y", "Ġev ery", "Ġeve ry", "us tom", "ust om", "usto m", "l ient", "li ent", "lie nt", "lien t", "oc ument", "ocu ment", "cri ption", "cript ion", "E rror", "Err or", "Er ror", "Erro r", "- b", "Ð ¾", "] [", "9 9", "t rans", "tr ans", "tra ns", "tran s", "Ġ point", "Ġp oint", "Ġpo int", "Ġpoi nt", "Ġ std", "Ġs td", "Ġst d", "Ġ fil", "Ġf il", "Ġfi l", "T ime", "Tim e", "Ti me", "8 0", "Ġ mod", "Ġm od", "Ġmo d", "Ġ ->", "Ġ- >", "Ġ error", "Ġe rror", "Ġerr or", "Ġer ror", "Ġerro r", "a h", "Ġ text", "Ġt ext", "Ġte xt", "Ġtex t", "r oller", "ro ller", "rol ler", "roll er", "l ose", "lo se", "los e", "q l", "Ġ pol", "Ġp ol", "Ġpo l", "> < /", "Ġ show", "Ġs how", "Ġsh ow", "Ġsho w", "U ser", "Us er", "Use r", "a sed", "as ed", "ase d", "Ġ {ĊĊ", "Ġ{ ĊĊ", "Ġ{Ċ Ċ", "Ġ find", "Ġf ind", "Ġfin d", "Ġfi nd", "Ð °", "E D", "s pan", "sp an", "spa n", "e nu", "en u", "Ġ current", "Ġc urrent", "Ġcur rent", "Ġcurr ent", "Ġ used", "Ġu sed", "Ġus ed", "Ġuse d", "c ept", "ce pt", "cep t", "cl ud", "Ġ play", "Ġp lay", "Ġpl ay", "Ġpla y", "Ġ log", "Ġl og", "Ġlo g", "u tion", "ut ion", "uti on", "f l", "Ġ see", "Ġs ee", "Ġse e", "ind ows", "indow s", "indo ws", "Ġ help", "Ġh elp", "Ġhe lp", "Ġhel p", "Ġ these", "Ġth ese", "Ġthe se", "Ġ pass", "Ġp ass", "Ġpas s", "Ġpa ss", "Ġ down", "Ġd own", "Ġdo wn", "Ġdow n", "Ġ even", "Ġe ven", "Ġev en", "Ġeve n", "a son", "as on", "aso n", "u ild", "ui ld", "uil d", "f rom", "fr om", "( d", "Ġ bl", "Ġb l", "l abel", "la bel", "lab el", "e lse", "el se", "els e", "Ð µ", "Ġ (!", "Ġ( !", "i zed", "iz ed", "ize d", "( ),", "() ,", "Ġ ob", "Ġo b", "Ġ item", "Ġit em", "Ġi tem", "u mp", "um p", "U R", "o rn", "or n", "Ġ don", "Ġd on", "Ġdo n", "S e", "m an", "ma n", "2 7", "am ple", "amp le", "t n", "= ===============", "== ==============", "==== ============", "======== ========", "=== =============", "============ ====", "============= ===", "=========== =====", "============== ==", "========= =======", "========== ======", "=============== =", "====== ==========", "===== ===========", "======= =========", "H e", "g ram", "gr am", "gra m", "Ġ did", "Ġd id", "Ġdi d", "w n", "_ h", "i ver", "iv er", "ive r", "Ġ sm", "Ġs m", "Ġ through", "Ġth rough", "Ġthr ough", "Ġthro ugh", "Ġ An", "ĠA n", "c he", "ch e", "Ġ inv", "Ġin v", "Ġi nv", "o use", "ou se", "ous e", "Ġ es", "Ġe s", "Ġ New", "ĠN ew", "ĠNe w", "ex port", "exp ort", "expo rt", "m ary", "ma ry", "mar y", "u to", "ut o", "l er", "le r", "Ġ last", "Ġl ast", "Ġla st", "Ġlas t", "Ġ event", "Ġe vent", "Ġeven t", "Ġev ent", "Ġeve nt", "t ry", "tr y", "ï ¼", "i ly", "il y", "ig ned", "ign ed", "igne d", "i nes", "in es", "ine s", "ol low", "oll ow", "ollo w", "ic ense", "icens e", "s ole", "so le", "sol e", "l ear", "le ar", "lea r", "( int", "(i nt", "(in t", "Ġ again", "Ġa gain", "Ġag ain", "Ġ high", "Ġh igh", "Ġhi gh", "h tml", "ht ml", "htm l", "In dex", "Ind ex", "ut hor", "uth or", "Ġ /**Ċ", "Ġ/ **Ċ", "Ġ/* *Ċ", "Ġ/** Ċ", "Ġ line", "Ġl ine", "Ġli ne", "Ġlin e", "E vent", "Even t", "Ev ent", "_ D", "Ġ does", "Ġd oes", "Ġdo es", "Ġdoe s", "it ial", "iti al", "itia l", "Ġ cr", "Ġc r", "a rs", "ar s", "2 8", "Ġ tem", "Ġt em", "Ġte m", "c ause", "ca use", "f ace", "fa ce", "fac e", "Ġ `", "_ A", "B utton", "But ton", "a ture", "at ure", "atur e", "atu re", "ect ed", "ec ted", "E S", "i ster", "is ter", "ist er", "iste r", "ĉ Ċ", "Ġ before", "Ġb efore", "Ġbe fore", "Ġbef ore", "a le", "al e", "o ther", "ot her", "oth er", "Ġ because", "Ġb ecause", "Ġbe cause", "Ġbec ause", "r oid", "ro id", "roi d", "Ġ ed", "Ġe d", "i k", "r eg", "re g", "Ġ De", "ĠD e", "Ġ dist", "Ġd ist", "Ġdis t", "Ġdi st", "} ,Ċ", "}, Ċ", "Ġ state", "Ġst ate", "Ġstat e", "Ġsta te", "Ġ cons", "Ġc ons", "Ġcon s", "Ġco ns", "r int", "ri nt", "rin t", "a tt", "at t", "Ġ here", "Ġh ere", "Ġhe re", "Ġher e", "i ned", "in ed", "ine d", "Ġ final", "Ġf inal", "Ġfin al", "Ġfi nal", "Ġ \"\"", "Ġ\" \"", "K ey", "Ke y", "L O", "Ġ del", "Ġd el", "Ġde l", "p ty", "pt y", "th ing", "thin g", "2 6", "Ġ And", "ĠA nd", "ĠAn d", "Ġ run", "Ġr un", "Ġru n", "Ġ X", "y m", ". app", ".ap p", ".a pp", "Ġ very", "Ġv ery", "Ġver y", "Ġve ry", "c es", "ce s", "_ N", "a red", "ar ed", "are d", "w ard", "wa rd", "war d", "l ist", "li st", "lis t", "i ted", "it ed", "ite d", "o log", "ol og", "olo g", "it ch", "B ox", "Bo x", "i fe", "if e", "3 3", "Ġ ac", "Ġa c", "Ġ model", "Ġm odel", "Ġmod el", "Ġmode l", "Ġmo del", "Ġ mon", "Ġm on", "Ġmo n", "Ġ way", "Ġw ay", "Ġwa y", "l ete", "le te", "let e", "Ġ call", "Ġc all", "Ġcal l", "Ġca ll", "Ġ att", "Ġa tt", "Ġat t", "Ġ cal", "Ġc al", "Ġca l", "v ert", "ver t", "ve rt", "Ġ dec", "Ġd ec", "Ġde c", "l ease", "le ase", "lea se", "o un", "ou n", "Ġ });Ċ", "Ġ} );Ċ", "Ġ}) ;Ċ", "Ġ}); Ċ", "f r", "form ation", "format ion", "forma tion", "e tail", "et ail", "eta il", "Ġ num", "Ġn um", "Ġnu m", "a j", "qu ery", "que ry", "quer y", "Ġ well", "Ġw ell", "Ġwe ll", "Ġwel l", "Ġ object", "Ġo bject", "Ġob ject", "Ġobj ect", "Ġ As", "ĠA s", "Ġ years", "Ġy ears", "Ġyear s", "Ġye ars", "C olor", "Col or", "Co lor", "I S", "Ġ default", "Ġd efault", "Ġde fault", "Ġdef ault", "Ġdefa ult", "W h", "Ġ ins", "Ġin s", "Ġi ns", "a int", "ain t", "ai nt", "Ġ java", "Ġj ava", "Ġjav a", "Ġja va", "Ġ sim", "Ġs im", "Ġsi m", "Ġ Ar", "ĠA r", "m on", "mo n", "t il", "ti l", "( );čĊ", "() ;čĊ", "(); čĊ", ") :", "S et", "Se t", "2 9", "at ter", "att er", "atte r", "Ġ view", "Ġv iew", "Ġvi ew", "Ġvie w", "Ġ pres", "Ġp res", "Ġpr es", "Ġpre s", "a rray", "ar ray", "arr ay", "arra y", "W e", "A t", "Ġ bel", "Ġb el", "Ġbe l", "Ġ many", "Ġm any", "Ġman y", "Ġma ny", "2 1", "M an", "Ma n", "e nder", "en der", "end er", "ende r", "Ġ being", "Ġb eing", "Ġbe ing", "Ġbei ng", "Ġ good", "Ġg ood", "Ġgo od", "Ġgoo d", "ĉ ĉĉĉĉĉ", "ĉĉ ĉĉĉĉ", "ĉĉĉĉ ĉĉ", "ĉĉĉ ĉĉĉ", "ĉĉĉĉĉ ĉ", "at ional", "ation al", "atio nal", "ati onal", "w are", "wa re", "war e", ". log", ".l og", ".lo g", "{ čĊ", "Ġ using", "Ġu sing", "Ġus ing", "_ B", "Ġ :=", "Ġ: =", "_ w", "i sts", "is ts", "ist s", "l ish", "li sh", "lis h", "Ġ stud", "Ġst ud", "Ġstu d", "Ġ Al", "ĠA l", "Ġ gu", "Ġg u", "con fig", "conf ig", "u ring", "ur ing", "uri ng", "t ime", "ti me", "tim e", "o ken", "ok en", "oke n", "ame space", "ames pace", "Ġ request", "Ġre quest", "Ġreq uest", "Ġrequ est", "Ġ child", "Ġch ild", "Ġchi ld", "Ġ Ã", "l ob", "lo b", "Ġ param", "Ġp aram", "Ġpar am", "Ġpara m", "Ġpa ram", "Ġ }čĊ", "Ġ} čĊ", "0 1", "Ġ echo", "Ġe cho", "Ġec ho", "Ġech o", "f unction", "func tion", "fun ction", "**** ****************************", "******** ************************", "**************** ****************", "************************ ********", "******************** ************", "**************************** ****", "************ ********************", "p s", "E lement", "El ement", "Elem ent", "Ele ment", "a lk", "al k", "l ication", "lic ation", "li cation", "lica tion", "b y", "S ize", "Si ze", "ra wing", "raw ing", "Ġ person", "Ġp erson", "Ġper son", "Ġpers on", "Ġperso n", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "\\ n", "o bject", "ob ject", "obj ect", "i nce", "in ce", "inc e", "E n", "F ile", "Fi le", "Fil e", "u f", "f fect", "ff ect", "ffe ct", "A C", "Ġ style", "Ġst yle", "Ġsty le", "Ġstyl e", "sum mary", "summ ary", "Ġ que", "Ġqu e", "Ġq ue", "_ r", "Ġ ($", "Ġ( $", "M odel", "Mode l", "Mod el", "Mo del", "i dent", "id ent", "ide nt", "iden t", "Ġ method", "Ġm ethod", "Ġmet hod", "Ġmeth od", "I L", "o tt", "ot t", "l ess", "le ss", "les s", "I NG", "IN G", "Ġ ()", "Ġ( )", "Ġ expect", "Ġex pect", "Ġexp ect", "y nc", "yn c", "p ackage", "pack age", "3 5", "u rs", "ur s", "Ġ prot", "Ġp rot", "Ġpro t", "Ġpr ot", ". /", "p re", "pr e", "Ġ )Ċ", "Ġ) Ċ", "m a", "Ġ sur", "Ġs ur", "Ġsu r", "Ġ found", "Ġf ound", "Ġfo und", "Ġfou nd", "In fo", "Inf o", "p ar", "pa r", "i mes", "im es", "ime s", ". e", "a ins", "ain s", "ai ns", "Ġ post", "Ġp ost", "Ġpos t", "Ġpo st", "- d", "4 5", "o lean", "ol ean", "ole an", "Ġ sl", "Ġs l", "P E", "Ġ such", "Ġs uch", "Ġsu ch", "Ġsuc h", "s elect", "se lect", "sel ect", "a iner", "ain er", "ai ner", "aine r", "Ġ think", "Ġth ink", "Ġthin k", "Ġthi nk", "Ġd iffer", "Ġdif fer", "Ġdi ffer", "Ġdiff er", ". r", "/ **Ċ", "/* *Ċ", "/** Ċ", "F F", "o ol", "oo l", "p late", "pl ate", "plat e", "pla te", "q ual", "qu al", "qua l", "Ġ For", "ĠF or", "ĠFo r", "Ġ much", "Ġm uch", "Ġmu ch", "Ġmuc h", "u c", "( new", "(n ew", "(ne w", "od ule", "odu le", "Ġ som", "Ġs om", "Ġso m", "Ġ http", "Ġh ttp", "Ġht tp", "Ġhtt p", "Ġ List", "ĠL ist", "ĠLi st", "ĠLis t", "Ġ count", "Ġc ount", "Ġco unt", "Ġcoun t", "Ġcou nt", "Ġ inst", "Ġin st", "Ġi nst", "Ġins t", "c har", "ch ar", "cha r", "m it", "mi t", ". id", ".i d", "a king", "ak ing", "aki ng", "akin g", "Ġ gener", "Ġg ener", "Ġge ner", "Ġgen er", "Ġgene r", "p x", "v ice", "vi ce", "vic e", "3 7", "_ data", "_d ata", "_dat a", "_da ta", "Ġ NULL", "ĠN ULL", "ĠNU LL", "} čĊ", "i dd", "id d", "ãĢ Ĥ", "Ġ med", "Ġm ed", "Ġme d", "o rg", "or g", "i der", "id er", "ide r", "a che", "ac he", "ach e", "w ork", "wo rk", "wor k", "Ġ check", "Ġc heck", "Ġch eck", "Ġche ck", "w een", "we en", "Ġ ((", "Ġ( (", "t he", "th e", "a nts", "an ts", "ant s", "> <", ". B", "- c", "Ġ open", "Ġo pen", "Ġop en", "Ġ est", "Ġe st", "Ġes t", "Ġ ĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ Ċ", "ĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĊ", "Ġ next", "Ġn ext", "Ġne xt", "Ġnex t", "I M", "Ñ Ĥ", "O T", "à ³", "Ġ follow", "Ġf ollow", "Ġfol low", "Ġfoll ow", "c ontent", "con tent", "cont ent", "conte nt", "Ġ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠ", "Ġin clud", "Ġincl ud", "Ġinclu d", "H E", "Ġ Res", "ĠR es", "ĠRe s", "Ġ href", "Ġh ref", "Ġhr ef", "Ð ¸", "Ġ car", "Ġc ar", "Ġca r", "y pes", "ype s", "yp es", "i mage", "im age", "ima ge", "imag e", "U n", "Ġ bool", "Ġb ool", "Ġbo ol", "Ġboo l", "A D", "Ġ game", "Ġg ame", "Ġgam e", "Ġga me", ". Form", ".F orm", ".For m", "r ows", "ro ws", "row s", "* /", "v elop", "ve lop", "vel op", ". Drawing", ".D rawing", ".Draw ing", "Ġ path", "Ġp ath", "Ġpat h", "Ġpa th", "is ion", "isi on", "Ġ each", "Ġe ach", "Ġea ch", "Ġ Pl", "ĠP l", "_ type", "_t ype", "_typ e", "_ty pe", "P ath", "Pa th", "Pat h", "n ection", "ne ction", "nect ion", "Ġ av", "Ġa v", "' ).", "') .", "Ġ support", "Ġs upport", "Ġsup port", "Ġsupp ort", "E NT", "EN T", "r em", "re m", "\" ).", "\") .", "Ġ own", "Ġo wn", "Ġow n", "Ġ cor", "Ġc or", "Ġco r", "c ount", "co unt", "cou nt", "m iss", "mi ss", "mis s", "u ally", "ual ly", "Ġ mem", "Ġm em", "Ġme m", "s td", "st d", "i ence", "ie nce", "ien ce", "s earch", "se arch", "sea rch", "\" ĊĊ", "\"Ċ Ċ", "F orm", "For m", "Fo rm", "Ġ sex", "Ġs ex", "Ġse x", "e name", "en ame", "ena me", "Ġ sign", "Ġs ign", "Ġsi gn", "Ġsig n", "Ġ et", "Ġe t", "Ġ ĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠ ĠĠĠĠ", "' ,'", "', '", "Ġ App", "ĠA pp", "ĠAp p", "Ġ those", "Ġth ose", "Ġtho se", "o ff", "of f", "Ġ err", "Ġe rr", "Ġer r", "Ġ system", "Ġs ystem", "Ġsys tem", "Ġsy stem", "Ġsyst em", "Ġ best", "Ġb est", "Ġbe st", "Ġbes t", "c ode", "co de", "cod e", "Ġ same", "Ġs ame", "Ġsa me", "Ġsam e", "Ġ di", "Ġd i", "u ss", "us s", "Ġ create", "Ġc reate", "Ġcre ate", "Ġcreat e", "Ġcrea te", "a ther", "at her", "ath er", "athe r", "A rray", "Ar ray", "Arr ay", ". in", ".i n", "f e", "S ervice", "Ser vice", "Serv ice", "U N", "a ts", "at s", "Ġ Z", "al th", "alt h", "Ġ made", "Ġm ade", "Ġma de", "Ġmad e", "tr ue", "tru e", "A B", "Ġ mark", "Ġm ark", "Ġmar k", "Ġma rk", "r id", "ri d", "if ied", "ifi ed", "ifie d", ", čĊ", "y n", "p ress", "pr ess", "pre ss", "pres s", "Ġ group", "Ġg roup", "Ġgr oup", "Ġgro up", "Ġgrou p", "Ġ fin", "Ġf in", "Ġfi n", "Ġ License", "ĠL icense", "ĠLic ense", "F ield", "Fi eld", "e ger", "eg er", "Ġ world", "Ġw orld", "Ġwor ld", "i ness", "in ess", "ine ss", "ines s", "t y", "Ġ process", "Ġp rocess", "Ġpro cess", "Ġproc ess", "Ġproces s", "( b", "Ġ cre", "Ġc re", "Ġcr e", "a rn", "ar n", "i ves", "iv es", "ive s", "Ġ main", "Ġm ain", "Ġma in", "Ġmai n", "i deo", "id eo", "ide o", "3 6", "_ g", "A G", "val id", "va lid", "i mg", "im g", "P I", "Ġ color", "Ġc olor", "Ġco lor", "Ġcol or", "Ġ report", "Ġre port", "Ġrep ort", "Ġrepo rt", "Ġ take", "Ġt ake", "Ġta ke", "Ġtak e", "r ib", "ri b", "O M", "Ġ day", "Ġd ay", "Ġda y", "Re quest", "Req uest", "Ġ sk", "Ġs k", "b ers", "ber s", "be rs", "ĉ s", ". Add", ".A dd", ".Ad d", "o ot", "oo t", "I mage", "Im age", "Ġcom ple", "Ġcomp le", "Ġcompl e", "ol lection", "oll ection", "ollect ion", "olle ction", "Ġ top", "Ġt op", "Ġto p", "Ġ free", "Ġf ree", "Ġfr ee", "Ġfre e", "A S", "D e", "Ġ On", "ĠO n", "I G", "9 0", "e ta", "et a", "D ate", "Da te", "Dat e", "Ġ action", "Ġa ction", "Ġact ion", "Ġac tion", "3 4", "O ver", "i tor", "it or", "ito r", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "n ot", "no t", "Ġ index", "Ġin dex", "Ġind ex", "Ġinde x", "h er", "he r", "i con", "ic on", "ico n", "O n", "; čĊčĊ", ";čĊ čĊ", "iv ity", "ivi ty", "m and", "man d", "ma nd", ". Windows", ".W indows", ".Window s", "O L", "Ġ real", "Ġre al", "Ġ max", "Ġm ax", "Ġma x", "l and", "la nd", "lan d", ". ...", ".. ..", "... .", "r aph", "ra ph", "rap h", "Ġ build", "Ġb uild", "Ġbu ild", "l eg", "le g", "as sword", "ass word", "? ĊĊ", "?Ċ Ċ", "âĢ ¦", "o ok", "oo k", "u ck", "uc k", "Ġ message", "Ġm essage", "Ġmess age", "Ġmes sage", "t est", "te st", "tes t", "i vers", "iv ers", "ive rs", "iver s", "3 8", "Ġ input", "Ġin put", "Ġinp ut", "Ġ art", "Ġa rt", "Ġar t", "Ġ between", "Ġb etween", "Ġbet ween", "G et", "Ge t", "en ter", "ent er", "ente r", "g round", "gr ound", "gro und", "e ne", "en e", "à ¡", ". length", ".l ength", ".len gth", ".le ngth", "N ode", "No de", "( i", "C lass", "Cl ass", "Cla ss", "f or", "fo r", "Ġ âĢĶ", "ĠâĢ Ķ", "t en", "te n", "o in", "oi n", "Ġ ke", "Ġk e", "u i", "Ġ IN", "ĠI N", "Ġ table", "Ġt able", "Ġtab le", "Ġta ble", "s ub", "su b", "Ġ Le", "ĠL e", "Ġ head", "Ġh ead", "Ġhe ad", "Ġ must", "Ġm ust", "Ġmus t", "Ġmu st", "//// ////////////", "//////// ////////", "//////////// ////", ". util", ".u til", ".ut il", "Cont ext", "Con text", "Ġ order", "Ġor der", "Ġord er", "Ġ mov", "Ġm ov", "Ġmo v", "o ver", "ov er", "ove r", "Ġ contin", "Ġcon tin", "Ġcont in", "Ġ say", "Ġs ay", "Ġsa y", "st atic", "stat ic", "sta tic", ". Text", ".T ext", ".Te xt", "Ġ className", "Ġclass Name", "p any", "pan y", "pa ny", "Ġ ter", "Ġt er", "Ġte r", "h ead", "he ad", "hea d", "r g", "Ġ product", "Ġpro duct", "Ġprodu ct", "Ġprod uct", "T his", "Th is", ". âĢĿ", "Ġ But", "ĠB ut", "ĠBu t", "7 0", "l oy", "lo y", "Ġ double", "Ġd ouble", "Ġdo uble", "Ġdoub le", "Ġdou ble", "s g", "Ġ place", "Ġp lace", "Ġpl ace", "Ġplac e", "Ġpla ce", ". x", "m essage", "mes sage", "mess age", "Ġ information", "Ġin formation", "Ġinform ation", "Ġinformat ion", "pr ivate", "priv ate", "Ġ oper", "Ġo per", "Ġop er", "c ed", "ce d", "d b", "\" > < /", "P aram", "Par am", "Pa ram", "Para m", "i cle", "ic le", "icl e", "Ġ week", "Ġwe ek", "Ġwee k", "Ġ prop", "Ġp rop", "Ġpro p", "Ġpr op", "t able", "ta ble", "tab le", "tabl e", "id get", "idge t", "p lace", "pl ace", "pla ce", "P rop", "Pro p", "Pr op", "Ġ All", "ĠA ll", "ĠAl l", "e ls", "el s", "b ox", "bo x", ". ĊĊĊĊ", ".ĊĊ ĊĊ", ".Ċ ĊĊĊ", ".ĊĊĊ Ċ", ". R", "Ġ To", "ĠT o", "i ter", "it er", "ite r", "S h", "u ration", "ur ation", "ura tion", "urat ion", "o lder", "ol der", "old er", "_ list", "_l ist", "_li st", "c ome", "com e", "co me", "Ġ sw", "Ġs w", "iz ation", "iza tion", "ĉ for", "ĉf or", "b l", "Ġ program", "Ġp rogram", "Ġpro gram", "Ġpr ogram", "Ġprog ram", "( e", "a pe", "ap e", "c heck", "ch eck", "che ck", ". Forms", ".Form s", ".For ms", "Ġ und", "Ġu nd", "Ġun d", "ateg ory", "ategor y", "atego ry", "7 5", "a gs", "ag s", "Ġ response", "Ġres ponse", "Ġrespons e", "Ġresp onse", "U S", "re quest", "req uest", "requ est", "Ġ struct", "Ġstr uct", "Ġstru ct", "e scription", "es cription", "Ġ code", "Ġc ode", "Ġco de", "Ġcod e", "_ H", "u ffer", "uff er", "uf fer", "Ġ without", "Ġwith out", "lo bal", "lob al", "M anager", "Man ager", "Manage r", "Mana ger", "il ter", "ilt er", "P O", "ĉ this", "ĉt his", "ĉth is", "o ption", "op tion", "opt ion", "Ġ sol", "Ġs ol", "Ġso l", "Ġ ===", "Ġ= ==", "Ġ== =", "a kes", "ak es", "ake s", "Cont roller", "Control ler", "Contr oller", "4 4", "M essage", "Mes sage", "Mess age", "Ġ ref", "Ġre f", "Ġr ef", "e ver", "ev er", "eve r", "Ġ So", "ĠS o", "a ining", "ain ing", "ai ning", ". append", ".app end", ".ap pend", "Ġ still", "Ġst ill", "Ġstil l", "Ġpro vid", "Ġpr ovid", "Ġprov id", "Ġ assert", "Ġas sert", "Ġass ert", "m ed", "me d", "Ġ cap", "Ġc ap", "Ġca p", "us iness", "usi ness", "Ġ rep", "Ġre p", "Ġr ep", "t ings", "ting s", "tin gs", "v ed", "ve d", ". N", "a pi", "ap i", "O D", "Ġ field", "Ġf ield", "Ġfi eld", "Ġfie ld", "i ven", "iv en", "ive n", "o to", "ot o", "âĢ ľ", "c ol", "co l", "( x", "g ht", "gh t", "Res ult", "C ode", "Co de", "Cod e", ". is", ".i s", "l ink", "li nk", "lin k", "Ġ cour", "Ġc our", "Ġco ur", "Ġcou r", "A n", "Ġ team", "Ġt eam", "Ġte am", "Ġtea m", "ĉ int", "ĉi nt", "ĉin t", "i ft", "if t", "5 5", "Ġ second", "Ġse cond", "Ġsec ond", "Ġ going", "Ġgo ing", "Ġ range", "Ġr ange", "Ġran ge", "Ġra nge", "Ġrang e", "_ E", "n ess", "ne ss", "nes s", "3 9", "Ġf am", "Ġfa m", "Ġ nil", "Ġn il", "Ġni l", "Ġ Cont", "ĠC ont", "ĠCon t", "ĠCo nt", "ail able", "u tes", "ut es", "ute s", "a tab", "at ab", "ata b", "Ġ fact", "Ġf act", "Ġfa ct", "Ġfac t", "Ġ vis", "Ġv is", "Ġvi s", "( &", "Ġ AN", "ĠA N", "3 1", "A l", "t itle", "ti tle", "tit le", "Ġ android", "Ġand roid", "C E", "\\ \"", "i rt", "ir t", "Ġw rit", "Ġwr it", "Ð ½", "ĉ m", "ft ware", "o nd", "on d", "Ġ ret", "Ġre t", "Ġr et", "os ition", "osi tion", "osit ion", "Ġ home", "Ġh ome", "Ġhom e", "Ġho me", "Ġ left", "Ġl eft", "Ġle ft", "ar gs", "arg s", "m eric", "mer ic", "me ric", "4 8", "Ġ direct", "Ġd irect", "Ġdi rect", "Ġdir ect", "Ġdire ct", "o ci", "oc i", "P l", "A s", "r et", "re t", "a do", "ad o", "O f", "c hn", "ch n", "Ġ Get", "ĠG et", "ĠGe t", "e e", "r oss", "ro ss", "ros s", "( );", "() ;", "_ ___", "__ __", "___ _", ". ph", ".p h", "I t", "o ute", "ou te", "out e", "Ġex per", "Ġexp er", "ch ool", "cho ol", "w ww", "ww w", "} ,", "Ġ allow", "Ġal low", "Ġall ow", "Ġallo w", "Ġ Â", "( ))", "() )", "s ize", "si ze", "siz e", "i sm", "is m", "a i", "t ract", "tr act", "tra ct", "a ne", "an e", ". ..ĊĊ", ".. .ĊĊ", "... ĊĊ", "...Ċ Ċ", "con text", "cont ext", "conte xt", "Ġ beg", "Ġb eg", "Ġbe g", "C H", "Ġ page", "Ġp age", "Ġpa ge", "Ġpag e", "h ip", "hi p", "n o", "c ore", "co re", "cor e", "s p", "Ġ different", "Ġd ifferent", "Ġdiffer ent", "i able", "ia ble", "Ġ Me", "ĠM e", "_ IN", "_I N", "b utton", "but ton", "butt on", "Ġ Is", "ĠI s", "erv ices", "ervice s", "Ġ ca", "Ġc a", "Ġ around", "Ġa round", "Ġar ound", "Ġarou nd", "A pp", "Ap p", "r ation", "ra tion", "rat ion", "ratio n", "Ġ rece", "Ġre ce", "Ġr ece", "Ġrec e", "Ġ really", "Ġre ally", "Ġreal ly", "Ġ image", "Ġi mage", "Ġim age", "Ġimag e", "Ġima ge", "Ġ target", "Ġt arget", "Ġtar get", "Ġtarg et", "Ġ dep", "Ġd ep", "Ġde p", "opy right", "t ra", "tr a", "i ngle", "in gle", "ing le", "i tal", "it al", "ita l", "L ayout", "Ġ both", "Ġb oth", "Ġbo th", "Ġbot h", "Over ride", "a rm", "ar m", "= >", "at erial", "ate rial", "ater ial", "ateria l", "i led", "il ed", "ile d", "Ġ put", "Ġp ut", "Ġpu t", "Q u", "Ñ Ģ", "u ng", "un g", "m ap", "ma p", "ĉ ĉĉĉĉĉĉĉ", "ĉĉ ĉĉĉĉĉĉ", "ĉĉĉĉ ĉĉĉĉ", "ĉĉĉ ĉĉĉĉĉ", "ĉĉĉĉĉ ĉĉĉ", "ĉĉĉĉĉĉ ĉĉ", "ĉĉĉĉĉĉĉ ĉ", "Ġ level", "Ġle vel", "Ġlev el", "Ġleve l", "Com ponent", "Comp onent", "b ook", "bo ok", "boo k", "c reen", "cre en", "cr een", "_ RE", "_R E", "Ġ config", "Ġcon fig", "Ġconf ig", "ã ģ", "O r", ". data", ".d ata", ".dat a", ".da ta", "Ġ document", "Ġd ocument", "Ġdoc ument", "\" ,\"", "\", \"", "trib ute", "u x", "L og", "Lo g", "f erence", "fer ence", "fe rence", "p ost", "pos t", "po st", "_ e", "Ġ local", "Ġl ocal", "Ġlo cal", "Ġloc al", "an dom", "and om", "ando m", "as sert", "ass ert", "asse rt", "asser t", "V al", "Va l", "l ected", "lect ed", "lec ted", "i na", "in a", "at abase", "ata base", "atab ase", "A dd", "Ad d", "Ġ content", "Ġc ontent", "Ġcon tent", "Ġcont ent", "Ġconten t", "Ġconte nt", ". print", ".p rint", ".pr int", "s igned", "sign ed", "sig ned", "r ic", "ri c", ". \"ĊĊ", ".\" ĊĊ", ".\"Ċ Ċ", "Ġ fa", "Ġf a", "! ĊĊ", "!Ċ Ċ", "- f", "i ved", "iv ed", "ive d", "Ġ quest", "Ġqu est", "Ġque st", "Ġq uest", "Ġques t", ". ex", ".e x", "Ġ float", "Ġf loat", "Ġflo at", "Ġ develop", "Ġde velop", "Ġdev elop", "Ġdeve lop", "Ġdevel op", "о Ð", "M ap", "Ma p", "a ding", "ad ing", "adi ng", "adin g", "Ġ poss", "Ġp oss", "Ġpos s", "Ġpo ss", "U E", "n amespace", "name space", "names pace", "_ O", "ĉ b", ". Get", ".G et", ".Ge t", "> (", "j son", "js on", "e tails", "et ails", "etail s", "eta ils", "6 6", "Ġ too", "Ġt oo", "Ġto o", "Ġ extends", "Ġext ends", "Ġextend s", "Ġ None", "ĠN one", "ĠNo ne", "ĠNon e", "Ġ fore", "Ġf ore", "Ġfor e", "Ġfo re", "( String", "(S tring", "(Str ing", "form at", "for mat", "forma t", "Ġ great", "Ġg reat", "Ġgr eat", "Ġgre at", "in ter", "int er", "inte r", "c ale", "ca le", "cal e", "Ñ ģ", "r on", "ro n", "i ving", "iv ing", "ivi ng", "E nt", "En t", "e ncy", "en cy", "enc y", "x t", "o y", "0 5", "Ġ month", "Ġm onth", "Ġmon th", "Ġmo nth", "Ġmont h", "Ġh app", "Ġha pp", "Ġhap p", "Ġ super", "Ġs uper", "Ġsu per", "Ġsup er", "b ar", "ba r", "d efault", "de fault", "def ault", "_ de", "_d e", "or ds", "ord s", "l n", "( {Ċ", "({ Ċ", "Ġ Ind", "ĠI nd", "ĠIn d", "a ses", "as es", "ase s", "Ġ title", "Ġt itle", "Ġtit le", "Ġti tle", "Ġ context", "Ġcon text", "Ġcont ext", "Ġconte xt", "0 8", "o h", "- p", "E m", "Ġ met", "Ġm et", "Ġme t", "T est", "Te st", "Tes t", "Ġ life", "Ġl ife", "Ġli fe", "Ġlif e", "_ v", "Ġ US", "ĠU S", "U I", "o cation", "oc ation", "oca tion", "m d", "Ġ [Ċ", "Ġ[ Ċ", "Ġ ]", "s w", "Ġ incre", "Ġin cre", "Ġinc re", "Ġincr e", "s cript", "scri pt", "scr ipt", "ent ial", "enti al", "w ays", "way s", "wa ys", ". de", ".d e", "Ġ src", "Ġs rc", "Ġsr c", "Ġ catch", "Ġc atch", "Ġcat ch", "Ġ Americ", "ĠA meric", "ĠAm eric", "ĠAmer ic", "/ /Ċ", "// Ċ", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "Ġ pay", "Ġp ay", "Ġpa y", "p lit", "pl it", "âĢ Ķ", "Ġc oun", "Ġco un", "Ġcou n", "o bj", "ob j", ". php", ".p hp", ".ph p", "Ġ change", "Ġch ange", "Ġchang e", "Ġcha nge", "Ġchan ge", "e thing", "eth ing", "' re", "'r e", "a ster", "as ter", "ast er", "aste r", "l os", "lo s", "l ation", "la tion", "lat ion", "Ġ ĠĊ", "ĠĠ Ċ", "L e", "à ¤", "( {", "re ady", "read y", "rea dy", "Ġ No", "ĠN o", "Ġ position", "Ġp osition", "Ġpos ition", "Ġposit ion", "Ġposi tion", "Ġ old", "Ġo ld", "Ġol d", "Ġ book", "Ġb ook", "Ġbo ok", "Ġboo k", "a bled", "ab led", "able d", "abl ed", "b ug", "bu g", "2 02", "20 2", "H and", "Ha nd", "Han d", "} ;ĊĊ", "};Ċ Ċ", "}; ĊĊ", "is play", "isp lay", "a ving", "av ing", "avi ng", "avin g", "0 4", "Ġg over", "Ġgo ver", "Ġgov er", "Ġ version", "Ġv ersion", "Ġvers ion", "S ystem", "Sys tem", "Sy stem", "n ect", "ne ct", "res ponse", "resp onse", "respons e", "St yle", "U p", "an gu", "ang u", "Ġ three", "Ġth ree", "Ġthr ee", "i nit", "in it", "ini t", "e ro", "er o", "Ġ law", "Ġl aw", "Ġla w", "en dif", "end if", "endi f", "Ġ base", "Ġb ase", "Ġbas e", "Ġba se", "e mail", "em ail", "ema il", "( l", "_ V", "Ġ conf", "Ġcon f", "Ġco nf", "A TE", "AT E", "Ġ during", "Ġd uring", "Ġdu ring", "Ġdur ing", "t es", "te s", "Ġ console", "Ġcon sole", "Ġcons ole", "Ġ Pr", "ĠP r", "Ġ spe", "Ġs pe", "Ġsp e", "v es", "ve s", "6 5", "p ath", "pat h", "pa th", "i alog", "ial og", "ia log", "d ition", "di tion", "dit ion", "_ to", "_t o", "ar ds", "ard s", "Ġ against", "Ġagain st", "et work", "Ġ Ph", "ĠP h", "_ L", "c ur", "cu r", "i mit", "im it", "imi t", "W ith", "Wi th", "Ġ power", "Ġp ower", "Ġpo wer", "Ġpow er", "i um", "iu m", "' ;ĊĊ", "';Ċ Ċ", "'; ĊĊ", "Ġw om", "Ġwo m", "l eft", "le ft", "lef t", "our ces", "ource s", "a tri", "at ri", "atr i", "Ġ Im", "ĠI m", "Ġ Man", "ĠM an", "ĠMa n", "or th", "ort h", "$ {", "8 8", "qu als", "qual s", "qua ls", "e se", "es e", "_ size", "_s ize", "_si ze", "Ġ iss", "Ġis s", "Ġi ss", "o tal", "ot al", "ota l", "- g", "i que", "iqu e", "iq ue", "r ame", "ra me", "ram e", "Ġ width", "Ġw idth", "Ġwid th", "e rg", "er g", ") (", "it tle", "itt le", "T R", "Ġ They", "ĠT hey", "ĠThe y", "ĠTh ey", "e nces", "en ces", "ence s", "enc es", "0 2", "r l", "o ns", "on s", "Ġ label", "Ġl abel", "Ġla bel", "Ġlab el", ". y", "- t", "up date", "upd ate", "a nel", "an el", "ane l", "s c", ". to", ".t o", "Ġ project", "Ġpro ject", "Ġproj ect", "Ġproje ct", "à ¼", "Ġ element", "Ġe lement", "Ġel ement", "Ġele ment", "Ġelem ent", "Ġ success", "Ġs uccess", "Ġsu ccess", "Ġsuc cess", "Ġsucc ess", "Ġsucces s", "ĉ ĉĊ", "ĉĉ Ċ", ". sh", ".s h", "r am", "ra m", "c hed", "ch ed", "che d", "( ))Ċ", "() )Ċ", "()) Ċ", "Ġ (Ċ", "Ġ( Ċ", "Ġ date", "Ġd ate", "Ġda te", "Ġdat e", "Ġ tot", "Ġt ot", "Ġto t", "_ ST", "_S T", "A ll", "Al l", "if ication", "ific ation", "ifi cation", "ifica tion", "ĉ var", "ĉv ar", "ĉva r", "Ġ tri", "Ġt ri", "Ġtr i", "c hem", "ch em", "che m", "m y", "Ġ big", "Ġb ig", "Ġbi g", "Ġ Ad", "ĠA d", "Ġ At", "ĠA t", "o ts", "ot s", "n um", "nu m", "A ct", "Ac t", "Ġ map", "Ġm ap", "Ġma p", "e ra", "er a", "c ope", "co pe", "cop e", ". $", ", âĢĿ", "Ġ pop", "Ġp op", "Ġpo p", "Ġ few", "Ġf ew", "Ġfe w", "Ġ len", "Ġl en", "Ġle n", "u id", "ui d", "e ters", "et ers", "eter s", "ete rs", "u les", "ul es", "ule s", "à Ń", "s ource", "ht tps", "http s", "htt ps", "Ġ dem", "Ġd em", "Ġde m", "Ġ ear", "Ġe ar", "Ġea r", "#### ############", "######## ########", "############ ####", "Ġ match", "Ġm atch", "Ġmat ch", "o ries", "or ies", "ori es", "orie s", "4 9", "a ces", "ace s", "ac es", "Ġ Cl", "ĠC l", "Ġ node", "Ġn ode", "Ġno de", "Ġnod e", "7 8", "i rc", "ir c", "l ocal", "lo cal", "loc al", "un ity", "unit y", "uni ty", "} ;Ċ", "}; Ċ", "Ġ another", "Ġan other", "Ġano ther", "< <", "o gle", "og le", "ogl e", "Ġ sit", "Ġs it", "Ġsi t", "e work", "ew ork", "T E", ". I", "N S", "o logy", "ol ogy", "olog y", "olo gy", "o ught", "ou ght", "ough t", "oug ht", ". Cont", ".C ont", ".Con t", ".Co nt", "> >", "Ġ care", "Ġc are", "Ġcar e", "Ġca re", "st ate", "stat e", "sta te", "ĉ private", "ĉpr ivate", "ĉpriv ate", "Ġ effect", "Ġe ffect", "Ġeff ect", "Ġef fect", "+ +)", "++ )", "_ file", "_f ile", "_fil e", "en ding", "end ing", "endi ng", "L ine", "Li ne", "Lin e", "F or", "Fo r", "i or", "io r", "Ġ Sc", "ĠS c", "Ġ fun", "Ġf un", "Ġfu n", ". Size", ".S ize", "ĉ else", "ĉe lse", "ĉel se", "] )", "st art", "star t", "sta rt", "v ious", "vi ous", "vio us", "Ġ },", "Ġ} ,", "o urs", "ou rs", "our s", "Ġ leg", "Ġl eg", "Ġle g", "Ġ service", "Ġs ervice", "Ġser vice", "Ġserv ice", "Ġservi ce", "Ġservic e", "Ġ since", "Ġs ince", "Ġsi nce", "Ġsin ce", "Ġsinc e", "i ron", "ir on", "iro n", "L abel", "La bel", "Lab el", "Ġ non", "Ġn on", "Ġno n", "Ġ los", "Ġl os", "Ġlo s", "i ction", "ic tion", "ict ion", "Ġ full", "Ġf ull", "Ġful l", "Ġfu ll", "a cter", "act er", "ac ter", "b oard", "bo ard", "boa rd", "g ress", "gr ess", "gre ss", "gres s", "Ġ turn", "Ġt urn", "Ġtu rn", "Ġtur n", "i ther", "it her", "ith er", "ithe r", "0 9", ". size", ".s ize", ".si ze", "Ġ body", "Ġb ody", "Ġbo dy", "Ġbod y", "r esh", "re sh", "res h", "e turn", "et urn", "etur n", "etu rn", "1 99", "19 9", "( _", "y les", "yle s", "yl es", "or mal", "orm al", "p i", "Ġ something", "Ġs omething", "Ġsome thing", "Ġsom ething", "! --", "u int", "ui nt", "uin t", "Ġ produ", "Ġp rodu", "Ġpro du", "Ġpr odu", "Ġprod u", "Ġ stand", "Ġst and", "Ġsta nd", "Ġstan d", "Ġpro ble", "Ġpr oble", "Ġprob le", "Ġprobl e", "Ġ available", "Ġa vailable", "Ġav ailable", "Ġavail able", "m t", "Ġ Bl", "ĠB l", "Ġ ...", "Ġ. ..", "Ġ.. .", "Ġ block", "Ġb lock", "Ġbl ock", "Ġblo ck", "Ġbloc k", "In put", "Ġ keep", "Ġke ep", "C ount", "Co unt", "Cou nt", "o pen", "op en", "ope n", "Ġ ['", "Ġ[ '", "Ġ throw", "Ġth row", "Ġthr ow", "Ġthro w", "u ilder", "uild er", "ui lder", "uil der", "A ction", "Act ion", "Ac tion", "Ġ things", "Ġth ings", "Ġthing s", "Ġthin gs", "Tr ue", "Ġ url", "Ġu rl", "Ġur l", "Ġ Bo", "ĠB o", "print f", "Ġ red", "Ġre d", "Ġr ed", "j s", ". create", ".c reate", "Ġ Or", "ĠO r", "S tatus", "St atus", "Stat us", "In stance", "Inst ance", "Ġ control", "Ġc ontrol", "Ġcont rol", "Ġcontr ol", "Ġcontro l", "Ġ come", "Ġc ome", "Ġcom e", "Ġco me", "Ġ custom", "Ġc ustom", "Ġcust om", "Ġcus tom", "l ocation", "lo cation", "loc ation", "0 7", "m odel", "mod el", "mo del", "mode l", "Ġ čĊ", "Ġč Ċ", "Ġ source", "Ġs ource", "Ġsour ce", "Ġe as", "Ġea s", ". out", ".o ut", "] ĊĊ", "]Ċ Ċ", "o ney", "on ey", "one y", "Ġ await", "Ġa wait", "Ġaw ait", "Ġp artic", "Ġpart ic", "Ġpar tic", "Ġparti c", "A P", "ub lish", "ubl ish", "o des", "od es", "ode s", "_ pro", "_p ro", "_pr o", "p ly", "pl y", "r iter", "ri ter", "rit er", "rite r", "Ġ prov", "Ġp rov", "Ġpro v", "Ġpr ov", "Ġ mill", "Ġm ill", "Ġmil l", "Ġmi ll", "H T", "] )Ċ", "]) Ċ", "Ġ chang", "Ġc hang", "Ġch ang", "Ġcha ng", "Ġchan g", "Ġ ask", "Ġa sk", "Ġas k", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "Ġ output", "Ġout put", "Ġ email", "Ġe mail", "Ġem ail", "6 8", ". push", ".p ush", "Ġ }čĊčĊ", "Ġ} čĊčĊ", "Ġ}čĊ čĊ", "i nation", "in ation", "ina tion", "inati on", "4 7", "at rix", "atri x", "atr ix", "T able", "Tab le", "Ta ble", "u ccess", "uc cess", "ucc ess", "] );Ċ", "]) ;Ċ", "]); Ċ", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġ disc", "Ġd isc", "Ġdis c", "Ġdi sc", "( [", "Ġ business", "Ġb usiness", "Ġbus iness", "h eight", "he ight", "hei ght", ". html", ".h tml", ".ht ml", ".htm l", "t a", "f ield", "fi eld", "Ġ required", "Ġre quired", "Ġrequire d", "Ġrequ ired", "_ R", "Ġg overn", "Ġgo vern", "Ġgover n", "Ġgov ern", "} čĊčĊ", "}čĊ čĊ", "l ex", "le x", "5 00", "50 0", ". ,", "Ġ Set", "ĠS et", "ĠSe t", "u rch", "ur ch", "/ //", "// /", "t s", "a f", "Ġ might", "Ġm ight", "Ġmi ght", "Ġmig ht", "i story", "ist ory", "istor y", "isto ry", "S tr", "St r", "Ġ never", "Ġn ever", "Ġne ver", "Ġnev er", "Res ponse", "Resp onse", "Respons e", "ar se", "ars e", "a da", "ad a", "Ġ How", "ĠH ow", "ĠHo w", "Ġ *)", "Ġ* )", "Ġ ;", "Ġ hard", "Ġh ard", "Ġhar d", "Ġha rd", "A d", "Ġ intern", "Ġin tern", "Ġint ern", "Ġinter n", "Ġinte rn", "u sed", "us ed", "use d", "( data", "(d ata", "(dat a", "(da ta", "m od", "mo d", "an nel", "ann el", "anne l", "Ġ np", "Ġn p", "u gg", "ug g", "Ġ />Ċ", "Ġ/ >Ċ", "Ġ/> Ċ", "Ġ called", "Ġc alled", "Ġcall ed", "Ġcal led", "Ġcalle d", "b ody", "bo dy", "Ġ cho", "Ġc ho", "Ġch o", "( r", "_ set", "_s et", "_se t", "i rd", "ir d", "Ġ >=", "Ġ> =", "Ġ };Ċ", "Ġ} ;Ċ", "Ġ}; Ċ", "Ġ options", "Ġo ptions", "Ġoption s", "Ġopt ions", "Ġ Gener", "ĠG ener", "ĠGe ner", "ĠGen er", "ĠGene r", "Ġ height", "Ġh eight", "Ġhe ight", "Ġhei ght", "P oint", "Po int", "Y ou", "Yo u", "e ty", "et y", "C lick", "Cl ick", "Cli ck", "Ġ small", "Ġs mall", "Ġsm all", "Ġ ide", "Ġi de", "Ġid e", "Ġ access", "Ġa ccess", "Ġacc ess", "Ġac cess", "Ġacces s", "angu age", "Ġ protected", "Ġprot ected", "Ġprotect ed", "Ġ job", "Ġj ob", "Ġjo b", "Ġ There", "ĠT here", "ĠThe re", "ĠTh ere", "ĠTher e", "D ef", "De f", "Ġ address", "Ġadd ress", "Ġad dress", "Ġaddr ess", "Ġ uint", "Ġu int", "Ġui nt", "N ot", "No t", "o o", "a ps", "ap s", "< div", "", "() ->", "()- >", "ĉ ĠĠĠ", "ĉĠ ĠĠ", "ĉĠĠ Ġ", "\" ))", "\") )", "C ontent", "Cont ent", "Con tent", "_ W", "p lement", "pl ement", "ple ment", "Ġ won", "Ġw on", "Ġwo n", "Ġ video", "Ġv ideo", "Ġvi deo", "Ġvid eo", "Ġvide o", "a di", "ad i", "p oint", "po int", "poi nt", "% %", "0 3", "Ġ gl", "Ġg l", "er ved", "erv ed", "erve d", "v iron", "vi ron", "vir on", "I F", "u ted", "ut ed", "ute d", "ã ĥ", "' m", "Ġ cert", "Ġc ert", "Ġce rt", "Ġcer t", "Ġ prof", "Ġp rof", "Ġpro f", "Ġpr of", "Ġ cell", "Ġc ell", "Ġce ll", "Ġcel l", "a ri", "ar i", "Ġ player", "Ġp layer", "Ġpl ayer", "Ġplay er", "Ġpla yer", "a is", "ai s", "Ġ cost", "Ġc ost", "Ġco st", "Ġcos t", "Ġ hum", "Ġh um", "Ġhu m", "( R", "Ġo ffic", "Ġof fic", "Ġoff ic", "k s", ". text", ".t ext", ".te xt", ".tex t", "a tures", "at ures", "ature s", "atur es", "atu res", "Ġ total", "Ġt otal", "Ġto tal", "Ġtot al", "Ġ */ĊĊ", "Ġ* /ĊĊ", "Ġ*/ ĊĊ", "Ġ*/Ċ Ċ", "o pe", "op e", "Ġ stat", "Ġs tat", "Ġst at", "Ġsta t", "U M", "Ġ load", "Ġl oad", "Ġlo ad", "Ġloa d", "ight s", "igh ts", "Ġ clear", "Ġc lear", "Ġcl ear", "Ġcle ar", "u ro", "ur o", "Ġ techn", "Ġt echn", "Ġte chn", "Ġtech n", "Ġtec hn", "up port", "upp ort", "I R", "Ġ row", "Ġr ow", "Ġro w", "Ġse em", "Ġsee m", "Ġ q", "Ġ short", "Ġs hort", "Ġsh ort", "Ġsho rt", "Ġ Not", "ĠN ot", "ĠNo t", "i pp", "ip p", "G roup", "Gr oup", "Gro up", "s ection", "se ction", "sec tion", "sect ion", "m ax", "ma x", "i rl", "ir l", "Ġ override", "Ġover ride", "Ġ company", "Ġcom pany", "Ġcomp any", "Ġcompan y", "Ġ done", "Ġd one", "Ġdo ne", "Ġdon e", "\" );čĊ", "\") ;čĊ", "\"); čĊ", "Ġ gre", "Ġg re", "Ġgr e", ". Re", ".R e", "Ġ belie", "Ġbe lie", "Ġbel ie", "r ist", "ri st", "ris t", "Ġ health", "Ġhe alth", "Ġheal th", "A NT", "AN T", "( )ĊĊ", "() ĊĊ", "()Ċ Ċ", "Ġ Be", "ĠB e", ". value", ".v alue", ".val ue", ".va lue", "Ġ Gr", "ĠG r", "ot tom", "ott om", "otto m", "Ġ args", "Ġar gs", "Ġarg s", "P T", "s tatus", "st atus", "stat us", "f unc", "fun c", "fu nc", "u ments", "um ents", "ument s", "ume nts", "umen ts", "- h", "N umber", "Num ber", ": čĊ", "Ġ Log", "ĠL og", "ĠLo g", "er ver", "erv er", "erve r", "Ġ ),Ċ", "Ġ) ,Ċ", "Ġ), Ċ", "a ment", "am ent", "ame nt", "amen t", "Ġ obj", "Ġo bj", "Ġob j", "i nc", "in c", "Ġ children", "Ġch ildren", "Ġchild ren", "i cy", "ic y", "I Z", "a nds", "an ds", "and s", "ab ly", "abl y", "Ġd istrib", "Ġdis trib", "Ġdist rib", "Ġdistr ib", "Ġ cur", "Ġc ur", "Ġcu r", "e rial", "er ial", "eri al", "eria l", "Ġ days", "Ġd ays", "Ġday s", "Ġda ys", "r eated", "re ated", "reate d", "reat ed", "rea ted", "r ect", "re ct", "rec t", "- l", "i rm", "ir m", "i dden", "id den", "idd en", "o mb", "om b", "Ġ initial", "Ġin itial", "Ġinit ial", "Ġiniti al", ". js", ".j s", "Ġ â", "Qu ery", "Que ry", "Ġ online", "Ġon line", "i mal", "im al", "ima l", ". con", ".c on", ".co n", "a u", "U rl", "Ur l", "c ontrol", "cont rol", "contr ol", "contro l", "i rection", "ir ection", "ire ction", "irect ion", "Ġ instance", "Ġin stance", "Ġinst ance", "O RT", "OR T", "Ġ Fr", "ĠF r", "w here", "wh ere", "Ġ javax", "Ġj avax", "Ġjav ax", "Ġjava x", "Ġ organ", "Ġo rgan", "Ġor gan", "Ġorg an", "a pter", "ap ter", "apt er", "Ġ reason", "Ġre ason", "o ptions", "option s", "opt ions", "5 9", "Ġ Mar", "ĠM ar", "ĠMa r", "( a", "Ġ within", "Ġwith in", "Ġwi thin", "Ġwit hin", ". âĢĿĊĊ", ".âĢĿ ĊĊ", ".âĢĿĊ Ċ", "O DE", "OD E", "_ DE", "_D E", "ad min", "adm in", "en ded", "end ed", "ende d", "Ġ design", "Ġd esign", "Ġde sign", "Ġdes ign", "Ġdesi gn", "Ġ Data", "ĠD ata", "ĠDa ta", "ĠDat a", "u ne", "un e", "Ġ File", "ĠF ile", "ĠFil e", "ĠFi le", "r oot", "ro ot", "Ġ cent", "Ġc ent", "Ġce nt", "Ġcen t", "Ġ arr", "Ġa rr", "Ġar r", "_ add", "_a dd", "_ad d", "l en", "le n", "p age", "pa ge", "pag e", ", '", "_ str", "_s tr", "_st r", "Ġ bro", "Ġb ro", "Ġbr o", "ab ility", "abil ity", "abilit y", "o uth", "ou th", "out h", "5 8", "/ c", "p ose", "pos e", "po se", "ir tual", "irt ual", "e arch", "ear ch", "ea rch", "_ url", "_u rl", "_ur l", "ar gin", "arg in", "H ttp", "Ġ school", "Ġs chool", "Ġsch ool", "Ġscho ol", "a va", "av a", "Ġ consider", "Ġcons ider", "Ġconsid er", ". label", ".l abel", ".lab el", "Ġ Array", "ĠA rray", "ĠAr ray", "ĠArr ay", "4 2", "w eb", "we b", "o pt", "op t", ". println", ".print ln", "u lation", "ul ation", "ula tion", "Ġ func", "Ġf unc", "Ġfun c", "Ġfu nc", "P L", "Ġ \"\\", "Ġ\" \\", "Ġ Text", "ĠT ext", "ĠTe xt", "ĠTex t", "act ory", "actor y", "( function", "(f unction", "(func tion", "(fun ction", "n ull", "nu ll", "nul l", "Ġ eng", "Ġe ng", "Ġen g", "d own", "do wn", "Ġ include", "Ġin clude", "Ġinclud e", "Ġinc lude", "Ġincl ude", "Ġinclu de", "Ġ En", "ĠE n", "Ġ Dr", "ĠD r", "Ġ db", "Ġd b", "! !", "s ide", "si de", "sid e", "Ġ init", "Ġin it", "Ġi nit", "Ġini t", "qu ired", "quire d", "quir ed", "qui red", "Ġ She", "ĠS he", "ĠSh e", "C olumn", "Col umn", "re act", "rea ct", "Ġ ann", "Ġa nn", "Ġan n", "Ġ stop", "Ġs top", "Ġst op", "Ġsto p", "Ġ later", "Ġl ater", "Ġla ter", "Ġlate r", "Ġlat er", "Ġ That", "ĠT hat", "ĠTh at", "en tion", "ent ion", "enti on", "d f", "U G", "I LE", "IL E", "Ġ client", "Ġc lient", "Ġcl ient", "Ġcli ent", "r aft", "ra ft", "raf t", "f fer", "ff er", "ffe r", "P OST", "PO ST", "POS T", "el per", "elp er", "Ġ love", "Ġl ove", "Ġlo ve", "Ġlov e", "qu ote", "quot e", "quo te", "o ud", "ou d", "Ġ json", "Ġj son", "Ġjs on", "Ġ able", "Ġa ble", "Ġab le", "Ġabl e", "Ġ men", "Ġm en", "Ġme n", "A X", "Ġ Copyright", "ĠC opyright", "ĠCopy right", "à ¶", "a vig", "av ig", "avi g", "r eq", "re q", "C lient", "Cl ient", "Cli ent", "} );Ċ", "}) ;Ċ", "}); Ċ", ". Com", ".C om", ".Co m", "e rc", "er c", "i lt", "il t", "p ecial", "pe cial", "pec ial", "pecia l", "_ com", "_c om", "_co m", "r oom", "ro om", ". Name", ".N ame", "Ġ give", "Ġg ive", "Ġgi ve", "a mb", "am b", "i ke", "ik e", "Ġ condition", "Ġcon dition", "Ġcond ition", "Ġcondi tion", "c lient", "cl ient", "cli ent", "a tors", "at ors", "ator s", "ato rs", ": \"", "Ġ copy", "Ġc opy", "Ġco py", "Ġcop y", "u ture", "ut ure", "ivers ity", "iversit y", "er nal", "ern al", "erna l", "{ {", "Ġ Can", "ĠC an", "ĠCa n", "o unc", "ou nc", "oun c", "d o", "Ġ occ", "Ġo cc", "Ġoc c", "Ġ appro", "Ġapp ro", "Ġap pro", "th ers", "ther s", "the rs", "z e", "Ġ either", "Ġe ither", "Ġei ther", "Ġ Fl", "ĠF l", "Ġ important", "Ġimport ant", "Ġ lead", "Ġl ead", "Ġle ad", "at tr", "att r", "A RT", "AR T", "E qual", "Equ al", "Eq ual", "Ġ da", "Ġd a", "et ch", "etc h", "e ntity", "ent ity", "enti ty", "Ġ family", "Ġf amily", "Ġfam ily", "Ġfamil y", "ad ding", "add ing", "addin g", "Ġ option", "Ġo ption", "Ġop tion", "Ġopt ion", "Ġ exist", "Ġex ist", "i ca", "ic a", "Ġ Object", "ĠO bject", "ĠOb ject", "ĠObj ect", "6 9", "' ve", "v ers", "ver s", "ve rs", "it ional", "ition al", "iti onal", "6 7", "out put", "Ġ True", "ĠTr ue", "ĠTru e", "Ġ OF", "ĠO F", "_ time", "_t ime", "_tim e", "_ti me", "Ġ offer", "Ġo ffer", "Ġof fer", "Ġoff er", "Ġ });ĊĊ", "Ġ} );ĊĊ", "Ġ});Ċ Ċ", "Ġ}) ;ĊĊ", "Ġ}); ĊĊ", "H ER", "HE R", "e gin", "eg in", "\" \"", "Ġ water", "Ġw ater", "Ġwa ter", "Ġwat er", "Ġ che", "Ġc he", "Ġch e", "Ġ My", "ĠM y", "o red", "or ed", "ore d", "Ġ step", "Ġs tep", "Ġst ep", "Ġste p", "a nces", "an ces", "ance s", "anc es", "C K", "A Y", "à ¸", "str uction", "struct ion", "stru ction", "( C", "3 00", "30 0", "o uch", "ou ch", "St ream", "Str eam", "act ive", "activ e", "a ma", "am a", "E ntity", "Ent ity", "pro duct", "produ ct", "prod uct", "( ){Ċ", "() {Ċ", "(){ Ċ", "Ġ government", "Ġg overnment", "Ġgovern ment", "Ġ ID", "ĠI D", "aj or", "ajo r", "A nd", "An d", "Ġ display", "Ġd isplay", "Ġdis play", "Ġdisp lay", "Ġdispl ay", "Ð »", "Ġ times", "Ġt imes", "Ġtime s", "Ġtim es", "Ġti mes", "Ġ four", "Ġf our", "Ġfo ur", "Ġfou r", "Ġ far", "Ġf ar", "Ġfa r", "Ġ present", "Ġp resent", "Ġpre sent", "Ġpres ent", "Ġ NS", "ĠN S", "Ġ \\Ċ", "Ġ\\ Ċ", "u est", "ue st", "ues t", "Ġ bas", "Ġb as", "Ġba s", "e cho", "ec ho", "ech o", "ch ild", "chi ld", "if ier", "ifi er", "ifie r", "H andler", "Hand ler", "Handle r", "Ġ lib", "Ġl ib", "Ġli b", "P roperty", "Pro perty", "Prop erty", "trans lation", "Ġ room", "Ġr oom", "Ġro om", "Ġ once", "Ġo nce", "Ġon ce", "Ġonc e", "Ġ []", "Ġ[ ]", "c enter", "cent er", "cen ter", "cente r", "================ ================", "Ġ results", "Ġres ults", "Ġresult s", "Ġ continue", "Ġcont inue", "Ġcontin ue", "Ġcontinu e", "Ġ talk", "Ġt alk", "Ġtal k", "Ġta lk", "_ get", "_g et", "_ge t", "Ġ grow", "Ġg row", "Ġgr ow", "Ġgro w", ". sw", ".s w", "e b", "Ġ Public", "ĠP ublic", "ĠPub lic", "O P", "ec ute", "ecut e", "o ls", "ol s", "Ġ **", "Ġ* *", "\" );ĊĊ", "\");Ċ Ċ", "\") ;ĊĊ", "\"); ĊĊ", "Ġ mass", "Ġm ass", "Ġma ss", "Ġmas s", "u red", "ur ed", "ure d", ". class", ".c lass", ".cl ass", "o mic", "om ic", "omi c", "Ġ mean", "Ġm ean", "Ġme an", "i ps", "ip s", "Ġ aut", "Ġa ut", "Ġau t", ") ;čĊčĊ", ");čĊ čĊ", "); čĊčĊ", "Ġ until", "Ġun til", "Ġunt il", "Ġ market", "Ġm arket", "Ġmark et", "Ġmar ket", "Ġ area", "Ġa rea", "Ġare a", "Ġar ea", "u it", "ui t", "Ġ length", "Ġl ength", "Ġle ngth", "Ġlen gth", "Ġleng th", "Ġ With", "ĠW ith", "ĠWi th", "ĠWit h", "str uctor", "struct or", "stru ctor", "e vent", "ev ent", "even t", "eve nt", "\" ><", "\"> <", "Ġ Sp", "ĠS p", "I V", "Ġ mus", "Ġm us", "Ġmu s", "i ff", "if f", "Ġ kind", "Ġk ind", "Ġki nd", "Ġkin d", "a uthor", "aut hor", "auth or", "o unds", "ou nds", "ound s", "oun ds", "m b", "_ key", "_k ey", "_ke y", "4 1", "w idth", "wid th", "pos itory", "posit ory", "positor y", "Ġ light", "Ġl ight", "Ġli ght", "Ġlig ht", "u k", "R ow", "Ro w", "o hn", "oh n", "a lf", "al f", "viron ment", "a pper", "ap per", "app er", "appe r", "ol lections", "oll ections", "ollection s", "ollect ions", "olle ctions", "Ġ side", "Ġs ide", "Ġsi de", "Ġsid e", "_ info", "_in fo", "_inf o", "Ġ example", "Ġex ample", "Ġexam ple", "i mary", "im ary", "ima ry", "imar y", "Ġ wr", "Ġw r", "Ġ camp", "Ġc amp", "Ġca mp", "Ġcam p", "cri be", "cr ibe", "2 55", "25 5", "\" /", "Ġ miss", "Ġm iss", "Ġmis s", "Ġmi ss", "w ay", "wa y", "Ġ based", "Ġb ased", "Ġbase d", "Ġbas ed", "Ġba sed", "Ġ plan", "Ġp lan", "Ġpl an", "Ġpla n", "V is", "Vi s", "o main", "om ain", "oma in", "u nk", "un k", "Ġ away", "Ġa way", "Ġaw ay", "U P", "< T", "O S", "i od", "io d", "Ġ Mon", "ĠM on", "ĠMo n", "âĢĻ re", "Ġ lik", "Ġl ik", "Ġli k", "à §", "i vely", "iv ely", "ive ly", "ivel y", ". v", "i mer", "im er", "ime r", "i zer", "iz er", "ize r", "S ub", "Su b", "Ġ button", "Ġb utton", "Ġbut ton", "Ġbutt on", "Ġbutto n", "Ġ Up", "ĠU p", "Ġ experience", "Ġex perience", "Ġexper ience", "Ġexperi ence", "C L", "Ġ render", "Ġre nder", "Ġr ender", "Ġren der", "Ġrend er", "_ value", "_v alue", "_val ue", "_va lue", "Ġ near", "Ġn ear", "Ġne ar", "U RL", "UR L", "a lt", "al t", "Ġ country", "Ġc ountry", "Ġcount ry", "Ġcoun try", "ib ility", "ibil ity", "ibilit y", "ibili ty", "5 7", "( ),Ċ", "() ,Ċ", "(), Ċ", "e ad", "ea d", "Ġ author", "Ġa uthor", "Ġaut hor", "Ġauth or", "Ġ specific", "Ġs pecific", "Ġspec ific", "Ġspeci fic", "b ase", "ba se", "bas e", "( name", "(n ame", "o nes", "on es", "one s", "Ġ Do", "ĠD o", "Ġ along", "Ġa long", "Ġal ong", "Ġalo ng", "y ear", "ye ar", "Ġ express", "Ġex press", "Ġexp ress", "Ġexpr ess", "Ġexpres s", ". '", "e nv", "en v", "Ġ begin", "Ġb egin", "Ġbe gin", "Ġbeg in", "Ġ software", "Ġs oftware", "Ġso ftware", "Ġsoft ware", "Ġ imp", "Ġi mp", "Ġim p", "Ġ win", "Ġw in", "Ġwi n", "ó n", "Ġ thing", "Ġth ing", "Ġthin g", "Ġthi ng", "T rans", "Tr ans", "Tra ns", "Ġ THE", "ĠT HE", "ĠTH E", "Ġ ", "Ġ? >", "Ġ den", "Ġd en", "Ġde n", "ob ile", "obi le", "obil e", "ch ange", "chan ge", "cha nge", "chang e", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "i ci", "ic i", "n a", "Ġ Form", "ĠF orm", "ĠFor m", "ĠFo rm", "Ġ sort", "Ġs ort", "Ġso rt", "Ġsor t", "S elect", "Se lect", "Sel ect", "Sele ct", "p are", "par e", "pa re", "Ġ thought", "Ġth ought", "Ġthough t", "Ġthou ght", "Ġtho ught", "_ con", "_c on", "_co n", "Ġ task", "Ġt ask", "Ġta sk", "Ġtas k", "o cus", "oc us", "ocu s", "Ġ DE", "ĠD E", "Ġ Min", "ĠM in", "ĠMi n", "Ġ opt", "Ġo pt", "Ġop t", "ĉ break", "ĉb reak", "u mer", "um er", "ume r", "K E", "t hen", "th en", "the n", "Ġ det", "Ġd et", "Ġde t", "Ġ Test", "ĠT est", "ĠTe st", "ĠTes t", "p orts", "port s", "por ts", "po rts", "Ġ review", "Ġre view", "Ġr eview", "Ġrev iew", "( '/", "(' /", "m ove", "mo ve", "mov e", "Ġ switch", "Ġs witch", "Ġsw itch", "E RT", "ER T", "p atch", "pat ch", "an not", "ann ot", "anno t", "ã Ĥ", "Ġ above", "Ġa bove", "Ġab ove", "it ive", "iti ve", "5 6", "Ġ question", "Ġqu estion", "Ġquest ion", "Ġq uestion", "Ġquesti on", "Ġques tion", "Ġ Qu", "ĠQ u", "ãĢĤ ĊĊ", "ãĢĤĊ Ċ", "g le", "gl e", "Ġ word", "Ġw ord", "Ġwor d", "Ġwo rd", "Ġ provide", "Ġpro vide", "Ġprovid e", "Ġprov ide", "Ġ Return", "ĠR eturn", "ĠRe turn", "ĠRet urn", "Ġ research", "Ġre search", "Ġres earch", "ã o", "u str", "us tr", "ust r", "Ġ publish", "Ġp ublish", "Ġpub lish", "Ġpubli sh", "ch ema", "che ma", "chem a", "} }", "Ġ CON", "ĠC ON", "ĠCO N", "- in", "-i n", "all back", "Ġ cover", "Ġc over", "Ġco ver", "Ġcov er", "\\ \\", "c olor", "co lor", "col or", "colo r", "Ġ IS", "ĠI S", "Ġ whether", "Ġwh ether", "Ġwhe ther", "i mate", "im ate", "ima te", "imat e", "i sc", "is c", "B ar", "Ba r", "Ġ div", "Ġd iv", "Ġdi v", "B e", "o urn", "ou rn", "our n", "Ġ having", "Ġh aving", "Ġha ving", "Ġhav ing", "l em", "le m", "p layer", "pl ayer", "play er", "pla yer", "a bs", "ab s", "am era", "ame ra", "amer a", "n ey", "ne y", "Ġ exc", "Ġe xc", "Ġex c", "g ether", "get her", "ge ther", "p lied", "pl ied", "a o", "[ $", "Ġ ++", "Ġ+ +", "i pe", "ip e", "s how", "sh ow", "/ d", "[ :", "a gement", "ag ement", "age ment", "agem ent", "l ev", "le v", "_ ID", "_I D", "9 7", "r ary", "ra ry", "rar y", "a des", "ad es", "ade s", "_ se", "_s e", "a use", "au se", "aus e", "Ġ employ", "Ġe mploy", "Ġem ploy", "Ġemp loy", "Ġ */čĊ", "Ġ* /čĊ", "Ġ*/ čĊ", "Ġ fre", "Ġf re", "Ġfr e", "Ġ' @", "Ġcom plet", "Ġcomp let", "Ġcomple t", "Ġcompl et", "Ġ large", "Ġl arge", "Ġlarg e", "Ġlar ge", "r al", "ra l", "\\ x", "Ġ fac", "Ġf ac", "Ġfa c", "< String", ">", "Ġ> >", "Ġ face", "Ġf ace", "Ġfa ce", "Ġfac e", "C TION", "CT ION", "Ġ save", "Ġs ave", "Ġsa ve", "Ġsav e", "Ġ typ", "Ġt yp", "Ġty p", "d ev", "de v", "( \"#", "(\" #", "A GE", "AG E", "cont ainer", "contain er", "e dit", "ed it", "edi t", "Q L", "Ġ items", "Ġit ems", "Ġi tems", "Ġitem s", "Ġ social", "Ġs ocial", "Ġso cial", "Ġsoc ial", "Ġsoci al", "i en", "ie n", "Ġ React", "ĠRe act", ") .ĊĊ", "). ĊĊ", ").Ċ Ċ", "Ġ mar", "Ġm ar", "Ġma r", "Ġre du", "Ġr edu", "Ġred u", "Ġ RE", "ĠR E", ". put", ".p ut", "Ġ major", "Ġm ajor", "Ġmaj or", "C ell", "Ce ll", "Cel l", "n ext", "ne xt", "nex t", "Ġ expected", "Ġex pected", "Ġexp ected", "Ġexpect ed", "Ġ yet", "Ġy et", "Ġye t", "Ġin div", "Ġind iv", "trib utes", "tribute s", "at is", "ati s", "a med", "am ed", "ame d", "Ġ food", "Ġf ood", "Ġfo od", "Ġfoo d", "S ource", "( string", "(s tring", "(str ing", "(st ring", "Ġ +Ċ", "Ġ+ Ċ", "i tes", "it es", "ite s", "d r", "Ġ members", "Ġm embers", "Ġmem bers", "Ġmember s", "Ġmemb ers", "Ġ comb", "Ġc omb", "Ġcom b", "Ġco mb", "i tems", "it ems", "ite ms", "item s", "Ġ Per", "ĠP er", "ĠPe r", "T H", "= True", "Ġ bar", "Ġb ar", "Ġba r", "_ SE", "_S E", "c omm", "com m", "co mm", "( w", ") ĊĊĊ", ")Ċ ĊĊ", ")ĊĊ Ċ", "Ġ send", "Ġs end", "Ġse nd", "Ġsen d", "Ġ inc", "Ġin c", "Ġi nc", "un signed", "uns igned", "F A", "Ġ params", "Ġpar ams", "Ġparam s", "Ġpara ms", "Ġpa rams", "a pping", "ap ping", "app ing", "r os", "ro s", "u gin", "ug in", "ugi n", "f a", "Ġ connection", "Ġcon nection", "Ġconn ection", "Ġconnect ion", "Ġ };ĊĊ", "Ġ} ;ĊĊ", "Ġ};Ċ Ċ", "Ġ}; ĊĊ", "Ġb ecome", "Ġbe come", "Ġbec ome", "M ode", "Mod e", "Mo de", "Ġ ev", "Ġe v", "Ġ diff", "Ġd iff", "Ġdif f", "Ġdi ff", "Ġ United", "ĠUn ited", "ĠUnit ed", "ĠUni ted", "H eight", "He ight", "f ully", "ful ly", "full y", "im ages", "image s", "ima ges", "imag es", "Ġ makes", "Ġm akes", "Ġmake s", "Ġma kes", "Ġmak es", "Ġ global", "Ġg lobal", "Ġglob al", "Ġglo bal", "Ġ contact", "Ġcont act", "Ġconta ct", "' :Ċ", "': Ċ", "Ġ abs", "Ġa bs", "Ġab s", "а Ð", "f loat", "flo at", "Ġ except", "Ġex cept", "Ġexc ept", "Ġexce pt", "Ġ Pol", "ĠP ol", "ĠPo l", "Ch ild", "Chi ld", "t yp", "ty p", "Ġc ertain", "Ġcert ain", "Ġcer tain", "i ón", "ió n", "O UT", "OU T", "Ġim pro", "Ġimp ro", "Ġimpr o", "i les", "il es", "ile s", "Ġ -->Ċ", "Ġ- ->Ċ", "Ġ-- >Ċ", "Ġ--> Ċ", "Ġ Part", "ĠP art", "ĠPar t", "ĠPa rt", "val ues", "value s", "valu es", "o ss", "os s", "/ **", "/* *", "i lit", "il it", "ili t", "Ġ Event", "ĠE vent", "ĠEven t", "ĠEv ent", "ĠEve nt", "c urity", "cur ity", "s ter", "st er", "ste r", "Ġ character", "Ġchar acter", "1 98", "19 8", "Ġ news", "Ġn ews", "Ġnew s", "Ġne ws", "Ġ \",", "Ġ\" ,", "Ġ device", "Ġd evice", "Ġde vice", "Ġdev ice", "c el", "ce l", "lo gin", "log in", "he et", "hee t", "D efault", "De fault", "Def ault", "@ \"", "ĉ Ġ", "c lick", "cl ick", "cli ck", "( value", "(v alue", "(val ue", "(va lue", "Ġ Ab", "ĠA b", "Ġ previous", "Ġpre vious", "Ġprev ious", "ERR OR", "o cal", "oc al", "oca l", "Ġ material", "Ġm aterial", "Ġmat erial", "Ġmate rial", "Ġmateria l", "Ġmater ial", "Ġmateri al", "Ġ below", "Ġb elow", "Ġbe low", "Ġbel ow", "Ġ Christ", "ĠCh rist", "ĠChris t", "ĠChr ist", "Ġ media", "Ġm edia", "Ġme dia", "Ġmed ia", "Ġmedi a", "c over", "co ver", "cov er", "Ġ UI", "ĠU I", "Ġ fail", "Ġf ail", "Ġfa il", "Ġ black", "Ġb lack", "Ġbl ack", "Ġbla ck", "Ġ component", "Ġcom ponent", "Ġcomp onent", "Ġ American", "ĠA merican", "ĠAmeric an", "ĠAmerica n", "ĠAmer ican", "Ġ added", "Ġadd ed", "Ġad ded", "Ġ buy", "Ġb uy", "Ġbu y", "s tit", "st it", "sti t", "Ġ came", "Ġc ame", "Ġca me", "Ġcam e", "Ġ delete", "Ġde lete", "Ġdel ete", "Ġdelet e", "Ġdele te", "p roperty", "pro perty", "prop erty", "proper ty", "o ding", "od ing", "odi ng", "Ġ card", "Ġc ard", "Ġcar d", "Ġca rd", "r ops", "ro ps", "rop s", "Ġ https", "Ġhttp s", "Ġht tps", "Ġhtt ps", "Ġ root", "Ġr oot", "Ġro ot", "Ġ handle", "Ġh andle", "Ġhand le", "Ġhan dle", "C C", "B ack", "Ba ck", "em plate", "emp late", "empl ate", "Ġ getting", "Ġg etting", "Ġget ting", "_ by", "_b y", "m ail", "ma il", "mai l", "_ sh", "_s h", ". assert", ".as sert", "Ġ Dec", "ĠD ec", "ĠDe c", "( true", "(tr ue", "Ġ comput", "Ġcom put", "Ġcomp ut", "Ġ claim", "Ġcl aim", "Ġcla im", "' =>", "'= >", "Ġ Sub", "ĠS ub", "ĠSu b", "Ġ air", "Ġa ir", "Ġai r", "o ps", "op s", "n av", "na v", "e ments", "em ents", "ement s", "eme nts", "emen ts", "( id", "(i d", "Ġ enter", "Ġen ter", "Ġent er", "an ged", "ang ed", "ange d", "E nd", "En d", "Ġ location", "Ġl ocation", "Ġlo cation", "Ġloc ation", "Ġ night", "Ġn ight", "Ġni ght", "Ġnig ht", "Ġ doing", "Ġdo ing", "Ġdoi ng", "Ġ Red", "ĠR ed", "ĠRe d", "l in", "li n", "} ĊĊĊ", "}Ċ ĊĊ", "}ĊĊ Ċ", "v ider", "vid er", "vi der", "vide r", "Ġ pick", "Ġp ick", "Ġpi ck", "Ġpic k", "Ġ watch", "Ġw atch", "Ġwat ch", "ess ages", "essage s", "essa ges", "Ġ human", "Ġh uman", "Ġhum an", "Ġhu man", "Ġ dam", "Ġd am", "Ġda m", "p end", "pe nd", "pen d", "d ir", "di r", "Ġ tax", "Ġt ax", "Ġta x", "Ġ girl", "Ġg irl", "Ġgi rl", "Ġgir l", "re et", "ree t", "Ġ box", "Ġb ox", "Ġbo x", "Ġ strong", "Ġst rong", "Ġstr ong", "Ġstro ng", "Ġstron g", "( v", "r el", "re l", "Ġ interface", "Ġinter face", "Ġinterf ace", "Ġ msg", "Ġm sg", "Ġms g", "f ect", "fe ct", "fec t", "_ at", "_a t", "Ġ house", "Ġh ouse", "Ġhous e", "Ġho use", "Ġ track", "Ġt rack", "Ġtr ack", "Ġtra ck", "' );ĊĊ", "') ;ĊĊ", "');Ċ Ċ", "'); ĊĊ", "j e", "Ġ John", "ĠJ ohn", "ĠJo hn", "ĠJoh n", "i str", "is tr", "ist r", "( S", "u be", "ub e", "Ġ ce", "Ġc e", "it ted", "itt ed", "itte d", "V ER", "VE R", "* )", "p arent", "par ent", "pare nt", "pa rent", "paren t", "Ġ application", "Ġapp lication", "Ġap plication", "Ġapplic ation", "Ġappl ication", "a ny", "an y", ". swing", ".s wing", ".sw ing", "Ġ pack", "Ġp ack", "Ġpa ck", "Ġpac k", "\\ u", "Ġp ract", "Ġpr act", "Ġpra ct", "Ġprac t", "Ġ section", "Ġs ection", "Ġse ction", "Ġsec tion", "Ġsect ion", "c tx", "ct x", "Ġ unsigned", "Ġun signed", "Ġuns igned", ". Point", ".P oint", "Ġ One", "ĠO ne", "ĠOn e", "Ä ±", "i ple", "ip le", "ipl e", "a id", "ai d", "Ñ ĥ", "V ector", "Vec tor", "Ve ctor", "Vect or", "b yte", "by te", "byt e", "Ġ wait", "Ġw ait", "Ġwa it", "Ġ Ãł", "Ġà ł", "à ¥", "Ġto gether", "Ġtog ether", "Ġ throws", "Ġth rows", "Ġthrow s", "Ġthr ows", "Ġthro ws", "F O", "' ))", "') )", "h ost", "ho st", "hos t", "i sing", "is ing", "isi ng", "isin g", ". view", ".v iew", "Ġ terms", "Ġte rms", "Ġter ms", "Ġterm s", "f ramework", "fr amework", "frame work", "fram ework", "- r", "Ġ apply", "Ġapp ly", "Ġap ply", "Ġappl y", "Ġ session", "Ġs ession", "Ġsess ion", "O ptions", "Option s", "Opt ions", "ug gest", "ugg est", "Ġ others", "Ġo thers", "Ġother s", "w itter", "wit ter", "Ġ fund", "Ġf und", "Ġfun d", "Ġfu nd", "I nit", "In it", "Ini t", "_ _(", "__ (", "ens or", "enso r", "G ET", "GE T", "Ġse veral", "Ġsever al", "Ġsev eral", "i i", "[ j", "I O", "Ġ template", "Ġt emplate", "Ġtem plate", "Ġtemp late", "Ġtempl ate", "P osition", "Pos ition", "Ġe con", "Ġec on", "Ġeco n", "a chine", "ach ine", "achi ne", "Ġ il", "Ġi l", ". spring", ".s pring", ".sp ring", "m ain", "ma in", "mai n", "e lt", "el t", "i ment", "im ent", "ime nt", "imen t", "R ec", "Re c", "m m", "Ġ University", "ĠUn iversity", "ĠUnivers ity", "urs or", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "G L", "ic ture", "ict ure", "it hub", "ith ub", "c er", "ce r", "c ast", "ca st", "cas t", "F rom", "Fr om", "a les", "al es", "ale s", "Ġ subject", "Ġsu bject", "Ġsub ject", "Ġsubj ect", "p assword", "pass word", "pas sword", "n y", "Ġ esc", "Ġe sc", "Ġes c", ". write", ".w rite", ".writ e", ".wr ite", "ï¼ Į", "W hat", "Wh at", ". H", "Ġ history", "Ġh istory", "Ġhistor y", "Ġhist ory", "Ġhi story", "Ġhisto ry", "Ġ Fe", "ĠF e", "Ġ individual", "Ġind ividual", "Ġindiv idual", "Ġindivid ual", "u nit", "un it", "uni t", "Ġ -->", "Ġ- ->", "Ġ-- >", "Ġ du", "Ġd u", "I ST", "IS T", "Ġ users", "Ġus ers", "Ġuse rs", "Ġuser s", "f s", "f alse", "fa lse", "fal se", "u nt", "un t", "T itle", "Tit le", "Ti tle", "Ġ mot", "Ġm ot", "Ġmo t", "Ġ future", "Ġf uture", "Ġfut ure", "Ġfu ture", "a ched", "ac hed", "ach ed", "ache d", "Ġ started", "Ġstart ed", "Ġstar ted", "Ġ mode", "Ġm ode", "Ġmod e", "Ġmo de", "Ġ '<", "Ġ' <", "_ array", "_a rray", "_arr ay", "_ar ray", "Ġ ax", "Ġa x", "' ];Ċ", "'] ;Ċ", "']; Ċ", "i res", "ir es", "ire s", "T here", "The re", "Th ere", "u ght", "ug ht", "ugh t", "t ml", "tm l", "p osed", "pos ed", "pose d", "po sed", "i cult", "ic ult", "Ġ took", "Ġt ook", "Ġto ok", "Ġtoo k", "Ġ games", "Ġg ames", "Ġgame s", "Ġgam es", "Ġga mes", "Ġ }}", "Ġ} }", "Ġ ?>Ċ", "Ġ? >Ċ", "Ġ?> Ċ", "Ġ products", "Ġproduct s", "Ġprodu cts", "I s", "Ġ bad", "Ġb ad", "Ġba d", "Ġ Des", "ĠD es", "ĠDe s", ". path", ".p ath", ".pa th", ".pat h", "' ĊĊ", "'Ċ Ċ", "Ġ Post", "ĠP ost", "ĠPo st", "ĠPos t", "a vel", "av el", "ave l", "( :", "1 50", "15 0", "Ġ needs", "Ġne eds", "Ġneed s", "Ġ known", "Ġk nown", "Ġkn own", "Ġknow n", "F l", "Ġ exec", "Ġe xec", "Ġex ec", "Ġexe c", "Ġ seen", "Ġs een", "Ġse en", "Ġsee n", "5 1", "u me", "um e", "Ġ border", "Ġb order", "Ġbor der", "Ġbord er", "Ġ live", "Ġl ive", "Ġli ve", "Ġliv e", "t emp", "te mp", "tem p", "P er", "Pe r", "Ġ variable", "Ġvar iable", "Ġvari able", "i et", "ie t", "Ġ Def", "ĠD ef", "ĠDe f", "Ġ ge", "Ġg e", "e me", "em e", "_ back", "_b ack", "f irst", "fi rst", "fir st", "Ġ provided", "Ġpro vided", "Ġprovid ed", "Ġprov ided", "Ġprovide d", "//////////////// ////////////////", "Ġ filename", "Ġf ilename", "Ġfile name", "Ġfil ename", "Ġfi lename", "Ġ hope", "Ġh ope", "Ġhop e", "Ġho pe", "u ly", "ul y", "a uto", "aut o", "au to", "f ind", "fin d", "fi nd", "_ string", "_s tring", "_st ring", "_str ing", "b tn", "bt n", "it ude", "itud e", "itu de", "At tribute", "Attrib ute", "Ġ young", "Ġyou ng", "Ġyo ung", ". txt", ".t xt", ".tx t", "Ġ website", "Ġwe bsite", "Ġweb site", "Ġwebs ite", "Ġ Prop", "ĠP rop", "ĠPro p", "ĠPr op", "Ġ ey", "Ġe y", "> ();Ċ", ">( );Ċ", ">() ;Ċ", ">(); Ċ", "i onal", "ion al", "io nal", "iona l", "A RR", "AR R", "iction ary", "ur ther", "urt her", ". ", ")- >", "t x", "Ġ pur", "Ġp ur", "Ġpu r", "u el", "ue l", "ym bol", "ymb ol", "u ation", "ua tion", "uat ion", "a nger", "an ger", "ang er", "ange r", "Ġ background", "Ġback ground", "e cess", "ec ess", "ece ss", "ef ined", ". .......", ".. ......", "... .....", ".... ....", "..... ...", "...... ..", "....... .", "Ġ description", "Ġd escription", "Ġde scription", "Ġdes cription", "Ġdescri ption", "Ġ represent", "Ġre present", "Ġrep resent", "\" ));Ċ", "\") );Ċ", "\")) ;Ċ", "\")); Ċ", "p ression", "pr ession", "press ion", "row ser", "rows er", "rowse r", "Ġ series", "Ġs eries", "Ġse ries", "Ġser ies", "Ġserie s", "Ġseri es", "w ards", "ward s", "war ds", "5 2", "( $_", "($ _", "a ise", "ai se", "ais e", "Ġ hot", "Ġh ot", "Ġho t", "a city", "ac ity", "aci ty", "r ies", "ri es", "rie s", "a ctions", "act ions", "action s", "C reate", "Cre ate", "Creat e", "a dio", "ad io", "adi o", "am ples", "amp les", "ample s", "Ġ original", "Ġor iginal", "Ġorig inal", "Ġorigin al", "ens ive", "ensi ve", "f ont", "fo nt", "fon t", "st ream", "str eam", "stre am", " using", ".spring framework", "0 01", "00 1", "s erver", "ser ver", "serve r", "serv er", "Ġ bill", "Ġb ill", "Ġbi ll", "Ġbil l", "A CK", "AC K", "i lename", "il ename", "ile name", "ilen ame", "Ġ frame", "Ġf rame", "Ġfr ame", "Ġfra me", "Ġfram e", "Ġ =Ċ", "Ġ= Ċ", "E dit", "Ed it", "ad ius", "adi us", "Ġ draw", "Ġd raw", "Ġdr aw", "Ġdra w", "an ks", "ank s", "Ġd eter", "Ġde ter", "Ġdet er", "Ġ comes", "Ġc omes", "Ġcom es", "Ġco mes", "Ġcome s", "_ int", "_in t", "_i nt", "Ġ foreach", "Ġf oreach", "Ġfor each", "Ġfore ach", "Ġfo reach", "a ngle", "an gle", "ang le", "angl e", "Ġ elect", "Ġe lect", "Ġel ect", "Ġele ct", "p ected", "pect ed", "pec ted", "He ader", "Head er", "i stration", "ist ration", "istr ation", "istra tion", "istrat ion", "F alse", "Fa lse", "Fal se", "Ġ Game", "ĠG ame", "ĠGa me", "ĠGam e", "Ġ filter", "Ġf ilter", "Ġfil ter", "Ġfilt er", "Act ivity", "Activ ity", "Ġ larg", "Ġl arg", "Ġla rg", "Ġlar g", "in ition", "init ion", "ini tion", "Ġ \"<", "Ġ\" <", "2 56", "25 6", "i sed", "is ed", "ise d", "Ġ remove", "Ġre move", "Ġrem ove", "Ġ Trans", "ĠT rans", "ĠTr ans", "ĠTra ns", "ĠTran s", "m et", "me t", "s ee", "se e", "Form at", "For mat", "Com mand", "Comm and", "Ġ EX", "ĠE X", "N one", "No ne", "Non e", "Ġ front", "Ġf ront", "Ġfr ont", "Ġfro nt", "Ġfron t", "A SE", "AS E", "Ġ Rec", "ĠR ec", "ĠRe c", "ound ation", "Ġ vo", "Ġv o", "9 6", "= \\\"", "=\\ \"", "( *", "Ch ange", "Chan ge", "Cha nge", ". Write", ".W rite", "g roup", "gr oup", "gro up", "i ents", "ie nts", "ient s", "ien ts", "u y", "******** ********************************************************", "**************** ************************************************", "******************************** ********************************", "************************ ****************************************", "**************************************** ************************", "************************************************ ****************", "******************************************************** ********", "Ġ dig", "Ġd ig", "Ġdi g", "h r", "( -", "Ġ gen", "Ġg en", "Ġge n", "n umber", "num ber", "v ec", "ve c", "ur ope", "uro pe", "en try", "ent ry", "entr y", "L L", "Ġ ste", "Ġs te", "Ġst e", "Val id", "Va lid", "' ],", "'] ,", "_ param", "_p aram", "_par am", "_para m", "_pa ram", "Ġ selected", "Ġse lected", "Ġselect ed", "Ġsel ected", "Ġ according", "Ġa ccording", "Ġacc ording", "Ġaccord ing", "Ġ Dis", "ĠD is", "ĠDi s", "Ġ util", "Ġu til", "Ġut il", "B uffer", "Buf fer", "Buff er", "Bu ffer", "_ error", "_e rror", "_err or", "_er ror", "Ġ associ", "Ġass oci", "Ġassoc i", "_ SIZE", "_S IZE", "_SI ZE", "Ġ wor", "Ġw or", "Ġwo r", "Ġ printf", "Ġprint f", "Ġprin tf", "r ag", "ra g", " ł", "D D", "Ġ Val", "ĠV al", "ĠVa l", "Ġ activ", "Ġact iv", "Ġac tiv", "E ng", "En g", "e time", "et ime", "eti me", "etim e", "Ġ virtual", "Ġv irtual", "Ġvir tual", "Ġvirt ual", "a ign", "ai gn", "a ur", "au r", "Ġ Pres", "ĠP res", "ĠPr es", "ĠPre s", "Ġ Exception", "ĠEx ception", "ĠExcept ion", "Ġ anything", "Ġany thing", "Ġ Off", "ĠO ff", "ĠOf f", "Ġ hours", "Ġh ours", "Ġhour s", "Ġho urs", "Ġ war", "Ġw ar", "Ġwa r", "Arg s", "Ar gs", "a ging", "ag ing", "agi ng", "Ġ models", "Ġmod els", "Ġmodel s", "Ġmode ls", "Ġ Time", "ĠT ime", "ĠTim e", "ĠTi me", "O b", "a ms", "am s", "j oy", "jo y", "Ġ early", "Ġear ly", ". read", ".re ad", ".r ead", "8 6", "Ġ center", "Ġc enter", "Ġcent er", "Ġcen ter", "Ġ Initial", "ĠIn itial", "ĠInit ial", "ĠIniti al", "Ġ language", "Ġl anguage", "Ġlangu age", "l ength", "le ngth", "len gth", "x y", "Ġ sn", "Ġs n", "Ġ inf", "Ġin f", "Ġi nf", "P ost", "Pos t", "Po st", "Ġ ago", "Ġa go", "Ġag o", "Ġ easy", "Ġe asy", "Ġeas y", "Ġea sy", "_ code", "_c ode", "_co de", "_cod e", "Ġ ANY", "ĠA NY", "ĠAN Y", "_ ch", "_c h", "Ġ download", "Ġd ownload", "Ġdown load", "( T", "a ved", "av ed", "ave d", "âĢ ĵ", "Ġ students", "Ġst udents", "Ġstud ents", "Ġstudent s", "Ġ fig", "Ġf ig", "Ġfi g", "l ight", "li ght", "lig ht", "x x", "Ġ buffer", "Ġb uffer", "Ġbu ffer", "Ġbuf fer", "Ġbuff er", "Ġ Dep", "ĠD ep", "ĠDe p", "Ġ Math", "ĠM ath", "ĠMat h", "ĠMa th", "I TH", "IT H", "Ġ vari", "Ġv ari", "Ġvar i", "Ġva ri", "Ġ due", "Ġd ue", "Ġdu e", "F actory", "Fact ory", "Factor y", "Ġ por", "Ġp or", "Ġpo r", "Ġ ep", "Ġe p", "o type", "ot ype", "otyp e", "oty pe", "Ġ cannot", "Ġc annot", "Ġcan not", "Ġcann ot", "Ġ white", "Ġwh ite", "Ġwhit e", "< int", "čĊ", "\"> čĊ", ". annot", ".an not", "Ġ collection", "Ġc ollection", "Ġcol lection", "Ġcoll ection", "Ġcollect ion", "Ġcolle ction", "' .", "Ġ similar", "Ġs imilar", "Ġsim ilar", "Ġsimil ar", "Ġ taken", "Ġt aken", "Ġtake n", "Ġta ken", "Ġtak en", "( \"%", "(\" %", "Or der", "Ord er", "' ]Ċ", "'] Ċ", "- md", "-m d", "Ġ TH", "ĠT H", "a ced", "ace d", "ac ed", "Ġis n", "Ġi sn", "/ j", "Ġ son", "Ġs on", "Ġso n", "g raph", "gr aph", "gra ph", "Ġ Integer", "ĠInt eger", "Ġn ecess", "Ġne cess", "Ġnec ess", "Ġneces s", "r een", "re en", "ree n", "Ġ um", "Ġu m", "Ġ \\<", "Ġ\\ <", "Ġ moment", "Ġm oment", "Ġmom ent", "Ġmo ment", "Ġ bring", "Ġb ring", "Ġbr ing", "Ġbri ng", "Ġ indic", "Ġin dic", "Ġind ic", "y sis", "ys is", "ysi s", "Le vel", "v erse", "ver se", "vers e", "ur renc", "urre nc", "urr enc", "_ test", "_t est", "_te st", "Ġent ire", "D own", "Do wn", "Ġ }ĊĊĊ", "Ġ} ĊĊĊ", "Ġ}Ċ ĊĊ", "Ġ}ĊĊ Ċ", "( result", "(res ult", "Ġ Read", "ĠR ead", "ĠRe ad", "à ¨", "M od", "Mo d", "Ġ trying", "Ġt rying", "Ġtr ying", "Ġtry ing", "\" ),Ċ", "\") ,Ċ", "\"), Ċ", "Ġ member", "Ġm ember", "Ġmem ber", "Ġmemb er", "Ġ Cor", "ĠC or", "ĠCo r", "O DO", "OD O", "- control", "-c ontrol", "-cont rol", "un time", "unt ime", "Ġ Sim", "ĠS im", "ĠSi m", "D ialog", "Di alog", "Dia log", "p lot", "pl ot", "_ on", "_o n", "Ġ phys", "Ġph ys", "Ġphy s", "} /", "Ġ namespace", "Ġn amespace", "Ġname space", "Ġnames pace", "ĉ čĊ", "a cc", "ac c", "P layer", "Pl ayer", "Play er", "A RE", "AR E", "8 9", "Ġ foot", "Ġf oot", "Ġfo ot", "Ġfoo t", "Ġ board", "Ġb oard", "Ġbo ard", "Ġboa rd", "p art", "par t", "pa rt", "Ġ sus", "Ġs us", "Ġsu s", "w ise", "wi se", "wis e", "Ġ Mc", "ĠM c", "Ġ push", "Ġp ush", "Ġpu sh", "Ġpus h", "A TA", "AT A", "Ġ please", "Ġp lease", "Ġpl ease", "Ġple ase", "Ġplea se", "Ġpleas e", "r ied", "ri ed", "rie d", "we et", "b it", "bi t", "i ded", "id ed", "ide d", "V E", "Ġ Sw", "ĠS w", "U B", "Ġ types", "Ġt ypes", "Ġtype s", "Ġtyp es", "Ġty pes", "e dia", "ed ia", "edi a", "Ġc los", "Ġcl os", "Ġclo s", "ace book", "W hen", "Wh en", "Ġ edit", "Ġe dit", "Ġed it", "Ġedi t", "i gger", "ig ger", "igg er", "Ġe nerg", "Ġen erg", "Ġener g", "Cont ainer", "Contain er", "Conta iner", "Ġ phot", "Ġp hot", "Ġph ot", "Ġ Count", "ĠC ount", "ĠCo unt", "ĠCou nt", "ĠCoun t", "Ġ Europe", "ĠE urope", "ĠEuro pe", "ĠEurop e", "ĠEur ope", ". Is", ".I s", "Ġ Russ", "ĠR uss", "ĠRu ss", "ĠRus s", "p eed", "pe ed", "pee d", "Ġ Str", "ĠS tr", "ĠSt r", "Ġ py", "Ġp y", "Ġ cult", "Ġc ult", "Ġcu lt", "Ġcul t", "Ġ defined", "Ġd efined", "Ġdef ined", "Ġdefine d", "Ġdefin ed", "c count", "cc ount", "cco unt", "Ġo bt", "Ġob t", ". Location", ".L ocation", ".Lo cation", "Ġ thread", "Ġt hread", "Ġth read", "Ġthr ead", "i lle", "il le", "ill e", "Ġ instead", "Ġin stead", "Ġinst ead", "st rong", "str ong", "stro ng", "Ġ Sec", "ĠS ec", "ĠSe c", "U RE", "UR E", "Ġ idea", "Ġi dea", "Ġid ea", "Ġide a", ". se", ".s e", "e my", "em y", "se lected", "select ed", "sel ected", "Con nection", "Conn ection", "Connect ion", "a cing", "ac ing", "aci ng", "acin g", "t hread", "th read", "thr ead", ". next", ".n ext", ".ne xt", "Ġ coll", "Ġc oll", "Ġco ll", "Ġcol l", "Ġ film", "Ġf ilm", "Ġfil m", "Ġfi lm", "is tic", "ist ic", "isti c", "Ġ compet", "Ġcom pet", "Ġcomp et", "Ġ conn", "Ġc onn", "Ġcon n", "Ġco nn", "th ough", "Ġ compan", "Ġcom pan", "Ġcomp an", "o cket", "oc ket", "ock et", "Ġt each", "Ġte ach", "Ġtea ch", "= (", "Ġ phone", "Ġp hone", "Ġph one", "Ġphon e", "Ġ active", "Ġact ive", "Ġactiv e", "7 9", "de lete", "del ete", "1 01", "10 1", "t ries", "tr ies", "trie s", "tri es", "Ġ mo", "Ġm o", "Ġ death", "Ġde ath", "} );ĊĊ", "});Ċ Ċ", "}) ;ĊĊ", "}); ĊĊ", "o col", "oc ol", "oco l", "W idget", "Ġ article", "Ġart icle", "Ġartic le", "r odu", "ro du", "rod u", "an did", "and id", "andi d", "Ñ ĭ", "Ġ Cr", "ĠC r", "k a", "( ):", "() :", "l ood", "lo od", "loo d", "ĉ ĉĉĊ", "ĉĉ ĉĊ", "ĉĉĉ Ċ", "Ġ almost", "Ġal most", "Ġalm ost", "Ġ sell", "Ġs ell", "Ġse ll", "Ġsel l", "erv let", "r ip", "ri p", "U nit", "Un it", "Uni t", "Ġapp lic", "Ġappl ic", "Ġ connect", "Ġcon nect", "Ġconn ect", "Ġ feature", "Ġf eature", "Ġfe ature", "Ġfeat ure", "Ġ via", "Ġv ia", "Ġvi a", "' ),", "') ,", "Ġ lim", "Ġl im", "Ġli m", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġ Gu", "ĠG u", "E ngine", "Eng ine", "Ġ ens", "Ġe ns", "Ġen s", "Ġ environment", "Ġen vironment", "Ġenviron ment", "b lock", "bl ock", "blo ck", "H ERE", "HE RE", "HER E", "N ULL", "NU LL", "g y", "t ag", "ta g", ") ).", ")) .", "e xp", "ex p", "Ġcom pl", "Ġco mpl", "Ġcomp l", "Ġ install", "Ġinst all", "Ġinstal l", "Ġ complete", "Ġcom plete", "Ġcomp lete", "Ġcomple te", "Ġcomplet e", "Ġcompl ete", "q ueue", "que ue", "at ural", "atur al", "atura l", "atu ral", "Ġ general", "Ġg eneral", "Ġgener al", "Ġgen eral", "Ġgene ral", "Ġgenera l", "t hon", "th on", "Ġas ked", "Ġask ed", "o res", "or es", "ore s", "( res", "(r es", "(re s", "Ġ reserved", "Ġres erved", "Ġreserve d", "Ġreserv ed", "S P", "Ġ âĢ¦", "ĠâĢ ¦", "Å Ĥ", "Ġsign ific", "O ff", "Of f", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġ Ag", "ĠA g", "Ġ Just", "ĠJ ust", "ĠJu st", "Ġ Error", "ĠE rror", "ĠEr ror", "ĠErr or", "Ġin fl", "Ġinf l", "a data", "ad ata", "ada ta", "Ġ icon", "Ġi con", "Ġic on", "as ks", "ask s", "' '", "_ LO", "_L O", "? .", "a ccount", "ac count", "acc ount", "acco unt", "Ġ (*", "Ġ( *", "' )ĊĊ", "') ĊĊ", "')Ċ Ċ", "r ap", "ra p", "_ var", "_v ar", "_va r", "Ġ FOR", "ĠF OR", "ĠFO R", "Ġ party", "Ġp arty", "Ġpart y", "Ġpar ty", "Ġ Your", "ĠY our", "ĠYou r", "ĠYo ur", "c at", "ca t", "s try", "st ry", "str y", ". new", ".n ew", ".ne w", "b oot", "bo ot", "boo t", "Ġ Nov", "ĠN ov", "ĠNo v", "Ġ vector", "Ġv ector", "Ġve ctor", "Ġvec tor", "Ġvect or", "Ġ normal", "Ġn ormal", "Ġnor mal", "Ġnorm al", "Ġf urther", "Ġfur ther", "Re pository", "8 00", "80 0", "Ġ database", "Ġd atabase", "Ġdata base", "Ġdat abase", "Ġdatab ase", "at tle", "att le", "Ġ music", "Ġm usic", "Ġmus ic", "Ġmu sic", "Ġ speed", "Ġs peed", "Ġsp eed", "Ġspe ed", "Ġ doc", "Ġd oc", "Ġdo c", "p rocess", "pro cess", "proc ess", "IG HT", "IGH T", ". parse", ".p arse", ".par se", "Ġ taking", "Ġt aking", "Ġta king", "Ġtak ing", "Ġ viol", "Ġv iol", "Ġvi ol", "c eed", "ce ed", "cee d", "Ġ After", "ĠA fter", "ĠAf ter", "Ġ forward", "Ġfor ward", "Ġ crit", "Ġc rit", "Ġcr it", "Ġcri t", "\" />Ċ", "\"/ >Ċ", "\"/> Ċ", "r ot", "ro t", "Ġ failed", "Ġf ailed", "Ġfa iled", "Ġfail ed", "e fore", "ef ore", "Ġcon cern", "Ġconc ern", "Ġconce rn", "o e", "b a", "Ġ sender", "Ġs ender", "Ġse nder", "Ġsend er", "Ġsen der", "Ġ term", "Ġt erm", "Ġte rm", "Ġter m", "h as", "ha s", "= \"#", "=\" #", "Ġ potential", "Ġpot ential", "Ġpotent ial", "N um", "Nu m", "Ġ published", "Ġp ublished", "Ġpublish ed", "Ġpubli shed", ". close", ".c lose", ".cl ose", "Ġ Image", "ĠI mage", "ĠIm age", "ĠImag e", "str aint", "stra int", "strain t", "U D", "Ġ Ob", "ĠO b", "Ġ probably", "Ġprob ably", "l im", "li m", "\" :Ċ", "\": Ċ", "ol ume", "olum e", "olu me", "Ġ consum", "Ġcon sum", "Ġcons um", "7 6", "a gue", "ag ue", "agu e", "ens ions", "ension s", "ensi ons", "Ġinvest ig", "- year", "-y ear", "' );", "') ;", "- sm", "-s m", "Ġen joy", "Ġenjo y", "o rig", "or ig", "ori g", "e ring", "er ing", "eri ng", "erin g", "c p", "le ased", "lease d", "lea sed", "p lements", "pl ements", "ple ments", "plement s", "Ġ returns", "Ġreturn s", "p at", "pa t", "B O", "Ġ House", "ĠH ouse", "ĠHo use", "ĠHou se", ". Label", ".L abel", "Ġ weight", "Ġw eight", "Ġwe ight", "Ġweigh t", "Ġwei ght", "ig hb", "igh b", "Ġ conditions", "Ġcondition s", "Ġcond itions", "Ġ exception", "Ġex ception", "Ġexcept ion", "Ġexce ption", "d escription", "de scription", "des cription", "Ġ trad", "Ġt rad", "Ġtr ad", "Ġtra d", "- to", "-t o", "Ġ {}", "Ġ{ }", "Ġ module", "Ġm odule", "Ġmod ule", "E ND", "EN D", ". ap", ".a p", ". props", ".p rops", ".pro ps", ".pr ops", ".prop s", "Ġ constructor", "Ġcon structor", "Ġconstruct or", "Ġconstr uctor", "a ves", "av es", "ave s", "Ġ favor", "Ġf avor", "Ġfa vor", "Ġfav or", "Ġ Now", "ĠN ow", "ĠNo w", "; i", "Ġ Main", "ĠM ain", "ĠMa in", "ĠMai n", "_ k", "e ries", "er ies", "erie s", "eri es", "âĢĻ ll", "âĢĻl l", "trans form", "ime stamp", "imest amp", "P re", "Pr e", "Ġ mer", "Ġm er", "Ġme r", ". res", ".re s", ".r es", "s tant", "st ant", "sta nt", "stan t", "L ocation", "Lo cation", "Loc ation", "_ NAME", "_N AME", "Ġ loss", "Ġl oss", "Ġlo ss", "Ġlos s", "Ġ ĊĊ", "ĠĊ Ċ", "n et", "ne t", "Ġ engine", "Ġe ngine", "Ġeng ine", "B lock", "Bl ock", "Bloc k", "Blo ck", "Ġ issues", "Ġiss ues", "Ġissue s", "Ġissu es", "Ġ parse", "Ġp arse", "Ġpar se", "Ġpars e", "Ġ Bar", "ĠB ar", "ĠBa r", "Ġ stay", "Ġst ay", "Ġsta y", "Ġ JSON", "ĠJ SON", "ĠJS ON", "Ġ dom", "Ġd om", "Ġdo m", "a irs", "air s", "ai rs", "w ner", "wn er", "Ġ lower", "Ġl ower", "Ġlo wer", "Ġlow er", "\" ,čĊ", "\", čĊ", "Ġ Dem", "ĠD em", "ĠDe m", "u fact", "uf act", "ufac t", "Ġ ps", "Ġp s", "Ġ perfect", "Ġper fect", "Ġperf ect", "R L", "Ġ educ", "Ġe duc", "Ġed uc", "Ġedu c", "l s", "em ory", "emo ry", "ARR ANT", "u ge", "ug e", "Ġ exact", "Ġex act", ". key", ".k ey", ".ke y", "al led", "all ed", "alle d", "e ch", "ec h", "i ef", "ie f", "\\ /", "o ke", "ok e", "Ġ former", "Ġfor mer", "Ġform er", "Ġforme r", "al loc", "all oc", "allo c", "Ġ six", "Ġs ix", "Ġsi x", "i da", "id a", "Ġ margin", "Ġm argin", "Ġmar gin", "Ġmarg in", "Ġ heart", "Ġhe art", "Ġhear t", "a ld", "al d", "p ack", "pa ck", "pac k", ".getElement ById", "ĠW ARRANT", "Ġ rather", "Ġr ather", "Ġrat her", "Ġra ther", "Ġ building", "Ġbuild ing", "er man", "erm an", "l ice", "lic e", "li ce", "Ġ questions", "Ġquest ions", "Ġquestion s", "Ġquesti ons", "i zes", "iz es", "ize s", "le ge", "leg e", "irect ory", "irector y", "Ġ je", "Ġj e", "Ġ cas", "Ġc as", "Ġca s", "p rops", "pr ops", "pro ps", "prop s", "u tf", "ut f", "Ġ security", "Ġs ecurity", "Ġse curity", "Ġsec urity", "Ġ however", "Ġhow ever", "w eight", "we ight", "wei ght", "weigh t", "Ġ inside", "Ġin side", "Ġins ide", "Ġp resident", "Ġpres ident", "C har", "Ch ar", "Cha r", "Ġ WITH", "ĠW ITH", "ĠWI TH", ". map", ".m ap", ".ma p", "Ġ graph", "Ġg raph", "Ġgr aph", "Ġgra ph", "Ġgrap h", "Ġ tag", "Ġt ag", "Ġta g", "_ status", "_s tatus", "_st atus", "_stat us", "Ġ attempt", "Ġat tempt", "Ġatt empt", "o pp", "op p", "u ses", "us es", "use s", "ĉ const", "ĉcon st", "Ġ round", "Ġr ound", "Ġro und", "Ġrou nd", ", $", "Ġ friends", "Ġf riends", "Ġfri ends", "Ġfriend s", "E mail", "Em ail", "? >", "Re source", "Res ource", "K EY", "KE Y", "o sp", "os p", ". query", ".qu ery", "Ġ North", "ĠN orth", "ĠNor th", "a bles", "ab les", "able s", "abl es", "is trib", "ist rib", "istr ib", "_ class", "_c lass", "_cl ass", "el lo", "ell o", "T hat", "Th at", "Ð º", "pec ially", "pecial ly", "Ġ President", "ĠP resident", "ĠPres ident", "Ġ campaign", "Ġc ampaign", "Ġcamp aign", "Ġ alt", "Ġa lt", "Ġal t", "a rea", "ar ea", "are a", "Ġc hall", "Ġch all", "Ġcha ll", "Ġop port", "Ġopp ort", ". Con", ".C on", ".Co n", "Ġ energy", "Ġe nergy", "Ġen ergy", "Ġenerg y", "Ġener gy", "l ike", "li ke", "lik e", ". string", ".s tring", ".st ring", ".str ing", "ing ton", "ingt on", ") *", "y y", "Ġ profession", "Ġprof ession", "Ġprofess ion", "ir th", "irt h", "Ġ seg", "Ġs eg", "Ġse g", "æ ľ", "Ġ hor", "Ġh or", "Ġho r", "i ers", "ie rs", "ier s", "c an", "ca n", "Ġbe hind", "Ġbeh ind", "Pro duct", "Produ ct", "Prod uct", "f g", "Ġ Sk", "ĠS k", ". jpg", ".j pg", ".jp g", "? :", "] ;ĊĊ", "];Ċ Ċ", "]; ĊĊ", "Ġ callback", "Ġc allback", "Ġcall back", "Ġ Http", "ĠH ttp", "Ñ Į", "l ong", "lo ng", "lon g", "M S", "A TH", "AT H", "Ġ raise", "Ġr aise", "Ġrais e", "Ġra ise", "Ġ wanted", "Ġw anted", "Ġwant ed", "Ġwan ted", "r own", "ro wn", "row n", "u tor", "ut or", "uto r", "l t", "] =", "e line", "el ine", "eli ne", "elin e", "M A", "Ġs epar", "Ġse par", "Ġsep ar", "c s", "s emb", "se mb", "sem b", "D is", "Di s", "b serv", "bs erv", "Ġ Will", "ĠW ill", "ĠWil l", "ĠWi ll", "Ġ policy", "Ġp olicy", "Ġpol icy", "Ġpolic y", "Ġ third", "Ġth ird", "Ġthi rd", "p hone", "ph one", "phon e", "Ġ bed", "Ġb ed", "Ġbe d", "/ g", ". __", "._ _", "Ġ Inc", "ĠI nc", "ĠIn c", "i zing", "iz ing", "izi ng", "izin g", ". remove", ".re move", ".rem ove", "in stance", "inst ance", "instanc e", ". type", ".t ype", ".typ e", "Ġ serv", "Ġs erv", "Ġse rv", "Ġser v", "E ach", "Ġ har", "Ġh ar", "Ġha r", "Ġ Message", "ĠM essage", "ĠMess age", "ĠMes sage", "( key", "(k ey", "SE LECT", "SEL ECT", "P os", "Po s", ") );čĊ", ")) ;čĊ", ")); čĊ", "Ġre comm", "Ġrec omm", "Ġrecom m", "Ġreco mm", "Ġ training", "Ġtr aining", "Ġtrain ing", "Ġtra ining", "Ġtrai ning", "Ġ Ent", "ĠE nt", "ĠEn t", "Ġ Char", "ĠC har", "ĠCh ar", "ĠCha r", "i cht", "ic ht", "ich t", "( file", "(f ile", "(fi le", "(fil e", "Ġ prior", "Ġp rior", "Ġpr ior", "Ġpri or", "Ġprio r", "G ame", "Gam e", "Ga me", "Ġ exit", "Ġe xit", "Ġex it", "Param s", "Par ams", "Pa rams", "Para ms", ". core", ".c ore", ".co re", ".cor e", "P C", "n es", "ne s", "an ced", "ance d", "anc ed", "( request", "(re quest", "(req uest", "P assword", "Pass word", "Pas sword", "} >Ċ", "}> Ċ", "Ġ mag", "Ġm ag", "Ġma g", "Ġ release", "Ġre lease", "Ġr elease", "Ġrel ease", "Ġrele ase", "Ġ shall", "Ġs hall", "Ġsh all", "Ġsha ll", "u dent", "ud ent", "ude nt", "uden t", "Ġ South", "ĠS outh", "ĠSo uth", "ĠSou th", "a ndo", "an do", "and o", ": '", ". TabIndex", ".Tab Index", "s k", "an ner", "ann er", "anne r", "is set", "iss et", "isse t", "Ġ outside", "Ġout side", "Ġouts ide", "l edge", "le dge", "led ge", "Ġ å", "Ġ Rob", "ĠR ob", "ĠRo b", "Ġ imm", "Ġi mm", "Ġim m", "! Ċ", "Ġ Web", "ĠW eb", "ĠWe b", "D es", "De s", "B C", "an cial", "anc ial", "ancia l", "R oute", "Ro ute", "D ec", "De c", "fer ences", "ference s", "Ġp urch", "Ġpur ch", "Ġpu rch", "Ġ Model", "ĠM odel", "ĠMod el", "ĠMo del", "ĠMode l", "c tor", "ct or", "g n", "_ start", "_st art", "_star t", "_sta rt", "_ un", "_u n", ". *", "i ses", "is es", "ise s", "Ġ ground", "Ġg round", "Ġgr ound", "Ġgro und", "Ġgrou nd", "Ġ unique", "Ġun ique", "Ġuniqu e", "Ġuni que", "Ġuniq ue", "Ġbe aut", "Ġbeau t", "{ \"", "Ġ pour", "Ġp our", "Ġpo ur", "Ġpou r", "Ġ Oct", "ĠO ct", "ĠOc t", "Ġ tree", "Ġt ree", "Ġtr ee", "Ġtre e", "s ets", "se ts", "set s", "_ res", "_re s", "_r es", "' )->", "') ->", "_ reg", "_re g", "_r eg", "( \"\\", "(\" \\", "Ġ byte", "Ġb yte", "Ġby te", "Ġbyt e", "B l", "Ġ dating", "Ġd ating", "Ġda ting", "Ġdat ing", "Ġdati ng", "Ġ matter", "Ġm atter", "Ġmat ter", "Ġmatt er", "Ġmatte r", "Ġ Rem", "ĠR em", "ĠRe m", "Ġ' ../", "Ġ'. ./", "Ġ'.. /", "Ġ Aug", "ĠA ug", "ĠAu g", "Ġ La", "ĠL a", "Ġ $(", "Ġ$ (", "o urnal", "our nal", "ourn al", "1 11", "11 1", "i am", "ia m", "Ġ shows", "Ġsh ows", "Ġshow s", "Ġsho ws", "w rite", "wr ite", "Ġ ball", "Ġb all", "Ġbal l", "Ġba ll", "Ġsim ply", "Ġsimp ly", "Ġsimpl y", "Ġ fast", "Ġf ast", "Ġfa st", "Ġfas t", "Ġ memory", "Ġm emory", "Ġmem ory", "Ġmemor y", "Ġmemo ry", "A SS", "AS S", "Ġ Of", "ĠO f", "o ved", "ov ed", "ove d", "a nte", "an te", "ant e", "a ul", "au l", "i stry", "is try", "ist ry", "istr y", ") ));Ċ", ")) );Ċ", "))) ;Ċ", "))); Ċ", "Ġ fit", "Ġf it", "Ġfi t", "< string", "_", "-> _", "\" )ĊĊ", "\") ĊĊ", "\")Ċ Ċ", "o x", "ap plication", "app lication", "appl ication", "Ġ ]Ċ", "Ġ] Ċ", "Ċ ĊĊĊĊĊ", "ĊĊ ĊĊĊĊ", "ĊĊĊĊ ĊĊ", "ĊĊĊ ĊĊĊ", "ĊĊĊĊĊ Ċ", "1 80", "18 0", "Ġ soon", "Ġs oon", "Ġso on", "Ġsoo n", "ct ions", "ction s", "i nger", "in ger", "ing er", "inge r", "Ġ join", "Ġj oin", "Ġjo in", "Ġ Pe", "ĠP e", "Ġ ë", "Ġ las", "Ġl as", "Ġla s", ". E", "c ss", "cs s", "/ or", "/o r", "Ġ Start", "ĠSt art", "ĠStar t", "ĠSta rt", "Ġ TO", "ĠT O", "Ġ subs", "Ġs ubs", "Ġsu bs", "Ġsub s", "c onn", "con n", "co nn", "com ponents", "comp onents", "component s", "DE BUG", "qu are", "qua re", "F unction", "Func tion", "Fun ction", "en dar", "end ar", "enda r", ". index", ".in dex", ".ind ex", "Ġ fill", "Ġf ill", "Ġfil l", "Ġfi ll", "Ä Ļ", "Ġ choose", "Ġch oose", "Ġcho ose", "h ow", "ho w", "Ġ America", "ĠAmeric a", "ĠAmer ica", "as sets", "ass ets", "asset s", "asse ts", "- -----------", "-- ----------", "---- --------", "-------- ----", "--- ---------", "----- -------", "---------- --", "------ ------", "----------- -", "------- -----", "--------- ---", "Ġ Value", "ĠV alue", "ĠVal ue", "ĠVa lue", "Ġ office", "Ġoff ice", "Ġoffic e", "Ġ veh", "Ġv eh", "Ġve h", "Ġ transform", "Ġtrans form", "Ġtransf orm", "Ġ Art", "ĠA rt", "ĠAr t", "Ġ inde", "Ġin de", "Ġi nde", "Ġind e", "Ġ fn", "Ġf n", "Ġ implements", "Ġim plements", "Ġimp lements", "Ġimplement s", "Ġimpl ements", "a ngo", "an go", "ang o", "p lete", "pl ete", "ple te", "plet e", "+ \"", "t mp", "tm p", "am ily", "ami ly", "amil y", "Ġ hash", "Ġh ash", "Ġhas h", "Ġha sh", "m issions", "miss ions", "mission s", "E ST", "ES T", "g t", "Pro vider", "Provid er", "Provide r", "Prov ider", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "Ġ flag", "Ġf lag", "Ġfl ag", "Ġfla g", "Ġpart icip", "Ġpartic ip", "Ġparti cip", "d en", "de n", "Ġ Returns", "ĠReturn s", "Ġ note", "Ġn ote", "Ġnot e", "Ġno te", "ü r", "p m", "id eos", "ide os", "ideo s", "Ġ specified", "Ġs pecified", "Ġspec ified", "Ġ EN", "ĠE N", "e ster", "es ter", "est er", "este r", "o lid", "ol id", "oli d", "Ġ upon", "Ġu pon", "Ġup on", "( std", "(s td", "(st d", "ĉ v", "Ġ '\\", "Ġ' \\", "u z", "Ġ vert", "Ġv ert", "Ġver t", "Ġve rt", "Ġv ict", "Ġvi ct", "Ġvic t", "ĉ self", "ĉs elf", "ĉse lf", "Ġ \"$", "Ġ\" $", "8 5", ". k", "Ġ groups", "Ġg roups", "Ġgroup s", "Ġgro ups", "Ġgrou ps", "g ithub", "git hub", "l ang", "la ng", "lan g", "Ġ mut", "Ġm ut", "Ġmu t", "T O", "Ġ ve", "Ġv e", "Ġ Please", "ĠP lease", "ĠPl ease", "ĠPle ase", "; ĊĊĊ", ";Ċ ĊĊ", ";ĊĊ Ċ", "a ccess", "ac cess", "acc ess", "Ġ {\"", "Ġ{ \"", "r ea", "re a", "Ġ risk", "Ġr isk", "Ġris k", "Ġri sk", "i cker", "ic ker", "ick er", "og gle", "ogg le", "ĉ while", "A NG", "AN G", ". send", ".s end", ".se nd", "7 2", "Ġ woman", "Ġw oman", "Ġwom an", "Ġwo man", "Ġ gets", "Ġg ets", "Ġget s", "Ġge ts", "Ġ ign", "Ġi gn", "Ġig n", "Ġ Id", "ĠI d", "_ log", "_l og", "_lo g", "O NE", "ON E", "Ġe vid", "Ġev id", "Ġ Har", "ĠH ar", "ĠHa r", "_ sub", "_s ub", "_su b", "Ġ endl", "Ġe ndl", "Ġen dl", "Ġend l", "Ġ included", "Ġin cluded", "Ġinclud ed", "Ġinclude d", "Ġincl uded", "Ġinclu ded", "( ));ĊĊ", "() );ĊĊ", "());Ċ Ċ", "()) ;ĊĊ", "()); ĊĊ", "Ġ Ap", "ĠA p", "i gr", "ig r", "Ġ sem", "Ġs em", "Ġse m", "Ġ Black", "ĠB lack", "ĠBl ack", "d oc", "do c", "_ table", "_t able", "_tab le", "_ta ble", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "- up", "-u p", "Ġ cause", "Ġc ause", "Ġca use", "Ġcaus e", "Ġ ..", "Ġ. .", "Ġ van", "Ġv an", "Ġva n", "_ dict", "_d ict", "_di ct", "_dic t", "Ġ focus", "Ġf ocus", "Ġfoc us", "Ġfo cus", "I ND", "IN D", "C ESS", "CE SS", "CES S", ". Log", ".L og", ".Lo g", "Ġ multiple", "Ġm ultiple", "Ġmult iple", "Ġmulti ple", "Ġmultip le", "i do", "id o", "Ġre gard", "Ġreg ard", "- M", "and ler", "andle r", "o urse", "our se", "ours e", "Ġ deg", "Ġd eg", "Ġde g", ". U", "Ġadd ition", "Ġad dition", "Ġv arious", "Ġvar ious", "Ġvari ous", "Ġva rious", "Ġ receive", "Ġre ceive", "Ġrece ive", "е н", "еР½", "Ġ HT", "ĠH T", "O bj", "Ob j", "D F", "Ġ increase", "Ġin crease", "Ġincre ase", "Ġincr ease", "Ġ Open", "ĠO pen", "ĠOp en", "] ;", "Ġ commit", "Ġcom mit", "Ġcomm it", "? Ċ", "ateg ories", "ategor ies", "ategori es", "ategorie s", "atego ries", "at ory", "ator y", "ato ry", "s hip", "sh ip", "shi p", "Ġ Mich", "ĠM ich", "ĠMi ch", "ĠMic h", "Ġ html", "Ġh tml", "Ġht ml", "ro mise", "rom ise", "Ġ leave", "Ġle ave", "Ġstr ateg", "Ġstrat eg", "Ġstra teg", "Ġstrate g", "a ven", "av en", "ave n", "Ġ Console", "ĠCon sole", "ĠCons ole", "k nown", "kn own", "know n", "- n", "_ LE", "_L E", ". component", ".com ponent", ".comp onent", "Ġ bre", "Ġb re", "Ġbr e", "S ession", "i ance", "ia nce", "ian ce", "Ġ align", "Ġal ign", "Ġali gn", "type def", "typ edef", "typed ef", "_ result", "_res ult", "Ġ WHERE", "ĠW HERE", "ĠWH ERE", ". split", ".s plit", ".sp lit", "Ġ reading", "Ġre ading", "Ġread ing", "FA ULT", "Ġ clo", "Ġc lo", "Ġcl o", "Ġ notice", "Ġnot ice", "_ pr", "_p r", "ar ter", "art er", "arte r", "Ġ lock", "Ġl ock", "Ġlo ck", "Ġloc k", "Ġ standard", "Ġst andard", "Ġstand ard", "e tic", "et ic", "eti c", "el low", "ell ow", "ello w", "Ġ padding", "Ġp adding", "Ġpad ding", "Ġpadd ing", "Ġ His", "ĠH is", "ĠHi s", "Ġ states", "Ġst ates", "Ġstate s", "Ġstat es", "Ġsta tes", "_ cast", "_c ast", "_ca st", "( P", "a a", "Ġ internal", "Ġin ternal", "Ġint ernal", "Ġinter nal", "Ġintern al", "e an", "ea n", "Ġ PRO", "ĠP RO", "ĠPR O", "Ġ Key", "ĠK ey", "ĠKe y", "Ġ especially", "Ġes pecially", "Ġespecial ly", "Ġespec ially", "m ing", "min g", "mi ng", "Ġ cross", "Ġc ross", "Ġcr oss", "Ġcro ss", "Ġ national", "Ġn ational", "Ġnation al", "Ġnat ional", "_ object", "_obj ect", "_o bject", "_ob ject", "f ilter", "fil ter", "filt er", "Ġ script", "Ġs cript", "Ġscr ipt", "Ġscri pt", ". update", ".up date", "_ i", "Ġ Assert", "ĠAs sert", "ĠAss ert", "/ core", "/c ore", "/co re", "% %%%", "%% %%", "%%% %", "Ġ problems", "Ġpro blems", "Ġproble ms", "Ġproblem s", "Ġprob lems", "Ġprobl ems", "i stor", "is tor", "ist or", "isto r", "Ġ .=", "Ġ. =", "Ġ arch", "Ġa rch", "Ġar ch", "Ġarc h", "Ġ written", "Ġw ritten", "Ġwrit ten", "Ġwr itten", "Ġm ilit", "Ġmil it", "Ġmi lit", "Ġmili t", "M ENT", "ME NT", ". ch", ".c h", "c ape", "ca pe", "cap e", "Ġ Mus", "ĠM us", "ĠMu s", "_ config", "_con fig", "_conf ig", "Ġ API", "ĠA PI", "ĠAP I", "f oot", "fo ot", "foo t", "Ġ images", "Ġim ages", "Ġimage s", "Ġimag es", "Ġima ges", "e ndl", "en dl", "end l", ". In", ".I n", "F irst", "Fi rst", "Ġ platform", "Ġpl atform", "Ġplat form", ". prot", ".p rot", ".pro t", ".pr ot", "O ption", "Op tion", "Opt ion", "s te", "st e", "Ġ TODO", "ĠT ODO", "ĠTO DO", "ĠTOD O", "Ġ force", "Ġf orce", "Ġfor ce", "Ġforc e", ". cont", ".c ont", ".con t", ".co nt", "ĉ echo", "ĉe cho", "ĠD av", "ĠDa v", "P tr", "Pt r", "( B", "R T", "Ġ Base", "ĠB ase", "ĠBa se", "ĠBas e", "] ['", "][ '", "Ġann ounc", "Ġanno unc", "con sole", "cons ole", "Ġ Py", "ĠP y", "d s", ". as", ".a s", "Ġ prevent", "Ġpr event", "Ġpre vent", "Ġprev ent", "a pan", "ap an", "apa n", "Ġ {'", "Ġ{ '", "} '", "Ġ dead", "Ġd ead", "Ġde ad", "V AL", "VA L", "Q UE", "QU E", "******** ****************************************************************", "**************** ********************************************************", "******************************** ****************************************", "**************************************************************** ********", "************************ ************************************************", "**************************************** ********************************", "************************************************ ************************", "******************************************************** ****************", "Ġ charg", "Ġch arg", "Ġchar g", "Ġcha rg", "R eturn", "Re turn", "Ret urn", "Ġ ful", "Ġf ul", "Ġfu l", "d om", "do m", "Ġ rules", "Ġr ules", "Ġrule s", "Ġru les", "Ġ modify", "Ġmod ify", "Ġ eval", "Ġe val", "Ġev al", "h am", "ha m", "a tement", "at ement", "ate ment", "atem ent", "\\ <", "u la", "ul a", "= False", "=F alse", "R A", "Ġ contains", "Ġcon tains", "Ġcont ains", "Ġcontain s", "Ġconta ins", "7 4", "Ġ stack", "Ġst ack", "Ġsta ck", "m ar", "ma r", "Ġ {}Ċ", "Ġ{ }Ċ", "Ġ{} Ċ", "Ġ undefined", "Ġun defined", "Ġund efined", "Ġundef ined", "A ss", "As s", "Ġ China", "ĠCh ina", "ĠChi na", "ĠChin a", "v ey", "ve y", "* Ċ", "Ġ playing", "Ġpl aying", "Ġplay ing", "Ġpla ying", ") /", "a ctor", "act or", "ac tor", "Ġ bottom", "Ġb ottom", "Ġbot tom", "Ġbott om", "l ier", "li er", "lie r", "Ġ Number", "ĠN umber", "ĠNum ber", "Ġc ouple", "Ġco uple", "Ġcou ple", "Ġcoup le", "D C", "Ġ SO", "ĠS O", "g or", "go r", ". setText", ".set Text", "s uccess", "su ccess", "succ ess", "com mand", "comm and", "comma nd", "F ilter", "Fil ter", "Ġ Our", "ĠO ur", "ĠOu r", "_ item", "_i tem", "_it em", "Ġ ctx", "Ġc tx", "Ġct x", "Ġ road", "Ġr oad", "Ġro ad", "V ersion", "Vers ion", "c ase", "ca se", "cas e", "u rt", "ur t", "av ior", "avi or", "y ch", "yc h", "semb ly", "sembl y", "Ġ Product", "ĠPro duct", "ĠProdu ct", "ĠProd uct", "Ġ held", "Ġh eld", "Ġhe ld", "Ġhel d", "a fe", "af e", "Ġ includes", "Ġin cludes", "Ġinclud es", "Ġinclude s", "Ġincl udes", "Ġinclu des", "< quote", "Ġ avoid", "Ġa void", "Ġav oid", "Ġ Fin", "ĠF in", "ĠFi n", "Ġ Mod", "ĠM od", "ĠMo d", "Ġ tab", "Ġt ab", "Ġta b", "a no", "an o", "à ±", "i pping", "ip ping", "ipp ing", "ippi ng", "- e", "Ġ insert", "Ġin sert", "Ġins ert", "Ġinser t", "Ġinse rt", "t arget", "tar get", "c han", "ch an", "cha n", ". Model", ".M odel", ".Mod el", ".Mode l", "I ME", "IM E", "\\ Ċ", "Ġ machine", "Ġm achine", "Ġma chine", "Ġmach ine", "a vy", "av y", "Ġ NO", "ĠN O", "Ġ Inter", "ĠIn ter", "ĠInt er", "Ġ operation", "Ġo peration", "Ġop eration", "Ġoper ation", "Ġopera tion", "m odal", "mod al", "mo dal", "T ag", "Ta g", "] :", "Ġ production", "Ġp roduction", "Ġpro duction", "Ġproduct ion", "Ġprodu ction", "Ġprod uction", "Ġ areas", "Ġa reas", "Ġare as", "Ġarea s", "Ġ ren", "Ġre n", "Ġr en", "_ from", "_f rom", "_fr om", "n bsp", "nb sp", "Ġ operator", "Ġo perator", "Ġop erator", "Ġoper ator", "Ġopera tor", "m en", "me n", "a pped", "ap ped", "app ed", "appe d", "_ per", "_p er", "_pe r", "z en", "ze n", "( \".", "(\" .", ". save", ".s ave", ".sa ve", ".sav e", "=\" {{", "=\"{ {", "Ġ tor", "Ġt or", "Ġto r", "( response", "(res ponse", "(resp onse", "Ġc andid", "Ġcan did", "Ġcand id", "Ġ conv", "Ġcon v", "Ġco nv", "a iled", "ail ed", "ai led", "Ġ Lib", "ĠL ib", "ĠLi b", "c omp", "com p", "co mp", "u ra", "ur a", "ï¿ ½", "Ġ Here", "ĠH ere", "ĠHe re", "ĠHer e", "Ġ argument", "Ġarg ument", "h ood", "ho od", "hoo d", "Ġ establish", "Ġest ablish", "ograph y", "ogra phy", "Ġ onClick", "Ġon Click", "amb da", "Ġ sch", "Ġs ch", "Ġsc h", "Ġ movie", "Ġm ovie", "Ġmov ie", "Ġmo vie", "Ġ sec", "Ġs ec", "Ġse c", "Ġ activity", "Ġact ivity", "Ġactiv ity", "Ø §", "Ġ sql", "Ġs ql", "Ġsq l", "_ all", "_a ll", "_al l", "in cip", "inc ip", "inci p", "Ġpro vides", "Ġprovid es", "Ġprov ides", "Ġprovide s", "Ġ sys", "Ġs ys", "Ġsy s", "a cket", "ack et", "ac ket", "Ġw asn", "Ġwas n", "Ġwa sn", "Ġ uses", "Ġu ses", "Ġus es", "Ġuse s", "Ġ Function", "ĠF unction", "ĠFun ction", "ĠFunc tion", ". google", ".g oogle", ".go ogle", "Ġ Result", "ĠRes ult", "8 4", "V isible", "Vis ible", "ag ma", "el come", "Ġ Sy", "ĠS y", "Ġ Cent", "ĠC ent", "ĠCe nt", "AL SE", "ALS E", "a ción", "ac ión", "aci ón", "E XT", "EX T", "Ġ license", "Ġl icense", "Ġlic ense", "Ġlicens e", "Ġ Long", "ĠL ong", "ĠLo ng", "ĠLon g", "Ġ accom", "Ġacc om", "Ġac com", "Ġ ability", "Ġab ility", ". height", ".h eight", ".he ight", "Act ive", "Activ e", "o logical", "olog ical", "ologic al", "ologi cal", "o ly", "ol y", ") ),", ")) ,", ". Se", ".S e", "Ġ parameter", "Ġparam eter", "Ġpara meter", "Ġparamet er", "p rite", "pr ite", "prit e", "pri te", "AB ILITY", ". service", ".s ervice", ".serv ice", ".ser vice", "Ġ Group", "ĠG roup", "ĠGr oup", "ĠGro up", "_ query", "_qu ery", "_que ry", "Ġ Item", "ĠI tem", "ĠIt em", "i ning", "in ing", "ini ng", "inin g", "Ġ jud", "Ġj ud", "Ġju d", "i ms", "im s", "f ix", "fi x", "i nder", "in der", "ind er", "inde r", "a gram", "ag ram", "agra m", "agr am", "Ġ functions", "Ġfunction s", "Ġfun ctions", "Ġfunct ions", "Ġex peri", "Ġexp eri", "Ġexper i", "Ġ Em", "ĠE m", "Ġ rot", "Ġr ot", "Ġro t", "Ġ pen", "Ġp en", "Ġpe n", ". btn", ".b tn", ".bt n", "Ġ AS", "ĠA S", "# ifdef", "#if def", "Ġ choice", "Ġch oice", "Ġcho ice", "Ġ Page", "ĠP age", "ĠPa ge", "ĠPag e", "_ PRO", "_P RO", "_PR O", "Q U", "å ı", "a ntity", "ant ity", "anti ty", " Ń", "w ords", "word s", "wor ds", "Ġ readonly", "Ġread only", "Ġ flex", "Ġf lex", "Ġfl ex", "Ġfle x", "prot ected", "protect ed", "Ġ Any", "ĠA ny", "ĠAn y", "Ġ characters", "Ġchar acters", "Ġcharacter s", "en ced", "ence d", "enc ed", "Ġ July", "ĠJ uly", "ĠJul y", "ĠJu ly", "i ler", "il er", "ile r", "C ard", "Car d", "Ca rd", "u rance", "ur ance", "ura nce", "uran ce", "Ġ rev", "Ġre v", "Ġr ev", ". event", ".e vent", ".ev ent", "a ly", "al y", "1 30", "13 0", "Ġw onder", "Ġwon der", "Ġwo nder", "Ġ Port", "ĠP ort", "ĠPo rt", "ĠPor t", "Ġ legal", "Ġl egal", "Ġle gal", "Ġleg al", "r ole", "ro le", "rol e", "Ġ ten", "Ġt en", "Ġte n", "Ġg oes", "Ġgo es", "M P", "wh ite", ") :čĊ", "): čĊ", ") )čĊ", ")) čĊ", "Ġ reference", "Ġre ference", "Ġref erence", "Ġrefer ence", "Ġrefere nce", "Ġ mis", "Ġm is", "Ġmi s", "Ġ Project", "ĠPro ject", "ĠProj ect", "i cks", "ic ks", "ick s", "> &", "C ON", "CO N", "Ġre pl", "Ġrep l", "Ġ regular", "Ġreg ular", "Ġregul ar", "St orage", "r amework", "rame work", "ram ework", "Ġ goal", "Ġgo al", "Ġ touch", "Ġt ouch", "Ġto uch", "Ġtou ch", ". widget", ".w idget", "Ġ built", "Ġb uilt", "Ġbu ilt", "d es", "de s", "P art", "Par t", "Pa rt", "( re", "(r e", "Ġ worth", "Ġw orth", "Ġwor th", "h ib", "hi b", "g ame", "ga me", "gam e", "9 1", "1 92", "19 2", "Ġ в", "ĠÐ ²", "a cion", "ac ion", "aci on", "acio n", "Ġ White", "ĠWh ite", "ĠWhit e", "( type", "(t ype", "(typ e", "( `", "8 1", "Ġ natural", "Ġn atural", "Ġnatur al", "Ġnat ural", "Ġin j", "Ġi nj", "Ġ calcul", "Ġcal cul", "Ġcalc ul", "Ġ April", "ĠApr il", "ĠAp ril", ". List", ".L ist", "Ġ associated", "Ġassoci ated", "Ġassociate d", "Ġassoc iated", "ĉ System", "ĉS ystem", "~ ~", "= [", "Ġ storage", "Ġst orage", "Ġstor age", "Ġsto rage", "Ġ bytes", "Ġby tes", "Ġbyte s", "Ġbyt es", "Ġ travel", "Ġt ravel", "Ġtr avel", "Ġtra vel", "Ġtrav el", "Ġ sou", "Ġs ou", "Ġso u", "Ġ passed", "Ġp assed", "Ġpass ed", "Ġpas sed", "Ġpasse d", "! =", "a script", "as cript", ". open", ".op en", ".o pen", "Ġ grid", "Ġg rid", "Ġgr id", "Ġgri d", "Ġ bus", "Ġb us", "Ġbu s", "Ġ recogn", "Ġrec ogn", "Ġreco gn", "A b", "Ġ hon", "Ġh on", "Ġho n", "Ġ Center", "ĠC enter", "ĠCent er", "Ġ prec", "Ġp rec", "Ġpr ec", "Ġpre c", "b uild", "bu ild", "7 3", "HT ML", "Ġ San", "ĠS an", "ĠSa n", "Ġ countries", "Ġc ountries", "Ġcount ries", "Ġcoun tries", "a led", "al ed", "ale d", "t oken", "to ken", "tok en", "k t", "Ġ qual", "Ġqu al", "Ġq ual", "Ġqua l", "L ast", "La st", "Las t", "ad ow", "ado w", "Ġ manufact", "Ġman ufact", "i dad", "id ad", "ida d", "j ango", "ja ngo", "jan go", "jang o", "N ext", "Ne xt", "x f", ". a", "Ġ porno", "Ġp orno", "Ġporn o", "Ġpor no", "Ġ PM", "ĠP M", "e rve", "er ve", "erv e", "i ting", "it ing", "iti ng", "itin g", "_ th", "_t h", "c i", "= None", "=N one", "g s", "Ġ login", "Ġlo gin", "Ġlog in", "at ives", "ative s", "ati ves", "ativ es", "' ]);Ċ", "'] );Ċ", "']) ;Ċ", "']); Ċ", "Ä ħ", "Ġ ill", "Ġi ll", "Ġil l", "I A", "ch ildren", "child ren", "D O", "Ġ levels", "Ġlevel s", "Ġlev els", "Ġleve ls", "Ġ {{", "Ġ{ {", "Ġ looks", "Ġl ooks", "Ġlo oks", "Ġlook s", "Ġ \"#", "Ġ\" #", "To String", "ToStr ing", "Ġ necessary", "Ġn ecessary", "Ġnecess ary", "Ġ ĠĠĊ", "ĠĠ ĠĊ", "ĠĠĠ Ċ", "c ell", "ce ll", "cel l", "En try", "Ent ry", "Entr y", "Ġ '#", "Ġ' #", "Ġext rem", "Ġextr em", "Se lector", "Select or", "Sel ector", "Sele ctor", "Ġ placeholder", "Ġplace holder", "L oad", "Lo ad", "Ġ released", "Ġre leased", "Ġrelease d", "Ġrele ased", "O RE", "OR E", "E numer", "En umer", "Enum er", "Ġ TV", "ĠT V", "S ET", "SE T", "in q", "P ress", "Pr ess", "Pre ss", "Pres s", "Ġ Department", "ĠDe partment", "ĠDep artment", "ĠDepart ment", "Ġ properties", "Ġp roperties", "Ġprop erties", "Ġproper ties", "Ġ respond", "Ġres pond", "Ġresp ond", "S earch", "Se arch", "Sea rch", "a el", "ae l", "Ġ requ", "Ġre qu", "Ġr equ", "Ġreq u", "Ġ Book", "ĠB ook", "ĠBo ok", "ĠBoo k", "/ Ċ", "( st", "(s t", "Ġ financial", "Ġfin ancial", "Ġfinanc ial", "Ġfinan cial", "i cket", "ic ket", "ick et", "_ input", "_in put", "_inp ut", "Ġ threat", "Ġth reat", "Ġthr eat", "( in", "(i n", "S trip", "St rip", "Str ip", "ì Ŀ", "ç ão", "7 1", "Ġe vidence", "Ġev idence", "Ġevid ence", ") );", ")) ;", "Ġ Bro", "ĠB ro", "ĠBr o", "Ġ [];Ċ", "Ġ[ ];Ċ", "Ġ[] ;Ċ", "Ġ[]; Ċ", "Ġ ou", "Ġo u", "b uf", "bu f", "S cript", "Scr ipt", "d at", "da t", "Ġ rule", "Ġr ule", "Ġru le", "# import", "= \"/", "=\" /", "S erial", "Se rial", "Ser ial", "Ġ starting", "Ġstart ing", "Ġstar ting", "[ index", "[in dex", "[ind ex", "a e", "Ġ contrib", "Ġcon trib", "Ġcont rib", "Ġcontr ib", "s ession", "sess ion", "_ new", "_n ew", "_ne w", "u table", "ut able", "uta ble", "o ber", "ob er", "obe r", "Ġ \"./", "Ġ\" ./", "Ġ\". /", "Ġ logger", "Ġlo gger", "Ġlog ger", "Ġrec ently", "Ġrecent ly", "Ġ returned", "Ġre turned", "Ġreturn ed", "č čĊ", ") ))Ċ", ")) )Ċ", "))) Ċ", "it ions", "ition s", "iti ons", "Ġ seek", "Ġse ek", "Ġsee k", "Ġ communic", "Ġcomm unic", "Ġcommun ic", "Ġ \".", "Ġ\" .", "Ġ username", "Ġuser name", "Ġusern ame", "E CT", "EC T", "D S", "Ġ otherwise", "Ġother wise", "Ġ German", "ĠG erman", "ĠGer man", "ĠGerm an", ". aw", ".a w", "Ad apter", "Ada pter", "ix el", "ixe l", "Ġ systems", "Ġs ystems", "Ġsystem s", "Ġsys tems", "Ġsyst ems", "Ġ drop", "Ġd rop", "Ġdr op", "Ġdro p", "8 3", "Ġ structure", "Ġstruct ure", "Ġ $(\"#", "Ġ$ (\"#", "Ġ$( \"#", "Ġ$(\" #", "en cies", "enc ies", "enci es", "an ning", "ann ing", "anni ng", "Ġ Link", "ĠL ink", "ĠLin k", "ĠLi nk", "Ġ Response", "ĠRes ponse", "ĠRespons e", "ĠResp onse", "Ġ stri", "Ġs tri", "Ġst ri", "Ġstr i", "Å ¼", "Ġ DB", "ĠD B", "æ Ĺ", "and roid", "andro id", "andr oid", "sub mit", "o tion", "ot ion", "oti on", "9 2", "( @", ". test", ".t est", ".te st", "8 2", "Ċ ĊĊĊĊĊĊĊ", "ĊĊ ĊĊĊĊĊĊ", "ĊĊĊĊ ĊĊĊĊ", "ĊĊĊ ĊĊĊĊĊ", "ĊĊĊĊĊĊ ĊĊ", "ĊĊĊĊĊ ĊĊĊ", "ĊĊĊĊĊĊĊ Ċ", "] ;čĊ", "]; čĊ", "Ġdirect ly", "Ġ \"%", "Ġ\" %", "r is", "ri s", "el ta", "elt a", "A IL", "AI L", ") {čĊ", "){ čĊ", "m ine", "min e", "mi ne", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "( k", "b on", "bo n", "a sic", "as ic", "asi c", "p ite", "pi te", "pit e", "_ __", "__ _", "M ax", "Ma x", "Ġ errors", "Ġerror s", "Ġerr ors", "Ġer rors", "Ġerro rs", "Ġ While", "ĠWh ile", "Ġ arguments", "Ġarg uments", "Ġargument s", "Ġ ensure", "Ġen sure", "Ġens ure", "R ight", "- based", "-b ased", "-base d", "W eb", "We b", "Ġ -=", "Ġ- =", "Ġint rodu", "Ġintr odu", "Ġintro du", "Ġ Inst", "ĠI nst", "ĠIn st", "ĠIns t", "ĠW ash", "ĠWas h", "ĠWa sh", "or din", "ord in", "j oin", "jo in", "D atabase", "Data base", "Dat abase", "Ġ grad", "Ġg rad", "Ġgr ad", "Ġgra d", "Ġ usually", "Ġus ually", "Ġusual ly", "Ġusu ally", "I TE", "IT E", "P rops", "Pro ps", "Prop s", "Pr ops", "? >Ċ", "?> Ċ", "Ġ Go", "ĠG o", "@ Override", "R EF", "RE F", "Ġ ip", "Ġi p", "ĠA ustral", "ĠAust ral", "ĠAu stral", "ĠAustr al", "Ġ ist", "Ġis t", "Ġi st", "View ById", "Ġ serious", "Ġse rious", "Ġser ious", "Ġseri ous", "Ġ customer", "Ġc ustomer", "Ġcustom er", "Ġcust omer", ". prototype", ".prot otype", ".proto type", "o do", "od o", "c or", "co r", "Ġ door", "Ġd oor", "Ġdo or", "Ġ WITHOUT", "ĠWITH OUT", "Ġ plant", "Ġp lant", "Ġpl ant", "Ġplan t", "Ġpla nt", "Ġb egan", "Ġbe gan", "Ġbeg an", "Ġ distance", "Ġd istance", "Ġdist ance", "Ġdi stance", "( )).", "() ).", "()) .", "Ġch ance", "Ġcha nce", "Ġchan ce", "Ġ ord", "Ġo rd", "Ġor d", "c ame", "ca me", "cam e", "pr agma", "Ġ protect", "Ġprot ect", "Ġprote ct", "r agment", "ra gment", "rag ment", "Ġ Node", "ĠN ode", "ĠNo de", "e ning", "en ing", "eni ng", "enin g", "Ñ ĩ", "Ġ route", "Ġr oute", "Ġro ute", "Ġrout e", "Ġrou te", "Ġ School", "ĠS chool", "ĠSch ool", "ĠScho ol", "h i", "Ġne ighb", "Ġneigh b", "A fter", "Af ter", "l icit", "lic it", "li cit", "Ġ contr", "Ġcon tr", "Ġcont r", "Ġ primary", "Ġpr imary", "Ġprim ary", "Ġpri mary", "Ġprima ry", "A A", ".Write Line", "ut ils", "util s", "uti ls", "Ġ bi", "Ġb i", "R ed", "Re d", ".L inq", ". object", ".o bject", ".obj ect", ".ob ject", "Ġ leaders", "Ġle aders", "Ġlead ers", "Ġleader s", "un ities", "unit ies", "uni ties", "Ġ gun", "Ġg un", "Ġgu n", "o nth", "on th", "ont h", "Ġ Dev", "ĠD ev", "ĠDe v", "F ILE", "FI LE", "Ġ comments", "Ġcom ments", "Ġcomm ents", "Ġcomment s", "Ġcomme nts", "_ len", "_l en", "_le n", "ar row", "arr ow", "arro w", "a mount", "am ount", "amo unt", "R ange", "Ra nge", "s ert", "se rt", "ser t", "Grid View", "Ġ updated", "Ġup dated", "Ġupdate d", "Ġupd ated", "Ġ Mo", "ĠM o", "Ġ inform", "Ġin form", "Ġinfo rm", "Ġinf orm", "oci ety", "a la", "al a", "A ccess", "Ac cess", "Acc ess", "Ġ hab", "Ġh ab", "Ġha b", "Ġ creat", "Ġc reat", "Ġcr eat", "Ġcre at", "Ġcrea t", "_ arg", "_a rg", "_ar g", "Ġ January", "ĠJan uary", "Ġ Day", "ĠD ay", "ĠDa y", "\" )čĊ", "\") čĊ", "u ple", "up le", "d ocument", "doc ument", "gor ith", "gorit h", "m enu", "me nu", "men u", "Ġ Over", "ĠO ver", "ĠOv er", "b b", ". title", ".t itle", "_ out", "_o ut", "Ġ led", "Ġl ed", "Ġle d", "u ri", "ur i", "Ġ ?> < /", "g l", "Ġ bank", "Ġb ank", "Ġban k", "Ġba nk", "ay ment", "ĉ printf", "ĉprint f", "M D", "Ġ sample", "Ġs ample", "Ġsam ple", "Ġsamp le", "Ġ hands", "Ġh ands", "Ġhand s", "Ġha nds", "Ġhan ds", "Ġ Version", "ĠV ersion", "ĠVers ion", "u ario", "ua rio", "uar io", "Ġ offers", "Ġof fers", "Ġoff ers", "Ġoffer s", "ity Engine", "Ġ shape", "Ġs hape", "Ġsh ape", "Ġsha pe", "Ġ sleep", "Ġs leep", "Ġsle ep", "Ġslee p", "_ point", "_p oint", "_po int", "S ettings", "Set tings", "Setting s", "Ġ achie", "Ġa chie", "Ġach ie", "Ġ sold", "Ġs old", "Ġso ld", "Ġsol d", "o ta", "ot a", ". bind", ".b ind", ".bin d", ".bi nd", "A m", "Ġ safe", "Ġs afe", "Ġsa fe", "Ġsaf e", "St ore", "Ġ shared", "Ġsh ared", "Ġshare d", "Ġsha red", "Ġshar ed", "Ġ priv", "Ġp riv", "Ġpr iv", "Ġpri v", "_ VAL", "_V AL", "Ġs ens", "Ġse ns", "Ġsen s", ") {", "Ġ remember", "Ġre member", "Ġrem ember", "sh ared", "sha red", "share d", "e lement", "el ement", "ele ment", "elem ent", "Ġ shoot", "Ġs hoot", "Ġsh oot", "Ġsho ot", "V ert", "Ver t", "Ve rt", "c out", "co ut", "cou t", "Ġ env", "Ġe nv", "Ġen v", "_ label", "_l abel", "_lab el", "_la bel", "Ġ >Ċ", "Ġ> Ċ", "r un", "ru n", "Ġ scene", "Ġs cene", "Ġsc ene", "Ġscen e", "Ġsce ne", "( array", "(a rray", "(arr ay", "(ar ray", "d evice", "de vice", "dev ice", "_ title", "_t itle", "_ti tle", "a gon", "ag on", "ago n", "] čĊ", "a by", "ab y", "Ġbe came", "Ġbec ame", "bo olean", "bool ean", "boo lean", "Ġ park", "Ġp ark", "Ġpar k", "Ġpa rk", "Ġ Code", "ĠC ode", "ĠCo de", "ĠCod e", "up load", "r iday", "ri day", "rid ay", "Ġ September", "ĠSept ember", "ĠSep tember", "F e", "Ġ sen", "Ġs en", "Ġse n", "c ing", "ci ng", "cin g", "F L", "C ol", "Co l", "u ts", "ut s", "_ page", "_p age", "_pag e", "_pa ge", "i nn", "in n", "Ġim plied", "Ġimp lied", "Ġimpl ied", "a ling", "al ing", "ali ng", "alin g", "Ġyour self", "Ġyours elf", ". Count", ".C ount", ".Co unt", "con f", "co nf", "Ġ aud", "Ġa ud", "Ġau d", "_ init", "_in it", "_i nit", "_ini t", ". )", "Ġw rote", "Ġwr ote", "0 03", "00 3", "N G", ". Error", ".E rror", ".Err or", "ä »", ". for", ".f or", "Ġ equal", "Ġe qual", "Ġequ al", "Ġeq ual", "Ġ Request", "ĠRe quest", "ĠReq uest", "Ġ serial", "Ġs erial", "Ġse rial", "Ġser ial", "Ġseria l", "Ġseri al", "Ġ allows", "Ġall ows", "Ġallow s", "Ġallo ws", "X X", "Ġ middle", "Ġm iddle", "Ġmid dle", "Ġmidd le", "c hor", "ch or", "cho r", "1 95", "19 5", "9 4", "à ¸", "er val", "erv al", "erva l", ". Column", ".C olumn", ".Col umn", "re ading", "read ing", "rea ding", "Ġ escort", "Ġesc ort", "Ġ August", "ĠAug ust", "Ġquick ly", "Ġwe ap", "Ġ CG", "ĠC G", "ro pri", "rop ri", "h o", "Ġ cop", "Ġc op", "Ġco p", "( struct", "(str uct", "Ġ Big", "ĠB ig", "ĠBi g", "Ġ vs", "Ġv s", "Ġf requ", "Ġfr equ", "Ġfre qu", "Ġfreq u", ". Value", ".V alue", ".Val ue", "Ġ actions", "Ġa ctions", "Ġact ions", "Ġaction s", "Ġ proper", "Ġpro per", "Ġpr oper", "Ġprop er", "Ġ inn", "Ġin n", "Ġi nn", "Ġ objects", "Ġobject s", "Ġobj ects", "Ġ matrix", "Ġm atrix", "Ġmat rix", "av ascript", "ava script", "Ġ ones", "Ġo nes", "Ġon es", "Ġone s", ". group", ".g roup", ".gr oup", "Ġ green", "Ġg reen", "Ġgr een", "Ġgre en", "Ġ paint", "Ġp aint", "Ġpain t", "Ġpa int", "Ġpai nt", "o ols", "ool s", "oo ls", "y cl", "yc l", "en code", "enc ode", "enco de", "o lt", "ol t", "com ment", "comm ent", ". api", ".ap i", ".a pi", "D ir", "Di r", "Ġ une", "Ġu ne", "Ġun e", "iz ont", "izon t", "izo nt", ". position", ".p osition", ".pos ition", "Ġde signed", "Ġdes igned", "Ġdesign ed", "_ val", "_v al", "_va l", "a vi", "av i", "i ring", "ir ing", "iri ng", "t ab", "ta b", "Ġ layer", "Ġl ayer", "Ġla yer", "Ġlay er", "Ġ views", "Ġview s", "Ġvi ews", "Ġvie ws", "Ġ reve", "Ġre ve", "Ġr eve", "Ġrev e", "r ael", "ra el", "Ġ ON", "ĠO N", "r ics", "ri cs", "ric s", "1 60", "16 0", "n p", "Ġ core", "Ġc ore", "Ġco re", "Ġcor e", "( ));čĊ", "() );čĊ", "()) ;čĊ", "()); čĊ", "M ain", "Ma in", "Ġ expert", "Ġex pert", "Ġexp ert", "Ġexper t", "ĉ ĉčĊ", "ĉĉ čĊ", "_ en", "_e n", "Ġ />", "Ġ/ >", "ut ter", "utt er", "I AL", "IA L", "a ils", "ail s", "ai ls", "Ġ King", "ĠK ing", "ĠKi ng", "ĠKin g", "* /ĊĊ", "*/ ĊĊ", "*/Ċ Ċ", "Ġ Met", "ĠM et", "ĠMe t", "_ end", "_e nd", "_en d", "ad dr", "add r", "o ra", "or a", "Ġ ir", "Ġi r", "M in", "Mi n", "Ġsur pr", "Ġre pe", "Ġrep e", "Ġ directory", "Ġd irectory", "Ġdirect ory", "Ġdirector y", "P UT", "PU T", "- S", "Ġ election", "Ġe lection", "Ġel ection", "Ġelect ion", "Ġele ction", "h aps", "ha ps", "hap s", ". pre", ".p re", ".pr e", "c m", "Value s", "Val ues", "Ġ \"Ċ", "Ġ\" Ċ", "c olumn", "col umn", "i vil", "iv il", "ivi l", "Log in", "Lo gin", "in ue", "inu e", "9 3", "Ġ beautiful", "Ġbe autiful", "Ġbeaut iful", "Ġ secret", "Ġs ecret", "Ġse cret", "Ġsec ret", "Ġsecre t", "( event", "(e vent", "(ev ent", "Ġ chat", "Ġc hat", "Ġch at", "Ġcha t", "u ms", "um s", "Ġ origin", "Ġor igin", "Ġorig in", "Ġori gin", "Ġ effects", "Ġe ffects", "Ġeffect s", "Ġeff ects", "Ġ management", "Ġman agement", "Ġmanage ment", "Ġmana gement", "i lla", "il la", "ill a", "t k", "Ġ setting", "Ġs etting", "Ġset ting", "Ġsett ing", "Ġ Cour", "ĠC our", "ĠCo ur", "ĠCou r", "Ġ massage", "Ġm assage", "Ġmass age", "Ġmas sage", "Ġmassa ge", "ĉ end", "ĉe nd", "ĉen d", "Ġ happy", "Ġh appy", "Ġhapp y", "Ġha ppy", "Ġhap py", "Ġ finish", "Ġf inish", "Ġfin ish", "Ġfi nish", "Ġ camera", "Ġc amera", "Ġcame ra", "Ġcam era", "Ġcamer a", "Ġ Ver", "ĠV er", "ĠVe r", "ĠDem ocr", "ĠDemo cr", "Ġ Her", "ĠH er", "ĠHe r", "( Q", "c ons", "con s", "co ns", "i ta", "it a", "Ġ '.", "Ġ' .", "{ }", "ĉ C", "Ġ stuff", "Ġst uff", "Ġstu ff", "1 94", "19 4", "Ġ :Ċ", "Ġ: Ċ", "Ġ AR", "ĠA R", "T ask", "Ta sk", "h idden", "hi dden", "hid den", "e ros", "er os", "ero s", "I GN", "IG N", "at io", "ati o", "Ġ Health", "ĠHe alth", "ĠHeal th", "ol ute", "olut e", "olu te", "En ter", "Ent er", "' >", "Ġ Twitter", "ĠT witter", "ĠTw itter", "Ġ County", "ĠC ounty", "ĠCount y", "ĠCou nty", "ĠCoun ty", "s cribe", "scri be", "scr ibe", "Ġ= >Ċ", "Ġ=> Ċ", "Ġ hy", "Ġh y", "f it", "fi t", "Ġm ilitary", "Ġmilit ary", "Ġmil itary", "Ġmilitar y", "Ġ sale", "Ġs ale", "Ġsa le", "Ġsal e", "re quired", "require d", "requ ired", "n on", "no n", "boot strap", "h old", "ho ld", "hol d", "r im", "ri m", "- old", "-o ld", "Ġ Down", "ĠD own", "ĠDo wn", "ĠDow n", "Ġ mention", "Ġm ention", "Ġmen tion", "Ġment ion", "cont act", "_ group", "_g roup", "_gr oup", "o day", "od ay", "oda y", "Ġ town", "Ġt own", "Ġto wn", "Ġtow n", "Ġ solution", "Ġs olution", "Ġsol ution", "u ate", "ua te", "uat e", "el ling", "ell ing", "elli ng", "] ->", "]- >", "o tes", "ot es", "ote s", "en tal", "ent al", "enta l", "o men", "om en", "ome n", "osp ital", "Ġ Sup", "ĠS up", "ĠSu p", "_ EN", "_E N", "Ġ slow", "Ġs low", "Ġsl ow", "Ġslo w", "SE SSION", "SES SION", "Ġ blue", "Ġb lue", "Ġbl ue", "a go", "ag o", "Ġl ives", "Ġli ves", "Ġlive s", "Ġliv es", "Ġ ^", ". un", ".u n", "i nst", "in st", "ins t", "e nge", "en ge", "eng e", "Ġ customers", "Ġcustom ers", "Ġcustomer s", "Ġcust omers", "Ġ cast", "Ġc ast", "Ġca st", "Ġcas t", "ud get", "udge t", "ï¼ ģ", "ic ens", "ice ns", "Ġd etermin", "Ġde termin", "Ġdeter min", "Ġdeterm in", "Se lected", "Select ed", "Sel ected", "_ pl", "_p l", "ue ue", "Ġ dark", "Ġd ark", "Ġda rk", "Ġdar k", "/ /ĊĊ", "// ĊĊ", "//Ċ Ċ", "s i", "th ern", "ther n", "the rn", "Ġ Japan", "ĠJ apan", "ĠJa pan", "ĠJap an", "/ w", "P U", "Ġ East", "ĠE ast", "ĠEa st", "o vie", "ov ie", "ovi e", "Ġ package", "Ġp ackage", "Ġpack age", "Ġ nor", "Ġn or", "Ġno r", "Ġ api", "Ġa pi", "Ġap i", "b ot", "bo t", "\" ];Ċ", "\"] ;Ċ", "\"]; Ċ", "_ post", "_p ost", "_pos t", "_po st", "u late", "ul ate", "ula te", "Ġ club", "Ġc lub", "Ġcl ub", "' ));Ċ", "') );Ċ", "')) ;Ċ", "')); Ċ", "Ġ loop", "Ġl oop", "Ġlo op", "P IO", "PI O", "i one", "ion e", "io ne", "s hot", "sh ot", "In itial", "Init ial", "Ġ played", "Ġpl ayed", "Ġplay ed", "reg ister", "regist er", "r ought", "ro ught", "rou ght", "rough t", "_ max", "_m ax", "_ma x", "ace ment", "ac ement", "m atch", "mat ch", "raph ics", "raphic s", "A ST", "AS T", "Ġ existing", "Ġex isting", "Ġexist ing", "Ġ complex", "Ġcom plex", "Ġcomp lex", "Ġcomple x", "Ġcompl ex", "D A", ". Ch", ".C h", ". common", ".com mon", ".comm on", "m o", "Ġ' ../../", "Ġ'../ ../", "Ġ'.. /../", "i to", "it o", "Ġ analysis", "Ġan alysis", "Ġanal ysis", "Ġanaly sis", "Ġanalys is", "Ġ deliver", "Ġdel iver", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "i dx", "id x", "à ł", "o ngo", "on go", "ong o", "Ġ English", "ĠEng lish", "ĠEngl ish", "< !--", " \";Ċ", ">\" ;Ċ", ">\"; Ċ", "_ UN", "_U N", "ĉ std", "ĉs td", "ĉst d", "o ded", "od ed", "ode d", "Ġ calls", "Ġc alls", "Ġcall s", "Ġcal ls", "h ere", "he re", "her e", "R el", "Re l", "Ġ brand", "Ġb rand", "Ġbr and", "Ġbra nd", "Ġbran d", "back ground", "g a", "_ address", "_add ress", "_addr ess", "_ad dress", "_ params", "_param s", "_par ams", "_para ms", "_pa rams", "C ategory", "1 03", "10 3", "Ġ India", "ĠIn dia", "ĠInd ia", "ĠIndi a", "_ event", "_e vent", "_ev ent", "_even t", "Ġ ing", "Ġin g", "Ġi ng", "R ender", "Re nder", "Ren der", ". cl", ".c l", "um py", "ump y", "Ġ pet", "Ġp et", "Ġpe t", "F C", "Ġ Ant", "ĠA nt", "ĠAn t", "E xt", "Ex t", "Ġ charge", "Ġch arge", "Ġchar ge", "Ġcharg e", "e ned", "en ed", "ene d", "g rad", "gr ad", "gra d", "E O", "Ġ depend", "Ġd epend", "Ġde pend", "Ġdep end", "Ġ .ĊĊ", "Ġ. ĊĊ", "Ġ.Ċ Ċ", "f rame", "fr ame", "fra me", "fram e", "Ġ df", "Ġd f", "Ġ huge", "Ġh uge", "Ġhug e", "Ġhu ge", "Ġ PART", "ĠP ART", "ĠPA RT", "ĠPAR T", "e ds", "ed s", "; ;", "Ġ AM", "ĠA M", "Ġ basic", "Ġb asic", "Ġbas ic", "Ġba sic", "Ġ Let", "ĠL et", "ĠLe t", "l ich", "lic h", "li ch", "Ġ arm", "Ġa rm", "Ġar m", "Ġ star", "Ġs tar", "Ġst ar", "Ġsta r", "Ġf ederal", "Ġfed eral", "Ġfeder al", "W ork", "Wo rk", "Ġ carry", "Ġc arry", "Ġcar ry", "Ġcarr y", "Ġ Israel", "ĠIs rael", "( obj", "(o bj", "(ob j", "= {{", "={ {", "Ġ saved", "Ġs aved", "Ġsa ved", "Ġsave d", "Ġsav ed", "Ġ syn", "Ġs yn", "Ġsy n", "Ġ constant", "Ġcon stant", "Ġconst ant", "Ġcons tant", "V ENT", "VE NT", "VEN T", "Ġ positive", "Ġpos itive", "Ġposit ive", "Ġ conduct", "Ġcon duct", "Ġcond uct", "Ġconduc t", "Ġcondu ct", "Ġ skin", "Ġs kin", "Ġsk in", "Ġski n", "Ġear lier", "Ġ layout", "Ġl ayout", "Ġlay out", "Ġ IP", "ĠI P", "O UR", "OU R", "Ġ tim", "Ġt im", "Ġti m", "style sheet", "styles heet", "_ cl", "_c l", "Ġ Card", "ĠC ard", "ĠCar d", "ĠCa rd", "++ ){Ċ", "++) {Ċ", "++){ Ċ", "Ġtem per", "Ġtemp er", "Ġ David", "ĠD avid", "ĠDav id", "ĠDa vid", "ĉ try", "ĉt ry", "ĉtr y", ". dart", ".d art", ".da rt", "Ġw ants", "Ġwant s", "Ġwa nts", "Ġwan ts", "Ġ picture", "Ġp icture", "Ġpic ture", "Ġpict ure", "Ġ videos", "Ġv ideos", "Ġvideo s", "Ġvid eos", "Ġvide os", "Ġ Comm", "ĠC omm", "ĠCom m", "ĠCo mm", "is ions", "ision s", "isi ons", "_ MAX", "_M AX", "_MA X", "M apping", "Map ping", "Ma pping", "- content", "-c ontent", "-con tent", "-cont ent", "Ġ Ear", "ĠE ar", "ĠEa r", "- de", "-d e", "Ġp rem", "Ġpr em", "Ġpre m", "b ruary", "br uary", "bru ary", "bruar y", "Ġ components", "Ġcom ponents", "Ġcomp onents", "Ġcomponent s", "Ġthrough out", "Ġ pull", "Ġp ull", "Ġpul l", "Ġpu ll", "Ġ pages", "Ġp ages", "Ġpage s", "Ġpa ges", "Ġpag es", "e nte", "en te", "ent e", "res pond", "resp ond", "Ġ gas", "Ġg as", "Ġga s", "cript or", "Ġ edge", "Ġe dge", "Ġed ge", "Ġ bound", "Ġb ound", "Ġbo und", "Ġbou nd", "A CT", "AC T", "* *****", "** ****", "**** **", "*** ***", "***** *", "Ġ creating", "Ġc reating", "Ġcr eating", "Ġcre ating", "Ġcreat ing", "Ġcrea ting", "Ġ CH", "ĠC H", "Ġ nullptr", "Ġnull ptr", "B r", "+ '", ". co", ".c o", "> ::", ">: :", "Ġ learning", "Ġl earning", "Ġle arning", "Ġlearn ing", "Ġlear ning", ". Length", ".L ength", ".Le ngth", ".Len gth", "_ SH", "_S H", "Ġ patients", "Ġpat ients", "Ġpatient s", "A IN", "AI N", "Ġ kids", "Ġk ids", "Ġkid s", "Ġki ds", "Ġ comfort", "Ġcom fort", "Ġ shown", "Ġsh own", "Ġshow n", "Ġsho wn", "ug ins", "ugin s", "ugi ns", "Ġ Back", "ĠB ack", "ĠBa ck", "ĠBac k", "e lla", "el la", "ell a", "_ CL", "_C L", "Ġ lat", "Ġl at", "Ġla t", "Ġ dispatch", "Ġdis patch", "Ġdisp atch", "Ġ classes", "Ġc lasses", "Ġclass es", "Ġcl asses", "Ġclasse s", "Ġclas ses", ". at", ".a t", ". begin", ".b egin", ".be gin", "Ġ successful", "Ġsuccess ful", "b an", "ba n", "Ġob tain", "Ġobt ain", "Ġ Sl", "ĠS l", "Ġ lack", "Ġl ack", "Ġla ck", "Ġlac k", "it erator", "iter ator", "T hread", "Th read", "Thr ead", "( size", "(s ize", "(si ze", "Ġ none", "Ġn one", "Ġno ne", "Ġnon e", ". has", ".h as", "_ X", "s ort", "so rt", "n ap", "na p", "p et", "pe t", "b in", "bi n", "7 00", "70 0", "Ġ Canada", "ĠCan ada", "T hey", "The y", "Th ey", "Ġd ans", "Ġda ns", "Ġdan s", "Ġ Mat", "ĠM at", "ĠMa t", "< td", "'", "'=> '", "'= >'", "Ġ Paul", "ĠP aul", "ĠPa ul", "m as", "ma s", "ĉ print", "ĉp rint", "ĉpr int", "( len", "(l en", "(le n", "f d", "Ġ );", "Ġ) ;", ". Event", ".E vent", "q li", "ql i", "i rit", "ir it", "iri t", "ie lds", "ield s", "iel ds", "o man", "om an", "oma n", "Ġ Top", "ĠT op", "ĠTo p", "Ġ vote", "Ġv ote", "Ġvo te", "Ġvot e", "Ġ mask", "Ġm ask", "Ġma sk", "Ġmas k", "Ġ theme", "Ġth eme", "Ġthe me", "Ġthem e", "- Ċ", "Ġ props", "Ġp rops", "Ġpro ps", "Ġpr ops", "Ġprop s", "Ġ fine", "Ġf ine", "Ġfin e", "Ġfi ne", "Ġ writer", "Ġw riter", "Ġwrit er", "Ġwr iter", "Ġwrite r", "_ offset", "_off set", "_o ffset", "c ar", "ca r", "Ġ altern", "Ġal tern", "Ġalt ern", "Ġalter n", "Ġalte rn", "Ġ copyright", "Ġc opyright", "Ġcopy right", "Ġ destroy", "Ġd estroy", "Ġde stroy", "Ġdest roy", "p per", "pp er", "ppe r", "Ġ generate", "Ġg enerate", "Ġgener ate", "Ġgen erate", "Ġgene rate", "Ġgenera te", "p ped", "pp ed", "ppe d", "âĢĻ d", "Ġ ĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĊ", "ĠĠĠĠ ĠĠĊ", "ĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠ Ċ", "m ake", "ma ke", "mak e", "Ġ Show", "ĠS how", "ĠSh ow", "ĠSho w", "Ġ browser", "Ġb rowser", "Ġbrows er", "Ġbrowse r", "Ġbrow ser", "Ġ favorite", "Ġf avorite", "Ġfavor ite", "Ġ career", "Ġcar eer", "Ġcare er", "Ġhapp ened", "Ġhappen ed", "( char", "(c har", "(ch ar", "Ġ recommend", "Ġre commend", "Ġrecomm end", "Ġ liter", "Ġl iter", "Ġli ter", "Ġlit er", "Ġlite r", ". filter", ".f ilter", ".fil ter", "g rade", "gr ade", "grad e", "gra de", "Ġ £", "Ġ £", "P hone", "Ph one", "o ms", "om s", "Ġ named", "Ġn amed", "Ġname d", "Ġna med", "Ġnam ed", "- label", "-l abel", "-la bel", "i po", "ip o", "Ġ Other", "ĠO ther", "ĠOt her", "Ġ panel", "Ġp anel", "Ġpa nel", "Ġpan el", "Ġpane l", "Ġ rock", "Ġr ock", "Ġro ck", "Ġroc k", "S cale", "Sc ale", "ĉ assert", "ĉas sert", "Ð ´", "Ġ trust", "Ġt rust", "Ġtr ust", "f ront", "fr ont", "Ġd emon", "Ġde mon", "Ġdem on", "Ġdemo n", "A r", "N et", "Ne t", "Ġ economic", "Ġe conomic", "Ġecon omic", "Ġeconom ic", "Ġec onomic", "f ooter", "fo oter", "foot er", "foo ter", "Ġ race", "Ġr ace", "Ġrac e", "Ġra ce", "( node", "(n ode", "(no de", "Ġ Option", "ĠO ption", "ĠOp tion", "ĠOpt ion", "s plit", "sp lit", "spl it", "Ġ physical", "Ġph ysical", "Ġphys ical", "Ġphysic al", "i fest", "if est", "ife st", "ifes t", "Ġ removed", "Ġre moved", "Ġrem oved", "Ġremove d", ". http", ".h ttp", ".ht tp", ") ),Ċ", ")) ,Ċ", ")), Ċ", "Ġlo oked", "Ġlook ed", "' ;", "d ing", "di ng", "din g", "g est", "ge st", "ges t", "atur day", "/ licenses", "/lic enses", "/license s", "P rice", "Pr ice", "Pri ce", "Ġ dro", "Ġd ro", "Ġdr o", "Ġt owards", "Ġto wards", "Ġtoward s", "Ġtow ards", "Ġ uns", "Ġu ns", "Ġun s", "Ġ CL", "ĠC L", "ĉ static", "ĉst atic", "ĉstat ic", "Ġ rows", "Ġr ows", "Ġro ws", "Ġrow s", "Ġ define", "Ġde fine", "Ġdef ine", "Ġdefin e", ". replace", ".re place", ".rep lace", "Ġ father", "Ġf ather", "Ġfa ther", "Ġfat her", "Ġ Design", "ĠD esign", "ĠDe sign", "ĠDes ign", "as sign", "ass ign", "assi gn", "m ut", "mu t", "D evice", "De vice", "Dev ice", "D id", "Di d", "' ))Ċ", "') )Ċ", "')) Ċ", "o metry", "om etry", "ome try", "omet ry", "ometr y", "ay load", "Ġ histor", "Ġh istor", "Ġhis tor", "Ġhist or", "Ġhi stor", "Ġhisto r", "Ġ Param", "ĠP aram", "ĠPar am", "ĠPa ram", "ĠPara m", "Ġ Boolean", "ĠBo olean", "ĠBool ean", "ĠBoo lean", "Ġ nature", "Ġn ature", "Ġna ture", "Ġnatur e", "Ġnat ure", "Ġ js", "Ġj s", "Ġ nation", "Ġn ation", "Ġna tion", "Ġnat ion", "i h", "Ġ discover", "Ġdis cover", "Ġdisc over", "Ġdisco ver", "s em", "se m", "H andle", "Hand le", "Han dle", "ĉ r", "Ġ Techn", "ĠT echn", "ĠTe chn", "ĠTech n", "ĠTec hn", "Ġ wall", "Ġw all", "Ġwa ll", "Ġwal l", "{ $", "@ property", "Ġ\" ../", "Ġ\". ./", "Ġ\".. /", "Ġ exam", "Ġex am", ". draw", ".d raw", ".dr aw", "o pping", "op ping", "opp ing", "Ġn early", "Ġnear ly", "Ġ cool", "Ġc ool", "Ġco ol", "Ġin depend", "Ġind epend", "Ġinde pend", "R ES", "RE S", "Ġ handler", "Ġh andler", "Ġhand ler", "Ġhandle r", "Ġ Monday", "ĠMon day", "ĠMond ay", "Ġ sun", "Ġs un", "Ġsu n", "St yles", "Style s", "ous ly", "Ġ ĉ", "v est", "ve st", "ves t", "D isplay", "Dis play", "Disp lay", "( y", "at ically", "atic ally", "atical ly", "Ġ predict", "Ġp redict", "Ġpre dict", "Ġpred ict", "Ġpredic t", "y ing", "yi ng", "Ġ sometimes", "Ġs ometimes", "Ġsome times", "Ġsom etimes", "Ġsometime s", "\" ]Ċ", "\"] Ċ", "Ġ drink", "Ġd rink", "Ġdr ink", "Ġdri nk", "Ġ bul", "Ġb ul", "Ġbu l", "if ications", "ific ations", "ification s", ". insert", ".in sert", ".ins ert", ". reg", ".re g", ".r eg", "Ġ tests", "Ġt ests", "Ġte sts", "Ġtest s", "Ġtes ts", "Al ignment", "Align ment", "Ġal leg", "Ġall eg", "Ġalle g", "Ġ attribute", "Ġat tribute", "Ġattrib ute", "Ġ Note", "ĠN ote", "ĠNo te", "ĠNot e", "Ġmy self", "Ġmys elf", "a rts", "ar ts", "art s", "N ow", "No w", "Ġ interesting", "Ġinter esting", "Ġinterest ing", "Ġinteres ting", "l ients", "li ents", "lient s", "lie nts", "lien ts", "Ġ population", "Ġpop ulation", "Ġpopul ation", "Ġ California", "ĠCal ifornia", "ĠCaliforn ia", "\" I", "å ¹", "Ġ greater", "Ġg reater", "Ġgreat er", "Ġgre ater", "ues day", "Ġth ous", "Ġthou s", "Ġtho us", "Ġco sts", "Ġcost s", "Ġcos ts", "Ġ launch", "Ġl aunch", "Ġla unch", "\\ Http", "k er", "ke r", "b and", "ba nd", "ban d", "Ġ Play", "ĠP lay", "ĠPl ay", "ĠPla y", "Ġ band", "Ġb and", "Ġban d", "Ġba nd", ". shape", ".s hape", ".sh ape", ".sha pe", "e some", "es ome", "eso me", "art icle", "arti cle", "artic le", ". rf", ".r f", "Ġ wer", "Ġw er", "Ġwe r", "á s", "em bers", "ember s", "emb ers", "u sr", "us r", "B A", "i can", "ic an", "ica n", "e tt", "et t", "valid ate", "ul ti", "ult i", "Ġim mediately", "Ġimmedi ately", "Ġimmediate ly", "z er", "ze r", "Ġ figure", "Ġf igure", "Ġfig ure", "Ġfigur e", "o es", "oe s", "e ller", "el ler", "ell er", "elle r", "ir cle", "irc le", "Ġ Sign", "ĠS ign", "ĠSi gn", "ĠSig n", ". db", ".d b", "Ġ rank", "Ġr ank", "Ġran k", "Ġra nk", "By tes", "Byte s", "Ġ projects", "Ġproject s", "Ġproj ects", "Ġproje cts", "_ rec", "_re c", "_r ec", "U LAR", "UL AR", "ULA R", "A PI", "AP I", "Ġ Line", "ĠL ine", "ĠLin e", "ĠLi ne", "P ort", "Por t", "Po rt", "Ġ poll", "Ġp oll", "Ġpol l", "Ġpo ll", "Ġg iving", "Ġgi ving", "id ence", "ide nce", "iden ce", "- -Ċ", "-- Ċ", "Ġ plot", "Ġp lot", "Ġpl ot", "Ġplo t", "i cial", "ic ial", "ici al", "icia l", "Ġw arrant", "Ġwar rant", "I TION", "IT ION", "Ġ Double", "ĠD ouble", "ĠDo uble", "ĠDou ble", "ĠDoub le", "Ġb illion", "Ġbill ion", "Ġbil lion", "gorith m", "gorit hm", "Ġ equipment", "Ġe quipment", "Ġequ ipment", "Ġequip ment", "D ATE", "DA TE", "DAT E", "Ġ @\"", "Ġ@ \"", "E E", "Ġ ple", "Ġp le", "Ġpl e", "i ation", "ia tion", "iat ion", "Ġ headers", "Ġhe aders", "Ġhead ers", "Ġheader s", "Ġpro ced", "Ġproc ed", ".Component Model", "Ġ Obama", "ĠOb ama", "Ġ pa", "Ġp a", "Ġ Best", "ĠB est", "ĠBe st", "ĠBes t", "im ately", "imate ly", "imat ely", ". getString", ".get String", ".getS tring", ". \\", "m ploy", "mp loy", "mpl oy", "Ġ raw", "Ġr aw", "Ġra w", "_ block", "_b lock", "_bl ock", "_bloc k", "und red", "\" },Ċ", "\"} ,Ċ", "\"}, Ċ", "1 12", "11 2", ".Group Layout", "Ġb rought", "Ġbr ought", "Ġbro ught", "NS String", "NSS tring", "th row", "thr ow", "thro w", "c reated", "cre ated", "create d", "cr eated", "creat ed", ". New", ".N ew", "_ view", "_v iew", "_vi ew", "C P", "e ps", "ep s", "O p", "Ġ gratis", "Ġgr atis", "Ġgrat is", "Ġ '\"", "Ġ' \"", "Ġint erview", "Ġinter view", "Ġinterv iew", "\" \"\"Ċ", "\"\" \"Ċ", "\"\"\" Ċ", "Ġ partial", "Ġp artial", "Ġpart ial", "Ġparti al", "Ġ aria", "Ġa ria", "Ġar ia", "b ing", "bin g", "bi ng", "A uthor", "Auth or", "Aut hor", "B ook", "Bo ok", "Ġ Pat", "ĠP at", "ĠPa t", "u man", "um an", "uma n", "User s", "Us ers", "Use rs", "p lus", "pl us", "1 93", "19 3", "Ġ Direct", "ĠD irect", "ĠDi rect", "ĠDir ect", "ĠDire ct", "ven ue", "al pha", "alph a", "UC CESS", "Ġ Call", "ĠC all", "ĠCal l", "ĠCa ll", "Ġ );čĊ", "Ġ) ;čĊ", "Ġ); čĊ", "im ated", "imate d", "ima ted", "imat ed", "Ġ remain", "Ġre main", "Ġr emain", "Ġrem ain", "Ġ anti", "Ġan ti", "Ġant i", "Ġ London", "ĠL ondon", "ĠLo ndon", "ĠLon don", "ĠLond on", "Ġs afety", "Ġsaf ety", "Ġsafe ty", "P OSE", "PO SE", "POS E", "o les", "ol es", "ole s", "cont roller", "control ler", "contr oller", "contro ller", "B yte", "By te", "Ġ Court", "ĠC ourt", "ĠCo urt", "ĠCour t", "ĠCou rt", "Ġ Phil", "ĠP hil", "ĠPh il", "ĠPhi l", "Ġ Associ", "ĠAss oci", "ĠAssoc i", "e na", "en a", "å IJ", "_ STR", "_S TR", "_ST R", "c oin", "co in", "res hold", "resh old", "Ġ batch", "Ġb atch", "Ġbat ch", "_ Click", "_C lick", "_Cl ick", "ent ication", "entic ation", "enti cation", "> ';Ċ", ">' ;Ċ", ">'; Ċ", "e nty", "en ty", "ent y", "Ġbeg inning", "Ġbegin ning", "Ġ zero", "Ġz ero", "Ġze ro", "Ġzer o", "Ġ Convert", "ĠCon vert", "ĠConv ert", "Ġ terr", "Ġt err", "Ġte rr", "Ġter r", "Ġ paid", "Ġp aid", "Ġpa id", "Ġpai d", "Ġincre ased", "Ġincrease d", "c atch", "cat ch", "- size", "-s ize", "1 15", "11 5", "act ivity", "activ ity", "e quals", "equ als", "equal s", "Ġ queue", "Ġque ue", "Ġq ueue", "Ġ \"'", "Ġ\" '", "Ġ International", "ĠInt ernational", "ĠIntern ational", "ĠInter national", "Ġf ür", "urs day", "Ġ scient", "Ġs cient", "Ġsc ient", "Ġsci ent", "al low", "all ow", "allo w", "a xis", "ax is", "Ġ appropri", "Ġapp ropri", "Ġap propri", "Ġappro pri", "e dge", "ed ge", "Ġ idx", "Ġi dx", "Ġid x", "S uccess", "Su ccess", "Suc cess", "ent ifier", ": \\", "x is", "xi s", "Ġ maximum", "Ġmax imum", "Ġmaxim um", "ar ks", "ark s", "Ġ birth", "Ġb irth", "Ġbir th", "( index", "(in dex", "(ind ex", "Ġ maybe", "Ġm aybe", "Ġmay be", ". py", ".p y", "f iles", "file s", "fi les", "fil es", "Ġ limited", "Ġl imited", "Ġlimit ed", "Ġlim ited", "Ġlimite d", "_ check", "_c heck", "_ch eck", "_che ck", "l ook", "lo ok", "loo k", "p lies", "pl ies", "Ġ movement", "Ġm ovement", "Ġmov ement", "Ġmove ment", "Ġmo vement", "' ].", "'] .", "Ġb road", "Ġbr oad", "Ġbro ad", "Ġ BE", "ĠB E", "Ġ UnityEngine", "ĠUn ityEngine", "ĠUnity Engine", ". cpp", ".c pp", ".cp p", "Ġ Every", "ĠE very", "ĠEv ery", "ĠEver y", "ĠEve ry", "Ad min", "Ġ fans", "Ġf ans", "Ġfa ns", "Ġfan s", "p ared", "par ed", "pare d", "pa red", "Ċ ĠĠĠĠĊ", "Ġ foreign", "Ġfore ign", "Ġ pan", "Ġp an", "Ġpa n", "Ġ tour", "Ġt our", "Ġto ur", "Ġtou r", "Ġ Order", "ĠOr der", "ĠOrd er", "Ġ moving", "Ġm oving", "Ġmov ing", "Ġmo ving", "Ġ auf", "Ġa uf", "Ġau f", "C all", "Cal l", "Ca ll", "c b", "Å Ł", "vent ory", "Ġ Sql", "ĠS ql", "ĠSq l", "Ġ fully", "Ġf ully", "Ġfull y", "Ġful ly", "Click Listener", "W ORD", "WO RD", "Ġann ounced", "Ġannounc ed", "Ġannounce d", ") čĊčĊ", ")čĊ čĊ", "Ġag reed", "Ġagre ed", "Ġagree d", "Ġagr eed", "r ie", "ri e", "Ġ earn", "Ġe arn", "Ġear n", "Ġea rn", "_ link", "_l ink", "_lin k", "_li nk", ". array", ".a rray", ".ar ray", ".arr ay", "( text", "(t ext", "(te xt", "(tex t", "Ġ materials", "Ġmaterial s", "Ġmateria ls", "Ġmater ials", "Ġmateri als", ", p", "f fff", "ff ff", "fff f", "v g", "Ġ ©", "Ġ ©", "Ġ unless", "Ġun less", "Ġunle ss", "Ġunl ess", "a jax", "aj ax", "aja x", "L OG", "LO G", "Ġ sexual", "Ġs exual", "Ġsex ual", "Ġ \\\"", "Ġ\\ \"", "- time", "-t ime", "Ġ coach", "Ġco ach", "Ġ supported", "Ġs upported", "Ġsup ported", "Ġsupport ed", "Ġsupp orted", "Ġ photos", "Ġph otos", "Ġphot os", "Ġphoto s", "i form", "if orm", "ifo rm", ". Create", ".C reate", ") ]", "r ier", "ri er", "rie r", "Ġ dialog", "Ġd ialog", "Ġdi alog", "Ġdia log", "Ġdial og", "a ver", "av er", "ave r", "i ge", "ig e", ") +", "_ idx", "_id x", "_i dx", ": [", "_ min", "_m in", "_mi n", "Ġ Cong", "ĠC ong", "ĠCon g", "ĠCo ng", "Ġ pressure", "Ġpres sure", "Ġpress ure", "Ġ teams", "Ġte ams", "Ġteam s", "Ġtea ms", "S ign", "Si gn", "Sig n", "b egin", "be gin", "beg in", "r ian", "ri an", "ria n", "N ESS", "NE SS", "NES S", "L S", "Ġim prove", "Ġimp rove", "Ġimpro ve", "Ġimpr ove", "Ġimprov e", "Ġ Sunday", "ĠS unday", "ĠSun day", "ĠSund ay", "Ġ definition", "Ġdef inition", "Ġdefinit ion", "Ġdefin ition", "i ger", "ig er", "ige r", "rol lers", "roll ers", "roller s", "Ġ thinking", "Ġth inking", "Ġthink ing", "Ġthin king", "T emplate", "Temp late", "Tem plate", "- F", "Ġem erg", "p lates", "pl ates", "plate s", "plat es", "pla tes", "Ġ USA", "ĠU SA", "ĠUS A", ". setState", ".set State", "Ġ Also", "ĠAl so", "ĠAls o", "r ev", "re v", "Ġ enable", "Ġe nable", "Ġen able", "Ġ CO", "ĠC O", "P ECT", "PE CT", "PEC T", "Ġ concept", "Ġcon cept", "Ġconc ept", "Ġconce pt", ") -", "Ġ âĢ¢", "ĠâĢ ¢", "Ġ sets", "Ġs ets", "Ġse ts", "Ġset s", "Ġ meaning", "Ġme aning", "Ġmean ing", "e mon", "em on", "emo n", "Ġ Cons", "ĠC ons", "ĠCon s", "ĠCo ns", "c mp", "cm p", "e der", "ed er", "ede r", "an ned", "ann ed", "anne d", "ic ensed", "icense d", "icens ed", "Ġ Super", "ĠS uper", "ĠSup er", "ĠSu per", "Ġ daily", "Ġd aily", "Ġda ily", "Ġdai ly", "Ġ multi", "Ġm ulti", "Ġmult i", "Ġmul ti", "_ u", "Ġch alleng", "Ġchall eng", "_ mode", "_m ode", "_mod e", "_mo de", "Ġ Promise", "ĠP romise", "ĠPro mise", "ĠProm ise", "Ġ strict", "Ġstr ict", "Ġstri ct", "j o", "i nton", "in ton", "int on", "into n", "( list", "(l ist", "(li st", "On ly", "> {", "Ġ vehicle", "Ġv ehicle", "Ġveh icle", "í ķ", "Ġ Player", "ĠP layer", "ĠPl ayer", "ĠPlay er", "ĠPla yer", "1 06", "10 6", "Ġ Del", "ĠD el", "ĠDe l", "Ġ pool", "Ġp ool", "Ġpo ol", "Ġpoo l", ". url", ".u rl", ".ur l", "nes day", "( );čĊčĊ", "() ;čĊčĊ", "();čĊ čĊ", "(); čĊčĊ", "9 00", "90 0", "Ġ \");Ċ", "Ġ\" );Ċ", "Ġ\") ;Ċ", "Ġ\"); Ċ", "L ocal", "Lo cal", "Loc al", ". \");Ċ", ".\" );Ċ", ".\") ;Ċ", ".\"); Ċ", "Ġ organization", "Ġo rganization", "Ġorgan ization", "Ġorganiz ation", "r ender", "re nder", "ren der", "rend er", "rende r", "Ġ Application", "ĠApp lication", "ĠAp plication", "ĠAppl ication", "Ġ summer", "Ġs ummer", "Ġsum mer", "Ġsumm er", "ex pected", "exp ected", "expect ed", "N A", "Ġ rap", "Ġr ap", "Ġra p", "_ obj", "_o bj", "_ob j", "Ġ surface", "Ġs urface", "Ġsur face", "Ġsurf ace", "Ġ PUR", "ĠP UR", "ĠPU R", "Ġ },ĊĊ", "Ġ} ,ĊĊ", "Ġ},Ċ Ċ", "Ġ}, ĊĊ", "Ġ variables", "Ġvariable s", "Ġvari ables", "( message", "(m essage", "Ġo pin", "Ġop in", "Ġopi n", ". back", ".b ack", ".ba ck", "а н", "аР½", "Ġ workers", "Ġwork ers", "Ġwor kers", "Ġworker s", "v m", "C o", "ught er", "ugh ter", "Ġ master", "Ġm aster", "Ġma ster", "Ġmas ter", "Ġmast er", "Ġ \"\",", "Ġ\" \",", "Ġ\"\" ,", "Ġ stories", "Ġst ories", "Ġstor ies", "Ġsto ries", ". User", ".U ser", ".Use r", "Ġcele br", "in ese", "ine se", "ines e", "B S", "Ġ Command", "ĠCom mand", "ĠComm and", "ash board", "Ġ og", "Ġo g", "k g", ". image", ".i mage", ".im age", ".imag e", ". style", ".st yle", "Ġ steps", "Ġst eps", "Ġstep s", "Ġste ps", "Ġ Ben", "ĠB en", "ĠBe n", "( args", "(arg s", "(ar gs", "4 04", "40 4", "Ġ Person", "ĠP erson", "ĠPer son", "ĠPers on", ", y", "Ġoffic ials", "Ġofficial s", "| Ċ", "Ġ skills", "Ġs kills", "Ġsk ills", "Ġskill s", "v c", "Ġ builder", "Ġb uilder", "Ġbu ilder", "Ġbuild er", "Ġ gar", "Ġg ar", "Ġga r", "A ccount", "Ac count", "Acc ount", "Ġ Auth", "ĠA uth", "ĠAut h", "ĠAu th", "ç Ķ", "' ])Ċ", "'] )Ċ", "']) Ċ", "Ġ AT", "ĠA T", "n n", ". Int", ".I nt", ".In t", "SS ERT", "Ġ effective", "Ġe ffective", "Ġeffect ive", "Ġeff ective", "LE TE", "LET E", "Ġ tools", "Ġt ools", "Ġto ols", "Ġtoo ls", "Ġtool s", "A RD", "AR D", "Ġ digital", "Ġd igital", "Ġdig ital", "Ġdigit al", "1 91", "19 1", "D ouble", "Do uble", "Dou ble", "Ġ Find", "ĠF ind", "ĠFin d", "ĠFi nd", "R C", "Ġ inline", "Ġin line", "/ r", "A RAM", "AR AM", "ARA M", "A SK", "AS K", "Ġ intent", "Ġin tent", "Ġint ent", "Ġinte nt", "a ight", "ai ght", "_ addr", "_add r", "_ad dr", "Ġ requests", "Ġre quests", "Ġrequest s", "Ġrequ ests", ". first", ".f irst", ".fi rst", "Ġ debug", "Ġde bug", "Ġdeb ug", "Ġ spent", "Ġs pent", "Ġsp ent", "Ġspe nt", "( )));Ċ", "() ));Ċ", "()) );Ċ", "())) ;Ċ", "())); Ċ", "Å Ľ", "Ġpr incip", "Ġprin cip", "Ġprinc ip", "Log ger", "Lo gger", "cl udes", "clude s", "clud es", ". use", ".u se", ".us e", "Ġs urv", "Ġsu rv", "Ġsur v", "m edia", "med ia", "me dia", "medi a", "Ġ February", "ĠFe bruary", "ĠFeb ruary", "Ġ Mac", "ĠM ac", "ĠMa c", "Ġ missing", "Ġm issing", "Ġmiss ing", "Ġmis sing", "Ġ wife", "Ġw ife", "Ġwi fe", "Ġt alking", "Ġtalk ing", "Ġtal king", "Ġ Make", "ĠM ake", "ĠMa ke", "ĠMak e", "Ġ cart", "Ġc art", "Ġcar t", "Ġca rt", "Ġ located", "Ġloc ated", "Ġlocate d", "E nc", "En c", "- a", "ch ron", "chr on", "Ġ cards", "Ġc ards", "Ġcar ds", "Ġcard s", "Ġg uy", "Ġgu y", "Ġ pers", "Ġp ers", "Ġper s", "Ġpe rs", "Ġ Yes", "ĠY es", "ĠYe s", "at ever", "ate ver", "Ġ Ang", "ĠA ng", "ĠAn g", "o lar", "ol ar", "ola r", "Ġ Even", "ĠE ven", "ĠEv en", "ĠEve n", "Ġ accur", "Ġacc ur", "Ġac cur", "Ġ Power", "ĠP ower", "ĠPo wer", "ĠPow er", "Ġ Gold", "ĠG old", "ĠGo ld", "ĠGol d", "c lear", "cl ear", "cle ar", "P rocess", "Pro cess", "Proc ess", "Ġ records", "Ġrec ords", "Ġrecord s", "Ġk illed", "Ġkill ed", "Ġkil led", ". clear", ".c lear", ".cl ear", "ĠWARRANT IES", "Ġ purpose", "Ġp urpose", "Ġpur pose", "Ġpurpos e", "p anel", "pan el", "pa nel", "pane l", "J ECT", "JE CT", "ÃŃ a", "Ġex erc", "Ġexe rc", "W S", "/ L", ". exports", ".ex ports", ".exp orts", ".export s", "Ġ ___", "Ġ_ __", "Ġ__ _", "Ġ sin", "Ġs in", "Ġsi n", "S ervlet", "Serv let", "Ġd é", ". delete", ".de lete", ".del ete", "r oke", "ro ke", "rok e", "S l", "u gh", "ug h", "e ars", "ear s", "ea rs", "Ġ pointer", "Ġpoint er", "Ġpo inter", "Ġ hop", "Ġh op", "Ġho p", "all ery", "alle ry", "aller y", "Ġ obs", "Ġo bs", "Ġob s", "c overy", "co very", "cover y", "cov ery", "ĉ char", "ĉc har", "ĉch ar", "ĉ ĉĉĉĉĉĉĉĉĉ", "ĉĉ ĉĉĉĉĉĉĉĉ", "ĉĉĉĉ ĉĉĉĉĉĉ", "ĉĉĉ ĉĉĉĉĉĉĉ", "ĉĉĉĉĉ ĉĉĉĉĉ", "ĉĉĉĉĉĉ ĉĉĉĉ", "ĉĉĉĉĉĉĉĉ ĉĉ", "ĉĉĉĉĉĉĉ ĉĉĉ", "ĉĉĉĉĉĉĉĉĉ ĉ", "ĉ def", "ĉd ef", "ĉde f", "o city", "oc ity", "oci ty", "it chen", "itch en", "u lations", "ul ations", "ulation s", "Ġ FIT", "ĠF IT", "ĠFI T", "Ġ ).", "Ġ) .", "straint s", "stra ints", "strain ts", "v ention", "vent ion", "ven tion", "Ġ requires", "Ġre quires", "Ġrequire s", "Ġrequ ires", "Ġ Oper", "ĠO per", "ĠOp er", "M E", "O UNT", "OUN T", "OU NT", "al let", "all et", "alle t", "Ġ norm", "Ġn orm", "Ġno rm", "Ġnor m", "I RE", "IR E", "ex as", "Ġ programs", "Ġpro grams", "Ġpr ograms", "Ġprogram s", "Ġprog rams", "Ġ weak", "Ġwe ak", "' .$", "'. $", "u ing", "ui ng", "uin g", "ĉ ĠĠĠĠĠĠĠ", "ĉĠĠĠ ĠĠĠĠ", "ĉĠ ĠĠĠĠĠĠ", "ĉĠĠ ĠĠĠĠĠ", "ĉĠĠĠĠĠ ĠĠ", "ĉĠĠĠĠ ĠĠĠ", "ĉĠĠĠĠĠĠ Ġ", "Ġ mil", "Ġm il", "Ġmi l", "Ġ firm", "Ġf irm", "Ġfi rm", "Ġfir m", "init ely", "inite ly", "_ VALUE", "_VAL UE", "ap se", "aps e", "atis f", "ati sf", "Ġ demand", "Ġd emand", "Ġde mand", "Ġdem and", "_ mod", "_m od", "_mo d", "Ġde scribed", "Ġdes cribed", "Ġdescri bed", "Ġdescribe d", "Ġ places", "Ġp laces", "Ġpl aces", "Ġplace s", "Ġplac es", "Ġpla ces", "V ID", "VI D", "Ġ alone", "Ġal one", "Ġalo ne", "Ġ export", "Ġex port", "Ġexp ort", "Ġexpo rt", "Ġ vec", "Ġv ec", "Ġve c", "Ġ Max", "ĠM ax", "ĠMa x", "Ġ activities", "Ġact ivities", "Ġactiv ities", "ic tures", "ict ures", "icture s", "g ener", "ge ner", "gen er", "gene r", "Ġ ma", "Ġm a", "Ĥ ¬", "Ġ expression", "Ġex pression", "Ġexp ression", "Ġexpress ion", "Ġexpr ession", "C allback", "Call back", "_ content", "_c ontent", "_con tent", "_cont ent", "Ġ Most", "ĠM ost", "ĠMo st", "ĠMos t", "Ġ testing", "Ġt esting", "Ġtest ing", "Ġtes ting", "E C", "CH ANT", "CHA NT", "CHAN T", "Ġ adjust", "Ġad just", "Ġadj ust", ".Th reading", ".Thread ing", "( ctx", "(c tx", "(ct x", "Ġ agree", "Ġa gree", "Ġag ree", "Ġagre e", "Ġagr ee", "i ghest", "ig hest", "igh est", "Ġ ui", "Ġu i", "Ġ Law", "ĠL aw", "ĠLa w", ". Y", "> < ?", "Ġ pod", "Ġp od", "Ġpo d", "- lg", "-l g", "âĢĿ ĊĊ", "âĢĿĊ Ċ", "Ġ describe", "Ġde scribe", "Ġdes cribe", "Ġdescri be", "Ġdescr ibe", "Ġ European", "ĠE uropean", "ĠEurope an", "ĠEurop ean", "- sh", "-s h", "ĠPUR POSE", "O RY", "OR Y", "Ġcon vers", "Ġconv ers", "Ġconver s", "Ġ Illuminate", "ĠI lluminate", "ĠIllum inate", "Ġ Av", "ĠA v", "( ch", "(c h", "? \"", "c hen", "ch en", "che n", "i ma", "im a", "D ocument", "Doc ument", "Ġ operations", "Ġoper ations", "Ġoperation s", "w in", "wi n", "ĉ function", "ĉf unction", "ĉfunc tion", "ĉfun ction", ". Image", ".I mage", ".Im age", "Ġs cen", "Ġsc en", "Ġsce n", "/ h", "Ġ SC", "ĠS C", "Ġ explo", "Ġexp lo", "Ġexpl o", ": %", "/ **čĊ", "/* *čĊ", "/** čĊ", "N AME", "NA ME", "æ Ī", "( var", "(v ar", "(va r", "Ġ director", "Ġd irector", "Ġdirect or", "Ġdir ector", "Ġdire ctor", "O NG", "ON G", "Ġ yield", "Ġy ield", "Ġyi eld", "Ġfe et", "Ġfee t", "Ġ Search", "ĠS earch", "ĠSe arch", "ĠSea rch", "Ġ Il", "ĠI l", "Ġrest aur", "Ġresta ur", "Ġrestau r", "d uc", "du c", "Ġ integer", "Ġint eger", "Ġinteg er", "Ġinte ger", "1 07", "10 7", "Ġ' ';Ċ", "Ġ'' ;Ċ", "Ġ''; Ċ", "Ġhigh ly", "check ed", "ĠPART IC", "ER CHANT", "ï¼ ī", "Ġ optim", "Ġop tim", "Ġopt im", "Q ueue", "Que ue", "Ġ LI", "ĠL I", "it ation", "ita tion", "itat ion", "Ġ transport", "Ġtrans port", "Ġtran sport", "iss ion", "f ill", "fi ll", "fil l", "us ion", "usi on", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĉ bool", "ĉb ool", "- th", "-t h", "u pt", "up t", "Ġ essential", "Ġess ential", "an ted", "ant ed", "ante d", "Ġbenef its", "Ġbenefit s", "Ġbene fits", "ĉ S", "' ;čĊ", "'; čĊ", "i ki", "ik i", "Ġ girls", "Ġgirl s", "Ġgir ls", "i ced", "ic ed", "ice d", "b uffer", "buf fer", "bu ffer", "buff er", "] +", "Ġ socket", "Ġs ocket", "Ġso cket", "Ġsock et", "Ġsoc ket", "Ġ prices", "Ġp rices", "Ġpr ices", "Ġprice s", "Ġpri ces", "Ġ Fre", "ĠF re", "ĠFr e", "Ġ sat", "Ġs at", "Ġsa t", "Ġ wood", "Ġw ood", "Ġwo od", "Ġwoo d", "Menu Item", "A RG", "AR G", "Ġ Admin", "ĠAd min", "O WN", "OW N", "d k", "Ġ reset", "Ġre set", "Ġres et", "Ġ forms", "Ġfor ms", "Ġform s", "Ġfo rms", "Ġ и", "ĠÐ ¸", "æ ĸ", "Ġ Tuesday", "ĠT uesday", "ĠTues day", "1 09", "10 9", "Ġ Initialized", "ĠInitial ized", "ĠInitialize d", "ĠInit ialized", "_ train", "_t rain", "_tr ain", "_tra in", "o rary", "or ary", "ora ry", "ate gor", "ateg or", "atego r", "Ġ dt", "Ġd t", "T otal", "To tal", "Tot al", "con struct", "i lies", "il ies", "ili es", "Ġgu ys", "Ġguy s", "е ÑĢ", "Ġ instruction", "Ġin struction", "Ġinstr uction", "Ġinstruct ion", "0 10", "01 0", "y led", "yle d", "yl ed", "Ġ internet", "Ġin ternet", "Ġint ernet", "Ġinter net", "Ġintern et", "et adata", "eta data", "a dy", "ad y", "f aces", "face s", "fa ces", "fac es", "j ection", "ject ion", "je ction", "jec tion", "Ġ Jack", "ĠJ ack", "ĠJac k", "ĠJa ck", "Ġ rect", "Ġre ct", "Ġr ect", "Ġrec t", "[ -", "Ġ Leg", "ĠL eg", "ĠLe g", "Ġ devices", "Ġdev ices", "Ġdevice s", "O C", "Ġ *čĊ", "Ġ* čĊ", "o ration", "or ation", "ora tion", "er tain", "ert ain", "erta in", "Ġ guard", "Ġg uard", "Ġgu ard", "Ġguar d", "o stream", "ost ream", "Ġ enum", "Ġe num", "Ġen um", ". layout", ".l ayout", "Ġ \";Ċ", "Ġ\" ;Ċ", "Ġ\"; Ċ", "v oke", "vo ke", "Ġ Ok", "ĠO k", "H ome", "Ho me", "Hom e", "( tr", "(t r", "E TH", "ET H", "Ġ delay", "Ġd elay", "Ġde lay", "Ġdel ay", "Ġdela y", "Ġ purchase", "Ġp urchase", "Ġpurch ase", "d c", "Ġ aren", "Ġa ren", "Ġare n", "Ġar en", "_ once", "_on ce", "_o nce", "ĉ ĉĉĉĊ", "ĉĉ ĉĉĊ", "ĉĉĉĉ Ċ", "ĉĉĉ ĉĊ", "r or", "ro r", "d raw", "dr aw", "dra w", ". run", ".r un", ".ru n", "( model", "(m odel", "(mod el", "(mode l", "Time out", "l ik", "li k", "Ġ Arg", "ĠA rg", "ĠAr g", ". en", ".e n", "Ġ fish", "Ġf ish", "Ġfi sh", "Ġfis h", "c py", "cp y", "_ fe", "_f e", "ERCHANT ABILITY", "( X", "_ output", "_out put", "? ?", "Ġ jo", "Ġj o", "and ard", "anda rd", "Ġ doll", "Ġd oll", "Ġdo ll", "Ġdol l", "er rors", "err ors", "error s", "erro rs", "_ base", "_b ase", "ĠPARTIC ULAR", "Ġ leader", "Ġle ader", "Ġlead er", "Ġcom par", "Ġco mpar", "Ġcomp ar", "Ġd oub", "Ġdo ub", "Ġdou b", "Ġ Vis", "ĠV is", "ĠVi s", "Stack Trace", "- C", "ĠSt ud", "stit ute", "M ore", "Mo re", "Mor e", "Ġ Description", "ĠD escription", "ĠDe scription", "ĠDes cription", "W ARE", "WA RE", "WAR E", "a ds", "ad s", "Ġ к", "ĠÐ º", "b ind", "bin d", "bi nd", "= self", "=s elf", "e mploy", "em ploy", "emp loy", "empl oy", "emplo y", "[ n", ". all", ".a ll", ".al l", "- B", "& &", "a lm", "al m", "Ġ culture", "Ġc ulture", "Ġcult ure", "Ġcul ture", "h ouse", "ho use", "hou se", "Ġs uffer", "Ġsu ffer", "Ġsuff er", "Ġsuf fer", "Ġ '%", "Ġ' %", "Ġ straight", "Ġstr aight", "Ġstra ight", "Ġ Star", "ĠS tar", "ĠSt ar", "ĠSta r", "u do", "ud o", "Ġ ded", "Ġd ed", "Ġde d", "Ġ COM", "ĠC OM", "ĠCO M", "Ġ confirm", "Ġcon firm", "Ġconf irm", "Ġ Good", "ĠG ood", "ĠGo od", ". sc", ".s c", "____ ____________", "________ ________", "____________ ____", "D R", "Config uration", "Date Time", "Ġ advert", "Ġad vert", "Ġadv ert", "Ġ couldn", "Ġcould n", "a sync", "as ync", "asy nc", "st ack", "sta ck", "' )čĊ", "') čĊ", "K it", "Ki t", "Ġh ous", "Ġho us", "Ġm echan", "Ġme chan", "Ġmec han", "Ġmech an", "r ate", "ra te", "rat e", "2 04", "20 4", "Ġ audio", "Ġa udio", "Ġaud io", "Ġau dio", "Ġaudi o", "ĉ cout", "ĉc out", "c ores", "co res", "core s", "cor es", "Ġ spot", "Ġs pot", "Ġsp ot", "Ġspo t", "Ġin creasing", "Ġincre asing", "Ġ ##", "Ġ# #", ") ))", ")) )", "p oints", "point s", "po ints", "poi nts", "Ġcom pared", "Ġcomp ared", "Ġcompar ed", "Ġcompare d", "l ig", "li g", "Ġ behavior", "Ġbeh avior", "Ġ BY", "ĠB Y", "Ġ Att", "ĠA tt", "ĠAt t", "c raft", "cr aft", "he aders", "head ers", "header s", "hea ders", "e te", "et e", "end region", "Ġ detail", "Ġd etail", "Ġde tail", "Ġdet ail", "U LE", "UL E", "Ġ Common", "ĠCom mon", "ĠComm on", "ĉ protected", "s ton", "st on", "sto n", "ĠFIT NESS", "Ġ fresh", "Ġf resh", "Ġfr esh", "Ġfre sh", "Ġfres h", "\" >ĊĊ", "\"> ĊĊ", "\">Ċ Ċ", ". example", ".ex ample", ".exam ple", "b erg", "ber g", "be rg", "Ġ moved", "Ġm oved", "Ġmov ed", "Ġmove d", "Ġmo ved", "ĉ e", "Ġ Saturday", "ĠS aturday", "Ġ payload", "Ġp ayload", "Ġpay load", "Ä ĩ", ") :ĊĊ", "):Ċ Ċ", "): ĊĊ", "Ġb ey", "Ġbe y", "u rer", "ur er", "ure r", "< script", " ,", "\" > < ?", "( num", "(n um", "ĉ inline", "ĉin line", "Trans action", ". On", ".O n", "Ġ mail", "Ġm ail", "Ġma il", "Ġmai l", "r ey", "re y", "res ults", "result s", "Ġ nav", "Ġn av", "Ġna v", "I MIT", "IM IT", "_ ids", "_id s", "_i ds", "M ake", "Ma ke", "å Ĭ", "M odal", "Mod al", "Mo dal", "Ġ LOG", "ĠL OG", "ĠLO G", "Ġ Sur", "ĠS ur", "ĠSu r", "Ġinstance of", "Ġ overall", "Ġover all", "Ġ Information", "ĠIn formation", "ĠInform ation", "Ġ construction", "Ġcon struction", "Ġconstruct ion", "Ġconstr uction", "_ FILE", "_F ILE", "b ut", "bu t", "Ġm edic", "Ġme dic", "Ġmed ic", "Ġmedi c", "Ġ duration", "Ġd uration", "Ġdu ration", "Ġdur ation", "it ness", "a gent", "ag ent", "age nt", "agen t", "A V", "Ġ seven", "Ġs even", "Ġse ven", "Ġsev en", "o lf", "ol f", "Ġ }}Ċ", "Ġ} }Ċ", "Ġ}} Ċ", "\" ],Ċ", "\"] ,Ċ", "\"], Ċ", "1 70", "17 0", "1 22", "12 2", "Ġ calling", "Ġc alling", "Ġcall ing", "Ġcal ling", "Ġ ans", "Ġa ns", "Ġan s", "th rows", "throw s", "thr ows", "thro ws", "or izontal", "Ġ useState", "Ġuse State", ". fl", ".f l", "Ġ Status", "ĠS tatus", "ĠSt atus", "ĠStat us", "Ġ Online", "ĠOn line", "R R", "Ġ Rich", "ĠR ich", "ĠRic h", "ĠRi ch", "ĠH ill", "ĠHi ll", "ĠHil l", "Ġ brain", "Ġb rain", "Ġbr ain", "Ġbra in", "Ġfollow ed", "Ġfoll owed", "2 40", "24 0", "e mic", "em ic", "emi c", "Ġs light", "Ġsl ight", "Ġ insurance", "Ġins urance", ". Array", ".A rray", ".Ar ray", "Ġ abstract", "Ġa bstract", "Ġab stract", "Ġabs tract", "Ġabst ract", "Ġ Sum", "ĠS um", "ĠSu m", "re direct", "red irect", "redi rect", "o wner", "ow ner", "own er", "( msg", "(m sg", "(ms g", "Ġ Clinton", "ĠCl inton", "ĠClin ton", "ĠClint on", "ĠCli nton", "N on", "No n", "ĉ ex", "ĉe x", "Ġ volume", "Ġv olume", "Ġvol ume", "Ġvolum e", "Ġ EventArgs", "ĠEvent Args", "- L", "Ġ Dim", "ĠD im", "ĠDi m", "Ġ Mart", "ĠM art", "ĠMar t", "ĠMa rt", "Ġ cursor", "Ġc ursor", "Ġcurs or", "Ġcurso r", "Ġ implementation", "Ġim plementation", "Ġimplement ation", "ur red", "urre d", "urr ed", "Ġl arger", "Ġlarge r", "Ġlarg er", "Ġlar ger", ") ;ĊĊĊ", ");Ċ ĊĊ", ");ĊĊ Ċ", "); ĊĊĊ", "' +", ". transform", ".trans form", "Ġ upload", "Ġup load", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "D raw", "Dr aw", "n el", "ne l", "ĉ float", "ĉf loat", "q rt", "qr t", "Ġ Network", "ĠN etwork", "ĠNet work", "Ġ tit", "Ġt it", "Ġti t", "A xis", "Ax is", ". android", ".and roid", "Ġ completed", "Ġcom pleted", "Ġcomp leted", "Ġcomple ted", "Ġcomplet ed", "Ġcompl eted", "Ġcomplete d", "Ġ mur", "Ġm ur", "Ġmu r", "Ġ columns", "Ġcolumn s", "Ġcolum ns", "x c", "Ġ supply", "Ġs upply", "Ġsup ply", "Ġsuppl y", "Ġsupp ly", "im inal", "imi nal", "imin al", "Ġ spr", "Ġs pr", "Ġsp r", "================ ================================================", "================================ ================================", "================================================ ================", "Ġ units", "Ġun its", "Ġunit s", "Ġuni ts", "( u", "m i", "re place", "rep lace", "[ key", "[k ey", "à ¹", "an tic", "ant ic", "anti c", "Ġ payment", "Ġp ayment", "Ġpay ment", ", B", "Ġ Apple", "ĠApp le", "ĠAp ple", "ĠAppl e", "g in", "gi n", "Re quired", "Require d", "# +", "l ands", "land s", "la nds", "lan ds", "Ġ squ", "Ġs qu", "Ġsq u", "Ġ factor", "Ġf actor", "Ġfact or", "Ġfa ctor", "Ġfac tor", "Ġfacto r", "d ec", "de c", "Ġ strength", "Ġst rength", "Ġstr ength", "Ġstre ngth", "Ġstren gth", "Ġ boy", "Ġb oy", "Ġbo y", "Ġ balance", "Ġb alance", "Ġbal ance", "Ġ sources", "Ġs ources", "Ġsource s", "Ġsour ces", "s creen", "sc reen", "scr een", "- top", "-t op", "-to p", "Ġ Amazon", "ĠA mazon", "ĠAm azon", "ĠAma zon", "Ġ hidden", "Ġh idden", "Ġhi dden", "Ġhid den", "е ÑĤ", "_ client", "_c lient", "_cl ient", "_cli ent", "Ġ eat", "Ġe at", "Ġea t", ". display", ".d isplay", ".dis play", "Ġ »", "Ġ »", "Ġ trigger", "Ġtr igger", "Ġtri gger", "Ġtrig ger", "an ager", "ana ger", "Ġ tro", "Ġt ro", "Ġtr o", "Ġ claims", "Ġcl aims", "Ġclaim s", "Ġcla ims", "f ord", "fo rd", "for d", "Ġ Company", "ĠCom pany", "ĠComp any", "Ġ gift", "Ġg ift", "Ġgi ft", "Ġgif t", ", :", "_ app", "_a pp", "_ap p", "h andle", "han dle", "hand le", "Ġ produce", "Ġp roduce", "Ġpro duce", "Ġprodu ce", "Ġprod uce", "/ lib", "/l ib", "/li b", "5 12", "51 2", "Ġ -*", "Ġ- *", "ĉ set", "ĉs et", "ĉse t", "' ];", "'] ;", "a rc", "ar c", "a nder", "an der", "and er", "ande r", "Ġ Engine", "ĠE ngine", "ĠEng ine", "Ġ attributes", "Ġat tributes", "Ġattribute s", "Ġattrib utes", "t ask", "ta sk", "tas k", "< =", "( N", "Ġ warm", "Ġw arm", "Ġwar m", "Ġwa rm", "wh ich", "Ġ Fore", "ĠF ore", "ĠFor e", "ĠFo re", "ag nost", "agn ost", "m ys", "my s", "Ġ tal", "Ġt al", "Ġta l", "Ġ Sal", "ĠS al", "ĠSa l", "g i", "Ġ Print", "ĠP rint", "ĠPr int", "ĠPri nt", "Ġ TRUE", "ĠTR UE", "Ġ о", "ĠÐ ¾", ". UI", ".U I", "Ġ flash", "Ġf lash", "Ġfl ash", "Ġfla sh", "ro perty", "rop erty", ". location", ".l ocation", ".loc ation", ".lo cation", "Ġ Mill", "ĠM ill", "ĠMil l", "ĠMi ll", "b i", "con tr", "cont r", ". request", ".re quest", ".req uest", "Ġ Sam", "ĠS am", "ĠSa m", "Ġ negative", "Ġn egative", "Ġneg ative", "k it", "ki t", "Ġ sett", "Ġs ett", "Ġse tt", "Ġset t", ".print StackTrace", "a be", "ab e", "ĉ i", "Ġ burn", "Ġb urn", "Ġbu rn", "Ġbur n", "Ġs ociety", "Ġsoci ety", "C ache", "Ca che", "Ġ Security", "ĠS ecurity", "ĠSe curity", "ĠSec urity", ". models", ".model s", ".mod els", ".mode ls", "ĠWARRANT Y", "_ up", "_u p", "ce ive", "Ġ clients", "Ġc lients", "Ġcl ients", "Ġclient s", "Ġcli ents", ". Tr", ".T r", "Ġprovid ing", "Ġprov iding", "Ġr out", "Ġro ut", "Ġrou t", "m aterial", "mat erial", "mate rial", "Ġ ||Ċ", "Ġ| |Ċ", "Ġ|| Ċ", "Ġ Ser", "ĠS er", "ĠSe r", "Ġ Office", "ĠOff ice", "FT WARE", "Ġ '$", "Ġ' $", "Ġf oc", "Ġfo c", "Ġex cell", "Ġexc ell", "Ġexcel l", "Ġexce ll", "Ġ cat", "Ġc at", "Ġca t", "n ormal", "norm al", "nor mal", "Ġd etermine", "Ġdeter mine", "Ġdetermin e", "Ġdeterm ine", "ĉ uint", "ĉu int", "ĉui nt", "P ane", "Pa ne", "Pan e", "Ġ employees", "Ġemploy ees", "Ġemployee s", "Ġ Texas", "ĠT exas", "ĠTex as", "Ġtr aff", "Ġtra ff", "Ġtraf f", "Ġ Report", "ĠRe port", "ĠRep ort", "ĠRepo rt", "an ta", "ant a", "Ġ Box", "ĠB ox", "ĠBo x", "Ġ django", "Ġd jango", "Ġdj ango", "Ġ partner", "Ġp artner", "Ġpart ner", "E B", "L INE", "LI NE", "LIN E", "Ġfe eling", "Ġfeel ing", "Ġfee ling", "Ġ civil", "Ġc ivil", "Ġci vil", "Ġciv il", "( float", "(f loat", "S ql", "Sq l", "Ġwould n", ". init", ".in it", ".i nit", ".ini t", ". left", ".l eft", ".le ft", "- v", "_ level", "_le vel", "' }", "A F", "Ġ loading", "Ġlo ading", "Ġload ing", "Ġloa ding", "Ġ Only", "ĠOn ly", "Ġ cookies", "Ġc ookies", "Ġco okies", "Ġcook ies", "Ġcookie s", "Ġ Gl", "ĠG l", "C O", "Ġ strategy", "Ġstr ategy", "Ġstrateg y", "Ġstrate gy", "(' ./", "('. /", "Ġ ship", "Ġs hip", "Ġsh ip", "p oses", "pos es", "pose s", "po ses", "Ġ signal", "Ġs ignal", "Ġsign al", "Ġsig nal", "Ġ alpha", "Ġal pha", "Ġalph a", ". pop", ".p op", ".po p", "R adius", "Rad ius", "Radi us", "Ġ replace", "Ġre place", "Ġrep lace", "Ġrepl ace", "_ DIR", "_D IR", "_DI R", "c ounter", "co unter", "count er", "bserv able", "e la", "el a", "W eight", "We ight", "Wei ght", "h ash", "ha sh", "has h", "b ose", "bo se", "bos e", "f x", "Ġ Email", "ĠE mail", "ĠEm ail", "Ġ refer", "Ġre fer", "Ġref er", "local host", "_ RO", "_R O", "i ques", "ique s", "iqu es", "iq ues", "S tep", "St ep", "Ste p", "Ġ ahead", "Ġa head", "Ġah ead", "( View", "(V iew", "Ġ Services", "ĠS ervices", "ĠService s", "ĠServ ices", "Ġ Json", "ĠJ son", "ĠJs on", "ess or", "esso r", "Ġ pun", "Ġp un", "Ġpu n", "Ġ appropriate", "Ġapp ropriate", "Ġappropri ate", "a kers", "ak ers", "ake rs", "aker s", "o sen", "os en", "ose n", "p osing", "pos ing", "po sing", "Ġ agent", "Ġa gent", "Ġag ent", "Ġage nt", "f c", "Ġ transfer", "Ġtrans fer", "Ġtransf er", "Ġ invalid", "Ġin valid", "Ġinval id", "Ġ Research", "ĠRe search", "ĠRes earch", "Vert ex", "Ver tex", "Ġ gay", "Ġg ay", "Ġga y", "Ġ journal", "Ġj ournal", "Ġjo urnal", "Ġjour nal", "[ x", "Ġ \"\",Ċ", "Ġ\" \",Ċ", "Ġ\"\" ,Ċ", "Ġ\"\", Ċ", "Ġ Well", "ĠW ell", "ĠWe ll", "ĠWel l", ". Tasks", ".T asks", ".Task s", "S pec", "Sp ec", "Spe c", "Ġ ol", "Ġo l", "Ġs pend", "Ġsp end", "Ġspe nd", "Ġ Australia", "ĠA ustralia", "ĠAustral ia", "ĠAustr alia", "M atch", "Mat ch", ".j unit", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġ MAX", "ĠM AX", "ĠMA X", "iz able", "iza ble", "cl usive", "clus ive", "_ valid", "_val id", "_va lid", "Ġ quarter", "Ġqu arter", "Ġquar ter", "Ġquart er", "y an", "ya n", "0 05", "00 5", "Ġ Edit", "ĠE dit", "ĠEd it", "ar den", "ard en", "arde n", "= new", "=n ew", "Ġ frag", "Ġf rag", "Ġfr ag", "Ġfra g", "B it", "Bi t", "z i", "a ine", "ain e", "ai ne", "u dd", "ud d", ". Object", ".O bject", ".Obj ect", "de bug", "deb ug", "Ġ cash", "Ġc ash", "Ġca sh", "Ġcas h", "_ IM", "_I M", "Ġ een", "Ġe en", "Ġee n", "Ġ commercial", "Ġcom mercial", "Ġcomm ercial", "Ġcommerc ial", "Ġ Video", "ĠV ideo", "ĠVi deo", "ĠVid eo", "ĠVide o", "l oader", "lo ader", "load er", "Ġ fixed", "Ġf ixed", "Ġfix ed", "Ġfi xed", "Ġ applications", "Ġapp lications", "Ġapplication s", "Ġapplic ations", "Ġappl ications", "Ġ _,", "Ġ_ ,", "Ġ Russia", "ĠR ussia", "ĠRuss ia", "it ect", "ite ct", "_ (", "Ġ Block", "ĠB lock", "ĠBl ock", "ĠBlo ck", "ĠBloc k", "Ġ san", "Ġs an", "Ġsa n", "Ġ Tom", "ĠT om", "ĠTo m", "Ġ perhaps", "Ġper haps", "Ġ sig", "Ġs ig", "Ġsi g", "le vant", "lev ant", "Ġc orpor", "Ġcor por", "Ġcorp or", "Ġcorpo r", "at aset", "ata set", "atas et", "r onic", "ro nic", "ron ic", "x e", "Ġ eth", "Ġe th", "Ġet h", "S ome", "So me", "Som e", "p op", "po p", "_ OK", "_O K", "Ġt end", "Ġte nd", "Ġten d", ". Res", ".R es", ".Re s", "_ and", "_a nd", "_an d", "Ġ reviews", "Ġre views", "Ġreview s", "Ġ wild", "Ġw ild", "Ġwi ld", "Ġwil d", "1 17", "11 7", "Ġ degree", "Ġd egree", "Ġde gree", "Ġdeg ree", ". O", ". objects", ".object s", ".obj ects", "_ args", "_arg s", "_ar gs", "n il", "ni l", "Ġ disabled", "Ġdis abled", "Ġdisable d", "P arent", "Par ent", "Pa rent", "Paren t", "Ġ notes", "Ġn otes", "Ġnot es", "Ġno tes", "Ġnote s", "Ġ \"\"Ċ", "Ġ\" \"Ċ", "Ġ\"\" Ċ", "( state", "(st ate", "(stat e", "i strict", "istr ict", "Ġ logging", "Ġlog ging", ". IO", ".I O", "Ġ Mal", "ĠM al", "ĠMa l", "D M", "Ġ xml", "Ġx ml", "Ġxm l", "Ġ Robert", "ĠRob ert", "ĠRo bert", "e len", "el en", "ele n", "l ayout", "lay out", "f ol", "fo l", "' ]))", "'] ))", "']) )", ", b", "Ġ Jer", "ĠJ er", "ĠJe r", "f ilename", "file name", "fi lename", "fil ename", "Ġ fan", "Ġf an", "Ġfa n", "Ġ Custom", "ĠC ustom", "ĠCust om", "= \"\"", "=\" \"", "Ġ Die", "ĠD ie", "ĠDi e", "B undle", ". utils", ".util s", ".ut ils", "Ġ trip", "Ġt rip", "Ġtr ip", "Ġtri p", "M B", "Ġ soft", "Ġs oft", "Ġso ft", "Ġsof t", "_ MODE", "_M ODE", "_MO DE", "_MOD E", "Ġapp licable", "Ġapplic able", "Ġappl icable", "Ġ upper", "Ġu pper", "Ġup per", "Ġupp er", "ER VER", "ERV ER", "ERVE R", "_ al", "_a l", "_ LOG", "_L OG", "_LO G", "H ere", "He re", "Her e", "w p", "Ġ Server", "ĠS erver", "ĠSer ver", "ĠServ er", "ĠServe r", "Ġ Client", "ĠC lient", "ĠCl ient", "ĠCli ent", "Ġ chem", "Ġc hem", "Ġch em", "Ġche m", "S croll", "Sc roll", "Scr oll", "Ġ highest", "Ġh ighest", "Ġhigh est", "Ġhi ghest", "Ġ Select", "ĠS elect", "ĠSe lect", "ĠSel ect", "ĠSele ct", "Ġ \"@", "Ġ\" @", "Ġ Why", "ĠW hy", "ĠWh y", "S ec", "Se c", "h eel", "he el", "hee l", "O peration", "Oper ation", "Op eration", "Opera tion", "Ġ connected", "Ġconn ected", "Ġconnect ed", "ir med", "irm ed", "Ġcit iz", "Ġ Che", "ĠC he", "ĠCh e", "Ġ forces", "Ġfor ces", "Ġforce s", "Ġforc es", "Ġ www", "Ġw ww", "Ġww w", "R oot", "Ro ot", "AN CE", "ANC E", "M any", "Man y", "Ma ny", "i cip", "ic ip", "ici p", "r gan", "rg an", "2 20", "22 0", "Ġ Tor", "ĠT or", "ĠTo r", "Ġ Press", "ĠP ress", "ĠPr ess", "ĠPres s", "ĠPre ss", "Ġ Mor", "ĠM or", "ĠMo r", "- line", "-l ine", "-li ne", "u led", "ul ed", "ule d", "> \\", "Ġ thus", "Ġt hus", "Ġth us", "Ġthu s", "Ġ Register", "ĠReg ister", "h ol", "ho l", "Ġ Chinese", "ĠCh inese", "ĠChin ese", "Ġ posted", "Ġpos ted", "Ġpost ed", "Ġpo sted", "Ġposte d", "Ġm agn", "Ġmag n", "Ġma gn", "ab ilities", "abil ities", "abilit ies", "Ġd isease", "Ġdis ease", "Ġdise ase", "Ġrem ains", "Ġremain s", "Ġ Prof", "ĠP rof", "ĠPro f", "ĠPr of", "- form", "-f orm", "-for m", "Ġ cin", "Ġc in", "Ġci n", "o rgan", "or gan", "org an", "i cate", "ic ate", "ica te", "Ġ stress", "Ġst ress", "Ġstr ess", "Ġstre ss", "] *", "Ġ ----------------------------------------------------------------", "Ġ---------------- ------------------------------------------------", "Ġ------------------------------------------------ ----------------", "Ġ-------------------------------- --------------------------------", "Ġ------------------------------------------------------------ ----", "_ context", "_con text", "_cont ext", "or ry", "orr y", "Ġd ied", "Ġdi ed", "Ġdie d", "m at", "ma t", "Ġ starts", "Ġst arts", "Ġstart s", "Ġstar ts", "Ġsta rts", ". Message", ".M essage", "Ġ runs", "Ġr uns", "Ġrun s", "Ġru ns", "Ġ guide", "Ġg uide", "Ġgu ide", "Ġguid e", "Ġgui de", "Ġw arranty", "Ġwarrant y", "ent ials", "ential s", "enti als", "d ict", "di ct", "dic t", "Ġ Size", "ĠS ize", "ĠSi ze", "ĠSiz e", "u ler", "ul er", "ule r", "Ġres ponsible", "Ġrespons ible", "_ SET", "_S ET", "_SE T", "Ġcont aining", "Ġcontain ing", "Ġconta ining", "Ġ Price", "ĠP rice", "ĠPr ice", "ĠPri ce", "| |", "3 50", "35 0", "F S", "Ġ emp", "Ġe mp", "Ġem p", "_ button", "_b utton", "_but ton", "( uint", "(u int", "(ui nt", "Ġs uff", "Ġsu ff", "Ġsuf f", "p th", "pt h", "Ġdef initely", "Ġdefinit ely", "Ġdefinite ly", "p ute", "put e", "pu te", "Ġ marketing", "Ġmark eting", "Ġmarket ing", "Ġ WH", "ĠW H", "Ġ Sie", "ĠS ie", "ĠSi e", "+ =", "OL OR", "Ġ consult", "Ġcons ult", "Ġconsul t", "Ġ signed", "Ġs igned", "Ġsign ed", "Ġsig ned", "Ġ sequence", "Ġse quence", "Ġsequ ence", "l ee", "le e", "Ġ requirements", "Ġrequire ments", "Ġrequirement s", "h y", "Ex press", "Exp ress", "Expr ess", "M T", "s ey", "se y", "Ġ ult", "Ġu lt", "Ġul t", "å ®", "ellig ence", "elli gence", "Ġ analy", "Ġan aly", "Ġanal y", "Ġana ly", "Ġ dress", "Ġd ress", "Ġdr ess", "Ġdre ss", "e ngine", "eng ine", "engin e", "Ġ Great", "ĠG reat", "ĠGr eat", "ĠGre at", "Ġ Android", "ĠAnd roid", "ĠAndr oid", "Ġ Alex", "ĠA lex", "ĠAl ex", "ĠAle x", "m ode", "mod e", "mo de", "D ictionary", ". Date", ".D ate", "ä ½", "V ICE", "VI CE", "VIC E", "Ġf amilies", "Ġfam ilies", "Ġfamil ies", "Ġ Russian", "ĠR ussian", "ĠRuss ian", "ĠRussia n", "ĠRus sian", "Ġ Times", "ĠT imes", "ĠTime s", "ĠTim es", "ĠTi mes", ". call", ".c all", ".ca ll", ".cal l", "$ (", "Pro file", "Pr ofile", "Prof ile", "Ġ folder", "Ġf older", "Ġfol der", "Ġfold er", "Ġfo lder", "c hes", "ch es", "che s", "Ġl egis", "Ġle gis", "Ġleg is", "_ row", "_r ow", "_ro w", "u nes", "un es", "une s", "Ù Ħ", "Ġ }).", "Ġ} ).", "Ġ}) .", "As sert", "Ass ert", "a gen", "ag en", "age n", "Ġ Hand", "ĠH and", "ĠHa nd", "ĠHan d", "I ter", "It er", "Ġbig gest", "o reach", "or each", "ore ach", "orea ch", "Ġp olic", "Ġpol ic", "Ġpo lic", "Ġ permissions", "Ġper missions", "Ġpermission s", "Ġperm issions", "Ġsh owed", "Ġshow ed", "Ġsho wed", "Ġ Element", "ĠE lement", "ĠEl ement", "ĠEle ment", "ĠElem ent", "Ġ topic", "Ġt opic", "Ġto pic", "Ġtop ic", "âĢĶ âĢĶ", "r oad", "ro ad", "Ġ Bank", "ĠB ank", "ĠBa nk", "ĠBan k", "re cord", "rec ord", "Ġ partners", "Ġpart ners", "Ġpartner s", "Ġ Ref", "ĠR ef", "ĠRe f", "ess ions", "ession s", "Ġas sess", "Ġass ess", "Ġasses s", "U ST", "US T", "Ġ Party", "ĠP arty", "ĠPart y", "ĠPar ty", "p rodu", "pr odu", "pro du", "prod u", "L C", "Ġ ul", "Ġu l", ". form", ".f orm", ".for m", "h ide", "hi de", "hid e", "c opy", "co py", "cop y", "U TF", "UT F", "Ġ SOFTWARE", "ĠSO FTWARE", "čĊ čĊčĊ", "čĊčĊ čĊ", "Ġ Lin", "ĠL in", "ĠLi n", "u na", "un a", "u gar", "ug ar", "uga r", "Ġ administration", "Ġadmin istration", "Ġadministr ation", "Ġ opening", "Ġop ening", "Ġopen ing", "Ġ scan", "Ġs can", "Ġsc an", "Ġsca n", "Ġ continued", "Ġcontin ued", "Ġcontinue d", "Ġcontinu ed", "com ponent", "comp onent", ". sp", ".s p", "Ġhapp ens", "Ġhappen s", "um my", "umm y", "Ġ PR", "ĠP R", ". File", ".F ile", "Ġ Download", "ĠD ownload", "ĠDown load", "Lo ading", "Load ing", "d i", "Ġ waiting", "Ġwait ing", "Ġwa iting", "_ ADD", "_A DD", "_AD D", "T ab", "Ta b", ". querySelector", ".query Selector", "Ġe conomy", "Ġecon omy", "Ġeconom y", "Ġec onomy", "Ġ French", "ĠF rench", "ĠFr ench", "t xt", "tx t", "Ġ fant", "Ġf ant", "Ġfa nt", "Ġfan t", "_ ;Ċ", "_; Ċ", "H older", "Hold er", "Ho lder", "Hol der", "S H", "0 04", "00 4", "Ġ numpy", "Ġn umpy", "Ġnum py", "Ġ street", "Ġst reet", "Ġstre et", "Ġ male", "Ġm ale", "Ġma le", "Ġmal e", "\\ Model", "\\M odel", "an ging", "ang ing", "angi ng", "3 33", "33 3", "Ġ Bill", "ĠB ill", "ĠBi ll", "ĠBil l", "Ġpre viously", "Ġprevious ly", "Ġprev iously", "B I", "Ġ Secret", "ĠS ecret", "ĠSe cret", "ĠSec ret", "Ġ mist", "Ġm ist", "Ġmis t", "Ġmi st", "Ġ Field", "ĠF ield", "ĠFi eld", "u ps", "up s", "Ġ Process", "ĠP rocess", "ĠPro cess", "ĠProc ess", "Ġ kept", "Ġk ept", "Ġke pt", "Ġkep t", "Ġ OT", "ĠO T", "Ġ traditional", "Ġtrad itional", "Ġtradition al", ". i", "a min", "am in", "ami n", "Ġh elps", "Ġhelp s", "Ġhel ps", "A ny", "An y", "or igin", "orig in", "ori gin", "il ters", "ilter s", "ilt ers", "j u", "d esc", "de sc", "des c", "Ġ Account", "ĠA ccount", "ĠAc count", "ĠAcc ount", "Ġ )čĊ", "Ġ) čĊ", "k top", "kt op", "ol ly", "oll y", "Ġ fs", "Ġf s", "Ġ ê", "Ġ ut", "Ġu t", "Ġ central", "Ġc entral", "Ġcent ral", "Ġcentr al", "( test", "(t est", "(te st", ". An", ".A n", "Ġs atisf", "G R", "Ġ Full", "ĠF ull", "ĠFu ll", "ĠFul l", "Ġ heat", "Ġh eat", "Ġhe at", "i ber", "ib er", "ibe r", "Ġ onto", "Ġon to", "Ġont o", "m os", "mo s", "S chema", "Sch ema", "Ġ factory", "Ġf actory", "Ġfact ory", "Ġfactor y", "Ġfacto ry", "\" .$", "\". $", "a ws", "aw s", "St atement", "State ment", "Stat ement", "( target", "(t arget", "ĉ new", "ĉn ew", ". be", ".b e", "Ġ guest", "Ġg uest", "Ġgu est", "Ġ mal", "Ġm al", "Ġma l", "A RY", "AR Y", "Ġre ached", "Ġreach ed", "Ġ mouse", "Ġm ouse", "Ġmo use", "Ġmou se", "Ġ challenge", "Ġch allenge", "Ġchall enge", "Ġchalleng e", "ĉ double", "ĉd ouble", "ĉdo uble", "Ġ Tem", "ĠT em", "ĠTe m", "Ġ terror", "Ġt error", "Ġte rror", "Ġter ror", "Ġterr or", "Ġ extract", "Ġex tract", "Ġext ract", "Ġextra ct", "Ġextr act", "_ TO", "_T O", "Ġse parate", "Ġsepar ate", "Ġseparat e", "Ġ mir", "Ġm ir", "Ġmi r", "h elp", "he lp", "hel p", "Ġ capacity", "Ġcap acity", "Ġcapac ity", "Ġcapacit y", "Ġ Property", "ĠP roperty", "ĠPro perty", "ĠProp erty", "ĠProper ty", "k an", "ka n", "_ create", "_c reate", "_cre ate", "Ġ Light", "ĠL ight", "ĠLi ght", "ĠLig ht", ". parent", ".p arent", ".par ent", ".pa rent", "Ġunder standing", "Ġunderstand ing", "Ġunderst anding", "Ġe asier", "Ġeas ier", "Ġ |=", "Ġ| =", "Ġ enh", "Ġe nh", "Ġen h", "Ġ fat", "Ġf at", "Ġfa t", "Ġpro test", "Ġprot est", "Ġprote st", "a mm", "am m", "_ AT", "_A T", "- of", "-o f", "i ls", "il s", "Ġ Oh", "ĠO h", "Ġ psych", "Ġps ych", "Ġpsy ch", "Ġ $.", "Ġ$ .", "i nds", "in ds", "ind s", "Ġ relative", "Ġrel ative", "Ġrelativ e", "Ġrelat ive", "s hop", "sh op", "s hort", "sh ort", "Ġ Sand", "ĠS and", "ĠSan d", "ĠSa nd", "2 10", "21 0", "u estion", "ues tion", "uest ion", "Ġf ear", "Ġfe ar", "/ ĊĊ", "/Ċ Ċ", ". context", ".con text", ".cont ext", "Ġ schools", "Ġschool s", "Ġsch ools", "Ġscho ols", "Ġ serve", "Ġs erve", "Ġse rve", "Ġser ve", "Ġserv e", "z one", "zo ne", "zon e", "_ db", "_d b", "Ġmajor ity", "ex ample", "exam ple", "Ġ lang", "Ġl ang", "Ġla ng", "Ġlan g", "ĉ ĠĠ", "ĉĠ Ġ", "Reg ister", "e ndo", "en do", "end o", "Ġ processing", "Ġp rocessing", "Ġprocess ing", "Ġproces sing", "_ template", "_t emplate", "_temp late", "_tem plate", "- user", "-use r", "-us er", "-u ser", "Ġ eg", "Ġe g", "C OM", "CO M", "Ġ Blue", "ĠB lue", "ĠBl ue", "ĠBlu e", "i ro", "ir o", "Ġ remote", "Ġrem ote", "Ġremot e", "Ġ IT", "ĠI T", "# !/", "#! /", "Ġred istrib", "Ġredis trib", "1 24", "12 4", "r az", "ra z", "Ġ Since", "ĠS ince", "ĠSi nce", "ĠSin ce", "Ġ Tur", "ĠT ur", "ĠTu r", "1 35", "13 5", "Back ground", "= ==", "== =", "Ġ reflect", "Ġref lect", "Ġrefl ect", "Ġp ros", "Ġpro s", "Ġpr os", "c md", "cm d", "Ġw hom", "Ġwh om", "Ġwho m", "Com pat", "Comp at", "Ġ Are", "ĠA re", "ĠAr e", "Id entifier", "Ident ifier", "ĠT hom", "ĠTh om", "_ port", "_p ort", "_po rt", "_por t", "g u", "Ġ monitor", "Ġm onitor", "Ġmon itor", "r m", "Ġ patient", "Ġp atient", "Ġpat ient", "ver ter", "vert er", "verte r", "Ġ gain", "Ġg ain", "Ġga in", "- ui", "-u i", "I nst", "In st", "Ins t", "Ġd ies", "Ġdi es", "Ġdie s", "1 18", "11 8", "A rea", "Ar ea", "Are a", "_ filter", "_f ilter", "_fil ter", "_filt er", "Ġg rat", "Ġgr at", "Ġgra t", "Ġre ality", "Ġreal ity", "ord inate", "ordin ate", "ol ved", "olve d", "olv ed", "Cont act", "Conta ct", "Ġcom pliance", "Ġcompl iance", "_ or", "_o r", "Ġ Var", "ĠV ar", "ĠVa r", "d l", "Ġ append", "Ġapp end", "Ġap pend", "Ġappe nd", "G ER", "GE R", "( max", "(m ax", ". render", ".re nder", ".r ender", "Ġ dynamic", "Ġd ynamic", "Ġdynam ic", "Ġdyn amic", "ordin ates", "ordinate s", "_ options", "_option s", "_o ptions", "_opt ions", "_ column", "_c olumn", "_col umn", "Ġb atter", "Ġbatt er", "Ġbat ter", "s pace", "sp ace", "spa ce", "L a", "Ġ Source", "ĠS ource", "ĠSour ce", "/ bin", "/b in", "Ġ dos", "Ġd os", "Ġdo s", "Ġ Board", "ĠB oard", "ĠBo ard", "Ġ Thread", "ĠT hread", "ĠTh read", "ĠThr ead", "Ġ AL", "ĠA L", "( config", "(con fig", "(conf ig", "1 44", "14 4", "Ġ Mer", "ĠM er", "ĠMe r", "Ġm iles", "Ġmil es", "Ġmi les", "Ġmile s", "_ header", "_head er", "_he ader", "ETH OD", "i zz", "iz z", "Ġb enefit", "Ġbenef it", "Ġbene fit", "Ġ integr", "Ġint egr", "Ġinteg r", "Ġinte gr", "( current", "(c urrent", "(cur rent", "(curr ent", "u lo", "ul o", ". default", ".d efault", ".de fault", ".def ault", "Ġ Div", "ĠD iv", "ĠDi v", "Ġ ton", "Ġt on", "Ġto n", "o th", "ot h", "er vation", "erv ation", "erva tion", "e dom", "ed om", "edo m", "Ġ baby", "Ġb aby", "Ġba by", "Ġbab y", "ce ived", "ceive d", ". top", ".t op", ".to p", "rior ity", "Ġ Local", "ĠL ocal", "ĠLo cal", "ĠLoc al", "r iage", "ri age", "ria ge", "Ġ attacks", "Ġatt acks", "Ġattack s", "Ġ hospital", "Ġh ospital", "Ġhosp ital", "1 68", "16 8", "Ġ female", "Ġf emale", "Ġfe male", "Ġfem ale", "Ġ Login", "ĠLog in", "ĠLo gin", "ĠF lor", "ĠFl or", "ĠFlo r", "Ġ chain", "Ġch ain", "Ġcha in", "Ġchai n", "ash ion", "ashi on", "Text ure", "Tex ture", "S ave", "Sa ve", "Ġ farm", "Ġf arm", "Ġfa rm", "Ġfar m", ". contains", ".con tains", ".cont ains", ". Test", ".T est", ".Te st", "Ġkn ows", "Ġknow s", "Ġgener ally", "Ġgeneral ly", "ip eline", "ipe line", "ipel ine", "Ġme ant", "Ġmean t", "e ncia", "en cia", "enc ia", "enci a", "Ġn icht", "Ġni cht", "Ġnic ht", "Ġnich t", "Ġ contents", "Ġcont ents", "Ġcontent s", "Ġconten ts", "Ġconte nts", "P M", "ched ule", "( line", "(l ine", "(li ne", "C G", "j ob", "jo b", "Ġ Real", "ĠRe al", "u er", "ue r", "f irm", "fi rm", "fir m", "Ġ Ø", "e tro", "et ro", "etr o", "\" `Ċ", "\"` Ċ", "Ġ speech", "Ġs peech", "Ġspe ech", "Ġ thr", "Ġt hr", "Ġth r", "f oreach", "fo reach", "fore ach", "for each", "Ġ warn", "Ġw arn", "Ġwar n", "Ġwa rn", "ĉ l", "Ġ heavy", "Ġhe avy", "Ġheav y", "< li", " )", "_ char", "_c har", "_ch ar", "re source", "res ource", "Ġ episode", "Ġep isode", "Ġepis ode", "Ġ '_", "Ġ' _", "Ġ Es", "ĠE s", "Ġ Earth", "ĠE arth", "ĠEar th", "Âł Âł", "UP DATE", "1 33", "13 3", "Ġ Sou", "ĠS ou", "ĠSo u", "u is", "ui s", "t ypes", "type s", "ty pes", "typ es", "Ġ mas", "Ġm as", "Ġma s", "Ġ fav", "Ġf av", "Ġfa v", "Ġ construct", "Ġcon struct", "Ġconstr uct", "_ rate", "_r ate", "_ra te", "_rat e", "e ras", "er as", "era s", "Ġ |Ċ", "Ġ| Ċ", "rop erties", "Ġ external", "Ġex ternal", "Ġext ernal", "Ġextern al", "Ġexter nal", "Ġapp lied", "Ġap plied", "Ġappl ied", "Ġ prefix", "Ġp refix", "Ġpre fix", "Ġpref ix", "o ted", "ot ed", "ote d", "l ers", "le rs", "ler s", "Ġ cold", "Ġc old", "Ġco ld", "Ġcol d", "Ġ SP", "ĠS P", "Ġ Church", "ĠCh urch", "ĠChu rch", "Ġ Output", "ĠOut put", "l osed", "lo sed", "lose d", "los ed", "ç ļ", "if icate", "ific ate", "ifi cate", "ifica te", "o peration", "op eration", "ope ration", "oper ation", "he rit", "her it", "x FF", "xF F", ". env", ".e nv", ".en v", "_ err", "_e rr", "_er r", "o sh", "os h", "D irection", "Dir ection", "Direct ion", "Di rection", "Dire ction", "C ancel", "Can cel", "Ġ Frank", "ĠF rank", "ĠFr ank", "ĠFra nk", "ĠFran k", "Ġ finding", "Ġf inding", "Ġfind ing", "Ġfin ding", ". )ĊĊ", ".) ĊĊ", ".)Ċ Ċ", "Ġ router", "Ġr outer", "Ġro uter", "Ġroute r", "Ġrout er", "Ġrou ter", "ãĥ »", "s es", "se s", "Ġ crow", "Ġc row", "Ġcr ow", "Ġcro w", "= ='", "== '", "Ġ sand", "Ġs and", "Ġsa nd", "Ġsan d", "Ġ rid", "Ġr id", "Ġri d", "i ture", "it ure", "itu re", "itur e", "Ġ entre", "Ġen tre", "Ġent re", "Ġentr e", "Ġ observ", "Ġo bserv", "Ġob serv", "Ġobs erv", "Ġ vac", "Ġv ac", "Ġva c", "ð Ł", "- T", "A rt", "Ar t", "n ight", "ni ght", ". search", ".s earch", ".se arch", "Ġ exchange", "Ġex change", "Ġ district", "Ġd istrict", "Ġdi strict", "Ġdistr ict", ". os", ".o s", "Ġ department", "Ġde partment", "Ġdep artment", "Ġdepart ment", "Ġ documents", "Ġdocument s", "Ġdoc uments", "Ġcent ury", "Ġ Next", "ĠN ext", "ĠNe xt", "ĠNex t", "H ost", "Ho st", "ĠK IND", "Ġs usp", "Ġsu sp", "Ġsus p", "- P", "r end", "re nd", "ren d", ". em", ".e m", "u ite", "ui te", "uit e", "i sters", "is ters", "ist ers", "ister s", "iste rs", "( json", "(j son", "(js on", "Ġ Ann", "ĠA nn", "ĠAn n", "w t", "a ti", "at i", "Ġ HTML", "ĠHT ML", "w hen", "wh en", "D irectory", "Direct ory", "Director y", "Ġsh ut", "< a", "e dy", "ed y", "Ġ healthy", "Ġhealth y", "Ġheal thy", "Ġ temperature", "Ġt emperature", "Ġtem perature", "Ġtemper ature", "Ġ Gen", "ĠG en", "ĠGe n", "Ġ metal", "Ġm etal", "Ġme tal", "Ġmet al", "Ġmeta l", "Ġ submit", "Ġsub mit", "Ġ DO", "ĠD O", "Ġat tract", "Ġatt ract", "Ġattr act", "Ġ {};Ċ", "Ġ{ };Ċ", "Ġ{} ;Ċ", "Ġ{}; Ċ", "Ġ Word", "ĠW ord", "ĠWo rd", "ĠWor d", "Ġ ll", "Ġl l", "Ġse emed", "Ġsee med", "Ġseem ed", "k o", "I ED", "IE D", "Ġl abor", "Ġla bor", "Ġlab or", ". Context", ".Cont ext", ".Con text", "Ġ asset", "Ġas set", "Ġass et", "y ou", "yo u", "Ġ cars", "Ġc ars", "Ġcar s", "Ġca rs", "Ġ Column", "ĠC olumn", "ĠCol umn", "ĠColum n", "Ġ ré", "Ġr é", "Ġ square", "Ġs quare", "Ġsqu are", "Ġ NSString", "ĠNS String", "ĠNSS tring", "âĢĿ ,", "a pes", "ap es", "ape s", ". ..Ċ", ".. .Ċ", "... Ċ", "Ġ thanks", "Ġth anks", "Ġthan ks", "Ġthank s", "( props", "(p rops", "(pro ps", "(pr ops", "(prop s", "Ġ tick", "Ġt ick", "Ġti ck", "Ġtic k", "Ġ experiment", "Ġex periment", "Ġexper iment", "Ġexperi ment", "Ġpr ison", "Ġpri son", "Ġpris on", "t ree", "tr ee", "tre e", "- text", "-t ext", "-te xt", "Ġ IOException", "ĠIO Exception", "- width", "-w idth", "_ STATUS", "_ST ATUS", "_STAT US", "f ast", "fa st", "fas t", "- body", "-b ody", "-bo dy", "- header", "-head er", "-he ader", "Ġg uar", "Ġgu ar", "c rete", "cre te", "cret e", "cr ete", "Ġ Tim", "ĠT im", "ĠTi m", "Ġcl early", "Ġclear ly", "Ġ Republican", "ĠRepublic an", "Ġ justify", "Ġjust ify", "и ÑĤ", "ĉ ĠĠĠĠ", "ĉĠĠĠ Ġ", "ĉĠ ĠĠĠ", "ĉĠĠ ĠĠ", "c ache", "ca che", "cac he", "; //", ";/ /", "Ġ presence", "Ġpres ence", "Ġf actors", "Ġfact ors", "Ġfa ctors", "Ġfac tors", "Ġfactor s", "Ġfacto rs", "Ġ employee", "Ġe mployee", "Ġemploy ee", "] ))", "]) )", "M ember", "Mem ber", "Ġ selector", "Ġse lector", "Ġselect or", "Ġsel ector", "Ġsele ctor", "b or", "bo r", "Ġ Mex", "ĠM ex", "ĠMe x", "çļ Ħ", "u tex", "ut ex", "ute x", "_ tag", "_t ag", "_ta g", "ail ure", "Ġ Net", "ĠN et", "ĠNe t", "Ġre li", "Ġr eli", "Ġrel i", "E G", "Ġ fprintf", "Ġf printf", "Ġ teen", "Ġt een", "Ġte en", "Ġtee n", "l oss", "lo ss", "los s", "Ġle aving", "1 34", "13 4", "De legate", "Ġ beat", "Ġb eat", "Ġbe at", "Ġ minute", "Ġmin ute", "Ġminut e", "sub scribe", "subs cribe", "Ġred istribute", "Ġredistrib ute", "Ġredis tribute", "Con stants", "Constant s", "Const ants", "Ġc ancer", "Ġcan cer", "Ġcanc er", "/ {", "B L", "Ġ span", "Ġs pan", "Ġsp an", "Ġspa n", "Ġ Child", "ĠCh ild", "ĠChi ld", "C enter", "Cent er", "Ġ earth", "Ġe arth", "Ġear th", "Y S", "Ġ Level", "ĠLe vel", "ĠLev el", "Ġ sea", "Ġs ea", "Ġse a", ". support", ".s upport", ".sup port", ". inner", ".in ner", ". Item", ".I tem", ".It em", "il ling", "ill ing", "illi ng", "illin g", "Ġ ĠĠĠĊĠĠĠĠĊ", "ĠĠ ĠĠĊĠĠĠĠĊ", "ĠĠĠĠ ĊĠĠĠĠĊ", "ĠĠĠ ĠĊĠĠĠĠĊ", "ĠĠĠĠĊ ĠĠĠĠĊ", "Ġ Label", "ĠL abel", "ĠLa bel", "ĠLab el", "3 20", "32 0", "Ġ Est", "ĠE st", "ĠEs t", "( arg", "(a rg", "(ar g", "1 45", "14 5", "bo Box", "ĉ foreach", "ĉf oreach", "ĉfor each", "c os", "co s", "F ailed", "Fail ed", "Fa iled", "s wers", "sw ers", "swer s", "E ditor", "Ed itor", "Edit or", "r ont", "ro nt", "ron t", "Ġ MP", "ĠM P", "ex pr", "exp r", "Ġ Life", "ĠL ife", "ĠLi fe", "ĠLif e", "Ġ ??", "Ġ? ?", "ö r", "Ġ attend", "Ġatt end", "Ġatte nd", "Ġ Que", "ĠQ ue", "ĠQu e", "Ġ species", "Ġs pecies", "Ġsp ecies", "Ġspec ies", "Ġspe cies", "Ġspeci es", "- D", "Ġ aus", "Ġa us", "Ġau s", "Str uct", "Ġadv antage", "Ġadvant age", "o ston", "os ton", "ost on", "osto n", "- block", "-b lock", "-bl ock", "in itial", "init ial", "C RE", "CR E", "Ġtr uly", "Ġ compare", "Ġcom pare", "Ġcomp are", "Ġcompar e", "or ney", "orn ey", "orne y", "Ġ spect", "Ġs pect", "Ġsp ect", "Ġspec t", "Ġspe ct", "F ull", "Fu ll", "b es", "be s", "Ġ visible", "Ġv isible", "Ġvis ible", "Ġ mess", "Ġm ess", "Ġme ss", "Ġmes s", "st ances", "stance s", "sta nces", "stan ces", "Ġ cloud", "Ġc loud", "Ġcl oud", "Ġclo ud", "_ version", "_v ersion", "Ġf urn", "Ġfur n", "Ġfu rn", "ic ago", "ica go", "L OW", "LO W", "Ġ traffic", "Ġtraff ic", "Ġtra ffic", "Ġtraf fic", "Ġ fol", "Ġf ol", "Ġfo l", "ry pto", "rypt o", "Ġ declar", "Ġde clar", "Ġdec lar", "Ġdecl ar", "Ġ slot", "Ġs lot", "Ġsl ot", "Ġslo t", "Ġ Ext", "ĠE xt", "ĠEx t", "Ġ England", "ĠEng land", "ĠEngl and", "Ġ Under", "ĠU nder", "ĠUn der", "ĠUnd er", "Ġ ta", "Ġt a", "l etter", "let ter", "lette r", "lett er", "2 03", "20 3", "Ġoff icer", "Ġoffic er", "Ġoffice r", "Ġ Donald", "ĠD onald", "ĠDon ald", "Y es", "Ye s", "_ json", "_j son", "_js on", "I TableView", "IT ableView", "Ġ USE", "ĠU SE", "ĠUS E", "mploy ee", "Ġop inion", "Ġopin ion", "Ġ Aut", "ĠA ut", "ĠAu t", "b order", "bor der", "Ġad vice", "Ġadv ice", "Ġautom atically", "Ġautomatic ally", "Ġautomat ically", "is co", "isc o", "Ġ mm", "Ġm m", ". vis", ".v is", "a ml", "am l", "Ġ initialize", "Ġinitial ize", "Ġ ({", "Ġ( {", "Ġ ;ĊĊ", "Ġ; ĊĊ", "Ġ;Ċ Ċ", "Ġ generation", "Ġg eneration", "Ġgener ation", "Ġgen eration", "Ġgene ration", "Ġgenera tion", "Ġ bits", "Ġb its", "Ġbit s", "Ġbi ts", "cl ipse", "clip se", "Ġu nf", "Ġun f", "u tors", "ut ors", "uto rs", "utor s", "p lt", "pl t", "Ġ delta", "Ġd elta", "Ġdel ta", "Ġdelt a", "e stroy", "est roy", "estr oy", "estro y", "i sis", "is is", "isi s", "< br", "Ċ", "'> Ċ", "a pers", "ap ers", "ape rs", "aper s", "] (", "cont inue", "contin ue", "s pec", "sp ec", "spe c", "Ġ Road", "ĠR oad", "ĠRo ad", "A SH", "AS H", "il iar", "ili ar", "ilia r", "Ġcontin ues", "Ġcontinue s", "Ġcontinu es", "Ġ appoint", "Ġapp oint", "Ġap point", "Ġ #Ċ", "Ġ# Ċ", "Ġ Vir", "ĠV ir", "ĠVi r", "Ġ ?>\"", "Ġ? >\"", "Ġ?> \"", "Ġ bin", "Ġb in", "Ġbi n", "} \",", "}\" ,", "go ing", "e ach", "ea ch", "B D", "1 85", "18 5", "Ġ Access", "ĠA ccess", "ĠAc cess", "ĠAcc ess", "D oc", "Do c", "Ġ Management", "ĠMan agement", "ĠManage ment", "ĠMana gement", "B ER", "BE R", "as ket", "ask et", ". getInstance", ".get Instance", ".getIn stance", "1 29", "12 9", "Ġestablish ed", "s ocket", "so cket", "sock et", "soc ket", "I NS", "IN S", "ĉ virtual", "ĉv irtual", "ĉ result", "ĉres ult", "RE AD", "REA D", "_ height", "_h eight", "_he ight", "1 52", "15 2", "Ġ Font", "ĠF ont", "ĠFo nt", "ĠFon t", "Ġ ();Ċ", "Ġ( );Ċ", "Ġ() ;Ċ", "Ġ(); Ċ", "_ html", "_h tml", "_ht ml", "Ġ neighbor", "Ġne ighbor", "Ġneighb or", "Ġneigh bor", "l or", "lo r", "Ġ gather", "Ġg ather", "Ġga ther", "Ġ })ĊĊ", "Ġ} )ĊĊ", "Ġ})Ċ Ċ", "Ġ}) ĊĊ", "Ġ identity", "Ġid entity", "Ġide ntity", "Ġident ity", "Ġ fab", "Ġf ab", "Ġfa b", "p adding", "pad ding", "Ġ Route", "ĠR oute", "ĠRo ute", "ĠRou te", "ĠRout e", "Enumer able", "Enum erable", "à ´", "Ġ forced", "Ġfor ced", "Ġforce d", "Ġforc ed", "/ jquery", "/j query", ". ĊĊĊĊĊĊ", ".ĊĊ ĊĊĊĊ", ".Ċ ĊĊĊĊĊ", ".ĊĊĊĊ ĊĊ", ".ĊĊĊ ĊĊĊ", ".ĊĊĊĊĊ Ċ", "res ents", "resent s", "rese nts", "_ left", "_l eft", "_le ft", ". Param", ".P aram", ".Par am", "ĉ throw", "ĉth row", "Ġ Ham", "ĠH am", "ĠHa m", "Ġevent ually", "Ġeventual ly", "a cer", "ace r", "ac er", "p ub", "pu b", "Ġ tra", "Ġt ra", "Ġtr a", "un ique", "uni que", "uniq ue", "d el", "de l", "Ġ Florida", "ĠFl orida", "ĠFlor ida", "Ġ Clean", "ĠC lean", "ĠCl ean", "ĠCle an", "x a", "Ġ ·", "Ġ ·", "Ġ validate", "Ġvalid ate", "Ġvalida te", "Vis ual", "Ex pression", "Exp ression", "Express ion", "Expr ession", "_ func", "_f unc", "_fun c", "_fu nc", "m ember", "mem ber", "ĉ h", "t rl", "tr l", "1 36", "13 6", "ĉ G", "nap shot", "Ġ PropTypes", "ĠProp Types", "v in", "vi n", "1 53", "15 3", "] )ĊĊ", "]) ĊĊ", "])Ċ Ċ", "o wl", "ow l", "if ies", "ifi es", "ifie s", "Ġ $('.", "Ġ$ ('.", "Ġ$( '.", "Ġ$(' .", "Ġ Context", "ĠCon text", "ĠCont ext", "ĠConte xt", "Ġ Toast", "ĠTo ast", ". Key", ".K ey", "Ġoff icers", "Ġoffic ers", "Ġoffice rs", "Ġofficer s", "/ n", "s n", "un defined", "und efined", "undef ined", ". items", ".i tems", ".item s", ".it ems", "ut ow", "uto w", "a mage", "am age", "ama ge", "Ġ accounts", "Ġac counts", "Ġaccount s", "o okie", "ook ie", "oo kie", "S ection", "Se ction", "Sec tion", "ic ians", "ici ans", "ician s", "icia ns", "Ġad vis", "Ġadv is", "( is", "(i s", "[ :,", "[: ,", "Ġ France", "ĠF rance", "ĠFr ance", "ĠFranc e", "ĠFra nce", "ĠFran ce", "F unc", "Fun c", "Fu nc", "ic ious", "ici ous", "icio us", "Ġ tok", "Ġt ok", "Ġto k", "Ch annel", "Chan nel", "Ġ AD", "ĠA D", "_ NUM", "_N UM", "Ġ timeout", "Ġtime out", "l emma", "le mma", "lem ma", "r eme", "re me", "rem e", "u j", ". Al", ".A l", "u clear", "uc lear", "ucle ar", "( os", "(o s", "( \"<", "(\" <", "[ Ċ", "f etch", "fet ch", "Ġ bal", "Ġb al", "Ġba l", "Ġ guid", "Ġg uid", "Ġgu id", "Ġgui d", "- align", "-al ign", "Ġ Write", "ĠW rite", "ĠWr ite", "Ġ Once", "ĠO nce", "ĠOn ce", "ĠOnc e", "utow ired", "OD ULE", "Ġ pitch", "Ġp itch", "Ġpit ch", "C F", "by tes", "byte s", "byt es", "Ġ Commission", "ĠCom mission", "ĠComm ission", "Ġin cred", "Ġincre d", "Ġinc red", "Ġincr ed", "P ER", "PE R", "_ response", "_res ponse", "_resp onse", "Ġ Los", "ĠL os", "ĠLo s", "p arser", "par ser", "parse r", "pars er", "Ġ assume", "Ġas sume", "Ġass ume", "Ġassum e", ". Request", ".Re quest", "Ġ Token", "ĠT oken", "ĠTo ken", "ĠTok en", "_ position", "_p osition", "_pos ition", "Ġ nom", "Ġn om", "Ġno m", "- term", "-t erm", "-te rm", "Ġ remaining", "Ġrem aining", "Ġremain ing", "i ostream", "io stream", "Ġ pieces", "Ġp ieces", "Ġpie ces", "Ġpiece s", "a py", "ap y", "Ġ Less", "ĠL ess", "ĠLe ss", "ĠLes s", "r ange", "ra nge", "ran ge", "rang e", "um bn", "umb n", "p rise", "pr ise", "pri se", "_ option", "_op tion", "_o ption", "_opt ion", "2 30", "23 0", "I mpl", "Im pl", "Imp l", "k wargs", "kw args", "Ġbusiness es", "Al ert", "Ale rt", "Ġpart ies", "Ġpar ties", "Ġpartie s", "Ġparti es", "Ġ Container", "ĠCont ainer", "Ġ Private", "ĠPr ivate", "ĠPriv ate", "Ġ Plan", "ĠP lan", "ĠPl an", "ĠPla n", "Ġ registered", "Ġregister ed", "Ġregist ered", "Ġ jour", "Ġj our", "Ġjo ur", "Ġjou r", "a cker", "ack er", "ac ker", "е ни", "ен и", "/ >", "c hat", "ch at", "cha t", "s ect", "se ct", "sec t", "Ġ creation", "Ġc reation", "Ġcre ation", "Ġcreat ion", "Ġcrea tion", "ol utely", "olute ly", "olut ely", "Ġ instant", "Ġin stant", "Ġins tant", "Ġinst ant", "Ġ delivery", "Ġd elivery", "Ġdel ivery", "Ġdeliver y", "i cken", "ic ken", "ick en", "y es", "ye s", "1 63", "16 3", "Ġ Franc", "ĠFr anc", "ĠFra nc", "ĠFran c", "b ling", "bl ing", "e nda", "en da", "end a", "[ (", "_ range", "_r ange", "_ra nge", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "Ġ schedule", "Ġs chedule", "Ġsched ule", "C onn", "Con n", "Co nn", "Ġ thank", "Ġth ank", "Ġthan k", "Ġtha nk", "x d", "Ġ hook", "Ġh ook", "Ġho ok", "Ġ documentation", "Ġdocument ation", "Param eters", "Parameter s", "H ello", "Hel lo", "Hell o", "v t", "Ġ articles", "Ġart icles", "Ġarticle s", "Ġartic les", "Ġ west", "Ġw est", "Ġwe st", "Ġwes t", "d efined", "def ined", "define d", ". select", ".s elect", ".se lect", ".sel ect", "ok ens", "oken s", "oke ns", "Ġ VAL", "ĠV AL", "ĠVA L", ". file", ".f ile", ".fi le", ".fil e", "re set", "res et", "rese t", "Ġ mys", "Ġm ys", "Ġmy s", "Ġ MA", "ĠM A", "] ),", "]) ,", "Ġ cities", "Ġc ities", "Ġcit ies", "Ġci ties", "re lated", "rel ated", "å Ľ", "Ġ appeared", "Ġappe ared", "Ġappear ed", "Ġ wid", "Ġw id", "Ġwi d", ". panel", ".p anel", ".pa nel", ".pan el", "Ġ Ins", "ĠI ns", "ĠIn s", ". entity", ".e ntity", ".ent ity", "Ġde cre", "Ġdec re", "Ġ Lou", "ĠL ou", "ĠLo u", "( time", "(t ime", "(ti me", "Ġ Thank", "ĠTh ank", "ĠThan k", ". createElement", ".create Element", "Ġ mentioned", "Ġmention ed", "Ġment ioned", "o unce", "ou nce", "oun ce", "ounc e", "Ġ Try", "ĠT ry", "ĠTr y", "Ġ Wall", "ĠW all", "ĠWal l", "ĠWa ll", "/ images", "/image s", "/im ages", "Ġ Menu", "ĠM enu", "ĠMe nu", "ĠMen u", "' čĊ", "Ġ Er", "ĠE r", "Ġc ritic", "Ġcr itic", "Ġcrit ic", "Ġcri tic", "Ġ Year", "ĠY ear", "ĠYe ar", "( param", "(p aram", "(par am", "(pa ram", "(para m", "Ġ flo", "Ġf lo", "Ġfl o", "N N", "o oter", "oot er", "oo ter", "Ġ ];Ċ", "Ġ] ;Ċ", "Ġ]; Ċ", "Ġ Aff", "ĠA ff", "ĠAf f", "\" github", "\"g ithub", "ro oms", "room s", "Ġ hyp", "Ġh yp", "Ġhy p", "g lobal", "glob al", "Ġ avec", "Ġa vec", "Ġav ec", "Ġave c", "æľ Ī", "Ġ completion", "Ġcom pletion", "Ġcomp letion", "Ġcomple tion", "Ġcomplet ion", "Ġ cond", "Ġc ond", "Ġcon d", "Ġco nd", "on ymous", "onym ous", "( temp", "(t emp", "(te mp", "Ġ stars", "Ġst ars", "Ġstar s", "Ġsta rs", "Ġ relevant", "Ġre levant", "Ġrele vant", "Ġrelev ant", "Ġ covered", "Ġcover ed", "Ġcov ered", "Ġ elim", "Ġe lim", "Ġel im", "_ types", "_t ypes", "_type s", "_typ es", "_ty pes", "( bool", "(b ool", "Ġ tu", "Ġt u", "_ exists", "_ex ists", "_exist s", "Ġ secure", "Ġs ecure", "Ġsec ure", "Ġ stored", "Ġst ored", "Ġstore d", "Ġstor ed", "Ġsto red", "] /", "x F", "Ġ Controller", "ĠCont roller", "ĠControl ler", "ĠContr oller", "Ġm igr", "Ġmi gr", "Ġmig r", "M I", "Ġ Den", "ĠD en", "ĠDe n", "Ġ annual", "Ġann ual", "U IL", "UI L", "- and", "-a nd", "-an d", "Ġ crime", "Ġcr ime", "Ġcri me", "Ġcrim e", "b el", "be l", "Ġk itchen", "Ġkit chen", "@ g", "_ ph", "_p h", "ourn ament", "Ġ Social", "ĠS ocial", "ĠSo cial", "ĠSoc ial", "ĠSoci al", "Ġ Special", "ĠS pecial", "ĠSp ecial", "ĠSpec ial", "ĠSpe cial", "lo gger", "log ger", "logg er", "Ġ tail", "Ġt ail", "Ġta il", "Ġtai l", "Ġ unknown", "Ġun known", "Ġunk nown", "Ġunknow n", "d ed", "de d", "Ġapp rec", "Ġap prec", "( db", "(d b", "c f", "1 55", "15 5", "Ġ assign", "Ġas sign", "Ġass ign", "- out", "-o ut", "Ġ Mont", "ĠM ont", "ĠMon t", "ĠMo nt", "d p", "w idget", "wid get", "Ġ stone", "Ġs tone", "Ġst one", "Ġsto ne", "- primary", "-pr imary", ". grid", ".g rid", ".gr id", "Res ults", "Result s", "a zz", "az z", "Ġ daughter", "Ġda ughter", "Ġ curr", "Ġc urr", "Ġcur r", "Ġcu rr", "1 75", "17 5", "Ġ lin", "Ġl in", "Ġli n", "Ġ south", "Ġs outh", "Ġso uth", "Ġsou th", "Ġsout h", "form s", "fo rms", "for ms", "Ġ OUT", "ĠO UT", "ĠOU T", "l ette", "le tte", "let te", "lett e", "a ks", "ak s", "ig ure", "igu re", "Ġ EU", "ĠE U", "var iable", "vari able", "Ġ brief", "Ġb rief", "Ġbr ief", "Ġbri ef", "Ġ Scott", "ĠS cott", "ĠSc ott", "ĠScot t", "ĠSco tt", "Ġ conference", "Ġcon ference", "Ġconf erence", "Ġconfer ence", "a nda", "an da", "and a", "_ lock", "_l ock", "_lo ck", "_loc k", "o ral", "or al", "ora l", "Ġe ine", "Ġein e", "Ġei ne", "O RS", "OR S", "//// ////////////////////////////////////////////////////////////", "//////// ////////////////////////////////////////////////////////", "//////////////// ////////////////////////////////////////////////", "//////////////////////////////// ////////////////////////////////", "//////////// ////////////////////////////////////////////////////", "//////////////////////////////////////////////// ////////////////", "//////////////////////////////////////////////////////// ////////", "//////////////////////////////////////////////////////////// ////", "//////////////////////////////////////////////////// ////////////", "es so", "ess o", "Ġ ris", "Ġr is", "Ġri s", "Ġ gender", "Ġg ender", "Ġge nder", "Ġgen der", "es tic", "est ic", "esti c", "L icense", "Lic ense", "( out", "(o ut", "Ġ ms", "Ġm s", "S ee", "Se e", "Ġw illing", "Ġwill ing", "Ġwil ling", "a ze", "az e", "Ġ sports", "Ġs ports", "Ġsp orts", "Ġsport s", "Ġspo rts", "Ġspor ts", "Ġ yes", "Ġy es", "Ġye s", "l u", "Ġp urs", "Ġpur s", "Ġpu rs", "/ javascript", "/j avascript", "/java script", "/jav ascript", "- pro", "-p ro", "-pr o", "nav bar", "_ product", "_pro duct", "_prod uct", "/ bootstrap", "/boot strap", "Ġdr iving", "Ġdriv ing", "Ġdri ving", "Ġ Ä", "Ġpro pos", "Ġprop os", "ul tip", "ult ip", "ulti p", "up lic", ". email", ".e mail", ".em ail", "Ġ approx", "Ġapp rox", "Ġap prox", "Ġappro x", "( cl", "(c l", "Ġ wear", "Ġw ear", "Ġwe ar", "Ġ reply", "Ġre ply", "Ġrep ly", "Ġrepl y", "as set", "ass et", "asse t", "Ġ ice", "Ġi ce", "Ġic e", "Ġ tx", "Ġt x", "k r", "Ġ Germany", "ĠGerman y", "ĠGer many", "ĠGerm any", "Ġ George", "ĠGe orge", "ĠGeorg e", "Ġ cb", "Ġc b", "ĉ err", "ĉe rr", "M ove", "Mo ve", "Mov e", "Ġ poly", "Ġp oly", "Ġpol y", "Ġpo ly", "v oice", "vo ice", "} \"", "Ġ animal", "Ġan imal", "Ġanim al", "Ġani mal", "A v", "Ġ Location", "ĠL ocation", "ĠLo cation", "ĠLoc ation", "Ġ native", "Ġn ative", "Ġnat ive", "] [\"", "][ \"", "< double", " \"", "s tat", "st at", "sta t", "Ġ },čĊ", "Ġ} ,čĊ", "Ġ}, čĊ", "< span", " =", "Ð ±", "1 39", "13 9", "i va", "iv a", ". AutoSize", ".Auto Size", "Ġ Lat", "ĠL at", "ĠLa t", "_ ext", "_e xt", "_ex t", "Initial ize", ". register", ".reg ister", "1 56", "15 6", "O PY", "OP Y", "Ġ reverse", "Ġre verse", "Ġrev erse", "Ġrevers e", "Ġrever se", "_ dis", "_d is", "_di s", "' ][", "'] [", "Ġ prompt", "Ġp rompt", "Ġprom pt", "on to", "ont o", "Ġ Journal", "ĠJ ournal", "ĠJo urnal", "r outer", "ro uter", "rou ter", "route r", "Ġ mysqli", "Ġm ysqli", "Ġmys qli", "Ġmysql i", "# else", ") \"", "- xs", "-x s", "l ets", "le ts", "let s", "p han", "ph an", "pha n", ". LE", ".L E", "1 37", "13 7", "W ill", "Wil l", "Wi ll", "Ġaff ord", "Ġaf ford", "Ġ skill", "Ġs kill", "Ġsk ill", "Ġski ll", "- toggle", "-t oggle", "N C", "B ind", "Bin d", "Bi nd", "T S", "J ust", "Ju st", "it eral", "ite ral", "iter al", "Y P", "ĉ unsigned", "ĉun signed", "Ġ wind", "Ġw ind", "Ġwin d", "Ġwi nd", "1 49", "14 9", ") ):Ċ", ")) :Ċ", ")): Ċ", "Ġ warning", "Ġw arning", "Ġwar ning", "Ġwarn ing", "Ġ Water", "ĠW ater", "ĠWat er", "ĠWa ter", "Ġ draft", "Ġd raft", "Ġdr aft", "Ġdra ft", "Ġ cm", "Ġc m", "Ġ sam", "Ġs am", "Ġsa m", "Ġ holding", "Ġh olding", "Ġhold ing", "Ġhol ding", "z ip", "zi p", "Ġ Science", "ĠSc ience", "ĠSci ence", "Ġsup posed", "Ġsuppose d", "Ġsupp osed", "G en", "Ge n", "Ġ diet", "Ġd iet", "Ġdi et", "Ġdie t", "< h", "Ġ Pass", "ĠP ass", "ĠPa ss", "ĠPas s", "v i", "Ġh usband", "Ġhus band", "� �", "n ote", "not e", "no te", "Ġ About", "ĠA bout", "ĠAb out", "ĠIn stitute", "ĠInstit ute", "ĠInstitut e", "Ġ climate", "Ġcl imate", "Ġclim ate", "Ġcli mate", ". Format", ".Form at", ".For mat", "Ġ nut", "Ġn ut", "Ġnu t", "e sted", "es ted", "est ed", "este d", "Ġapp arent", "Ġap parent", "Ġappar ent", "Ġappare nt", "Ġ holds", "Ġh olds", "Ġhold s", "Ġho lds", "Ġhol ds", "f i", "n ews", "ne ws", "new s", "C M", "v ideo", "vid eo", "vi deo", "vide o", "' :'", "': '", "D ITION", "DI TION", "p ing", "pi ng", "pin g", "Ġsen ior", "Ġseni or", "w a", "- ->Ċ", "-- >Ċ", "--> Ċ", "_ default", "_d efault", "_de fault", "_def ault", "Ġ Database", "ĠD atabase", "ĠData base", "ĠDat abase", "r ep", "re p", "E SS", "ES S", "n ergy", "ner gy", "nerg y", ". Find", ".F ind", "_ mask", "_m ask", "_ma sk", "_mas k", "Ġ rise", "Ġr ise", "Ġris e", "Ġri se", "Ġ kernel", "Ġk ernel", "Ġker nel", "Ġkern el", ": :$", ":: $", ". Q", "Ġoff ering", "Ġoffer ing", "de cl", "dec l", "Ġ CS", "ĠC S", "Ġ listed", "Ġl isted", "Ġli sted", "Ġlist ed", "Ġliste d", "Ġlis ted", "Ġ mostly", "Ġmost ly", "e nger", "en ger", "eng er", "enge r", "Ġ blocks", "Ġb locks", "Ġbl ocks", "Ġblock s", "Ġblo cks", "Ġbloc ks", "o lo", "ol o", "Ġgover ning", "Ġgovern ing", "\\ F", "Ġcon cent", "Ġconc ent", "Ġconce nt", ". getText", ".get Text", "Ġ mb", "Ġm b", "Ġocc urred", "Ġoccur red", "Ġ changing", "Ġch anging", "Ġchang ing", "Ġchan ging", "S cene", "Sc ene", "_ CODE", "_C ODE", "_CO DE", "_COD E", "B eh", "Be h", "\" The", "\"T he", "Ġ tile", "Ġt ile", "Ġti le", "Ġtil e", "Ġ Association", "ĠAssoci ation", "ĠAssoc iation", "ĉ P", "al ty", "alt y", "_ ad", "_a d", "od ies", "odi es", "odie s", "i ated", "ia ted", "iate d", "iat ed", "Ġ prepared", "Ġpre pared", "Ġprepar ed", "Ġprepare d", "Ġprep ared", "p ossible", "poss ible", "Ġ mort", "Ġm ort", "Ġmor t", "Ġmo rt", "T EST", "TE ST", "TES T", "1 42", "14 2", "Ġ ignore", "Ġi gnore", "Ġign ore", "Ġig nore", "Ġignor e", "Ġ calc", "Ġc alc", "Ġcal c", "Ġca lc", "Ġ rs", "Ġr s", "Ġ assertEquals", "Ġassert Equals", "Ġ sz", "Ġs z", "Ġ THIS", "ĠTH IS", ". \"Ċ", ".\" Ċ", "Ġ canvas", "Ġc anvas", "Ġcan vas", "Ġcanv as", "j ava", "ja va", "jav a", "Ġd ut", "Ġdu t", "VAL ID", ". sql", ".s ql", ". input", ".in put", "Ġ aux", "Ġa ux", "Ġau x", "S up", "Su p", "Ġ artist", "Ġart ist", "V ec", "Ve c", "_ TIME", "_T IME", "_TIM E", "_TI ME", ".string ify", "et ween", "Ġ Category", "ĠC ategory", "Ġ [-", "Ġ[ -", "Ġ DevExpress", "ĠDev Express", "Ġ Jul", "ĠJ ul", "ĠJu l", "Ġ ring", "Ġr ing", "Ġri ng", "Ġrin g", ". ed", ".e d", "Y Y", "L et", "Le t", "T extField", "Text Field", "Ġ flat", "Ġf lat", "Ġfl at", "Ġfla t", "_ print", "_p rint", "_pr int", "_pri nt", "Ġ OTHER", "ĠOT HER", "ad ian", "adi an", "adia n", "Ġ checked", "Ġcheck ed", "e le", "el e", "Al ign", "Ali gn", "st anding", "stand ing", "stan ding", "Ġ [],", "Ġ[ ],", "Ġ[] ,", "Ġ lab", "Ġl ab", "Ġla b", "u cky", "uc ky", "uck y", "Ġ Christmas", "ĠChrist mas", "( image", "(i mage", "(im age", ". module", ".m odule", ".mod ule", "Ġ lots", "Ġl ots", "Ġlo ts", "Ġlot s", "Ġs lightly", "Ġsl ightly", "Ġslight ly", "( final", "(f inal", "(fin al", "(fi nal", "er ge", "erg e", "è ¿", "1 47", "14 7", "Ġ Police", "ĠPol ice", "ĠPo lice", "ĠPolic e", "1 43", "14 3", "Ġ Right", "ĠR ight", "ĠRig ht", "ĠRi ght", "Ġ award", "Ġa ward", "Ġaw ard", "Ġ OS", "ĠO S", "Ġ {}ĊĊ", "Ġ{ }ĊĊ", "Ġ{} ĊĊ", "Ġ{}Ċ Ċ", "Ġ ptr", "Ġp tr", "Ġpt r", "o ves", "ov es", "ove s", "ic ated", "ica ted", "icate d", "е м", "еР¼", "Ġ manage", "Ġman age", "Ġma nage", "Ġmana ge", "ol iday", "olid ay", "oli day", "A mount", "Am ount", "ool Strip", "t body", "tb ody", "N av", "Na v", "w rap", "wr ap", "B B", "Ġw atching", "Ġwatch ing", "Ġwat ching", "a rios", "ar ios", "ari os", "ario s", "Ġ optional", "Ġoption al", "Ġopt ional", "_ K", "Ġ Licensed", "ĠL icensed", "ĠLicense d", "ĠLic ensed", ". Map", ".M ap", ".Ma p", "T imer", "Time r", "Tim er", "Ti mer", "Ġ AP", "ĠA P", "Ġ Rev", "ĠR ev", "ĠRe v", "( o", ", c", "u min", "um in", "umi n", "et ailed", "etail ed", "eta iled", "Ġ Hy", "ĠH y", "Ġ blank", "Ġbl ank", "Ġbla nk", "a gger", "ag ger", "agg er", "Ġ Self", "ĠS elf", "ĠSe lf", "ĠSel f", "( )[", "() [", ". make", ".m ake", ".ma ke", "e arn", "ear n", "ea rn", "ch annel", "chan nel", "< pre", "

;Ċ", ">; Ċ", "W orld", "Ġ python", "Ġp ython", "Ġpy thon", "Ġpyt hon", "Ġ lif", "Ġl if", "Ġli f", "Ġt rav", "Ġtr av", "Ġtra v", "Ġcon ven", "Ġconv en", "com pany", "comp any", "compan y", "Ġ Club", "ĠC lub", "ĠCl ub", "1 38", "13 8", "V er", "Ve r", "B tn", "Ġ zone", "Ġz one", "Ġzo ne", "product s", "produ cts", "Ġ Educ", "ĠE duc", "ĠEd uc", "ĠEdu c", "Ġ verify", "Ġver ify", "Ġveri fy", "Ġ Mil", "ĠM il", "ĠMi l", "o no", "on o", "] );ĊĊ", "]) ;ĊĊ", "]);Ċ Ċ", "]); ĊĊ", "EN CE", "ENC E", "Ġ packet", "Ġp acket", "Ġpack et", "Ġpa cket", "Ġpac ket", "Ġ cer", "Ġc er", "Ġce r", "Ġ enumer", "Ġe numer", "Ġen umer", "Ġenum er", "Ġ pars", "Ġp ars", "Ġpar s", "Ġpa rs", "form ed", "for med", "forme d", "Ġ occup", "Ġocc up", "Ġoc cup", "t re", "tr e", "Ġ exercise", "Ġex ercise", "Ġexerc ise", "D ay", "Da y", "_ sum", "_s um", "_su m", "Ġ asking", "Ġas king", "Ġask ing", "a ption", "ap tion", "apt ion", "Ġ orders", "Ġor ders", "Ġorder s", "Ġord ers", "Ġs pending", "Ġsp ending", "Ġspend ing", "Ġ ERR", "ĠE RR", "ĠER R", ". Dis", ".D is", ".Di s", "Ġ Util", "ĠU til", "ĠUt il", "âĢľ I", "\\ '", "? )", "/ >Ċ", "/> Ċ", "Ġe mot", "Ġem ot", "Ġemo t", "Ġin fluence", "Ġinflu ence", "Ġ Africa", "ĠA frica", "ĠAfr ica", "ĠAf rica", "at ters", "att ers", "atter s", "atte rs", "Ù ħ", ". session", ".s ession", ".sess ion", "Ġ chief", "Ġch ief", "Ġchi ef", "ĉ ĉĉĉĉĉĉĉĉĉĉ", "ĉĉ ĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉ ĉĉĉĉĉĉĉ", "ĉĉĉ ĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉ ĉĉĉĉĉĉ", "ĉĉĉĉĉĉ ĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉ ĉĉĉ", "ĉĉĉĉĉĉĉ ĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉ ĉĉ", "ĉĉĉĉĉĉĉĉĉĉ ĉ", "Ġ tom", "Ġt om", "Ġto m", "cl uded", "clude d", "clud ed", "s erial", "se rial", "ser ial", "_ handler", "_h andler", "_handle r", "_hand ler", ". Type", ".T ype", "a ped", "ap ed", "ape d", "Ġp olicies", "Ġpol icies", "Ġpolic ies", "- ex", "-e x", "- tr", "-t r", "bl ank", "bla nk", "m erce", "mer ce", "merc e", "Ġ coverage", "Ġco verage", "Ġcover age", "Ġ rc", "Ġr c", "_ matrix", "_m atrix", "_mat rix", "_ box", "_b ox", "_bo x", "Ġ charges", "Ġch arges", "Ġchar ges", "Ġcharg es", "Ġcharge s", "Ġ Boston", "ĠB oston", "ĠBo ston", "ĠBos ton", "P e", "Ġcirc um", "Ġcir cum", "Ġ filled", "Ġf illed", "Ġfil led", "Ġfill ed", "Ġfille d", "1 48", "14 8", "Ġ north", "Ġn orth", "Ġnor th", "icture Box", "ĉ res", "ĉr es", "ĉre s", "è ®", "Ġ termin", "Ġter min", "Ġterm in", "Ġ [âĢ¦", "Ġ[ âĢ¦", "I RECT", "IR ECT", "IRE CT", "Ġ ber", "Ġb er", "Ġbe r", "Ġ\" ../../", "Ġ\"../ ../", "Ġ\".. /../", "r etch", "ret ch", ". code", ".c ode", ".co de", ".cod e", "_ col", "_c ol", "_co l", "Ġ Government", "ĠG overnment", "ĠGovern ment", "Ġ argv", "Ġar gv", "Ġarg v", "Ġ Lord", "ĠL ord", "ĠLo rd", "ĠLor d", "a si", "as i", "E xec", "Ex ec", "ĉ let", "ĉl et", "vert is", "Ġ discussion", "Ġdisc ussion", "Ġdiscuss ion", "en ance", "ena nce", "enan ce", "ou tube", "out ube", "outu be", "type of", "typ eof", "Ġs erved", "Ġser ved", "Ġserv ed", "Ġserve d", "Ġ Put", "ĠP ut", "ĠPu t", "ĉ x", "Ġ sweet", "Ġs weet", "Ġswe et", "B efore", "Be fore", "ate gy", "ateg y", ". of", ".o f", "Ġ Material", "ĠM aterial", "ĠMat erial", "ĠMate rial", "ĠMater ial", "S ort", "So rt", "O NT", "ON T", "ig ital", "igit al", "igi tal", "W hy", "Wh y", "Ġs ust", "Ġsu st", "Ġsus t", "Ġ ç", "a bet", "ab et", "abe t", "Ġ segment", "Ġs egment", "Ġse gment", "Ġseg ment", "Ġ [],Ċ", "Ġ[ ],Ċ", "Ġ[] ,Ċ", "Ġ[], Ċ", "Ġ Muslim", "ĠM uslim", "ĠMus lim", "Ġ findViewById", "Ġfind ViewById", "c ut", "cu t", "_ TEXT", "_T EXT", "_TE XT", "_TEX T", "Ġ Mary", "ĠM ary", "ĠMar y", "ĠMa ry", "Ġl oved", "Ġlo ved", "Ġlove d", "Ġlov ed", "Ġ lie", "Ġl ie", "Ġli e", "Ġ JO", "ĠJ O", "Ġ isset", "Ġis set", "Ġiss et", "m onth", "mon th", "mo nth", "mont h", "Ġ prime", "Ġpr ime", "Ġprim e", "Ġpri me", "t i", "Ġ Carol", "ĠCar ol", "ĠCa rol", "U se", "Us e", "1 46", "14 6", "Ġ Pop", "ĠP op", "ĠPo p", "Ġ Save", "ĠS ave", "ĠSa ve", "ĠSav e", "Int erval", "Inter val", "ex ecute", "exec ute", "d y", "Ġ Iran", "ĠI ran", "ĠIr an", "_ cont", "_c ont", "_con t", "_co nt", "ĉ T", "Ġ phase", "Ġph ase", "Ġpha se", "check box", "we ek", "Ġ hide", "Ġh ide", "Ġhi de", "Ġhid e", "Ġ til", "Ġt il", "Ġti l", "Ġ ju", "Ġj u", "C ustom", "b urg", "bur g", "bu rg", "/ M", "T ON", "TO N", "Ġ quant", "Ġqu ant", "Ġq uant", "Ġqua nt", "Ġquan t", "Ġ rub", "Ġr ub", "Ġru b", "ix els", "ixel s", "ixe ls", "Ġ installed", "Ġinst alled", "Ġinstall ed", "Ġinstal led", "Ġ dump", "Ġd ump", "Ġdu mp", "Ġdum p", "Ġproper ly", "( List", "(L ist", "Ġdec ide", "Ġdecid e", "ap ply", "app ly", "appl y", "H as", "Ha s", "Ġ keeping", "Ġke eping", "Ġkeep ing", "Ġcit izens", "Ġcitiz ens", "Ġcitizen s", "Ġ joint", "Ġj oint", "Ġjoin t", "Ġjo int", "p ool", "po ol", "S ocket", "So cket", "Sock et", "_ op", "_o p", "Ġ weapon", "Ġwe apon", "Ġweap on", "g nore", "gn ore", "Ġ Exec", "ĠE xec", "ĠEx ec", "ot ten", "ott en", "otte n", "Ġ MS", "ĠM S", "Ġ (-", "Ġ( -", "Ġ Review", "ĠR eview", "ĠRe view", "ĠRev iew", "Ġ examples", "Ġex amples", "Ġexample s", "Ġexam ples", "Ġ tight", "Ġt ight", "Ġti ght", "! (", "D P", "Ġ MessageBox", "ĠMessage Box", "Ġphot ograph", "Ġphoto graph", "1 64", "16 4", "U RI", "UR I", "é t", "l ow", "lo w", "Ġ Grand", "ĠG rand", "ĠGr and", "ĠGran d", "ĠGra nd", ".p ersistence", ".persist ence", "Ġmain tain", "Ġmaint ain", "Ġ nums", "Ġn ums", "Ġnum s", "Ġnu ms", "Ġ zip", "Ġz ip", "Ġzi p", "i als", "ial s", "ia ls", "Ġ Gets", "ĠG ets", "ĠGet s", "ĠGe ts", "p eg", "pe g", "Ġ Buffer", "ĠB uffer", "ĠBuff er", "ĠBu ffer", "ĠBuf fer", "~~ ~~", "ra structure", "Ġ PL", "ĠP L", "u en", "ue n", "ob by", "obb y", "size of", "siz eof", "Ġ pic", "Ġp ic", "Ġpi c", "Ġ seed", "Ġs eed", "Ġse ed", "Ġsee d", "Ġex perienced", "Ġexperience d", "Ġexperi enced", "Ġ odd", "Ġo dd", "Ġod d", "Ġ kick", "Ġk ick", "Ġki ck", "Ġ procedure", "Ġpro cedure", "Ġproced ure", "av igator", "avig ator", "- on", "-o n", ", j", "Ġ Although", "ĠAl though", "Ġ userId", "Ġuser Id", "ac cept", "acc ept", "B lue", "Bl ue", "I Color", "IC olor", "l ayer", "la yer", "lay er", "a vailable", "av ailable", "avail able", "Ġ ends", "Ġe nds", "Ġen ds", "Ġend s", ". table", ".t able", ".tab le", ".ta ble", "Ġ dataset", "Ġd ataset", "Ġdata set", "Ġdat aset", "Ġdatas et", "b us", "bu s", "Ġ explain", "Ġex plain", "Ġexp lain", "Ġexpl ain", "( pro", "(p ro", "(pr o", "ĠCommit tee", "Ġn oted", "Ġnot ed", "Ġno ted", "Ġnote d", "] :Ċ", "]: Ċ", "D im", "Di m", "st dio", "std io", "1 54", "15 4", ". \",Ċ", ".\" ,Ċ", ".\", Ċ", "_ source", "_s ource", "1 81", "18 1", "Ġ Week", "ĠWe ek", "Ġ Edge", "ĠE dge", "ĠEd ge", "Ġoper ating", "Ġopera ting", "Ġ este", "Ġe ste", "Ġes te", "Ġest e", "i pl", "ip l", "3 30", "33 0", "ag ination", "agi nation", "agina tion", "Ġpro ceed", "Ġproc eed", "Ġ animation", "Ġan imation", "Ġanim ation", ". Models", ".Model s", ".Mod els", ".Mode ls", "Ġ Watch", "ĠW atch", "ĠWat ch", "i at", "ia t", "Ġop pon", "Ġopp on", "/ A", "Re port", "Rep ort", "Repo rt", "Ġ sounds", "Ġs ounds", "Ġso unds", "Ġsound s", "Ġsou nds", "_ buf", "_b uf", "IE LD", "IEL D", "Ġb und", "Ġbu nd", "Ġbun d", "ĉ get", "ĉg et", ". pr", ".p r", "( tmp", "(t mp", "(tm p", "Ġ kid", "Ġk id", "Ġki d", "> ĊĊĊ", ">Ċ ĊĊ", ">ĊĊ Ċ", "Ġ yang", "Ġy ang", "Ġya ng", "Ġyan g", "Not Found", "Ñ Ĩ", "m ath", "ma th", "mat h", "@ gmail", "@g mail", "Ġ LIMIT", "ĠL IMIT", "ĠLI MIT", "red ients", "redient s", "redi ents", "Ġ vent", "Ġv ent", "Ġve nt", "Ġven t", "av igate", "avig ate", "avi gate", "L ook", "Lo ok", "Ġ religious", "Ġrel igious", "Ġrelig ious", "Ġ rand", "Ġr and", "Ġran d", "Ġra nd", "r io", "ri o", "( GL", "(G L", "_ ip", "_i p", "u an", "ua n", "ici ency", "icie ncy", "Ġ Change", "ĠCh ange", "ĠCha nge", "ĠChan ge", "ĠChang e", "> čĊčĊ", ">čĊ čĊ", "Ġ Entity", "ĠE ntity", "ĠEnt ity", "Ġren contre", "Ġrencont re", "Ġrencontr e", "Ġ Ret", "ĠR et", "ĠRe t", "p lan", "pl an", "pla n", "é n", "BO OL", "u ries", "ur ies", "uri es", "t rain", "tr ain", "tra in", "Def inition", "= ===========", "== ==========", "==== ========", "======== ====", "=== =========", "=========== =", "========= ===", "========== ==", "====== ======", "===== =======", "======= =====", "z z", "4 50", "45 0", "An imation", "Anim ation", "Ġ OK", "ĠO K", "_ menu", "_m enu", "_me nu", ". bl", ".b l", "_ score", "_s core", "_sc ore", "Ġ acad", "Ġa cad", "Ġac ad", "( System", "(S ystem", "Ġ refresh", "Ġre fresh", "Ġref resh", "Ġrefr esh", "' =>$", "'=> $", "'= >$", ". Graphics", ".G raphics", ".Graph ics", "am ento", "ament o", "amen to", "p id", "pi d", "t c", "Ġ tips", "Ġt ips", "Ġti ps", "Ġtip s", "Ġ homes", "Ġh omes", "Ġhome s", "Ġhom es", "Ġho mes", "Ġ fuel", "Ġf uel", "Ġfu el", "Ġfue l", "â ĸ", "_ helper", "_h elper", "_help er", "Ġ ĠčĊ", "ĠĠ čĊ", "Ġ Room", "ĠR oom", "ĠRo om", "ĠRoo m", ". Close", ".C lose", ".Cl ose", "_ attr", "_at tr", "_att r", "Ġ Mount", "ĠM ount", "ĠMo unt", "ĠMou nt", "Ġ Ev", "ĠE v", "ar ser", "ars er", "arse r", "_ top", "_t op", "_to p", "e ah", "ea h", "Ġ Delete", "ĠDe lete", "ĠDel ete", "ãĢ į", "u ke", "uk e", "Ġ usage", "Ġu sage", "Ġus age", "Ġusa ge", "a ria", "ar ia", "ari a", "_ dev", "_d ev", "_de v", "Ġ texture", "Ġtext ure", "Ġtex ture", "Ġtextu re", "Ġ conversation", "Ġcon versation", "Ġconvers ation", "e per", "ep er", "B ean", "Be an", "d one", "do ne", "don e", "non atomic", "Ġ Second", "ĠSe cond", "ĠSec ond", "Ġsh ooting", "Ġshoot ing", "Ġsho oting", "_ pre", "_p re", "_pr e", "Com ponents", "Component s", "Comp onents", "Ġ ]ĊĊ", "Ġ] ĊĊ", "Ġ]Ċ Ċ", "_ _,", "__ ,", "st itution", "stit ution", ". Char", ".C har", ".Ch ar", "> ();ĊĊ", ">( );ĊĊ", ">();Ċ Ċ", ">() ;ĊĊ", ">(); ĊĊ", "Ġpres ented", "Ġpresent ed", "Ġpresente d", "Ġ wa", "Ġw a", "o ker", "ok er", "oke r", "- ĊĊ", "-Ċ Ċ", "i ner", "in er", "ine r", "Ġbe coming", "Ġbec oming", "Ġ incident", "Ġinc ident", "Ġincid ent", "A tt", "At t", "1 62", "16 2", "Ġreve aled", "Ġreveal ed", "f orc", "fo rc", "for c", "Ġ boot", "Ġb oot", "Ġbo ot", "Ġboo t", ". page", ".p age", ".pa ge", ".pag e", "En umerator", "Enumer ator", "Enum erator", "1 65", "16 5", "_ ->", "_- >", "Ph oto", "Phot o", "Ġ spring", "Ġs pring", "Ġsp ring", "Ġspr ing", ". \",", ".\" ,", "Ġ Dictionary", "ĠD ictionary", "B JECT", "BJ ECT", "Ġ locations", "Ġl ocations", "Ġloc ations", "Ġlocation s", "Ġ samples", "Ġs amples", "Ġsample s", "Ġsam ples", "Ġsamp les", "Input Stream", "Ġ Brown", "ĠB rown", "ĠBr own", "ĠBro wn", "ĠBrow n", "Ġ stats", "Ġst ats", "Ġstat s", "Ġsta ts", "q uality", "qu ality", "qual ity", "Ñ ħ", "- dis", "-d is", "-di s", "Ġhelp ing", "Ġhel ping", "Ġ ped", "Ġp ed", "Ġpe d", "2 24", "22 4", "( se", "(s e", "Ġ Who", "ĠW ho", "ĠWh o", "a lian", "al ian", "ali an", "alia n", "in ternal", "int ernal", "inter nal", "intern al", "Ġ ft", "Ġf t", "> ().", ">( ).", ">() .", "- >{", "-> {", "Ġ mine", "Ġm ine", "Ġmin e", "Ġmi ne", "Ġ sector", "Ġs ector", "Ġse ctor", "Ġsec tor", "Ġsect or", "Ġ gro", "Ġg ro", "Ġgr o", "Ġopport unities", "Ġopportun ities", "Ġ ü", "Ġà ¼", "Ġ mp", "Ġm p", "Ġalleg ed", "Ġalle ged", "Ġallege d", "Ġdoub t", "Ġdou bt", "M ouse", "Mo use", "A bout", "Ab out", "_ part", "_p art", "_par t", "_pa rt", "Ġ chair", "Ġc hair", "Ġch air", "Ġcha ir", "Ġchai r", "Ġ stopped", "Ġst opped", "Ġstop ped", "Ġsto pped", "1 61", "16 1", "l oop", "lo op", "loo p", "ent ities", "enti ties", "Ġ apps", "Ġa pps", "Ġapp s", "Ġap ps", "ans ion", "ansi on", "Ġ mental", "Ġm ental", "Ġmen tal", "Ġment al", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "F R", "Ġdef end", "Ġdefe nd", "c are", "ca re", "car e", "Ġ ideal", "Ġi deal", "Ġide al", "Ġidea l", "/ api", "/a pi", "/ap i", "ur face", "urf ace", "0 11", "01 1", "Ġ ele", "Ġe le", "Ġel e", "u lator", "ul ator", "ula tor", "Ġ Rights", "ĠR ights", "ĠRight s", "angu ages", "anguage s", "Ġf unds", "Ġfun ds", "Ġfund s", "Ġfu nds", "Ġ adapt", "Ġad apt", "Ġada pt", "Ġadap t", "At tributes", "Attribute s", "Attrib utes", "Ġ deploy", "Ġde ploy", "Ġdep loy", "o pts", "op ts", "opt s", "Ġ validation", "Ġvalid ation", "Ġvalida tion", "Ġconcern s", "u ce", "uc e", ". num", ".n um", "ul ture", "ult ure", "ultur e", "i la", "il a", "Ġ cup", "Ġc up", "Ġcu p", "Ġ pure", "Ġp ure", "Ġpur e", "Ġpu re", ". Fore", ".F ore", ".For e", "1 83", "18 3", "Ġ HashMap", "ĠHash Map", ". valueOf", ".value Of", "a sm", "as m", "M O", "Ġ cs", "Ġc s", "Ġ stores", "Ġst ores", "Ġstore s", "Ġstor es", "Ġsto res", "Ġ ************************************************************************", "Ġ**************************************************************** ********", "Ġ******************************** ****************************************", "Ġ******** ****************************************************************", "Ġ**************** ********************************************************", "Ġ************************ ************************************************", "Ġ**************************************** ********************************", "Ġ******************************************************** ****************", "Ġ************************************************ ************************", "Ġ communication", "Ġcomm unication", "Ġcommunic ation", "Ġcommun ication", "m em", "me m", ". EventHandler", ".Event Handler", ". Status", ".S tatus", ".St atus", ".Stat us", "_ right", "_r ight", ".set On", "S heet", "She et", "Ġ identify", "Ġident ify", "ener ated", "ene rated", "enerate d", "ord ered", "order ed", "orde red", "Ġ \"[", "Ġ\" [", "Ġs we", "Ġsw e", "Con dition", "Cond ition", "Ġ According", "ĠA ccording", "ĠAcc ording", "ĠAccord ing", "Ġ prepare", "Ġpre pare", "Ġprepar e", "Ġprep are", "Ġ rob", "Ġr ob", "Ġro b", "P ool", "Po ol", "Ġ sport", "Ġs port", "Ġsp ort", "Ġspo rt", "Ġspor t", "r v", "Ġ Router", "ĠR outer", "ĠRoute r", "ĠRo uter", "ĠRou ter", "ĠRout er", "Ġ alternative", "Ġaltern ative", "Ġalter native", "( []", "([ ]", "Ġ Chicago", "ĠCh icago", "ĠChic ago", "i pher", "ip her", "iph er", "is che", "isc he", "isch e", "Ġ Director", "ĠD irector", "ĠDirect or", "ĠDir ector", "ĠDire ctor", "k l", "Ġ Wil", "ĠW il", "ĠWi l", "ke ys", "key s", "Ġ mysql", "Ġm ysql", "Ġmy sql", "Ġmys ql", "Ġ welcome", "Ġw elcome", "Ġwel come", "k ing", "ki ng", "kin g", "Ġ Manager", "ĠM anager", "ĠMan ager", "ĠManage r", "ĠMana ger", "Ġ caught", "Ġca ught", ") }Ċ", ")} Ċ", "S core", "Sc ore", "_ PR", "_P R", "Ġ survey", "Ġs urvey", "Ġsur vey", "Ġsurv ey", "Ġsurve y", "h ab", "ha b", "He aders", "Header s", "Head ers", "A DER", "AD ER", "ADE R", "Ġ decor", "Ġde cor", "Ġdec or", "Ġdeco r", "Ġturn s", "Ġtur ns", "Ġ radius", "Ġr adius", "Ġrad ius", "Ġradi us", "er rupt", "err upt", "C or", "Co r", "Ġ mel", "Ġm el", "Ġme l", "Ġ intr", "Ġin tr", "Ġint r", "( q", "Ġ AC", "ĠA C", "a mos", "am os", "amo s", "M AX", "MA X", "Ġ Grid", "ĠG rid", "ĠGr id", "ĠGri d", "Ġ Jesus", "ĠJ esus", "ĠJes us", "ĠJe sus", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", ". DE", ".D E", "Ġ ts", "Ġt s", "Ġ linked", "Ġlink ed", "Ġlin ked", "f ree", "fr ee", "fre e", "Ġ Qt", "ĠQ t", "Ġ /**čĊ", "Ġ/ **čĊ", "Ġ/* *čĊ", "Ġ/** čĊ", "Ġf aster", "Ġfa ster", "Ġfast er", "Ġfas ter", "c tr", "ct r", "_ J", "D T", ". Check", ".C heck", ".Ch eck", "Ġ combination", "Ġcomb ination", "Ġcombin ation", "Ġint ended", "Ġintend ed", "- the", "-t he", "-th e", "- type", "-t ype", "1 82", "18 2", "e ctors", "ect ors", "ec tors", "ector s", "a mi", "am i", "u ting", "ut ing", "uti ng", "utin g", "Ġ uma", "Ġu ma", "Ġum a", "X ML", "XM L", "U CT", "UC T", "A p", "Ġ Random", "ĠR andom", "ĠRand om", "ĠRan dom", "Ġ ran", "Ġr an", "Ġra n", ". sort", ".s ort", ".so rt", "Ġ sorted", "Ġs orted", "Ġsort ed", "Ġsor ted", "Ġsorte d", ". Un", ".U n", "4 01", "40 1", "_ PER", "_P ER", "_PE R", "it ory", "itor y", "ito ry", "Ġ priority", "Ġp riority", "Ġprior ity", "Ġpriorit y", "Ġ Gal", "ĠG al", "ĠGa l", "Ġ Old", "ĠO ld", "ĠOl d", "h ot", "ho t", "Ġ Display", "ĠD isplay", "ĠDis play", "ĠDisp lay", "( sub", "(s ub", "_ TH", "_T H", "_ Y", "Ġ Care", "ĠC are", "ĠCar e", "ĠCa re", "lo ading", "load ing", "K ind", "Kin d", "Ki nd", "_ handle", "_h andle", "_hand le", ", ,", "r ase", "ra se", "ras e", "_ replace", "_re place", "_rep lace", ". addEventListener", ".add EventListener", "Ġ RT", "ĠR T", "1 72", "17 2", "Ġ entered", "Ġen tered", "Ġent ered", "Ġenter ed", "g ers", "ge rs", "ger s", "Ġ ich", "Ġi ch", "Ġic h", "( start", "(st art", "2 05", "20 5", "/ app", "/a pp", "/ap p", "Ġbr other", "Ġbro ther", "Ġbroth er", "M emory", "Mem ory", "Memo ry", "Out let", "Ġ utf", "Ġu tf", "Ġut f", "p rec", "pr ec", "pre c", "Ġ navigation", "Ġn avigation", "Ġnav igation", "Ġnavig ation", "O RK", "OR K", "Ġ dst", "Ġd st", "Ġds t", "D etail", "De tail", "Det ail", "Ġaud ience", "Ġaudi ence", "Ġ dur", "Ġd ur", "Ġdu r", "Ġ cluster", "Ġcl uster", "un ched", "unc hed", "unch ed", "Ġ ],", "Ġ] ,", "Ġcomfort able", ". values", ".value s", ".val ues", "Ġ Total", "ĠT otal", "ĠTo tal", "ĠTot al", "Ġ snap", "Ġs nap", "Ġsn ap", "Ġsna p", "Ġstand ards", "Ġstandard s", "Ġper formed", "Ġperform ed", "Ġperfor med", "h and", "ha nd", "han d", "( \"@", "(\" @", "å Ń", "Ġ phil", "Ġp hil", "Ġph il", "Ġphi l", "i br", "ib r", "t rim", "tr im", "tri m", "Ġ forget", "Ġf orget", "Ġfor get", "Ġforg et", "Ġforge t", "1 57", "15 7", "Ġ doctor", "Ġdo ctor", "Ġdoc tor", ". TextBox", ".Text Box", "3 77", "37 7", "i cons", "ic ons", "icon s", "ico ns", ", s", "Ġ Op", "ĠO p", "S m", "S top", "St op", "ĉ List", "ĉL ist", "ĉ u", "Com ment", "Comm ent", "_ VERSION", "_V ERSION", "_VER SION", ".X tra", "P erson", "Per son", "Pers on", "r b", "L OB", "LO B", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "Ġ Central", "ĠC entral", "ĠCent ral", "2 70", "27 0", "I CK", "IC K", "r aq", "ra q", "Ġp utting", "Ġput ting", "Ġputt ing", "Ġ md", "Ġm d", "Ġ Love", "ĠL ove", "ĠLo ve", "ĠLov e", "P rogram", "Pro gram", "Pr ogram", "Prog ram", "B order", "o or", "oo r", "Ġall owing", "Ġallow ing", "Ġallo wing", "a fter", "af ter", "aft er", "Ġ entries", "Ġen tries", "Ġent ries", "Ġentr ies", "Ġ Maybe", "ĠM aybe", "ĠMay be", "] ).", "]) .", "Ġ Short", "ĠS hort", "ĠSh ort", "ĠSho rt", ") \\", ". now", ".n ow", ".no w", "f riend", "Ġ prefer", "Ġp refer", "Ġpre fer", "Ġpref er", "Ġ GPIO", "ĠG PIO", "ĠGP IO", "ĠGPI O", "o sis", "os is", "osi s", "Ġ GameObject", "ĠGame Object", "Ġ skip", "Ġs kip", "Ġsk ip", "Ġski p", "Ġ competition", "Ġcom petition", "Ġcompet ition", "Ġcompetit ion", "_ match", "_m atch", "_mat ch", "l ications", "lic ations", "lication s", "_ CONT", "_C ONT", "_CON T", "_CO NT", ". groupBox", ".group Box", "Ġ als", "Ġa ls", "Ġal s", "6 66", "66 6", "\" We", "\"W e", "_ eq", "_e q", "l an", "la n", "_ search", "_s earch", "_se arch", "Ġ Music", "ĠM usic", "ĠMus ic", "ĠMu sic", "a sis", "as is", "asi s", "Ġ bind", "Ġb ind", "Ġbi nd", "Ġbin d", "ĠIs land", "ĠIsl and", "r um", "ru m", "( E", "Ġ seat", "Ġs eat", "Ġse at", "Ġsea t", "V ideo", "Vi deo", "Ġ ack", "Ġa ck", "Ġac k", "re ek", "ree k", "={ ()", "={( )", "Ġ rating", "Ġr ating", "Ġrat ing", "Ġra ting", "Ġ restaurant", "Ġrest aurant", "Ġrestaur ant", "Ġrestau rant", "4 56", "45 6", "D EX", "DE X", "( buf", "(b uf", "p ping", "pp ing", "u ality", "ual ity", "uali ty", "Ġ league", "Ġle ague", "1 76", "17 6", "Ġ focused", "Ġf ocused", "Ġfocus ed", "Ġfoc used", "a pon", "ap on", "apo n", "$ data", "$d ata", "CL UD", "CLU D", "CLUD ING", "Ġ absolute", "Ġa bsolute", "Ġabs olute", "Ġabsolut e", "Ġabsol ute", "( query", "(qu ery", "Ġt ells", "Ġtell s", "Ġtel ls", "A ng", "An g", "Ġcomm unities", "Ġcommun ities", "Ġh onest", "Ġhon est", "Ġho nest", "Ġhone st", "o king", "ok ing", "oki ng", "okin g", "Ġa part", "Ġap art", "Ġapar t", "Ġapa rt", "ar ity", "ari ty", "/ $", "_ module", "_m odule", "_mod ule", "Ġ Enc", "ĠE nc", "ĠEn c", ". an", ".a n", ". Config", ".Con fig", "C re", "Cr e", "Ġsh ock", "Ġsho ck", "ĠA rab", "ĠAr ab", "ĠAra b", "I ENT", "IE NT", "/ re", "/r e", "Ġre trie", "Ġret rie", "Ġretr ie", "yc ler", "ycle r", "ycl er", "i sa", "is a", "Ġ Organ", "ĠO rgan", "ĠOr gan", "ĠOrg an", ". graph", ".g raph", ".gr aph", "Ġ í", "ĠB AS", "ĠBA S", "E num", "En um", "Ġ possibly", "Ġposs ibly", "ÑĢ аÐ", "ÑĢа Ð", "Ġ Japanese", "ĠJ apanese", "ĠJapan ese", "Ġ craft", "Ġc raft", "Ġcr aft", "Ġcra ft", "Ġ Place", "ĠP lace", "ĠPl ace", "ĠPla ce", "Ġt alent", "Ġtal ent", "Ġtale nt", "Ġf unding", "Ġfun ding", "Ġfund ing", "Ġ confirmed", "Ġconf irmed", "Ġconfirm ed", "Ġ cycle", "Ġc ycle", "Ġcy cle", "Ġcycl e", "Ġcyc le", "/ x", "G E", "Ġh earing", "Ġhe aring", "Ġhear ing", "Ġ plants", "Ġpl ants", "Ġplan ts", "Ġplant s", "Ġpla nts", "Ġ mouth", "Ġm outh", "Ġmo uth", "Ġmou th", "p ages", "page s", "pa ges", "pag es", "o ria", "or ia", "ori a", "Ġ Remove", "ĠRe move", "ĠRem ove", "_ total", "_t otal", "_to tal", "_tot al", "Ġ od", "Ġo d", "oll apse", "d oor", "do or", "Ġb ought", "Ġbo ught", "Ġbou ght", "Ġ addr", "Ġadd r", "Ġad dr", "AR CH", "ARC H", "_ dim", "_d im", "_di m", "d den", "dd en", "dde n", "Ġdec ades", "Ġdecade s", "Ġdecad es", "RE QUEST", "REQ UEST", "Ġ versions", "Ġv ersions", "Ġversion s", "Ġvers ions", "f ire", "fi re", "fir e", "0 06", "00 6", "Ġ moves", "Ġm oves", "Ġmov es", "Ġmove s", "Ġmo ves", "f b", "Ġ coffee", "Ġc offee", "Ġco ffee", "Ġcoff ee", "Ġcof fee", ". connect", ".con nect", ".conn ect", "Ġ Row", "ĠR ow", "ĠRo w", "Ġ schema", "Ġs chema", "Ġsch ema", "Ġschem a", "Ġsche ma", "S cope", "Sc ope", "- Type", "-T ype", "Ġf ighting", "Ġfight ing", "Ġre tail", "Ġr etail", "Ġret ail", "Ġ modified", "Ġmod ified", "T F", "F iles", "File s", "Fi les", "Fil es", "n ie", "ni e", "_ command", "_com mand", "_comm and", "s tone", "st one", "ston e", "sto ne", "Ġ ÑĤ", "ĠÑ Ĥ", "_ thread", "_t hread", "_th read", "_thr ead", "Ġ bond", "Ġb ond", "Ġbo nd", "Ġbon d", "Ġ Development", "ĠDe velopment", "ĠDev elopment", "ĠDevelop ment", "Ġ pt", "Ġp t", "F ORM", "FO RM", "FOR M", "p let", "pl et", "ple t", "Ġ identified", "Ġident ified", "c pp", "cp p", "2 06", "20 6", "2 25", "22 5", "Ġ coding", "Ġc oding", "Ġco ding", "Ġcod ing", "o ked", "ok ed", "oke d", "Ġ Master", "ĠM aster", "ĠMa ster", "ĠMas ter", "ĠMast er", "ID TH", "Ġres idents", "Ġresident s", "Ġresid ents", "Ġreside nts", "r edit", "re dit", "red it", "redi t", "Ġ Photo", "ĠPh oto", "ĠPhot o", "= -", "u nte", "un te", "unt e", "at eur", "ate ur", "1 59", "15 9", "_ STATE", "_ST ATE", "_STAT E", "_STA TE", "Ġ Sing", "ĠS ing", "ĠSi ng", "ĠSin g", "Ġ sheet", "Ġs heet", "Ġshe et", ". val", ".v al", ".va l", "or se", "ors e", "Ġh ers", "Ġhe rs", "Ġher s", "Ġd etermined", "Ġdetermin ed", "Ġdetermine d", "Ġdeterm ined", "Com mon", "Comm on", "Ġ wed", "Ġw ed", "Ġwe d", "_ queue", "_q ueue", "_que ue", "P H", "Ġ Atl", "ĠA tl", "ĠAt l", "c red", "cre d", "cr ed", "/ LICENSE", "/L ICENSE", "Ġ mes", "Ġm es", "Ġme s", "Ġ advanced", "Ġad vanced", "Ġadv anced", "Ġadvance d", ". java", ".j ava", ".jav a", ". Sh", ".S h", "G o", "k ill", "ki ll", "kil l", "f p", "_ settings", "_s ettings", "_set tings", "_setting s", "Ġ pal", "Ġp al", "Ġpa l", "Ġ truck", "Ġtr uck", "Ġ combined", "Ġcomb ined", "Ġcombine d", "Ġcombin ed", "Ġ \"${", "Ġ\" ${", "Ġ\"$ {", "ĠC orpor", "ĠCor por", "ĠCorp or", "Ġ joined", "Ġj oined", "Ġjoin ed", "Ġjo ined", "Ġ Jose", "ĠJ ose", "ĠJo se", "ĠJos e", "ĠC up", "ĠCu p", "u ns", "un s", "est ival", "esti val", "le vision", "lev ision", "Ġ broken", "Ġb roken", "Ġbr oken", "Ġbro ken", "Ġbroke n", "Ġmar riage", "Ġ Western", "ĠWest ern", "ĠWes tern", "Ġrep resents", "Ġrepresent s", "Ġ Title", "ĠT itle", "ĠTi tle", "ĠTit le", "Ġ ss", "Ġs s", ". Ass", ".A ss", ".As s", "ong oose", "ongo ose", "i ento", "ient o", "ien to", "< >();Ċ", "<>( );Ċ", "<> ();Ċ", "Ġabs olutely", "Ġabsolute ly", "Ġabsolut ely", "Ġabsol utely", "Ġ smooth", "Ġsm ooth", "Ġsmo oth", "T ERN", "TE RN", "TER N", "Ġ Unless", "ĠUn less", "W ord", "Wo rd", "Ġ merge", "Ġm erge", "Ġmer ge", "Ġmerg e", "i gan", "ig an", "iga n", "Ġ Vol", "ĠV ol", "ĠVo l", "Ġ nn", "Ġn n", ". getId", ".get Id", "Ġ з", "ĠÐ ·", "1 71", "17 1", "Ġ sexy", "Ġse xy", "Ġsex y", "Ġse eking", "Ġsee king", "Ġseek ing", "S ingle", "Si ngle", "Sin gle", "Sing le", ". this", ".t his", ".th is", "1 79", "17 9", "Ġ kom", "Ġk om", "Ġko m", "b ound", "bo und", "bou nd", "; \"", "Ġ fontSize", "Ġfont Size", "_ df", "_d f", "Ġin jury", "Ġinj ury", "( H", "Ġ issued", "Ġiss ued", "Ġissue d", "Ġissu ed", "_ END", "_E ND", "_EN D", ": self", ":s elf", "0 20", "02 0", "Ġ patch", "Ġp atch", "Ġpat ch", "Ġle aves", "Ġleave s", "Ġ adopt", "Ġad opt", "Ġado pt", "File Name", "ãĢ IJ", "Ġexec utive", "Ġexecut ive", "Ġ Byte", "ĠB yte", "ĠBy te", "] ))Ċ", "]) )Ċ", "])) Ċ", "Ġ nu", "Ġn u", "o uting", "ou ting", "out ing", "cl uding", "clud ing", "- R", ". options", ".o ptions", ".opt ions", ".option s", "Ġsub stant", "Ġsubs tant", "Ġsubst ant", "av ax", "ava x", "ĠB UT", "ĠBU T", "Ġ technical", "Ġtechn ical", "Ġtw ice", "Ġm ás", "Ġmá s", "Ġun ivers", "Ġuni vers", "y r", "Ġ drag", "Ġd rag", "Ġdr ag", "Ġdra g", "Ġ DC", "ĠD C", "Ġ sed", "Ġs ed", "Ġse d", "Ġ bot", "Ġb ot", "Ġbo t", "Ġ Pal", "ĠP al", "ĠPa l", "Ġ Hall", "ĠH all", "ĠHa ll", "ĠHal l", "force ment", "forc ement", "Ġa uch", "Ġau ch", "Ġauc h", ". mod", ".m od", ".mo d", "n otation", "not ation", "nota tion", "_ files", "_f iles", "_file s", "_fil es", ". line", ".l ine", ".li ne", ".lin e", "_ flag", "_f lag", "_fl ag", "[ name", "[n ame", "Ġ resolution", "Ġre solution", "Ġres olution", "Ġb ott", "Ġbo tt", "Ġbot t", "( \"[", "(\" [", "e nde", "en de", "end e", "( arr", "(a rr", "(ar r", "F ree", "Fr ee", "Fre e", "( @\"", "(@ \"", "Ġ District", "ĠD istrict", "ĠDi strict", "P EC", "PE C", ": -", "P icker", "Pic ker", "Pi cker", "Pick er", "Ġ Jo", "ĠJ o", "Ġ ĠĠĠĠĊ", "ĠĠ ĠĠĠĊ", "ĠĠĠĠ ĠĊ", "ĠĠĠ ĠĠĊ", "ĠĠĠĠĠ Ċ", "Ġ River", "ĠR iver", "ĠRiv er", "ĠRi ver", "_ rows", "_r ows", "_row s", "_ro ws", "Ġhelp ful", "Ġmass ive", "- --Ċ", "-- -Ċ", "--- Ċ", "Ġme asures", "Ġmeasure s", "Ġmeas ures", "0 07", "00 7", "Ġ Runtime", "ĠR untime", "ĠRun time", "Ġw orry", "Ġwor ry", "Ġ Spec", "ĠS pec", "ĠSp ec", "ĠSpe c", "ĉ D", "ãĢ ij", "Ġ ){Ċ", "Ġ) {Ċ", "Ġ){ Ċ", "Ġw orse", "Ġwor se", "Ġwors e", "( filename", "(f ilename", "(file name", "(fi lename", "(fil ename", "Ġ lay", "Ġl ay", "Ġla y", "Ġ magic", "Ġm agic", "Ġmag ic", "Ġ Their", "ĠThe ir", "o ul", "ou l", "st roy", "str oy", "stro y", "Ġ Where", "ĠW here", "ĠWh ere", "ĠWhe re", "2 80", "28 0", "Ġs udden", "Ġsu dden", "Ġsud den", "Ġd efe", "Ġde fe", "Ġdef e", "Ġ binding", "Ġb inding", "Ġbin ding", "Ġbind ing", "Ġ flight", "Ġf light", "Ġfl ight", "Ġ OnInit", "ĠOn Init", "Ġ Women", "ĠW omen", "ĠWo men", "Ġ Policy", "ĠP olicy", "ĠPol icy", "ĠPolic y", "Ġdr ugs", "Ġdrug s", "Ġdru gs", "ish ing", "ishi ng", "(' ../", "('. ./", "Ġ Mel", "ĠM el", "ĠMe l", "p eat", "pe at", "t or", "to r", "Ġpro posed", "Ġprop osed", "Ġpropos ed", "Ġpropose d", "Ġst ated", "Ġstate d", "Ġstat ed", "Ġsta ted", "_ RES", "_RE S", "_R ES", "Ġ east", "Ġe ast", "Ġeas t", "Ġea st", "2 12", "21 2", "Ġ CONDITION", "ĠCON DITION", "_ desc", "_d esc", "_de sc", "_des c", "Ġw inning", "Ġwin ning", "f olio", "fo lio", "fol io", "M apper", "Map per", "Ma pper", "Ġ Pan", "ĠP an", "ĠPa n", "ĠA nge", "ĠAn ge", "ĠAng e", ".s ervlet", ".serv let", "Ġ copies", "Ġc opies", "Ġco pies", "Ġcop ies", "L M", "Ġ vm", "Ġv m", "å į", "Ġ dictionary", "Ġd ictionary", "S eg", "Se g", "1 77", "17 7", "e lines", "el ines", "eline s", "eli nes", "elin es", "Ġ Send", "ĠS end", "ĠSe nd", "ĠSen d", "Ġ iron", "Ġi ron", "Ġir on", "Ġ Fort", "ĠF ort", "ĠFor t", "ĠFo rt", "1 66", "16 6", ". domain", ".d omain", ".do main", ".dom ain", "Ġde bate", "Ġdeb ate", "Not Null", "e q", "a cher", "ac her", "ach er", "ache r", "l f", "ĉ fmt", "ĉf mt", "Ġl awy", "Ġla wy", "Ġlaw y", "1 78", "17 8", "Ä Ł", "Ġ Men", "ĠM en", "ĠMe n", "Ġ trim", "Ġt rim", "Ġtr im", "Ġtri m", "( NULL", "(N ULL", "Ġ !!", "Ġ! !", "Ġ pad", "Ġp ad", "Ġpa d", "Ġfollow s", "Ġfoll ows", "\" ][\"", "\"] [\"", "\"][ \"", "r equ", "re qu", "req u", "Ġ Ep", "ĠE p", ". github", ".g ithub", ".git hub", "( img", "(i mg", "(im g", "e to", "et o", "( '\\", "(' \\", "S ervices", "Service s", "Serv ices", "umbn ail", "_ main", "_m ain", "_ma in", "p leted", "pl eted", "ple ted", "plete d", "plet ed", "fort unately", "fortunate ly", "Ġ windows", "Ġw indows", "Ġwindow s", "Ġwind ows", "Ġ plane", "Ġp lane", "Ġpl ane", "Ġplan e", "Ġpla ne", "Ġ Connection", "ĠCon nection", "ĠConnect ion", "ĠConn ection", ". local", ".l ocal", ".loc al", ".lo cal", "u ard", "ua rd", "uar d", "} \\", "= =\"", "== \"", "a ndon", "an don", "and on", "ando n", "Ġ Roy", "ĠR oy", "ĠRo y", "w est", "we st", "1 58", "15 8", "ig inal", "igin al", "igi nal", "em ies", "emi es", "emie s", "i tz", "it z", "' ):Ċ", "') :Ċ", "'): Ċ", "Ġ Peter", "ĠP eter", "ĠPe ter", "ĠPet er", "ĠPete r", "Ġt ough", "Ġto ugh", "Ġtou gh", "Ġre duced", "Ġred uced", "Ġredu ced", "Ġreduce d", "Ġ calculate", "Ġc alculate", "Ġcal culate", "Ġcalcul ate", "Ġcalc ulate", "Ġ rapid", "Ġr apid", "Ġrap id", "Ġra pid", "c ustomer", "custom er", "cust omer", "Ġ efficient", "Ġeff icient", "Ġeffic ient", "Ġ medium", "Ġm edium", "Ġmed ium", "Ġmedi um", "Ġ fell", "Ġf ell", "Ġfe ll", "Ġfel l", ". ref", ".re f", ".r ef", "Ġ Cas", "ĠC as", "ĠCa s", "Ġ feedback", "Ġfe edback", "Ġfeed back", "S peed", "Sp eed", "Spe ed", "( output", "(out put", "a je", "aj e", "Ġ categories", "Ġc ategories", "Ġcategor ies", "Ġcategorie s", "Ġ fee", "Ġf ee", "Ġfe e", "} ;", "Ġ deleted", "Ġde leted", "Ġdel eted", "Ġdelete d", "Ġdelet ed", "Ġdele ted", "r eh", "re h", "Ġ proof", "Ġp roof", "Ġpro of", "D esc", "De sc", "Des c", "B uild", "Bu ild", "Ġs ides", "Ġside s", "Ġsi des", "Ġsid es", ". ArrayList", ".Array List", "- %", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "Ø ±", ". match", ".m atch", ".mat ch", "л и", "Ġfe els", "Ġfeel s", "Ġfee ls", "Ġachie ve", "Ġach ieve", "Ġc lim", "Ġcl im", "Ġcli m", "_ ON", "_O N", "Ġ CD", "ĠC D", "Ġ teacher", "Ġt eacher", "Ġte acher", "Ġteach er", "Ġtea cher", "_ current", "_c urrent", "_cur rent", "_curr ent", "b n", "_ PL", "_P L", "is ting", "ist ing", "isti ng", "E nable", "En able", "G EN", "GE N", "Ġ tv", "Ġt v", "Ġ sock", "Ġs ock", "Ġso ck", "Ġsoc k", "Ġ plays", "Ġp lays", "Ġpl ays", "Ġplay s", "Ġpla ys", "Ġ discount", "Ġdis count", "Ġdisc ount", "Ġdisco unt", "Ġ KE", "ĠK E", "Ġ Debug", "ĠDe bug", "ĠDeb ug", "F ore", "For e", "Fo re", "Ġ Iraq", "ĠI raq", "ĠIr aq", "Ġ appearance", "Ġap pearance", "Ġappear ance", "M on", "Mo n", "Ġ styled", "Ġst yled", "Ġstyle d", "Ġsty led", "Ġstyl ed", "Ġ Human", "ĠH uman", "ĠHum an", "ĠHu man", "i ot", "io t", "Ġ History", "ĠH istory", "ĠHi story", "ĠHistor y", "ĠHist ory", "Ġs ac", "Ġsa c", "Ġ Collection", "ĠC ollection", "ĠCol lection", "ĠColl ection", "ĠCollect ion", "Ġ recommended", "Ġre commended", "Ġrecomm ended", "Ġrecommend ed", ". Selected", ".Se lected", ".Select ed", "Ġ organizations", "Ġorgan izations", "Ġorganization s", "Ġorganiz ations", "Ġdis covered", "Ġdiscover ed", "co hol", "coh ol", "a das", "ad as", "ada s", "Ġ Thomas", "ĠTh omas", "ĠThom as", "M ay", "Ma y", "Ġcon serv", "Ġcons erv", "Ġconse rv", "Ġd omin", "Ġdo min", "Ġdom in", "Ġ Follow", "ĠF ollow", "ĠFol low", "Ġ Section", "ĠS ection", "ĠSe ction", "ĠSec tion", "ĠSect ion", "Ġ Thanks", "ĠTh anks", "ĠThank s", "ĠThan ks", "User name", "Ġ recipe", "Ġrec ipe", "Ġrecip e", "Ġwonder ful", ". sleep", ".s leep", "_ if", "_i f", "ĉ ĊĉĊ", "ĉĊ ĉĊ", "or no", "orn o", "Ġ ru", "Ġr u", "_ target", "_t arget", "_tar get", ". \"\"", ".\" \"", "à ¦", "Event Args", "Ġ inputs", "Ġin puts", "Ġinput s", "Ġinp uts", "Ġf if", "Ġfi f", "Ġ vision", "Ġv ision", "Ġvis ion", "c y", "Ġ Series", "ĠS eries", "ĠSe ries", "ĠSer ies", "ĠSerie s", ") (((", ")( ((", ")(( (", "Ġtr ading", "Ġtrad ing", "Ġtra ding", "Ġtradi ng", "Ġ marker", "Ġm arker", "Ġmark er", "Ġmar ker", "B egin", "Be gin", "Ġ typically", "Ġtyp ically", "Ġtypical ly", "Ġc auses", "Ġca uses", "Ġcause s", "Ġcaus es", "d ropdown", "drop down", "_ DEBUG", "_DE BUG", "2 60", "26 0", "Ġ detect", "Ġd etect", "Ġdet ect", "c ountry", "count ry", "! \");Ċ", "!\" );Ċ", "!\"); Ċ", "!\") ;Ċ", "ĉ R", "a ppy", "ap py", "app y", "Ġc ref", "Ġcr ef", "Ġcre f", "( '<", "(' <", "\" =>", "Ġ LE", "ĠL E", "re ader", "read er", "rea der", "Ġ administr", "Ġadmin istr", "à µ", "u cket", "uc ket", "uck et", "Ġf ashion", ". char", ".c har", ".ch ar", "i zar", "iz ar", "iza r", "Ġ disable", "Ġd isable", "Ġdis able", "Ġs uc", "Ġsu c", "Ġ Live", "ĠL ive", "ĠLi ve", "ĠLiv e", "i ssue", "iss ue", "Ġ metadata", "Ġm etadata", "Ġmet adata", "Ġmeta data", "f lags", "fl ags", "flag s", "Ġ ðŁ", "Ġð Ł", "Ġ committed", "Ġcom mitted", "Ġcomm itted", "Ġcommit ted", "Ġ va", "Ġv a", "Ġ rough", "Ġr ough", "Ġro ugh", "Ġrou gh", "Ġ '''Ċ", "Ġ' ''Ċ", "Ġ'' 'Ċ", "Ġ''' Ċ", "Ġ highlight", "Ġhigh light", "_ vars", "_v ars", "_var s", "_va rs", "V O", "Ġ encoding", "Ġen coding", "Ġenc oding", "- Z", "_ sign", "_s ign", "_sig n", "_si gn", "$ (\"#", "$( \"#", "$(\" #", "Ġ rain", "Ġr ain", "Ġra in", "re atest", "reate st", "reat est", "rea test", "Ġ END", "ĠE ND", "ĠEN D", "S election", "Se lection", "Select ion", "Sel ection", "Sele ction", "Ġ candidates", "Ġc andidates", "Ġcandid ates", "Ġcandidate s", "Ġ sav", "Ġs av", "Ġsa v", ". Empty", "Ġdec isions", "Ġdecision s", "Ġdecis ions", "Ġcoll abor", "r idge", "ri dge", "rid ge", "f eed", "fe ed", "fee d", "r ession", "ress ion", "Ġ persons", "Ġper sons", "Ġperson s", "Ġpers ons", "Ġperso ns", "V M", "0 08", "00 8", "e ga", "eg a", "_ BIT", "_B IT", "A ccording", "Acc ording", "ack ed", "ac ked", "Ġdoll ars", "Ġdollar s", "_ loss", "_l oss", "_lo ss", "Ġ Cost", "ĠC ost", "ĠCo st", "ĠCos t", "} \"Ċ", "}\" Ċ", "Not ification", "Ġpro stit", "Ġpros tit", "Ġprost it", "Ġ authority", "Ġauthor ity", ". rec", ".re c", ".r ec", "Ġsp okes", "Ġspoke s", "Ġspo kes", "Ġ Today", "ĠT oday", "ĠTo day", "ĠTod ay", "i stant", "is tant", "ist ant", "istan t", "ista nt", "Ġ Head", "ĠH ead", "ĠHe ad", "âĢĿ .", "er tainment", "ert ainment", "ertain ment", "c ean", "ce an", "cea n", "c ulate", "cul ate", "cu late", "Ġ ven", "Ġv en", "Ġve n", "How ever", "_ arr", "_a rr", "_ar r", "Ġ tokens", "Ġt okens", "Ġtoken s", "Ġtok ens", "G raph", "Gr aph", "Ġ Jud", "ĠJ ud", "ĠJu d", "Ġ Virgin", "ĠVir gin", "Ġ Serial", "ĠS erial", "ĠSe rial", "ĠSer ial", "un ning", "unn ing", "M utable", "Mu table", "Mut able", "a gers", "ag ers", "age rs", "ager s", ". csv", ".c sv", ".cs v", "Ġdevelop ing", "Ġdevel oping", "Ġ instructions", "Ġin structions", "Ġinstruction s", "Ġinstr uctions", "Ġinstruct ions", "Ġ promise", "Ġp romise", "Ġpro mise", "Ġprom ise", "Ġ requested", "Ġrequest ed", "Ġrequ ested", "_ encode", "_en code", "_enc ode", "/ \"", "Ġ Icon", "ĠI con", "u ilt", "ui lt", "uil t", "- day", "-d ay", "-da y", "Ġ intelligence", "Ġint elligence", "Ġintellig ence", ". IS", ".I S", "Ġ Observable", "ĠO bservable", "ĠObserv able", "Ġ Hard", "ĠH ard", "ĠHar d", "ĠHa rd", "B ool", "Bo ol", "2 11", "21 1", "id ential", "ident ial", ". Anchor", ".An chor", "Ġ selling", "Ġs elling", "Ġsell ing", "Ġsel ling", "C I", "A GES", "AG ES", "AGE S", "t le", "tl e", "b ur", "bu r", "UFF ER", "UF FER", "R Y", "Ġb igger", "Ġbig ger", "Ġbi gger", "Ġ rat", "Ġr at", "Ġra t", "Ġf amous", "Ġfam ous", "Ġ typename", "Ġtype name", "Ġtyp ename", "Ġ explained", "Ġexpl ained", "Ġexplain ed", "} }Ċ", "}} Ċ", "Ġn uclear", "Ġnu clear", "Ġnucle ar", "- N", "Ġcr isis", "Ġcri sis", "Ġcris is", "Ġ Enter", "ĠEn ter", "ĠEnt er", "Ġ answers", "Ġan swers", "Ġanswer s", "Ġans wers", "/ ${", "/$ {", "/ pl", "/p l", "Ġ sequ", "Ġs equ", "Ġse qu", "Ġseq u", "_ next", "_n ext", "_ne xt", "m ask", "ma sk", "mas k", "Ġ standing", "Ġst anding", "Ġstand ing", "Ġstan ding", "Ġpl enty", "Ġple nty", "Ġ Cross", "ĠC ross", "ĠCr oss", "ĠCro ss", "ĠCros s", "ĉ ret", "ĉr et", "ĉre t", "d ro", "dr o", "Ġ Cast", "ĠC ast", "ĠCas t", "ĠCa st", "1 67", "16 7", "= true", "Ġ Chris", "ĠCh ris", "ĠChr is", "i cio", "ic io", "ici o", "Ġ Mike", "ĠM ike", "ĠMi ke", "ĠMik e", "D ecimal", "De cimal", "Dec imal", "add Component", "L en", "Le n", "Ġ cock", "Ġc ock", "Ġco ck", "Ġcoc k", "Ġ #{", "Ġ# {", "U RN", "UR N", "< tr", "", "\\\" >", "Ġ *=", "Ġ* =", "Ġ PS", "ĠP S", "Ġdanger ous", "[ p", "O ME", "OM E", "O ther", "Ot her", "Ġ StringBuilder", "ĠString Builder", "P oints", "Point s", "Po ints", "he ading", "head ing", "hea ding", "Ġ currency", "Ġc urrency", "Ġcurr ency", "Ġ percentage", "Ġper centage", "Ġpercent age", "_ API", "_A PI", "_AP I", "Ġ classic", "Ġclass ic", "Ġcl assic", "Ġclas sic", "t head", "th ead", "the ad", "Ġ MO", "ĠM O", "F E", "I dx", "Id x", "a wait", "aw ait", "awa it", "awai t", "Ġ è", "Ġà ¨", "Ġacc ident", "Ġ variant", "Ġv ariant", "Ġvar iant", "Ġvari ant", "Ġm yst", "Ġmy st", "Ġmys t", "Ġ Land", "ĠL and", "ĠLa nd", "ĠLan d", "Ġ Bre", "ĠB re", "ĠBr e", "Ġh arm", "Ġhar m", "Ġha rm", "Ġ Acc", "ĠA cc", "ĠAc c", "Ġ charged", "Ġch arged", "Ġchar ged", "Ġcharg ed", "Ġcharge d", "i ones", "ion es", "io nes", "ione s", "Vis ibility", "ar ry", "arr y", "Ġ Language", "ĠL anguage", "Ġ walking", "Ġw alking", "Ġwalk ing", "Ġwal king", "\" .ĊĊ", "\". ĊĊ", "\".Ċ Ċ", "i fer", "if er", "ife r", "Ġleaders hip", "Ġleader ship", ". From", ".F rom", "y nam", "yn am", "yna m", "Ġ timestamp", "Ġt imestamp", "Ġtime stamp", "i pt", "ip t", "Ġ Has", "ĠH as", "ĠHa s", "RE FER", "REF ER", "Ġ Its", "ĠI ts", "ĠIt s", "Ġ listener", "Ġlist ener", "Ġlisten er", "Ġliste ner", "Ġlis tener", "U TE", "UT E", "2 13", "21 3", "_ description", "_d escription", "_de scription", "_des cription", "Ġex periences", "Ġexper iences", "Ġexperience s", "Ġexperi ences", "Ġ creates", "Ġcreate s", "Ġcre ates", "Ġcreat es", "Ġcrea tes", "R S", "c art", "ca rt", "car t", "b lack", "bl ack", "bla ck", "Ġ choices", "Ġcho ices", "Ġchoice s", "w ar", "wa r", "7 50", "75 0", "Ġ '''", "Ġ' ''", "Ġ'' '", "Ġ ordered", "Ġorder ed", "Ġord ered", "Ġeven ing", "Ġev ening", "Ġeve ning", "Ġp il", "Ġpi l", "Ġt un", "Ġtu n", "Ġ Bad", "ĠB ad", "ĠBa d", "( app", "(a pp", "(ap p", "r andom", "ran dom", "rand om", "Ġ explicit", "Ġexp licit", "Ġexpl icit", "Ġexplic it", "Ġarr ived", "Ġarrive d", "Ġarriv ed", "Ġ fly", "Ġf ly", "Ġfl y", "Ġe conom", "Ġecon om", "Ġec onom", "Ġeco nom", "- mail", "-m ail", "Ġ lists", "Ġl ists", "Ġli sts", "Ġlist s", "Ġlis ts", "Ġarch itect", "Ġarchit ect", "2 34", "23 4", "Ġ Pay", "ĠP ay", "ĠPa y", "Ġ ds", "Ġd s", "Ġ Sol", "ĠS ol", "ĠSo l", "Ġ vehicles", "Ġv ehicles", "Ġveh icles", "Ġvehicle s", "H z", "- com", "-c om", "-co m", "Ġ king", "Ġk ing", "Ġki ng", "Ġkin g", "_ equal", "_e qual", "_eq ual", "_equ al", "Ġ Help", "ĠH elp", "ĠHe lp", "ĠHel p", "Ġab use", "4 80", "48 0", "1 69", "16 9", "-- ;Ċ", "--; Ċ", "Ġ extr", "Ġex tr", "Ġext r", "Ġ chemical", "Ġchem ical", "ä ¿", "Ġ orient", "Ġo rient", "Ġor ient", "Ġori ent", "Ġbre ath", "Ġbreat h", "Ġ Space", "ĠS pace", "ĠSp ace", "ĠSpa ce", "( element", "(e lement", "(el ement", "(elem ent", "(ele ment", "w ait", "wa it", "D ED", "DE D", "ig ma", "igm a", "Ġ entr", "Ġen tr", "Ġent r", "Ġ sob", "Ġs ob", "Ġso b", "- name", "-n ame", "-na me", "Ġ affected", "Ġaff ected", "Ġaffect ed", "i ka", "ik a", "Ġ coal", "Ġco al", "_ work", "_w ork", "_wo rk", "Ġh undreds", "Ġhundred s", "Ġpol itics", "Ġpolit ics", "Ġpolitic s", "sub ject", "su bject", "subj ect", "Ġ consumer", "Ġcon sumer", "Ġcons umer", "Ġconsum er", "Ġconsume r", "AN GE", "ANG E", "Ġre peated", "Ġrep eated", "Ġrepe ated", "Ġrepeat ed", "S end", "Se nd", "Sen d", "Ġ #[", "Ġ# [", "Ġ protocol", "Ġprot ocol", "Ġproto col", "Ġ leads", "Ġle ads", "Ġlead s", "us eum", "use um", "E very", "Ev ery", "Ever y", "8 08", "80 8", "1 74", "17 4", "Im port", "Imp ort", "( count", "(c ount", "(co unt", "Ġch allenges", "Ġchalleng es", "Ġchallenge s", "Ġn ovel", "Ġno vel", "Ġnov el", "Ġ depart", "Ġde part", "Ġdep art", "b its", "bit s", "bi ts", ". Current", ".C urrent", "Ġ `${", "Ġ` ${", "Ġ`$ {", "o ting", "ot ing", "oti ng", "( \\", "Ġ creative", "Ġc reative", "Ġcre ative", "Ġcreat ive", "Ġ buff", "Ġb uff", "Ġbu ff", "Ġbuf f", "Ġint roduced", "Ġintrodu ced", "Ġintroduce d", "Ġintro duced", "u sic", "us ic", "usi c", "mod ules", "module s", "A re", "Ar e", "- doc", "-d oc", "-do c", "l anguage", "_ cache", "_c ache", "_ca che", "Ġ tod", "Ġt od", "Ġto d", "? > < /", "om ething", "ome thing", "Ġh un", "Ġhu n", "å º", "a ters", "at ers", "ate rs", "ater s", "In tent", "Int ent", "Ġ implemented", "Ġim plemented", "Ġimplement ed", "Ġ Case", "ĠC ase", "ĠCas e", "ĠCa se", "Ch ildren", "Child ren", "Ġ notification", "Ġnot ification", "Render er", "W rapper", "Wrap per", "Wr apper", "Object s", "Obj ects", "t l", ". Contains", ".Cont ains", ".Con tains", "Pl ugin", "Plug in", ". row", ".r ow", ".ro w", "Ġ forg", "Ġf org", "Ġfor g", "Ġfo rg", "Ġ permit", "Ġper mit", "Ġperm it", "Ġ targets", "Ġtarget s", "Ġtar gets", "Ġtarg ets", "Ġ IF", "ĠI F", "Ġ tip", "Ġt ip", "Ġti p", "s ex", "se x", "Ġ supports", "Ġsup ports", "Ġsupport s", "Ġsupp orts", "Ġ fold", "Ġf old", "Ġfol d", "Ġfo ld", "ph oto", "phot o", "} ,čĊ", "}, čĊ", "Ġ google", "Ġg oogle", "Ġgo ogle", "Ġgoog le", "Ġgoo gle", "$ ('#", "$( '#", "$(' #", "Ġ sharing", "Ġsh aring", "Ġsha ring", "Ġshar ing", "Ġ goods", "Ġg oods", "Ġgo ods", "Ġgood s", "Ġgoo ds", "v s", "Ġ Dan", "ĠD an", "ĠDa n", "R ate", "Ra te", "Ġ Martin", "ĠM artin", "ĠMar tin", "ĠMart in", "Ġm anner", "Ġman ner", "Ġmann er", "l ie", "li e", ". The", ".T he", ".Th e", "In ternal", "Int ernal", "Inter nal", "Intern al", "ĠCON TR", "ĠCONT R", "M ock", "Mo ck", "R IGHT", "Ġ '{", "Ġ' {", "Ġ controls", "Ġcontrol s", "Ġcontr ols", "Ġcontro ls", "M at", "Ma t", "Ġ mand", "Ġm and", "Ġman d", "Ġma nd", "Ġ extended", "Ġext ended", "Ġextend ed", "O k", "Ġ embed", "Ġem bed", "Ġemb ed", "Ġ planet", "Ġplan et", "Ġplane t", "Ġpla net", "Ġ Non", "ĠN on", "ĠNo n", "- ch", "-c h", ") \",", ")\" ,", "e par", "ep ar", "Ġbel ieved", "Ġbelie ved", "Ġbelieve d", "Ġ Environment", "ĠEn vironment", "Ġ Friend", "ĠF riend", "ĠFri end", "- res", "-r es", "-re s", "Ġ handling", "Ġhand ling", "Ġhan dling", "n ic", "ni c", "- level", "-le vel", "s cri", "sc ri", "scr i", "X ml", "B E", "u ngen", "un gen", "ung en", "unge n", "Ġ alter", "Ġal ter", "Ġalt er", "Ġalte r", "[ idx", "[i dx", "[id x", "P op", "Po p", "c am", "ca m", "Ġ (((", "Ġ( ((", "Ġ(( (", "Ġ shipping", "Ġsh ipping", "Ġship ping", "Ġ battery", "Ġb attery", "Ġbatter y", "Ġbatt ery", "Ġbat tery", "iddle ware", "M C", "Ġ impl", "Ġi mpl", "Ġim pl", "Ġimp l", "ot ation", "ota tion", "Ġ Lab", "ĠL ab", "ĠLa b", "< form", " {{", ">{ {", "Ġ Resource", "ĠRe source", "ĠRes ource", "Ġ Standard", "ĠSt andard", "ĠStand ard", "Ġ Prem", "ĠP rem", "ĠPr em", "ĠPre m", "up dated", "update d", "upd ated", "iv alent", "ival ent", "Ġ assets", "Ġas sets", "Ġass ets", "Ġasset s", "_ temp", "_t emp", "_tem p", "_te mp", "Ġinter ests", "Ġinterest s", "Ġinteres ts", "Ġ hardware", "Ġh ardware", "Ġhard ware", "Ġ Rom", "ĠR om", "ĠRo m", "Ġ Share", "ĠS hare", "ĠSh are", "ĠSha re", "ĠShar e", "Ġ ''Ċ", "Ġ' 'Ċ", "Ġ'' Ċ", "Ġ *,", "Ġ* ,", "Ġ Take", "ĠT ake", "ĠTa ke", "ĠTak e", "Ġ Images", "ĠIm ages", "ĠImage s", "ĠImag es", "_ CHECK", "_C HECK", "( typeof", "(type of", "(typ eof", "Ġ Jun", "ĠJ un", "ĠJu n", "\\< ^", "Ġ liqu", "Ġl iqu", "Ġli qu", "Ġw orst", "Ġwor st", "Ġwo rst", "Ġwors t", "ymb ols", "ymbol s", "ĉ ĉĉĠĠĠ", "ĉĉ ĉĠĠĠ", "ĉĉĉ ĠĠĠ", "ĉĉĉĠ ĠĠ", "ĉĉĉĠĠ Ġ", "Ġ drivers", "Ġdr ivers", "Ġdriver s", "Ġdrive rs", "Ġdriv ers", "Ġdri vers", "Ġ Document", "ĠD ocument", "ĠDoc ument", "e no", "en o", "Ġ Technology", "ĠTechn ology", "Ġ approved", "Ġap proved", "Ġappro ved", "Ġapprove d", "u mps", "um ps", "ump s", "Ġ snow", "Ġs now", "Ġsn ow", "Ġsno w", "form ance", "forma nce", "forman ce", "_ ASSERT", "_A SSERT", "_ASS ERT", "u its", "ui ts", "uit s", "2 07", "20 7", "Ù Ĩ", "Ġd ifferences", "Ġdif ferences", "Ġdiffer ences", "Ġdifference s", ". Visible", ".V isible", "ĉ ĉĉčĊ", "ĉĉ ĉčĊ", "ĉĉĉ čĊ", "Ġ Ps", "ĠP s", "_ fetch", "_f etch", "Ġ todo", "Ġt odo", "Ġto do", "Ġtod o", ". ',Ċ", ".' ,Ċ", ".', Ċ", "Ġ sel", "Ġs el", "Ġse l", "ur ers", "ure rs", "urer s", "in valid", "Ġ tweet", "Ġt weet", "Ġtwe et", "Ġtwee t", "V EL", "VE L", "Ġresearch ers", "Ġresearcher s", "Ġ sprintf", "Ġs printf", "Ġsprint f", "Ġ RO", "ĠR O", "Ġ pel", "Ġp el", "Ġpe l", ". Trans", ".T rans", ".Tr ans", "Ġ illegal", "Ġil legal", "Ġill egal", "Ġilleg al", "d ialog", "di alog", "dia log", "sm arty", "smart y", "l g", "_ MIN", "_M IN", "_MI N", "Ġ hero", "Ġh ero", "Ġhe ro", "Ġher o", "f inal", "fin al", "fi nal", "Ġ pp", "Ġp p", ". Le", ".L e", "Ġ ci", "Ġc i", "ĉ RT", "ĉR T", "Ġs uggested", "Ġsuggest ed", "p df", "pd f", "a ching", "ach ing", "achi ng", "Ġ Ro", "ĠR o", "Ġ Properties", "ĠP roperties", "ĠProp erties", "ĠProper ties", "Ġ Si", "ĠS i", "Ġbu ying", "Ġbuy ing", "Ġ mu", "Ġm u", "Ġ lands", "Ġl ands", "Ġla nds", "Ġland s", "Ġlan ds", "if iers", "ifier s", "ifi ers", "ifie rs", "Ġ FILE", "ĠF ILE", "ĠFI LE", "ĠFIL E", "RO UP", "Ġ holder", "Ġh older", "Ġhold er", "Ġho lder", "Ġhol der", "Ġ Son", "ĠS on", "ĠSo n", "Ġsym pt", "Ġsymp t", ". route", ".r oute", ".ro ute", ") ?", "Ġ argc", "Ġar gc", "Ġarg c", "Ġ fort", "Ġf ort", "Ġfor t", "Ġfo rt", "Ġcas ino", "Ġcasi no", "_ category", "_c ategory", "Ġ forum", "Ġf orum", "Ġfor um", "Ġfo rum", "2 15", "21 5", "p refix", "pre fix", "pref ix", "ap ture", "apt ure", "T ube", "Tu be", "e ms", "em s", "im ize", "imi ze", "imiz e", "Ġn ue", "Ġnu e", "a us", "au s", "c ourse", "co urse", "cour se", "A TOR", "AT OR", "ATO R", "( )),", "() ),", "()) ,", "Ad vertis", "IN GS", "ING S", "Ġac know", "Ġack now", "ĠK orea", "ĠKore a", "ĠKo rea", "ĠKor ea", "p ling", "pl ing", "Ġ worker", "Ġwork er", "Ġwor ker", "PL IED", "h al", "ha l", "Ġ Richard", "ĠRich ard", "ĠRic hard", "E lements", "Element s", "El ements", "Elem ents", "Ele ments", "ĉ ĉĉĠ", "ĉĉ ĉĠ", "ĉĉĉ Ġ", "s tar", "st ar", "sta r", "Ġ relationships", "Ġrelations hips", "Ġrelationship s", "Ġrelation ships", "Ġ cheap", "Ġc heap", "Ġche ap", "A CH", "AC H", "Ġ XML", "ĠX ML", "ĠXM L", ", &", "Ġ Louis", "ĠL ouis", "ĠLo uis", "ĠLou is", "Ġ ride", "Ġr ide", "Ġrid e", "Ġri de", "_ FAIL", "_F AIL", "_FA IL", "Ġ chunk", "Ġch unk", "Ġchu nk", "[ s", "_ OUT", "_O UT", "Ġ chosen", "Ġch osen", "Ġcho sen", "Ġchose n", "_ [", "/ (", "Ġ Jeff", "ĠJ eff", "ĠJe ff", "_ sl", "_s l", "p riv", "pr iv", "pri v", "Ġ Canadian", "ĠCan adian", "Ġ unable", "Ġu nable", "Ġun able", "Ġuna ble", "Ġunab le", "_ FLAG", "_F LAG", "_FL AG", "Ġ nos", "Ġn os", "Ġno s", "h igh", "hi gh", "Ġ lift", "Ġl ift", "Ġli ft", "Ġlif t", "f un", "fu n", "( ){", "() {", "el ly", "ell y", "ycler View", "_ as", "_a s", "_ LIST", "_L IST", "Ġ radi", "Ġr adi", "Ġrad i", "Ġra di", ". getValue", ".get Value", "3 04", "30 4", "ĠAnge les", "ĠAngel es", "Ġ Span", "ĠS pan", "ĠSp an", "ĠSpa n", "_ instance", "_in stance", "_inst ance", "i tors", "it ors", "itor s", "ito rs", "2 08", "20 8", "Ġ migration", "Ġm igration", "Ġmigr ation", "Ġmig ration", "A K", "O h", " ®", ". selected", ".se lected", ".select ed", ".sel ected", "Ġ GT", "ĠG T", "Ġ advance", "Ġadv ance", "Ġ Style", "ĠSt yle", "ĠSty le", ". DataGridView", ".Data GridView", "e ction", "ect ion", "ec tion", "Ñ İ", "p io", "pi o", "r og", "ro g", "Ġ shopping", "Ġsh opping", "Ġshop ping", "Ġsho pping", "Ġ Rect", "ĠR ect", "ĠRe ct", "ĠRec t", "I lluminate", "O U", "ĉ array", "ĉa rray", "ĉarr ay", "ĉar ray", "Ġsub stantial", "Ġsubstant ial", "Ġp regn", "Ġpre gn", "Ġpreg n", "Ġprom ote", "Ġpromot e", "Ġpromo te", "I EW", "IE W", ". Layout", ".L ayout", "Ġsign s", "Ġsig ns", "/ .", "Ġ letters", "Ġlet ters", "Ġletter s", "Ġlett ers", "B oard", "Bo ard", "c trl", "ct rl", "ctr l", "\" \\", "Ġ Jones", "ĠJ ones", "ĠJo nes", "ĠJon es", "Ġ vertex", "Ġver tex", "Ġvert ex", "Ġverte x", "Ġ ja", "Ġj a", "Ġaff ili", "Ġ wealth", "Ġwe alth", "ĉ default", "ĉd efault", "ĉdef ault", "ĉde fault", "Ġsign ificantly", "Ġsignific antly", "Ġsignificant ly", "Ġ ec", "Ġe c", "Ġ xs", "Ġx s", "act ual", "ac tual", ". per", ".p er", ".pe r", "_ step", "_s tep", "_st ep", "_ste p", "an vas", "m ac", "ma c", "Ġtrans l", "Ġtran sl", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "It erator", "Iter ator", "Ġ och", "Ġo ch", "Ġoc h", "ag nostic", "agnost ic", "Ġ During", "ĠD uring", "ĠDu ring", "ĠDur ing", "Ġ DEFAULT", "ĠD EFAULT", "ĠDE FAULT", "Ġt ill", "Ġti ll", "Ġtil l", "Ġ signature", "Ġsign ature", "Ġsig nature", "Ġ bird", "Ġb ird", "Ġbi rd", "Ġbir d", "Ġ Ol", "ĠO l", "3 10", "31 0", "Ġ Ir", "ĠI r", "H S", "av atar", "ava tar", "ESS AGE", "Ġe lev", "Ġel ev", "Ġele v", "Ġ mt", "Ġm t", "Ġ Nav", "ĠN av", "ĠNa v", "Ġrel ax", "Ġ plate", "Ġp late", "Ġpl ate", "Ġpla te", "Ġplat e", "I TEM", "IT EM", "ITE M", "( date", "(d ate", "(dat e", "(da te", ". not", ".n ot", ".no t", "Ġ grade", "Ġg rade", "Ġgr ade", "Ġgrad e", "Ġgra de", "Ġ }),Ċ", "Ġ} ),Ċ", "Ġ}) ,Ċ", "Ġ}), Ċ", "? \"ĊĊ", "?\" ĊĊ", "?\"Ċ Ċ", "i ences", "ie nces", "ience s", "ien ces", "H igh", "Hi gh", "Ġ DIS", "ĠD IS", "ĠDI S", "2 31", "23 1", "dis abled", "disable d", "Q UI", "QU I", "Ġ noise", "Ġn oise", "Ġno ise", "Ġnoi se", "a ux", "au x", "Ġ UP", "ĠU P", "8 88", "88 8", "o sa", "os a", "Ġv oc", "Ġvo c", "Ġ ))", "Ġ) )", "o com", "oc om", "oco m", "_ OFF", "_O FF", "_OF F", "Ġ Db", "ĠD b", "L ock", "Lo ck", "Loc k", ".e clipse", ", d", "Ġ Draw", "ĠD raw", "ĠDr aw", "ĠDra w", "Ġ \"(", "Ġ\" (", "Ġ visited", "Ġvis ited", "Ġvisit ed", "Ġvisite d", "Ġ âĪ", "Ġâ Ī", "Ġs ucceed", "Ġsuc ceed", "Ġsucc eed", "Ġim possible", "Ġimp ossible", "Ġimposs ible", "a ire", "air e", "ai re", "Ġ Turn", "ĠT urn", "ĠTur n", "ĠTu rn", "Ġ dish", "Ġd ish", "Ġdis h", "Ġdi sh", "F G", "Ġ sensor", "Ġs ensor", "Ġsens or", "A NN", "AN N", "a ba", "ab a", "Ġs urg", "Ġsu rg", "Ġsur g", "] );čĊ", "]) ;čĊ", "]); čĊ", "Ġ fp", "Ġf p", "_ an", "_a n", "- J", "- G", "Ġ Job", "ĠJ ob", "ĠJo b", "Con vert", "Conv ert", "Ġ KEY", "ĠK EY", "ĠKE Y", "Ġ authors", "Ġauthor s", "Ġauth ors", "_ server", "_s erver", "_serv er", "_ser ver", "\\ r", "Ġ -*-", "Ġ- *-", "Ġ-* -", "f lex", "fl ex", "Ġ soc", "Ġs oc", "Ġso c", "R et", "Re t", "Ġ salt", "Ġs alt", "Ġsa lt", "Ġsal t", "Ġ âĢ¦ĊĊ", "ĠâĢ¦ ĊĊ", "ĠâĢ¦Ċ Ċ", "Ġ Clear", "ĠC lear", "ĠCl ear", "ĠCle ar", "( page", "(p age", "(pa ge", "- danger", "-d anger", "-da nger", "Ġ rooms", "Ġro oms", "Ġroom s", "con v", "co nv", "# {", ". op", ".o p", "Ġ Area", "ĠA rea", "ĠAr ea", "ĠAre a", "_ SC", "_S C", "h en", "he n", "Ġbeg ins", "Ġbegin s", "- y", "Ġexc ited", "Ġexcit ed", "Ġ ignored", "Ġign ored", "Ġignore d", "Ġignor ed", "Ġ bonus", "Ġb onus", "Ġbon us", "st udent", "stu dent", "stud ent", "Ġ Member", "ĠM ember", "ĠMem ber", "Ġrel atively", "Ġrelative ly", "Ġrelativ ely", "Ġrelat ively", "Ġ Low", "ĠL ow", "ĠLo w", "Ġ Produ", "ĠP rodu", "ĠPro du", "ĠPr odu", "ĠProd u", "at eway", "ate way", "pos ure", "po sure", "Ġth ick", "Ġthi ck", "an iel", "ani el", "anie l", "( view", "(v iew", "ĠC rush", "ĠCr ush", "ĠCru sh", "ĠCrus h", "Ext ension", "I l", "e ed", "ee d", "L OC", "LO C", ". im", ".i m", ". Items", ".I tems", ".Item s", ".It ems", "Ġconf lict", "Ġconflic t", "Ġconfl ict", ". prevent", ".pre vent", ".pr event", ".prev ent", "2 52", "25 2", "Ġon Create", "u v", "i ser", "is er", "ise r", "Ġ wave", "Ġw ave", "Ġwa ve", "Ġwav e", "M ar", "Ma r", "Ġ Community", "ĠComm unity", "ĠCommun ity", "i che", "ic he", "ich e", "Ġ Nothing", "ĠNo thing", "[ m", "Ġ Lee", "ĠL ee", "ĠLe e", "ri ends", "riend s", "rie nds", "rien ds", "2 32", "23 2", "è re", "! !!", "!! !", "a nz", "an z", ". result", ".res ult", "Ġ SK", "ĠS K", "_ PARAM", "_P ARAM", "_PA RAM", "_PAR AM", "Ġdem ocr", "Ġdemo cr", "Back Color", ". exists", ".ex ists", ".exist s", "\" It", "\"I t", "( options", "(o ptions", "(opt ions", "(option s", "r azy", "ra zy", "raz y", "a ser", "as er", "ase r", "\\ Database", "\\Data base", "\\D atabase", "al endar", "alen dar", "_ ass", "_a ss", "_as s", "; }Ċ", ";} Ċ", "ver tex", "vert ex", "verte x", "ine craft", "W arning", "War ning", "Warn ing", "ar go", "arg o", "Ġ actor", "Ġa ctor", "Ġact or", "Ġac tor", "Ġ Instead", "ĠIn stead", "ĠInst ead", "Ġ Using", "ĠU sing", "ĠUs ing", "S elf", "Se lf", "Sel f", "@ interface", "Ġspe aking", "Ġspeak ing", "Ġ Paris", "ĠP aris", "ĠPar is", "ĠPa ris", "Ġ LICENSE", "ĠL ICENSE", "ĠLIC ENSE", ". node", ".n ode", ".no de", "Ġ Food", "ĠF ood", "ĠFo od", "ĠFoo d", "E IF", "EI F", "Ġ Bi", "ĠB i", ". Start", ".St art", "Ġ IB", "ĠI B", "Ġun iversity", "Ġunivers ity", "2 54", "25 4", "Ġ Header", "ĠHe ader", "ĠHead er", ". product", ".pro duct", ".prod uct", "4 09", "40 9", "C opy", "Co py", "Cop y", "e tc", "et c", "r ical", "ri cal", "ric al", "rica l", "Ġ >>>", "Ġ> >>", "Ġ>> >", "b ooks", "bo oks", "book s", "boo ks", "Ġ algorithm", "Ġal gorithm", "Ġ' __", "Ġ'_ _", "( javax", "(j avax", "(java x", "Ġnumer ous", "Ġnumero us", "S hare", "Sh are", "Shar e", "Sha re", "H ave", "Ha ve", "Ġrec ru", "Ġ prove", "Ġp rove", "Ġpro ve", "Ġpr ove", "Ġprov e", ". substring", ".sub string", ".substr ing", "he alth", "е л", "еР»", "Ġ decimal", "Ġd ecimal", "Ġde cimal", "Ġdec imal", "Ġ commission", "Ġcom mission", "Ġcomm ission", "s cription", "script ion", "scri ption", "x C", "Ġ summary", "Ġsum mary", "Ġsummar y", "Ġsumm ary", "at ted", "att ed", "atte d", "Ġc loser", "Ġcl oser", "Ġclose r", "Ġclos er", "Ġclo ser", "f inished", "fin ished", "finish ed", "( )){Ċ", "() ){Ċ", "()) {Ċ", "()){ Ċ", "Ġ Wood", "ĠW ood", "ĠWo od", "ĠWoo d", "3 01", "30 1", "_ fields", "_f ields", "_field s", "k u", "_ items", "_i tems", "_item s", "_it ems", "F lag", "Fl ag", "Ġ confidence", "Ġconf idence", "Ġ Federal", "ĠF ederal", "ĠFeder al", "ĠFed eral", "d ux", "du x", "Ġ compat", "Ġcom pat", "Ġcomp at", "Ġ vertical", "Ġver tical", "Ġvert ical", "Ð ¹", "è s", "; \">Ċ", ";\" >Ċ", ";\"> Ċ", "_ manager", "_m anager", "_man ager", "_manage r", "( )))Ċ", "() ))Ċ", "()) )Ċ", "())) Ċ", "I DE", "ID E", ": \",", ":\" ,", "2 35", "23 5", "_ _Ċ", "__ Ċ", "Ġ Way", "ĠW ay", "ĠWa y", "2 21", "22 1", "Ñ Ī", "T emp", "Te mp", "Tem p", "Ġ STR", "ĠS TR", "ĠST R", "r itten", "rit ten", "ritt en", "ritte n", "S ync", "Sy nc", "Syn c", "Ġ AV", "ĠA V", "Ġ CEO", "ĠC EO", "ĠCE O", "Ġ Guid", "ĠG uid", "ĠGu id", "ĠGui d", "Ġenvironment al", "Ġenviron mental", "Ġcorrespond ing", "ĉ console", "ĉcon sole", "Ġ justice", "Ġjust ice", "Ġju stice", "Ġ JS", "ĠJ S", "Ġl ived", "Ġli ved", "Ġlive d", "Ġliv ed", "g ar", "ga r", "Ġ Graph", "ĠG raph", "ĠGr aph", "ĠGra ph", "Ġ Stat", "ĠS tat", "ĠSt at", "ĠSta t", "Ġ iPhone", "Ġi Phone", "ĠiP hone", ". al", ".a l", "Ġ HD", "ĠH D", "Ġocc ur", "Ġoc cur", "Ġ threshold", "Ġth reshold", "Ġthresh old", "5 09", "50 9", "Ġ onclick", "Ġon click", "Ġonc lick", "R EG", "RE G", ".Graphics Unit", "M eta", "Me ta", "Met a", "Å ¾", "Ġ cum", "Ġc um", "Ġcu m", ". gnu", ".g nu", "à «", "Ġobt ained", "Ġobtain ed", "Ġcom plaint", "Ġcompl aint", "Ġcomplain t", "Ġ eating", "Ġe ating", "Ġeat ing", "Ġea ting", "Ġ tar", "Ġt ar", "Ġta r", "_ task", "_t ask", "_ta sk", "Ġ opts", "Ġo pts", "Ġop ts", "Ġopt s", "2 16", "21 6", "( to", "(t o", "P ass", "Pa ss", "Pas s", "Ġp lastic", "Ġpl astic", "Ġplast ic", "t ility", "til ity", "Ġ Win", "ĠW in", "ĠWi n", ".prevent Default", "p ile", "pi le", "Ġ Gar", "ĠG ar", "ĠGa r", "Ġ quantity", "Ġqu antity", "Ġquant ity", "Ġqua ntity", "_ last", "_l ast", "_la st", "Ġg reatest", "Ġgreat est", "Ġgre atest", "D ao", "Da o", "_ DIS", "_D IS", "_DI S", "Ġ Used", "ĠU sed", "ĠUs ed", "ĠUse d", "Ġ HP", "ĠH P", "r iting", "ri ting", "rit ing", "S ION", "SI ON", "b lue", "bl ue", "d omain", "do main", "dom ain", "Ġ scores", "Ġs cores", "Ġsc ores", "Ġscore s", "Ġsco res", "Ġscor es", "N ormal", "Norm al", "Nor mal", "_ admin", "_ad min", "Ġ ASSERT", "ĠA SSERT", "ĠASS ERT", "T hen", "The n", "Th en", "* **", "** *", "d ist", "dis t", "di st", "l on", "lo n", "Ġh ate", "Ġha te", "Ġhat e", "s hal", "sh al", "sha l", "Image View", "d atabase", "data base", "dat abase", "Ġp and", "Ġpa nd", "Ġpan d", "Ġ logic", "Ġlog ic", "= false", "=f alse", "b g", "Ġ Configuration", "ĠConfig uration", "Ġn ur", "Ġnu r", "O G", "Ġ married", "Ġmar ried", ": +", "Ġd ropped", "Ġdr opped", "Ġdrop ped", "Ġdro pped", "0 40", "04 0", "Ġ registration", "Ġreg istration", "Ġregistr ation", "Ġregist ration", "о м", "оР¼", "ult iple", "ulti ple", "ultip le", "i zers", "iz ers", "ize rs", "izer s", "s hape", "sh ape", "sha pe", ". copy", ".c opy", ".co py", "Ġw earing", "Ġwe aring", "Ġwear ing", "ĠC ath", "ĠCa th", "ĠCat h", "Ġded icated", "Ġdedic ated", "Ġdedicate d", "Ġ ...Ċ", "Ġ. ..Ċ", "Ġ... Ċ", "Ġ.. .Ċ", "Ġadv oc", "Ġ Family", "ĠF amily", "ĠFam ily", "ĠFamil y", "Ġ statements", "Ġstate ments", "Ġstat ements", "Ġstatement s", "e matic", "em atic", "ema tic", "emat ic", "ampions hip", "ampion ship", "Ġmot iv", "Ġmo tiv", "Ġ Have", "ĠH ave", "ĠHa ve", "ĠHav e", "Ġb low", "Ġbl ow", "Ġblo w", "J ob", "Jo b", "c ert", "ce rt", "cer t", "_ vector", "_v ector", "_vec tor", "_vect or", "_ve ctor", "inst all", "Ġ COPY", "ĠC OPY", "ĠCO PY", "ĠCOP Y", "em bed", "emb ed", "D IR", "DI R", "Ġ Spring", "ĠS pring", "ĠSp ring", "ĠSpr ing", "Ġex hib", "Ġexh ib", "2 23", "22 3", "c dn", "cd n", "Ġ Comment", "ĠCom ment", "ĠComm ent", "Ġ Optional", "ĠOption al", "ĠOpt ional", ". player", ".p layer", ".pl ayer", ".play er", "Ġ Dark", "ĠD ark", "ĠDa rk", "ĠDar k", "( pos", "(p os", "(po s", "Ġ Should", "ĠSh ould", "ĠSho uld", "Ġ centre", "Ġc entre", "Ġcent re", "Ġcentr e", "Ġcen tre", "Ġ Guard", "ĠG uard", "ĠGu ard", "ĠGuar d", "ó w", "Ġtr ouble", "Ġtro uble", "Ġtroub le", "Ġtrou ble", "E NER", "EN ER", "ENE R", "( unsigned", "(un signed", "_ service", "_s ervice", "_serv ice", "_ser vice", "Ġ ns", "Ġn s", "u ling", "ul ing", "ulin g", "uli ng", "Ġ Mexico", "ĠMe xico", "ĠMex ico", "Ġ NY", "ĠN Y", "m ysql", "my sql", "mys ql", "Ġ lic", "Ġl ic", "Ġli c", "å ľ", "M r", "- fl", "-f l", "Ġ Customer", "ĠC ustomer", "ĠCustom er", "ĠCust omer", "i di", "id i", "Ġ ?>ĊĊ", "Ġ? >ĊĊ", "Ġ?> ĊĊ", "Ġ?>Ċ Ċ", "r ible", "ri ble", "rib le", "Ġ пÑĢ", "Ġп ÑĢ", "Ġ sizes", "Ġs izes", "Ġsize s", "Ġsi zes", "Ġsiz es", "_ STRING", "_ST RING", "_STR ING", "valid ation", "Ġ Jon", "ĠJ on", "ĠJo n", "( Http", "(H ttp", "add Class", "N odes", "Node s", "No des", "Ġ fragment", "Ġf ragment", "Ġfr agment", "Ġfra gment", "Ġfrag ment", "Ġs poke", "Ġsp oke", "Ġspo ke", "Ġw aste", "Ġwas te", "Ġwa ste", "Ġwast e", "J oin", "Jo in", "Ġ illustr", "Ġill ustr", "Ġillust r", "e li", "el i", "c ient", "ci ent", "cie nt", "Ġ aid", "Ġa id", "Ġai d", "Ġpro sec", "Ġpros ec", "Ġprose c", "' ){Ċ", "') {Ċ", "'){ Ċ", "Ġp assing", "Ġpass ing", "Ġpas sing", "Ġ faces", "Ġf aces", "Ġfa ces", "Ġfac es", "Ġface s", "S hape", "Sh ape", "Sha pe", "_ Z", "i ti", "it i", "Ġ alle", "Ġa lle", "Ġal le", "Ġall e", "Ġ robot", "Ġr obot", "Ġro bot", "Ġrob ot", "Ġ ĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĊ", "ĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠ Ċ", "ĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠĠ ĠĊ", "Ġ Spe", "ĠS pe", "ĠSp e", "Ġre ceiving", "Ġrece iving", "Ġ Details", "ĠD etails", "ĠDe tails", "ĠDet ails", "ĠDetail s", "Ġ \")", "Ġ\" )", "m g", "_ REF", "_RE F", "_R EF", "Ġ comparison", "Ġcom parison", "Ġcompar ison", "* ,", "Ġ Found", "ĠF ound", "ĠFo und", "ĠFou nd", "_ session", "_s ession", "_sess ion", "( U", "/ F", "Ġ xxx", "Ġx xx", "Ġxx x", "N etwork", "Net work", "d ers", "de rs", "der s", "Ġ capture", "Ġc apture", "Ġcap ture", "Ġcapt ure", "Ġc orre", "Ġcor re", "Ġcorr e", "Ġ Ltd", "ĠL td", "ĠLt d", "Ġ Adv", "ĠA dv", "ĠAd v", "[ @", "Ġ clip", "Ġc lip", "Ġcl ip", "Ġcli p", "M ill", "Mi ll", "Mil l", "Ġ Profile", "ĠPro file", "ĠPr ofile", "ĠProf ile", "Ġ endif", "Ġen dif", "Ġend if", "Ġob lig", "Ġobl ig", "de scribe", "des cribe", "descr ibe", ". element", ".e lement", ".el ement", ".elem ent", "riter ion", "L D", "e red", "er ed", "ere d", "Ġf avour", "Ġfav our", "s core", "sc ore", "Ġ Filter", "ĠF ilter", "ĠFil ter", "at tributes", "attribute s", "attrib utes", "Ġ checks", "Ġcheck s", "Ġche cks", "In flater", "Inf later", "Ġ Plus", "ĠP lus", "ĠPl us", "Ġ scientific", "Ġscient ific", "Ġ privacy", "Ġpriv acy", "H ead", "He ad", "Ġ feat", "Ġf eat", "Ġfe at", "Ġ degrees", "Ġdeg rees", "Ġdegree s", "Ġ Pale", "ĠP ale", "ĠPal e", "ĠPa le", "; \">", ";\" >", "Ġ films", "Ġfil ms", "Ġfilm s", "Ġ Audio", "ĠA udio", "ĠAud io", "ĠAu dio", "ĠAudi o", "Ġ Tag", "ĠT ag", "ĠTa g", "Ġ Energy", "ĠE nergy", "ĠEn ergy", "ĠEnerg y", "i tar", "it ar", "ita r", "par ator", "para tor", "Ġf ellow", "Ġfell ow", "Ġfel low", "Ġ evt", "Ġe vt", "Ġev t", "Ġ Tri", "ĠT ri", "ĠTr i", "ĠD AM", "ĠDA M", "c loud", "cl oud", "clo ud", "Ġ Password", "ĠP assword", "ĠPass word", "ĠPas sword", "Ġ Democrats", "ĠDem ocrats", "ĠDemocr ats", "ĠDemocrat s", "ĠA cad", "ĠAc ad", "$ lang", "$l ang", "Ġ reb", "Ġre b", "Ġr eb", "( ))ĊĊ", "() )ĊĊ", "()) ĊĊ", "())Ċ Ċ", "н Ñĭ", "Ġ Bur", "ĠB ur", "ĠBu r", "read cr", "Ġ hex", "Ġh ex", "Ġhe x", "2 09", "20 9", "Con sole", "Cons ole", "c tl", "ct l", "ou sel", "ous el", "ouse l", "Ġ William", "ĠWill iam", "ĠWilli am", "Ġ az", "Ġa z", "_ PORT", "_P ORT", "_PO RT", "Ġpr actices", "Ġpract ices", "Ġpractice s", "Ġany where", "Ġ Position", "ĠP osition", "ĠPos ition", "Ġ ->Ċ", "Ġ- >Ċ", "Ġ-> Ċ", "i ams", "ia ms", "iam s", ". username", ".user name", "place holder", "Ġ oder", "Ġo der", "Ġod er", "Ġode r", "Ġ Secretary", "ĠSecret ary", "Ġ iT", "Ġi T", "m ond", "mon d", "mo nd", "e vents", "event s", "ev ents", "even ts", "eve nts", "? âĢĿ", ". Sub", ".S ub", "Ġ attached", "Ġatt ached", "Ġattach ed", "Ġn ão", "Ġ estate", "Ġe state", "Ġest ate", "Ġesta te", "3 65", "36 5", ". action", ".a ction", ".ac tion", ".act ion", "Ġ figures", "Ġfig ures", "Ġfigure s", "Ġfigur es", "Ġ });čĊ", "Ġ} );čĊ", "Ġ}) ;čĊ", "Ġ}); čĊ", "Ġsub scri", "Ġsubs cri", ". tag", ".t ag", ".ta g", "n am", "na m", ". plot", ".p lot", ".pl ot", "n oon", "no on", "li ament", "lia ment", "Char acter", ". tab", ".t ab", ".ta b", "Ġ winter", "Ġw inter", "Ġwin ter", "Ġ Variable", "ĠVar iable", "ĠVari able", "Ġ trees", "Ġt rees", "Ġtr ees", "Ġtre es", "Ġtree s", "Ġp roud", "Ġpro ud", "Ġpr oud", "( V", "_ load", "_l oad", "_lo ad", "Ġh ier", "Ġhi er", "ĠE con", "ĠEc on", "ĠEco n", "Ġ fd", "Ġf d", "Ġvict ims", "Ġvictim s", "R est", "Re st", "Res t", "i ana", "ia na", "ian a", "Ġ fake", "Ġf ake", "Ġfa ke", "Ġfak e", ".Print ln", "Ġ strlen", "Ġst rlen", "Ġstr len", "Ġ sad", "Ġs ad", "Ġsa d", "Ġ ble", "Ġb le", "Ġbl e", "P rot", "Pro t", "Pr ot", "Ġ buttons", "Ġbut tons", "Ġbutton s", "Ġbutt ons", "Ġbutto ns", "Ġte levision", "Ġtele vision", "Ġtelevis ion", "Ġtelev ision", "Ġ logo", "Ġl ogo", "Ġlo go", "Ġlog o", "ext ension", "ĉ j", "s tein", "st ein", "ste in", "ac iones", "acion es", "aci ones", "acio nes", "Ġ \"\"\"ĊĊ", "Ġ\"\" \"ĊĊ", "Ġ\"\"\"Ċ Ċ", "Ġ\"\"\" ĊĊ", "Ġ simp", "Ġs imp", "Ġsim p", "Ġsi mp", "Ġrecord ed", "Ġbr ings", "Ġbring s", "Ġ principal", "Ġpr incipal", "Ġprincip al", "Ġf ees", "Ġfe es", "Ġfee s", "( source", "(s ource", "k dir", "kd ir", "Ġ utils", "Ġutil s", "Ġut ils", "Ġcorrect ly", "f il", "fi l", "Ġ wel", "Ġw el", "Ġwe l", "P air", "Pa ir", "- button", "-b utton", "-but ton", "s cale", "sc ale", "scal e", "ver ify", "[ c", "Ġ ---", "Ġ- --", "Ġ-- -", "Ġ escape", "Ġe scape", "Ġes cape", "Ġesc ape", "Ġescap e", "i kes", "ik es", "ike s", "Lower Case", "ic ian", "ici an", "icia n", "Ġ chapter", "Ġch apter", "Ġcha pter", "Ġchap ter", "Ġ TYPE", "ĠT YPE", "ĠTY PE", "Ġ shadow", "Ġsh adow", "Ġ awesome", "Ġaw esome", "Ġawe some", "W E", "e lif", "el if", "eli f", "Ġ lambda", "Ġl ambda", "Ġlamb da", "Ġ distinct", "Ġdist inct", "Ġ bare", "Ġb are", "Ġbar e", "Ġba re", "- off", "-of f", "-o ff", "Ġ colour", "Ġcol our", ". appendChild", ".append Child", "o lec", "ol ec", "ole c", "a ga", "ag a", ". fill", ".f ill", ".fi ll", ".fil l", "ĉ super", "ĉs uper", "Ġ adj", "Ġa dj", "Ġad j", "( position", "(p osition", "(pos ition", ". getItem", ".get Item", "2 42", "24 2", "S hort", "Sh ort", "Ġtot ally", "Ġtotal ly", "V D", "Ġ Tre", "ĠT re", "ĠTr e", "_ ep", "_e p", "v ements", "ve ments", "vement s", "vem ents", "Ġ Solution", "ĠS olution", "ĠSol ution", "Ġfund ament", "F ollow", "Ġ facility", "Ġfac ility", "Ġfacilit y", "Ġfacil ity", "Ġhapp ening", "Ġhappen ing", "O F", ". textBox", ".text Box", "S pan", "Sp an", "Ġ «", "Ġ «", "i den", "id en", "ide n", "Ġex ceed", "Ġexc eed", "Ġexce ed", "( parent", "(p arent", "(par ent", "(paren t", "(pa rent", "Ġ cp", "Ġc p", "ç »", "Ġh asn", "Ġhas n", "Ġha sn", "Ġ pri", "Ġp ri", "Ġpr i", "Ġcon sequ", "Ġcons equ", "Ġconse qu", "n en", "ne n", "ĠIN TO", "ĠINT O", "I gnore", "Ign ore", "Ġ Future", "ĠF uture", "ĠFu ture", "ĠFut ure", "Ġ carbon", "Ġc arbon", "Ġcar bon", "Ġcarb on", "Ġ Steel", "ĠSt eel", "ĠSte el", "f mt", "fm t", "o kie", "ok ie", "oki e", "Ġ spl", "Ġs pl", "Ġsp l", "( title", "(t itle", "(ti tle", "- info", "-in fo", "-inf o", "Ġde als", "Ġdeal s", "Ġ fixture", "Ġf ixture", "Ġfix ture", "e a", "D iv", "Di v", "Ġ tested", "Ġt ested", "Ġte sted", "Ġtest ed", "Ġtes ted", "Ġteste d", "_ return", "_re turn", "_r eturn", "_ret urn", ") ĊĊĊĊ", ")Ċ ĊĊĊ", ")ĊĊ ĊĊ", ")ĊĊĊ Ċ", "up ported", "upport ed", "upp orted", "Ġ Cook", "ĠC ook", "ĠCo ok", "Ġp aying", "Ġpay ing", "Ġpa ying", "Ġ Ill", "ĠI ll", "ĠIl l", "Ġarr ested", "Ġarrest ed", "Ġ Prime", "ĠPr ime", "ĠPri me", "ĠPrim e", "_ callback", "_c allback", "_call back", "> ,Ċ", ">, Ċ", "d river", "dr iver", "drive r", "O nce", "On ce", "a bb", "ab b", "_ bytes", "_by tes", "_byte s", "Ġ Sets", "ĠS ets", "ĠSe ts", "ĠSet s", "( Object", "(O bject", "Ġ cc", "Ġc c", "Ġ shell", "Ġs hell", "Ġsh ell", "Ġshe ll", "Ġshel l", "a lo", "al o", ") ;//", "); //", "( log", "(l og", "(lo g", "2 64", "26 4", "c tors", "ct ors", "ctor s", ") ", "2 18", "21 8", "Ġ $(\".", "Ġ$ (\".", "Ġ$( \".", "Ġ$(\" .", ". pos", ".p os", ".po s", "Ġ boys", "Ġbo ys", "Ġboy s", "Ġwed ding", "Ġ agents", "Ġag ents", "Ġage nts", "Ġagent s", "= \"_", "=\" _", "Ġ Army", "ĠAr my", "ĠArm y", "Ġ hint", "Ġh int", "Ġhi nt", "Ġhin t", "v ision", "vis ion", "Ġ tech", "Ġt ech", "Ġte ch", "Ġtec h", "Ġ Connect", "ĠCon nect", "ĠConn ect", "Ġ legend", "Ġl egend", "Ġle gend", "Ġleg end", "Ġ Bet", "ĠB et", "ĠBe t", ". Base", ".B ase", "Sub ject", "Su bject", "Ġ lit", "Ġl it", "Ġli t", "Re move", "Rem ove", "Ġ \":", "Ġ\" :", "Ġ Final", "ĠF inal", "ĠFin al", "ĠFi nal", "pear ance", "ĠiT unes", "Ġ participants", "Ġpart icipants", "Ġparticip ants", "Ġparticipant s", "Ġ Python", "ĠP ython", "ĠPy thon", "Ġ busy", "Ġbu sy", "Ġbus y", "i el", "ie l", "vert ices", "Ġtemplate Url", "Ġ Close", "ĠC lose", "ĠCl ose", "ĠClo se", "I mg", "Im g", "ĠCorpor ation", "ĠCorp oration", "t imestamp", "time stamp", "Ġ extend", "Ġext end", "Ġwe bsites", "Ġweb sites", "Ġwebsite s", "Ġwebs ites", "Ġposs ibility", "Ġpossibilit y", "о ÑĤ", "Ġ kö", "Ġk ö", "Ġm eat", "Ġme at", "Ġ representation", "Ġre presentation", "Ġrep resentation", "Ġrepresent ation", "Ġrepresenta tion", "2 41", "24 1", "Ġ ĉĉ", "Ġĉ ĉ", "_ START", "_ST ART", "_STAR T", "_STA RT", ". apply", ".app ly", ".ap ply", "ĠV alley", "ĠVal ley", "ĠValle y", "ĠVall ey", "Ġ Success", "ĠS uccess", "ĠSu ccess", "ĠSuc cess", "ĠSucc ess", "H i", "Ġ nob", "Ġn ob", "Ġno b", "Ġ IEnumerable", "ĠI Enumerable", "_ select", "_s elect", "_se lect", "_sel ect", "g eo", "ge o", ". \")Ċ", ".\" )Ċ", ".\") Ċ", "Ġt urning", "Ġturn ing", "Ġtur ning", "Ġ fabric", "Ġf abric", "Ġfab ric", "(\" \");Ċ", "(\"\" );Ċ", "(\"\") ;Ċ", "(\"\"); Ċ", "Ġp erspective", "Ġpers pective", "é Ĺ", "Ġ Sn", "ĠS n", "Th ank", "Than k", "; j", ". Parameters", ".Param eters", ".Parameter s", "ĉ ĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠ ĠĠĠĠĠĠĠĠ", "ĉĠ ĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠ ĠĠĠĠ", "ĉĠĠ ĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠ ĠĠĠĠĠĠ", "ĉĠĠĠĠ ĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠ ĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠ ĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠ ĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġ facts", "Ġf acts", "Ġfact s", "Ġfa cts", "Ġfac ts", "3 05", "30 5", "Ġ unt", "Ġu nt", "Ġun t", ". instance", ".in stance", ".inst ance", "#### ############################################################", "######## ########################################################", "################ ################################################", "################################ ################################", "################################################ ################", "######################################## ########################", "######################## ########################################", "######################################################## ########", "############################################################ ####", "- end", "-e nd", "-en d", "Ġ JOIN", "ĠJO IN", "Ġ Hen", "ĠH en", "ĠHe n", "Ġ uri", "Ġu ri", "Ġur i", "åIJ į", "Ġ на", "Ġн а", "Ġ Info", "ĠIn fo", "ĠInf o", "Ġcon ducted", "Ġconduct ed", "Ġconduc ted", "Ġ Ã¥", "Ġà ¥", "OUR CE", "Ġ wine", "Ġw ine", "Ġwin e", "Ġwi ne", "J ohn", "Jo hn", ".Error f", ".Err orf", "Ġ Age", "ĠA ge", "ĠAg e", "o unded", "ound ed", "oun ded", "Ġreal ize", "Ġrealiz e", "3 12", "31 2", "Ġ ];", "Ġ] ;", "Ġsub sequ", "Ġsubs equ", ", m", "( User", "(U ser", "i ano", "ia no", "ian o", "Ġaccom pl", "Ġaccomp l", "i sp", "is p", ". std", ".s td", ".st d", "é ĩ", "Ġ Bed", "ĠB ed", "ĠBe d", ". setAttribute", ".set Attribute", "B R", "ke ep", "kee p", "Ġ ALL", "ĠA LL", "ĠAL L", "Ġ isol", "Ġis ol", "Ġi sol", "Ġiso l", "a mma", "am ma", "amm a", "P ackage", "Pack age", "Ġ occasion", "Ġocc asion", "Ġoccas ion", "- success", "-s uccess", "-su ccess", "е д", "еР´", "ĠLIMIT ED", "s trip", "st rip", "str ip", "stri p", "( )ĊĊĊ", "() ĊĊĊ", "()Ċ ĊĊ", "()ĊĊ Ċ", "is tribution", "istrib ution", "Color s", "Col ors", "Ġ+ :+", "Did Load", "a ler", "al er", "ale r", "Ġ tid", "Ġt id", "Ġti d", "Ġ LED", "ĠL ED", "ĠLE D", "Ġ Linked", "ĠLink ed", "ĠLin ked", "Ġ Cart", "ĠC art", "ĠCar t", "ĠCa rt", "( ))čĊ", "() )čĊ", "()) čĊ", "_ READ", "_RE AD", "Ġk illing", "Ġkill ing", "Ġkil ling", "Ġ PHP", "ĠP HP", "ĠPH P", "f ection", "fe ction", "fect ion", "fec tion", "Ġ instances", "Ġin stances", "Ġinst ances", "Ġinstance s", "c v", "\" />", "\"/ >", "Ġ sf", "Ġs f", "Ġt axes", "Ġtax es", "Ġta xes", "_ location", "_l ocation", "_lo cation", "_loc ation", "Ġ Bitcoin", "ĠBit coin", "u able", "ua ble", "r ank", "ra nk", "ran k", "i gnore", "ig nore", "ign ore", "t rack", "tr ack", "tra ck", "к а", "Ġshould n", "Ġ OP", "ĠO P", "= >{Ċ", "=> {Ċ", "Ġ km", "Ġk m", "Ġ helper", "Ġh elper", "Ġhelp er", "Ġhel per", "_ head", "_h ead", "_he ad", "Ġ Whether", "ĠWh ether", "ĠWhe ther", "o co", "oc o", "_ bl", "_b l", "Ġ statistics", "Ġstat istics", "Ġstatist ics", "Ġstatistic s", "Ġbe auty", "Ġbeaut y", "Ġbeau ty", "Ġ tog", "Ġt og", "Ġto g", "t ip", "ti p", "ëĭ ¤", "Ġ csv", "Ġc sv", "Ġcs v", "( sql", "(s ql", "(sq l", "std lib", "we ak", "Ġ likes", "Ġl ikes", "Ġli kes", "Ġlike s", "Ġlik es", "Ä į", "Ġ repeat", "Ġre peat", "Ġrep eat", "Ġrepe at", "Ġa partment", "Ġap artment", "Ġapart ment", "Ġe mph", "Ġem ph", "Ġemp h", "_ edit", "_e dit", "_ed it", "Ġ vit", "Ġv it", "Ġvi t", "ĉ type", "ĉt ype", "ĉtyp e", "2 17", "21 7", "E ven", "Ev en", "u ten", "ut en", "ute n", "Ġcircum stances", "Ġcircumstance s", "b ian", "bi an", "bia n", "Ġs ugar", "Ġsu gar", "Ġsug ar", "W indows", "Window s", "Wind ows", "ì ŀ", "Ġobs erved", "Ġobserv ed", "Ġobserve d", "/ data", "/d ata", "/dat a", "Ġ calendar", "Ġc alendar", "Ġcal endar", "Ġcalend ar", "Ġ strike", "Ġst rike", "Ġstr ike", "Ġstri ke", "Ġ RES", "ĠR ES", "ĠRE S", "_ sc", "_s c", "f ony", "fo ny", "fon y", "o rem", "or em", "ore m", "( z", "p ower", "po wer", "pow er", "et ect", "ete ct", "Ġ Sat", "ĠS at", "ĠSa t", ". description", ".d escription", ".de scription", ".des cription", "Ġ gang", "Ġg ang", "Ġga ng", "Ġgan g", "Ġ Sports", "ĠS ports", "ĠSp orts", "ĠSport s", "ĠSpo rts", "ĠSpor ts", "on gs", "ong s", "Ġ Bundle", "ĠB undle", "ĠBund le", "ĠBun dle", ". sum", ".s um", "o nce", "on ce", "Ġacc used", "Ġaccus ed", "Ġaccuse d", "Ġexp lore", "Ġexpl ore", "Ġexplo re", "Ġexplor e", "Ġ approximately", "Ġapprox imately", "Ġapproximate ly", "Ġ losing", "Ġl osing", "Ġlo sing", "Ġlos ing", "th esis", "the sis", "thes is", "Ġ Fund", "ĠF und", "ĠFun d", "ĠFu nd", "Ġdi agn", "Ġdia gn", "Ġdiag n", "A utowired", "p roperties", "prop erties", "proper ties", "Ġ _.", "Ġ_ .", "Ġ cnt", "Ġc nt", "Ġcn t", "ced ure", "Ġ yy", "Ġy y", "Ġ grant", "Ġg rant", "Ġgr ant", "Ġgran t", "Ġgra nt", "s ock", "so ck", "soc k", ". innerHTML", ".inner HTML", "Ġ ]);Ċ", "Ġ] );Ċ", "Ġ]) ;Ċ", "Ġ]); Ċ", "Ġ CONFIG", "ĠCON FIG", "ĠCONF IG", "= '$", "=' $", "5 50", "55 0", "] ];Ċ", "]] ;Ċ", "]]; Ċ", "U ND", "UN D", "Ġ glob", "Ġg lob", "Ġgl ob", "Ġglo b", "Ġ dire", "Ġd ire", "Ġdi re", "Ġdir e", "uff le", "_ MEM", "_M EM", "_ME M", "Ġauth entic", "> (\"", ">( \"", "Ġde cade", "Ġdec ade", "Ġdecad e", "Ġ Import", "ĠIm port", "ĠImp ort", "Ġorig inally", "Ġoriginal ly", "Ġorigin ally", "Ġ jQuery", "Ġj Query", "Ġind icate", "Ġindic ate", "Ġindica te", "Ġours elves", "S w", ". lbl", ".l bl", ".lb l", "en erate", "ener ate", "ene rate", "Ġbas ically", "Ġbasic ally", "Ġ Hom", "ĠH om", "ĠHo m", "Ġ+ #+", "Ġ+# +", "Ġ Britain", "ĠBrit ain", "ĠBri tain", "Ġ Kar", "ĠK ar", "ĠKa r", "to Equal", ". stop", ".s top", ".st op", "Ġ modal", "Ġm odal", "Ġmod al", "Ġmo dal", "Ġmoda l", "i si", "is i", "Ġsuggest s", "Ġ dtype", "Ġd type", "Ġdt ype", "Ġ tur", "Ġt ur", "Ġtu r", "b f", "Ġ connections", "Ġconnection s", "Ġconn ections", "Ġconnect ions", "Ġ Before", "ĠB efore", "ĠBe fore", "i sted", "is ted", "ist ed", "iste d", "m ouse", "mo use", "Ġp ulled", "Ġpull ed", "Ġpul led", ". build", ".b uild", "Ġleg islation", "Ġlegis lation", "Ġlegisl ation", "Ġ forth", "Ġf orth", "Ġfor th", "Ġfort h", "p ad", "pa d", "e go", "eg o", ". Now", ".N ow", ".No w", "Ġexc iting", "Ġexcit ing", "} ĊĊĊĊ", "}Ċ ĊĊĊ", "}ĊĊ ĊĊ", "}ĊĊĊ Ċ", "Ġcom pr", "Ġco mpr", "Ġcomp r", "Ġ shares", "Ġsh ares", "Ġshare s", "Ġsha res", "Ġshar es", "Ġ rig", "Ġr ig", "Ġri g", "g reen", "gr een", "gre en", "gree n", "_ vec", "_v ec", "_ve c", "Ġ enumerate", "Ġenum erate", "Ġenumer ate", "A uto", "Aut o", "Au to", "ic ator", "ica tor", "Ġ Ray", "ĠR ay", "ĠRa y", "a sse", "as se", "ass e", "Ġ holiday", "Ġh oliday", "Ġhol iday", "Ġ nullable", "Ġnull able", "Ġnulla ble", "g un", "gu n", "_ details", "_d etails", "_de tails", "_detail s", "_det ails", "Ġ wrapper", "Ġw rapper", "Ġwr apper", "Ġwrap per", "s eq", "se q", "Ġ Young", "ĠYou ng", "ĠYo ung", "ju ana", "juan a", "Ġ\" __", "Ġ\"_ _", "l icense", "lic ense", "s erve", "se rve", "ser ve", "serv e", "^ (", "i ders", "id ers", "ide rs", "ider s", ". Remove", ".Re move", ".Rem ove", "rop down", "' S", "p in", "pi n", "( token", "(t oken", "(to ken", "(tok en", ". Default", ".D efault", ".De fault", ".Def ault", "Ġ reasonable", "Ġreason able", "amp ion", "ĠS ociety", "ĠSoci ety", "Ġ bei", "Ġb ei", "Ġbe i", "er ves", "erv es", "erve s", "r ad", "ra d", "Ġ Fox", "ĠF ox", "ĠFo x", "_ images", "_image s", "_im ages", "_imag es", "Ġ wheel", "Ġw heel", "Ġwh eel", "Ġwhe el", "' )[", "') [", "Ġ cfg", "Ġc fg", "Ġcf g", "( By", "(B y", "Con structor", "Construct or", "Ġ vary", "Ġv ary", "Ġvar y", "Ġva ry", ". swift", ".sw ift", "Ġ proxy", "Ġpro xy", "Ġpr oxy", "Ġprox y", "ĉ H", "Ġ Another", "ĠAn other", "Ġ Pen", "ĠP en", "ĠPe n", "Ġ checking", "Ġcheck ing", "Ġ jest", "Ġj est", "Ġje st", "Ġjes t", "m anager", "man ager", "manage r", "mana ger", "Or igin", "Orig in", "u gs", "ug s", "o ir", "oi r", "> čĊ", "Ġ-- >čĊ", "Ġ--> čĊ", "Ġrel ief", "Ġreli ef", "Ġrelie f", "l ap", "la p", "q uer", "qu er", "que r", "_ parent", "_p arent", "_par ent", "_pa rent", "he ap", "hea p", "L OSE", "LO SE", "LOS E", "Ġ combine", "Ġc ombine", "Ġcom bine", "Ġcomb ine", "Ġcombin e", "Ġ Rose", "ĠR ose", "ĠRo se", "ĠRos e", "o wers", "ow ers", "ower s", "owe rs", "Ġpro cedures", "Ġproced ures", "Ġprocedure s", "Ġ Sort", "ĠS ort", "ĠSo rt", "ĠSor t", "a nim", "an im", "ani m", "v ariant", "var iant", "vari ant", "eh icle", "Ġsign ing", "Ġsig ning", "Ġsignin g", "Pr imary", "Prim ary", "Pri mary", "c urrency", "curr ency", "Ġs exe", "Ġse xe", "Ġsex e", "o en", "oe n", "th eta", "the ta", "e man", "em an", "ema n", "Ġim pressive", "Ġimp ressive", "Ġimpress ive", "( '_", "(' _", "ĉ U", "Ġ TextStyle", "ĠText Style", "_ cnt", "_c nt", "_cn t", "Ġ slice", "Ġs lice", "Ġsl ice", "Ġslic e", "( ':", "(' :", "Ġunder stood", "Ġunderst ood", "H is", "Hi s", "2 77", "27 7", "0 13", "01 3", "Ġin formed", "Ġinform ed", "Ġ nick", "Ġn ick", "Ġni ck", "Ġnic k", "4 29", "42 9", "( TAG", "(T AG", "h d", "Ġe lections", "Ġel ections", "Ġelect ions", "Ġelection s", "Ġele ctions", "es ture", "est ure", "Ġ Santa", "ĠS anta", "ĠSan ta", "ĠSant a", "ĠCo ast", ". pdf", ".p df", "inc iple", "incip le", "inci ple", ". clone", ".cl one", "b orn", "bo rn", "bor n", "u ta", "ut a", "Ġ licensed", "Ġl icensed", "Ġlicense d", "Ġlic ensed", "Ġlicens ed", "C r", "Ġ bread", "Ġb read", "Ġbr ead", "Ġbre ad", "Ġ Houston", "ĠH ouston", "ĠHou ston", "Ġ nod", "Ġn od", "Ġno d", "Ġh opes", "Ġhope s", "Ġhop es", "Ġho pes", "Ġ CGRect", "ĠCG Rect", "Ġgu ilty", "Ġguilt y", ". gif", ".g if", "Ġ rose", "Ġr ose", "Ġro se", "Ġros e", ". Common", ".Com mon", ".Comm on", "T ip", "Ti p", "A NK", "AN K", "Ġ FC", "ĠF C", "D uring", "Du ring", "Dur ing", "Ġ Symfony", "ĠSym fony", "Ġdef ensive", "k m", ") >", "a rchive", "arch ive", "arc hive", "Ġ URI", "ĠU RI", "ĠUR I", "y cling", "yc ling", "ycl ing", "- o", "Ġ Website", "ĠWe bsite", "ĠWeb site", "A MP", "AM P", "4 05", "40 5", "ish ment", "Ġdo ctors", "Ġdoc tors", "Ġdoctor s", "D irect", "Dir ect", "Di rect", "Dire ct", "A RI", "AR I", "Ġ Redirect", "ĠRe direct", "ĠRed irect", "i eren", "ie ren", "ier en", "iere n", "9 60", "96 0", "_ dist", "_d ist", "_dis t", "_di st", "y o", "Ġ Progress", "ĠPro gress", "Ġz um", "Ġzu m", "Ġme mor", "Ġmem or", "Ġmemo r", "Ġ ED", "ĠE D", "Ġ jur", "Ġj ur", "Ġju r", "æį ®", "_ TABLE", "_T ABLE", "_TAB LE", "_TA BLE", "Ġ uuid", "Ġu uid", "Ġuu id", "Ex pr", "Exp r", ". head", ".h ead", ".he ad", "( '%", "(' %", "point er", "po inter", "Ġ estimate", "Ġest imate", "Ġestim ate", "Ġ Greg", "ĠG reg", "ĠGr eg", "ĠGre g", "Ġ loader", "Ġl oader", "Ġlo ader", "Ġload er", "Ġloa der", "Ġ iOS", "Ġi OS", "Ġ mens", "Ġm ens", "Ġme ns", "Ġmen s", "[ y", "Ġref used", "Ġrefuse d", "Ġ precision", "Ġp recision", "Ġpre cision", "Ġprec ision", "Ġprecis ion", "i sch", "is ch", "isc h", "Ġ ACTION", "ĠA CTION", "ĠAC TION", "ĠACT ION", "C loud", "Cl oud", "Clo ud", "s With", "( ret", "(r et", "(re t", "2 92", "29 2", "_ ADDR", "_A DDR", "_ADD R", "_AD DR", "_ conf", "_con f", "_co nf", "( df", "(d f", "Ġ locked", "Ġl ocked", "Ġloc ked", "Ġlock ed", "Ġ rising", "Ġr ising", "Ġris ing", "Ġri sing", "ãĥ» ãĥ»", "Ġ Ms", "ĠM s", "Ġ scenes", "Ġsc enes", "Ġscene s", "Ġscen es", "Ġsce nes", "_ EXT", "_E XT", "_EX T", "_ raw", "_r aw", "_ra w", "_ the", "_t he", "_th e", "pe ople", "Ġre con", "Ġrec on", "Ġreco n", "Ġ Fun", "ĠF un", "ĠFu n", "Ġb less", "Ġbl ess", "Ġble ss", "Ġ Updated", "ĠUp dated", "ĠUpdate d", "4 22", "42 2", "ü n", "Ġ ĠĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠčĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠčĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠčĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠčĊ", "p ection", "pe ction", "pect ion", "pec tion", "R elease", "Re lease", "Rel ease", ". logger", ".log ger", ".lo gger", "Ġ SY", "ĠS Y", "Ġc ounsel", "Ġcoun sel", "u rd", "ur d", "_ true", "_tr ue", "Ġevery body", "iv ot", "ivo t", "Ġh ence", "Ġhe nce", "Ġhen ce", "Ġ NAS", "ĠN AS", "ĠNA S", "7 89", "78 9", "Ġop posed", "Ġopp osed", "Ġoppos ed", "Ġoppose d", "un known", "unk nown", "Ġ DESC", "ĠD ESC", "ĠDE SC", "ĠDES C", "Ġ Chair", "ĠC hair", "ĠCh air", "ĠCha ir", "f ailed", "fa iled", "fail ed", "Ġ INCLUDING", "ĠIN CLUDING", "3 86", "38 6", "3 52", "35 2", "Ġ writers", "Ġw riters", "Ġwrit ers", "Ġwrite rs", "Ġwriter s", "{ }Ċ", "{} Ċ", "ÃŃ t", "_ copy", "_c opy", "_co py", "} :", "Ġ Bat", "ĠB at", "ĠBa t", "Ġ converted", "Ġcon verted", "Ġconvert ed", "Ġconver ted", "e ding", "ed ing", "edi ng", "edin g", "pl acement", "place ment", "Ġ Host", "ĠH ost", "ĠHo st", "ĠHos t", "S ound", "So und", "Sou nd", "и м", "Ġs ought", "Ġso ught", "Ġsou ght", "4 02", "40 2", "m id", "mi d", "Ġ salary", "Ġs alary", "Ġsal ary", "Ġsala ry", "o gg", "og g", "âĦ ¢", "b ul", "bu l", "Ġw ir", "Ġwi r", "valid ator", "_ STAT", "_ST AT", "_STA T", ". store", ".st ore", "Ġ Battle", "ĠB attle", "ĠBat tle", "ĠBatt le", "ı n", "Ġ -->ĊĊ", "Ġ-- >ĊĊ", "Ġ-->Ċ Ċ", "Ġ--> ĊĊ", "Tr ump", "d ot", "do t", "Ġ CONT", "ĠC ONT", "ĠCON T", "ĠCO NT", ". fetch", ".f etch", "Ġcont inu", "Ġcontin u", "w as", "wa s", "Ġf raud", "Ġfr aud", "Ġfra ud", "Ġfrau d", "_ tmp", "_t mp", "_tm p", "m itter", "mit ter", "mitt er", ". pictureBox", ".p ictureBox", ".picture Box", "G A", "Ġ tournament", "Ġt ournament", ". Input", ".In put", "3 43", "34 3", "[ r", "ex ion", "cent age", "ĠK orean", "ĠKore an", "ĠKorea n", "ĠKor ean", "u ndef", "un def", "und ef", "unde f", "Ġ Available", "ĠA vailable", "ĠAv ailable", "re shape", "res hape", "resh ape", "Ġ kit", "Ġk it", "Ġki t", "Ġ Struct", "ĠStr uct", "Ġ SUB", "ĠS UB", "ĠSU B", "An swer", "Ans wer", "_ lib", "_l ib", "_li b", ". twitter", ".t witter", ".tw itter", "Ġ ore", "Ġo re", "Ġor e", "Ġ Dragon", "ĠD ragon", "ĠDr agon", "ĠDrag on", "ĠDra gon", ". Ext", ".Ex t", ".E xt", ", k", "Ġex planation", "Ġexplan ation", "r efs", "re fs", "ref s", "Ġ Drive", "ĠD rive", "ĠDr ive", "Ġ Training", "ĠTr aining", "ĠTra ining", "ĠTrain ing", "2 82", "28 2", ". Has", ".H as", "3 41", "34 1", "int age", "inta ge", "b ig", "bi g", "olog ist", "olo gist", "ologi st", "en nis", "enn is", "4 60", "46 0", "Ù ĩ", "Ġch icken", "Ġchi cken", "Ġchick en", "Ġchic ken", "Ġ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ Ċ", "ç Ľ", "ãģ §", "Ġ peak", "Ġpe ak", "Ġpea k", "Ġdr inking", "Ġdrink ing", "Ġ encode", "Ġen code", "Ġenc ode", "Ġ NEW", "ĠN EW", "ĠNE W", "m alloc", "mal loc", "mall oc", "ĉ fprintf", "ĉf printf", "Ġ= ================================================================", "Ġ================= ================================================", "Ġ================================================= ================", "Ġ================================= ================================", "Ġ============================================================== ===", "in cluding", "incl uding", "inclu ding", "Ġprincip les", "Ġprinciple s", "Ġprinc iples", "Ġ Mah", "ĠM ah", "ĠMa h", "2 67", "26 7", "st orage", "sto rage", "stor age", "- key", "-k ey", "Ġ keyword", "Ġkey word", "% ;", "Ġ trained", "Ġtr ained", "Ġtrain ed", "Ġtra ined", "Ġtrai ned", ". contrib", ".con trib", ".cont rib", "Ġ kv", "Ġk v", "__ ':Ċ", "Ġ Boy", "ĠB oy", "ĠBo y", "param eter", "para meter", "Ġ suite", "Ġs uite", "Ġsu ite", "Ġsuit e", "Ġsui te", "Ġth ousand", "Ġthous and", "Ġthou sand", "Ġ coordinate", "Ġco ordinate", "Ġcoord inate", "Ġcoordin ate", "- generated", "-g enerated", "íķ ĺ", "g enerated", "gener ated", "generate d", "gene rated", "Ġad mitted", "Ġadm itted", "Ġadmit ted", "Ġp ussy", "Ġpus sy", "Ġpuss y", "# w", "Ġsw im", "un ion", "uni on", "N a", "2 74", "27 4", "Ġ Royal", "ĠR oyal", "ĠRoy al", "ĠRo yal", ". channel", ".ch annel", "Up dated", "Update d", "_ ROOT", "_R OOT", "_RO OT", "Ġv ital", "Ġvi tal", "Ġvit al", "Ġvita l", "3 35", "33 5", "r action", "ra ction", "rac tion", "ract ion", "ĠCr usher", "ĠCrush er", "ĠCru sher", "ĠCrus her", "Ġ preced", "Ġpr eced", "Ġpre ced", "Ġprec ed", "Ġ horizontal", "Ġh orizontal", "Ġhor izontal", "Ġhorizon tal", "Blue print", "Ġ attrs", "Ġat trs", "Ġatt rs", "Ġattr s", "Ġsm oke", "Ġsmo ke", "Ð Ĵ", ". Equals", ".E quals", ".Equal s", "F B", "Ġ Resources", "ĠRe sources", "ĠRes ources", "ĠResource s", "rol ling", "roll ing", "Ġ passes", "Ġp asses", "Ġpass es", "Ġpas ses", "Ġpasse s", "Ġ Num", "ĠN um", "ĠNu m", "r otate", "rot ate", "e type", "et ype", "ety pe", "\\ \",", "\\\" ,", "Ġs ensitive", "Ġsens itive", "Ġsensit ive", "Ġt all", "Ġtal l", "Ġta ll", "? âĢĿĊĊ", "?âĢĿ ĊĊ", "Pro xy", "Pr oxy", "i y", "_ section", "_s ection", "_se ction", "_sec tion", "âĢĶâĢĶ âĢĶâĢĶ", "b rid", "br id", "Ġc ircuit", "Ġcirc uit", "a tan", "at an", "ata n", "E NC", "EN C", "Ġdr iven", "Ġdrive n", "Ġdriv en", "Ġdri ven", "Ġv oted", "Ġvo ted", "Ġvot ed", "Ġvote d", "Ġeduc ational", "Ġeducation al", "Ġ interaction", "Ġinter action", "Ġinteract ion", "Ġinte raction", "ab etes", "abe tes", "abet es", "Ġ tone", "Ġt one", "Ġto ne", "Ġton e", "ĠInitialize Component", "Ġmer ely", "Ġmere ly", "Ġ ìŀ", "Ġì ŀ", "c ookie", "co okie", "cook ie", "_ div", "_d iv", "_di v", "Ġ UILabel", "ĠUI Label", "ĠUIL abel", "v ely", "ve ly", "vel y", "} );čĊ", "}) ;čĊ", "}); čĊ", "_ ENT", "_E NT", "_EN T", "#+ #+", "art icles", "article s", "arti cles", "artic les", "Ġ Southern", "ĠSouth ern", "ĠSou thern", "Ġstrong er", "Ġstro nger", "Ġstron ger", "Ġ Given", "ĠG iven", "ĠGi ven", "ĠGive n", "Ġ Eric", "ĠE ric", "ĠEr ic", "Ġ IR", "ĠI R", "a bstract", "ab stract", "abs tract", "U nder", "Un der", "Und er", "n able", "na ble", "Ġ increment", "Ġin crement", "Ġincre ment", "Ġinc rement", "Ġincr ement", "o ven", "ov en", "ove n", "Ġ coin", "Ġc oin", "Ġco in", "Ġcoi n", "_ timer", "_t imer", "_time r", "_tim er", "_ti mer", "Ġsuffer ed", "Ġsuff ered", "Ġ FREE", "ĠF REE", "ĠFR EE", "ĠFRE E", "' ].\"", "'] .\"", "']. \"", "Ġ Queen", "ĠQu een", "ĠQue en", "st ats", "stat s", "sta ts", "Ġmeet ings", "Ġmeeting s", "Ġmee tings", "2 76", "27 6", "Ġen tering", "Ġent ering", "Ġenter ing", "Ġalong side", "( session", "(s ession", "(sess ion", "it als", "ital s", "ita ls", "Ġ foundation", "Ġf oundation", "Ġfound ation", "Ġ Credit", "ĠC redit", "ĠCr edit", "ĠCre dit", ". div", ".d iv", ".di v", "_ ALL", "_A LL", "_AL L", "p cion", "pc ion", "pci on", "_ stat", "_s tat", "_st at", "_sta t", "ic king", "ick ing", "Default s", "_ src", "_s rc", "_sr c", "Ġ outputs", "Ġout puts", "Ġoutput s", "/ B", "Ġen thus", "Ġent hus", "Ġenth us", "- bl", "-b l", ". ForeColor", ".Fore Color", "ĉ temp", "ĉt emp", "F ace", "Fac e", "Fa ce", "Ġinter act", "Ġinte ract", "Ġwe ird", "Ġwei rd", "Ġweir d", "M ount", "Mo unt", "r ell", "re ll", "rel l", "ud ents", "ude nts", "udent s", "uden ts", "Ġrequire ment", "Ġrequ irement", "Ġ Sus", "ĠS us", "ĠSu s", "I ER", "IE R", "Ġe lected", "Ġel ected", "Ġelect ed", "re ference", "ref erence", "refer ence", "Ġ ME", "ĠM E", "Ġ servers", "Ġs ervers", "Ġser vers", "Ġserver s", "Ġserv ers", "Ġserve rs", ". wait", ".w ait", "Ġ snapshot", "Ġs napshot", "Ġsnap shot", "Ġsnaps hot", "il ton", "ilt on", "Ġ tries", "Ġt ries", "Ġtr ies", "Ġtri es", "Ġtrie s", "Ġ tipo", "Ġt ipo", "Ġti po", "Ġtip o", ". Time", ".T ime", "> w", "Ġm ountain", "Ġmount ain", "Ġp ounds", "Ġpo unds", "Ġpou nds", "Ġpound s", "Ġ[ ...", "Ġ[. ..", "ex ists", "exist s", "Ġng On", "_ MAP", "_M AP", "_MA P", "Ġf lying", "Ġfl ying", "Ġfly ing", "3 31", "33 1", "xi ety", "ĉ value", "ĉv alue", "ĉval ue", "ĉva lue", "_ DB", "_D B", "u no", "un o", "Ġse ats", "Ġsea ts", "Ġseat s", "T URN", "TU RN", ". author", ".a uthor", ".auth or", ".aut hor", "! )", "or ce", "orc e", "Ġind icated", "Ġindic ated", "Ġindicate d", "Ġindica ted", "3 17", "31 7", ". sin", ".s in", ".si n", "Ġ assignment", "Ġass ignment", "Ġassign ment", "im iento", "imi ento", "Ġ Frame", "ĠF rame", "ĠFr ame", "ĠFra me", "ĠFram e", "3 24", "32 4", "_ gen", "_g en", "_ge n", "in ery", "ine ry", "iner y", "_ )", "m essages", "message s", "mess ages", ". settings", ".s ettings", ".set tings", ".setting s", "Ġ Mean", "ĠM ean", "ĠMe an", "ĠM useum", "ĠMus eum", "ĠMuse um", "i rq", "ir q", "at tach", "att ach", "atta ch", "ĠPale stin", "ĠPalest in", "_ QU", "_Q U", "_ tags", "_t ags", "_tag s", "_ta gs", "Ġcas ual", "e men", "em en", "eme n", "ASS WORD", "4 32", "43 2", "$ s", "ĠC irc", "ĠCi rc", "ĠCir c", "о й", "оР¹", "et ric", "etr ic", "etri c", "/ P", "0 18", "01 8", "Ġ epoch", "Ġep och", "< head", " The", ">T he", "Ġ Ak", "ĠA k", "Ġ grass", "Ġgr ass", "Ġgra ss", "Ġgras s", "/ *čĊ", "/* čĊ", "( dis", "(d is", "(di s", "Ġ guns", "Ġg uns", "Ġgu ns", "Ġgun s", "Ġ tb", "Ġt b", "Ġ Kevin", "ĠK evin", "ĠKe vin", ". args", ".ar gs", ".arg s", "Ġ Ah", "ĠA h", "o ped", "op ed", "ope d", "( J", "column s", "arg uments", "argument s", "ĠWith Events", "_ full", "_f ull", "_fu ll", "Ġ Defense", "ĠDef ense", "S imple", "Sim ple", "Ġde aths", "Ġdeath s", "2 95", "29 5", "Ġext ensive", "Ġ Still", "ĠSt ill", "Ġ Expression", "ĠEx pression", "ĠExp ression", "ĠExpress ion", "ĠExpr ession", "Ġ Agency", "ĠA gency", "ĠAg ency", "ĠAge ncy", "Ġper forming", "Ġperform ing", "Ġperfor ming", "F X", "Ġ usuario", "Ġus uario", "Ġusu ario", "U AL", "UA L", "S ide", "Si de", "Sid e", "o dos", "od os", "odo s", "ap top", "apt op", "Ġ credentials", "Ġc redentials", "Ġcred entials", "Ġcredential s", "_ cap", "_c ap", "_ca p", "at ient", "ati ent", "atie nt", "Ġ Disney", "ĠDis ney", "Ġ ai", "Ġa i", "Ġ chip", "Ġc hip", "Ġch ip", "Ġchi p", "Ġ volt", "Ġv olt", "Ġvo lt", "Ġvol t", ".make Text", "%%%%%%%% %%%%%%%%", "Ġ belief", "Ġbel ief", "Ġbelie f", "_ LOC", "_L OC", "_LO C", "Ġ Civil", "ĠC ivil", "ĠCi vil", "ĠCiv il", "N avigation", "Nav igation", "Navig ation", "Ġ reveal", "Ġre veal", "Ġreve al", "Ġ violent", "Ġviol ent", "Ġ Fil", "ĠF il", "ĠFi l", "Ġ catalog", "Ġc atalog", "Ġcat alog", "Ġcata log", "Ġcatal og", "e med", "em ed", "eme d", "s can", "sc an", ". control", ".c ontrol", ".cont rol", "Ġ constitution", "Ġcon stitution", "Ġconst itution", "Ġconstit ution", "Ġconstitu tion", "C ountry", "Count ry", "S eparator", "Se parator", "Separ ator", "_ APP", "_A PP", "_AP P", "t opic", "to pic", "top ic", "u etooth", "uet ooth", "M IN", "MI N", "Ġ descriptor", "Ġdes criptor", "y t", "ET HER", "ETH ER", "Ġd istribute", "Ġdis tribute", "Ġdistrib ute", "' }Ċ", "'} Ċ", ". trim", ".t rim", ".tr im", ". Line", ".L ine", "Ġ lbl", "Ġl bl", "Ġlb l", "assert Equals", "Ġ Det", "ĠD et", "ĠDe t", "omb ok", "ombo k", "( width", "(w idth", "Ġt ort", "Ġto rt", "Ġtor t", "ĠEX PRESS", "ĠEXP RESS", "a co", "ac o", "U sing", "Us ing", "Ġ Brand", "ĠB rand", "ĠBr and", "ĠBra nd", "ĠBran d", "w all", "wa ll", "wal l", "E MENT", "EM ENT", "Ġ Communic", "ĠComm unic", "ĠCommun ic", "< uint", " (Ċ", ">( Ċ", "? >\"", "?> \"", "Ġ ///Ċ", "Ġ// /Ċ", "Ġ/ //Ċ", "Ġ/// Ċ", "Ġe iner", "Ġein er", "Ġeine r", "Ġei ner", "Ġ weekly", "Ġweek ly", "ĉ logger", "ĉlog ger", "_ pop", "_p op", "_po p", "_ man", "_m an", "_ma n", "Ġm igrations", "Ġmigr ations", "Ġmigration s", "Ġ asks", "Ġas ks", "Ġask s", "Ġ bs", "Ġb s", "Ġ falls", "Ġf alls", "Ġfall s", "Ġfal ls", ". Where", ".W here", ".Wh ere", "- height", "-h eight", "-he ight", "_ feature", "_f eature", "_fe ature", "_feat ure", ". Min", ".M in", "Ġ hyper", "Ġh yper", "Ġhy per", "Ġhyp er", "Ġhype r", "Ġ volatile", "Ġv olatile", "Ġvol atile", "Ġ twenty", "Ġtw enty", "Ġtwe nty", "Typ ography", "U nable", "Un able", "Una ble", "D et", "De t", ", f", "- mod", "-m od", "Ġset tlement", "Ġsett lement", "Ġsettle ment", "Ġ contracts", "Ġcon tracts", "Ġcontract s", "Ġcontr acts", "Ġcontra cts", "n ome", "no me", "nom e", "B ad", "Ba d", "Ġ Brian", "ĠB rian", "ĠBr ian", "ĠBri an", "7 68", "76 8", "( username", "(user name", "! !!!", "!! !!", "!!! !", "Ġ hack", "Ġh ack", "Ġha ck", "Ġhac k", ". Field", ".F ield", "H R", "Ġ Jordan", "ĠJ ordan", "ĠJord an", "i za", "iz a", "Ġ Âł", "Ġ ł", "Ġ Sher", "ĠS her", "ĠSh er", "ĠShe r", ". header", ".head er", ".he ader", "( other", "(o ther", "Ġ Dub", "ĠD ub", "ĠDu b", "( op", "(o p", "Ġ Round", "ĠR ound", "ĠRo und", "ĠRou nd", "Ġ vie", "Ġv ie", "Ġvi e", "Ġ appl", "Ġapp l", "Ġap pl", "ĉ J", "Ġ Insert", "ĠIn sert", "ĠIns ert", "Ġ LP", "ĠL P", "re gon", "reg on", "rego n", "Ġ MPI", "ĠM PI", "ĠMP I", "Ġ anchor", "Ġan chor", "Ġanch or", "Ġanc hor", "a ca", "ac a", "ø r", "Ġ ade", "Ġa de", "Ġad e", "an chor", "anc hor", "anch or", "qu ee", "que e", "Ġ TreeNode", "ĠT reeNode", "ĠTree Node", "Ġtarget ed", "Ġtarg eted", "Ġl aid", "Ġla id", "Ġlai d", "A BEL", "AB EL", "v et", "ve t", "Ġ Origin", "ĠOr igin", "ĠOri gin", "ĠOrig in", "A nt", "An t", ". ');Ċ", ".' );Ċ", ".') ;Ċ", ".'); Ċ", "ex pect", "exp ect", "ed Reader", "Ġ Major", "ĠM ajor", "ĠMaj or", "Ġ inch", "Ġin ch", "Ġinc h", "Com par", "Co mpar", "Comp ar", "Ġ preview", "Ġp review", "Ġpr eview", "Ġpre view", "Ġprev iew", "Ġill ness", "ĠCON TRACT", "ĠCONTR ACT", "ĠCONT RACT", "Ġ Independ", "ĠIn depend", "ĠInd epend", "u uid", "uu id", "Ġ nome", "Ġn ome", "Ġno me", "Ġnom e", "Ġ tc", "Ġt c", "ĠA venue", "i san", "is an", "isa n", "Ġ phrase", "Ġph rase", "_ move", "_m ove", "_mov e", "_mo ve", "\" )[", "\") [", "4 12", "41 2", "Ġpro vision", "Ġprov ision", "Ġconc entr", "Ġconcent r", "_ IR", "_I R", "Ġ Ut", "ĠU t", "( )+", "() +", "Ġ nas", "Ġn as", "Ġna s", "! ,", "Ġ Robin", "ĠRob in", "ĠRo bin", "i ations", "iation s", "iat ions", "at itude", "Ġ px", "Ġp x", "Ġ Without", "ĠWith out", "/ bash", "/b ash", "e kt", "ek t", "re ement", "ree ment", "3 42", "34 2", "Ob server", "Observ er", "Obs erver", "3 18", "31 8", "Ġ Region", "ĠReg ion", "UB LIC", "UBL IC", "Ġ {//", "Ġ{ //", "K N", "å ·", "Game Object", "å ¾", "en coding", "enc oding", "enco ding", "Ġ ***", "Ġ* **", "Ġ** *", "project s", "proj ects", "Ġ tk", "Ġt k", "Ġche ese", "Ġchees e", "EM PL", "EMP L", "a ro", "ar o", "Ġ اÙĦ", "Ġا ÙĦ", "6 10", "61 0", "3 37", "33 7", "Ġcons ists", "Ġconsist s", "re fresh", "ref resh", "u reau", "ure au", "Ġ Scanner", "ĠSc anner", "ĠScan ner", "Ġs oil", "Ġso il", "Ġfl avor", "Ġflav or", "Ġfla vor", "Data Source", "Ex ecute", "Exec ute", "е ние", "ен ие", "ени е", "Ġ shit", "Ġs hit", "Ġsh it", "åĪ Ĩ", "< any", " < /", "Qu antity", "Quant ity", "qu iry", "quir y", "qui ry", "_ tab", "_t ab", "_ta b", "Ġ alg", "Ġa lg", "Ġal g", "To ast", "re size", "res ize", "resi ze", "quest ions", "question s", "s chema", "sch ema", "L iteral", "Lite ral", "Lit eral", "Liter al", "( entity", "(e ntity", "(ent ity", "NE CTION", "NECT ION", "ch anged", "change d", "chan ged", "chang ed", "_ FIELD", "_F IELD", "_ HEIGHT", "_HE IGHT", "Ġ organic", "Ġorg anic", "Ġorgan ic", "P RE", "PR E", "Ġ Cat", "ĠC at", "ĠCa t", ". Draw", ".D raw", "E s", "Ġ loud", "Ġl oud", "Ġlo ud", "Ġlou d", "6 80", "68 0", "Ġ ĠĠĠĠĠĠĠĉ", "ĠĠ ĠĠĠĠĠĠĉ", "ĠĠĠĠ ĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠ ĉ", "ĠĠĠ ĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠ Ġĉ", "ĠĠĠĠĠ ĠĠĠĉ", "ĠĠĠĠĠĠ ĠĠĉ", "Ġ Kat", "ĠK at", "ĠKa t", "Ġ heap", "Ġhe ap", "âĢľ It", "âĢľI t", "0 70", "07 0", "e tr", "et r", "Ġ unlikely", "Ġun likely", "Ġunlike ly", "er als", "era ls", "eral s", "/ auth", "/a uth", "5 02", "50 2", "t odo", "to do", "tod o", "P lace", "Pl ace", "Post ed", "Pos ted", "Po sted", "Com ments", "Comment s", "Comm ents", "Ġ Tech", "ĠT ech", "ĠTe ch", "ĠTec h", "Ġ Finally", "ĠF inally", "ĠFin ally", "ĠFinal ly", "eg ration", "egr ation", "egra tion", "Ġ minimal", "Ġmin imal", "Ġmini mal", "Ġminim al", "Ġ Files", "ĠF iles", "ĠFile s", "ĠFil es", "ĠFi les", "Ġt amb", "Ġta mb", "Ġtam b", "ë¡ ľ", "Ġ Release", "ĠR elease", "ĠRe lease", "ĠRel ease", "4 25", "42 5", ". resize", ".re size", ".res ize", "Ġ Ï", "c ollect", "col lect", "coll ect", "= p", "ĠLI ABLE", "Ġp roducing", "Ġprodu cing", "Ġprod ucing", "- wrapper", "-w rapper", "-wrap per", "Ġs ingles", "Ġsingle s", "Ġsin gles", "Ġsing les", "ĠN BA", "ĠNB A", "o rr", "or r", "e ren", "er en", "ere n", ". addAction", ".add Action", "Ġ thesis", "Ġth esis", "Ġthe sis", "d n", "P TY", "PT Y", ". des", ".d es", ".de s", "Ġb acter", "Ġba cter", "Ġbac ter", "Ġ Express", "ĠEx press", "ĠExp ress", "ĠExpr ess", "Ġ *)Ċ", "Ġ* )Ċ", "Ġ*) Ċ", "å ij", "/ admin", "/ad min", "se conds", "sec onds", "second s", "åĬ Ł", "uss ion", "a beth", "ab eth", "abe th", "abet h", "Ġ Computer", "ĠCom puter", "ĠComp uter", "ĠCompute r", "ĠComput er", "Ġr uling", "Ġru ling", "(\" ../", "(\". ./", "(\".. /", ". GET", ".G ET", "ĠMe dal", "ĠMed al", "ition ally", "itional ly", "com mit", "comm it", "f ocus", "fo cus", "_ LEVEL", "_LE VEL", "i nda", "in da", "ind a", "F act", "Fac t", "Fa ct", "= np", "=n p", "=\" \">Ċ", "=\"\" >Ċ", "=\"\"> Ċ", "Ġsub sequent", "Ġsubsequ ent", "pos able", "- fluid", "-fl uid", "Ġth orough", "Ġtho rough", "Ġthor ough", "Ġpublic ly", "Ġpubli cly", "ap ters", "apt ers", "apter s", "Ġ Wilson", "ĠWil son", "_ PRE", "_P RE", "_PR E", "y ard", "ya rd", "yar d", "ä ¼", "ĉ in", "ĉi n", "3 39", "33 9", "Ġre vers", "Ġrev ers", "Ġreve rs", "Ġrever s", "Ġ bullet", "Ġb ullet", "Ġbul let", "Ġbull et", "cri bed", "cribe d", "nes ota", "Ġ ($_", "Ġ( $_", "Ġ($ _", "an non", "ann on", "anno n", "c ursor", "curso r", "Ġclo thing", "Ġcloth ing", "Ġ Multi", "ĠM ulti", "ĠMult i", "ĠMul ti", "2 87", "28 7", ": ',", ":' ,", "Ġv ess", "Ġve ss", "Ġves s", "ord inator", "ordin ator", "Ġe inem", "Ġein em", "Ġeine m", "Ġei nem", "C annot", "Can not", "Ġ armed", "Ġar med", "Ġarm ed", "ĉ V", "ä¸ Ĭ", ". Flat", ".F lat", ".Fl at", "Ġ Sep", "ĠS ep", "ĠSe p", "Ġ Subject", "ĠSub ject", "ĠSu bject", "_ font", "_f ont", "Ġcharacter istics", "Ġcharacteristic s", "D one", "Do ne", "Don e", "e ln", "el n", "#### ########", "######## ####", "##### #######", "###### ######", "####### #####", "P OS", "PO S", "Ġ density", "Ġd ensity", "Ġdens ity", "Ġ Platform", "ĠPl atform", "ĠPlat form", "- items", "-item s", "-i tems", "-it ems", "Ġ overs", "Ġo vers", "Ġover s", "Ġov ers", "Ġp ushing", "Ġpush ing", "ç ¤", ". Connection", ".Con nection", ".Connect ion", ".Conn ection", "_ term", "_t erm", "_te rm", "_ter m", "Ġinitial ization", "________________ ________________", "ç ¬", ". document", ".d ocument", ".doc ument", "l esh", "le sh", "les h", "ĉ document", "ĉd ocument", "ĉdoc ument", "Ġ Pin", "ĠP in", "ĠPi n", "ç a", "Ġ definitions", "Ġdefinition s", "Ġdefinit ions", "Ġdefin itions", ". Path", ".P ath", "_ WRITE", "_W RITE", "_WR ITE", "Ġ ĉĊ", "Ġĉ Ċ", "? >ĊĊ", "?> ĊĊ", "?>Ċ Ċ", "Ġter rible", "Ġterr ible", "b ean", "be an", "ick ets", "icket s", "Ġ SV", "ĠS V", "B uy", "Bu y", "( task", "(t ask", "Ġreg ime", "g oogle", "go ogle", "goog le", "goo gle", "Ġc rack", "Ġcr ack", "Ġcra ck", ". visit", ".vis it", "N UM", "NU M", "e nergy", "en ergy", "ener gy", "Ġs truck", "Ġstr uck", "Ġstru ck", "_ sample", "_s ample", ". payload", ".p ayload", ".pay load", "Ġre vis", "Ġrev is", "Ġ Scene", "ĠS cene", "ĠSc ene", "Ġ pg", "Ġp g", "Ġbreak fast", "URRE NT", ". charAt", ".char At", "_ exception", "_ex ception", "_except ion", "ĠA nton", "ĠAn ton", "ĠAnt on", "Ġguide lines", "Ġguid elines", "Ġguideline s", "Ġex haust", "Ġ Financial", "ĠFin ancial", "Ġ indent", "Ġin dent", "Ġind ent", "Ġinde nt", "Ġ desktop", "Ġd esktop", "Ġdes ktop", "Ġdesk top", "H idden", "Hi dden", "F ailure", "Fail ure", "Ġpr inciple", "Ġprincip le", "Ġprinc iple", "Ġ iv", "Ġi v", "Ġs eks", "Ġse ks", "Ġsek s", "n etwork", "net work", "Ġ numberOf", "Ġnumber Of", "Ġ Albert", "ĠAl bert", "ĠAlb ert", "ĉ long", "ĉl ong", "8 01", "80 1", ", .", "Ġ zeros", "Ġz eros", "Ġzero s", "Ġze ros", "Ġzer os", "f ade", "fa de", "fad e", "Ġ Typ", "ĠT yp", "ĠTy p", "Ġ Term", "ĠT erm", "ĠTe rm", "ĠTer m", "ĠA rts", "ĠAr ts", "ĠArt s", ". Application", ".App lication", ".Ap plication", "Ġbe half", "Ġbeh alf", "æĪ ·", "Ġ mere", "Ġm ere", "Ġme re", "Ġmer e", "( `${", "(` ${", "Ġaware ness", "el pers", "elp ers", "elper s", "f lix", "fl ix", "Ġ weigh", "Ġwe igh", "Ġwei gh", "Ġest imates", "Ġestim ates", "Ġestimate s", ". child", ".ch ild", "/ O", "Ġ Bitmap", "ĠB itmap", "ĠBit map", ". bottom", ".b ottom", ".bot tom", "Ġ** ************************************************************************", "Ġ************************************************************************ **", "Ex pect", "Exp ect", "en to", "ent o", "Ġ Forum", "ĠF orum", "ĠFor um", "ĠFo rum", "v eral", "ver al", "ve ral", "Ġj ail", "Ġja il", "Ġ abilities", "Ġab ilities", "ĠH OLD", "ĠHO LD", "ĠHOL D", "Ġ Cit", "ĠC it", "ĠCi t", "Ġd ynam", "Ġdy nam", "Ġdyn am", "Ġ gray", "Ġg ray", "Ġgr ay", "Ġgra y", "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", ". nextInt", ".next Int", "ant ly", "ĠAR ISING", "( private", "(pr ivate", "(priv ate", "Ġ rejected", "Ġre jected", "Ġreject ed", "Ġrej ected", "Ġ Nic", "ĠN ic", "ĠNi c", "Ġle ather", "= {Ċ", "={ Ċ", "aly tics", "t hetic", "th etic", "the tic", ". Top", ".T op", ".To p", "3 73", "37 3", ". Page", ".P age", "={ `", "Ġ ;čĊ", "Ġ; čĊ", "de pth", "dep th", "dept h", "m ann", "man n", "ma nn", "W D", "Ġ Som", "ĠS om", "ĠSo m", ". Right", ".R ight", "Ġ )}Ċ", "Ġ) }Ċ", "Ġ trait", "Ġt rait", "Ġtr ait", "Ġtra it", "Ġtrai t", "à Ĺ", "i ac", "ia c", "Ġ rv", "Ġr v", "S ample", "Sam ple", ". Xml", ".X ml", "o pped", "op ped", "opp ed", "Ġ ÑĦ", "ĠÑ Ħ", "l ists", "li sts", "list s", "lis ts", "Ġt ear", "Ġte ar", "Ġtea r", "ivers ary", ". collection", ".c ollection", ".col lection", ".collect ion", ".coll ection", "ĠCon stitution", "ĠConst itution", "ĠConstit ution", "Ġ HttpResponse", "ĠHttp Response", "Ġbr ill", "Ġbri ll", "Ġ Prom", "ĠP rom", "ĠPro m", "ĠPr om", "h over", "ho ver", "3 66", "36 6", "Ġ Miami", "ĠM iami", "ĠMi ami", "ĠMia mi", "Ġar gue", "Ġarg ue", "_ float", "_f loat", "5 04", "50 4", "Ġ ãĤ", "Ġ nat", "Ġn at", "Ġna t", "ĠT al", "ĠTa l", "Ġ integration", "Ġint egration", "Ġinteg ration", "Ġintegr ation", "( cur", "(c ur", "Ġre moving", "Ġrem oving", "Ġ coeff", "Ġc oeff", "Ġco eff", "Ġcoef f", "Ġ Though", "ĠTh ough", "ĠThou gh", "Ġ forecast", "Ġfor ecast", "Ġfore cast", "4 08", "40 8", "ĠV egas", "ĠVe gas", "ĠVega s", "ĠVeg as", "S ite", "Si te", "Sit e", "3 46", "34 6", "Ġt rab", "Ġtr ab", "Ġtra b", "Ġ Henry", "ĠHen ry", "- i", "Ġinv olves", "Ġinvol ves", "Ġinvolve s", "B T", "Ġs lo", "Ġsl o", "In voke", "Inv oke", "Ġl ucky", "Ġluck y", "Ġlu cky", "Ġluc ky", "0 25", "02 5", "r at", "ra t", "Ġ ?Ċ", "Ġ? Ċ", "Ġ handled", "Ġhand led", "Ġhandle d", "( fd", "(f d", "cont ents", "content s", "conte nts", "Ġ OFF", "ĠO FF", "ĠOF F", "R F", "Ġ sty", "Ġs ty", "Ġst y", "Ġ Motor", "ĠM otor", "ĠMo tor", "ĠMot or", "ĠMoto r", "t ery", "ter y", "te ry", "t ax", "ta x", "M AP", "MA P", "Ġ Mrs", "ĠM rs", "ĠMr s", "Ġ phones", "Ġph ones", "Ġphone s", "Ġphon es", "Ġ UIView", "ĠUI View", "\" )));Ċ", "\") ));Ċ", "\")) );Ċ", "\"))) ;Ċ", "\"))); Ċ", "( dev", "(d ev", "(de v", "ĠI rish", "ĠIr ish", "ĠIris h", "0 19", "01 9", "Ġ ws", "Ġw s", "D I", "_ OFFSET", "_OFF SET", "Ġ Events", "ĠE vents", "ĠEvent s", "ĠEven ts", "ĠEv ents", "ĠEve nts", "Ġst ages", "Ġstage s", "Ġsta ges", "Ġstag es", "Ġ }//", "Ġ} //", "Ġh aben", "Ġhab en", "Ġha ben", "Ġhabe n", "ST ANCE", "Ġ Sin", "ĠS in", "ĠSi n", "Ġ Money", "ĠM oney", "ĠMon ey", "ĠMo ney", "( top", "(t op", "(to p", "Ġ appointment", "Ġapp ointment", "Ġappoint ment", "V ERSION", "VER SION", "VERS ION", "m etadata", "met adata", "meta data", "_ comment", "_com ment", "_comm ent", "Ġcolle agues", "Ġcolleague s", "m aps", "ma ps", "map s", "â ĺ", "Ċ ĉĊ", "( al", "(a l", "_ req", "_re q", "_r eq", "Ġf ut", "Ġfu t", "Ġ architecture", "Ġarch itecture", "Ġarchitect ure", "Ġarchit ecture", "3 51", "35 1", "ĠWH ETHER", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "_ screen", "_s creen", "_sc reen", "_scr een", "Ġstyle Urls", "Ġ monster", "Ġmon ster", ". up", ".u p", "ph ia", "phi a", "Ġ processor", "Ġprocess or", "Ġproc essor", "Ġprocesso r", "Ġ Terr", "ĠT err", "ĠTe rr", "ĠTer r", "= ',", "=' ,", "Ġ Manufact", "ĠMan ufact", "Ġ NT", "ĠN T", "k el", "ke l", "i bern", "ib ern", "iber n", "ibe rn", "ĉ file", "ĉf ile", "ĉfi le", "A li", "Al i", "rient ation", "Ġ //!", "Ġ// !", "ap ore", "apor e", "apo re", "an eous", "ane ous", "Ġ Creat", "ĠC reat", "ĠCr eat", "ĠCre at", "f older", "fo lder", "fol der", "fold er", "4 15", "41 5", "Ġ hay", "Ġh ay", "Ġha y", "Sup press", "( left", "(l eft", "(le ft", "Ġe uro", "Ġeu ro", "Ġdis claimer", "u stry", "us try", "ust ry", "ustr y", "s hips", "sh ips", "ship s", "shi ps", "_ fd", "_f d", "Ġ Fa", "ĠF a", "_ insert", "_in sert", "_ins ert", "Ġ rol", "Ġr ol", "Ġro l", "if ting", "ift ing", "Ġ Comments", "ĠCom ments", "ĠComm ents", "ĠComment s", "_ br", "_b r", "Ġlos ses", "Ġloss es", "Ġ Added", "ĠAd ded", "ĠAdd ed", "ch arg", "char g", "cha rg", "Ġ по", "Ġп о", "_ system", "_s ystem", "_sys tem", "_sy stem", "Ġ Sometimes", "ĠS ometimes", "ĠSome times", "ĠSom etimes", "Ġ Spain", "ĠS pain", "ĠSp ain", "ĠSpa in", "( group", "(g roup", "(gr oup", "i alis", "ial is", "ia lis", "iali s", "Ġd ollar", "Ġdoll ar", "Ġdol lar", "Ġ Args", "ĠAr gs", "ĠArg s", "4 99", "49 9", "2 97", "29 7", "qu ires", "quire s", "quir es", "qui res", "Ġ Ten", "ĠT en", "ĠTe n", ". scss", ".s css", ".sc ss", "Ġsurv ive", "Ġsurviv e", "u sage", "us age", "usa ge", "Ġ jun", "Ġj un", "Ġju n", "im iter", "imit er", "imi ter", "ï¼ģ ĊĊ", "ï¼ģĊ Ċ", "Ġf ifth", "Ġfif th", "t oggle", "tog gle", "Ġde cline", "Ġdec line", "Ġdecl ine", "( $\"", "($ \"", "( Long", "(L ong", "i nge", "in ge", "ing e", "Ġp ilot", "Ġpi lot", "Ġpil ot", "- light", "-l ight", "-li ght", "- radius", "-r adius", "-rad ius", "Ġp odcast", "Ġpod cast", "Ġn aturally", "Ġnatural ly", "Ġnatur ally", "Ġnat urally", "P ages", "Page s", "Pa ges", "Pag es", "ä¸ º", "Ġ Despite", "ĠDes pite", "Ġl ighting", "Ġlight ing", "Ġ crate", "Ġc rate", "Ġcr ate", "Ġcra te", "Ġ Binary", "ĠB inary", "ĠBin ary", "Ġred ucing", "Ġredu cing", "Ġe leg", "Ġel eg", "Ġele g", "Ġ Mouse", "ĠM ouse", "ĠMo use", "ĠMou se", "ĠTest Bed", "Ġbefore Each", "_ ARRAY", "_AR RAY", "_ARR AY", "Re direct", "Red irect", "3 29", "32 9", "Ġf lood", "Ġfl ood", "Ġflo od", "Ġ ships", "Ġs hips", "Ġsh ips", "Ġship s", "3 63", "36 3", "Ġelectric ity", "Ġelectr icity", ") *(", ")* (", "ê ¸", "Ġ Viet", "ĠV iet", "ĠVi et", "ĠVie t", "h ero", "he ro", "her o", "Ġ dia", "Ġd ia", "Ġdi a", "Ġ Kent", "ĠK ent", "ĠKe nt", "ĠKen t", "he art", "hea rt", "hear t", "Ġthreat s", "_ acc", "_a cc", "_ac c", "Ġ symbols", "Ġs ymbols", "Ġsymbol s", "Ġsymb ols", "is chen", "isc hen", "ische n", "isch en", "_ inst", "_in st", "_i nst", "_ins t", "C riterion", "Ġ TIM", "ĠT IM", "ĠTI M", ". Height", ".H eight", ".He ight", "5 80", "58 0", "Ġ âĢĻ", "ĠâĢ Ļ", "( );ĊĊĊ", "() ;ĊĊĊ", "();Ċ ĊĊ", "();ĊĊ Ċ", "(); ĊĊĊ", "Product s", "Produ cts", "_ SP", "_S P", "Ġ Cy", "ĠC y", "Ġ dependent", "Ġdep endent", "Ġdepend ent", "e ste", "es te", "est e", "Ġ datos", "Ġd atos", "Ġda tos", "Ġdat os", "Ġdato s", "d it", "di t", "а в", "аР²", "IG NAL", "IGN AL", "Ġ lesson", "Ġl esson", "Ġle sson", "Ġless on", "Ġles son", "\" >'", "\"> '", "Ġ Cover", "ĠC over", "ĠCo ver", "ĠCov er", "ĠCove r", "Ġ Hope", "ĠH ope", "ĠHo pe", "ĠHop e", "Ġ Timer", "ĠT imer", "ĠTime r", "ĠTim er", "ĠTi mer", "Ġ dad", "Ġd ad", "Ġda d", "v iders", "vid ers", "vider s", "vi ders", "vide rs", "Ġ Phot", "ĠP hot", "ĠPh ot", "/ ?", "r opy", "ro py", "rop y", "o ming", "om ing", "omin g", "omi ng", "as ion", "asi on", "asio n", "Ġ \\(", "Ġ\\ (", "Ġ ET", "ĠE T", "Ġ Reading", "ĠRe ading", "ĠRead ing", "Ġep isodes", "Ġepisode s", "Ġepis odes", "l m", "4 21", "42 1", "e cha", "ec ha", "ech a", "Ġne uro", "Ġneu ro", "Ġneur o", "8 20", "82 0", "Ġhar mon", "Ġharm on", "Ġlib eral", "Ġliber al", "- ind", "-in d", "-i nd", "3 93", "39 3", "D ATA", "DA TA", "DAT A", "Ġevery day", "Ġdi vided", "Ġdiv ided", "Ġdivide d", "Ġdivid ed", "ĠActive Record", "f igure", "fig ure", "figur e", "U A", "ä ¹", "riend ly", "t ech", "te ch", "tec h", "6 01", "60 1", ". gameObject", ".game Object", "и ÑĤÑĮ", "иÑĤ ÑĮ", "3 74", "37 4", "Ġ moon", "Ġm oon", "Ġmo on", "Ġmoo n", "f time", "ft ime", "Ġ noch", "Ġn och", "Ġno ch", "Ġnoc h", "ĠT ORT", "ĠTO RT", "ĠTOR T", "Ġ VM", "ĠV M", ". initial", ".in itial", ".init ial", "( child", "(ch ild", "Ġmus ical", "Ġmusic al", "Ġmusica l", "Ġ oc", "Ġo c", "b as", "ba s", "Ġ Hay", "ĠH ay", "ĠHa y", "3 61", "36 1", "_ long", "_l ong", "_lo ng", "_lon g", "Ġ memset", "Ġmem set", "i ley", "il ey", "ile y", "adel phia", "S V", "ro at", "_ tx", "_t x", "Ġ lon", "Ġl on", "Ġlo n", "Ġng OnInit", "ĠngOn Init", "b p", "Ġ Golden", "ĠGold en", "ĠGol den", "A CHE", "AC HE", "ACH E", "Ġwor ried", "a zi", "az i", "E ar", "T ake", "Ta ke", "Tak e", "( fp", "(f p", "burg h", "bur gh", "_ Data", "_D ata", "g res", "gr es", "gre s", "Ġ Ont", "ĠO nt", "ĠOn t", "p us", "pu s", "Ġ transparent", "Ġtrans parent", "Ġp ocket", "Ġpo cket", "Ġpoc ket", "Ġ ram", "Ġr am", "Ġra m", "igration s", "igr ations", ". čĊčĊ", ".čĊ čĊ", "Ġ [(", "Ġ[ (", "Ġadopt ed", "Ġreport edly", "Ġreported ly", "Ġ Dream", "ĠD ream", "ĠDr eam", "ĠDre am", "Ġ }));Ċ", "Ġ} ));Ċ", "Ġ}) );Ċ", "Ġ})) ;Ċ", "l osing", "lo sing", "los ing", "Ġte eth", "Ġtee th", "Ġ Books", "ĠB ooks", "ĠBo oks", "ĠBook s", "ĠBoo ks", "\" ,&", "\", &", "en ny", "enn y", "L EMENT", "LE MENT", "LEM ENT", "Ġ gel", "Ġg el", "Ġge l", "Ġ Plant", "ĠP lant", "ĠPl ant", "ĠPlan t", "ĠPla nt", "4 37", "43 7", "! âĢĿ", ". host", ".h ost", "Ġ Reply", "ĠRe ply", "ĠRep ly", "3 76", "37 6", "r ength", "re ngth", "ren gth", "Ġrec ognition", "Ġrecogn ition", "Ġ }}>Ċ", "Ġ} }>Ċ", "Ġ}} >Ċ", "Ġ}}> Ċ", "L A", "Ġ mirror", "Ġm irror", "Ġmir ror", "Ġmi rror", "Ġ assistant", "Ġass istant", "Ġassist ant", "( device", "(d evice", "(de vice", "(dev ice", "Ġspirit ual", "b uilder", "build er", "bu ilder", " §", "Ġo utr", "Ġout r", "Ġou tr", "Ġ tt", "Ġt t", "Ġ PER", "ĠP ER", "ĠPE R", "Ġrad ical", "Ġradi cal", "Method s", "Ġ pace", "Ġp ace", "Ġpa ce", "Ġpac e", "u dy", "ud y", "Ġg ut", "Ġgu t", "Ġ Greek", "ĠG reek", "ĠGre ek", "ĠGree k", "Ġ nonatomic", "Ġnon atomic", "Ġ Paper", "ĠP aper", "ĠPa per", "ĠPap er", "_ GPIO", "_G PIO", "_GP IO", "Ġo bst", "Ġob st", "Ġobs t", ". Ad", ".A d", "viron ments", "vironment s", "ĠS ov", "ĠSo v", "3 56", "35 6", "( con", "(c on", "(co n", "Ġ Transaction", "ĠTrans action", ". assign", ".as sign", "ĉ catch", "ĉc atch", "ĉcat ch", "el ter", "elt er", "Ġ bitcoin", "Ġbit coin", "_ GR", "_G R", "Ġ čĊ", "Ġ/ >čĊ", "Ġ/> čĊ", "m etic", "me tic", "met ic", "Ġtrans formation", "Ġtransform ation", "åı ·", "Ġ rgb", "Ġr gb", "Ġrg b", "istrib utions", "istribution s", "Ġ implicit", "Ġimp licit", "Ġimpl icit", "Ġimplic it", "/ in", "/i n", "d estination", "dest ination", "а ÑĤÑĮ", "аÑĤ ÑĮ", "Z ero", "Ze ro", "Ġ unset", "Ġun set", "Ġuns et", "9 20", "92 0", ". where", ".w here", ".wh ere", ". go", ".g o", "Ġ formation", "Ġform ation", "Ġformat ion", "Ġforma tion", "Ġ declaration", "Ġde claration", "Ġdeclar ation", "( )čĊčĊ", "() čĊčĊ", "()čĊ čĊ", "Ġ Expl", "ĠEx pl", "ĠExp l", "ĉ ĉĉĠĠ", "ĉĉ ĉĠĠ", "ĉĉĉ ĠĠ", "ĉĉĉĠ Ġ", "/ pro", "/p ro", "/pr o", ". JSON", ".J SON", "4 41", "44 1", "Ġ desk", "Ġd esk", "Ġde sk", "Ġdes k", ". substr", ".sub str", "// ----------------------------------------------------------------------------", "//---------------------------------------------------------------- ------------", "//------------------------------------------------ ----------------------------", "l yn", "ly n", "p son", "ps on", "4 07", "40 7", "d isable", "dis able", "Ġ Func", "ĠF unc", "ĠFun c", "ĠFu nc", "ĉ Assert", "Ġ MARK", "ĠM ARK", "ĠMA RK", "ĠMAR K", "Ġde feat", "Ġdef eat", "Ġdefe at", "Ġ blind", "Ġbl ind", "Ġbli nd", "Ġ constants", "Ġcon stants", "Ġconst ants", "Ġconstant s", "3 62", "36 2", ". headers", ".head ers", ".header s", ".he aders", "U ILD", "UI LD", "UIL D", "Ġ expenses", "Ġexp enses", "Ġexpense s", "P ixel", "Pix el", "Ġ hr", "Ġh r", "Ġ fel", "Ġf el", "Ġfe l", "Ġ Eastern", "ĠEast ern", "ĠEaster n", "4 24", "42 4", "4 90", "49 0", "_ del", "_d el", "_de l", "3 57", "35 7", "ĠC ub", "ĠCu b", "Ġ sq", "Ġs q", "ĉ count", "ĉc ount", "Ġ Directory", "ĠD irectory", "ĠDirect ory", "ĠDirector y", "Ġex clus", "Ġexc lus", "Ġexcl us", "Ġ historic", "Ġhistor ic", "Ġhist oric", "Ġhisto ric", "Ġ ------------------------------------------------", "Ġ---------------- --------------------------------", "Ġ-------------------------------- ----------------", "Ġ-------------------- ----------------------------", "Ġ composition", "Ġcom position", "Ġcomp osition", "Ġcompos ition", "Ġ dataGridView", "Ġdata GridView", "Ġ Burn", "ĠB urn", "ĠBur n", "ĠBu rn", "Ġ BC", "ĠB C", "M aster", "Ma ster", "Mas ter", "Ġ spawn", "Ġs pawn", "Ġsp awn", "Ġspa wn", "Ġ bearing", "Ġb earing", "Ġbe aring", "Ġbear ing", ". SetActive", ".Set Active", "i lo", "il o", "Ġ gallery", "Ġg allery", "Ġgall ery", "Ġf ounded", "Ġfound ed", "Ġfo unded", "Ġ availability", "Ġa vailability", "Ġav ailability", "Ġavail ability", ". sqrt", ".s qrt", "Ġ pes", "Ġp es", "Ġpe s", "Ġ DOM", "ĠD OM", "ĠDO M", "m ate", "ma te", "mat e", "O ct", "Ġ matched", "Ġm atched", "Ġmatch ed", "Ġmat ched", "it ivity", "Ġan xiety", ". price", ".p rice", ".pr ice", "Ġ Instant", "ĠIn stant", "ĠInst ant", "ĠIns tant", "ì Ĭ", "Ġt ut", "Ġtu t", "I Collection", "IC ollection", ". shared", ".sh ared", ".share d", ".sha red", "_ sql", "_s ql", "_sq l", "t bl", "tb l", "l ibrary", "lib rary", "_ destroy", "_d estroy", "_de stroy", "_dest roy", "er mal", "erm al", "Ġ Notes", "ĠN otes", "ĠNo tes", "ĠNot es", "ĠNote s", "Ġ Ein", "ĠE in", "Ġsou thern", "Ġsouth ern", "ĠOTHER WISE", "Ġ macro", "Ġm acro", "Ġma cro", "Ġmac ro", ". lower", ".l ower", ".lo wer", ".low er", "c ls", "cl s", "Content View", ". link", ".l ink", ".li nk", ".lin k", "con stant", "const ant", "cons tant", "Ġ Bes", "ĠB es", "ĠBe s", "Ġsome body", "n b", "3 99", "39 9", "\" >{", "\"> {", "( local", "(l ocal", "(loc al", "(lo cal", ". ....", ".. ...", "... ..", ".... .", "Ġ Null", "ĠN ull", "ĠNu ll", "m x", "Ġ ç", "Ġà §", "Ġ pause", "Ġp ause", "Ġpa use", "Ġpau se", "- ----------", "-- ---------", "---- -------", "-------- ---", "--- --------", "----- ------", "---------- -", "------ -----", "------- ----", "--------- --", "_ MO", "_M O", "Ġ CM", "ĠC M", "Ġfor Key", "Ġ DVD", "ĠD VD", "ĠDV D", "Ġ closest", "Ġclose st", "Ġclos est", "Ġcloses t", "_ DEVICE", "_DE VICE", "_DEV ICE", "Ġ Stephen", "ĠSte phen", "ĠStep hen", "ĠSteph en", "Ġ BBC", "ĠB BC", "ĠBB C", "Ġ Travel", "ĠT ravel", "ĠTr avel", "ĠTra vel", "ĠTrav el", "P aint", "Pa int", "Ġ Results", "ĠRes ults", "ĠResult s", "Ġ Rule", "ĠR ule", "ĠRu le", "Ġ tp", "Ġt p", "Ġ ratings", "Ġr atings", "Ġrating s", "Ġrat ings", "Ġra tings", "c in", "ci n", "c sv", "cs v", "> /", "Ġ GOP", "ĠG OP", "ĠGO P", "l ad", "la d", "Ġ ÑĢ", "ĠÑ Ģ", "Ġ indexPath", "Ġindex Path", "m atrix", "mat rix", "= f", "ar sed", "ars ed", "arse d", "Ġ });", "Ġ} );", "Ġ}) ;", "Ġ Cos", "ĠC os", "ĠCo s", "Ġ Score", "ĠS core", "ĠSc ore", "ĠSco re", "ĠScor e", "Ġ tak", "Ġt ak", "Ġta k", "Ġ ESP", "ĠE SP", "ĠES P", "Ġ INC", "ĠI NC", "ĠIN C", "_ NULL", "_N ULL", "- flex", "-f lex", "-fl ex", "\" ][", "\"] [", "in to", "int o", "e land", "el and", "ela nd", "elan d", "Author ization", "_ FALSE", "_F ALSE", "Ġ gate", "Ġg ate", "Ġga te", "Ġ vid", "Ġv id", "Ġvi d", "is tent", "ist ent", "iste nt", "isten t", "T IME", "TI ME", "TIM E", "Ġ rewrite", "Ġre write", "Ġrew rite", "Ġ tie", "Ġt ie", "Ġti e", "Ġ archive", "Ġa rchive", "Ġarch ive", "Ġarc hive", "5 11", "51 1", ". events", ".e vents", ".event s", ".ev ents", ". getParameter", ".get Parameter", ".getParam eter", "Ġ Permission", "ĠPer mission", "ĠPerm ission", "Ġprogram me", "Ġprogramm e", "Ġ é", "j ud", "ju d", "Ġcame ras", "Ġcamera s", "Ġcam eras", "Ġcamer as", "3 38", "33 8", "3 49", "34 9", "( sys", "(s ys", "ĠSy rian", "ĠSyria n", "Ġimpro vements", "Ġimprove ments", "Ġimprovement s", "Ġimprov ements", "Ġ hip", "Ġh ip", "Ġhi p", "Ġsu icide", "Ġsuic ide", "Ġsch olar", "Ġscho lar", "Ġ compatible", "Ġcom patible", "Ġcompat ible", "0 22", "02 2", "rem ote", ". down", ".d own", ".do wn", "F UNCTION", "FUNC TION", "FUN CTION", "Ġman aging", "Ġmana ging", "Ġ UIKit", "ĠUI Kit", ". raw", ".r aw", ".ra w", "> >>>", ">> >>", ">>> >", "3 71", "37 1", "Ġdem ands", "Ġdemand s", "el lite", "ell ite", "elli te", "Ġ dent", "Ġd ent", "Ġde nt", "Ġden t", "Ġ Micro", "ĠM icro", "ĠMi cro", "ĠMic ro", "åı ĸ", "' ][$", "'] [$", "'][ $", "Ġ IE", "ĠI E", "im ension", "imens ion", "Ġt rem", "Ġtr em", "Ġtre m", "6 30", "63 0", "Ġg ained", "Ġgain ed", "Ġga ined", ". with", ".w ith", ". ok", ".o k", "h ou", "ho u", "Ġb om", "Ġbo m", "amp aign", "ampa ign", "Ġ joining", "Ġjoin ing", "Ġjo ining", "f ish", "fi sh", "Ġadd Subview", "8 60", "86 0", "Ġnor thern", "Ġnorth ern", ". cor", ".c or", ".co r", "o ret", "or et", "ore t", "D ie", "Di e", "i nish", "in ish", "ini sh", "inis h", "_ comp", "_c omp", "_com p", "_co mp", "Ġ attended", "Ġatt ended", "Ġattend ed", "Ġ collapse", "Ġc ollapse", "Ġcoll apse", "Ġcollaps e", "Ġ SS", "ĠS S", "a cent", "ace nt", "ac ent", "acen t", "_ EQUAL", "_E QUAL", "_EQ UAL", "Ġ Deep", "ĠDe ep", "ĠDee p", "R GB", "RG B", "ĉ test", "ĉt est", "ol ves", "olve s", "olv es", "u set", "us et", "use t", "Un ityEngine", "Unity Engine", "w riter", "write r", "wr iter", "Re solver", "Res olver", "Resolve r", ", %", "if ference", "iff erence", "iffer ence", "iffe rence", "_ remove", "_re move", "_rem ove", "o nda", "on da", "ond a", "Ġf emme", "Ġfem me", "3 85", "38 5", "de code", "dec ode", "Br anch", "Ġ flush", "Ġf lush", "Ġfl ush", "Ġflu sh", "Ġinnov ative", "T ests", "Test s", "Te sts", "Tes ts", "Ġ[' ./", "Ġ['. /", "Ġ covering", "Ġcover ing", "Ġcov ering", ". admin", ".ad min", "ulti part", "ultip art", "( lambda", "(l ambda", " namespace", "Ġ Sport", "ĠS port", "ĠSp ort", "ĠSpo rt", "ĠSpor t", "Ġ !(", "Ġ! (", "a cles", "ac les", "acle s", "acl es", "Ġde pression", "Ġdep ression", "Ġdepr ession", "Ġdepress ion", "ĠK ong", "ĠKon g", "ĠKo ng", "5 70", "57 0", "Ġ pert", "Ġp ert", "Ġper t", "Ġpe rt", "Ġ Conn", "ĠC onn", "ĠCon n", "ĠCo nn", "Ġ Otherwise", "ĠOther wise", "/ home", "/h ome", "s upported", "sup ported", "support ed", "Ġ pink", "Ġp ink", "Ġpi nk", "Ġpin k", "Ġinv ited", "Ġinvite d", "Ġinvit ed", "ñ os", "ño s", "_ enabled", "_en abled", "_enable d", "Ġ -Ċ", "Ġ- Ċ", "F W", "e ners", "en ers", "ener s", "ene rs", "Ġ MY", "ĠM Y", "Ġs uggestions", "Ġsuggest ions", "Ġsuggestion s", "C anvas", "Can vas", "Ġ fer", "Ġf er", "Ġfe r", "Ġ Marketing", "ĠMark eting", "ĠMarket ing", "@ Test", "un tu", "unt u", "Ġ Ven", "ĠV en", "ĠVe n", "Ġ Cou", "ĠC ou", "ĠCo u", "i vals", "iv als", "ival s", "iva ls", "D onald", "Don ald", "l imited", "lim ited", "limit ed", "ĉ ĉĉĉĉĉĊ", "ĉĉ ĉĉĉĉĊ", "ĉĉĉĉ ĉĉĊ", "ĉĉĉ ĉĉĉĊ", "ĉĉĉĉĉ ĉĊ", "ĉĉĉĉĉĉ Ċ", "Ġanal yst", "Ġanaly st", "Ġanalys t", "( entry", "(en try", "(ent ry", "Ġrepresent ative", "_ attributes", "_at tributes", "_attribute s", "_attrib utes", "Ġ fur", "Ġf ur", "Ġfu r", ". hide", ".h ide", "r esp", "re sp", "res p", "ad ores", "ado res", "ador es", "r ides", "ri des", "ride s", "rid es", "Ġ Josh", "ĠJ osh", "ĠJo sh", "ĠJos h", "r obot", "ro bot", "rob ot", "ĠN AT", "ĠNA T", "Ġs esso", "Ġses so", "Ġsess o", "Ġint egrated", "Ġinteg rated", "Ġintegr ated", "Ġintegrate d", ": true", "p arts", "par ts", "part s", "pa rts", "Ġst upid", "Ġstu pid", "Ġstup id", ": event", ":e vent", "@end section", "Ġ pu", "Ġp u", ". Table", ".T able", ".Tab le", "Ġ Yii", "ĠY ii", "ĠYi i", "` ;ĊĊ", "`;Ċ Ċ", "`; ĊĊ", "Ġ clang", "Ġc lang", "Ġcl ang", "Ġclan g", "Ġcla ng", "=\" \">", "=\"\" >", "en gan", "eng an", "enga n", "_ parameters", "_param eters", "_parameter s", ". internal", ".in ternal", ".int ernal", ".inter nal", "Ġ Modern", "ĠMod ern", "ĠMode rn", "ĠModer n", "Ġ metric", "Ġm etric", "Ġmet ric", "Ġ semi", "Ġs emi", "Ġse mi", "Ġsem i", "={ {Ċ", "={{ Ċ", "7 07", "70 7", ". amazon", ".a mazon", ".am azon", "Ġ BB", "ĠB B", "ain ty", "aint y", "ai nty", "view port", "3 67", "36 7", "Ġstart Activity", "dis patch", "disp atch", "* ****", "** ***", "**** *", "*** **", "Ġf lav", "Ġfl av", "Ġfla v", "iffer ent", "iffe rent", "3 82", "38 2", "[ this", "[t his", "Ġs take", "Ġst ake", "Ġsta ke", "Ġarg ued", "Ġargue d", "v iously", "vious ly", "vi ously", ". work", ".w ork", "Ġ Oak", "ĠO ak", "O ld", "Ol d", "( async", "(a sync", "(as ync", "n otes", "not es", "no tes", "note s", "Ġ flip", "Ġf lip", "Ġfl ip", "Ġdis ag", "Ġ TE", "ĠT E", "ĉ error", "ĉe rror", "ĉerr or", "< '", "Ġ »ĊĊ", "Ġ» ĊĊ", "Ġ»Ċ Ċ", "Ġ filtered", "Ġfil tered", "Ġfilter ed", "Ġfilt ered", "ĠM ach", "ĠMac h", "ĠMa ch", "Ġ hung", "Ġh ung", "Ġhun g", "Ġhu ng", "_ dump", "_d ump", "_ samples", "_s amples", "_sample s", "- dismiss", "-dis miss", "Ġ ray", "Ġr ay", "Ġra y", "Im plemented", "Implement ed", "D K", "Ġ jed", "Ġj ed", "Ġje d", "0 90", "09 0", "Ġbreak s", "Ġbre aks", "Ġ fits", "Ġf its", "Ġfit s", "Ġfi ts", ". gr", ".g r", "Ġ Zero", "ĠZ ero", "ĠZe ro", "o ro", "or o", "Ġequ ally", "Ġequal ly", "Ġeq ually", "Ġ '[", "Ġ' [", "Ġconcern ing", "< meta", "<", "'> <", "Ġpro mot", "Ġprom ot", "Ġpromo t", "Ġ incl", "Ġin cl", "Ġinc l", "_ only", "_on ly", "ë¥ ¼", "ĠAtt orney", "- date", "-d ate", "-da te", "-dat e", "Ġ landscape", "Ġl andscape", "Ġland scape", "Ġlands cape", "Ġlandsc ape", "Ġ fu", "Ġf u", "S Y", ". prop", ".p rop", ".pro p", ".pr op", "Ġ Arr", "ĠA rr", "ĠAr r", "p ag", "pa g", "Parallel Group", "' :čĊ", "': čĊ", "Ġ logs", "Ġl ogs", "Ġlo gs", "Ġlog s", "a unch", "un ci", "unc i", "n ama", "na ma", "nam a", "Table Cell", "iss ues", "issue s", ". {", "e curity", "ec urity", "_ exec", "_e xec", "_ex ec", "_exe c", "o lds", "ol ds", "old s", "Ġ hosts", "Ġhost s", "Ġho sts", "Ġhos ts", "Ġ proto", "Ġpro to", "Ġpr oto", "Ġprot o", "_ import", "_im port", "_imp ort", "_ sort", "_s ort", "_so rt", "Ġ Bow", "ĠB ow", "ĠBo w", "Ġ Normal", "ĠN ormal", "ĠNor mal", "ĠNorm al", "Ġ Farm", "ĠF arm", "ĠFar m", "ĠFa rm", ".create ParallelGroup", "R otation", "Rot ation", ". err", ".e rr", ".er r", "Ġp leased", "Ġplease d", "Ġple ased", "Ġplea sed", "Ġpleas ed", "it age", "ita ge", "itag e", ". Wh", ".W h", "ĉ ĉĠĠĠĠ", "ĉĉ ĠĠĠĠ", "ĉĉĠĠĠ Ġ", "ĉĉĠ ĠĠĠ", "ĉĉĠĠ ĠĠ", "M R", "Ġ MORE", "ĠM ORE", "ĠMO RE", "ĠMOR E", "Ġ Natural", "ĠN atural", "ĠNat ural", "ĠNatur al", "_ transform", "_trans form", "B ASE", "BA SE", "en eral", "ener al", "ene ral", "u tdown", "ut down", ". commons", ".com mons", ".common s", ".comm ons", "W T", "Ġ aan", "Ġa an", "Ġaa n", ". Result", ".Res ult", "d og", "do g", "Ġcl icking", "Ġclick ing", "Ġclic king", ") ,ĊĊ", "), ĊĊ", "),Ċ Ċ", "# line", "O perator", "Oper ator", "Op erator", "Opera tor", "Ġc iv", "Ġci v", "Ġm erg", "Ġme rg", "Ġmer g", "o buf", "ob uf", "ng then", "ngth en", "Ġ [{", "Ġ[ {", "Ġc ancell", "Ġcan cell", "Ġcancel l", "Ġcanc ell", "tr igger", "tri gger", ". :", "W ORK", "WO RK", "de clare", "decl are", "declar e", "Ġde crease", "Ġdecre ase", "ÅĽ ci", "l oom", "lo om", "loo m", ". None", ".N one", ".No ne", ".Non e", "Ġ MI", "ĠM I", "Ġ Jason", "ĠJ ason", "ĠJa son", "ĠJas on", "Ġhealth care", "ia mond", "iam ond", "iamo nd", "s ylvania", "* x", "Ġ Ra", "ĠR a", "[ b", "Ġ printing", "Ġprint ing", "Ġprin ting", "ph abet", "pha bet", "Ġ Labour", "ĠLa bour", "ĠLab our", "o pper", "op per", "opp er", "Ġz ijn", "Ġzi jn", "Ġzij n", "- target", "-t arget", "_ FUNCTION", "_F UNCTION", "_FUNC TION", "_FUN CTION", "Ġ oct", "Ġo ct", "Ġoc t", "е ниÑı", "ен иÑı", "ени Ñı", "åľ ¨", "Ġ western", "Ġwest ern", "Ġwes tern", "Ġcomp uters", "Ġcomput ers", "Ġcomputer s", "Ġcompute rs", "Ġ RET", "ĠR ET", "ĠRE T", "Hash Map", "[ String", "[S tring", "get Value", "_ DATE", "_D ATE", "_DAT E", "_DA TE", ". Next", ".N ext", "ĠF if", "ĠFi f", "é l", "ic ked", "ick ed", "æ İ", "- MM", "-M M", "Ġ {ĊĊĊ", "Ġ{ ĊĊĊ", "Ġ{Ċ ĊĊ", "Ġ{ĊĊ Ċ", "Ġ contacts", "Ġcont acts", "Ġcontact s", "Ġconta cts", "Ġ digits", "Ġd igits", "Ġdig its", "Ġdigit s", "P rodu", "Pro du", "Pr odu", "Prod u", "Ġun usual", "Ġunus ual", "Ġrapid ly", "t ures", "ture s", "tu res", "tur es", "Ġang ry", "c ancel", "can cel", "x xxx", "xx xx", "xxx x", "_ parser", "_p arser", "_parse r", "_par ser", "_pars er", "id ity", "idi ty", "_ PREFIX", "_P REFIX", "_PRE FIX", "_PREF IX", "7 10", "71 0", "Ġm ehr", "Ġme hr", "Ġrare ly", "Ġrar ely", "e the", "et he", "eth e", "o pes", "op es", "ope s", "Ġ %.", "Ġ% .", "w orks", "work s", "wor ks", "Ġ theta", "Ġth eta", "Ġthe ta", "Ġcon tribution", "Ġcontrib ution", "Ġ Tony", "ĠT ony", "ĠTo ny", "ĠTon y", "Ġs quad", "Ġsqu ad", "5 37", "53 7", "а й", "аР¹", "Ġî n", "t here", "th ere", "ther e", "the re", "o uted", "ou ted", "out ed", "oute d", "ĉ q", "Ļ Ĥ", "g ood", "go od", "goo d", "L I", "é¡ µ", "Ġ Living", "ĠL iving", "ĠLi ving", "ĠLiv ing", "iz abeth", "iza beth", "Ġ kt", "Ġk t", "Ġ Dallas", "ĠD allas", "ĠDal las", "] ],Ċ", "]] ,Ċ", "]], Ċ", "Ġ />ĊĊ", "Ġ/ >ĊĊ", "Ġ/>Ċ Ċ", "Ġ/> ĊĊ", "Ġ raising", "Ġr aising", "Ġrais ing", "Ġra ising", "/ router", "/r outer", "_ game", "_g ame", "3 68", "36 8", "Ġ CUR", "ĠC UR", "ĠCU R", "z ens", "ze ns", "zen s", ". es", ".e s", "Ġ fontWeight", "Ġfont Weight", "( func", "(f unc", "(fun c", "not ification", "notif ication", "Ġ' ../../../", "Ġ'../ ../../", "Ġ'../../ ../", "Ġbl ame", "Ġbla me", "ãĢĤ ĊĊĊĊ", "ãĢĤĊĊ ĊĊ", "ãĢĤĊ ĊĊĊ", "an co", "anc o", "9 80", "98 0", "Id entity", "Ident ity", "Ide ntity", "f ollow", "fol low", "Ġ arts", "Ġa rts", "Ġar ts", "Ġart s", "x s", "Ġoffic ially", "Ġofficial ly", "Ġ Studio", "ĠSt udio", "ĠStud io", "ĠStudi o", "Ġrecommend ations", "Ġrecommendation s", "Ġ locale", "Ġl ocale", "Ġlo cale", "Ġloc ale", "Ġlocal e", "Ġam ateur", "Ġamat eur", "Ġ Enable", "ĠE nable", "ĠEn able", "Ġ caps", "Ġc aps", "Ġcap s", "Ġca ps", ". End", ".E nd", ".En d", "3 88", "38 8", "- add", "-a dd", "-ad d", "_g shared", "Ġ CT", "ĠC T", "F orce", "For ce", "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "Ġ orange", "Ġo range", "Ġor ange", "Ġorang e", "Ġora nge", "Ġoran ge", "Ġ lp", "Ġl p", "Ġ answered", "Ġanswer ed", ". Grid", ".G rid", ".Gr id", "Ġd ual", "Ġdu al", "Ġdua l", "Ġstr ategic", "Ġstrateg ic", "Ġn obody", "Ġno body", "Ġnob ody", "Ġ fatal", "Ġf atal", "Ġfa tal", "Ġfat al", "_ est", "_e st", "_es t", "( el", "(e l", "Ġ ìł", "Ġì ł", "ĠB udd", "ĠBu dd", "ĠBud d", "A IT", "AI T", "_ factor", "_f actor", "_fac tor", "_fact or", "_fa ctor", "- one", "-on e", "-o ne", "Ġ HAVE", "ĠH AVE", "ĠHA VE", "\" čĊčĊ", "\"čĊ čĊ", "7 60", "76 0", "P rof", "Pro f", "Pr of", "Ġ är", "Ġä r", "str ings", "string s", "Ġ dirty", "Ġd irty", "Ġdir ty", "Ġdirt y", "Ġ Face", "ĠF ace", "ĠFac e", "ĠFa ce", "Ġ Begin", "ĠB egin", "ĠBe gin", "ĠBeg in", "Ġ Bus", "ĠB us", "ĠBu s", "Ġ wis", "Ġw is", "Ġwi s", "åŃ Ĺ", "Ġ speaker", "Ġs peaker", "Ġspe aker", "Ġspeak er", "Ġ carrier", "Ġcar rier", "Ġcarr ier", "Ġ Om", "ĠO m", "Ġhad n", "Ġha dn", "Al low", "All ow", ":: __", "::_ _", "Ġ verb", "Ġv erb", "Ġver b", "Ġve rb", "Ġ Complete", "ĠCom plete", "ĠComp lete", "ĠComple te", "Ġ Easy", "ĠE asy", "ĠEa sy", "Ġb ills", "Ġbill s", "Ġbil ls", "Ġ ĠĊĊ", "ĠĠ ĊĊ", "ĠĠĊ Ċ", "Vert ical", "Ver tical", "Ġ pron", "Ġp ron", "Ġpro n", "Ġpr on", "Ġ Define", "ĠDe fine", "ĠDef ine", "Ġ lookup", "Ġlook up", "variable s", "vari ables", "Ġp andas", "Ġpan das", "Ġpand as", "Ġpanda s", "u mes", "um es", "ume s", "Ġin noc", "Ġinn oc", "Ġ setUp", "Ġset Up", "ĠCh ampionship", "ĠChampions hip", "ĠChampion ship", "art ist", "arti st", "ĠC Type", "ĠCT ype", "F oundation", "Found ation", "๠Ī", "Ġ Setup", "ĠSet up", "4 28", "42 8", "Ġ recipes", "Ġrec ipes", "Ġrecipe s", "Ġrecip es", "Ġ UIColor", "ĠU IColor", "ĠUI Color", "Ġ Fight", "ĠF ight", "ĠFig ht", "ĠFi ght", "Ġ authorized", "Ġauthor ized", "Ġauthorize d", "_ click", "_c lick", "_cl ick", "_cli ck", "9 90", "99 0", "_ success", "_s uccess", "_succ ess", "_su ccess", "an gan", "ang an", "anga n", "Ġ Mountain", "ĠM ountain", "ĠMount ain", "Ġ Doctor", "ĠDo ctor", "ĠDoc tor", "Ġ egg", "Ġe gg", "Ġeg g", "ĠM edicine", "ĠMed icine", "ĠMedic ine", "c les", "cl es", "cle s", "` .Ċ", "`. Ċ", "[ int", "[i nt", "[in t", "d ashboard", "dash board", "Ġ Appro", "ĠApp ro", "ĠAp pro", "- dr", "-d r", "Ġprodu ces", "Ġproduce s", "Ġprod uces", "Ġr ental", "Ġren tal", "Ġrent al", "Ġ reload", "Ġre load", "Ġr eload", "Ġrel oad", "3 81", "38 1", "Ġ arrival", "Ġarr ival", "Ġarriv al", "s pot", "sp ot", "spo t", "Ġunder t", "Ġund ert", "Ġunde rt", "3 78", "37 8", "Ġequ ipped", "Ġequip ped", "Ġ proved", "Ġpro ved", "Ġpr oved", "Ġprov ed", "Ġprove d", "Ġ centers", "Ġcent ers", "Ġcenter s", "Ġcen ters", "Ġ defines", "Ġdef ines", "Ġdefine s", "Ġdefin es", "al so", "als o", "Ġ opacity", "Ġop acity", "Ġ Unfortunately", "ĠUn fortunately", "ĠIll inois", "Ġ не", "Ġн е", "ĠT emple", "ĠTem ple", "ĠTemp le", "ĠTempl e", "Ġ Trail", "ĠT rail", "ĠTr ail", "ĠTra il", "Ġ Kelly", "ĠK elly", "ĠKel ly", "Ġ measurement", "Ġme asurement", "Ġmeasure ment", "Ġmeas urement", "Ġse parated", "Ġsepar ated", "Ġseparate d", "Ġseparat ed", "- circle", "-c ircle", "H ey", "He y", "Ġ READ", "ĠRE AD", "ig its", "igit s", "igi ts", "Ġ ib", "Ġi b", "Ġ MOD", "ĠM OD", "ĠMO D", "at tery", "att ery", "atter y", "atte ry", "а з", "аР·", "Ġv end", "Ġve nd", "Ġven d", "е нÑĤ", "ен ÑĤ", "Ġ HttpClient", "ĠHttp Client", "3 59", "35 9", "s afe", "sa fe", "_ ASS", "_A SS", "_AS S", "i cit", "ic it", "ici t", "Ġ Construct", "ĠCon struct", "ĠConstr uct", "Ġ Clo", "ĠC lo", "ĠCl o", "Ġ Six", "ĠS ix", "ĠSi x", "_ TOKEN", "_T OKEN", "_TO KEN", "( block", "(b lock", "(bl ock", "Ġwar ned", "Ġwarn ed", "/* !", "! Ċ", "}/ >Ċ", "}/> Ċ", "Ġin novation", "Ġinn ovation", "Ġinnov ation", "_ \"", "Ġ );čĊčĊ", "Ġ) ;čĊčĊ", "Ġ); čĊčĊ", "Ġ);čĊ čĊ", "Ġ spots", "Ġsp ots", "Ġspot s", "Ġspo ts", "Ġcho osing", ". cs", ".c s", "Ġf lexible", "Ġflex ible", "U Int", "UI nt", "4 35", "43 5", "9 30", "93 0", "Ġ scratch", "Ġs cratch", "Ġscr atch", "- al", "-a l", "Ġf estival", "Ġfest ival", "Ġout standing", "================ ================================", "================================ ================", "M ean", "Me an", "Ġ Oregon", "ĠO regon", "ĠOre gon", "s ymbol", "sym bol", ". account", ".a ccount", ".ac count", ".acc ount", "d ney", "dn ey", "' ''", "'' '", "! \",", "!\" ,", "9 01", "90 1", "Ġ particle", "Ġp article", "Ġpart icle", "Ġpartic le", "Ġparti cle", "à ĥ", "[ MAX", "[M AX", "I VER", "IV ER", "IVE R", "ER ENCE", "NS Mutable", "ĠC olumbia", "ĠColum bia", "_ ĊĊ", "_Ċ Ċ", ". fr", ".f r", "Ġc ogn", "Ġco gn", "Ġcog n", "V R", "Ġ Methods", "ĠMethod s", "ĠMeth ods", "Ġ Made", "ĠM ade", "ĠMad e", "ĠMa de", "Ġ BR", "ĠB R", "Ġ Else", "ĠE lse", "ĠEl se", "Ġeg gs", "Ġegg s", "Ġ swing", "Ġs wing", "Ġsw ing", "Ġ Inv", "ĠI nv", "ĠIn v", "Ġdise ases", "Ġdisease s", "Ġf irms", "Ġfirm s", "Ġfi rms", "Ġfir ms", "Ġ lemma", "Ġl emma", "Ġle mma", "Ġlem ma", "} `);Ċ", "}` );Ċ", "l ings", "ling s", "lin gs", "Ġg ym", "Ġgy m", "umin um", "umi num", ". Trim", ".T rim", ".Tr im", "M em", "Me m", "Ġcrit icism", "Ġcritic ism", "ibern ate", "_ TX", "_T X", "i oni", "ion i", "io ni", "Ġguid ance", "Ġgui dance", "Ġrepeated ly", "Ġrepeat edly", "Ġ supplier", "Ġs upplier", "Ġsup plier", "Ġsuppl ier", "Ġsupp lier", "Ġp ainting", "Ġpaint ing", "Ġpain ting", "8 64", "86 4", ". Fragment", ".F ragment", "ed Exception", "Ġw iring", "Ġwir ing", "Ġwi ring", "Ġcour ts", "Ġcou rts", "Ġcourt s", "W EB", "WE B", "æľ ī", "\\ .", "ill ance", "illa nce", "Ġb rows", "Ġbr ows", "Ġbro ws", "Ġbrow s", "Ġ Pattern", "ĠP attern", "ĠPat tern", "ĠPatt ern", "PL ICATION", "PLIC ATION", "Ġ Summer", "ĠS ummer", "ĠSum mer", "Ch ain", "Cha in", "Ġc ute", "Ġcut e", "Ġcu te", "m ercial", "mer cial", "merc ial", "Ġd il", "Ġdi l", "ĠFrank lin", "ĉ global", "ĉg lobal", "IN CLUDING", "h istory", "hi story", "hist ory", "histor y", "Ġ lst", "Ġl st", "Ġls t", "Q t", "S DL", "SD L", "a lia", "al ia", "ali a", "i ere", "ie re", "ier e", "( ...", "(. ..", "(.. .", "ĉ cin", "ĉc in", "if fs", "iff s", "v elope", "ve lope", "vel ope", "velop e", "Ġ Root", "ĠR oot", "ĠRo ot", "ĠRoo t", "cl uster", "clus ter", "User Name", "i gne", "ig ne", "ign e", "< S", "Ġ fest", "Ġf est", "Ġfe st", "4 19", "41 9", "Ġindic ating", "Ġindica ting", "k eeper", "ke eper", "keep er", "kee per", "Ġc ada", "Ġca da", "Ġcad a", "é g", "con sin", "cons in", "Ġ GB", "ĠG B", "Ġ lb", "Ġl b", "e mony", "em ony", "emo ny", "emon y", "- icons", "-icon s", "-i cons", "_ doc", "_d oc", "_do c", "A ctor", "Act or", "Ac tor", "e lem", "el em", "ele m", ". Delete", ".De lete", "Ġin fection", "Ġinf ection", "Ġinfect ion", "Ġ Privacy", "ĠPriv acy", "Ġgreat ly", "Ġ Pos", "ĠP os", "ĠPo s", "ĠT reat", "ĠTr eat", "ĠTre at", "F low", "Fl ow", "Flo w", "Ġat tractive", "Ġattr active", "Ġattract ive", "Ġ Marc", "ĠM arc", "ĠMar c", "ĠMa rc", "s udo", "su do", "t esy", "te sy", "tes y", "- an", "-a n", "9 98", "99 8", "ab ama", "aba ma", "Ġ Would", "ĠW ould", "ĠWo uld", "Ġs uck", "Ġsu ck", "Ġsuc k", "index Path", "Ġ Et", "ĠE t", "T imes", "Time s", "Tim es", "Ti mes", "7 80", "78 0", "Ġ clubs", "Ġcl ubs", "Ġclub s", "_ assoc", "_as soc", "_ass oc", "Ġac quired", "Ġacqu ired", "Ġacquire d", "( \":", "(\" :", "Ġint ense", "Ġintens e", ". maps", ".m aps", ".map s", ".ma ps", "Ex pected", "Exp ected", "Expect ed", "T oggle", "Ġ ay", "Ġa y", "Ġl ifestyle", "Ġlife style", "Ġlif estyle", "- called", "-c alled", "-cal led", "-call ed", "Ġ Snow", "ĠS now", "ĠSn ow", "ĠSno w", "V olume", "Vol ume", "Ġcann abis", "Ġ Direction", "ĠD irection", "ĠDirect ion", "ĠDi rection", "ĠDir ection", "ĠDire ction", "Ġ Limited", "ĠL imited", "ĠLim ited", "ĠLimit ed", "- specific", "-s pecific", "-spec ific", "Ġd owntown", "Ġdown town", "Ġdownt own", "/ icons", "/i cons", "/icon s", "/ic ons", "Ġre ven", "Ġr even", "Ġrev en", "Ġreve n", "L eg", "Le g", "8 85", "88 5", "= null", "=n ull", "4 96", "49 6", "Key board", "' )).", "') ).", "')) .", "Ġ\" \";čĊ", "Ġ\"\" ;čĊ", "Ġ\"\"; čĊ", "Ġatt itude", ". navigate", ".n avigate", ".nav igate", "- error", "-e rror", "AM PLE", "AMP LE", "AMPL E", "Ġ Jay", "ĠJ ay", "ĠJa y", "v r", "c ow", "co w", ". compile", ".com pile", ".comp ile", "Ġmem ories", "Ġmemor ies", "Ġmemo ries", "_ mark", "_m ark", "_mar k", "_ma rk", "Ġ Minnesota", "ĠMin nesota", "Ġk osten", "Ġko sten", "Ġkos ten", "Ġkost en", "Ġ probability", "Ġprob ability", "Ġprobabil ity", "w arning", "war ning", "warn ing", "Ġgen etic", "Ġgene tic", "F ixture", "Fix ture", "Ġ HashSet", "ĠHash Set", "N ombre", "Nom bre", "_ month", "_m onth", "_mon th", "_mo nth", "Æ °", "- start", "-st art", "-star t", "xy gen", "ĉ ft", "ĉf t", "i agnostics", "Ġ Matthew", "ĠMat thew", "ĠMatth ew", "Ġcon cepts", "Ġconcept s", "Ġconce pts", "Ġcon str", "Ġconst r", "Ġcons tr", ". State", ".St ate", ".Stat e", "и н", "N ov", "No v", "Î ±", "Ġ Panel", "ĠP anel", "ĠPan el", "ĠPa nel", "ĠPane l", "ä¸ ª", "com pare", "comp are", "> ()Ċ", ">( )Ċ", ">() Ċ", "Ġapp lying", "Ġapply ing", "Ġappl ying", "Ġprom ised", "Ġpromise d", "Ġ ox", "Ġo x", "n cia", "nc ia", "Ġ Validation", "ĠValid ation", "o rts", "or ts", "ort s", "_ cur", "_c ur", "_cu r", "e lect", "el ect", "ele ct", "e ye", "ey e", "( Data", "(D ata", "Ġre porter", "Ġreport er", "Ġ Buff", "ĠB uff", "ĠBu ff", "ĠBuf f", "3 95", "39 5", "Ġ sr", "Ġs r", "Ġ \";", "Ġ\" ;", "i cky", "ic ky", "ick y", "Ġt empor", "Ġtem por", "Ġtemp or", "Ġtempo r", "S N", "Ġ resident", "Ġres ident", "Ġresid ent", "Ġreside nt", "p ires", "pi res", "pir es", "pire s", "ys ical", "ysi cal", "Ġend orse", "Ġendors e", "Ġ Song", "ĠS ong", "ĠSo ng", "ĠSon g", "is Empty", "le et", "lee t", "_ util", "_u til", "_ut il", "Ġd istingu", "Ġdist ingu", "Ġ Talk", "ĠT alk", "ĠTal k", "ĠTa lk", "Ġ Mot", "ĠM ot", "ĠMo t", "( default", "(d efault", "(de fault", "(def ault", ". Arg", ".A rg", ".Ar g", "gorith ms", "gorithm s", "_ words", "_w ords", "_word s", "im mer", "imm er", "_ reset", "_re set", "_res et", "f amily", "W W", "Ġs avings", "Ġsav ings", "Ġsaving s", "Ġ âĢĿ", "ĠâĢ Ŀ", "_ enable", "_e nable", "_en able", "s idebar", "side bar", "R unning", "Run ning", "Ġ ali", "Ġa li", "Ġal i", "Ġte stim", "Ġtest im", "Ġtes tim", "Ġ warnings", "Ġw arnings", "Ġwar nings", "Ġwarn ings", "Ġwarning s", "Ġ Chem", "ĠC hem", "ĠCh em", "ĠChe m", "Ġ Exit", "ĠE xit", "ĠEx it", "Ġf ounder", "Ġfound er", "Ġfo under", "Ġfou nder", "p ector", "pe ctor", "pect or", "pec tor", "Ġ rm", "Ġr m", "_ dataset", "_d ataset", "_data set", "_dat aset", "_datas et", "Ġ Das", "ĠD as", "ĠDa s", "Ġ han", "Ġh an", "Ġha n", "G etty", "Get ty", "Ge tty", "á l", "Ġ ny", "Ġn y", "Ġpo verty", "Ġpov erty", "Ġresult ed", ". by", ".b y", "Ġ Visit", "ĠVis it", "ĠVi sit", "Ġobt aining", "Ġobtain ing", "/ '.$", "/' .$", "/'. $", "Ġ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ Ċ", "ĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĊ", "s hall", "sh all", "shal l", "sha ll", "_ LEFT", "_LE FT", "UI Image", "_ Name", "_N ame", "h ave", "ha ve", "ĠN ob", "ĠNo b", "l r", "- footer", "-f ooter", "-foot er", "Ġn aked", "Ġna ked", "Ġnak ed", "ĠG arden", "ĠGar den", "ĠGard en", "\\F acades", "Ġ graduate", "Ġgrad uate", "Ġgradu ate", "4 17", "41 7", "Ġfr anchise", "Ġfranch ise", "p lane", "pl ane", "plan e", "pla ne", "Ġcontrib utions", "Ġcontribution s", "Ġstring With", "Ġ crypto", "Ġc rypto", "Ġcrypt o", "Ġcry pto", "Ġmov ements", "Ġmove ments", "Ġmo vements", "Ġmovement s", "a thers", "ath ers", "ather s", "athe rs", "Ġ lifetime", "Ġl ifetime", "Ġlife time", "Ġlif etime", "Ġcommunic ate", "Ġcommun icate", "j ar", "ja r", "Ġ Fragment", "ĠF ragment", "ĠFr agment", "ĠFra gment", "ĠFrag ment", "_ IF", "_I F", "ĠN avy", "ĠNav y", "ĠNa vy", "Ġ Figure", "ĠF igure", "ĠFig ure", "Ġ simulation", "Ġs imulation", "Ġsim ulation", "Ġsimul ation", "_ stop", "_s top", "_st op", "Ġreport ers", "Ġreporter s", "Ġver sus", "Ġvers us", "a ja", "aj a", "Ġ α", "ĠÎ ±", "Ġgover nor", "Ġgovern or", "Ġgoverno r", "List Item", "Ġ sealed", "Ġse aled", "Ġsea led", "Ġseal ed", ". Background", ".Back ground", "e di", "ed i", "ash ing", "ashi ng", "Ġ lip", "Ġl ip", "Ġli p", "ĠI h", "m erge", "mer ge", "Ġn ec", "Ġne c", "0 24", "02 4", "el ocity", "elo city", "AT EG", "ATE G", "Ġse eds", "Ġsee ds", "Ġseed s", "Ġ floating", "Ġf loating", "Ġfloat ing", "Ġflo ating", "7 01", "70 1", "_ FA", "_F A", "w alk", "wa lk", "wal k", "ĉ user", "ĉu ser", "ĉuse r", "ĉus er", "_ depth", "_de pth", "_dep th", "_dept h", "Ġw age", "Ġwa ge", "Ġwag e", "@ app", "@a pp", "N il", "Ni l", "( [\"", "([ \"", "( vector", "(v ector", "(vec tor", "Ġsecret ary", "4 61", "46 1", "Ġj Panel", "v ez", "ve z", "Âł ³³³", "³³ ³³", "³³³ Âł", "d irection", "dir ection", "di rection", "direct ion", "dire ction", "Ġ EP", "ĠE P", "Ġ hunt", "Ġh unt", "Ġhun t", "Ġhu nt", "3 96", "39 6", "Json Property", "Ġ PORT", "ĠP ORT", "ĠPO RT", "ĠPOR T", "] \",", "]\" ,", "а п", "аР¿", "Ġ Foreign", "ĠFore ign", "p anic", "pan ic", "pa nic", "Ġtr ials", "Ġtri als", "Ġtrial s", "Ġ Ale", "ĠA le", "ĠAl e", "Ġr ural", "Ġru ral", "- value", "-v alue", "-val ue", "-valu e", "author ized", "authorize d", "Ġ Scotland", "ĠSc otland", "ĠScot land", ". drop", ".d rop", ".dr op", "Ġ MT", "ĠM T", "ç ±", "3 91", "39 1", "row th", "5 15", "51 5", "File Path", "Ġ recall", "Ġre call", "Ġrec all", "Ġrecal l", "if le", "Ġ cel", "Ġc el", "Ġce l", "Ġ SELECT", "ĠSE LECT", "ĠSEL ECT", "k n", "_ case", "_c ase", "_ca se", "Ġ crop", "Ġc rop", "Ġcr op", "Ġcro p", "5 43", "54 3", "s ure", "sur e", "su re", "p ot", "po t", "I CS", "IC S", "Ġ stem", "Ġs tem", "Ġst em", "Ġste m", "Ġindust ries", "Ġindustri es", "P ut", "Pu t", "Ġ aber", "Ġa ber", "Ġab er", "road cast", "I cons", "Icon s", ") \")Ċ", ")\" )Ċ", ")\") Ċ", "æĪIJ åĬŁ", "g ui", "gu i", "Ġass umed", "Ġassum ed", "Ġassume d", "Ġ rx", "Ġr x", "E A", "è §", "E LL", "EL L", "Ġd ose", "Ġdo se", "Ġdos e", "Ġ ine", "Ġin e", "Ġi ne", "Ġd eeper", "Ġde eper", "Ġdeep er", "Ġdee per", "l ider", "li der", "lide r", "lid er", "Ġ ordinary", "Ġord inary", "Ġordin ary", "Ġg olf", "Ġgo lf", "Ġgol f", "6 05", "60 5", "_ IMAGE", "_IM AGE", "Ġ NAME", "ĠN AME", "ĠNA ME", "( module", "(m odule", "(mod ule", "Ġ atom", "Ġa tom", "Ġat om", "Ġ belt", "Ġb elt", "Ġbe lt", "Ġbel t", "Ġoff ices", "Ġoffic es", "Ġoffice s", "5 06", "50 6", "b eta", "be ta", "bet a", "Ġphilosoph y", "( JSON", "(J SON", "(JS ON", "- field", "-f ield", "-fi eld", "Ġint roduce", "Ġintrodu ce", "Ġintro duce", "Ġcon venience", "Ġconven ience", "op tim", "opt im", "> \"Ċ", ">\" Ċ", "a thy", "at hy", "ath y", "Ġ employer", "Ġemploy er", "q uate", "qu ate", "qua te", "quat e", "Ġ edited", "Ġed ited", "Ġedit ed", "Ġedi ted", "Arg uments", "Argument s", "ĠN ations", "ĠNation s", "ĠNat ions", "_ _)", "__ )", "Ġn ose", "Ġno se", "Ġnos e", "Ġ Sample", "ĠS ample", "ĠSam ple", "ĠSamp le", "' )ĊĊĊ", "') ĊĊĊ", "')Ċ ĊĊ", "')ĊĊ Ċ", "Ġ cake", "Ġc ake", "Ġca ke", ". getAttribute", ".get Attribute", "H D", "3 92", "39 2", "Mod ified", "4 45", "44 5", "Ġ predicted", "Ġpred icted", "Ġpredict ed", "Ġpredic ted", "Å Ħ", "a nie", "an ie", "ani e", "S orry", "( doc", "(d oc", "(do c", "w ind", "win d", "wi nd", "i eve", "ie ve", "iev e", "Ġpro visions", "Ġprov isions", "Ġprovision s", "A TER", "AT ER", "ATE R", "O TE", "OT E", "M Y", ". Autowired", ".A utowired", "ĠB ath", "ĠBa th", "ĠBat h", "4 23", "42 3", ". Boolean", ".Bool ean", "Ġ backend", "Ġback end", ". Mouse", ".M ouse", "at eral", "ate ral", "ater al", "p aper", "pa per", "Con st", "Co nst", "Cons t", "Ġ VR", "ĠV R", "_ entity", "_e ntity", "_ent ity", "_ CTRL", "_C TRL", "_CT RL", "Ġ Protection", "ĠPro tection", "ĠProt ection", "ĠProte ction", "ĠProtect ion", "Ġ GM", "ĠG M", "Ġ Study", "ĠSt udy", "ĠStud y", "Ġ soup", "Ġs oup", "Ġso up", "Ġsou p", "o time", "ot ime", "oti me", "' use", "'u se", "] \"", "/ users", "/user s", "/use rs", "/us ers", "a ug", "au g", "Ġ Hong", "ĠH ong", "ĠHon g", "ĠHo ng", "_ norm", "_n orm", "_no rm", "ãģ ¨", "Ġse cre", "Ġsec re", "( Build", "(B uild", "Ġ Contract", "ĠCon tract", "ĠCont ract", "ĠContr act", "o las", "ol as", "ola s", "Ġs auce", "Ġsa uce", "Ġsau ce", "Ġag gressive", "Ġaggress ive", "Ġagg ressive", "Ġ racial", "Ġr acial", "Ġrac ial", "Ġra cial", "char acter", "@ @", "Ġ compile", "Ġcom pile", "Ġcomp ile", "Ġcompil e", "Ġ Void", "ĠV oid", "ĠVo id", "_ rem", "_re m", "_r em", "_ memory", "_m emory", "_mem ory", "3 48", "34 8", "k k", "Ġ mic", "Ġm ic", "Ġmi c", "S ame", "Sam e", "Sa me", "U tility", "Util ity", "Ut ility", "Ġ Html", "ĠH tml", "Ġ Xml", "ĠX ml", "ĠXm l", "Re ady", "Read y", "Ġg all", "Ġga ll", "Ġgal l", "Ġalleg edly", "Ġalleged ly", "ĉ ĉĉĉĠĠĠ", "ĉĉ ĉĉĠĠĠ", "ĉĉĉĉ ĠĠĠ", "ĉĉĉ ĉĠĠĠ", "ĉĉĉĉĠ ĠĠ", "ĉĉĉĉĠĠ Ġ", "Ġ Metal", "ĠM etal", "ĠMe tal", "ĠMet al", "ĠMeta l", "Ġ Personal", "ĠPerson al", "ĠPers onal", "ĠPersona l", "Ġborder Radius", "rx js", "object s", "obj ects", "Ġwant ing", "Ġwan ting", "Ġb owl", "Ġbo wl", "Ġbow l", "v endor", "offset of", "Ġ Rs", "ĠR s", "Ġ Rating", "ĠR ating", "ĠRa ting", "ĠRat ing", "Ġr ally", "Ġrall y", "_ NODE", "_N ODE", "_NO DE", "4 18", "41 8", "Ġ Mix", "ĠM ix", "ĠMi x", "Ġad vertis", "Ġadvert is", "4 85", "48 5", "6 67", "66 7", "Ġnarr ative", "s al", "sa l", "Ġ mc", "Ġm c", "S Error", "SE rror", "Ġf ingers", "Ġfin gers", "Ġfinger s", "Ġfing ers", "Ġac company", "Ġaccom pany", "Ġaccomp any", "Ġt ired", "Ġti red", "Ġtire d", "Ġtir ed", "Ġ stride", "Ġst ride", "Ġstr ide", "Ġstri de", "Ġ gui", "Ġg ui", "Ġgu i", "e list", "el ist", "eli st", "L ocale", "Lo cale", "Local e", "Loc ale", "Ġre leases", "Ġrelease s", "Ġrele ases", "i king", "ik ing", "iki ng", "Ġ anger", "Ġa nger", "Ġan ger", "Ġang er", "Ġange r", ") ))ĊĊ", ")) )ĊĊ", ")))Ċ Ċ", "))) ĊĊ", "al lest", "all est", "alle st", "alles t", "Sum mary", "( O", "( for", "(f or", "Ġbasket ball", "Ġ roads", "Ġro ads", "Ġroad s", "Ġ Install", "ĠInst all", "Ġ Fab", "ĠF ab", "ĠFa b", "it map", "itm ap", "4 75", "47 5", "Ġ ))Ċ", "Ġ) )Ċ", "Ġ)) Ċ", "Ġ intersection", "Ġinter section", "Ġintersect ion", "Ġinters ection", "igh bor", "ighb or", "ĠB ry", "ĠBr y", "Ġ HERE", "ĠH ERE", "ĠHE RE", "ĠHER E", "S oftware", "So ftware", "Soft ware", "el fare", "elf are", "a cs", "ac s", "6 22", "62 2", "Ġtr ailer", "Ġtrail er", "Ġtra iler", "Ġtrai ler", ". getClass", ".get Class", ".getC lass", "ch ars", "char s", "cha rs", "Ġreg ulation", "Ġregul ation", "Ġre fers", "Ġref ers", "Ġrefer s", "Ġd estruction", "Ġde struction", "Ġdestruct ion", "Ġ continuous", "Ġcontin uous", "Ġcontinu ous", "Ġ Austin", "ĠA ustin", "ĠAust in", "ĠAus tin", "ĠAu stin", "é ¢", "a kan", "ak an", "aka n", ". window", ".w indow", ".wind ow", "Ġ Templates", "ĠT emplates", "ĠTem plates", "ĠTemplate s", "ĠTemp lates", "ĠTempl ates", "Ġabs ence", ": n", "Ġdis order", "f lash", "fl ash", "Ġde let", "Ġdel et", "Ġdele t", "bo ards", "board s", "Ġ Ġĉ", "ĠĠ ĉ", "R OP", "RO P", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġac qu", "Ġlaw suit", "Ġlaws uit", "Ġ Reviews", "ĠRe views", "ĠReview s", "Ġgar age", "Ġga rage", "t imer", "time r", "ti mer", "tim er", "Ġ ej", "Ġe j", "Ġ Rectangle", "ĠRect angle", "Ġ flowers", "Ġfl owers", "Ġflow ers", "Ġflo wers", "Ġflower s", "3 98", "39 8", "i lst", "il st", "ils t", "Ġ Instance", "ĠIn stance", "ĠInst ance", "S uper", "Sup er", "Su per", "d et", "de t", "dis posing", "disp osing", "Ġ ES", "ĠE S", "Ġ IC", "ĠI C", "v ere", "ver e", "ve re", "S k", "_ channels", "_ch annels", "_channel s", "_chan nels", "p uted", "put ed", "pu ted", "pute d", "/ null", "/n ull", "n nen", "nn en", "4 31", "43 1", "Ġ Gallery", "ĠG allery", "ĠGall ery", "_ global", "_g lobal", "_glob al", "Auth entication", "Ġ Rank", "ĠR ank", "ĠRa nk", "ĠRan k", "Ġ blocked", "Ġb locked", "Ġbl ocked", "Ġblock ed", "Ġbloc ked", "Ġc alm", "Ġcal m", "Ġca lm", "m arket", "mark et", "mar ket", "ĉ val", "ĉv al", "ĉva l", "Ġ aug", "Ġa ug", "Ġau g", "p eriod", "per iod", "peri od", "Ġ Constant", "ĠCon stant", "ĠCons tant", "ĠConst ant", "Ġ ?>\">Ċ", "Ġ?> \">Ċ", "Ġ?>\" >Ċ", "Ġ?>\"> Ċ", "Ġ lobby", "Ġl obby", "Ġlob by", "p al", "pa l", "3 79", "37 9", "Ġ sink", "Ġs ink", "Ġsi nk", "Ġsin k", "5 08", "50 8", "i ah", "ia h", "Ð ¡", "ur name", "urn ame", "Ġcon ver", "Ġconv er", "Ġinvest igate", "Ġinvestig ate", "Ch rist", "Chris t", "Chr ist", "H ub", "Hu b", "Ġ IND", "ĠI ND", "ĠIN D", "Ġ Ped", "ĠP ed", "ĠPe d", "u ras", "ur as", "ura s", "ĉ url", "ĉu rl", "Ġ Tro", "ĠT ro", "ĠTr o", "Ġ preferences", "Ġp references", "Ġpre ferences", "Ġprefer ences", "Ġpreference s", "Ġguar anteed", "Ġguarante ed", "Ġguarantee d", "` ĊĊ", "`Ċ Ċ", "Ġport ions", "Ġportion s", "Ġe valu", "Ġev alu", "Ġeval u", "' > < /", "( ){ĊĊ", "() {ĊĊ", "(){Ċ Ċ", "(){ ĊĊ", "en coded", "enc oded", "encode d", "enco ded", "z illa", "zi lla", ". Class", ".C lass", ".Cl ass", "Ġ *_", "Ġ* _", "_ '", "Ġview ed", "Ġvi ewed", "Ġvie wed", "Ġ Philadelphia", "ĠPhil adelphia", ". rows", ".r ows", ".row s", ".ro ws", "Add ed", "Ad ded", "Ġ Touch", "ĠT ouch", "ĠTo uch", "ĠTou ch", "8 40", "84 0", ". delegate", ".de legate", "quee ze", "s lide", "sl ide", "Ġ Senior", "ĠSen ior", "( tag", "(t ag", "Ġinter views", "Ġinterview s", "Ġs ua", "Ġsu a", "a tas", "at as", "ata s", "@ ĊĊ", "@Ċ Ċ", "d istance", "di stance", "dist ance", "Ġ sein", "Ġs ein", "Ġse in", "Ġsei n", "l atest", "la test", "late st", "lat est", "lates t", "Ġ Prince", "ĠPr ince", "ĠPri nce", "Ġlux ury", "Ġre fr", "Ġref r", "Ġ Kitchen", "ĠK itchen", "ĠKit chen", "Ñ Ħ", "( at", "(a t", "F inal", "Fin al", "Fi nal", "ü ck", "üc k", "_ zero", "_z ero", "Ġ ABC", "ĠA BC", "ĠAB C", "Ġ Manchester", "ĠMan chester", "Ġ cow", "Ġc ow", "Ġco w", "C OL", "CO L", "_ NUMBER", "_NUM BER", "ch anges", "change s", "chan ges", "chang es", "g enerate", "gen erate", "gener ate", "gene rate", ". Printf", ".Print f", "3 69", "36 9", "s hare", "sh are", "sha re", "St ock", "Ġ PT", "ĠP T", "A nim", "An im", "a nga", "an ga", "ang a", "Ġ ig", "Ġi g", "up loads", "upload s", "Ġ packed", "Ġp acked", "Ġpack ed", "Ġpac ked", "Ġ }];Ċ", "Ġ} ];Ċ", "Ġ}] ;Ċ", "( sender", "(s ender", "(se nder", "(send er", "Ġ Wire", "ĠW ire", "ĠWi re", "ĠWir e", "i sons", "is ons", "ison s", "iso ns", "Ġplay off", "\\ E", "6 08", "60 8", "/ R", "Ġ headed", "Ġhe aded", "Ġhead ed", "Al pha", "( order", "(ord er", "(or der", "Ġop ponents", "Ġopp onents", "Ġoppon ents", "Ġopponent s", "ack son", "acks on", "_ member", "_m ember", "_mem ber", "T urn", "Tur n", "Tu rn", "ĠSov iet", "ìĹ IJ", "a uge", "au ge", "aug e", "4 48", "44 8", "Ġ incoming", "Ġin coming", "Ġinc oming", "Ġincom ing", "Ġ jak", "Ġj ak", "Ġja k", "- game", "-g ame", "Ġ Male", "ĠM ale", "ĠMal e", "ĠMa le", "Ġ Month", "ĠM onth", "ĠMon th", "ĠMo nth", "ĠMont h", "St age", ". exe", ".e xe", ".ex e", "Own Property", ". setItem", ".set Item", "Ġ dc", "Ġd c", "ä½ ľ", "Ġb rut", "Ġbr ut", "Ġbru t", "Ġattempt ing", ". len", ".l en", ".le n", "Ġjud gment", "Ġs ab", "Ġsa b", "Ġ cad", "Ġc ad", "Ġca d", "Ġ Items", "ĠI tems", "ĠIt ems", "ĠItem s", "com fort", "el ize", "eli ze", "/ log", "/l og", "/lo g", "Ġentre prene", "Ġ compiler", "Ġc ompiler", "Ġcom piler", "Ġcomp iler", "Ġcompile r", "Ġcompil er", "_ validation", "_valid ation", "r eview", "re view", "rev iew", "Ġ textBox", "Ġtext Box", "Ġ fraction", "Ġf raction", "Ġfr action", "Ġfra ction", "Ġfract ion", "Ġfrac tion", "Ġ Bal", "ĠB al", "ĠBa l", "> ;ĊĊ", ">;Ċ Ċ", ">; ĊĊ", ".AutoScale Mode", "Ġ cats", "Ġc ats", "Ġca ts", "Ġcat s", "4 65", "46 5", "Ġ registry", "Ġreg istry", "Ġregistr y", "Ġregist ry", "u lus", "ul us", "ulu s", "F I", "p ayload", "pay load", "- search", "-s earch", "-se arch", "Ġst aying", "Ġstay ing", "Ġsta ying", "ac ious", "aci ous", "acio us", "De coration", "Dec oration", "Decor ation", "R eview", "Re view", "Rev iew", "I nf", "In f", "Ke ep", "it is", "iti s", ", String", ",S tring", "C oord", "Co ord", "Ġp ero", "Ġper o", "Ġpe ro", "S ex", "Se x", "Ġ Atlanta", "ĠAtl anta", "u esta", "ue sta", "ues ta", "uest a", "A rgb", "Arg b", "Ar gb", "> *", "} _", "F ooter", "Foo ter", "Foot er", "Fo oter", "Ġ employed", "Ġemploy ed", "_ bound", "_b ound", "_bo und", "v ide", "vid e", "vi de", ". func", ".f unc", ".fun c", "$ scope", "$s cope", "Ġ spo", "Ġs po", "Ġsp o", "Ġ Anal", "ĠA nal", "ĠAn al", "ĠAna l", "oun ced", "ounc ed", "ounce d", "a round", "ar ound", "aro und", "Ġ restriction", "Ġre striction", "Ġrestrict ion", "Ġrestr iction", "Ġ shops", "Ġsh ops", "Ġshop s", "Ġsho ps", "å Ģ", "Ġ Latin", "ĠL atin", "ĠLa tin", "ĠLat in", "- col", "-c ol", "-co l", "Ġbar ely", "Ġbare ly", "Ġ Euro", "ĠE uro", "ĠEu ro", "ĠEur o", "E r", "Ġf aire", "Ġfa ire", "Ġfair e", "_ distance", "_d istance", "_dist ance", "_di stance", "_ unlock", "_un lock", "Qu ote", "IV ATE", "IVA TE", "Ġ åĪ", "Ġå Ī", "Ġa imed", "Ġaim ed", "Ġai med", "Ġaime d", "ĠRe trie", "ĠRet rie", ". iter", ".i ter", ".it er", "Ġ wrapped", "Ġw rapped", "Ġwr apped", "Ġwrap ped", "Ġag reements", "Ġagre ements", "Ġagree ments", "Ġagreement s", "str ument", "stru ment", "( product", "(pro duct", "(prod uct", "Ġstud ied", "Ġstudi ed", ". setValue", ".set Value", "Ġ ye", "Ġy e", "Ġ Cache", "ĠC ache", "ĠCa che", "MB OL", "Ġquarter back", "Ġ syntax", "Ġs yntax", "Ġsy ntax", "Ġsyn tax", "Ġsynt ax", ".get ElementsBy", ".getElements By", ". version", ".v ersion", "we bsite", "web site", "webs ite", "R unner", "Run ner", "_ single", "_s ingle", "_si ngle", "_sin gle", "a tiv", "at iv", "ati v", "Ġ Altern", "ĠAl tern", "ĠAlt ern", "ĠAlter n", "Ġ Beautiful", "ĠBe autiful", "ĠBeaut iful", "right arrow", "Ġd iversity", "Ġdivers ity", "p lash", "pl ash", "pla sh", "( co", "(c o", ". Fill", ".F ill", "Ġ typing", "Ġtyp ing", "Ġty ping", "3 87", "38 7", "0 23", "02 3", "Ġ clar", "Ġc lar", "Ġcl ar", "Ġcla r", "H it", "Hi t", "O O", "a cco", "ac co", "acc o", "5 07", "50 7", "w orth", "wort h", "wor th", "Ġ scripts", "Ġs cripts", "Ġscript s", "Ġscri pts", "ĠMuslim s", "Ġ LL", "ĠL L", "er ving", "erv ing", "( boolean", "(bool ean", "Ġbase ball", "Ġ CAN", "ĠC AN", "ĠCA N", "3 94", "39 4", "0 44", "04 4", "M AIL", "MA IL", "d epend", "de pend", "dep end", "Ġres pective", "Ġrespect ive", "Ġresp ective", "Ġ constexpr", "Ġconst expr", ".* ;ĊĊ", ".*;Ċ Ċ", "' ]))Ċ", "'] ))Ċ", "']) )Ċ", "'])) Ċ", "Ġ yard", "Ġy ard", "Ġya rd", "Ġyar d", "Ġident ical", "if ecycle", "ife cycle", "U SH", "US H", "up iter", ". validate", ".valid ate", "c li", "cl i", "I STER", "IS TER", "IST ER", "Ind icator", "F ail", "Fa il", "Ġdem ocracy", "Ġdemocr acy", ". var", ".v ar", ".va r", "Ġs atisfied", "Ġsatisf ied", "- ------------", "-- -----------", "---- ---------", "-------- -----", "--- ----------", "------------ -", "----- --------", "---------- ---", "------ -------", "----------- --", "------- ------", "--------- ----", "en cer", "ence r", "enc er", "h or", "ho r", "Ġr ounds", "Ġro unds", "Ġround s", "Ġrou nds", "D AO", "DA O", "o a", "Ġfl ask", "Ġfla sk", "= c", "[ ]Ċ", "[] Ċ", "/ dist", "/d ist", "/dis t", "Ġp arte", "Ġpart e", "Ġpar te", "Ġ confirmation", "Ġconfirm ation", "e ron", "er on", "ero n", "a ware", "aw are", "awa re", "< ?>", "", "Ġ dependencies", "Ġdep endencies", "Ġdepend encies", "Ġ Videos", "ĠV ideos", "ĠVideo s", "ĠVid eos", "ĠVide os", "- row", "-r ow", "-ro w", "Ġ **/Ċ", "Ġ* */Ċ", "Ġ** /Ċ", "Ġ nou", "Ġn ou", "Ġno u", "Ġ hover", "Ġh over", "Ġho ver", "æ ŀ", "Ġ nin", "Ġn in", "Ġni n", "Ġ USD", "ĠU SD", "ĠUS D", "M ac", "Ma c", "_ Load", "_L oad", "Ġout comes", "Ġoutcome s", "_ socket", "_s ocket", "_sock et", "_so cket", "_soc ket", "Ġ queries", "Ġqu eries", "Ġque ries", "Ġquer ies", "w m", "5 92", "59 2", "Ġh itting", "Ġhit ting", "in ux", "inu x", "M ich", "Mi ch", "Mic h", "u dge", "ud ge", "A TAB", "AT AB", "ATA B", "Ġv ulnerable", "Ġvulner able", "ä ¾", "Ġ portfolio", "Ġport folio", ": YES", "ĉ map", "ĉm ap", "B ound", "Bo und", "Ġ iteration", "Ġit eration", "Ġiter ation", "in cess", "ince ss", "inc ess", "inces s", "Ġ actors", "Ġa ctors", "Ġact ors", "Ġac tors", "Ġactor s", "Ġ Qual", "ĠQ ual", "ĠQu al", "_ clean", "_c lean", "_cl ean", "ãĢij ãĢIJ", "M SG", "MS G", "G reen", "Gr een", "Gre en", "ĠOff icer", "ĠOffice r", "Ġsm oking", "Ġsmo king", "> ',", ">' ,", "Ġ Flo", "ĠF lo", "ĠFl o", "++ ;", "4 33", "43 3", "oly gon", "Ġ bulk", "Ġb ulk", "Ġbu lk", "Ġbul k", "Ġd rama", "Ġdr ama", "Ġdram a", "Ġdra ma", "Ġ exceptions", "Ġex ceptions", "Ġexcept ions", "Ġexception s", "Ġexce ptions", "o sed", "os ed", "ose d", "Ġ+ čĊ", "Ġ legacy", "Ġleg acy", "C V", "Ġcontrib uted", "Ġcontribute d", "Ġ Terms", "ĠTe rms", "ĠTer ms", "ĠTerm s", "Ġ bt", "Ġb t", "4 34", "43 4", "Ġun tuk", "Ġunt uk", "Ġ alien", "Ġa lien", "Ġal ien", "Ġali en", "= ==Ċ", "== =Ċ", "=== Ċ", "ĉ Vector", "ĉV ector", "ĉVec tor", "Ġ ls", "Ġl s", "On line", ". facebook", ".f acebook", ".face book", "n umeric", "num eric", "nu meric", "numer ic", "ock ets", "ocket s", "A ut", "Au t", "b ury", "bur y", "bu ry", "- redux", "-re dux", "-red ux", "ĠRed istributions", "ĠRedistribution s", "GLOBAL S", "urrenc ies", "urr encies", "Ġ tons", "Ġt ons", "Ġto ns", "Ġton s", "âĢĻ ,", "Ġ ê", "Ġà ª", "( col", "(c ol", "(co l", "Ġ Symbol", "ĠS ymbol", "ĠSym bol", "Ġst ayed", "Ġstay ed", "Ġ ML", "ĠM L", "Ġm unicip", "Ġmun icip", "Ġ sexo", "Ġs exo", "Ġse xo", "Ġsex o", "S en", "Se n", "n r", "Ġg ains", "Ġgain s", "Ġga ins", "Ġshort ly", ". Menu", ".M enu", ".Me nu", "à ½", "KN OWN", "Ġ operators", "Ġoper ators", "Ġoperator s", "Ġopera tors", "- V", "Ġ Patrick", "ĠPat rick", "ĠPatri ck", "/ add", "/a dd", "/ad d", "_ CO", "_C O", "i ration", "ir ation", "ira tion", "( post", "(p ost", "(pos t", "(po st", "Post s", "Pos ts", "Po sts", "/ _", "Ġ plug", "Ġp lug", "Ġpl ug", "Ġplu g", "Ġintel lectual", "Ġintellect ual", "Ġme tab", "Ġmet ab", "Ġmeta b", "Ġpregn ancy", "ĠPrem ier", "ĠPremi er", "n m", "Ġ prediction", "Ġpred iction", "Ġpredict ion", "Ġpredic tion", "6 06", "60 6", "ĠMin istry", "ĠMini stry", "ĠMinist ry", "Th ree", "Thr ee", "val uate", "valu ate", "Ġ Mini", "ĠM ini", "ĠMin i", "ĠMi ni", "b u", "о з", "оР·", "< ul", " \";čĊ", ">\" ;čĊ", ">\"; čĊ", "ĠS av", "ĠSa v", ". Bold", ".B old", "Ġen ables", "Ġenable s", "ĉ tmp", "ĉt mp", "Ġman ually", "Ġmanual ly", "ĠS qu", "ĠSq u", "use rid", "user id", ". function", ".f unction", ".func tion", ".fun ction", ". cache", ".c ache", ".ca che", "L OPT", "LO PT", ". Services", ".S ervices", ".Service s", "5 88", "58 8", "d dit", "dd it", "t im", "ti m", "< img", " >>", ">> >", "st ation", "stat ion", "sta tion", "l ore", "lo re", "lor e", "a type", "at ype", "aty pe", "i shop", "is hop", "ish op", "/ ****************************************************************", "/******************************** ********************************", "/************************ ****************************************", "/******************************************************** ********", "/******** ********************************************************", "/**************** ************************************************", "/**************************************** ************************", "/************************************************ ****************", "5 21", "52 1", "Com boBox", "Combo Box", "Ġvac ation", "Ġva cation", "Ġinit iative", "Ġiniti ative", "Ġ defaultValue", "Ġdefault Value", "7 70", "77 0", "con cat", "conc at", "Ġ Kh", "ĠK h", "6 32", "63 2", "Ġ Welcome", "ĠW elcome", "ĠWel come", "ized Name", "M igration", "Ġ gradient", "Ġg radient", "Ġgrad ient", "H ot", "Ho t", "Ġhard ly", "e lo", "el o", "Ġ Students", "ĠSt udents", "ĠStud ents", "ĠStudent s", "Ġl oose", "Ġlo ose", "Ġloos e", "7 30", "73 0", "a tz", "at z", ". Send", ".S end", ".Se nd", "' /", "Ġ universal", "Ġun iversal", "Ġunivers al", "Ġ enterprise", "Ġenter prise", "Ġ regex", "Ġreg ex", "Ġ visitor", "Ġvis itor", "Ġvisit or", "Ġ Fly", "ĠF ly", "ĠFl y", "S eq", "Se q", "ภĻ", "Ġ Visual", "ĠVis ual", "Ġ libraries", "Ġl ibraries", "Ġlib raries", "Ġlibr aries", "at oes", "ato es", "P ayment", "Pay ment", "4 47", "44 7", "Ġ pent", "Ġp ent", "Ġpe nt", "Ġpen t", "Ġgather ed", "VR TX", "VRT X", "Ġ DM", "ĠD M", "S plit", "Sp lit", "Spl it", "Ġl etting", "Ġlet ting", "Ġlett ing", "Ð Ŀ", "_ errors", "_error s", "_err ors", "_er rors", "ep och", "P ARAM", "PA RAM", "PAR AM", "c u", "ÑģÑĤ в", "ol utions", "olution s", "olut ions", "Ed iting", "Edit ing", "fo nts", "font s", "fon ts", "Ġ allocated", "Ġal located", "Ġalloc ated", "Ġallocate d", "Ġ Based", "ĠB ased", "ĠBase d", "ĠBa sed", "ĠBas ed", "( Y", "Ġ Judge", "ĠJ udge", "ĠJud ge", "ĠJu dge", "Ġbr others", "Ġbro thers", "Ġbrother s", "Ġbroth ers", "F ILES", "FILE S", "FI LES", "ç o", "5 31", "53 1", "w b", "_ PI", "_P I", "' ^", "Ġ sword", "Ġs word", "Ġsw ord", "Ġswo rd", ". services", ".s ervices", ".service s", ".serv ices", "Ġ nl", "Ġn l", "T im", "Ti m", "i gg", "ig g", "ĠMo ore", "ĠMoo re", "ĠMoor e", "Ġcrypt oc", "Ġcrypto c", "åĩ º", "_ posts", "_post s", "_pos ts", "_po sts", "ot ate", "ota te", "? '", ". ...ĊĊ", ".. ..ĊĊ", "... .ĊĊ", ".... ĊĊ", "....Ċ Ċ", "Ġ kl", "Ġk l", "= \"$", "=\" $", "Ġde coration", "Ġdec oration", "Ġdecor ation", "Ġdeco ration", "Ạ¡", "Ġ DIRECT", "ĠD IRECT", "ĠDI RECT", "ĠDIR ECT", "G UI", "GU I", ") =>{Ċ", ")= >{Ċ", ")=> {Ċ", "Ġ newsletter", "Ġnews letter", "Ġpre cis", "Ġprec is", "( point", "(p oint", "(po int", "Ġ Equipment", "ĠE quipment", "ĠEqu ipment", "ĠEquip ment", "u ty", "ut y", "Ġ Dave", "ĠD ave", "ĠDav e", "ĠDa ve", "Ġpart icipation", "Ġpartic ipation", "Ġparticip ation", "u arios", "ua rios", "uario s", "uar ios", "x it", "xi t", ". As", ".A s", "E TER", "ET ER", "o rous", "or ous", "oro us", "Ġ shield", "Ġsh ield", "[ ]>", "[] >", "il itary", "ilit ary", ". origin", ".or igin", ".orig in", "Ġ promotion", "Ġpro motion", "Ġprom otion", "Ġpromot ion", "Ġpromo tion", "U nt", "Un t", "Ġ ct", "Ġc t", "T RA", "TR A", "5 56", "55 6", "View Holder", "Ġ sigma", "Ġs igma", "Ġsig ma", "d elta", "del ta", "are house", "con tract", "cont ract", "contr act", "contra ct", "( Vector", "(V ector", "(Vec tor", "7 21", "72 1", "Ġcomp ete", "Ġcompet e", "/ form", "/f orm", "/ components", "/com ponents", "/component s", "Ġ nr", "Ġn r", "ĠInd ones", "ĠIndo nes", "Ġ оÑĤ", "Ġо ÑĤ", "Ġ Volume", "ĠV olume", "ĠVol ume", ". files", ".f iles", ".file s", ".fi les", ".fil es", "( resp", "(r esp", "(res p", "(re sp", "/ models", "/model s", "/mod els", "Ġ surf", "Ġs urf", "Ġsu rf", "Ġsur f", "st andard", "stand ard", "/ o", "ĠXCT Assert", "V ICES", "VICE S", "VI CES", "VIC ES", ". Code", ".C ode", ".Co de", "S ED", "SE D", "Ġ activate", "Ġact ivate", "Ġactiv ate", "D elta", "Del ta", "Ġlimit ation", "Ġlim itation", "r ij", "ri j", "Ġpregn ant", "Ġpreg nant", ": ^(", ":^ (", "Ġs our", "Ġso ur", "Ġsou r", "p ie", "pi e", "8 03", "80 3", "Ġ expense", "Ġexp ense", "i cation", "ic ation", "ica tion", "Ġ Large", "ĠL arge", "ĠLar ge", "Ġ ±", "Ġ ±", "ĠB owl", "ĠBo wl", "ĠBow l", "( models", "(model s", "(mod els", "(mode ls", "/ N", "8 57", "85 7", "P a", ". reload", ".re load", ".r eload", ".rel oad", "Ġwonder ing", "4 62", "46 2", "Exec ution", "ĉ ĠĠĠĠĠĠ", "ĉĠĠĠ ĠĠĠ", "ĉĠ ĠĠĠĠĠ", "ĉĠĠ ĠĠĠĠ", "ĉĠĠĠĠĠ Ġ", "ĉĠĠĠĠ ĠĠ", "Ġ Graphics", "ĠG raphics", "ĠGraph ics", "ĠGraphic s", "Ġ Contin", "ĠCon tin", "ĠCont in", "_ job", "_j ob", "Ġ getName", "Ġget Name", "Ġ Magn", "ĠM agn", "ĠMag n", "ĠMa gn", "Ġ DWORD", "ĠD WORD", "ĠDW ORD", "m ad", "ma d", "Ġ nh", "Ġn h", "f eatures", "fe atures", "feature s", "feat ures", "fea tures", "} \");Ċ", "}\" );Ċ", "}\") ;Ċ", "he ets", "heet s", "hee ts", "( train", "(t rain", "(tr ain", "z n", "Ġrec ruit", "Ġrecru it", ". connection", ".con nection", ".connect ion", ".conn ection", "Ġbar rel", "Ġbarr el", "Ġ steam", "Ġs team", "Ġst eam", "Ġste am", "_ setting", "_s etting", "_set ting", "Ġ angular", "Ġang ular", "ane ously", "aneous ly", "Ġ bil", "Ġb il", "Ġbi l", "Ġ Norm", "ĠN orm", "ĠNo rm", "ĠNor m", "5 22", "52 2", "(! $", "i bt", "ib t", "% (", "Ġ posit", "Ġp osit", "Ġpos it", "Ġpo sit", "Ġposi t", "Ġ Father", "ĠF ather", "ĠFa ther", "ĠFat her", "int endo", "inte ndo", "5 65", "56 5", "L ive", "Li ve", "Liv e", "0 41", "04 1", "Ġ ports", "Ġp orts", "Ġport s", "Ġpo rts", "Ġpor ts", "Ġm ej", "Ġme j", "Ġ landing", "Ġl anding", "Ġland ing", "Ġlan ding", "p onder", "pon der", "pond er", "po nder", "ponde r", "Ġ cod", "Ġc od", "Ġco d", "_ HEADER", "_HE ADER", "_HEAD ER", ". Margin", ".M argin", ".Mar gin", "Ġ balls", "Ġb alls", "Ġball s", "Ġbal ls", "Ġdisc ussions", "Ġdiscuss ions", "Ġdiscussion s", "Ġ blend", "Ġbl end", "Ġble nd", "H ex", "He x", "Ġfar mers", "Ġfarm ers", "Ġfarmer s", "Ġmaint aining", "Ġmaintain ing", "Ġ ĠĠčĊ", "ĠĠ ĠčĊ", "ĠĠĠ čĊ", "s yn", "sy n", "[ T", "r us", "ru s", "4 39", "43 9", "uff ers", "uf fers", "uffer s", "Ġ contributors", "Ġcontrib utors", "Ġcontributor s", "_ sys", "_s ys", "_sy s", ". Debug", ".De bug", "Ġ constructed", "Ġconstruct ed", "o mes", "om es", "ome s", "? id", "s lider", "sl ider", "slide r", "Ġsup pliers", "Ġsupplier s", "Ġsuppl iers", "Ġsupp liers", "6 11", "61 1", "scribe r", "scri ber", "scr iber", "p es", "pe s", "Ð ŀ", "\" :čĊ", "\": čĊ", "\\ Controller", ") )ĊĊĊ", ")) ĊĊĊ", "))Ċ ĊĊ", "))ĊĊ Ċ", "Ġ lua", "Ġl ua", "Ġlu a", "M ulti", "Mult i", "Mul ti", "E NS", "EN S", "S rc", "Sr c", "Ġ petition", "Ġpet ition", "Ġpetit ion", "Ġ slave", "Ġsl ave", "Ġsla ve", "Ġslav e", "lo oking", "look ing", "loo king", "V ERT", "VER T", "VE RT", "ĉ vector", "ĉv ector", "ĉvec tor", "S pecial", "Sp ecial", "Spec ial", "Spe cial", "h h", "an ne", "ann e", "ĠN iger", "ĠNi ger", "/ views", "/view s", "z ing", "zi ng", "zin g", "end ant", "enda nt", "< C", "s peed", "sp eed", "spe ed", "5 14", "51 4", "Ġ{ };ĊĊ", "Ġ{} ;ĊĊ", "Ġ{};Ċ Ċ", "Ġ{}; ĊĊ", "Begin Init", "Ġf open", "Ġfo pen", "@ RequestMapping", "End Init", "Ġp unch", "Ġpun ch", "S ender", "Se nder", "Send er", "Sen der", "6 03", "60 3", "é Ķ", "get Message", "/ types", "/t ypes", "/type s", ". PI", ".P I", "(' ');Ċ", "oc used", "ocus ed", "ocu sed", "( all", "(a ll", "(al l", "Ġ dropdown", "Ġd ropdown", "Ġdrop down", ") .__", "). __", ")._ _", "Ġ Vin", "ĠV in", "ĠVi n", ". ForeignKey", ".Fore ignKey", "6 12", "61 2", "ca nf", "can f", "o ured", "ou red", "our ed", "oure d", "Ġ Organization", "ĠO rganization", "ĠOrgan ization", "Ġ а", "ĠÐ °", "Ġ Culture", "ĠC ulture", "ĠCult ure", "ĠCul ture", "( cls", "(c ls", "(cl s", ", _", "9 02", "90 2", "r gba", "rg ba", "rgb a", "ìĿ ĺ", ". dataGridView", ".data GridView", "Ġdo zen", "Ġdoz en", "ĠG es", "ĠGe s", "8 05", "80 5", "4 64", "46 4", "_ shared", "_sh ared", "_share d", "_sha red", "n ick", "ni ck", "nic k", "Ġh osp", "Ġho sp", "Ġhos p", "o meter", "om eter", "ome ter", "omet er", "4 95", "49 5", "Ġclaim ing", "Ġcla iming", "0 32", "03 2", "i bles", "ib les", "ible s", "r ik", "ri k", "æĺ ¯", "en ario", "ena rio", "Ġd engan", "Ġden gan", "o bb", "ob b", "m ont", "mon t", "mo nt", "_ rank", "_r ank", "_ra nk", "(' /',", "('/ ',", "Ġap olog", "Ġapo log", "P s", "_ power", "_p ower", "_pow er", "_po wer", "ĠG ree", "ĠGr ee", "ĠGre e", "Ġf ulfill", "Ġful fill", "Ġfulfil l", "Ġ firebase", "Ġf irebase", "Ġfire base", "9 10", "91 0", "Ġ fare", "Ġf are", "Ġfa re", "Ġfar e", "ĠH im", "ĠHi m", "Ġ bean", "Ġb ean", "Ġbe an", "âĢ¦ .", "Ġ SPI", "ĠS PI", "ĠSP I", "_ RX", "_R X", "Ġper ception", "Ġperce ption", "Ġpercept ion", "rel ative", "com pile", "comp ile", "u um", "uu m", "u tos", "ut os", "uto s", "a uc", "au c", "Ġ Ask", "ĠA sk", "ĠAs k", "Ġ indicator", "Ġind icator", "Ġindic ator", "Ġindica tor", "/ th", "/t h", ".set String", "ĠWis consin", ". Domain", ".D omain", ".Do main", ".Dom ain", "Ġart ificial", "De velop", "Dev elop", "Ġ Sarah", "ĠS arah", "ĠSar ah", "ĠSa rah", "ĠSara h", "Ġ lying", "Ġl ying", "Ġly ing", "( search", "(s earch", "(se arch", "ĠEm pire", "ĠEmp ire", "ur ring", "urr ing", "æŶ éĹ´", "= \"${", "=\" ${", "=\"$ {", "Ġ getId", "Ġget Id", "Ġ Payment", "ĠP ayment", "ĠPay ment", "t ransition", "trans ition", "Ġ ].", "Ġ] .", "i xin", "ix in", "V T", "- select", "-s elect", "-se lect", "Ġdemonstr ated", "Ġdemonstrate d", "Ġ lastName", "Ġlast Name", "em ployment", "emp loyment", "employ ment", ". getProperty", ".get Property", ".getP roperty", "Ġf ought", "Ġfo ught", "Ġfou ght", "file Name", "Ġ Pers", "ĠP ers", "ĠPer s", "ĠPe rs", "4 52", "45 2", "- card", "-c ard", "-car d", "-ca rd", "a str", "as tr", "ast r", "at trs", "att rs", "attr s", "Ġpro minent", "Ġprom inent", "Ġpromin ent", "D esign", "De sign", "Des ign", "anc ouver", "ãģĹ ãģ", "ar do", "ard o", "s ecret", "se cret", "sec ret", "Ġ rag", "Ġr ag", "Ġra g", "Ġpo ison", "Ġpoi son", "Ġpois on", "- man", "-m an", ", omitempty", "7 40", "74 0", "ĉ un", "ĉu n", "it zer", "itz er", "ĠCas ino", "Ġ Ross", "ĠR oss", "ĠRo ss", "ĠRos s", "- foot", "-f oot", "( results", "(result s", "(res ults", "P lan", "Pl an", "Ġl aser", "Ġla ser", "Ġlas er", "ê¸ °", "_ DR", "_D R", "5 23", "52 3", "F acebook", "Face book", "4 49", "44 9", "Ġ boards", "Ġbo ards", "Ġboard s", "s ta", "st a", "] ],", "]] ,", "6 75", "67 5", "Ġ tiles", "Ġt iles", "Ġti les", "Ġtile s", "Ġtil es", "S IZE", "SI ZE", "Ġ =~", "Ġ= ~", "9 70", "97 0", "Ġprem ier", "Ġpremi er", "o cab", "oc ab", "oca b", "Ġ encoded", "Ġen coded", "Ġenc oded", "Ġencode d", "Ġ reserve", "Ġre serve", "Ġres erve", "Ġreserv e", "6 09", "60 9", "ĠAfghan istan", "Ġ ListNode", "ĠList Node", "ur ls", "url s", "Ġ submission", "Ġsub mission", "Ġn eu", "Ġne u", "4 77", "47 7", "Ġ# +#", "_ POST", "_P OST", "_PO ST", "_POS T", "Ġmo ist", "Ġmoi st", "Ġmois t", "e lli", "el li", "ell i", "ellig ent", "elli gent", ". alert", ".al ert", "ó d", "b re", "br e", "Ġ Collect", "ĠC ollect", "ĠCol lect", "ĠColl ect", "Ġ graphic", "Ġg raphic", "Ġgraph ic", "Ġgrap hic", "Ġ longitude", "Ġlong itude", "Ġlongitud e", "Ġ Provid", "ĠPro vid", "ĠPr ovid", "ĠProv id", "Ġ Calculate", "ĠC alculate", "ĠCal culate", "ĠCalcul ate", "ĠCalc ulate", "x ffff", "xf fff", "xff ff", "xfff f", "c riteria", "crit eria", "Ġ waters", "Ġw aters", "Ġwater s", "Ġwa ters", "Ġwat ers", "r ock", "ro ck", "roc k", "lo quent", "ĠT rib", "ĠTr ib", "ĠTri b", "5 13", "51 3", "Ġ burst", "Ġb urst", "Ġbu rst", "Ġbur st", "Ġ suffix", "Ġs uffix", "Ġsuff ix", "Ġsuf fix", ". Extensions", ".Ext ensions", ".Extension s", "is hes", "ish es", "i vel", "iv el", "ive l", "Ġ LIKE", "ĠL IKE", "ĠLI KE", "Ġ Getty", "ĠG etty", "ĠGet ty", "ĠGe tty", ".Action Event", ".s lf", ".sl f", "Ġ HAL", "ĠH AL", "ĠHA L", "u pal", "up al", "upa l", "E AR", "EA R", "5 24", "52 4", "u di", "ud i", "_ timeout", "_time out", "U F", "Ġ Singapore", "ĠSing apore", "ĠSingap ore", "ĠAd vent", "ĠAdv ent", "_ interval", "_int erval", "_inter val", "c haft", "ch aft", "cha ft", "Ġ Emer", "ĠE mer", "ĠEm er", "Ġ telephone", "Ġtele phone", "ĠTur k", "ĠTu rk", "_ interface", "_inter face", "Ġ Own", "ĠO wn", "ĠOw n", "Ġencour aged", "Ġencourage d", "< Object", "(", "<> (", "5 44", "54 4", ". Product", ".Pro duct", ".Produ ct", "Form s", "For ms", "Fo rms", "N EW", "NE W", "P ay", "Pa y", "ĉ boolean", "ĉbool ean", "_ contact", "_cont act", "Ġ Electric", "ĠE lectric", "ĠElect ric", "s kip", "sk ip", "ski p", "Ġw ur", "Ġch ronic", "Ġchron ic", "Ġchr onic", "_ driver", "_d river", "_dr iver", "_drive r", "9 40", "94 0", "Ġ Sab", "ĠS ab", "ĠSa b", "Ġ Ult", "ĠU lt", "ĠUl t", "Ġ Rad", "ĠR ad", "ĠRa d", "ST ATUS", "STAT US", "Ġ Lewis", "ĠL ewis", "ĠLe wis", "ĠLew is", "O B", "Ġgift s", "Ġgi fts", "Ġgif ts", ". Rec", ".R ec", ".Re c", "TR UE", "Ġint ensity", "Ġintens ity", "M arker", "Mark er", "Mar ker", ". compare", ".com pare", ".comp are", "f fic", "ff ic", "ffi c", "C ookie", "Co okie", "Cook ie", "Ġ Baby", "ĠB aby", "ĠBa by", "ĠBab y", "Ġ BigDecimal", "ĠB igDecimal", "ĠBig Decimal", "i let", "il et", "ile t", "ĠHOLD ERS", "ĠHOLDER S", "Ġ Lady", "ĠL ady", "ĠLa dy", "ĠLad y", "Ġ lung", "Ġl ung", "Ġlu ng", "Ġlun g", "Ġ Alabama", "ĠAl abama", "Ġ dess", "Ġd ess", "Ġde ss", "Ġdes s", "` );Ċ", "`) ;Ċ", "Ġ Builder", "ĠB uilder", "ĠBuild er", "ĠBu ilder", "_ region", "_reg ion", "Ġ neutral", "Ġne utral", "Ġneut ral", "Ġneutr al", "9 09", "90 9", "B oth", "Bo th", "Bot h", "Ġ hp", "Ġh p", "Ġ horn", "Ġh orn", "Ġhor n", "Ġho rn", "Ġ segments", "Ġse gments", "Ġseg ments", "Ġsegment s", "Ġ EC", "ĠE C", "\" =>\"", "\"=> \"", "( rec", "(r ec", "(re c", "Ġ Pi", "ĠP i", "G M", "Ġl aptop", "Ġlap top", "S calar", "Sc alar", "Scala r", "4 63", "46 3", "i sd", "is d", "- dialog", "-d ialog", "-di alog", "Ġ Anderson", "ĠAnd erson", "ĠAnders on", "Ġmis takes", "Ġmist akes", "Ġmi stakes", "Ġmistake s", "7 08", "70 8", "Ġ Han", "ĠH an", "ĠHa n", "j es", "je s", "est ination", "esti nation", "4 36", "43 6", "Ġprom ises", "Ġpromise s", "b id", "bi d", "Ġ Scient", "ĠS cient", "ĠSc ient", "ĠSci ent", "G IN", "GI N", "Ġ Performance", "ĠPer formance", "ĠPerform ance", "b age", "ba ge", "bag e", ". users", ".user s", ".use rs", ".us ers", "le ading", "lead ing", "lea ding", "Ġ oral", "Ġo ral", "Ġor al", "Ġora l", "G raphics", "Graph ics", "Graphic s", "4 88", "48 8", "_ PTR", "_P TR", "_PT R", "5 18", "51 8", "h ang", "ha ng", "han g", "Ġin ev", "Ġi nev", "Ġine v", "p rocessing", "process ing", "F actor", "Fact or", "Fac tor", "Fa ctor", "Ġ NA", "ĠN A", "$ string", "$s tring", "$str ing", "Ġ grounds", "Ġgr ounds", "Ġground s", "Ġgro unds", "Ġgrou nds", ".Save Changes", "c lock", "cl ock", "clo ck", "9 41", "94 1", "cri pcion", "Ġ Newton", "ĠNew ton", "g c", ". includes", ".in cludes", ".include s", "Ġ blast", "Ġb last", "Ġbl ast", "Ġblas t", "Ġbla st", "Ġ' -'", "Ġ'- '", "Ġp uede", "Ġpued e", "Ġpu ede", "4 69", "46 9", ". Session", ".S ession", "Ġ grep", "Ġg rep", "Ġgr ep", "Ġgre p", "_ final", "_f inal", "_fin al", "Ġ Gay", "ĠG ay", "ĠGa y", "Ġ Give", "ĠG ive", "ĠGi ve", "i ri", "ir i", "- star", "-s tar", "-st ar", "Ġ UIImage", "ĠUI Image", "_ epoch", "_ep och", "u bb", "ub b", "e nth", "en th", "ent h", "Ġ elite", "Ġe lite", "Ġel ite", "Ġelit e", "Ġcampaign s", "Ġ Porno", "ĠP orno", "ĠPorn o", "ĠPor no", "_ assign", "_as sign", "_ass ign", "Prot ocol", "Proto col", "Ġ Being", "ĠB eing", "ĠBe ing", "ĠBei ng", "Ġ Airport", "ĠAir port", "Ġcon ventional", "Ġconvent ional", "Ġconvention al", "Ġ Wat", "ĠW at", "ĠWa t", "Ġ CI", "ĠC I", "E TA", "ET A", "Ġ Anthony", "ĠAnth ony", "Ġ tablet", "Ġtable t", "Ġtab let", "( format", "(form at", "(for mat", "Ġconsist ently", "Ġconsistent ly", "ĠI owa", "ĠIo wa", "4 74", "47 4", "Ġ avatar", "Ġav atar", "Ġava tar", "0 27", "02 7", ". cursor", ".c ursor", "! [", "Ġh anging", "Ġhang ing", "Ġhan ging", "Ġhangi ng", "H er", "He r", "S uch", "Su ch", "Suc h", "' ;ĊĊĊ", "';Ċ ĊĊ", "';ĊĊ Ċ", "'; ĊĊĊ", "org eous", "orge ous", "( )==", "() ==", "Ġ viewModel", "Ġview Model", "Ġ ãĥ", "Ġ els", "Ġe ls", "Ġel s", "Ġ Agent", "ĠA gent", "ĠAg ent", "ĠAge nt", "F etch", "a por", "ap or", "apo r", "Ġ cx", "Ġc x", "p read", "pr ead", "pre ad", "ĠP ier", "ĠPi er", "ĠPie r", "o eff", "oe ff", "6 16", "61 6", "S n", "8 90", "89 0", "Ġ Virtual", "ĠV irtual", "ĠVir tual", "ĠVirt ual", "A pr", "Ap r", ". White", ".Wh ite", "6 15", "61 5", "_ MOD", "_M OD", "_MO D", "Ġ Points", "ĠP oints", "ĠPoint s", "ĠPo ints", "å¤ ±", "Ġ genes", "Ġg enes", "Ġge nes", "Ġgen es", "Ġgene s", "Ġ vendor", "Ġv endor", "Ġvend or", "Ġmain stream", "< src", "Ċ", "Ġ< >Ċ", "Ġ<> Ċ", "F ilename", "File name", "Fi lename", "Fil ename", "Ġs ne", "Ġsn e", "Ġ Football", "ĠF ootball", "ĠFoot ball", "Ġr ival", "Ġri val", "Ġriv al", "Ġdis aster", "i onic", "ion ic", "io nic", "ioni c", "Ġ Damage", "ĠD amage", "ĠDa mage", "ĠDam age", ". Resource", ".Re source", ".Res ource", "- en", "-e n", "Ġ Types", "ĠT ypes", "ĠType s", "ĠTy pes", "ĠTyp es", "get String", "( board", "(b oard", "Ġ bol", "Ġb ol", "Ġbo l", "p lain", "pl ain", "pla in", "z ym", "zy m", "ภ²", "Ġ scanner", "Ġsc anner", "Ġscan ner", "i lder", "il der", "ild er", "ilde r", "_ msgs", "_msg s", "_ms gs", "æ ı", "( intent", "(int ent", "(in tent", "Ġ destruct", "Ġd estruct", "Ġde struct", "Ġb ust", "Ġbu st", "Ġbus t", "Ġ Employ", "ĠE mploy", "ĠEm ploy", "ĠEmp loy", "o ni", "on i", "Ġ UIViewController", "ĠUI ViewController", "ĠUIView Controller", "Ġo dds", "Ġodd s", "Ġod ds", "e arer", "ear er", "ea rer", "Ge ometry", "Geo metry", "Geom etry", "Ġ yii", "Ġy ii", "Ġyi i", "_ EXPORT", "_EX PORT", "_EXP ORT", "Ġ Attack", "ĠAtt ack", "Ġn iet", "Ġnie t", "Ġni et", "Ġim pression", "Ġimp ression", "Ġimpress ion", "Ġimpr ession", "Ġ Gil", "ĠG il", "ĠGi l", "_ prob", "_p rob", "_pro b", "_pr ob", "5 28", "52 8", "Ġ CF", "ĠC F", "Ġ Experience", "ĠEx perience", "/ plugins", "/pl ugins", "/plugin s", ". Method", ".M ethod", "Ġbel iefs", "Ġbelie fs", "Ġbelief s", "N ative", "Nat ive", "_ build", "_b uild", "Ġ vig", "Ġv ig", "Ġvi g", "Ġr anks", "Ġrank s", "Ġran ks", "cover ed", "cov ered", "7 05", "70 5", "s uch", "su ch", "G uard", "Gu ard", ". pack", ".p ack", ".pa ck", "ad der", "add er", "8 09", "80 9", "i via", "iv ia", "ivi a", "l ng", "ln g", "Ġ вÑĭ", "Ġв Ñĭ", "5 52", "55 2", "T imestamp", "Time stamp", "_ now", "_n ow", "_no w", "Ġp oker", "Ġpo ker", "Ġpok er", "Ġpoke r", "Ġ unc", "Ġu nc", "Ġun c", "Ġ shapes", "Ġsh apes", "Ġshape s", "Ġsha pes", "- types", "-t ypes", "-type s", "_ period", "_p eriod", "_per iod", "p k", "Ġveter an", "Ġ sono", "Ġs ono", "Ġso no", "Ġson o", "Ġ appointed", "Ġapp ointed", "Ġappoint ed", "over flow", ". driver", ".d river", ".dr iver", ".drive r", "_ cat", "_c at", "_ca t", "u tt", "ut t", "p lant", "pl ant", "plan t", "pla nt", "i mb", "im b", "Ġ Accept", "ĠAc cept", "ĠAcc ept", "Ġ concert", "Ġcon cert", "Ġconc ert", "Ġconce rt", "ĉ node", "ĉn ode", "ĉno de", "ĉ z", "? >čĊ", "?> čĊ", "Ġb anned", "Ġban ned", "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĉĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĉĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġt oxic", "Ġto xic", "Ġtox ic", "Ġdis appe", "Ġdisap pe", "4 73", "47 3", "È Ľ", "Ġg race", "Ġgr ace", "Ġgra ce", "Ġgrac e", "at eful", "ate ful", "Re ply", "Rep ly", "ĠC ruz", "ĠCr uz", "ĠCru z", "4 86", "48 6", "Ġs crap", "Ġsc rap", "Ġscr ap", "Ġ keywords", "Ġkey words", "Ġkeyword s", "s imp", "si mp", "sim p", "Ġmort gage", "Ġcy ber", "Ġ Execute", "ĠEx ecute", "ĠExec ute", "Ġ latitude", "Ġl atitude", "Ġlat itude", "i fu", "if u", ". COM", ".C OM", ".CO M", "d bo", "db o", "Ġs orts", "Ġso rts", "Ġsort s", "Ġsor ts", "Ġ Gas", "ĠG as", "ĠGa s", "om ial", "omi al", ". Local", ".L ocal", ".Lo cal", "C ells", "Cell s", "Cel ls", ". Replace", ".Re place", "String s", "Str ings", ". fit", ".f it", ".fi t", "Ġ Third", "ĠTh ird", "ĠThi rd", "% \",Ċ", "%\" ,Ċ", "%\", Ċ", "Ġ {}\".", "Ġ{ }\".", "Ġ{} \".", "Ġ Sony", "ĠS ony", "ĠSo ny", "ĠSon y", "Ġ [:", "Ġ[ :", "5 85", "58 5", "Ġf allen", "Ġfa llen", "Ġfall en", "Ġfal len", ". ')Ċ", ".' )Ċ", ".') Ċ", "i nh", "in h", "Ġ MC", "ĠM C", "Ġ redis", "Ġre dis", "Ġr edis", "Ġred is", "C odes", "Code s", "Co des", "Cod es", "Ġ profiles", "Ġpro files", "Ġprof iles", "Ġprofile s", "Ġprofil es", "h ook", "ho ok", "hoo k", "Re ducer", "Red ucer", "Reduc er", "Reduce r", "_ FUNC", "_F UNC", "_FUN C", "Ġ navigate", "Ġn avigate", "Ġnav igate", "Ġnavig ate", "st rlen", "str len", "Ġh orm", "Ġhor m", "Ġho rm", "á ŀ", "Ġ SR", "ĠS R", ". boot", ".b oot", ".bo ot", "Ġ digest", "Ġd igest", "Ġdi gest", "Ġdig est", "ĉ header", "ĉhead er", ". findOne", ".find One", "æ ģ", "Db Type", "n ia", "ni a", "_ merge", "_m erge", "Ġd onne", "Ġdon ne", "Ġdonn e", "/ Getty", "/G etty", "_ CHAR", "_CH AR", "Ġ bands", "Ġb ands", "Ġband s", "Ġban ds", "Ġba nds", ". URL", ".U RL", ".UR L", "art ial", "arti al", "Ġ freq", "Ġf req", "Ġfr eq", "Ġfre q", "Ġs ist", "Ġsi st", "Ġsis t", "N g", "Ġrender ing", "Ġrend ering", "\\ Core", "\\C ore", "Widget s", "Ġ VA", "ĠV A", "Ġactiv ists", "Ġactivist s", "S te", "St e", "= _", "a lla", "al la", "all a", "St amp", "Ġ loads", "Ġlo ads", "Ġload s", "Ġloa ds", "Ġ xx", "Ġx x", "Ġ Learning", "ĠL earning", "ĠLe arning", "ĠLearn ing", "ĠLear ning", ". Mvc", ".M vc", "u ir", "ui r", "( \"$", "(\" $", "Ġ connecting", "Ġconnect ing", "Read Only", "u ru", "ur u", "ĠE ag", "ĠEa g", "B IT", "BI T", "_ DEL", "_D EL", "_DE L", "å §", "arr ass", "arra ss", "ex ternal", "ext ernal", "extern al", "exter nal", "Ġ YOUR", "ĠY OUR", "ĠYOU R", "ĠB rew", "ĠBr ew", "ĠBre w", "Ġ Five", "ĠF ive", "ĠFi ve", "Ġ resize", "Ġre size", "Ġres ize", "i gid", "ig id", "igi d", "e ration", "er ation", "era tion", "6 53", "65 3", "Ġ Ñį", "ĠÑ į", "5 36", "53 6", "åĬ ł", "0 39", "03 9", "Ġ Catch", "ĠC atch", "ĠCat ch", "Ù ģ", "Ġ Leon", "ĠL eon", "ĠLe on", "ĠLeo n", "a mil", "am il", "ami l", ". Body", ".B ody", "C lip", "Cl ip", "Cli p", "/ list", "/l ist", "/li st", ". br", ".b r", "Edit Text", "ĉ db", "ĉd b", ". Game", ".G ame", "( BuildContext", "(Build Context", "back end", ". Red", ".R ed", ".Re d", "f acebook", "face book", "5 29", "52 9", ". urls", ".url s", ".ur ls", "m r", "rol led", "roll ed", "- ------", "-- -----", "---- ---", "--- ----", "----- --", "------ -", "Ġint ervention", "Ġinter vention", "Ġinterven tion", "Ġinterv ention", "Ġret irement", "Ġretire ment", "Ġretir ement", "Ġ Kit", "ĠK it", "ĠKi t", "Ġ PRE", "ĠP RE", "ĠPR E", "Upper Case", "Ġ Socket", "ĠS ocket", "ĠSo cket", "ĠSoc ket", "Ġ :-", "Ġ: -", "Ġstud ying", "Ġstudy ing", "Ġ Metro", "ĠM etro", "ĠMe tro", "ĠMet ro", "ar ded", "ard ed", "arde d", "Ġcon versations", "Ġconvers ations", "Ġconversation s", "C alled", "Call ed", "Cal led", "Ġex amine", "Ġexam ine", "ert ificate", ". gz", ".g z", "- responsive", "-res ponsive", "Ġ refund", "Ġre fund", "Ġref und", "_ network", "_n etwork", "_net work", "0 26", "02 6", "all owed", "allow ed", "allo wed", "em pt", "emp t", "Ġme als", "Ġmeal s", "C ategories", "Ġtravel ing", "Ġtrav eling", "Ġ kg", "Ġk g", "Ġsh ame", "Ġsha me", "Ġsham e", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġexplicit ly", "Ġmath ematic", "Ġ Suite", "ĠS uite", "ĠSu ite", "ĠSuit e", "Ġ RGB", "ĠR GB", "ĠRG B", "****** /", "***** */", "Ġm ixture", "Ġmix ture", "l earning", "le arning", "lear ning", "learn ing", ". template", ".t emplate", ".temp late", ".tem plate", "at ts", "att s", "w x", "ĉ ctx", "ĉc tx", "ĉct x", ". properties", ".p roperties", ".prop erties", "Ġdr inks", "Ġdrink s", "Ġ Either", "ĠE ither", "set Text", ". getData", ".get Data", ".getD ata", ". zip", ".z ip", "Ġreve als", "Ġreveal s", "< table", ".Ċ", "/> .Ċ", "/>. Ċ", "Ġr anked", "Ġrank ed", "Ġran ked", "_ impl", "_i mpl", "_im pl", "_imp l", "Ġ Handles", "ĠH andles", "ĠHand les", "ĠHandle s", "Ġhost ed", "Ġho sted", "Ġhos ted", "Ġ updating", "Ġup dating", "Ġupd ating", "al bum", "é Ŀ", "Ġ shader", "Ġsh ader", "Ġsha der", "Ġshade r", "Ed itors", "Edit ors", "Editor s", "- round", "-r ound", "-ro und", "[ ]{", "[] {", "Ġ sep", "Ġs ep", "Ġse p", "Ġ Hi", "ĠH i", "T EM", "TE M", "look up", "loo kup", ". man", ".m an", ".ma n", "_ INPUT", "_IN PUT", "Ġthreat ened", "Ġthreaten ed", "_ IMPORT", "_IM PORT", "_IMP ORT", "Ġ drops", "Ġd rops", "Ġdr ops", "Ġdrop s", "Ġdro ps", "r uit", "ru it", "s id", "si d", "b oth", "bo th", "bot h", "Ġ Excel", "ĠEx cel", "ĠExc el", "Ġ jer", "Ġj er", "Ġje r", "ord inary", "ordin ary", "е й", "еР¹", "V IEW", "VI EW", "re ply", "rep ly", "Ġ ):Ċ", "Ġ) :Ċ", "Ġ): Ċ", "col ors", "color s", "colo rs", "ver ified", "_ Tr", "_T r", "_ parse", "_p arse", "_par se", "_pars e", "Ġcon gress", "Ġcongr ess", "Ġcong ress", "6 17", "61 7", "P romise", "Pro mise", "Prom ise", "i nts", "in ts", "int s", "Ġ Mother", "ĠM other", "ĠMo ther", "ĠMot her", ". Api", ".A pi", ".Ap i", "Ġ Duration", "ĠD uration", "ĠDu ration", "ĠDur ation", "Ġ firstName", "Ġfirst Name", "inherit doc", "ĠM ars", "ĠMar s", "ĠMa rs", "Ġ apr", "Ġa pr", "Ġap r", "O DY", "OD Y", "Ġvis its", "Ġvisit s", "6 31", "63 1", "Ġhe aling", "Ġheal ing", "let ters", "letter s", "lette rs", "lett ers", ") ));čĊ", ")) );čĊ", "))) ;čĊ", "))); čĊ", "f uture", "fu ture", ". Framework", ".F ramework", ".Frame work", "Ġk iss", "Ġki ss", "Ġinv olve", "Ġinvol ve", "Ġ silent", "Ġs ilent", "Ġsil ent", "ad ows", "ado ws", "adow s", "Ġany body", "s ch", "sc h", "6 90", "69 0", "Ġsol ely", "Ġsole ly", "- img", "-i mg", "-im g", "Ġ propri", "Ġp ropri", "Ġpro pri", "Ġprop ri", "Ġin struct", "Ġinstr uct", "Ġ licenses", "Ġlicense s", "Ġlic enses", "Ġlicens es", "Ġ meth", "Ġm eth", "Ġme th", "Ġmet h", "Ġcon dem", "Ġcond em", "Ġ Domain", "ĠD omain", "ĠDo main", "ĠDom ain", "ĠH arris", "ĠHar ris", "ĠHarr is", "Ġs Ã¥", "CE PT", "B atch", "Bat ch", "@ extends", "ĠCONTR IBUT", ". 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GetString", ".Get String", "e ks", "ek s", "( project", "(pro ject", "(proj ect", "5 98", "59 8", "ĉ msg", "ĉm sg", "ĉms g", "ĉ output", "ĉout put", "Ġcomplaint s", "Ġcomplain ts", "5 51", "55 1", ", S", "Ġ tbl", "Ġt bl", "Ġtb l", "Ġ ,ĊĊ", "Ġ, ĊĊ", "Ġ,Ċ Ċ", "r iors", "ri ors", "rior s", "rio rs", "ah ren", "ahr en", "Ġlaw yers", "Ġlawy ers", "Ġlawyer s", "re dux", "red ux", "_ symbol", "_s ymbol", "_sym bol", "o ffee", "of fee", "off ee", "_ RESULT", "_RES ULT", "( Name", "(N ame", "U TC", "UT C", ". currentTime", ".current Time", "Ġorgan is", ". arg", ".a rg", ".ar g", "5 33", "53 3", "Ġmin im", "Ġmi nim", "Ġmini m", "w ick", "wi ck", "Ġrece ives", "Ġreceive s", "B alance", "Bal ance", "Ġspe aks", "Ġspeak s", "Ġ Days", "ĠD ays", "ĠDay s", "ĠDa ys", "Ġ Below", "ĠB elow", "ĠBe low", "ĠBel ow", "4 83", "48 3", "t ipo", "ti po", "tip o", "P resent", "Pre sent", "Pres ent", "Ġre serv", "Ġres erv", "h p", "Ġ rit", "Ġr it", "Ġri t", "_ RIGHT", "_R IGHT", "- -)", "-- )", "Ġchair man", "7 81", 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T", "Ġ spy", "Ġs py", "Ġsp y", "Ġ Professional", "ĠProf essional", "ĠProfession al", "Ġ\" \";ĊĊ", "Ġ\"\" ;ĊĊ", "Ġ\"\";Ċ Ċ", "Ġ\"\"; ĊĊ", "Ġstr iking", "Ġstri king", "Ġd iscrimination", "Ġdiscrim ination", "Ġdiscrimin ation", "Ġp ays", "Ġpay s", "Ġpa ys", "9 37", "93 7", "l ict", "lic t", "li ct", "en tes", "ent es", "ente s", "Ġth rowing", "Ġthrow ing", "Ġthr owing", "Ġthro wing", "Ġ Plugin", "ĠPl ugin", "ĠPlug in", "( def", "(d ef", "(de f", "Ġ RuntimeException", "ĠRuntime Exception", "Ġ Migration", "ĠM igration", "ĠMig ration", "5 99", "59 9", "Ġ dic", "Ġd ic", "Ġdi c", "b ag", "ba g", "o nia", "on ia", "oni a", "Ġcor ruption", "Ġcorrupt ion", "7 04", "70 4", "( Map", "(M ap", "Ġp rz", "Ġpr z", ". dto", ".d to", ".dt o", "Ġac quire", "Ġacqu ire", "State ToProps", "Ġl oving", "Ġlo ving", "Ġlov ing", "о ж", "оР¶", "_ pattern", "_p attern", "_pat tern", "Ġem otions", "Ġemot ions", "Ġemotion s", "Ġ publisher", "Ġp ublisher", "Ġpublish er", "Ġpubli sher", "_ be", "_b e", "Ġc ouples", "Ġco uples", "Ġcou ples", "Ġcouple s", "Ġcoup les", "4 98", "49 8", "o j", "Ġ Chart", "ĠC hart", "ĠCh art", "ĠChar t", "ĠCha rt", "Ġt rop", "Ġtr op", "Ġtro p", ". tool", ".t ool", ".to ol", "Ġestablish ment", "Ġ dol", "Ġd ol", "Ġdo l", "6 54", "65 4", "Ġ tower", "Ġt ower", "Ġto wer", "Ġtow er", "Ġ lane", "Ġl ane", "Ġla ne", "Ġlan e", "ĠSy dney", "Ġf illing", "Ġfil ling", "Ġfill ing", "claim ed", "cla imed", "6 44", "64 4", "Ġdialog ue", "Ġdia logue", "Ġdial ogue", "Ġcon vention", "Ġconv ention", "Ġconven tion", "Ġconvent ion", "bo oking", "book ing", "boo king", "par ency", "pare ncy", "paren cy", "æ ±", "Ġ Generic", "ĠG eneric", "ĠGener ic", "ĠGen eric", "ĠGene ric", "7 18", "71 8", "\\ Schema", "\\S chema", "4 82", "48 2", "6 18", "61 8", "Ġ ranges", "Ġr anges", "Ġrange s", "Ġran ges", "Ġrang es", "/ ch", "/c h", "Ġ panels", "Ġpanel s", "Ġpa nels", "Ġpan els", "Ġpane ls", "Ġr uled", "Ġrule d", "Ġru led", "çĶ Ł", ". ts", ".t s", "_ sets", "_s ets", "_set s", "_se ts", "Ġ cleanup", "Ġc leanup", "Ġclean up", "Pre vious", "Prev ious", "Ġ Animal", "ĠAn imal", "ĠAnim al", "6 07", "60 7", "( $(", "($ (", "ĠA ve", "ĠAv e", "ol lar", "oll ar", "olla r", "0 28", "02 8", "_ eval", "_e val", "_ev al", "ĉ Name", "ĉN ame", "( tree", "(t ree", "(tr ee", "Ġ \"]", "Ġ\" ]", "5 71", "57 1", "Ġdu ties", "Ġdut ies", "= '/", "=' /", "Click ed", "Cl icked", "Ġdiffer ently", "Ġdifferent ly", "Ġ Clark", "ĠCl ark", "ĠClar k", "ĠCla rk", "Ġ dit", "Ġd it", "Ġdi t", "olog ists", "ologist s", "ologi sts", "Ġsy nd", "Ġsyn d", "Ġs ends", "Ġse nds", "Ġsend s", "Ġsen ds", "- known", "-k nown", "-know n", "k b", "Ġ Modal", "ĠM odal", "ĠMod al", "ĠMo dal", "it ative", "itat ive", "Ġr acing", "Ġrac ing", "Ġra cing", "Ġhigh lights", "Ġhighlight s", "Ġ Simon", "ĠS imon", "ĠSim on", "ĠSi mon", "Ġ Captain", "ĠCap tain", "ĠCapt ain", "ä¿ ¡", "Ġ CB", "ĠC B", "con tin", "cont in", "conti n", "a ran", "ar an", "ara n", "Ġ physics", "Ġph ysics", "Ġphys ics", "Ġphysic s", "r etty", "re tty", "ret ty", "rett y", "e tal", "et al", "eta l", ". md", ".m d", "ax ios", "Ġspe akers", "Ġspeak ers", "Ġspeaker s", "Ġ prep", "Ġp rep", "Ġpr ep", "Ġpre p", "Ġaw arded", "Ġaward ed", "ì§ Ģ", "Ġ Corn", "ĠC orn", "ĠCo rn", "ĠCor n", "Ġ Nature", "ĠN ature", "ĠNa ture", "ĠNat ure", "ĠNatur e", "UD IO", "7 37", "73 7", "Ġ proj", "Ġp roj", "Ġpro j", "Ġpr oj", "- pre", "-p re", "-pr e", "[ u", "F eatures", "Fe atures", "Feature s", "Feat ures", "Ġ isEqual", "Ġis Equal", "B inary", "Bin ary", "s ig", "si g", "Ġcon fusion", "Ġconf usion", "5 46", "54 6", "5 68", "56 8", "Ġ Hat", "ĠH at", "ĠHa t", "Ġkt ó", ". configure", ".con figure", ".config ure", ".conf igure", "M ON", "MO N", "4 94", "49 4", "/ edit", "/e dit", "_ Add", "_A dd", "_Ad d", ", true", ",tr ue", "5 41", "54 1", "Ġ cli", "Ġc li", "Ġcl i", "Error Message", "- loader", "-l oader", "-lo ader", "-load er", "Dim ensions", "Dimension s", "ulti ply", "ultip ly", "Ġ {!!", "Ġ{ !!", "Ġ{! !", "Ġ SqlCommand", "ĠSql Command", "Ġ spoken", "Ġsp oken", "Ġspoke n", "Ġspo ken", "Ġ pics", "Ġp ics", "Ġpi cs", "Ġpic s", "Ġ toy", "Ġt oy", "Ġto y", "( Key", "(K ey", "Ġ Loop", "ĠL oop", "ĠLo op", "Ø ¨", "E ATURE", "EA TURE", "in ction", "inc tion", "inct ion", "_ setup", "_set up", "w rapper", "wrap per", "wr apper", "Ġt ong", "Ġto ng", "Ġton g", "c ular", "cul ar", "cu lar", "O pt", "Op t", ". Pl", ".P l", "= \",", "=\" ,", "( length", "(l ength", "(len gth", "(le ngth", "u mn", "um n", "Ġ chrom", "Ġch rom", "Ġchr om", "Ġs event", "Ġse vent", "Ġseven t", "Ġsev ent", "Ġ IllegalArgumentException", "ĠIl legalArgumentException", "ĠIllegal ArgumentException", "4 78", "47 8", "ĉ start", "ĉst art", "Ġbe gun", "Ġbeg un", "CE PTION", "CEPT ION", "d ataset", "data set", "dat aset", "datas et", "8 25", "82 5", "Ġ Failed", "ĠF ailed", "ĠFa iled", "ĠFail ed", "c ols", "co ls", "col s", "4 59", "45 9", "Ġk nee", "Ġkn ee", "Ġkne e", "i more", "im ore", "imo re", ". splice", ".sp lice", "s hell", "sh ell", "she ll", "ig gers", "igger s", "igg ers", "Ġ themes", "Ġth emes", "Ġthe mes", "Ġthem es", "Ġtheme s", "9 95", "99 5", "Ġ DJ", "ĠD J", "Ġ Assistant", "ĠAss istant", "ĠAssist ant", "- $", "M aybe", "May be", "Ġ ordering", "Ġorder ing", "Ġord ering", "ĠInt elligence", "ĠIntelli gence", "ĠMass achusetts", "Ġf ailing", "Ġfa iling", "Ġfail ing", "el son", "els on", "G reat", "Gr eat", "Gre at", "= i", ". rest", ".re st", ".r est", ".res t", "Ġ invite", "Ġinv ite", "Ġinvit e", "- disable", "-d isable", "-dis able", ". GroupBox", ".Group Box", "âĢĻ est", "âĢĻe st", "âĢĻes t", "Ġt ackle", "Ġtack le", "Ġtac kle", "g v", "et ter", "ette r", "ett er", "Ġ ),čĊ", "Ġ) ,čĊ", "Ġ), čĊ", "_ rules", "_r ules", "_rule s", "_ru les", ". warn", ".w arn", "function s", "fun ctions", "ĠChrist ians", "ĠChristian s", "Ġb acked", "Ġback ed", "Ġbac ked", "Ġ slider", "Ġs lider", "Ġsl ider", "Ġslide r", "Ġslid er", "Ġenjoy ing", "Ġenjo ying", "n est", "ne st", "nes t", "Ġh ij", "Ġhi j", "_ ms", "_m s", "/ /*", "// *", "An notations", "Annotation s", "Ġ Variables", "ĠVariable s", "ĠVari ables", "< V", "( server", "(s erver", "(serv er", "Ġ Oracle", "ĠOr acle", "e lements", "el ements", "element s", "ele ments", "elem ents", "Ġ organisation", "Ġorgan isation", "Ġorganis ation", "_ pointer", "_point er", "_po inter", "Ġ Headers", "ĠHe aders", "ĠHead ers", "ĠHeader s", "[ d", "Ġ deadline", "Ġdead line", "i ssa", "is sa", "iss a", "Ġ knife", "Ġkn ife", "Ġ NASA", "ĠN ASA", "ĠNAS A", "ĠNA SA", "Ġ Height", "ĠH eight", "ĠHe ight", "7 84", "78 4", "Ġ Async", "ĠA sync", "ĠAs ync", "Ġ venue", "Ġven ue", ". dom", ".d om", ".do m", "bour ne", "bou rne", "ĠH awai", "ĠHaw ai", "Ġ memo", "Ġm emo", "Ġme mo", "Ġmem o", "i ctions", "ict ions", "iction s", "Ġsur veillance", "Ġsurve illance", "o mi", "om i", "/ assets", "/as sets", "5 87", "58 7", "Ġ edu", "Ġe du", "Ġed u", "Ä Ľ", "Ġr oster", "Ġro ster", "Ġros ter", "Ġrost er", "Ġh ired", "Ġhi red", "Ġhire d", "Ġ Tok", "ĠT ok", "ĠTo k", "Ġ placement", "Ġpl acement", "Ġplace ment", "Ġplac ement", "ur ations", "uration s", "urat ions", "Ġ setState", "Ġset State", "ĠMag azine", "Ġhor ror", "Ġho rror", "Ġhorr or", "T ry", "Tr y", "Ġ lag", "Ġl ag", "Ġla g", "Ġ Everyone", "ĠEvery one", "t hur", "th ur", ") );čĊčĊ", ")) ;čĊčĊ", "));čĊ čĊ", ")); čĊčĊ", ". return", ".re turn", ".r eturn", ".ret urn", "Ġsy mp", "Ġsym p", "âĸĪ âĸĪ", "Ġn ights", "Ġnight s", "work er", "wor ker", "Ġ ale", "Ġa le", "Ġal e", "ennes see", ". step", ".s tep", ".st ep", "Ġs ynchronized", "Ġsynchron ized", "Ġsynchronize d", "4 87", "48 7", "o uri", "ou ri", "our i", "D oes", "Do es", ". change", ".ch ange", "f on", "fo n", ". setBackground", ".set Background", "ir cular", "irc ular", "4 76", "47 6", "+ -", "ĠC IA", "ĠCI A", "7 29", "72 9", "Ġ Jane", "ĠJ ane", "ĠJan e", "ĠJa ne", "Ġ Similar", "ĠS imilar", "ĠSim ilar", "- I", "level and", "lev eland", "Ġpro spect", "Ġpros pect", "_ found", "_f ound", "ĉ color", "ĉc olor", "ĉcol or", ".D iagnostics", ".Di agnostics", "Ġ announce", "Ġann ounce", "Ġannounc e", "Ġanno unce", "Ġass umes", "Ġassum es", "Ġassume s", "/ tr", "/t r", "Ġ bd", "Ġb d", "9 87", "98 7", "Ġ Carbon", "ĠC arbon", "ĠCar bon", "ĠCarb on", "Ġanal ys", "Ġanaly s", "Ġana lys", "5 64", "56 4", ". dest", ".d est", ".de st", ".des t", "n ik", "ni k", "Ġ Lie", "ĠL ie", "ĠLi e", "- index", "-in dex", "-ind ex", "Draw able", "Ġ TAG", "ĠT AG", "ĠTA G", "Ġ triangle", "Ġt riangle", "Ġtr iangle", "Ġtri angle", "Ġtriang le", "_ FLOAT", "_F LOAT", "ĉ ĉĠĠĠĠĠ", "ĉĉ ĠĠĠĠĠ", "ĉĉĠĠĠ ĠĠ", "ĉĉĠ ĠĠĠĠ", "ĉĉĠĠ ĠĠĠ", "ĉĉĠĠĠĠ Ġ", ". black", ".b lack", ".bl ack", "v ue", "vu e", "c uracy", "cur acy", "cura cy", "Ġa ffects", "Ġaff ects", "Ġaffect s", "9 06", "90 6", "Ġsur ely", "Ġsure ly", "S lider", "Sl ider", "Slide r", "u ki", "uk i", "c ery", "ce ry", "cer y", "Ġ unter", "Ġun ter", "Ġunt er", ". profile", ".pro file", ".pr ofile", ".prof ile", "or don", "ord on", "ordo n", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "le ave", "lea ve", "Ġsmart phone", "g ie", "gi e", "Ġcon spir", "Ġcons pir", "Ġ tutorial", "Ġt utorial", "Ġtut orial", "Ġtutor ial", "Ġtuto rial", "ç± »", "Ġ cab", "Ġc ab", "Ġca b", "7 65", "76 5", "Ġ Summary", "ĠSum mary", "* ĊĊ", "*Ċ Ċ", "ä h", "\" This", "\"T his", "Ġ slides", "Ġsl ides", "Ġslide s", "Ġslid es", "\" ", "() >", "c ycle", "cy cle", "cycl e", "ĠB ull", "ĠBul l", "ĠBu ll", "p aths", "path s", "pat hs", "pa ths", "Ġu np", "Ġun p", "Ġview DidLoad", "_ Model", "_M odel", "_Mode l", "_Mod el", "Ġ assertTrue", "Ġassert True", "Ġ rated", "Ġr ated", "Ġrate d", "Ġrat ed", "Ġra ted", "De cl", "Dec l", "ver ted", "vert ed", "verte d", "Ġ Dat", "ĠD at", "ĠDa t", "b rew", "br ew", "bre w", "Ġpoint ing", "M s", "Ġ Pointer", "ĠPoint er", "ĠPo inter", ") '", "_ non", "_n on", "_no n", "5 27", "52 7", "Ġ SEC", "ĠS EC", "ĠSE C", "Ġ yeah", "Ġy eah", "Ġye ah", "g ency", "ge ncy", "gen cy", "initial ize", "f ly", "fl y", "7 11", "71 1", "[ pos", "[p os", ", g", "T ele", "Te le", "Tel e", "0 34", "03 4", "Ġj oke", "Ġjo ke", "Ġ clause", "Ġcl ause", "Ġcla use", ". findById", ".find ById", ".findBy Id", "e nes", "en es", "ene s", "( instance", "(in stance", "(inst ance", "6 26", "62 6", " £", "9 15", "91 5", "Ġs lic", "Ġsl ic", "_ home", "_h ome", "_hom e", "Ġ */}Ċ", "Ġ*/ }Ċ", "_ pages", "_p ages", "_page s", "_pag es", "_pa ges", "( service", "(s ervice", "(serv ice", "9 05", "90 5", "R P", "Ġ Among", "ĠA mong", "ĠAm ong", ". getCurrent", ".get Current", ".getC urrent", "8 06", "80 6", "ã Ĥ¹", "ãĤ ¹", "Ġs lee", "Ġsl ee", "Ġsle e", "= [Ċ", ">[ Ċ", "o ler", "ol er", "ole r", "Ġli bert", "Ġlib ert", "Ġliber t", "Ġ `Ċ", "Ġ` Ċ", "Ġw enn", "Ġwe nn", "Ġwen n", "l ated", "la ted", "late d", "lat ed", "Ġ immune", "Ġimm une", "Ġimmun e", "( Node", "(N ode", "Ġ Problem", "ĠPro blem", "ĠProb lem", "ĠProble m", "Ġ Abs", "ĠA bs", "ĠAb s", "l ogs", "lo gs", "log s", "Ġ ../", "Ġ. ./", "Ġ.. /", "Ġ ADC", "ĠA DC", "ĠAD C", "Ġ }}\">Ċ", "Ġ} }\">Ċ", "Ġ}} \">Ċ", "Ġ}}\" >Ċ", "Ġ}}\"> Ċ", "> ');Ċ", ">' );Ċ", ">') ;Ċ", ">'); Ċ", "= b", "Ġ Wind", "ĠW ind", "ĠWin d", "ĠWi nd", "l ahoma", "lah oma", "Ġ allocate", "Ġal locate", "Ġall ocate", "Ġalloc ate", "Ġallo cate", "o rian", "or ian", "oria n", "ori an", "Ġpr escription", "Ġpre scription", "Ġpres cription", "- quality", "-q uality", "-qu ality", "Ġ Mayor", "ĠMay or", "ĠMa yor", "ĠMayo r", "8 55", "85 5", "in ely", "ine ly", "inel y", "end foreach", "Ġ Complex", "ĠCom plex", "ĠComp lex", "ĠComple x", "k om", "ko m", "7 09", "70 9", "T Y", "7 90", "79 0", "] ].", "]] .", ". Style", ".St yle", "_ many", "_m any", "_man y", "_ma ny", "', '$", "',' $", "Ġbar rier", "Ġbarr ier", "Ġ Fetch", "ĠF etch", "ĠFet ch", "Ġ Marvel", "ĠMar vel", "Ġres ist", "о го", "ог о", "b idden", "bi dden", "bid den", "Ġ Runnable", "ĠR unnable", "ĠRun nable", ": false", ":f alse", "8 99", "89 9", "Ġbuild s", "Ġ Stage", "ĠSt age", "ĠSta ge", "Ġ dub", "Ġd ub", "Ġdu b", "em po", "emp o", ". site", ".s ite", ".si te", "5 58", "55 8", "; ĊĊĊĊ", ";Ċ ĊĊĊ", ";ĊĊ ĊĊ", ";ĊĊĊ Ċ", "9 94", "99 4", "Ġ Denver", "ĠDen ver", "Ġre vel", "Ġrev el", "Ġreve l", "Ġtrigger ed", "Ġ dice", "Ġd ice", "Ġdi ce", "Ġdic e", "_ fail", "_f ail", "_fa il", "Ġ gc", "Ġg c", "8 33", "83 3", "5 89", "58 9", "ĉ X", "Ġ Throwable", "ĠTh rowable", "ĠThrow able", "7 75", "77 5", ". router", ".r outer", ".route r", ".ro uter", "ĠRe volution", "ĠRev olution", "ÑĢ а", "_ NON", "_N ON", "_NO N", "0 55", "05 5", "Ł ¥", "5 78", "57 8", "Ġ elder", "Ġe lder", "Ġel der", "Ġelde r", "Ġab road", "Ġ е", "ĠÐ µ", "Ġ Adult", "ĠAd ult", "b lr", "bl r", "g lyphicon", "glyph icon", "6 13", "61 3", "Ġprom oting", "Ġpromot ing", "Ġpromo ting", "Ġ iz", "Ġi z", "Ġ Solid", "ĠS olid", "ĠSo lid", "ĠSol id", "6 45", "64 5", "_ loader", "_l oader", "_lo ader", "_load er", "ear ly", ". enabled", ".en abled", ".enable d", "- edit", "-e dit", "-ed it", "Ġ UL", "ĠU L", "_ play", "_p lay", "_pl ay", "Ġ Interrupt", "ĠInt errupt", "ĠInter rupt", "ĠInterr upt", "Ġadv antages", "Ġadvant ages", "Ġadvantage s", "u cle", "uc le", "Ġmechan ical", "Ġmechanic al", "Ġmech anical", ".table LayoutPanel", "Ġ Working", "ĠWork ing", "ĠWor king", "Ġ anonymous", "Ġan onymous", "Ġanonym ous", "Ġanon ymous", "R ating", "Ra ting", "ig ious", "igi ous", "_ phone", "_p hone", "_ph one", ".addAction Listener", "Ġf ran", "Ġfr an", "Ġfra n", "un den", "und en", "unde n", "Ġ *)&", "Ġ* )&", "Ġ*) &", "_ bool", "_b ool", "_bo ol", "ul ative", "Ġ cone", "Ġc one", "Ġcon e", "Ġco ne", "Ġ Mult", "ĠM ult", "ĠMu lt", "ĠMul t", "Ġm ö", "Ġ Forward", "ĠFor ward", "] ):Ċ", "]) :Ċ", "]): Ċ", "Ġconvin ced", "Ġconvince d", "Ġconvinc ed", "act ed", "ac ted", "6 43", "64 3", "ãģ ĵ", "Ġ Configure", "ĠCon figure", "ĠConfig ure", "ĠConf igure", "Ġce iling", "Ġceil ing", "D er", "De r", "Ġpass engers", "Ġpassenger s", "G roups", "Group s", "Gro ups", "Ġs occer", "Ġsoc cer", "/ W", "av iors", "avior s", "avi ors", "s with", "sw ith", "Ġ Zone", "ĠZ one", "ĠZo ne", ". Options", ".O ptions", ".Option s", "Ġ Mom", "ĠM om", "ĠMo m", "i eder", "ie der", "ied er", "Array s", "Ar rays", "Arr ays", "Ġtreat ments", "Ġtreatment s", "Ġprotect ing", "f ac", "fa c", "Ġ pickle", "Ġp ickle", "Ġpick le", "Ġpic kle", "Button Item", "7 13", "71 3", "Ġ blocking", "Ġb locking", "Ġbl ocking", "Ġblock ing", "Ġbloc king", "st rar", "str ar", "stra r", "à ²", "Ġ Export", "ĠEx port", "ĠExp ort", "ĠExpo rt", "Ġth rew", "Ġthr ew", "ot ta", "ott a", "Ġ BASE", "ĠB ASE", "ĠBAS E", "ĠBA SE", ". ws", ".w s", ".LE ADING", "order By", "_ delay", "_d elay", "_de lay", "_del ay", "Ġ Pu", "ĠP u", ". dll", ".d ll", "Ġ Choose", "ĠCh oose", "ĠCho ose", "9 92", "99 2", "Pol ice", "Po lice", "Ġ BEGIN", "ĠB EGIN", "ĠBE GIN", "bo xes", "box es", "Ġ diamond", "Ġd iamond", "Ġdiam ond", "Ġdia mond", ", l", "Ġ ĉĉĉ", "Ġĉ ĉĉ", "Ġĉĉ ĉ", "Ġc urious", "Ġcur ious", "Ġcu rious", "6 24", "62 4", "t v", "Ġerot ische", "Ġerotisch e", "ack ages", "ackage s", "ĉ Set", "ĉS et", "T ick", "Ti ck", ". border", ".b order", "static method", "Ġ cher", "Ġc her", "Ġch er", "Ġche r", "in voice", "inv oice", "Ġc ru", "Ġcr u", "Ġde fect", "Ġdef ect", "Ġdefe ct", "_ metadata", "_m etadata", "_meta data", "_met adata", "re lation", "rel ation", "i kan", "ik an", "ika n", "[ N", "( Qt", "(Q t", "( Base", "(B ase", "æģ ¯", "b eat", "be at", "Ġ Empty", "ĠEm pty", "ĠEmp ty", "ĉ o", "_ shift", "_s hift", "_sh ift", "Ġreg ret", "7 22", "72 2", "Th ose", "Tho se", "C ent", "Ce nt", "ĠPort ug", "ĠIs lands", "ĠIsl ands", "ĠIsland s", "Ġ TIME", "ĠT IME", "ĠTIM E", "ĠTI ME", "Man agement", "Manage ment", "Mana gement", "9 96", "99 6", "- sp", "-s p", "5 39", "53 9", "ê me", "êm e", "Ġn otion", "Ġnot ion", "Ġno tion", "un ifu", "uni fu", "P K", "8 26", "82 6", "è¡ Į", "ĠC URLOPT", "ĠCUR LOPT", "ĠCURL OPT", "\\ \"\\", "\\\" \\", "U V", "ç º", "d ra", "dr a", "c ou", "co u", "= `", "Ġ Destroy", "ĠD estroy", "ĠDe stroy", "ĠDest roy", "r p", ". cancel", ".c ancel", ".can cel", "G G", "r untime", "run time", "Ġ Vue", "ĠV ue", "ĠVu e", "Ġpro gressive", "Ġprogress ive", "Ġprog ressive", "/ services", "/s ervices", "/service s", "Ġ runner", "Ġr unner", "Ġrun ner", "_ FRAME", "_FR AME", ". ToolStripMenuItem", ".ToolStrip MenuItem", "Ġ ','", "Ġ' ,'", "Ġ', '", "d elay", "de lay", "del ay", "= utf", "=u tf", "Ġscreen ing", "Ġscre ening", "Ġp ulling", "Ġpull ing", "Ġpul ling", "o mas", "om as", "oma s", "Ġ anth", "Ġa nth", "Ġan th", "Ġant h", "- new", "-n ew", "-ne w", "/ local", "/l ocal", "/lo cal", "Ġ iPad", "Ġi Pad", "ĠiP ad", "Ġ twitter", "Ġt witter", "Ġtw itter", "Ġd ying", "Ġdy ing", "Ġhe aven", "Ġheav en", "Ġ UInt", "ĠU Int", "ĠUI nt", "Ġ Senator", "ĠSen ator", "Ġpre sum", "Ġpres um", "Ġ Walker", "ĠW alker", "ĠWalk er", "ĠWal ker", "Ġover come", "e tection", "et ection", "ete ction", "etect ion", "Ġemb arrass", "Ch ina", "Chi na", "6 39", "63 9", "In clude", "Inc lude", "R OLL", "RO LL", "ROL L", "Ġ dataType", "Ġdata Type", "D avid", "Da vid", "ภ£", "l op", "lo p", "- month", "-m onth", "-mon th", "Ġ scar", "Ġs car", "Ġsc ar", "Ġsca r", "Ġ Safe", "ĠS afe", "ĠSaf e", "ĠSa fe", "Ġ ****************************************************************", "Ġ******************************** ********************************", "Ġ******** ********************************************************", "Ġ**************** ************************************************", "Ġ************************ ****************************************", "Ġ**************************************** ************************", "Ġ******************************************************** ********", "Ġ************************************************ ****************", "Ġaccess ories", "Ġaccessor ies", "Ġr amp", "Ġra mp", "Ġram p", "_ USE", "_U SE", "_US E", "Ġcon trad", "Ġcont rad", "Ġcontr ad", "Ġcontra d", ") )]Ċ", ")) ]Ċ", "))] Ċ", "Ġp rest", "Ġpr est", "Ġpre st", "Ġpres t", "Ġ HR", "ĠH R", "ĠR ap", "ĠRa p", "Ġ usize", "Ġu size", "Ġus ize", "Ġ capability", "Ġcap ability", "Ġc ort", "Ġco rt", "Ġcor t", "- next", "-n ext", "-ne xt", "0 77", "07 7", "6 27", "62 7", "Ġbur den", "8 22", "82 2", "_ reader", "_re ader", "_read er", "Ġ @@", "Ġ@ @", "reg ular", "Ġ Ka", "ĠK a", "0 36", "03 6", "M AN", "MA N", "Ġ astr", "Ġa str", "Ġas tr", "Ġast r", "Ġ' ')Ċ", "Ġ'' )Ċ", "Ġ'') Ċ", "Ġ fed", "Ġf ed", "Ġfe d", "Ġp arsing", "Ġpar sing", "Ġpars ing", "Ġ Years", "ĠY ears", "ĠYear s", "ĠYe ars", "Ġ broker", "Ġb roker", "Ġbr oker", "Ġbro ker", "Ġbroke r", "\": {\"", "Ġ akt", "Ġa kt", "Ġak t", "In ventory", "ab eled", "abel ed", "abe led", "Ġarg parse", "* ******Ċ", "** *****Ċ", "**** ***Ċ", "****** *Ċ", "*** ****Ċ", "***** **Ċ", "******* Ċ", "vers ation", "Ġ cord", "Ġc ord", "Ġco rd", "Ġcor d", "Ġ Ti", "ĠT i", "Ġ hopefully", "Ġhope fully", "Ġhop efully", "Ġhopeful ly", "Ġ ah", "Ġa h", "v erb", "ver b", "ve rb", "Ġst olen", "Ġstole n", "Ġsto len", "Ġstol en", ". Entry", ".En try", ".Ent ry", "Ġex pecting", "Ġexpect ing", "O rientation", "Ġ powered", "Ġp owered", "Ġpower ed", "Ġpow ered", "Ġ persist", "Ġp ersist", "Ġpers ist", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "' ]);", "'] );", "']) ;", "' )),Ċ", "') ),Ċ", "')) ,Ċ", "')), Ċ", "Ġ Cash", "ĠC ash", "ĠCas h", "ĠCa sh", "ĉ item", "ĉi tem", "ĉit em", "8 18", "81 8", "g rades", "gr ades", "grad es", "grade s", "gra des", "r opol", "ro pol", "rop ol", "b asic", "ba sic", "bas ic", "Ġ \");čĊ", "Ġ\" );čĊ", "Ġ\") ;čĊ", "Ġ\"); čĊ", "Ġa wards", "Ġaw ards", "Ġaward s", "( range", "(r ange", "(ra nge", "- all", "-a ll", "-al l", "Ġ IBOutlet", "ĠIB Outlet", "Ġ Indeed", "ĠInd eed", "---------------------------------------------------------------- ------------", "------------ ----------------------------------------------------------------", "------------------------------------------------ ----------------------------", "------ ----------------------------------------------------------------------", "---------------------------- ------------------------------------------------", "---------------------------------------------------------------------- ------", "Ġstom ach", "Ġsto mach", "Ġ flower", "Ġf lower", "Ġfl ower", "Ġflow er", "Ġflo wer", "Ġs ew", "Ġse w", "_ times", "_t imes", "_time s", "_tim es", "_ti mes", "a vis", "av is", "avi s", "Q String", "QS tring", "Ġ Routes", "ĠR outes", "ĠRoute s", "ĠRo utes", "ĠRou tes", "ĠRout es", "_ prot", "_p rot", "_pro t", "_pr ot", "Ġcom edy", "Ġcome dy", "Ġcomed y", "Ġ logout", "Ġlog out", "Ġlogo ut", "Ġwood en", "Ġwo oden", "Ġwoo den", "Ġ poster", "Ġp oster", "Ġpos ter", "Ġpost er", "Ġpo ster", "Ġposte r", "p iece", "pi ece", "pie ce", ". Join", ".J oin", "Ġ Pok", "ĠP ok", "ĠPo k", "cel ona", "m utex", "mut ex", "mu tex", "mute x", "; čĊčĊčĊ", ";čĊ čĊčĊ", ";čĊčĊ čĊ", "Ġst rikes", "Ġstr ikes", "Ġstri kes", "Ġstrike s", "7 87", "78 7", "Lo aded", "Load ed", ") arg", ")a rg", "e sa", "es a", "Un ited", "Unit ed", "Uni ted", "E p", "P ELL", "PE LL", "8 07", "80 7", "Ġ Atlantic", "ĠAtl antic", "ul let", "ull et", "ulle t", "6 52", "65 2", "ap ple", "app le", "appl e", "Ġsett led", "Ġsettle d", "a con", "ac on", "aco n", "Ġ printer", "Ġpr inter", "Ġprint er", "Ġprin ter", "Ġ GC", "ĠG C", "å® ļ", "Ġrender ed", "Ġrend ered", ", âĢĻ", "he it", "hei t", "s ocial", "so cial", "soc ial", ". ge", ".g e", "7 14", "71 4", "Ġ Rick", "ĠR ick", "ĠRic k", "ĠRi ck", "ĠU tah", "ĠUt ah", "g ot", "go t", "on ical", "onic al", "oni cal", "onica l", "Ġ Scroll", "ĠS croll", "ĠSc roll", "ĠScr oll", "ĠSc iences", "ĠScience s", "ĠSci ences", "Ġ jug", "Ġj ug", "Ġju g", "Ġa mpl", "Ġam pl", "Ġamp l", "en ti", "ent i", "LE FT", "Ġ tabs", "Ġt abs", "Ġtab s", "Ġta bs", "Ġenorm ous", ". getKey", ".get Key", "l ocate", "lo cate", "loc ate", ". EX", ".E X", ". storage", ".st orage", ". We", ".W e", "Ġ toast", "Ġto ast", "Ġ Additionally", "ĠAdd itionally", "ĠAdditional ly", "ĠAddition ally", "8 82", "88 2", "Ġ NOW", "ĠN OW", "ĠNO W", "5 47", "54 7", "_ UPDATE", "_UP DATE", "Ġtrans ferred", "Ġtransfer red", "t ha", "th a", ". Display", ".D isplay", ".Dis play", "_ ui", "_u i", "ID EO", "IDE O", "Ġmeaning ful", "ĠMos cow", ", this", ",t his", "Ġ Victoria", "ĠVict oria", "ĠVictor ia", "æĶ ¹", "Ġ ÐŁ", "ĠÐ Ł", ". stack", ".st ack", "ĠB arn", "ĠBar n", "ĠBa rn", "pared Statement", ": string", ":s tring", ":str ing", "Ġ bij", "Ġb ij", "Ġbi j", "Ġ STATE", "ĠST ATE", "ĠSTAT E", "ĠSTA TE", "Ġemploy ers", "Ġemployer s", "ĉ input", "ĉin put", "( |", "Ġ lex", "Ġl ex", "Ġle x", "in voke", "inv oke", "ĉ num", "ĉn um", "+ +,", "++ ,", "at ial", "ati al", "or ses", "ors es", "orse s", "Ġ fork", "Ġf ork", "Ġfor k", "Ġfo rk", "_ txt", "_t xt", "_tx t", "ĠAnt onio", "ĠAnton io", "Ġ (<", "Ġ( <", "a verse", "av erse", "aver se", "avers e", "Ġdev ast", "ãĢ Ģ", ". Dec", ".D ec", ".De c", "ĠG ard", "ĠGar d", "ĠGa rd", "/ ui", "/u i", ". %", "t ri", "tr i", "Ġ rolled", "Ġroll ed", "Ġrol led", "Value Pair", "it ten", "itt en", "itte n", "ĠT her", "ĠThe r", "ĠTh er", "Ġv rou", "Ġvr ou", "Ġ Flow", "ĠF low", "ĠFl ow", "ĠFlo w", "Ġ Finance", "ĠF inance", "ĠFin ance", "Ġ Comb", "ĠC omb", "ĠCom b", "ĠCo mb", "H C", ". setVisible", ".set Visible", "i sl", "is l", "Ġ pk", "Ġp k", "7 73", "77 3", "Ġup set", "Ġups et", "( raw", "(r aw", "(ra w", "ĠV ice", "ĠVi ce", "ĠVic e", "e atures", "ea tures", "eature s", "eat ures", "Ġ Lang", "ĠL ang", "ĠLa ng", "ĠLan g", "0 29", "02 9", "Lo oking", "Look ing", "7 67", "76 7", "Ġ AST", "ĠA ST", "ĠAS T", "Ġt rips", "Ġtr ips", "Ġtri ps", "Ġtrip s", "Ġ Justin", "ĠJ ustin", "ĠJust in", "ĠJu stin", "b rowser", "browse r", "=\" '.$", "=\"' .$", "=\"'. $", ". vertices", ".vert ices", "8 21", "82 1", "- co", "-c o", "} /{", "}/ {", "Ġ ?,", "Ġ? ,", "Ġ Domin", "ĠD omin", "ĠDo min", "ĠDom in", "ĠBe lg", "ĠBel g", "\" <", "Ġsup pose", "Ġsupp ose", "a ddy", "ad dy", "add y", "Ġwalk s", "Ġwal ks", "6 88", "68 8", "ER RU", "ERR U", "_ filters", "_f ilters", "_filter s", "_fil ters", "_filt ers", "Pre ferred", "s cene", "sc ene", "е Ñģ", "ĠAff airs", "Ġ\" #{", "Ġ\"# {", "Ġon Submit", "Ġ stocks", "Ġst ocks", "Ġstock s", "Ġsto cks", "/ view", "/v iew", "g ree", "gr ee", "gre e", "- get", "-g et", "9 03", "90 3", "h it", "hi t", "J o", ". getC", ".get C", "7 25", "72 5", "Init ialized", "Initial ized", "Initialize d", "ÑĤ и", "c uts", "cut s", "cu ts", "( Type", "(T ype", "ĠAg reement", "ĠAgree ment", "ĠViet nam", "Ġ /*!", "Ġ/* !", "Ġ pizza", "Ġp izza", "Ġpi zza", "- view", "-v iew", "_ em", "_e m", "Ġ lhs", "Ġl hs", "Ġlh s", "Ġm uy", "Ġmu y", "Ġ Ident", "ĠI dent", "ĠId ent", "ĠIde nt", "Ġ Friends", "ĠF riends", "ĠFriend s", "ĠFri ends", "0 61", "06 1", "Ġab und", "_ AD", "_A D", ". timestamp", ".t imestamp", ".time stamp", "- '", "Ġ duplicate", "Ġd uplicate", "Ġdup licate", "Ġduplic ate", "Ġh unting", "Ġhun ting", "Ġhunt ing", "Ġreg ulatory", "Ġregul atory", "Ġregulator y", "i ao", "ia o", "am ous", "amo us", "ĠEnt ertainment", "ĠEnter tainment", "[ A", "iat ric", "_ CLIENT", "_CL IENT", "_CLI ENT", "Ġ Kids", "ĠK ids", "ĠKi ds", "ĠKid s", "/ pkg", "/p kg", "B reak", "Bre ak", ") ));ĊĊ", ")) );ĊĊ", ")));Ċ Ċ", "))) ;ĊĊ", "))); ĊĊ", "Ġ Shape", "ĠS hape", "ĠSh ape", "ĠSha pe", "Ġrel ating", "Ġrelat ing", "Int errupt", "Inter rupt", "able Opacity", "em bre", "emb re", "embr e", "Ġm ystery", "Ġmy stery", "Ġmys tery", "Ġmyst ery", "Ġmyster y", "Ġjournal ists", "Ġjournalist s", "r itable", "ri table", "rit able", "rita ble", ". Link", ".L ink", "Ġst opping", "Ġstop ping", "Ġsto pping", "C RET", "CR ET", "CRE T", ". DB", ".D B", "Ġpop ularity", "Ġpopular ity", "Ġpopul arity", "Ġ gew", "Ġg ew", "Ġge w", "Ġi mpr", "Ġim pr", "Ġimp r", "set Value", "F LAG", "FL AG", "ĉ max", "ĉm ax", "Ġb ake", "Ġba ke", "Ġbak e", "w y", "ĠE conomic", "ĠEcon omic", "ĠEc onomic", "ĠEconom ic", "Ġen contr", "Ġ fname", "Ġf name", "Ġfn ame", "/ de", "/d e", "R ank", "Ra nk", "Ġ bugs", "Ġb ugs", "Ġbu gs", "Ġbug s", ". sm", ".s m", "Ġ median", "Ġm edian", "Ġmed ian", "Ġmedia n", "Ġmedi an", "D OWN", "DO WN", "Ġ Sure", "ĠS ure", "ĠSur e", "ĠSu re", "At Index", "Ġ Dick", "ĠD ick", "ĠDi ck", "Ġ (__", "Ġ( __", "Ġ(_ _", ". delta", ".d elta", ".del ta", "F r", "Ġsuggest ing", "Ġ RecyclerView", "ĠRec yclerView", ", e", "ST ART", "STAR T", "STA RT", "/ ****************************************************************************", "/************************************************************************ ****", "/**************************************************************** ************", "/******************************************************** ********************", "/************************************************ ****************************", "x ford", "xf ord", "Ġ receipt", "Ġre ceipt", "Ġrece ipt", "CL AIM", "CLA IM", "read only", "9 68", "96 8", "Ġeng aging", "6 19", "61 9", "C a", "as ma", "asm a", "Ġens uring", "Eng lish", "ĠV ancouver", "h yth", "hy th", "Ġpurch asing", "Ġ PI", "ĠP I", ". word", ".w ord", "( sp", "(s p", ". home", ".h ome", ".hom e", ": def", ":d ef", "Ġg ig", "Ġgi g", "5 74", "57 4", "6 71", "67 1", "Ġ Ve", "ĠV e", "f orum", "fo rum", "for um", "Ġ Mitch", "ĠM itch", "ĠMit ch", "B ay", "Ba y", "_ FL", "_F L", "6 51", "65 1", "Ġs oll", "Ġso ll", "Ġsol l", "5 77", "57 7", "_ columns", "_column s", "Ġminor ity", "b ird", "bi rd", "bir d", "Ġh anded", "Ġhand ed", "Ġhan ded", "S SL", "SS L", "ST AT", "STA T", "Ġnerv ous", "Ġner vous", "ĥ ½", "Ġ filePath", "Ġfile Path", "C REATE", "CRE ATE", "A w", "Ġp ens", "Ġpe ns", "Ġpen s", "8 35", "83 5", "s eed", "se ed", "see d", "Ġ Compute", "ĠCom pute", "ĠComp ute", "ĠComput e", "o lk", "ol k", "5 94", "59 4", "Ġ Asset", "ĠAs set", "ĠAss et", "r each", "re ach", "rea ch", "' ),čĊ", "') ,čĊ", "'), čĊ", "n avigation", "nav igation", "L F", "/ util", "/u til", "Ġ Pub", "ĠP ub", "ĠPu b", "Ġ âĶ", "Ġâ Ķ", "c ion", "ci on", "cio n", "# #Ċ", "## Ċ", "0 72", "07 2", "I II", "II I", "Tag Name", "Ġa mid", "Ġam id", "Ġami d", "per mission", "perm ission", "if iable", "ifi able", "x FFFFFFFF", "xFF FFFFFF", "xFFFF FFFF", "xFFFFFF FF", "н и", ". Buffer", ".B uffer", "_ irq", "_i rq", "_ir q", "d ark", "da rk", "dar k", "Ġ retval", "Ġret val", ". fire", ".f ire", ".fi re", "p roduction", "pro duction", "product ion", "produ ction", "prod uction", ". listen", ".l isten", ".list en", ".li sten", "Ġ Weather", "ĠWe ather", "Ġbu yers", "Ġbuy ers", "Ġbuyer s", ". ne", ".n e", "e rp", "er p", "ĠP ent", "ĠPe nt", "ĠPen t", "6 99", "69 9", "Ġw elfare", "Ġwel fare", "Ġ pageSize", "Ġpage Size", "ĠSt adium", "ĠStad ium", "er ta", "ert a", "Ġ lev", "Ġl ev", "Ġle v", "am pa", "amp a", "P ager", "Page r", "Pa ger", "Pag er", "6 65", "66 5", "Ġ charging", "Ġch arging", "Ġchar ging", "Ġcharg ing", "Ġ Netflix", "ĠNet flix", "| null", "_ random", "_r andom", "_rand om", ". xpath", ".x path", "Ġs tere", "Ġst ere", "Ġste re", "Ġster e", "Ġ ISIS", "ĠIS IS", "ĠISI S", "pon ses", "ponse s", "pons es", "( loc", "(l oc", "(lo c", "5 66", "56 6", "ey ond", "Ġ Official", "ĠOff icial", "6 57", "65 7", "ĠMary land", "Data Type", "_ par", "_p ar", "_pa r", "{ },", "{} ,", "Ġ Enjoy", "ĠEn joy", "7 27", "72 7", "_ SHIFT", "_SH IFT", "ĠA wards", "ĠAward s", "ĠAw ards", "_ ENTRY", "_EN TRY", "_ENT RY", "Ġseem ingly", "Ġseeming ly", "ent icate", "entic ate", "enti cate", "Ġhe arts", "Ġheart s", "Ġhear ts", "5 83", "58 3", "_ ;ĊĊ", "_;Ċ Ċ", "_; ĊĊ", "ĠH IV", "ĠHI V", "Ġin divid", "Ġind ivid", "Ġindiv id", "Ġ Flag", "ĠF lag", "ĠFl ag", "ĠFla g", "_ ctrl", "_c trl", "_ct rl", "_ctr l", "Ġ Callback", "ĠC allback", "ĠCall back", ", z", "Ġ GPU", "ĠG PU", "ĠGP U", "ĉ obj", "ĉo bj", "ĉob j", "Ġ Phoenix", "ĠPh oenix", "Ġ BUS", "ĠB US", "ĠBU S", "9 07", "90 7", "Ġr ubber", "Ġrub ber", "_ AUTH", "_A UTH", "_AUT H", "ĠS olutions", "ĠSol utions", "ĠSolution s", "( location", "(l ocation", "(loc ation", "(lo cation", "Variable s", "Vari ables", ". setEnabled", ".set Enabled", "_ high", "_h igh", "_hi gh", "W O", "G esture", "Ġ retry", "Ġre try", "Ġr etry", "Ġret ry", "Ġretr y", "Ġobject ForKey", "allow een", "allo ween", "Ġ mos", "Ġm os", "Ġmo s", "Ġ Cele", "ĠC ele", "ĠCe le", "ĠCel e", "Ġi kke", "Ġik ke", "( cell", "(c ell", "Ġ MODE", "ĠM ODE", "ĠMO DE", "ĠMOD E", "r ena", "re na", "ren a", "Ġdes cribing", "Ġdescri bing", "6 41", "64 1", "Ġ phi", "Ġp hi", "Ġph i", "Ġ rd", "Ġr d", "Ġde serve", "Ġdes erve", "Ġdese rve", "Ġdeser ve", "Ġw heels", "Ġwheel s", "Ġwhe els", "å¸ Ĥ", "Ġcr itics", "Ġcrit ics", "Ġcritic s", "Ġcri tics", "7 55", "75 5", "N amespace", "Name space", "Names pace", "Ġ Fra", "ĠF ra", "ĠFr a", "Ġ ĊĊĊĊ", "ĠĊ ĊĊĊ", "ĠĊĊ ĊĊ", "ĠĊĊĊ Ċ", "Ġ alla", "Ġa lla", "Ġal la", "Ġall a", "Ġre quiring", "Ġrequ iring", "æľ Ł", "ut ation", "uta tion", "Ġdel ayed", "Ġdelay ed", "Ġadministr ative", "Ġ bay", "Ġb ay", "Ġba y", ". hidden", ".h idden", "T ex", "Te x", "0 51", "05 1", "Ġbound aries", "Ġ ]);ĊĊ", "Ġ] );ĊĊ", "Ġ]);Ċ Ċ", "Ġ]) ;ĊĊ", "Ġ]); ĊĊ", "Ġ Following", "ĠFollow ing", "~ /", "F i", "_ conv", "_con v", "_co nv", "_ TITLE", "_T ITLE", "Ġdes de", "I CollectionView", "ICollection View", "A lias", "Al ias", "Ali as", "Ġ bite", "Ġb ite", "Ġbit e", "Ġbi te", "p atient", "pat ient", "_ COMMAND", "_COM MAND", "_COMM AND", "Com pleted", "Complete d", "Comp leted", "Comple ted", "ĉ elif", "ĉe lif", "ĉel if", "( <", "B usiness", "Bus iness", "Ġ Pool", "ĠP ool", "ĠPo ol", "Ġpurs ue", "Ġ Ban", "ĠB an", "ĠBa n", "_ steps", "_st eps", "_step s", "_ste ps", "_ DECL", "_DE CL", "_DEC L", "um ble", "umb le", "Ġ combo", "Ġc ombo", "Ġcom bo", "Ġcomb o", "Ġ Layer", "ĠL ayer", "ĠLa yer", "ĠLay er", ". xr", ".x r", "Ġ dup", "Ġd up", "Ġdu p", "- --------", "-- -------", "---- -----", "-------- -", "--- ------", "----- ----", "------ ---", "------- --", "6 28", "62 8", "Ġ modifier", "Ġmod ifier", "r ob", "ro b", "r ez", "re z", "6 96", "69 6", "Ġath letes", "Ġathlete s", "U sed", "Us ed", "Use d", "w ear", "we ar", "8 15", "81 5", "Ġleg itimate", "Ġlegit imate", "Ġlegitim ate", "Ġ \"ĊĊ", "Ġ\" ĊĊ", "Ġ\"Ċ Ċ", "Ġ hv", "Ġh v", "S td", "St d", "0 37", "03 7", "Ġ Hold", "ĠH old", "ĠHol d", "ĠHo ld", "Ġsurv iv", "ĠAll iance", "Ġ Early", "ĠEar ly", "ĠEarl y", "7 78", "77 8", "Beh avior", "( font", "(f ont", "/ libs", "/lib s", "/l ibs", "/li bs", "Ġ rectangle", "Ġrect angle", "Ġs inger", "Ġsi nger", "Ġsin ger", "Ġsing er", "Ġ amp", "Ġa mp", "Ġam p", "Equal To", "Ġ \".\"", "Ġ\" .\"", "Ġ\". \"", "Ġgirl friend", "å ±", "l inear", "li near", "line ar", "lin ear", "o bserv", "ob serv", "obs erv", "Ġpi ù", "Ġcom plement", "Ġcomp lement", "Ġcomple ment", "Ġcompl ement", "With Value", "( password", "(p assword", "(pass word", "t ake", "ta ke", "tak e", "Bl ank", "Ġ Compar", "ĠCom par", "ĠCo mpar", "ĠComp ar", "' \",", "'\" ,", "_ policy", "_p olicy", "_pol icy", "m ongoose", "mongo ose", "mong oose", "_ FAILED", "_FAIL ED", "_FA ILED", ". report", ".re port", ".repo rt", ".rep ort", "R atio", ".Perform Layout", "7 47", "74 7", "us able", "usa ble", "m ers", "mer s", "me rs", "_ render", "_re nder", "_r ender", "PE ED", "7 72", "77 2", "Ġle sb", "Ġles b", "ĉ E", "_ tool", "_t ool", "_to ol", "Ġl adies", "Ġlad ies", "9 08", "90 8", "о Ñģ", ") )))Ċ", ")) ))Ċ", "))) )Ċ", ")))) Ċ", "; ;;;", ";; ;;", ";;; ;", ". dot", ".d ot", ".do t", "Ġ nest", "Ġn est", "Ġne st", "Ġnes t", "pe ak", "uk kit", "e ca", "ec a", "_ SW", "_S W", "Ġ &(", "Ġ& (", "ĠOk lahoma", "Ġb anking", "Ġbank ing", "Ġban king", "5 69", "56 9", "Ġ Nintendo", "ĠN intendo", "7 52", "75 2", "Ġre produce", "Ġrep roduce", "Ġreprodu ce", "Ġrepro duce", "_ elements", "_e lements", "_element s", "_el ements", "_elem ents", "_ele ments", "_ mac", "_m ac", "_ma c", "pr oxy", "pro xy", "prox y", "Ġremark able", "} /${", "}/ ${", "Ġ outs", "Ġo uts", "Ġout s", "Ġou ts", ".has Next", "M ODE", "MO DE", "MOD E", "6 58", "65 8", "Ġ anime", "Ġan ime", "Ġanim e", "Ġani me", ". conn", ".c onn", ".con n", ".co nn", "Un ique", "Uni que", "D om", "Do m", "Ġimport antly", "Ġimportant ly", "i tty", "it ty", "itt y", "Ġju ice", "T w", "ĠPart ners", "ĠPartner s", "Ġatt acking", "Ġattack ing", "Ġport able", "Ġpor table", "Ġporta ble", "am iento", "ami ento", "amient o", ". PictureBox", ".P ictureBox", ". gen", ".g en", ".ge n", "Ġopt imal", "Ġoptim al", "5 82", "58 2", "Ġre cre", "Ġrec re", "Ġjournal ist", "Ġ Extract", "ĠEx tract", "ĠExt ract", "ĠExtra ct", "ĠExtr act", "Ġ Moreover", "ĠMore over", "Ġ marginTop", "Ġmargin Top", ". Ap", ".A p", "Ġf iring", "Ġfi ring", "Ġfir ing", "N aN", "Na N", "ĉ template", "ĉt emplate", "ĉtemp late", "а д", "аР´", ". En", ".E n", "Ġdef ence", "Ġdefe nce", "Ġ Tel", "ĠT el", "ĠTe l", "i len", "il en", "ile n", "j an", "ja n", "= data", "=d ata", "Ġ Url", "ĠU rl", "ĠUr l", "Ġ Reuters", "ĠRe uters", "( total", "(t otal", "(to tal", "ĠF ifth", "ĠFif th", "Ġes says", "Ġess ays", "Ġessay s", "Ġessa ys", "Ġinterpret ation", "Ġch arity", "Ġchar ity", "Ġ Rules", "ĠR ules", "ĠRule s", "ĠRu les", "Ġ subsection", "Ġsub section", "Ġsubs ection", "st yled", "style d", "sty led", "styl ed", "a zer", "az er", "aze r", "l ags", "la gs", "lag s", "L IST", "LI ST", "Ġ uploaded", "Ġup loaded", "Ġupload ed", "Ġ trash", "Ġtr ash", "Ġtra sh", "Ġtras h", "Ġ registr", "Ġreg istr", "Ġregist r", "Ġ seller", "Ġs eller", "Ġse ller", "Ġsell er", "Ġsel ler", "> ';čĊ", ">' ;čĊ", ">'; čĊ", "Ġ startTime", "Ġstart Time", "ç Ļ", "s y", "( HttpServletRequest", "(Http ServletRequest", "Ġ trap", "Ġt rap", "Ġtr ap", "Ġtra p", "G C", "Ġ embedded", "Ġembed ded", "Ġsur rounded", "Ġsurround ed", "8 16", "81 6", "i mits", "im its", "imit s", "imi ts", "T X", "yl inder", "6 85", "68 5", "Ġ Fal", "ĠF al", "ĠFa l", "Ġsent ences", "Ġsentence s", "Ġ Ja", "ĠJ a", "IF ICATION", "IFIC ATION", "we apon", "o vation", "ov ation", "ova tion", "ovat ion", "Ġ coat", "Ġco at", "Ġinter pol", "Ġinterp ol", "Ġl ips", "Ġli ps", "Ġlip s", "Ġ Ky", "ĠK y", "Ġv ectors", "Ġvector s", "Ġve ctors", "Ġvec tors", "Ġvect ors", "_ am", "_a m", "Ġin take", "Ġint ake", ". world", ".w orld", "Ġ inbox", "Ġin box", "Ġ MAC", "ĠM AC", "ĠMA C", "_ ab", "_a b", "( nameof", "(name of", "6 33", "63 3", "Ġent ert", "Ġenter t", "Ġg athering", "Ġgather ing", "Ġ SIM", "ĠS IM", "ĠSI M", "+ +.", "++ .", "n ya", "ny a", "' }}", "'} }", "Ġ UPDATE", "ĠUP DATE", "Ġ pac", "Ġp ac", "Ġpa c", "( html", "(h tml", "(ht ml", "ĠS ant", "ĠSan t", "ĠSa nt", "i ating", "ia ting", "iat ing", "ĠIde as", "ĠIdea s", "Ġs pray", "Ġsp ray", "Ġspr ay", "ĠH art", "ĠHar t", "ĠHa rt", "Ġ verification", "Ġver ification", "Ġverifica tion", "ad esh", "ade sh", "ades h", "/ modules", "/mod ules", "/module s", "Ġ Mind", "ĠM ind", "ĠMin d", "ĠMi nd", "ĠSized Box", "Ġsh elter", "Ġshel ter", "Ġ heroes", "Ġher oes", "Ġhero es", "a tty", "at ty", "att y", "Ġcert ified", "Ġcertif ied", "s j", "Ġ être", "Ġê tre", "ÅĤ o", "Ġpublish ing", "ĠMal ays", "ĠMa lays", "ĠMalay s", ". getUser", ".get User", "Ġ Provider", "ĠPro vider", "ĠProvid er", "ĠProvide r", "ĠProv ider", "Ġ LinkedList", "ĠLink edList", "ĠLinked List", "ĠB or", "ĠBo r", "R OUND", "RO UND", "d id", "di d", "t ain", "ta in", "p ire", "pi re", "pir e", "ĠJ enn", "ĠJe nn", "ĠJen n", "t el", "te l", "a nde", "an de", "and e", "7 57", "75 7", "_ front", "_f ront", "_fr ont", "ĠMc G", "Test Method", "ภŃ", "Ġocc asionally", "Ġoccasion ally", "Ġoccasional ly", "ĠW ales", "ĠWal es", "ĠWa les", "Ġex ercises", "Ġexerc ises", "Ġexercise s", "Ġ ÐĴ", "ĠÐ Ĵ", "0 45", "04 5", "- plus", "-p lus", "-pl us", "Ġ validator", "Ġvalid ator", "Ġvalida tor", "Ġpr ayer", "Ġpray er", "Ġpra yer", "L ATED", "LA TED", "LAT ED", "_ author", "_a uthor", "_auth or", "_aut hor", "Ġla bour", "Ġlab our", "+ +Ċ", "++ Ċ", "- equiv", "-e quiv", "-equ iv", "Ġ GPL", "ĠG PL", "ĠGP L", "Ġ facebook", "Ġf acebook", "Ġface book", "s imple", "sim ple", "simp le", "g ly", "gl y", "Process or", "Proc essor", "i py", "ip y", "7 44", "74 4", "Ġ *>", "Ġ* >", "6 48", "64 8", "Ġc leared", "Ġclear ed", "Ġcle ared", "Ġ Push", "ĠP ush", "ĠPu sh", "8 58", "85 8", "Ġp enis", "Ġpe nis", "Ġpen is", "Struct ure", "l ij", "li j", "ĠM organ", "ĠMo rgan", "ĠMor gan", "ĠMorg an", "Ġhand ful", "\" .Ċ", "\". Ċ", "9 84", "98 4", "| \\", "Ġ ********************************", "Ġ**** ****************************", "Ġ******** ************************", "Ġ**************** ****************", "Ġ************************ ********", "Ġ Aqu", "ĠA qu", "5 84", "58 4", "_ IC", "_I C", ". loads", ".load s", ".lo ads", "Ġ meter", "Ġm eter", "Ġme ter", "Ġmet er", "ĠM arine", "ĠMar ine", "ĠMa rine", "ĠMari ne", "ĠMarin e", ": :{", ":: {", "Ġ TS", "ĠT S", "7 76", "77 6", "Ġ Arrays", "ĠAr rays", "ĠArray s", "ĠArr ays", ". Title", ".T itle", "G RAM", "GR AM", "GRA M", "ter min", "term in", "Ġco inc", "Ġcoin c", "Ġcoi nc", "E lse", "El se", "_ states", "_st ates", "_state s", "_stat es", "_sta tes", "- run", "-r un", "m embers", "member s", "mem bers", "7 82", "78 2", "a stro", "as tro", "ast ro", "astr o", "0 66", "06 6", "Ġon Press", "Ġbe ings", "Ġbeing s", "Ġab andoned", "Ġabandon ed", "Ġtax p", "Ġta xp", "ow ners", "own ers", "owner s", ". mode", ".m ode", ".mod e", ".mo de", "Ġdi agnosis", "Ġdiagn osis", "Ġdiag nosis", "Ġ _Ċ", "Ġ_ Ċ", "Ġ Knight", "ĠK night", "ĠKn ight", "ĉ A", "Ġ observe", "Ġob serve", "Ġobs erve", "Ġobserv e", ") ,'", "), '", "8 23", "82 3", "! \")Ċ", "!\" )Ċ", "!\") Ċ", "Ġ Para", "ĠP ara", "ĠPar a", "ĠPa ra", "Ġ variation", "Ġvar iation", "Ġvari ation", "( False", "(F alse", "Ġ Anti", "ĠAn ti", "ĠAnt i", "Ġg ri", "Ġgr i", "Ġhome less", "Ġhom eless", "? v", "Ġb ez", "Ġbe z", ". Server", ".S erver", ".Serve r", "r elease", "re lease", "rel ease", "ĠP atri", "ĠPat ri", "ĠPa tri", "Ġ chars", "Ġch ars", "Ġchar s", "Ġcha rs", "Ġ ranking", "Ġr anking", "Ġrank ing", "Ġran king", "act ivation", "activ ation", "5 81", "58 1", "Ġw ides", "Ġwide s", "Ġwid es", "Ġwi des", "q r", ". Sql", ".S ql", "a cular", "ac ular", "acula r", "Ġ Bot", "ĠB ot", "ĠBo t", "_ sync", "_s ync", "_syn c", "_sy nc", "Ġh appiness", "Ġhapp iness", "Ġvol unteers", "Ġvolunte ers", "Ġvolunteer s", "8 77", "87 7", "Ġs its", "Ġsit s", "Ġsi ts", "/ <", "[ e", "( fileName", "(file Name", "Ġcap ac", "Ġca pac", "8 32", "83 2", "Ġ Maria", "ĠM aria", "ĠMar ia", "ĠMa ria", "ĠMari a", "f ather", "fa ther", "fat her", "Ġ gram", "Ġg ram", "Ġgr am", "Ġgra m", "* i", "Ġc aso", "Ġca so", "Ġcas o", "_ draw", "_d raw", "_dr aw", "Ġ Raw", "ĠR aw", "ĠRa w", "Ġ Iterator", "ĠIt erator", "ĠIter ator", "6 64", "66 4", "Ġ Padding", "ĠP adding", "ĠPad ding", "9 24", "92 4", "P D", "B OX", "BO X", "ĠS PECIAL", "ĠSPEC IAL", "Ġ fecha", "Ġf echa", "Ġfe cha", "Ġfec ha", "Ġ vide", "Ġv ide", "Ġvi de", "Ġvid e", "Ġ Leader", "ĠLe ader", "ĠLead er", "ä» ¥", "$ (\".", "$( \".", "$(\" .", "Ġd iameter", "Ġdiam eter", "Ġdia meter", "Ġm ild", "Ġmil d", "Ġmi ld", "7 45", "74 5", "Ġr ocks", "Ġro cks", "Ġrock s", "Ġroc ks", "app ings", "apping s", "0 48", "04 8", "d irectory", "direct ory", "director y", "5 57", "55 7", ". flush", ".f lush", ".fl ush", "Ġ Jess", "ĠJ ess", "ĠJes s", "ĠJe ss", "UN IT", "Ġ Pear", "ĠP ear", "ĠPe ar", "Ġ mandatory", "Ġm andatory", "Ġmand atory", "S ur", "Su r", "q t", "Ġ streams", "Ġstream s", "Ġstre ams", "Ġco operation", "Ġcooper ation", "Ġcoop eration", "Ġ Sac", "ĠS ac", "ĠSa c", "Ġche aper", "Ġcheap er", "ĉ ch", "ĉc h", "an imation", "anim ation", "f are", "fa re", "far e", "( height", "(h eight", "( True", "N Y", "Ġw rest", "Ġwr est", "Ġwre st", "Ġp olls", "Ġpol ls", "Ġpoll s", "Ġencounter ed", "Ġencount ered", "ĠMark etable", "ĠMarket able", "_ PASSWORD", "_P ASSWORD", "_PASS WORD", "7 16", "71 6", "_ SELECT", "_SE LECT", "_SEL ECT", "ĠArab ia", "ĠAra bia", "_ clock", "_c lock", "_cl ock", "Ġ voy", "Ġv oy", "Ġvo y", "Ġ из", "Ġи з", "Ġs tir", "Ġst ir", "is ible", "isi ble", "- effect", "-e ffect", "-eff ect", ". created", ".c reated", ".create d", ".cr eated", "Ġto ys", "Ġtoy s", "ĠTrad able", "Ġ rust", "Ġr ust", "Ġru st", "Ġrus t", "Ġ strcpy", "Ġstr cpy", "_ timestamp", "_t imestamp", "_time stamp", "Ġtal ented", "Ġtalent ed", ", null", ",n ull", "Ġ Jobs", "ĠJ obs", "ĠJo bs", "ĠJob s", "Ġ Portland", "ĠPort land", "Ġweak ness", "Th row", "Thr ow", "Ġ Angel", "ĠAn gel", "ĠAng el", "ĠAnge l", "ä¿ ®", "7 54", "75 4", "Ġun cert", "Ġunc ert", "ï¼ī Ċ", "Ġ ìĿ´", "ĠìĿ ´", "Wh ich", "Ġ[- ]:", "S omething", "Some thing", "Som ething", "Ġconv icted", "Ġconvict ed", "k le", "kl e", "ed ium", "edi um", "Ġ branches", "Ġbr anches", "Ġbranch es", "Ġbran ches", "Ġ bases", "Ġb ases", "Ġbase s", "Ġbas es", "Ġba ses", "ç ®", "Ġcomplex ity", "Ġ Fig", "ĠF ig", "ĠFi g", ". reshape", ".re shape", ".res hape", "$ db", "$d b", "7 36", "73 6", "_ CONST", "_CON ST", "_CO NST", "Ġ Tes", "ĠT es", "ĠTe s", ". runtime", ".r untime", ".run time", "Ġ deny", "Ġd eny", "Ġde ny", "Ġden y", "Ġ BSD", "ĠB SD", "ĠBS D", "Ġ kr", "Ġk r", "h att", "ha tt", "hat t", "Ġ Static", "ĠSt atic", "ĠStat ic", "ĠSta tic", "Ġunivers ities", "Re place", "Rep lace", "Ġd rove", "Ġdr ove", "Ġdro ve", "Ġad oles", "Ġado les", "_ plugin", "_pl ugin", "ĠL GBT", "ĠLG BT", "Ġ tex", "Ġt ex", "Ġte x", "d uction", "du ction", "duct ion", "duc tion", "7 51", "75 1", "7 99", "79 9", "E DI", "ED I", "Ġ Ted", "ĠT ed", "ĠTe d", "_ URI", "_U RI", "Ġre ception", "Ġrece ption", "Ġrecept ion", "Ġrecep tion", "ar ten", "art en", "arte n", ". Single", ".S ingle", ".Sin gle", "r ice", "ri ce", "ric e", "sc ious", "sci ous", "8 43", "84 3", "_ bg", "_b g", "Ġw ages", "Ġwa ges", "Ġwage s", "Ġwag es", "Ġ Servlet", "ĠS ervlet", "ĠServ let", "UI Layout", "UIL ayout", "Ġ formatted", "Ġform atted", "Ġformat ted", ". Mod", ".M od", "< class", " ',Ċ", ">' ,Ċ", ">', Ċ", "Ġexp anding", "Ġexpand ing", "Ġ Hamilton", "ĠHam ilton", "Ġ Contrib", "ĠCon trib", "ĠCont rib", "ĠContr ib", ". Tables", ".T ables", ".Tab les", ".Table s", "7 28", "72 8", "Act iv", "Ac tiv", "H H", "o commerce", "ocom merce", "_ ;", "Ġamong st", "o wing", "ow ing", "owi ng", "8 59", "85 9", "Ġ Cold", "ĠC old", "ĠCo ld", "ĠCol d", "A PH", "AP H", "Ġpsych ological", "Ġpsycho logical", "_ tensor", "_t ensor", "Ġpack aging", "Ġ Sweden", "ĠSw eden", "ĠSwe den", "Ġ pare", "Ġp are", "Ġpar e", "Ġpa re", "Ġ aggregate", "Ġag gregate", "Ġaggreg ate", "Ġmod erate", "Ġmode rate", "Ġmoder ate", "8 62", "86 2", "_ hand", "_h and", "Ġdesign ated", "Ġdesignate d", "Ġd rum", "Ġdr um", "Ġdru m", "Ġ getUser", "Ġget User", "ĠC reek", "ĠCre ek", "ĠCree k", "_ scope", "_s cope", "_sc ope", "Ġ Transfer", "ĠTrans fer", "Ġ Marg", "ĠM arg", "ĠMar g", "ĠMa rg", "Ġ fighters", "Ġfight ers", "Ġfighter s", "W nd", "Ġ Sel", "ĠS el", "ĠSe l", "Ġ Launch", "ĠL aunch", "ĠLa unch", "Ġem erging", "Ġemerg ing", "i frame", "if rame", "ifr ame", "Ġ Additional", "ĠAdd itional", "ĠAddition al", "Ġf ears", "Ġfe ars", "Ġfear s", "Ġsat ellite", "_ :", "Ġ disposing", "Ġdis posing", "Ġdisp osing", "Ġdispos ing", "Get Value", "Http Post", "AT IVE", "ul ary", "ular y", "ula ry", "View s", "Vi ews", "Ġatt ending", "Ġattend ing", "ĠT ennessee", "Ġ Mission", "ĠM ission", "ĠMiss ion", "Ġmed ication", "Ġmedic ation", "Ġmedi cation", "Ġ Wy", "ĠW y", "Ġ Anna", "ĠAn na", "ĠAnn a", "Ø ¹", "Ġ Vertex", "ĠVer tex", "ĠVert ex", ". types", ".t ypes", ".type s", ".typ es", "O rgan", "Or gan", "Org an", ". DataGridViewTextBoxColumn", ".DataGridView TextBoxColumn", "Ġ RS", "ĠR S", "Ġt empo", "Ġtem po", "Ġtemp o", "( App", "(A pp", "8 92", "89 2", "Version UID", ". point", ".p oint", ".po int", ".poi nt", "ĠD utch", "ĠDut ch", "H ours", "Hour s", "Ho urs", "L U", "Ġ quoted", "Ġqu oted", "Ġquote d", "Ġquot ed", "Ġquo ted", ". builder", ".b uilder", ".build er", "Ġ Perfect", "ĠPer fect", "ĠPerf ect", "Ġ Always", "ĠAl ways", "_ two", "_t wo", "_tw o", "Ġexclusive ly", "Ġexclus ively", "ĠC ra", "ĠCr a", "ific ar", "ifi car", "ifica r", "Ġ AWS", "ĠA WS", "ĠAW S", "ing ham", "com plex", "comp lex", "k ernel", "ker nel", "Ġ gravity", "Ġgr avity", "Ġgrav ity", "Ġ wi", "Ġw i", "0 52", "05 2", "Ġ overview", "Ġover view", "Ġov erview", "6 61", "66 1", "Ġ Want", "ĠW ant", "ĠWa nt", "ĠWan t", "Ġ WP", "ĠW P", "( sh", "(s h", ". rotation", ".r otation", ".rot ation", "St ates", "State s", "Stat es", "Ġ Teen", "ĠT een", "ĠTe en", "ĠTee n", "_ components", "_com ponents", "_comp onents", "_component s", "ì Īĺ", "ìĪ ĺ", "Re ceived", "Receive d", "Ġly rics", "Ġlyric s", "Ġlyr ics", "r ites", "ri tes", "rit es", "rite s", "ĉ ĉĉĉĉĠ", "ĉĉ ĉĉĉĠ", "ĉĉĉĉ ĉĠ", "ĉĉĉ ĉĉĠ", "ĉĉĉĉĉ Ġ", "- American", "-A merican", "-Americ an", "[ num", "[n um", "/ python", "/p ython", "/py thon", "Ġ UART", "ĠU ART", "ĠUA RT", "Ġ apple", "Ġapp le", "Ġap ple", "Ġappl e", "Ġ Jonathan", "ĠJon athan", "Ġm omentum", "Ġmoment um", "ภ±", "Ĥ ¹", "Ġm ich", "Ġmi ch", "Ġmic h", "an dra", "and ra", "andr a", "Ġb iological", "Ġbi ological", "Ġbio logical", "ĠM ens", "ĠMe ns", "ĠMen s", "Ġ %%", "Ġ% %", "el sea", "else a", "els ea", "ĠMex ican", ".rand int", "Ġt ale", "Ġtal e", "Ġta le", "Ġ Validate", "ĠValid ate", "Ġdef eated", "Ġdefe ated", "Ġdefeat ed", ". htm", ".h tm", ".ht m", "Ġc opper", "Ġco pper", "Ġcop per", "Ġcopp er", "= /", "co system", "cos ystem", "Ġ rip", "Ġr ip", "Ġri p", "d ecimal", "de cimal", "dec imal", ". VISIBLE", ".V ISIBLE", "Ġ Ta", "ĠT a", "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", "Ġdown loaded", "Ġdownload ed", "en vironment", "Ġno mine", "Ġnom ine", "Ġnomin e", "build ing", "Ġ Spot", "ĠS pot", "ĠSp ot", "ĠSpo t", "ipher al", "iph eral", "Ġ alto", "Ġal to", "Ġalt o", "q uet", "qu et", "que t", "Ġ FT", "ĠF T", "/ get", "/g et", "/ge t", "/ master", "/m aster", "W IN", "WI N", "åħ ĥ", "6 76", "67 6", "W est", "We st", "ar gc", "arg c", "Ġpro ducers", "Ġprodu cers", "Ġproduce rs", "Ġproducer s", "Ġ Much", "ĠM uch", "ĠMu ch", "_ storage", "_st orage", "c redit", "cre dit", "cr edit", "cred it", "C ONT", "CON T", "CO NT", "Ġ vet", "Ġv et", "Ġve t", "Ġ voices", "Ġvo ices", "Ġvoice s", "Ġvoi ces", "( '',", "(' ',", "Ġin struments", "Ġinstr uments", "Ġinstrument s", "6 62", "66 2", "Ġ MSG", "ĠM SG", "ĠMS G", "e sse", "es se", "ess e", "re pository", "repos itory", "om ics", "omic s", "omi cs", "Ġ dealer", "Ġde aler", "Ġdeal er", "St ill", "Ġ banner", "Ġb anner", "Ġban ner", "asc ii", "Ġ remarks", "Ġre marks", "Ġr emarks", "Ġrem arks", "Ġremark s", "Ġremar ks", "[ js", "[j s", "Ġshort er", "g ulp", "gu lp", "Ġm yster", "Ġmy ster", "Ġmys ter", "Ġmyst er", "Ġk un", "Ġku n", "Ġ Bird", "ĠB ird", "ĠBi rd", "ĠBir d", "Ġt iene", "Ġti ene", "Ġtie ne", "7 88", "78 8", "n ut", "nu t", "Ġ Um", "ĠU m", "Ġ wise", "Ġw ise", "Ġwis e", "Ġwi se", "Y eah", "Ye ah", "I NESS", "IN ESS", "INE SS", "INES S", "0 46", "04 6", "_ begin", "_b egin", "_be gin", "_beg in", "- heading", "-head ing", "-he ading", "C ourse", "Co urse", "Cour se", "Ġ čĊčĊ", "ĠčĊ čĊ", "om bie", "omb ie", "gr aded", "grad ed", "grade d", "gra ded", "Ġ GPS", "ĠG PS", "ĠGP S", "Ġ że", "Ġż e", "F it", "Fi t", "c aption", "ca ption", "cap tion", "capt ion", "ö n", "/ image", "/i mage", "/im age", "l ia", "li a", "( mod", "(m od", "Ġle ak", "en za", "enz a", "6 29", "62 9", "/ H", "Ġ Happy", "ĠH appy", "ĠHa ppy", "ĠHapp y", "9 93", "99 3", "D ist", "Dis t", "Di st", "n x", "ĠGovern or", "ĠGover nor", "( last", "(l ast", "t eacher", "te acher", "tea cher", "Ġ Sent", "ĠS ent", "ĠSe nt", "ĠSen t", "s upport", "sup port", "8 38", "83 8", "ject ory", "Ġ Ùħ", "ĠÙ ħ", "Reg istration", "Registr ation", "0 63", "06 3", "Ġ Gray", "ĠG ray", "ĠGr ay", "ĠGra y", ", false", ",f alse", "Ġ adjusted", "Ġadjust ed", "Ġadj usted", "( settings", "(s ettings", "(set tings", "(setting s", "< R", "Ġ Mage", "ĠM age", "ĠMag e", "ĠMa ge", "Ġ plaint", "Ġpl aint", "Ġplain t", "Ġpla int", "_ )Ċ", "_) Ċ", "ĉ it", "ĉi t", "o metric", "om etric", "omet ric", "ometr ic", ". bootstrap", ".boot strap", "Ġcar ries", "Ġcarr ies", "I p", "Ġ! $", "Ġsw imming", "Ġswim ming", "Ġ Mario", "ĠM ario", "ĠMar io", "ĠMa rio", "ĠMari o", "Ġ Questions", "ĠQuest ions", "ĠQuestion s", "P ACE", "PA CE", "æĸ ¹", "e or", "eo r", "} }\"", "}} \"", "Ġ oven", "Ġo ven", "Ġov en", "Ġ Kon", "ĠK on", "ĠKo n", "Ġwis dom", "Ġac quisition", "ess ment", "ag ine", "agi ne", "Ġex pressions", "Ġexpress ions", "Ġexpression s", "Ġexpr essions", "Sequential Group", "F ront", "Fr ont", "ul pt", "ulp t", "a wk", "aw k", "' ])ĊĊ", "'] )ĊĊ", "']) ĊĊ", "'])Ċ Ċ", "8 13", "81 3", "7 32", "73 2", "_ AR", "_A R", "Ġan alog", "Ġanal og", "Ġana log", "u lin", "ul in", "uli n", "_ PRINT", "_PR INT", "_PRI NT", "Ġ LG", "ĠL G", "Ġ blob", "Ġb lob", "Ġbl ob", "Ġblo b", "Ġ Furthermore", "ĠFurther more", "_ component", "_com ponent", "_comp onent", "Ġ Cole", "ĠC ole", "ĠCo le", "ĠCol e", "L AN", "LA N", "SC RIPTION", "SCRI PTION", "SCRIPT ION", "Ġ lap", "Ġl ap", "Ġla p", "ic ensing", "icens ing", "_TIME OUT", "ĠF ro", "ĠFr o", "Ġl iability", "Ġli ability", "Ġ composed", "Ġcom posed", "Ġcomp osed", "Ġcompose d", "Ġcompos ed", "6 34", "63 4", ".create SequentialGroup", "_ person", "_p erson", "_per son", "Ġ beam", "Ġb eam", "Ġbe am", "ĉ ĠĠĠĠĠĠĠĠ", "ĉĠĠĠ ĠĠĠĠĠ", "ĉĠ ĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠ Ġ", "ĉĠĠ ĠĠĠĠĠĠ", "ĉĠĠĠĠĠ ĠĠĠ", "ĉĠĠĠĠ ĠĠĠĠ", "ĉĠĠĠĠĠĠ ĠĠ", "Ġ NotFound", "ĠNot Found", "6 84", "68 4", ". 'Ċ", ".' Ċ", "ÃŃ s", ". TextView", ".T extView", ".Text View", "P DF", "PD F", "Ġ kar", "Ġk ar", "Ġka r", "_ _('", "__ ('", "__( '", "Ġ \":\"", "Ġ\" :\"", "Ġ\": \"", "_ messages", "_m essages", "_message s", "Ġhar vest", ". history", ".h istory", ".hist ory", "> 'Ċ", ">' Ċ", "- fold", "-f old", "æ Ĭ", "Ġ Better", "ĠB etter", "ĠBet ter", "ĠBett er", "Ġ\" \\<", "Ġ\"\\ <", "sp acing", "spa cing", "Ġf urnished", "Ġfurn ished", "Ġfurnish ed", "9 13", "91 3", "o ser", "os er", "ose r", "] }Ċ", "]} Ċ", "Ġ $\"", "Ġ$ \"", "p ull", "pu ll", ". Post", ".P ost", ".Pos t", "9 19", "91 9", "( ip", "(i p", "Ĺ ı", ". front", ".f ront", ".fr ont", "n te", "nt e", "Ġ FM", "ĠF M", "g uid", "gu id", "gui d", "8 44", "84 4", "Ġnegot iations", "Ġnegotiation s", "ag onal", "agon al", "ago nal", "9 34", "93 4", "Ġtrem end", "ung eon", "unge on", "A dv", "Ad v", "car ousel", "ÃŁ e", "_ DESC", "_D ESC", "_DE SC", "Ġ hammer", "Ġh ammer", "Ġham mer", "Ġhamm er", "ẠŃ", "Ġ ĠĠĠĠĠĠĠĊĊ", "ĠĠ ĠĠĠĠĠĠĊĊ", "ĠĠĠĠ ĠĠĠĠĊĊ", "ĠĠĠĠĠĠĠĠ ĊĊ", "ĠĠĠ ĠĠĠĠĠĊĊ", "ĠĠĠĠĠĠĠ ĠĊĊ", "ĠĠĠĠĠ ĠĠĠĊĊ", "ĠĠĠĠĠĠ ĠĠĊĊ", "ĠĠĠĠĠĠĠĠĊ Ċ", "- core", "-c ore", "-co re", "-cor e", "- service", "-s ervice", "-ser vice", "Ġc orners", "Ġcor ners", "Ġcorner s", "Ġcorn ers", "Ġ SF", "ĠS F", "p red", "pr ed", "pre d", "> A", "ĠJ Label", "ĠJL abel", "Ġrom antic", "Ġroman tic", "Ġromant ic", "Ġtest imony", "Ġtestim ony", "Ġtestimon y", "o sc", "os c", "Ġ Generation", "ĠG eneration", "ĠGener ation", "ĠGen eration", "ĠGene ration", "as ures", "asure s", "asu res", "_ internal", "_in ternal", "_int ernal", "_inter nal", "Ġ prints", "Ġpr ints", "Ġprint s", "Ġpri nts", "Ġprin ts", "Ġ ])Ċ", "Ġ] )Ċ", "Ġ]) Ċ", "ĠC leveland", "re po", "rep o", "D isc", "Dis c", "Di sc", "6 77", "67 7", "7 62", "76 2", "Ġ \">Ċ", "Ġ\" >Ċ", "Ġ\"> Ċ", "� ���", "�� ��", "��� �", "Ġ nearest", "Ġne arest", "Ġnear est", "5 91", "59 1", "_ tb", "_t b", "( require", "(re quire", "(req uire", "E OF", "EO F", "- child", "-ch ild", "Ġb udd", "Ġbu dd", "Ġbud d", ".Xtra Editors", "al ties", "alt ies", "7 23", "72 3", "\\\" :\\\"", "\\\": \\\"", "W ords", "Word s", "9 17", "91 7", "Ġloc ally", "Ġlocal ly", "Ġp urchases", "Ġpurch ases", "Ġpurchase s", "6 95", "69 5", "D rawer", "Draw er", "ex tract", "ext ract", "extra ct", "extr act", "Ġex ecut", "Ġexec ut", "Ġexe cut", "} '.", "}' .", "user data", "Ġfocus es", "Ġfoc uses", "- minute", "-min ute", "7 64", "76 4", "Ġ Publish", "ĠP ublish", "ĠPub lish", "o go", "og o", "Ġm ountains", "Ġmount ains", "Ġmountain s", "B ot", "Bo t", "} >{", "}> {", "Ġt ension", "Ġtens ion", "r od", "ro d", "m esh", "me sh", "mes h", "Ġtrans formed", "Ġtransform ed", ", R", "( )}Ċ", "() }Ċ", "()} Ċ", ". long", ".l ong", ".lo ng", ".lon g", "Ġg orgeous", "Ġgorge ous", "Ġ Schedule", "ĠS chedule", "Ġold est", "Ġol dest", "Ġsub process", "( IN", "(I N", "y ect", "ye ct", "ĠCo oper", "ar ness", "arn ess", "Ġ Monitor", "ĠM onitor", "ĠMon itor", ". part", ".p art", ".par t", ".pa rt", "9 72", "97 2", "Ġ NBC", "ĠN BC", "ĠNB C", "6 68", "66 8", "Ġc otton", "Ġcot ton", "Ġcott on", "Ġ hol", "Ġh ol", "Ġho l", "7 26", "72 6", "Ġ rgba", "Ġr gba", "Ġrgb a", "Ġrg ba", "Ġ Bio", "ĠB io", "ĠBi o", "Cont inue", "Contin ue", "P od", "Po d", "Ġparticip ating", "cl usions", "clus ions", "clusion s", "(By Val", "7 34", "73 4", "à ¬", "Ġ HOW", "ĠH OW", "ĠHO W", "_set opt", "Ġaccompany ing", "0 91", "09 1", "a ton", "at on", "ato n", "Ġ /\\", "Ġ/ \\", "Ġ Authentication", "ĠAuth entication", "ĠAuthentic ation", "i én", "ĠBar ack", "ĠBa rack", "/ *.", "/* .", "Ġe ager", "Ġea ger", "Ġeag er", "Ġ Cancel", "ĠC ancel", "ĠCan cel", "ĠCanc el", "< lemma", " $", "O LEAN", "OLE AN", "OK IE", "IB ILITY", "U AGE", "UA GE", "Ġ Survey", "ĠS urvey", "ĠSur vey", "ĠSurv ey", "0 71", "07 1", "Ġre sign", "Ġr esign", "Ġres ign", "w ing", "win g", "wi ng", "Ġse crets", "Ġsec rets", "Ġsecret s", "Ġsecre ts", "Ġc hips", "Ġch ips", "Ġchip s", "Ġchi ps", "JSON Object", "D esktop", "Des ktop", "Desk top", "5 96", "59 6", "_SY MBOL", "( resource", "(res ource", "(re source", "Ġ< />Ċ", "ĠĊ", "Ġnew est", "Ġne west", "u li", "ul i", "Ġde sert", "Ġdes ert", "Ġdese rt", "Ġdeser t", "Ġd ip", "Ġdi p", "Ġ Pow", "ĠP ow", "ĠPo w", "Ġequ ation", "Ġeq uation", "Ġposs ibilities", "Ġpossibilit ies", "Ġ Fed", "ĠF ed", "ĠFe d", "os ph", "osp h", "Ġ [%", "Ġ[ %", "Ġ bubble", "Ġb ubble", "Ġbu bble", "Ġbub ble", "Ġbubb le", "ether lands", "7 93", "79 3", "Ġc ement", "Ġce ment", ". auto", ".a uto", ".au to", ".aut o", "_ AN", "_A N", "âĢĻ .", "s election", "se lection", "select ion", "sel ection", "Ġ Bond", "ĠB ond", "ĠBo nd", "ĠBon d", "9 88", "98 8", "D en", "De n", "- O", ". getType", ".get Type", "8 96", "89 6", ". Window", ".W indow", "p res", "pr es", "pre s", "Ġsw inger", "Ġswing er", "\" })Ċ", "\"} )Ċ", "\"}) Ċ", "Ġ pip", "Ġp ip", "Ġpi p", "Ġm ice", "Ġmi ce", "Ġmic e", "Ġ compound", "Ġcomp ound", "- plugin", "-pl ugin", "i ko", "ik o", "Ġcent uries", "i cular", "ic ular", "- inline", "-in line", "ĉ key", "ĉk ey", "> \\<", ">\\ <", "EN SION", "ENS ION", "Ġ[ čĊ", "Ġprecis ely", "Ġprecise ly", "Ġ été", "Ġé té", "Ġét é", "Ġ Past", "ĠP ast", "ĠPa st", "ĠPas t", "ĠCam bridge", "ĠCamb ridge", "- full", "-f ull", "Ġ analyze", "Ġan alyze", "Ġanaly ze", "Ġ Steven", "ĠSt even", "ĠSte ven", "ĠSteve n", "Ġ nem", "Ġn em", "Ġne m", "d ue", "du e", "o ren", "or en", "ore n", "Ġmus cles", "Ġmuscle s", "i jing", "ij ing", "iji ng", "8 52", "85 2", "/ -", "ĠKenn edy", "5 97", "59 7", "R M", "oss ible", "Ġact ress", "Ġd olor", "Ġdo lor", "Ġdol or", "9 14", "91 4", "å½ ķ", "N eed", "Ne ed", ". toggle", ".t oggle", "Ġ Race", "ĠR ace", "ĠRa ce", "ĠRac e", "w ers", "we rs", "wer s", ". material", ".m aterial", ".mat erial", "Ġ Due", "ĠD ue", "ĠDu e", "Ġ Pel", "ĠP el", "ĠPe l", "# print", "Ġin dependence", "Ġindepend ence", "ex us", "Sh adow", "Ġ encoder", "Ġe ncoder", "Ġen coder", "Ġenc oder", "Ġencode r", "( level", "(le vel", "Ġ Swift", "ĠSw ift", ". doc", ".d oc", ".do c", "_ selection", "_s election", "_se lection", "_select ion", "_sel ection", "9 52", "95 2", "Ġserial VersionUID", "9 45", "94 5", "Label s", "Lab els", "Ġperform ances", "Ġperformance s", "Ġperforman ces", ". Tag", ".T ag", "ĠN HL", "ĠNH L", "i zen", "iz en", "ize n", "/ UIKit", "/UI Kit", "9 91", "99 1", "_ CONTROL", "_CONT ROL", "Ġe arnings", "Ġear nings", "Ġearn ings", "Ġearning s", "9 75", "97 5", "Ġ Alt", "ĠA lt", "ĠAl t", "_ HANDLE", "_H ANDLE", "_HAND LE", "C tx", "Ġper su", "Ġpers u", "Ġ tran", "Ġt ran", "Ġtr an", "Ġtra n", "ç ¨", "_ CHANNEL", "_CH ANNEL", "_CHAN NEL", "Ġs atisfaction", "Ġsatisf action", "Ġ GP", "ĠG P", "7 69", "76 9", "i ox", "io x", "m itt", "mit t", "mi tt", "l ando", "land o", "la ndo", "lan do", "Ġ pig", "Ġp ig", "Ġpi g", "in als", "inal s", "ina ls", "ê ncia", "ên cia", "7 31", "73 1", "S urface", "Sur face", "Ġ UUID", "ĠU UID", "Ġbenef icial", "Ġbenefici al", "Ġ sequences", "Ġse quences", "Ġsequence s", "Ġsequ ences", "ĉ memset", "ĉmem set", "Ġmag ical", "Ġmagic al", " «", "Ġw orn", "Ġwor n", "Ġwo rn", "A SC", "AS C", "p opup", "pop up", "C OMP", "CO MP", "COM P", "_ before", "_b efore", "_be fore", "e ness", "en ess", "ene ss", "enes s", "U i", "L es", "Le s", ". require", ".re quire", ".req uire", ". Serializable", ".Serial izable", "add Gap", "Ġ authorization", "Ġauthor ization", "0 85", "08 5", ".py plot", "u rray", "ur ray", "urr ay", "l atitude", "lat itude", "8 45", "84 5", "f rames", "fr ames", "frame s", "fra mes", "fram es", "a js", "aj s", "Ġ compass", "Ġcom pass", "Ġcomp ass", "Ġ observations", "Ġobs ervations", "Ġobserv ations", "Ġobservation s", "_ sup", "_s up", "_su p", ".en viron", ".env iron", "Ġtr iple", "Ġtri ple", "Ġtrip le", "Ġ Ruby", "ĠR uby", "ĠRub y", "ĠRu by", "Ġd rain", "Ġdr ain", "Ġdra in", "_ FILTER", "_F ILTER", "S an", "Sa n", "U MP", "UM P", "Null Exception", "Ġ Gab", "ĠG ab", "ĠGa b", "o we", "ow e", "ĠTur kish", "ĠTurk ish", "_ sequence", "_se quence", "Ġ Grant", "ĠG rant", "ĠGr ant", "ĠGran t", "ĠGra nt", "u ela", "ue la", "uel a", "Ġ wo", "Ġw o", "Ġ cube", "Ġc ube", "Ġcu be", "Ġcub e", "i q", "Ġdis orders", "Ġdisorder s", "Ġextra ordinary", "Ġextraordin ary", "Ġ ctrl", "Ġc trl", "Ġct rl", "Ġctr l", "Ġ Seq", "ĠS eq", "ĠSe q", "en tr", "ent r", "8 65", "86 5", "Ġsan ctions", "Ġsanct ions", "Ġsanction s", "9 49", "94 9", "ut sch", "uts ch", "Re ports", "Report s", "Rep orts", "Repo rts", "Ġ inherit", "Ġin herit", "Ġinher it", "P eriod", "Per iod", "Ġphot ography", "Ġphotograph y", "Ġ Framework", "ĠF ramework", "ĠFr amework", "ĠFrame work", "ĠFram ework", "Ġspecial ist", "Ġspeci alist", "Ġ ?ĊĊ", "Ġ? ĊĊ", "Ġ?Ċ Ċ", "_ selected", "_se lected", "_select ed", "_sel ected", ". Player", ".P layer", ".Pl ayer", ".Play er", "Ġ allocation", "Ġal location", "Ġall ocation", "Ġalloc ation", "Ġallo cation", "( account", "(a ccount", "(ac count", "(acc ount", "Ġ structural", "Ġstruct ural", "v able", "va ble", "- offset", "-off set", "-o ffset", "-offs et", ".App CompatActivity", "а м", "аР¼", ".Add WithValue", "Ġ icons", "Ġi cons", "Ġicon s", "Ġic ons", "Ġ shutdown", "Ġsh utdown", "Ġshut down", "_ low", "_l ow", "_lo w", "Ġ Compare", "ĠCom pare", "ĠComp are", "ĠCompar e", "Ġ Ce", "ĠC e", "= head", "=h ead", "l am", "la m", ". predict", ".p redict", ".pre dict", ".pred ict", "_ DEC", "_D EC", "_DE C", "Ġ Sleep", "ĠS leep", "ĠSlee p", "ĠSle ep", "Ġ Gratis", "ĠGr atis", "ĠGrat is", "Ġs uggestion", "Ġsuggest ion", "Ġ DEL", "ĠD EL", "ĠDE L", "c aff", "ca ff", "caf f", "av irus", "avi rus", "avir us", "No thing", "ŀ ĭ", "Ġwide spread", "Ġwides pread", "Ġmechan isms", "Ġmechanism s", "Ġ textAlign", "Ġtext Align", "oc cup", "occ up", "Ġ Rail", "ĠR ail", "ĠRa il", "ĠRai l", ": NS", ":N S", "Ġ fiber", "Ġf iber", "Ġfi ber", "Ġfib er", "Ġ mk", "Ġm k", "Ġv intage", "- long", "-l ong", "-lo ng", ". reduce", ".re duce", ".red uce", ". Entities", ".Ent ities", "( record", "(re cord", "(rec ord", "Ġ pleasant", "Ġple asant", "Ġpleas ant", "F RING", "FR ING", ". Cells", ".C ells", ".Cell s", "O TT", "OT T", "ĉ elseif", "ĉelse if", "6 49", "64 9", "7 24", "72 4", "_ confirm", "_con firm", "_conf irm", "ĠView Group", "s ym", "sy m", "Ġ pray", "Ġp ray", "Ġpr ay", "Ġpra y", "Ġsus pected", "Ġsusp ected", "Ġsuspect ed", "Cont ains", "Con tains", "Contain s", "Conta ins", "9 83", "98 3", "Ġb orders", "Ġborder s", "Ġbor ders", "Ġbord ers", "Ġcomponent Did", "A SSERT", "ASS ERT", "Ġin finite", "Ġinf inite", "Ġinfinit e", "- order", "-or der", "Ġ hello", "Ġh ello", "Ġhel lo", "Ġhell o", "Ġ Grade", "ĠG rade", "ĠGr ade", "ĠGrad e", "ĠGra de", ".currentTime Millis", "ap olis", "apol is", "apo lis", "z h", "ĉ Object", "ĉO bject", ": \\\\", ":\\ \\", "H O", "val uation", "valu ation", "Ġ vocab", "Ġv ocab", "Ġvo cab", "Ġvoc ab", "7 19", "71 9", "Ġ coupon", "Ġc oupon", "Ġco upon", "Ġcou pon", "Ġcoup on", "ata bases", "atab ases", "atabase s", ". GetType", ".Get Type", "L earn", "Le arn", "7 92", "79 2", "] =\"", "]= \"", "Ġ Gary", "ĠG ary", "ĠGar y", "ĠGa ry", "ot ive", "oti ve", "Ġ ash", "Ġa sh", "Ġas h", "Ġ bib", "Ġb ib", "Ġbi b", "X XXX", "XX XX", "XXX X", "Ġ balanced", "Ġbalance d", "Ġbal anced", "VAL UE", "Ġ Nat", "ĠN at", "ĠNa t", "_ Ad", "_A d", "< E", "åĮ º", "Ġ MethodInfo", "ĠMethod Info", "8 97", "89 7", "L IB", "LI B", "Ġconsider able", "Ġconsid erable", "Ġ Industry", "ĠInd ustry", "ĠIndust ry", "t ests", "te sts", "test s", "tes ts", ". setTitle", ".set Title", "Ġ Bluetooth", "ĠB luetooth", "ĠBl uetooth", "ĠBlu etooth", "Ġ mapped", "Ġm apped", "Ġmap ped", "Ġma pped", "Ġ Bruce", "ĠBr uce", "ĠBru ce", "Ġ MainWindow", "ĠMain Window", "ĉ status", "ĉs tatus", "ĉst atus", "ĉstat us", "Ġ raz", "Ġr az", "Ġra z", "ĠM and", "ĠMan d", "ĠMa nd", "Ġ classification", "Ġclass ification", "Per missions", "Permission s", "Perm issions", "9 69", "96 9", "Ġ ----------------------------------------------------------------------------", "Ġ---------------------------------------------------------------- ------------", "Ġ------------------------------------------------ ----------------------------", "Ġ------------ ----------------------------------------------------------------", "Ġ------ ----------------------------------------------------------------------", "Ġ------------------------------------------------------------ ----------------", "Ġ------------------------------------------------------------------------- ---", "Ġ containers", "Ġcont ainers", "Ġcontainer s", "Ġcontain ers", "Ġconta iners", ": set", ":s et", "_ xml", "_x ml", "Ġwh ilst", "Th rough", "Thr ough", "Ġv align", "Ġval ign", "Ġworld s", "Ġwor lds", "C ORD", "CO RD", "COR D", "ED IA", "EDI A", "ÑĢ ов", "ÑĢо в", "Ġs pare", "Ġsp are", "Ġspa re", "Ġspar e", "Ġ Had", "ĠH ad", "ĠHa d", "Ġ DEF", "ĠD EF", "ĠDE F", "( ptr", "(p tr", "(pt r", "Ġw arming", "Ġwar ming", "Ġwarm ing", "8 98", "89 8", "ठ¾", "Ġcons ensus", "a gne", "ag ne", "agn e", "C TL", "CT L", "Ġ ìķ", "Ġì ķ", ". Main", ".M ain", ".Ma in", "web Element", "Ġp ist", "Ġpi st", "Ġpis t", "F lash", "Fl ash", "App end", "Ap pend", "Appe nd", ".tw img", "T ap", "Ta p", "Ġveget ables", "Ġvegetable s", "a lg", "al g", "0 58", "05 8", ". sample", ".s ample", ".sam ple", "Ġco aching", "Ġcoach ing", "( ind", "(i nd", "(in d", "Cell Value", "Check Box", "Ġ Hell", "ĠH ell", "ĠHe ll", "ĠHel l", "R OOT", "RO OT", "7 96", "79 6", "Ġst adium", "Ġstad ium", "Ġinvestig ating", ") %", "s ted", "st ed", "ste d", "9 65", "96 5", "Ġ Writing", "ĠW riting", "ĠWr iting", "Ġ ê²", "Ġê ²", "Ġ uno", "Ġu no", "Ġun o", "Ġ {{--", "Ġ{{ --", "Ġ coords", "Ġco ords", "Ġcoord s", "Ġun ser", "Ġuns er", "o rganization", "organ ization", "Ġ Crime", "ĠCr ime", "ĠCri me", "ĠDem ocrat", "ĠDemocr at", "5 79", "57 9", "Ġ vin", "Ġv in", "Ġvi n", "/ file", "/f ile", "0 78", "07 8", "- api", "-a pi", "-ap i", "Ġ Ay", "ĠA y", "Ġf unded", "Ġfun ded", "Ġfund ed", "ĠBr exit", "ĠBre xit", "ĠG h", "ent ina", "enti na", "entin a", "c ases", "ca ses", "case s", "cas es", "Ġ dash", "Ġd ash", "Ġda sh", "Ġdas h", "Ġ!! }Ċ", "Ġ!!} Ċ", "H I", "Off ice", "Ġcap tain", "Ġcapt ain", "Ġwor ship", "Ġwors hip", "Ġworsh ip", "\\ C", "7 33", "73 3", "8 51", "85 1", "Ġg lobe", "Ġgl obe", "Ġglob e", "Ġglo be", "_ board", "_b oard", "_bo ard", "Ġb abies", "Ġba bies", "Ġbab ies", "8 76", "87 6", "Ġcon secutive", "Ġconsec utive", "Ġenh anced", "Ġenhance d", "er eum", "ere um", "ĠAd vis", "ĠAdv is", "Ġg rain", "Ġgr ain", "Ġgra in", "7 71", "77 1", "Ġc raw", "Ġcr aw", "Ġcra w", "ancell ationToken", "ancellation Token", ". alpha", ".al pha", "_ WITH", "_W ITH", "ĠO tt", "ĠOt t", "Ġ Cool", "ĠC ool", "ĠCo ol", ". batch", ".b atch", ".bat ch", "Ġ verified", "Ġver ified", "( callback", "(c allback", "(call back", "Ġreg ards", "Ġregard s", "6 83", "68 3", "Ġ IntPtr", "ĠInt Ptr", "o ucher", "ou cher", "ouch er", "Ġ kin", "Ġk in", "Ġki n", "Ġt ouched", "Ġtouch ed", "Ġtou ched", "it Ãł", "a thon", "at hon", "ath on", "Ġadj acent", "Ġaccom panied", "L EAR", "LE AR", "Ġim plies", "Ġimp lies", "Ġimpl ies", "Ġ hill", "Ġh ill", "Ġhi ll", "Ġhil l", "ĠB altimore", "ĠBalt imore", "= \"-", "=\" -", "F inally", "Fin ally", "Final ly", "8 83", "88 3", "S am", "Sa m", "ic opt", "ico pt", "Ġs od", "Ġso d", "Ġ maj", "Ġm aj", "Ġma j", "Ġ Shipping", "ĠSh ipping", "ĠShip ping", "ĠShi pping", "Ġ getAll", "Ġget All", "Ġco aches", "Ġcoach es", "Ġdon ations", "Ġdonation s", "i lot", "il ot", "ilo t", "Ġ Tar", "ĠT ar", "ĠTa r", "c err", "ce rr", "cer r", "Ġ badge", "Ġb adge", "Ġbad ge", "Ġba dge", "Ġ markers", "Ġm arkers", "Ġmark ers", "Ġmar kers", "Ġmarker s", "Ġ Rand", "ĠR and", "ĠRa nd", "ĠRan d", "a ised", "ai sed", "ais ed", "aise d", "iss ance", "issa nce", "issan ce", "Ġexpl oring", "Ġexplo ring", "Ġexplor ing", "8 27", "82 7", "u ced", "uc ed", "uce d", "ĠInd onesia", "ĠIndones ia", "ĠIndo nesia", "Ġbe neath", "Ġbene ath", "Ġm agnetic", "Ġmagn etic", "Ġmagnet ic", "Ġm useum", "Ġmus eum", "Ġmuse um", "match Condition", "Ġdis rupt", "Ġre mind", "Ġrem ind", "Ġremin d", "Ġ TM", "ĠT M", "Ġ /><", "Ġ/ ><", "Ġ/> <", "Ġf ool", "Ġfo ol", "Ġfoo l", "Ġ esk", "Ġe sk", "Ġes k", ". Null", ".N ull", "Ġ Dies", "ĠD ies", "ĠDi es", "ĠDie s", "_ OUTPUT", "_OUT PUT", "_TYPE D", "_TYP ED", "Ġp ainted", "Ġpaint ed", "Ġpain ted", "6 73", "67 3", "7 35", "73 5", "Ġsoph istic", "Ġ Bear", "ĠB ear", "ĠBe ar", "ĠBea r", "* n", "_ PACK", "_P ACK", "_PA CK", "Ġdel ivering", "Ġdeliver ing", "Ġ COUNT", "ĠC OUNT", "ĠCO UNT", "åį ķ", "Ġj eg", "Ġje g", "- car", "-c ar", "-ca r", "f name", "fn ame", "Ġr anging", "Ġran ging", "Ġrang ing", "8 48", "84 8", "Ġ Neg", "ĠN eg", "ĠNe g", "/ ******/", "Ġ CHAR", "ĠCH AR", "Ġu ltra", "Ġult ra", "Ġul tra", "Ġultr a", "G rad", "Gr ad", "= t", "Ġjud ges", "Ġjudge s", "ĠD ise", "ĠDis e", "ĠDi se", "an ners", "ann ers", "anner s", "anne rs", "9 85", "98 5", "8 91", "89 1", "8 61", "86 1", "Ġ scal", "Ġs cal", "Ġsc al", "Ġsca l", "_ cal", "_c al", "_ca l", "ĠCON NECTION", "ĠCONNECT ION", "_ embed", "_em bed", "_emb ed", "( fn", "(f n", "Ġ Craft", "ĠC raft", "ĠCr aft", "ĠCra ft", "0 47", "04 7", "Ġ Pas", "ĠP as", "ĠPa s", "\" )->", "\") ->", ". convert", ".con vert", ".conv ert", ". resource", ".re source", ".res ource", "Ġ STATUS", "ĠST ATUS", "ĠSTAT US", "ô ng", "ôn g", "Ġ Tit", "ĠT it", "ĠTi t", "Ġclass room", "ĠArch itect", "ĠK ings", "ĠKing s", "ĠKin gs", "Ġ steady", "Ġste ady", "Ġstead y", "/* !Ċ", "/*! Ċ", "Ġ Gene", "ĠG ene", "ĠGe ne", "ĠGen e", ") \";Ċ", ")\" ;Ċ", "i cia", "ic ia", "ici a", "s tan", "st an", "sta n", "Ġ Construction", "ĠCon struction", "ĠConstruct ion", "ĠConstr uction", "um per", "ump er", "9 51", "95 1", "w c", "Ġ CBS", "ĠC BS", "ĠCB S", "in ging", "ing ing", "- party", "-p arty", "-part y", "-par ty", "( driver", "(d river", "(dr iver", "M ARK", "MA RK", "MAR K", "0 82", "08 2", "Ġ nested", "Ġn ested", "Ġne sted", "Ġnest ed", "Ġneste d", "Ġnes ted", "e ward", "ew ard", "Ġ dependency", "Ġd ependency", "Ġdep endency", "Ġdepend ency", "Ġm ales", "Ġma les", "Ġmale s", "Ġmal es", "9 28", "92 8", "Ġ ONE", "ĠO NE", "ĠON E", "Ġ Production", "ĠP roduction", "ĠPro duction", "ĠProduct ion", "ĠProdu ction", "ĠProd uction", "] [$", "][ $", "ãĥ¼ ãĥ", "_ LOAD", "_L OAD", "_LO AD", "ĠB ol", "ĠBo l", "el ry", "8 31", "83 1", "ł éĻ¤", "Ġ Require", "ĠRe quire", "ĠReq uire", "Ġ placing", "Ġpl acing", "Ġplac ing", "Ġpla cing", "x xx", "xx x", "C ALE", "CA LE", "CAL E", "Ġ thumb", "Ġth umb", "Ġthu mb", "8 24", "82 4", "Ch oose", "Cho ose", "Ġ prototype", "Ġprot otype", "Ġproto type", "V OID", "VO ID", "Ġles bian", "Ġlesb ian", "Ġlesbi an", "7 41", "74 1", "Ġ traits", "Ġt raits", "Ġtr aits", "Ġtra its", "Ġtrait s", "Ġtrai ts", "Sh arp", "Shar p", "Sha rp", "Ġ consume", "Ġcon sume", "Ġcons ume", "Ġconsum e", "Tr uth", "Ġaction Performed", "Ġ Environmental", "ĠEnvironment al", "Ġ Dean", "ĠD ean", "ĠDe an", "Ġ estado", "Ġest ado", "Ġesta do", "Ġestad o", "s ame", "sa me", "sam e", "Ġ numeric", "Ġn umeric", "Ġnum eric", "Ġnumer ic", "Ġnu meric", "Ġtrans it", "Ġtran sit", ". Email", ".E mail", "- side", "-s ide", "_ RUN", "_R UN", "ĠV illage", "ĠVill age", "ĠVilla ge", "ĠVil lage", "_ OPEN", "_OP EN", "è ¦", ". rem", ".re m", ".r em", "- warning", "-w arning", "-war ning", "a nya", "an ya", "any a", "Property Changed", "Ġ (!_", "Ġ(! _", "( check", "(c heck", "(ch eck", "i lia", "il ia", "ili a", "Ġ Soft", "ĠS oft", "ĠSo ft", "ĠSof t", "st eps", "ste ps", "step s", "ĠMad rid", "Memory Warning", "Ġ handlers", "Ġhand lers", "Ġhandle rs", "Ġhandler s", "Ġexperi encing", "Ġ inspect", "Ġin spect", "Ġins pect", "Ġinsp ect", "button s", "but tons", "butt ons", "Receive MemoryWarning", "ch emy", "che my", "chem y", "L inks", "Link s", "Lin ks", "Ġurl lib", "Ġur llib", ".System Colors", "Ġ Eigen", "ĠE igen", "ĠEig en", "Ġpun ishment", "Ġpunish ment", ":UI Control", "b ara", "bar a", "ba ra", "- set", "-s et", "-se t", "Ġ }čĊčĊčĊ", "Ġ} čĊčĊčĊ", "Ġ}čĊ čĊčĊ", "Ġ}čĊčĊ čĊ", "Ġt olerance", "Ġtoler ance", "Ġ interfaces", "Ġinter faces", "Ġinterface s", "Ġinterf aces", ". redirect", ".re direct", ".red irect", "ighb ors", "ighbor s", "cs rf", "csr f", "_ background", "_back ground", ". Utils", ".Util s", "_ HT", "_H T", "6 92", "69 2", "Ġ Interest", "ĠInter est", "i mos", "im os", "imo s", "Ġgr ants", "Ġgrant s", "Ġgran ts", "Ġgra nts", "0 83", "08 3", "Ġexam ined", "Ġexamine d", "Ð Ķ", "Ġ cf", "Ġc f", "f orge", "for ge", "forg e", "b acks", "back s", "ba cks", "bac ks", "Ġ Objects", "ĠObject s", "ĠObj ects", "_ sent", "_s ent", "_se nt", ". entry", ".en try", ".ent ry", "Ġ THEN", "ĠT HEN", "ĠTHE N", "ĠTH EN", "ell ido", "elli do", "c ia", "ci a", ", res", ",r es", ",re s", "6 59", "65 9", "6 81", "68 1", "/st dc", "/std c", ". nd", ".n d", "( Int", "(I nt", "(In t", "Ġ Authors", "ĠAuthor s", "ĠAuth ors", "ĠApp CompatActivity", "' {", "Ġ medi", "Ġm edi", "Ġme di", "Ġmed i", "M usic", "Mu sic", "Mus ic", "i gm", "ig m", "ce ipt", "Ġ auss", "Ġa uss", "Ġau ss", "Ġaus s", "Ġtarget ing", "Ġtarg eting", "Ġ Keys", "ĠKey s", "ĠKe ys", "h n", ": ]Ċ", ":] Ċ", "Ġmin eral", "Ġmine ral", "Ġminer al", "à ®", ". ca", ".c a", "7 61", "76 1", "o med", "om ed", "ome d", "Ġ sheets", "Ġs heets", "Ġshe ets", "Ġsheet s", "Ġc amb", "Ġca mb", "Ġcam b", "Ġdead ly", ". inject", ".in ject", "( unit", "(u nit", "(un it", "Ġ Selection", "ĠS election", "ĠSe lection", "ĠSelect ion", "ĠSel ection", "ĠSele ction", ".g ms", "( connection", "(con nection", "(conn ection", "(connect ion", "Ġ $(\"", "Ġ$ (\"", "Ġ$( \"", "é mon", "ém on", "Ġ Currently", "ĠCurrent ly", "p te", "pt e", "_ paths", "_p aths", "_path s", "_pa ths", "_pat hs", "8 47", "84 7", "le af", "lea f", "Ġimp lications", "Ġimpl ications", "Ġimplication s", "Ġimplic ations", "p osal", "pos al", "po sal", "ä½ į", "[ /", "a ncia", "an cia", "anc ia", "é Ľ", "m ul", "mu l", "c ie", "ci e", "Ġge ile", "Ġgeil e", "6 79", "67 9", "im als", "imal s", "ima ls", "UI View", "Ġs urre", "Ġsur re", "s erialize", "serial ize", "I SO", "IS O", "Ġar bitrary", "Ġarbit rary", "Ġarbitr ary", "Ġsock addr", ". fn", ".f n", "Ġ Merc", "ĠM erc", "ĠMe rc", "ĠMer c", "Ġ casting", "Ġc asting", "Ġcas ting", "Ġcast ing", "Key Down", "Ġ newValue", "Ġnew Value", "ĠnewVal ue", "op ens", "open s", "ope ns", "7 17", "71 7", "T odo", "To do", "Ġflex ibility", "ĉ ĉĉĉĠĠ", "ĉĉ ĉĉĠĠ", "ĉĉĉĉ ĠĠ", "ĉĉĉ ĉĠĠ", "ĉĉĉĉĠ Ġ", "V elocity", "Vel ocity", "ú n", "r owing", "ro wing", "row ing", "Ġ computed", "Ġcom puted", "Ġcomp uted", "Ġcomput ed", "Ġcompute d", "` )Ċ", "`) Ċ", "st atement", "state ment", "stat ement", "sta tement", "Ġ ri", "Ġr i", "_ cart", "_c art", "_car t", "_ca rt", "L ow", "Lo w", "trans fer", ". nav", ".n av", "Ġ grave", "Ġgr ave", "Ġgra ve", "Ġgrav e", "Ġ Door", "ĠD oor", "ĠDo or", "ĉ alert", "ĉal ert", "6 91", "69 1", "6 98", "69 8", ". subscribe", ".sub scribe", "- profile", "-pro file", "-pr ofile", "-prof ile", "ĉ base", "ĉb ase", "Ġ âĪĴ", "ĠâĪ Ĵ", "_ _ĊĊ", "__ ĊĊ", "__Ċ Ċ", "Ġengine ers", "Ġengineer s", "Ġexp losion", "Ġexplos ion", "Ġd ari", "Ġda ri", "Ġdar i", "6 82", "68 2", "ĉ Log", "ĉL og", "o nal", "on al", "ona l", "Ġis olated", "Ġisol ated", "Ġiso lated", "Ġisolate d", "{ i", "Ġ Msg", "ĠM sg", "ĠMs g", "F uture", "Fu ture", "Ġr acist", "Ġrac ist", "- wrap", "-w rap", "Ġ Vers", "ĠV ers", "ĠVer s", "ĠVe rs", "b org", "bo rg", "bor g", "I SION", "IS ION", "Ġ ÑĢаÐ", "ĠÑĢ аÐ", "ĠÑĢа Ð", "ĠY an", "ĠYa n", "8 36", "83 6", "init With", "Ġn omin", "Ġno min", "Ġnom in", "( empty", "(em pty", "(emp ty", "ÃŃ n", "ã Ĥ¤", "ãĤ ¤", "ĉ width", "ĉw idth", "Ġch amber", "Ġcham ber", "/ ajax", "/a jax", "E MP", "EM P", "0 93", "09 3", "Ġne ces", "Ġnec es", "i vos", "iv os", "ivo s", "log ic", "* )&", "*) &", "cri pts", "cript s", "9 76", "97 6", "Row At", "0 53", "05 3", "i blings", "ib lings", "ibling s", "Ġ ears", "Ġe ars", "Ġear s", "Ġea rs", "Ġcomp uting", "Ġcomput ing", "Ġ maker", "Ġm aker", "Ġmake r", "Ġma ker", "Ġmak er", "Ġ Neither", "ĠN either", "ĠNe ither", "b readcrumb", "Ġ serialize", "Ġs erialize", "Ġserial ize", "Ġ Within", "ĠWith in", "ĠWi thin", "ĠWit hin", "Ġd ell", "Ġde ll", "Ġdel l", "_ TRACE", "_TR ACE", "_TRA CE", "0 92", "09 2", "= a", "Ġw ishes", "Ġwish es", "Ġwis hes", "- inch", "-in ch", "-inc h", "ĠD or", "ĠDo r", "Ġinnoc ent", "ĠD ol", "ĠDo l", "Ġint ens", "Ġinte ns", "for ced", "force d", "forc ed", "0 54", "05 4", "Ġ BIT", "ĠB IT", "ĠBI T", "Ġphot ographs", "Ġphoto graphs", "Ġphotograph s", "Ġc asa", "Ġca sa", "Ġcas a", "Ġ Len", "ĠL en", "ĠLe n", "\\ Framework", "\\F ramework", ". Simple", ".S imple", "Ġd ear", "Ġde ar", "8 95", "89 5", ") /(", ")/ (", "ip pi", "ipp i", "Ġ owns", "Ġown s", "Ġow ns", "P layers", "Pl ayers", "Player s", "Play ers", "Ġprop osals", "Ġpropos als", "Ġproposal s", ". pi", ".p i", "us alem", "usa lem", "usal em", "D amage", "Da mage", "Dam age", "Ġcal ories", "Ġcalorie s", "Ġcalor ies", "Ġ Creative", "ĠC reative", "ĠCre ative", "ĠCreat ive", "Ġ [$", "Ġ[ $", "Ġ //čĊ", "Ġ// čĊ", "Ġ/ /čĊ", "7 86", "78 6", "And View", "è me", "èm e", ". custom", ".c ustom", "_ factory", "_f actory", "_factor y", "_fact ory", "comm ands", "command s", "comma nds", "_ look", "_l ook", "_lo ok", "Ġ strcmp", "Ġstr cmp", "Y N", "a ired", "air ed", "ai red", "aire d", "Ġ audit", "Ġa udit", "Ġaud it", "Ġau dit", "Ġaudi t", "о ÑģÑĤ", "оÑģ ÑĤ", "Ġ Reverse", "ĠRe verse", "ĠRev erse", "ĠRever se", "ropri ate", "e tics", "et ics", "etic s", "eti cs", "< vector", "';Ċ", "\"> ';Ċ", "\">' ;Ċ", "Ġpe pper", "Ġpepp er", "Ġpep per", "9 89", "98 9", "Ġ shed", "Ġs hed", "Ġsh ed", "Ġshe d", "Ġ Medium", "ĠM edium", "ĠMed ium", "ĠMedi um", "Ġ Cookie", "ĠC ookie", "ĠCo okie", "ĠCook ie", "8 89", "88 9", "Ġoverse as", "ed or", "edo r", "as urement", "asure ment", "asu rement", "7 66", "76 6", "åŃ ĺ", "Ġ' .'", "Ġ'. '", "Ġ php", "Ġp hp", "Ġph p", "Ġ PROC", "ĠP ROC", "ĠPRO C", "ĠPR OC", "Ġexcept ional", "Ġexception al", "( th", "(t h", "Ġ Jet", "ĠJ et", "ĠJe t", "Ġ occupied", "Ġoccup ied", ". setImage", ".set Image", "Ġ Related", "ĠRe lated", "ĠRel ated", "u cker", "uc ker", "uck er", "M embers", "Member s", "Mem bers", "PR INT", "PRI NT", "ĠG lo", "ĠGl o", "_ VIEW", "_V IEW", "} \",Ċ", "}\", Ċ", "}\" ,Ċ", "Ġad option", "Ġadopt ion", "Ġado ption", "[ ])Ċ", "[] )Ċ", "[]) Ċ", "8 42", "84 2", "ĠMiss ouri", "ĠLin coln", "er ald", "era ld", "eral d", "P opup", "Pop up", "Ġf ate", "Ġfa te", "Ġfat e", "- bootstrap", "-boot strap", "f ections", "fe ctions", "fect ions", "fection s", "Ġ Poll", "ĠP oll", "ĠPol l", "ĠPo ll", "_ ARGS", "_ARG S", "_AR GS", "in ance", "ina nce", "inan ce", "6 97", "69 7", "- home", "-h ome", ". ),", ".) ,", "_ done", "_d one", "_do ne", "_don e", "6 94", "69 4", ": ĊĊĊ", ":Ċ ĊĊ", ":ĊĊ Ċ", "Ġdiscuss ing", "Ġ SQLException", "ĠSQL Exception", "Ġelect ro", "Ġelectr o", "ĉ req", "ĉr eq", "ĉre q", "Ġ zw", "Ġz w", "8 86", "88 6", "Ġl ui", "Ġlu i", "9 32", "93 2", "Ġover night", "$ user", "Ġ WAY", "ĠW AY", "ĠWA Y", "Ġall erg", "Ġalle rg", "Ġaller g", "Ġdis appointed", "Ġdisappoint ed", "Ġrad iation", "Ġradi ation", "Ġim pressed", "Ġimp ressed", "Ġimpress ed", "Ġimpr essed", "Ġimpres sed", "if icates", "ific ates", "ificate s", "ifica tes", "Ġt ob", "Ġto b", "CL ASS", "CLA SS", "Ġ cuda", "Ġc uda", "Ġcu da", "Ġcud a", "_ det", "_d et", "_de t", "- post", "-p ost", "-pos t", "-po st", "u lu", "ul u", "Trans lation", "- hand", "-h and", ". year", ".y ear", "Ġ Mongo", "ĠM ongo", "ĠMon go", "ĠMo ngo", "ĠMong o", "Ġun clear", "Ġunc lear", "Ġuncle ar", ". engine", ".e ngine", ".eng ine", "WEB PACK", "r ices", "ri ces", "ric es", "rice s", "_ ACCESS", "_AC CESS", "_ACC ESS", "Ġh olidays", "Ġholiday s", "per cent", "perc ent", ". Identity", ".Id entity", "Ġ Gov", "ĠG ov", "ĠGo v", "Ġpass ionate", "Ġpassion ate", "! !.", "!! .", "ĠG reece", "ĠGre ece", "ĠGree ce", "plus plus", "' ));", "') );", "')) ;", "G P", "Ġex cit", "Ġexc it", ".tab Page", "_ cond", "_c ond", "_con d", "_co nd", "Ġ sponsor", "Ġs ponsor", "Ġspons or", "M ODULE", "MOD ULE", "_ proc", "_p roc", "_pro c", "_pr oc", "Ġ $Ċ", "Ġ$ Ċ", "Ġr ational", "Ġrat ional", "Ġratio nal", "Ġration al", ". Tool", ".T ool", ".To ol", "Ġi hr", "Ġih r", "c ca", "cc a", "åĵ ģ", "ĠE state", "ĠEst ate", "ĠEsta te", "IB UTE", "IBUT E", "Action Performed", "Ġ Solar", "ĠS olar", "ĠSo lar", "ĠSol ar", "¦ Ĥ", "Ġequ ity", "Ġeq uity", "t id", "ti d", "9 38", "93 8", "Ġre cip", "Ġrec ip", ". simple", ".s imple", ".sim ple", "m k", "6 89", "68 9", "Ġ Luke", "ĠL uke", "ĠLu ke", "ĠLuk e", "ĠGuard ian", "Ġ encrypted", "Ġenc rypted", "Ġencrypt ed", "Ġdom inant", "Ġdomin ant", "Ġdomina nt", ". place", ".p lace", ".pl ace", "Ġ NV", "ĠN V", "8 39", "83 9", "Ġton gue", "Ġtong ue", "( Get", "(G et", "Ġst ainless", "Ġstain less", ". Play", ".P lay", ".Pl ay", "Ġ eb", "Ġe b", "a ci", "ac i", ". buffer", ".b uffer", ".buf fer", "readcr umbs", "readcrumb s", "Ġv accine", "Ġvacc ine", "p rom", "pr om", "pro m", "9 79", "97 9", "Ġ userInfo", "Ġuser Info", "Ġ slug", "Ġs lug", "Ġsl ug", "Ġslu g", "Serial izedName", "Serialized Name", "- wide", "-w ide", "Ġre actions", "Ġreaction s", "Ġreact ions", "Ġ Yang", "ĠY ang", "ĠYan g", "ĠYa ng", "Ġ Adds", "ĠA dds", "ĠAd ds", "ĠAdd s", "( userId", "(user Id", "Ġ plates", "Ġp lates", "Ġpl ates", "Ġplate s", "Ġpla tes", "Ġplat es", "Ġ MEM", "ĠM EM", "ĠME M", "Ġb ail", "Ġba il", "In side", "Ins ide", "e ted", "et ed", "ete d", "Ġ elsif", "Ġels if", "Ġs ake", "Ġsa ke", "Ġsak e", "Ġ cycles", "Ġc ycles", "Ġcy cles", "Ġcycle s", "Ġcycl es", "Ġcyc les", "Ġ ìĹ", "Ġì Ĺ", "ĉ I", "- collapse", "-c ollapse", "8 41", "84 1", "Ġ GMT", "ĠG MT", "ĠGM T", "8 14", "81 4", "De claration", "Ġg ros", "Ġgr os", "Ġgro s", "Ġre aches", "Ġreach es", "Ġcust ody", "Un til", "Unt il", "7 53", "75 3", "8 56", "85 6", "t u", "ĠC hen", "ĠCh en", "ĠChe n", "Ġ nx", "Ġn x", "( addr", "(add r", "(ad dr", "Ġ Offer", "ĠO ffer", "ĠOff er", "ĠOf fer", "Ġcol leg", "Ġcoll eg", "Ġcolle g", "ass ador", "6 74", "67 4", "Ġ mapper", "Ġm apper", "Ġmap per", "Ġma pper", "8 54", "85 4", "ĠS IGNAL", "ĠSIG NAL", "ĠSIGN AL", "ĠB loom", "ĠBl oom", "ĠBlo om", "ĠH oll", "ĠHol l", "ĠHo ll", "ĠIm per", "ĠImp er", "- des", "-d es", "-de s", "_ site", "_s ite", "_si te", "P roc", "Pro c", "Pr oc", "E qu", "Eq u", "Ġ atomic", "Ġat omic", "Ġatom ic", "Ġ Woman", "ĠW oman", "ĠWo man", "s ent", "se nt", "sen t", "7 38", "73 8", "8 17", "81 7", "s car", "sc ar", "Ġint elligent", "Ġintellig ent", "Ġ Getting", "ĠG etting", "ĠGet ting", "Ġ Registration", "ĠReg istration", "ĠRegistr ation", "Ġ Phill", "ĠP hill", "ĠPh ill", "ĠPhil l", "ĠPhi ll", "Ġ killer", "Ġk iller", "Ġkill er", "Ġkil ler", "Ġki ller", "un icode", "unic ode", "uni code", "Ċ ĉĉĊ", "Ġ Jacob", "ĠJ acob", "ĠJac ob", "ĠJa cob", "Ġ Const", "ĠCon st", "ĠCo nst", "ĠCons t", "Ġ locate", "Ġl ocate", "Ġlo cate", "Ġloc ate", "Ġc aus", "Ġca us", "7 49", "74 9", "ĠSch olar", "ĠScho lar", "Ġ constitutional", "Ġconstitution al", "Ġin flation", "Ġinf lation", "Ġinfl ation", "Ġ Got", "ĠG ot", "ĠGo t", "= array", "=a rray", "en dum", "end um", "Ġ translated", "Ġtrans lated", "Ġtransl ated", "Ġtranslate d", "Ġdiv orce", "Ġdivor ce", "En tries", "Ent ries", "Entr ies", "Ġs or", "Ġso r", "Ġ Quote", "ĠQu ote", "ir lines", "irl ines", "U K", "Ġ excel", "Ġex cel", "Ġexc el", "Ġexce l", "( opt", "(o pt", "(op t", "Ġ ADV", "ĠA DV", "ĠAD V", ", :,", ",: ,", "Ġcont acted", "Ġcontact ed", "7 42", "74 2", "Ġ DA", "ĠD A", "Ġr ings", "Ġring s", "Ġrin gs", "Ġ Industrial", "ĠInd ustrial", "ĠIndust rial", ". getContext", ".get Context", "Ġforg otten", "Ġforgot ten", "Ġ Tan", "ĠT an", "ĠTa n", "Ġ pants", "Ġp ants", "Ġpa nts", "Ġpan ts", "Ġpant s", "Ġ ov", "Ġo v", "Ġ decoder", "Ġde coder", "Ġdec oder", "Ġdecode r", "Ġdeco der", "Ġ Partial", "ĠP artial", "ĠPart ial", "ĠParti al", "Ġ vc", "Ġv c", "Ġb attles", "Ġbattle s", "Ġbatt les", "A rial", "Ar ial", "FRING EMENT", "i rates", "ir ates", "ira tes", "irate s", ", w", "aint enance", "Ġ Od", "ĠO d", "ĠTechn ologies", "åī į", "ĠC arter", "ĠCar ter", "ĠCart er", ". findAll", ".find All", "N ome", "No me", "Nom e", "B en", "Be n", "Ġ Usage", "ĠU sage", "ĠUs age", "ĠUsa ge", "Ġ Picture", "ĠP icture", "ĠPic ture", "Ġbad ly", "_ panel", "_p anel", "_pa nel", "_pan el", "Ġpat ent", "Ġpa tent", "Ġ Protocol", "ĠProt ocol", "ĠProto col", "l otte", "lo tte", "lot te", "ĉ player", "ĉp layer", "ĉpl ayer", "ĉplay er", "j ections", "ject ions", "je ctions", "jection s", "7 46", "74 6", "Ġ dou", "Ġd ou", "Ġdo u", "_ release", "_re lease", "_r elease", "_rel ease", "urn iture", "_ tax", "_t ax", "_ta x", "Ġ Fields", "ĠF ields", "ĠField s", ". dataset", ".d ataset", ".data set", ".dat aset", ".datas et", "_ master", "_m aster", "_ma ster", "_mas ter", "CLUD E", "CLU DE", "ĠPh arm", "ĠPhar m", "b st", "bs t", "Ġoper ational", "Ġoperation al", ". cell", ".c ell", ".ce ll", "Ġident ifying", "Ġidentify ing", "Ġ jwt", "Ġj wt", "t uple", "tu ple", "Ġ TC", "ĠT C", "Ġ Cro", "ĠC ro", "ĠCr o", "9 36", "93 6", "ix map", "- components", "-com ponents", "-component s", "-comp onents", "g eneral", "gen eral", "gener al", "gene ral", "Ġ oz", "Ġo z", "_ De", "_D e", "_ double", "_d ouble", "_do uble", "Ġ Too", "ĠT oo", "ĠTo o", "0 88", "08 8", ".View Group", "8 79", "87 9", "g ate", "ga te", "d ings", "ding s", "din gs", "ph otos", "photo s", "phot os", "Ġgr ande", "Ġgrand e", "Ġgran de", "Ġgra nde", "ol lect", "oll ect", "olle ct", "_ lin", "_l in", "_li n", "Ġaw ful", "f ilters", "filter s", "fil ters", "filt ers", "Ġ alternate", "Ġaltern ate", "e sp", "es p", "Ġ compress", "Ġcom press", "Ġcomp ress", "Ġcompr ess", "e o", "Ġ Scale", "ĠS cale", "ĠSc ale", "ĠScal e", "Ġin direct", "Ġind irect", "Ġindir ect", "Ġ invoice", "Ġin voice", "Ġinv oice", "Ġinvo ice", "ĊĊ ĊĊĊĊĊĊĊĊĊĊĊĊĊĊ", "ĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊĊĊ", "ĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊ", "ĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊ", "ĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊĊ", "ĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊ", "ĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊ", "ĊĊĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊ", "ĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊ", "ĊĊĊĊĊĊĊĊĊĊĊĊĊĊ ĊĊ", "ĊĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊ", "Start ing", "Star ting", "Ġ Players", "ĠP layers", "ĠPl ayers", "ĠPlay ers", "ĠPlayer s", "ĠPla yers", "i ele", "ie le", "iel e", ". then", ".t hen", ".th en", ".the n", "9 81", "98 1", "O rd", "Or d", "Ġ Tuple", "ĠT uple", "ĠTu ple", "ĠTup le", "Ġ bout", "Ġb out", "Ġbo ut", "Ġbou t", "Ġ Statistics", "ĠStat istics", "P review", "Pr eview", "Pre view", "Prev iew", "Ġp uzzle", "Ġpu zzle", "Ġpuzz le", "Ġ Width", "ĠW idth", "ĠWid th", "ST ATE", "STAT E", "STA TE", "Ġ overlay", "Ġover lay", "Ġoverl ay", "ĉ on", "ĉo n", "Ġin fr", "Ġinf r", "Ġsm allest", "Ġsmall est", "l ocked", "lock ed", "loc ked", "ÑĤ о", "s sl", "ss l", "7 79", "77 9", "Ġde emed", "Ġdee med", "Ġdeem ed", "Ġs co", "Ġsc o", "r eck", "re ck", "rec k", "Ġj Button", "Ġ missions", "Ġm issions", "Ġmiss ions", "Ġmission s", "8 71", "87 1", "ç§ °", ".Selected Index", "T ABLE", "TA BLE", "TAB LE", "S ept", "Se pt", "Sep t", "Ġac knowledge", "Ġack nowledge", "Ġacknow ledge", "Ġacknowled ge", "Ġ strtotime", "Ġstrt otime", "Ġ Tell", "ĠT ell", "ĠTe ll", "ĠTel l", "ĠD ak", "ĠDa k", "Ġal uminum", "Ġf ence", "Ġfe nce", "Ġfen ce", "Ġ Stars", "ĠSt ars", "ĠStar s", "ĠSta rs", "CON FIG", "CONF IG", "Ġr etrofit", "Ġretro fit", "Ġ emphasis", "Ġem phasis", "Ġemph asis", "Ġemphas is", "/ header", "/head er", "/he ader", "Ġ Something", "ĠS omething", "ĠSome thing", "ĠSom ething", "in ished", "ini shed", "inish ed", "inis hed", "=' \".$", "='\" .$", "='\". $", "Ġ Validators", "ĠValid ators", "ĠValidator s", "Ġp olar", "Ġpol ar", "Ġpo lar", "s ections", "se ctions", "section s", "sect ions", "9 44", "94 4", ".as px", ".asp x", "Ġa spir", "Ġas pir", "Ġasp ir", ". Mock", ".M ock", "Code Gen", "Ġp eut", "Ġpe ut", "Ġpeu t", "9 71", "97 1", "Ġaccept ing", "Ġb acking", "Ġback ing", "Ġbac king", "P icture", "Pic ture", "/ ap", "/a p", "е г", "еР³", "_ SEC", "_S EC", "_SE C", "- use", "-us e", "-u se", "an notation", "ann otation", "annot ation", "Ġc ognitive", "Ġcogn itive", "Ġg rip", "Ġgr ip", "Ġgri p", "h our", "ho ur", "hou r", "Ġ Legal", "ĠL egal", "ĠLe gal", "ĠLeg al", "Ġe pic", "Ġep ic", ". toolStrip", ".t oolStrip", ".tool Strip", ". notify", ".n otify", ".not ify", ". Last", ".L ast", "OR IZ", "M iddleware", "Middle ware", "cri ptions", "cript ions", "cription s", "l ash", "la sh", "las h", "_ FOUND", "_F OUND", "Ġ Liverpool", "ĠLiver pool", "Ġ {}\",", "Ġ{ }\",", "Ġ{} \",", "9 31", "93 1", "Inst all", "Ġ nit", "Ġn it", "Ġni t", "Ġfig ured", "Ġfigure d", "Ġfigur ed", "[ len", "[l en", ". Win", ".W in", ". platform", ".pl atform", "8 53", "85 3", "Ġgam bling", "Ġgamb ling", "( dt", "(d t", "a very", "av ery", "ave ry", "aver y", "ĉ include", "ĉin clude", "Wh ether", "R outing", "Ro uting", "Ġth erap", "Ġthe rap", "Ġther ap", "Rem ote", "Ġ Loss", "ĠL oss", "ĠLo ss", "ĠLos s", "y ll", "yl l", "Ġappro ached", "Ġapproach ed", "Ġ Vehicle", "ĠV ehicle", "Ġ Alpha", "ĠAl pha", "Ġv ocê", "Ġvoc ê", "an swers", "ans wers", "answer s", "NS Dictionary", "9 54", "95 4", "cons ider", "un used", "unu sed", "Ġ Fan", "ĠF an", "ĠFa n", "or able", "ora ble", "f re", "fr e", "8 73", "87 3", "ĠDIS CLAIM", "Ġ Actor", "ĠA ctor", "ĠAct or", "ĠAc tor", ". ]", "to Have", ". userId", ".user Id", "Ġspe eds", "Ġspeed s", "e way", "ew ay", "Ġrec urs", "Ġrecur s", "Ġ г", "ĠÐ ³", "_ priv", "_p riv", "_pr iv", "_pri v", "! âĢĿĊĊ", "!âĢĿ ĊĊ", "Ch oice", "Cho ice", "Ġs ettle", "Ġset tle", "Ġsett le", "Ġ planes", "Ġpl anes", "Ġplan es", "Ġplane s", "Ġpla nes", "' },", "'} ,", "T om", "To m", "I TER", "IT ER", "ITE R", "! \"Ċ", "!\" Ċ", "å »", "ach elor", "ache lor", "achel or", "Ġse paration", "Ġsepar ation", "Ġseparat ion", "Ġ dal", "Ġd al", "Ġda l", "a dj", "ad j", "Ġ registers", "Ġreg isters", "Ġregister s", "Ġregist ers", "r iz", "ri z", "Ġ Notice", "ĠNot ice", "Ġ lu", "Ġl u", "Ġc ourage", "Ġcour age", "Ġcou rage", "Ġ axes", "Ġa xes", "Ġax es", "Ġaxe s", "cell ent", ". async", ".as ync", ".a sync", "0 73", "07 3", "Ġcom patibility", "Ġcompat ibility", "ç «", "Ġ !ĊĊ", "Ġ! ĊĊ", "Ġ!Ċ Ċ", "ĉ title", "ĉt itle", "ĉti tle", "Y LE", "YL E", "ĉ message", "ĉm essage", "U UID", "UU ID", "OL DER", "OLD ER", "Ġ HH", "ĠH H", "Ġ StyleSheet", "ĠStyle Sheet", "Ġacc essed", "Ġaccess ed", "Ġacces sed", ". validation", ".valid ation", "t asks", "task s", "tas ks", "Ġpoll ution", "Ġpollut ion", ". canvas", ".c anvas", ".can vas", "Ġ ingredient", "Ġing redient", "ĠC abin", "ĠCa bin", "ĠCab in", "A h", "ol down", "old own", "ĠN OI", "ĠNO I", "Ġ ÃĹ", "Ġà Ĺ", "[ f", "e duc", "ed uc", "edu c", "y alty", "yal ty", "( not", "(n ot", "(no t", "_ State", "_St ate", "9 33", "93 3", "a men", "am en", "ame n", "7 95", "79 5", "7 39", "73 9", "Ġ dao", "Ġd ao", "Ġda o", "u dad", "ud ad", "uda d", "el lers", "ell ers", "elle rs", "eller s", "} &", "l icity", "lic ity", "li city", "licit y", "_ WINDOW", "_W INDOW", "Ġt atto", "Ġtat to", "val or", "va lor", ". Range", ".R ange", "Ġreference d", "Ġrefer enced", "ĠRe serve", "ĠRes erve", "M oney", "Mon ey", "Mo ney", "8 74", "87 4", "SC RIPT", "SCRI PT", "/ product", "/pro duct", "cho ices", "choice s", "Ġ tin", "Ġt in", "Ġti n", "ãĤ ĵ", "9 18", "91 8", "Ġ separator", "Ġs eparator", "Ġse parator", "Ġsepar ator", "Ġseparat or", "Ġ pkg", "Ġp kg", "Ġpk g", "am med", "amm ed", "Ġ MAT", "ĠM AT", "ĠMA T", "! !ĊĊ", "!! ĊĊ", "!!Ċ Ċ", "Ġ raid", "Ġr aid", "Ġra id", "Ġmot ivation", "Ġmotiv ation", "Ġ XP", "ĠX P", "Ġ Background", "ĠBack ground", "Ġ Quaternion", "ĠQu aternion", ".define Property", "i ker", "ik er", "ike r", "ĉ parent", "ĉp arent", "Ġ Originally", "ĠOrigin ally", "ĠOriginal ly", "ĠOrig inally", "ant age", "anta ge", "ĠH ans", "ĠHa ns", "ĠHan s", "Ġ timeline", "Ġt imeline", "Ġtime line", "Ġtim eline", ". cur", ".c ur", "o pic", "op ic", "opi c", "ĠS equ", "ĠSe qu", "ĠSeq u", "m ust", "mu st", "mus t", "Ġ Coal", "ĠCo al", "Ġ formatter", "Ġfor matter", "Ġform atter", "Ġformat ter", "_ RGB", "_R GB", "_RG B", "Ġ _(\"", "Ġ_ (\"", "Ġ_( \"", "' }),Ċ", "'} ),Ċ", "'}) ,Ċ", "Ġ= ================", "Ġ== ===============", "Ġ=== ==============", "Ġ===== ============", "Ġ==== =============", "Ġ========== =======", "Ġ======= ==========", "Ġ FUNCTION", "ĠF UNCTION", "ĠFUN CTION", "ĠFUNC TION", "ĠFUNCT ION", "Ġ lng", "Ġl ng", "Ġln g", "ic ates", "ica tes", "icate s", "l ive", "li ve", "liv e", "_ engine", "_e ngine", "_eng ine", "Ġt owns", "Ġtown s", "Ġtow ns", "8 68", "86 8", "' ))ĊĊ", "') )ĊĊ", "')) ĊĊ", "'))Ċ Ċ", "Ġ PK", "ĠP K", "( api", "(a pi", "(ap i", "ĉ scanf", "ĉs canf", "0 89", "08 9", "p acket", "pack et", "pa cket", "pac ket", ". phone", ".p hone", ".ph one", "á Ģ", "Ġ Andy", "ĠAn dy", "ĠAnd y", "_N AMES", "_NAME S", "9 82", "98 2", "P LY", "PL Y", "9 55", "95 5", "Ġ mins", "Ġm ins", "Ġmin s", "Ġmi ns", "i mi", "im i", "Ġ brick", "Ġb rick", "Ġbr ick", "Ġbri ck", "Ġ blade", "Ġbl ade", "Ġbla de", ". stdout", ".std out", "} `;Ċ", "}` ;Ċ", "S hift", "Sh ift", "ĉ sb", "ĉs b", "Ġ Checks", "ĠCheck s", "ĠChe cks", "Ġphenomen on", "Av atar", "Ġmin istry", "Ġmini stry", "Ġminist ry", "r ose", "ro se", "ros e", "ĉ File", "ĉF ile", "8 78", "87 8", "Ġt itled", "Ġtitle d", "Ġtit led", "( LOG", "(L OG", "Ġ gan", "Ġg an", "Ġga n", "d esign", "de sign", "des ign", "( ),čĊ", "() ,čĊ", "(), čĊ", "Ġ bones", "Ġb ones", "Ġbo nes", "Ġbon es", "Ġbone s", "s tm", "st m", "ÅĽ Äĩ", "Ġ InputStream", "ĠInput Stream", "Ġvol unt", "Ġ Serializable", "ĠSerial izable", "Ġ fighter", "Ġf ighter", "Ġfight er", "Ġ Drag", "ĠD rag", "ĠDr ag", "ĠDra g", "T witter", "Tw itter", "Ġsub sid", "Ġsubs id", "ç ¼", "Ġ forums", "Ġfor ums", "Ġforum s", ". loading", ".load ing", ".lo ading", "log ged", "logg ed", "_ this", "_t his", "_th is", "Ġ terrain", "Ġter rain", "Ġterr ain", "Ġterra in", "Ġir re", "Ġirr e", "Ġ Ing", "ĠI ng", "ĠIn g", "Ġ CN", "ĠC N", "_ objects", "_object s", "_obj ects", ". uid", ".ui d", ".u id", "Ġconscious ness", "T INGS", "TING S", "ĠG all", "ĠGal l", "ĠGa ll", "Ġport ray", "0 56", "05 6", "Ġ Developer", "ĠDe veloper", "ĠDevelop er", "Ġ participant", "Ġpart icipant", "Ġparticip ant", "Ġ \";čĊ", "Ġ\" ;čĊ", "Ġ\"; čĊ", "/ model", "/m odel", "/mod el", "7 94", "79 4", "Ġ Operations", "ĠOper ations", "ĠOperation s", "^ \\", "Ġ Later", "ĠL ater", "ĠLa ter", "ĠLat er", "ĠLate r", "Ġ raises", "Ġr aises", "Ġraise s", "Ġrais es", "Ġra ises", "- none", "-n one", "-no ne", "-non e", ". meta", ".m eta", ".me ta", ".met a", "= '.$", "=' .$", "='. $", "F inished", "Fin ished", "Finish ed", "Ġre placing", "Ġrepl acing", "Ġ sampling", "Ġs ampling", "Ġsam pling", "Ġsamp ling", "ĠJ en", "ĠJe n", "\" There", "\"The re", "\"T here", "RE AL", "REA L", "A LE", "AL E", "ìĬ ¤", "Or ders", "Order s", "Ord ers", "_ parameter", "_param eter", "_para meter", "ĠOlymp ic", "Ġtr ès", "Ġ arena", "Ġa rena", "Ġare na", "Ġar ena", "Ġaren a", "i ol", "io l", "; ?>", "Ġimp acts", "Ġimpact s", "Ġ WS", "ĠW S", ": get", ":g et", "Ġf lights", "Ġfl ights", "Ġflight s", "ĠRuss ell", "ĠRus sell", "c amera", "came ra", "cam era", "F n", "s igma", "sig ma", "Ġ forcing", "Ġfor cing", "Ġforc ing", "Ġ locals", "Ġloc als", "Ġlocal s", "Ġ departure", "Ġdepart ure", "Ġcelebr ation", "Ġ Say", "ĠS ay", "ĠSa y", "8 84", "88 4", "ï¼ Ĵ", "ĠH ills", "ĠHill s", "ĠHil ls", ".has OwnProperty", "Ġ typings", "Ġtyp ings", "Ġtyping s", ". API", ".A PI", ".AP I", "Ġd onation", "Ġdo nation", "Ġdon ation", "Operation Exception", ". Activity", ".Act ivity", "c plusplus", "Ġ Charlie", "ĠChar lie", "ĠCharl ie", "Ġim ported", "Ġimport ed", "Ġimp orted", "Ġd ann", "Ġda nn", "Ġdan n", "Ġocc asions", "Ġoccas ions", "Ġoccasion s", "Ġimplement ing", "Ġ purple", "Ġp urple", "Ġpur ple", ". dialog", ".d ialog", ".di alog", "SQL Exception", "er no", "ern o", "Ġw ars", "Ġwar s", "Ġwa rs", "Ġ paste", "Ġp aste", "Ġpast e", "Ġpas te", "Ġpa ste", "Ġdecre ased", "Ġdecrease d", "Ġhar sh", "Ġel abor", "Ġela bor", "in puts", "input s", "inp uts", "Ġ Views", "ĠView s", "ĠVi ews", "ĠVie ws", "Ġ errorMessage", "Ġerror Message", "_ mul", "_m ul", "_mu l", "ĉ write", "ĉw rite", "Ġ Cop", "ĠC op", "ĠCo p", "Ġ Annual", "ĠAnn ual", "( button", "(b utton", "Ġ vida", "Ġv ida", "Ġvi da", "Ġvid a", "b ars", "bar s", "ba rs", "ĠHar vard", "ĉ expect", "ĉex pect", "ĉexp ect", "Ġ indexes", "Ġindex es", "Ġinde xes", "Ġdocument ary", "Ġf lesh", "Ġfl esh", "Ġfle sh", "OR LD", "Ġ Delta", "ĠD elta", "ĠDel ta", "M AND", "MA ND", "MAN D", "B rush", "Br ush", "Bru sh", "- column", "-c olumn", "-col umn", "Ġdevelop ments", "Ġdevelopment s", "9 74", "97 4", "7 83", "78 3", "method Visitor", "s lice", "sl ice", "Ġ PDO", "ĠP DO", "ĠPD O", "Ġinv esting", "Ġinvest ing", "8 67", "86 7", "ir able", "ira ble", "Ġ xmlns", "Ġxml ns", "ï¼ Ľ", "ar ta", "art a", "Ġthe ories", "Ġtheor ies", "Ġtheo ries", "_ city", "_c ity", "_ci ty", "Ġ $__", "Ġ$ __", "Ġ$_ _", "C reating", "Cre ating", "Cr eating", "Creat ing", "( pr", "(p r", "D ropdown", "Drop down", "is match", "ism atch", "Ġ NET", "ĠN ET", "ĠNE T", "9 26", "92 6", "' ])){Ċ", "'] )){Ċ", "']) ){Ċ", "'])) {Ċ", "'])){ Ċ", "Ġ Values", "ĠVal ues", "ĠValue s", "Ġ SEO", "ĠS EO", "ĠSE O", "Ġ STAT", "ĠST AT", "ĠSTA T", "Ġe cosystem", "Ġeco system", "Ġ tempt", "Ġt empt", "Ġtem pt", "Ġtemp t", "Ġ \\\\", "Ġ\\ \\", "Ġ //{Ċ", "Ġ// {Ċ", "Ġ//{ Ċ", "Ġ Christopher", "ĠChrist opher", "ĠChristoph er", "ĠKent ucky", "ĠHttp ServletResponse", "ĠHttpServlet Response", "Ġh ybrid", "Ġhy brid", "y on", "yo n", "Ġ feeding", "Ġfe eding", "Ġfeed ing", "Ġfee ding", "Ġ Extra", "ĠEx tra", "ĠExt ra", "ĠExtr a", "N orm", "No rm", "Nor m", "IT CH", "Ġ Sean", "ĠS ean", "ĠSe an", "ĠSea n", "Ġ Upload", "ĠUp load", "m un", "mu n", "p ur", "pu r", "Ġ persistent", "Ġp ersistent", "Ġpers istent", "Ġpersist ent", "ĠI DC", "ĠID C", "Ġ Perform", "ĠPer form", "ĠPerf orm", "8 63", "86 3", ". merge", ".m erge", "_ room", "_r oom", "_ro om", "Mean while", "! ='", "!= '", "Ġ Wel", "ĠW el", "ĠWe l", "Args Constructor", "8 87", "88 7", ". Database", ".D atabase", ".Data base", "Ġco unting", "Ġcount ing", "Ġcoun ting", "( )*", "() *", "Ķ åĽŀ", "Ġ TOP", "ĠT OP", "ĠTO P", "m ill", "mi ll", "mil l", "Ġ DT", "ĠD T", "IGN ED", "9 56", "95 6", "Ġ KB", "ĠK B", "Ġcom ply", "Ġcomp ly", "Ġcompl y", "S outh", "So uth", "Sou th", "_ collection", "_c ollection", "_col lection", "_coll ection", "_collect ion", "Ch apter", "Cha pter", "Ġexpl aining", "Ġexplain ing", "_ AM", "_A M", "_ ts", "_t s", "c ards", "card s", "car ds", "Ġ quel", "Ġqu el", "Ġque l", "Ġq uel", "Ġ pole", "Ġp ole", "Ġpol e", "Ġpo le", "Ġtouch down", "Ġ Others", "ĠO thers", "ĠOther s", "Ġpe ers", "Ġpeer s", "Ġpee rs", "Ġ TypeError", "ĠType Error", "7 63", "76 3", "Ġsix th", "Ġch eer", "Ġche er", "Ġdis pute", "Ġdisp ute", "Ġdisput e", "9 63", "96 3", "8 93", "89 3", "u sc", "us c", ") ],", ")] ,", "th umb", "Ġh iding", "Ġhi ding", "Ġhid ing", "Ġ SIG", "ĠS IG", "ĠSI G", "l ikes", "li kes", "like s", "lik es", "Ġ PAGE", "ĠP AGE", "ĠPA GE", ". Reflection", ".Ref lection", "Ġhead quarters", "T ING", "TI NG", "Ġ Ghost", "ĠG host", "ĠGh ost", "M LE", "ML E", "$ Ċ", "Ġcont rary", "Ġcontr ary", "Ġcontra ry", "ext end", "' ]).", "'] ).", "']) .", "FF ECT", "FFE CT", "Ġ Pinterest", "ĠP interest", "úmer o", "ric ane", "rica ne", "ĉ session", "ĉs ession", "Ġcr ystal", "Ġcry stal", "Ġcryst al", "- Control", "-C ontrol", "overn ment", "o graf", "og raf", "ogr af", "ogra f", "9 61", "96 1", "- action", "-a ction", "-ac tion", "v olume", "vol ume", "f ten", "ft en", "fte n", "Ġun con", "Ġunc on", "Ġ animate", "Ġan imate", "Ġanim ate", "Ġani mate", "Ġ lease", "Ġl ease", "Ġle ase", "s cr", "sc r", "Ġre fuse", "Ġref use", "ãĢ ĭ", "f tp", "ft p", "in formation", "inform ation", "Ġeval uated", "Ġevaluate d", "Ġevalu ated", "Ġin jection", "Ġinj ection", "Ġinject ion", "Ġ jack", "Ġj ack", "Ġja ck", "Ġjac k", "Ġwork shop", "Ġworks hop", "æ³ ¨", "P TH", "PT H", "Ġ Ts", "ĠT s", "o ffer", "of fer", "off er", "ĉ os", "ĉo s", "Ġking dom", "M issing", "Miss ing", "Mis sing", "Ġlaw makers", "Ġlawmaker s", "ext Field", "Ġs inging", "Ġsin ging", "Ġsing ing", "a bi", "ab i", "/ client", "/c lient", "/cl ient", "/cli ent", ". media", ".m edia", ".me dia", ".med ia", "ATEG ORY", "Sign ature", "Sig nature", "% ',Ċ", "%' ,Ċ", "%', Ċ", "Ġ Fuck", "ĠF uck", "ĠFu ck", "] [:", "][ :", "Ġs ensors", "Ġsens ors", "Ġsensor s", "/ com", "/c om", "/co m", "Ġ Primary", "ĠPr imary", "ĠPri mary", "ĠPrim ary", ". SQL", ".S QL", "_ program", "_p rogram", "_pro gram", "_pr ogram", "_prog ram", "Ġp ills", "Ġpil ls", "Ġpill s", "Ġ integral", "Ġint egral", "Ġinteg ral", "Ġintegr al", "Ġ fleet", "Ġf leet", "Ġfle et", "Ġflee t", "Ġd ropping", "Ġdr opping", "Ġdrop ping", "Ġdro pping", ". sl", ".s l", "B een", "Be en", "Ġ pets", "Ġp ets", "Ġpe ts", "Ġpet s", "Ġad vised", "Ġadv ised", "Ġadvis ed", "Ġadvise d", "Ġ dragon", "Ġd ragon", "Ġdr agon", "Ġdrag on", "Ġdra gon", "_ EDIT", "_ED IT", "( im", "(i m", "9 39", "93 9", "F ER", "FE R", "Ġ Drug", "ĠD rug", "ĠDr ug", "( random", "(r andom", "(rand om", "Ġ compression", "Ġcom pression", "Ġcomp ression", "Ġcompr ession", "Ġcompress ion", "o ust", "ou st", "ous t", "[ %", "Ġ buyer", "Ġbu yer", "Ġbuy er", "h op", "ho p", "R oles", "Role s", "Ro les", "Rol es", "man age", "ma nage", "mana ge", "Ġpain ful", "Ġ Branch", "ĠBr anch", "ĠBran ch", "- modal", "-m odal", "-mod al", "e nant", "en ant", "ena nt", "enan t", "Ġ Mesh", "ĠM esh", "ĠMe sh", "ĠMes h", "/ font", "/f ont", "ĠG raham", "ĠGra ham", "Ġ âĺ", "Ġâ ĺ", "Ġ nc", "Ġn c", "ĠFranc is", "ĠFran cis", "Ġspec ification", "Ġspecific ation", "Ġdam ages", "Ġdamage s", "- config", "-con fig", "-conf ig", "Ġthe oret", "Ġtheor et", "Ġtheo ret", "s ecure", "sec ure", "_ multi", "_m ulti", "_mul ti", "_mult i", "aceut ical", "Ġdem anding", "Ġdemand ing", "en ne", "enn e", "I STS", "IS TS", "IST S", "0 94", "09 4", "( )));ĊĊ", "() ));ĊĊ", "()) );ĊĊ", "()));Ċ Ċ", "())) ;ĊĊ", "())); ĊĊ", "Re ason", "Re cent", "Rec ent", "ph ase", "pha se", "phas e", "Ġ psy", "Ġp sy", "Ġps y", "_ MAN", "_M AN", "_MA N", "Ġvol unteer", "Ġvolunte er", "Ġvolunt eer", "å ¿", "istrib uted", "istribute d", "l io", "li o", "Ġproduct ivity", "_ comm", "_c omm", "_com m", "_co mm", "S pring", "Sp ring", "Spr ing", "n is", "ni s", ". weight", ".w eight", ".we ight", "ĠC ancer", "ĠCan cer", "ĠCanc er", "Al loc", "All oc", "Ġ Tweet", "ĠT weet", "ĠTwe et", "Ġsepar ately", "Ġseparate ly", "Ġseparat ely", "ĉ check", "ĉc heck", "ĉch eck", "_ properties", "_p roperties", "_prop erties", ". Unit", ".U nit", ".Un it", "8 29", "82 9", "_ CLK", "_C LK", "_CL K", "Ġ gt", "Ġg t", "Ġ ();ĊĊ", "Ġ( );ĊĊ", "Ġ() ;ĊĊ", "Ġ();Ċ Ċ", "Ġ(); ĊĊ", "Ġh andy", "Ġhand y", "Ġhan dy", "8 34", "83 4", "ĠTh ompson", "ĠThom pson", "Ġun necessary", "Ġunn ecessary", "Ġ Reader", "ĠRe ader", "ĠRead er", "8 94", "89 4", "G N", "= request", "=re quest", "=req uest", "Ġ Utility", "ĠU tility", "ĠUtil ity", "ĠUt ility", ". Repository", ".Re pository", "Ġ Ax", "ĠA x", "hy dr", "7 91", "79 1", "i eu", "ie u", "Ġ thy", "Ġt hy", "Ġth y", "Ġ lt", "Ġl t", "_ mail", "_m ail", "_ma il", "ä¿® æĶ¹", "a iland", "ail and", "ai land", "Ġ Philip", "ĠPh ilip", "ĠPhil ip", "ĠPhi lip", "Ġb itter", "Ġbit ter", "Ġbitte r", "Ġb etting", "Ġbet ting", "8 37", "83 7", "Ġt imed", "Ġtime d", "Ġtim ed", "Ġti med", "o cks", "oc ks", "ock s", "0 76", "07 6", "' a", "Ġal gorithms", "Ġalgorithm s", "Ġ reinterpret", "Ġre interpret", "Ġt oss", "Ġto ss", "r ogen", "ro gen", "rog en", "Ġh oped", "Ġhope d", "Ġhop ed", "Ġho ped", "( selected", "(se lected", "(select ed", "(sel ected", "Ġ venture", "Ġvent ure", "Ġven ture", "T EX", "TE X", "Ġ Leave", "ĠLe ave", ". Substring", ".Sub string", "Ġgr ateful", "Ġgrat eful", "Ġgrate ful", "7 43", "74 3", "u ka", "uk a", "Ġ Consumer", "ĠCon sumer", "ĠCons umer", "ĠConsum er", "Ġag greg", "Ġagg reg", "C ircle", "ภģ", "_ blocks", "_b locks", "_block s", "_bl ocks", "_bloc ks", "Ġleg ally", "Ġlegal ly", "Ġ \"|", "Ġ\" |", "ãĥ ĥ", ". board", ".b oard", ".bo ard", ". Ab", ".A b", "Function s", "Fun ctions", "rec ipe", "è ĩ", "ĠO xford", "ĠOx ford", "Ġw holes", "Ġwh oles", "Ġwho les", "Ġwhole s", ". Build", ".B uild", "_ changed", "_ch anged", "_change d", "_chan ged", "h ai", "ha i", "Ġ departments", "Ġdepartment s", "Ġdepart ments", "9 64", "96 4", "I mp", "Im p", "Ġcoal ition", "IN FRINGEMENT", "Ġem power", "Ġemp ower", "it ches", "itch es", "N orth", "Nor th", "Ġin flamm", "Ġinfl amm", "O NSE", "ON SE", "ONS E", "Ġmiss ile", "ĠR aj", "ĠRa j", "Ġ Issue", "ĠI ssue", "ĠIss ue", "Ġ atoi", "Ġa toi", "Ġat oi", "c aled", "ca led", "cale d", "cal ed", ". Controllers", ".Cont rollers", ".Control lers", ".Controller s", "Ġ Wolf", "ĠW olf", "ĠWo lf", "ĠWol f", "Ġcrush ers", "Ġcrusher s", "á» ĩ", ". Auth", ".A uth", ".add Attribute", "h is", "hi s", "Ġbo ots", "Ġboot s", "Ġboo ts", ". clean", ".c lean", ".cl ean", "c amp", "ca mp", "cam p", "Ġ tenant", "Ġt enant", "Ġte nant", "Ġten ant", "Ġt une", "Ġtu ne", "Ġtun e", "Ġ {}'.", "Ġ{ }'.", "Ġ{} '.", "Ġwork out", "Re po", "Rep o", "Ġpart ially", "Ġpartial ly", "Ġparti ally", "MI SSION", "MISS ION", "j amin", "ja min", "jam in", "Ġ SB", "ĠS B", "Ġd etermination", "Ġde termination", "Ġdeter mination", "Ġdetermin ation", "Ġdeterm ination", "Ġ' ');Ċ", "Ġ'' );Ċ", "Ġ'') ;Ċ", "Ġ''); Ċ", "ĠB eng", "ĠBe ng", "ĠBen g", "Ġ vos", "Ġv os", "Ġvo s", "Ġin hab", "Ġinh ab", "/ lang", "/l ang", "s burgh", "sburg h", "Exec utor", "h one", "ho ne", "hon e", "Ġ Challenge", "ĠCh allenge", "ĠChall enge", "_ links", "_l inks", "_link s", "_lin ks", ". Level", ".Le vel", "Ġunder ground", "- code", "-c ode", "-co de", "9 59", "95 9", "Ġopt imization", "Ġoptim ization", "log ging", "logg ing", "_ dest", "_d est", "_de st", "_des t", "Ġ snake", "Ġsn ake", "Ġsna ke", "Ġchem icals", "Ġchemical s", "_IMPORT ED", "ad oop", "ado op", "adoo p", "ĠTH AT", "man aged", "manage d", "mana ged", "Ġred uces", "Ġredu ces", "Ġreduce s", "Ġ REAL", "ĠRE AL", "Ġ Guy", "ĠG uy", "ĠGu y", "_GENER IC", "_GEN ERIC", "/ ********************************", "/************************ ********", "/******** ************************", "/**************** ****************", ". amount", ".a mount", ".am ount", "Ġ dere", "Ġd ere", "Ġde re", "Ġder e", "get Time", "Ġp ant", "Ġpa nt", "Ġpan t", "an onymous", "anon ymous", "Ġhar mony", "Ġharm ony", "Ġharmon y", "Ġ Alan", "ĠA lan", "ĠAl an", "ĠAla n", "Ġsc enarios", "Ġscen arios", "Ġscenario s", "Ġd irt", "Ġdi rt", "Ġdir t", "h tags", "ht ags", "htag s", "hta gs", "M c", "S hell", "Sh ell", "She ll", "r in", "ri n", "{ čĊčĊ", "{čĊ čĊ", ". pow", ".p ow", ".po w", "ĉ client", "ĉc lient", "ĉcl ient", "ĉcli ent", "Ġcon spiracy", "Ġconspir acy", "Ġad mission", "Ġadm ission", "Ġ Regional", "ĠReg ional", "ĠRegion al", "Ġ ViewController", "ĠView Controller", "ĠPhil ippines", "ĠPhilipp ines", "ĠPhilippine s", "Ġde pos", "Ġdep os", "Ġp ap", "Ġpa p", "9 62", "96 2", "Ġ Pad", "ĠP ad", "ĠPa d", "P aul", "Pa ul", ". ComboBox", ".Com boBox", "Ġt utor", "Ġtu tor", "Ġtut or", "Ġtuto r", "Ġ Recipe", "ĠRec ipe", "w riting", "wr iting", "Ġcontrib utor", "O TH", "OT H", "S mall", "Sm all", "V I", "Ġh acer", "Ġha cer", "Ġhace r", "Ġhac er", "e qu", "eq u", "Ġ Examples", "ĠEx amples", "ĠExample s", "ĠExam ples", "h uman", "hu man", "hum an", ". messages", ".m essages", ".message s", "ĉ typ", "ĉt yp", "Ġ (čĊ", "Ġ( čĊ", "Ġ SSL", "ĠS SL", "ĠSS L", "L EN", "LE N", "ĠRom ney", "( grid", "(g rid", "(gr id", "ĉ min", "ĉm in", "Ġ >ĊĊ", "Ġ> ĊĊ", "Ġ>Ċ Ċ", "Ġf ruits", "Ġfr uits", "Ġfruit s", "Ġv oter", "Ġvo ter", "Ġvot er", "Ġvote r", "In line", "p ane", "pan e", "pa ne", "Ġ Collections", "ĠC ollections", "ĠCol lections", "ĠCollection s", "ĠColl ections", "ĠCollect ions", "char set", "chars et", "Ġ spam", "Ġsp am", "Ġspa m", "z b", "it emap", "ite map", "item ap", "Ġs ucceeded", "Ġsuc ceeded", "Ġsucceed ed", "_ COL", "_C OL", "_CO L", "Ġ elapsed", "Ġel apsed", "i meter", "im eter", "ime ter", "imet er", "Ġre covered", "Ġrecover ed", "T ensor", "hat tan", "hatt an", ". setup", ".set up", "i sto", "is to", "ist o", "( head", "(h ead", "9 77", "97 7", "Ġ SIZE", "ĠS IZE", "ĠSI ZE", "Ġt actics", "Ġtact ics", "Ġtactic s", "Ġtac tics", "Ġdis tur", "Ġdist ur", "Ġpr eval", "Ġpre val", "Ġprev al", "ic ios", "ici os", "icio s", "( Value", "(V alue", "_ cols", "_c ols", "_col s", "_co ls", "Ġ Fat", "ĠF at", "ĠFa t", "Ġse al", "Ġsea l", "Ġ sons", "Ġs ons", "Ġso ns", "Ġson s", "Ġens ures", "Ġensure s", "0 95", "09 5", "Ġp ressing", "Ġpres sing", "Ġpress ing", "= &", "igen ous", "Ġharass ment", "_ JSON", "_J SON", "_JS ON", "Ġign or", "Ġig nor", "yn omial", "ynom ial", "o mer", "om er", "ome r", "_ static", "_st atic", "_stat ic", "_sta tic", "Ġsign ificance", "Ġsignific ance", "Ġsignifica nce", "Ġc ircles", "Ġcirc les", "Ġcircle s", "Ġcir cles", "_ System", "_S ystem", "Ġdisc ipline", "Ġdiscipl ine", "Ġd ressed", "Ġdr essed", "Ġdress ed", "Ġ sphere", "Ġs phere", "Ġsp here", "Ġsph ere", "9 27", "92 7", "Ġcl imb", "Ġclim b", "Ġcli mb", "7 59", "75 9", "_ actions", "_a ctions", "_action s", "_act ions", "ĠB ab", "ĠBa b", "Ġ' =',", "Ġ'=' ,", "Ġ'= ',", "_ schema", "_s chema", "\" use", "Ġ unders", "Ġun ders", "Ġunder s", "Ġund ers", "Ġunde rs", "Ġc ups", "Ġcu ps", "Ġcup s", ". screen", ".s creen", ".sc reen", "/ new", "/n ew", "/ne w", "Ġapp earing", "Ġappe aring", "Ġappear ing", "T OP", "TO P", "v ised", "vis ed", "vi sed", "vise d", "c lang", "cl ang", "cla ng", "Ġinvest igators", "Ġinvestig ators", "Ġinvestigator s", "Ġm ysterious", "Ġmyster ious", "Ġprom ising", "Ġqual ify", "Ġqua lify", "Ġquali fy", "Ġc ave", "Ġca ve", "Ġcav e", "Ġ equip", "Ġe quip", "Ġequ ip", "= x", "G T", "( link", "(l ink", "(li nk", ". velocity", ".v elocity", ".vel ocity", ". erase", ".e rase", ".er ase", "o ter", "ot er", "ote r", "++++ ++++", "pro fit", "prof it", "Ġ zones", "Ġz ones", "Ġzone s", "Ġzo nes", "_ uid", "_u id", "_ui d", "- ser", "-s er", "-se r", "Ġob jectives", "Ġobject ives", "Ġobjective s", "Ġmil f", "Ġmi lf", "web kit", "( match", "(m atch", "(mat ch", "n eh", "ne h", "Ġ Associated", "ĠAssoci ated", "ĠAssociate d", "ĠAssoc iated", "Ġ Todo", "ĠT odo", "ĠTo do", "ĠTod o", "= d", "0 65", "06 5", "C am", "Ca m", "Ġv ocal", "Ġvo cal", "Ġvoc al", "Ġ sudo", "Ġs udo", "Ġsu do", "Ġsud o", "( EX", "(E X", "Ġt rou", "Ġtr ou", "Ġtro u", "A BC", "AB C", ". bean", ".b ean", ".be an", "Ġ Ground", "ĠG round", "ĠGr ound", "ĠGro und", "Ġ REST", "ĠR EST", "ĠRE ST", "ĠRES T", "we ets", "weet s", "I ng", "In g", "i mon", "im on", "imo n", "9 46", "94 6", "_ bus", "_b us", "Ġ COLOR", "ĠC OLOR", "ĠCOL OR", "un to", "unt o", "Ġf oss", "Ġfo ss", "Ġfos s", "Ġ Links", "ĠL inks", "ĠLink s", "ĠLin ks", "8 69", "86 9", "ä ng", "än g", "/ forms", "/form s", "pr ises", "prise s", "pri ses", "Ġ achievement", "Ġachie vement", "Ġachieve ment", "C ALL", "CA LL", "CAL L", "е лÑĮ", "ел ÑĮ", "Ġ Verify", "ĠVer ify", "_ SOURCE", "_S OURCE", "apt cha", "I DD", "ID D", "_ reference", "_re ference", "_ref erence", "_refer ence", "G old", "Go ld", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "9 47", "94 7", "Re ceiver", "Receive r", "0 99", "09 9", "Ġ aj", "Ġa j", "_ direction", "_d irection", "_dir ection", "_di rection", "_direct ion", "} ]", "Ġ Compet", "ĠCom pet", "ĠComp et", "Ġ bang", "Ġb ang", "Ġban g", "Ġba ng", "7 98", "79 8", "ĠC ass", "ĠCas s", "ĠCa ss", "- url", "-u rl", "t echn", "te chn", "tech n", "tec hn", "ĠJer usalem", "long itude", "' );čĊčĊ", "') ;čĊčĊ", "'); čĊčĊ", "');čĊ čĊ", "Ġwin ners", "Ġwinner s", "T asks", "Task s", "Ġ DMA", "ĠD MA", "ĠDM A", "Ġ tooltip", "Ġto oltip", "Ġtool tip", "İ ·", "ĠB ra", "ĠBr a", "_ duration", "_d uration", "_dur ation", "c ury", "cur y", "cu ry", "par ents", "pare nts", "parent s", "paren ts", "- --- >(", ">> (", "Ġ Kir", "ĠK ir", "ĠKi r", "Ġ intros", "Ġint ros", "Ġintr os", "Ġintro s", "Ġsk etch", "Ġsk illed", "Ġskill ed", "Ġ immer", "Ġim mer", "Ġimm er", "Ġade quate", "Ġadequ ate", "_ rep", "_re p", "_r ep", "( header", "(head er", "_ like", "_l ike", "_li ke", "Ġper ceived", "Ġperceive d", "Ġperce ived", "s sh", "ss h", "Ġ assuming", "Ġas suming", "Ġass uming", "Ġassum ing", "Ġ ff", "Ġf f", "_ uuid", "_u uid", "u las", "ul as", "ula s", "Ġdem ocratic", "Ġdemocr atic", "Ġdemocrat ic", ". entities", ".ent ities", "S eries", "Se ries", "Ser ies", "aph ore", "Ġnew er", "Ġne wer", "} (", "S EC", "SE C", "a iro", "air o", "ai ro", "Ġcom mod", "Ġcomm od", "Ġpriv ilege", "Ġprivile ge", "Ġde ux", "Ġ Hop", "ĠH op", "ĠHo p", ". '/", ".' /", "c tic", "ct ic", ". ';Ċ", ".' ;Ċ", "< ?=", " C", "ĠWar ren", "Ġ optimizer", "Ġopt imizer", "Ġoptim izer", "Ġoptimize r", "ĠSER VICES", "ĠSERVICE S", "_ oper", "_op er", "_o per", "get Attribute", "ĠMc K", "_ self", "_s elf", "_se lf", "_sel f", "0 84", "08 4", ". rs", ".r s", "\" )ĊĊĊ", "\") ĊĊĊ", "\")Ċ ĊĊ", "\")ĊĊ Ċ", "Get Component", "er ce", "erc e", "Ġt ous", "Ġto us", "Ġtou s", "un its", "unit s", "uni ts", "' ]);čĊ", "'] );čĊ", "']) ;čĊ", "']); čĊ", "Z oom", "/ E", "Ġob sc", "Ġobs c", "Ġfast est", "Ġfas test", "on line", "Ġpeace ful", "f fen", "ff en", "ffe n", "Ġ cargo", "Ġc argo", "Ġcar go", "Ġcarg o", "ĉ pr", "ĉp r", "Ġse eks", "Ġsee ks", "Ġseek s", "z u", "0 74", "07 4", "T rim", "Tr im", "Tri m", "Ġ ward", "Ġw ard", "Ġwar d", "Ġwa rd", "Ġv erd", "Ġver d", "Ġve rd", "Ġ blogs", "Ġb logs", "Ġbl ogs", "Ġblog s", "Ġblo gs", ". exceptions", ".ex ceptions", ".exception s", "Ġ Premium", "ĠP remium", "ĠPre mium", "ĠPrem ium", "ĠPremi um", "ĠN etherlands", "S afe", "Sa fe", "F inish", "Fin ish", "Fi nish", "Ġ Album", "ĠAl bum", "ĠAlb um", "_ ACC", "_A CC", "_AC C", "= this", "=t his", "v irtual", "vir tual", "virt ual", "] >", "_ LABEL", "_L ABEL", "_LA BEL", "Ġ Nich", "ĠN ich", "ĠNic h", "ĠNi ch", "_ win", "_w in", "Ġ Aaron", "ĠA aron", "W P", "; $", "a ims", "ai ms", "aim s", "Ġ ImageView", "ĠImage View", "Ġend less", "Ġendl ess", "E RA", "ER A", "_ DISABLE", "_DIS ABLE", "Ġ cancelled", "Ġcancel led", "Ġcancell ed", "Ġcanc elled", "- us", "-u s", "Ġ inspection", "Ġins pection", "Ġinspect ion", "Ġinsp ection", "e min", "em in", "emi n", "Ġ Grey", "ĠG rey", "ĠGr ey", "ĠGre y", "- open", "-o pen", "-op en", "Ġ iterations", "Ġiter ations", "Ġiteration s", ". owner", ".o wner", "Ġk eras", "Ġke ras", "Ġker as", ". Password", ".P assword", ".Pass word", "ĠR y", "Ġ INS", "ĠI NS", "ĠIN S", "A ir", "Ai r", "Ġ Several", "ĠSe veral", "ĠSever al", "ĠSev eral", ".Tab Stop", "IN GLE", "ING LE", "Ġ Hair", "ĠH air", "ĠHa ir", "ĠHai r", "Ġ Canvas", "ĠC anvas", "ĠCan vas", "A AAA", "AA AA", "AAA A", "Ġf law", "Ġfl aw", "Ġfla w", "c edes", "ce des", "ced es", "cede s", ". Report", ".Re port", "í Ĭ", "Ġ Tips", "ĠT ips", "ĠTi ps", "ĠTip s", "cript ors", "criptor s", ". transaction", ".trans action", ". Spring", ".S pring", ".Sp ring", "Ġ viewer", "Ġview er", "Ġvie wer", "Ġins ights", "Ġinsight s", "è¾ ĵ", "ord ion", "U INT", "UI NT", "se ek", "see k", "Ġ Auf", "ĠA uf", "ĠAu f", "ìŀ IJ", "Ġ strain", "Ġs train", "Ġst rain", "Ġstr ain", "Ġstra in", "To oltip", "Tool tip", "Ġ dz", "Ġd z", "ig nal", "ign al", "a dt", "ad t", "Ġ uc", "Ġu c", "f inite", "fin ite", "fi nite", "Ġ nm", "Ġn m", ". cmd", ".c md", ".cm d", "ĠMy Sql", "[ data", "[d ata", ".j ackson", ". tree", ".t ree", ".tr ee", "Request Param", "_ agent", "_a gent", "_ag ent", "_age nt", "\" )]čĊ", "\") ]čĊ", "\")] čĊ", "Ġas sass", "Ġass ass", "( Constants", "(Constant s", "(Const ants", ": ss", ":s s", "Ġ MAN", "ĠM AN", "ĠMA N", "+- +-", "Ġ Bottom", "ĠB ottom", "ĠBot tom", "ĠBott om", "pr ints", "print s", "pri nts", "Ġ Same", "ĠS ame", "ĠSam e", "ĠSa me", "@ Autowired", "s wap", "sw ap", "i ción", "ic ión", "ici ón", "Ġprot esters", "Ġprote sters", "Ġprotest ers", "Ġprotester s", "Ġh oney", "Ġhon ey", "Ġho ney", "Ġhone y", "Ġ Veter", "ĠV eter", "ĠVe ter", "ĠVet er", "( Calendar", "(C alendar", "- ad", "-a d", "ĠBro oklyn", "ĠBrook lyn", "L ife", "Li fe", "_ VAR", "_V AR", "z ech", "ze ch", "Ġ CALL", "ĠC ALL", "ĠCA LL", "ĠCAL L", "_ CAST", "_C AST", "_CA ST", "ĠE lection", "ĠEl ection", "ĠElect ion", "ĠEle ction", "Ġ thickness", "Ġth ickness", "Ġthick ness", "V ery", "Ver y", "Ve ry", "_ INTEGER", "_IN TEGER", "- dev", "-d ev", "-de v", ") )))", ")) ))", "))) )", "a pat", "ap at", "apa t", "o ooo", "oo oo", "ooo o", "d emo", "de mo", "dem o", "Ġ parseFloat", "Ġparse Float", "Ġ Rather", "ĠR ather", "ĠRa ther", "ĠRat her", "ĠRath er", "ST IT", "m aker", "ma ker", "make r", "mak er", "[ current", "[c urrent", "[cur rent", "[curr ent", "chron o", "chr ono", "Ġ christ", "Ġch rist", "Ġchr ist", "ãģ ª", "Ġ Detail", "ĠD etail", "ĠDe tail", "ĠDet ail", "Æ° á»", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "Ġs ul", "Ġsu l", "id ency", "ide ncy", "iden cy", "Q ue", "Qu e", "Ġe legant", "Ġeleg ant", "a pons", "ap ons", "apon s", "apo ns", "Ġd ishes", "Ġdis hes", "Ġdish es", "Ġint egers", "Ġinteg ers", "Ġinteger s", "Ġinte gers", "( read", "(r ead", "(re ad", "0 57", "05 7", "find ViewById", "Ġ Amount", "ĠA mount", "ĠAm ount", "Ġ Skip", "ĠS kip", "ĠSk ip", "ĠSki p", "Ġhab its", "Ġha bits", "Ġhabit s", "* )(", "*) (", "Ġmon sters", "Ġmonster s", "M AC", "MA C", ": end", ":e nd", "Ġf rank", "Ġfr ank", "Ġfra nk", "Ġfran k", "As sembly", "Ġ dfs", "Ġd fs", "Ġdf s", "Ġn eut", "Ġne ut", "Ġneu t", "_ TYPES", "_T YPES", "_TYPE S", "_TYP ES", "e qual", "eq ual", "equ al", "lo yd", "loy d", "( uri", "(u ri", "(ur i", "Ġ chi", "Ġc hi", "Ġch i", "Ġdef endant", "Ġdefend ant", "Ġconf licts", "Ġconflic ts", "Ġconflict s", "Ġconfl icts", "Ġ vil", "Ġv il", "Ġvi l", "- js", "-j s", "Ġ Peace", "ĠPe ace", "Ġ mutable", "Ġm utable", "Ġmut able", "Ġmu table", ") sender", ")s ender", "Ġ Focus", "ĠF ocus", "ĠFo cus", "å» º", "Ġapprec iated", "Ġappreciate d", "s leep", "Ġ RED", "ĠR ED", "ĠRE D", "C ulture", "Ġdesign ers", "Ġdesigner s", "_ generator", "_g enerator", "_gen erator", "_gener ator", "c odes", "co des", "code s", "cod es", "/ ex", "/e x", ". GetValue", ".Get Value", "um bled", "umb led", "umble d", ".scal ajs", ".scala js", "pe ror", "per or", "Ġveter ans", "Ġveteran s", "Ġ })čĊ", "Ġ} )čĊ", "Ġ}) čĊ", "Ġun fortunately", "Ġunfortunate ly", "_ CREATE", "_C REATE", "_CRE ATE", "_CREAT E", "M ass", "Ma ss", "Mas s", "Ġ CLAIM", "ĠCL AIM", "Ġ Meet", "ĠMe et", "_ support", "_s upport", "_sup port", "_supp ort", "B ank", "Ban k", "Ba nk", "( ).Ċ", "() .Ċ", "(). Ċ", "D ark", "Da rk", "Dar k", "_ LOW", "_L OW", "_LO W", "Ġ Mining", "ĠM ining", "ĠMin ing", "ĠMini ng", "ĠMi ning", "Ġ Owner", "ĠO wner", "ĠOwn er", "ĠOw ner", "i era", "ie ra", "ier a", "Client e", "Cl iente", "Cli ente", "Ġencour aging", "> S", "Ġboy friend", "Ġ Half", "ĠH alf", "ĠHa lf", "ĠHal f", "Ġ ACC", "ĠA CC", "ĠAC C", "A ff", "Af f", "_ ar", "_a r", "- life", "-l ife", "-li fe", "c x", ".J Button", "iz ado", "iza do", ". zero", ".z ero", ".open qa", "o ton", "ot on", "oto n", ". textContent", ".text Content", "Ġt oll", "Ġto ll", "Ġtol l", "a tie", "at ie", "ati e", "Ġball ot", "Ġbal lot", "Ġballo t", "- number", "-n umber", "-num ber", ". Exception", ".Ex ception", "ĉ params", "ĉparam s", "c ircle", "circ le", "cir cle", "- map", "-m ap", "Ġ nap", "Ġn ap", "Ġna p", "Ġ Robot", "ĠR obot", "ĠRob ot", "ĠRo bot", "Ġ Ich", "ĠI ch", "reg istration", "registr ation", "regist ration", "A mazon", "Am azon", "roll ment", "( exp", "(e xp", "(ex p", "Ġt anks", "Ġtank s", "Ġtan ks", "ĠG ordon", "ĠGor don", "ĠGord on", "Ġm achinery", "Ġmachine ry", "Ġmach inery", "Ġ baseline", "Ġb aseline", "Ġbase line", "Ġbas eline", "æ ĭ", "0 86", "08 6", "Ø ©", "Ġ Convention", "ĠCon vention", "ĠConv ention", "ĉ config", "ĉcon fig", "ĉconf ig", "o okies", "ook ies", "ookie s", "m ult", "mu lt", "mul t", "Rec ords", "Record s", "Ġ EST", "ĠE ST", "ĠES T", "Ġgar bage", "Ġcon form", "Ġconf orm", "i dal", "id al", "ida l", "Ġb arg", "Ġbar g", "Ġba rg", "Ġsurv ived", "Ġsurvive d", "Ġsurviv ed", "Ġinvestig ations", "Ġinvestigation s", "9 35", "93 5", ".contains Key", "---- ----------------------------------------------------------------------Ċ", "---------------------------------------------------------------- ----------Ċ", "---------------------------------------------------------------------- ----Ċ", "or tion", "ort ion", "Ġh orr", "Ġhor r", "Ġho rr", "_ http", "_h ttp", "_ht tp", "Ġ mant", "Ġm ant", "Ġman t", "Ġma nt", "] ;čĊčĊ", "]; čĊčĊ", "];čĊ čĊ", "b inary", "bin ary", "9 48", "94 8", "e mpl", "em pl", "emp l", "Ġin quiry", "Ġ Meanwhile", "ĠMean while", "0 98", "09 8", "Ġcollect ing", ".Entity Framework", "\" ,ĊĊ", "\", ĊĊ", "\",Ċ Ċ", "Ġ Pic", "ĠP ic", "ĠPi c", "@ Inject", "ick ness", "Ġ Binding", "ĠB inding", "ĠBind ing", "ĠBin ding", "Ġcont rolling", "Ġcontrol ling", "re verse", "rev erse", "Ġch airs", "Ġchair s", "Ġcha irs", "Ġchai rs", "semb led", "sem bled", "semble d", "sembl ed", "( add", "(a dd", "(ad d", "Dis abled", "Disable d", "a nas", "an as", "ana s", ". translate", ".trans late", "- ----------Ċ", "-- ---------Ċ", "---- -------Ċ", "-------- ---Ċ", "--- --------Ċ", "----- ------Ċ", "---------- -Ċ", "------ -----Ċ", "----------- Ċ", "------- ----Ċ", "--------- --Ċ", "Ġref lected", "Ġreflect ed", "Ġrefl ected", "\" ]ĊĊ", "\"] ĊĊ", "\"]Ċ Ċ", "Ex ternal", "Ext ernal", "Extern al", "Ar row", "Arr ow", "S ingleton", "Single ton", "Sing leton", "% x", "Ġ Å", "Ġan cest", "Ġance st", "Ġanc est", "ĠOr leans", "ĉ cmd", "ĉc md", "ĉcm d", "Ġpro hibited", "Ġprohib ited", "Ġprohibit ed", "ith metic", "( channel", "(ch annel", "(chan nel", "_ css", "_c ss", "_cs s", "For ward", ". socket", ".s ocket", ".so cket", ".sock et", "Ġl uc", "Ġlu c", "â Ĩ", "Ġ Firefox", "ĠFire fox", "Ġ Movies", "ĠM ovies", "ĠMovie s", "ĠMov ies", ") _", ". ends", ".e nds", ".end s", ".en ds", "( shape", "(s hape", "(sh ape", "Ġde alt", "Ġdeal t", "Ġs aves", "Ġsa ves", "Ġsave s", "Ġsav es", "Ġgl ory", "Ġglo ry", "Ġglor y", "Ġmej or", "Ġbre athing", "Ġbreath ing", "Ġ eller", "Ġe ller", "Ġel ler", "Ġell er", "Ġelle r", "get Data", "Ġ angles", "Ġan gles", "Ġang les", "Ġangle s", "Ġangl es", "Ġ toolbar", "Ġtool bar", "Ġ spacing", "Ġsp acing", "Ġspa cing", "Ġspac ing", "0 59", "05 9", "I PS", "IP S", "Ġfloor s", "Ġflo ors", "_ ACTIVE", "_ACT IVE", "_ACTIV E", "Ġ shuffle", "Ġsh uffle", "/ shared", "/sh ared", "/share d", "Ġ Ele", "ĠE le", "ĠEl e", "e dish", "ed ish", "edi sh", "edis h", "Ġweb cam", ". expect", ".ex pect", ".exp ect", "i loc", "il oc", "ilo c", "Ġ Includes", "ĠIn cludes", "ĠInclude s", "Ġt weeted", "Ġtweet ed", "Ġtwe eted", "Ġtwee ted", "Ġ :)", "Ġ: )", "Ġ Essay", "ĠEs say", "ĠEss ay", "F ix", "Fi x", "- between", "-b etween", "_ web", "_w eb", "_we b", ". conv", ".con v", ".co nv", "Ġrac ism", "Ġreflect s", "Ġrefl ects", "u mm", "um m", "и ÑĤе", "иÑĤ е", "_ footer", "_f ooter", "_foot er", "/ docs", "/d ocs", "/doc s", "/do cs", "Ġ Pour", "ĠP our", "ĠPo ur", "ĠPou r", "Ng Module", ". initialize", ".initial ize", "pattern s", "_ In", "_I n", "Ġ Abb", "ĠA bb", "ĠAb b", "* čĊ", "Ġsent iment", "b uff", "buf f", "bu ff", "_ counts", "_count s", "_co unts", "_cou nts", "Ġ reuse", "Ġre use", "ch unk", "chu nk", "Ġim posed", "Ġimp osed", "Ġimpose d", "Primary Key", "Fore ground", "Ġcons umed", "Ġconsum ed", "Ġconsume d", "? !", "Ġd ick", "Ġdi ck", "Ġdic k", "Ġ chron", "Ġch ron", "Ġchr on", "ĠF ern", "ĠFe rn", "ĠFer n", "Ġ responsive", "Ġres ponsive", "Ġrespons ive", "9 58", "95 8", "Ġin sect", "Ġins ect", "Ġinse ct", "ic ulty", "icult y", "Ġ rw", "Ġr w", "Ġa like", "Ġal ike", "Ġali ke", "Ġ subset", "Ġsub set", "Ġsubs et", "Ġ Cookies", "ĠC ookies", "ĠCo okies", "ĠCook ies", "ĠCookie s", "Ġ Pair", "ĠP air", "ĠPa ir", "ĠPai r", "Ġ tier", "Ġt ier", "Ġti er", "Ġtie r", "I FO", "IF O", "av our", "avo ur", "avou r", "Ġ QU", "ĠQ U", ", sizeof", ",size of", "Ġ merged", "Ġm erged", "Ġmer ged", "Ġmerge d", "Ġmerg ed", "m v", "i tol", "it ol", "ito l", "y lon", "yl on", "Ġj umped", "Ġjump ed", ". role", ".r ole", ".ro le", "ens aje", "ensa je", "R ules", "Rule s", "Ru les", "Ġ browse", "Ġb rowse", "Ġbrows e", "Ġbrow se", "An imator", "Anim ator", "Ġy oga", "Ġyo ga", "Ġyog a", "Ġ variants", "Ġvar iants", "Ġvari ants", "Ġvariant s", "Ġcour tesy", "Ġcourt esy", "Ġcourte sy", "u ran", "ur an", "ura n", "p bs", "pb s", "else if", "A lt", "Al t", "Ġ Lane", "ĠL ane", "ĠLa ne", "ĠLan e", "C LK", "CL K", "I MARY", "IM ARY", "IMA RY", "_ PROPERTY", "_P ROPERTY", "_PRO PERTY", "ï¼ IJ", "Ġ chan", "Ġc han", "Ġch an", "Ġcha n", "Ġgrad ually", "Ġgradu ally", "Ġgradual ly", "Ġ shake", "Ġsh ake", "Ġsha ke", "Ġbl onde", "Ġblo nde", "Ġblond e", ".. .\");Ċ", "... \");Ċ", "...\" );Ċ", "- sex", "-s ex", "-se x", "Ġgame play", "a cies", "ac ies", "aci es", "acie s", ". refresh", ".re fresh", ".ref resh", "U SB", "US B", "Ġ Plot", "ĠP lot", "ĠPl ot", "W as", "Wa s", "iss ippi", "Ġ Tensor", "ĠT ensor", "Ġcrypt ocurrency", "Ġcrypto currency", "Ġcryptoc urrency", "Ġdifficult ies", "De leted", "Delete d", "Del eted", "With out", "_ append", "_app end", "_ap pend", "_ ver", "_v er", "_ve r", "9 67", "96 7", "\" ))čĊ", "\") )čĊ", "\")) čĊ", "Ġh onestly", "Ġhonest ly", "Ġ pivot", "Ġp ivot", "Ġpiv ot", "Ġ temps", "Ġte mps", "Ġtem ps", "Ġtemp s", "_ ps", "_p s", "Ġ Unlike", "ĠUn like", "[ :-", "[: -", "V S", "_ inf", "_in f", "_i nf", "Ġjun ior", "Ġjuni or", "Ġ animations", "Ġan imations", "Ġanim ations", "Ġanimation s", "Ġ filepath", "Ġfile path", "? {{$", ">{ {$", ">{{ $", "Ġ unicode", "Ġun icode", "Ġuni code", "Ġunic ode", "p laces", "pl aces", "place s", "pla ces", "Ġ Coffee", "ĠC offee", "ĠCo ffee", "ĠCoff ee", ". SE", ".S E", "Ġ PAR", "ĠP AR", "ĠPA R", "( txt", "(t xt", "(tx t", "g ebra", "ge bra", "geb ra", "Ġ fires", "Ġf ires", "Ġfire s", "Ġfi res", "Ġfir es", "Main Window", "m edium", "med ium", "medi um", "Ġ( âĢľ", "Ġ lg", "Ġl g", "Ġ cmp", "Ġc mp", "Ġcm p", "/ base", "/b ase", "_ layers", "_l ayers", "_layer s", "_la yers", "_ entries", "_en tries", "_ent ries", "Ġadmin ister", "ĠS UCH", "ĠSU CH", "B P", "ĠScott ish", "ĉ čĊĉčĊ", "ĉčĊ ĉčĊ", "g uard", "gu ard", "gua rd", "Ġ Strong", "ĠSt rong", "ĠStr ong", "In sn", "Ins n", "Ġ CAP", "ĠC AP", "ĠCA P", "as ury", "asu ry", "Ġ SEE", "ĠS EE", "ĠSE E", "C lock", "Cl ock", "Clo ck", "e rie", "er ie", "eri e", "\\ models", "\\model s", "Ġ $$", "Ġ$ $", "Ġ Cab", "ĠC ab", "ĠCa b", "Ġwur de", "Ġsold ier", "Ġcl ips", "Ġclip s", "Ġcli ps", "Ġarr angement", "Ġarrang ement", "Ġarrange ment", "Ġ Wonder", "ĠW onder", "ĠWo nder", "ĠWon der", "ĠH orn", "ĠHor n", "ĠHo rn", "Ġsc ared", "Ġsca red", "Ġscar ed", "Ġscare d", "Ġc ure", "Ġcur e", "Ġcu re", "m kdir", "mk dir", "Ġ aligned", "Ġal igned", "Ġalign ed", "Ġ Pink", "ĠP ink", "ĠPin k", "ĠPi nk", "Ġl anded", "Ġland ed", "Ġlan ded", "D imension", "Dim ension", "Scroll Pane", ". chat", ".c hat", ".ch at", ". With", ".W ith", "Ġ Train", "ĠT rain", "ĠTr ain", "ĠTra in", "] .Ċ", "]. Ċ", "Ġth irty", "Ġd urable", "Ġdur able", "Ġ ld", "Ġl d", "Ġlate init", "Ġ charts", "Ġch arts", "Ġchar ts", "Ġchart s", "Ġcha rts", "Ġins ult", ". Fatal", ".F atal", ".Fat al", "_ ct", "_c t", "Ġm asks", "Ġmask s", "Ġmas ks", "CLUD ED", "CLU DED", "CLUDE D", "P resident", "Pres ident", "Ġcol ours", "Ġcolour s", "g ments", "gment s", "gm ents", ". attributes", ".at tributes", ".attribute s", ".attrib utes", "Ġ Flex", "ĠF lex", "ĠFl ex", "ĠFle x", "Ġ Clock", "ĠC lock", "ĠCl ock", "ĠClo ck", "ÃŃ cul", "ÃŃc ul", "i men", "im en", "ime n", "J O", "Ġ Regex", "ĠReg ex", "_ LINK", "_L INK", "Ġc ouch", "Ġco uch", "Ġcou ch", "Ġ INPUT", "ĠIN PUT", "Ġb eating", "Ġbe ating", "Ġbeat ing", "b usiness", "bus iness", "pr eced", "pre ced", "prec ed", ". unit", ".un it", ".u nit", ".uni t", "Ġ Fel", "ĠF el", "ĠFe l", "N ever", "Ne ver", "os pel", "osp el", ". startswith", ".start swith", "ĠE PA", "ĠEP A", ". only", ".on ly", "Ġpre venting", "Ġprevent ing", "Ġprev enting", "y er", "ye r", "Column Name", "Ġe levation", "Ġele vation", "Ġelev ation", "f lu", "fl u", "i cycle", "ic ycle", "icy cle", "Ġ offline", "Ġoff line", "Tool bar", "Ġcomp eting", "Ġcompet ing", ") ].", ")] .", "Ġm og", "Ġmo g", "Ġ isValid", "Ġis Valid", "A sk", "As k", "_ av", "_a v", "_ lat", "_l at", "_la t", "A NC", "AN C", "ĠJ oh", "ĠJo h", "k ers", "ke rs", "ker s", "Ġ guards", "Ġg uards", "Ġgu ards", "Ġguard s", "Ġguar ds", "Ġ chains", "Ġch ains", "Ġchain s", "Ġcha ins", "Ġchai ns", "ĠSimple DateFormat", ". static", ".st atic", ".stat ic", "Ġv essel", "Ġve ssel", "Ġvess el", "Ġves sel", "Ġm ud", "Ġmu d", "Ġst abil", "Ġstab il", "Ġsta bil", "Ġst ret", "Ġstr et", "Ġstre t", "g m", "am ation", "ama tion", "amat ion", "ç ľ", "- with", "-w ith", "Ġ ros", "Ġr os", "Ġro s", "_ PA", "_P A", "Ġ resultado", "Ġresult ado", "Ġconf idential", "Ġconfident ial", "ĠTok yo", "ĉ using", "ĉu sing", "ĉus ing", "Ġ Mathf", "ĠMath f", "ĠMat hf", "om bine", "omb ine", "Ġ ESPN", "ĠESP N", "ĠES PN", "Ġde alers", "Ġdeal ers", "Ġdealer s", "Ġdismiss ed", "T RY", "TR Y", "Ġte ens", "Ġteen s", "Ġtee ns", "rec ords", "record s", "Ġw ings", "Ġwin gs", "Ġwing s", "g allery", "ac counts", "account s", "acco unts", "_ LIB", "_L IB", "Ġj acket", "Ġja cket", "Ġjack et", "Ġjac ket", "Ġ NSObject", "ĠNS Object", "Ġ stones", "Ġs tones", "Ġst ones", "Ġstone s", "Ġsto nes", "Ġ Delivery", "ĠD elivery", "ĠDel ivery", "ĠDeliver y", "ĠD iet", "ĠDi et", "ĠDie t", "/ watch", "/w atch", "Ġto ilet", "Ġtoile t", "Ġtoi let", "Ġ Guest", "ĠG uest", "ĠGu est", ". day", ".d ay", ".da y", "0 67", "06 7", "Ġ intval", "Ġint val", "0 87", "08 7", "Vis it", "Vi sit", "Ġinvest igated", "Ġinvestig ated", "Ġinvestigate d", "Ġpen tru", "Ġpent ru", "ĠThe atre", "andid ates", "andidate s", "andi dates", "L ang", "La ng", "Ġ Serv", "ĠS erv", "ĠSe rv", "ĠSer v", "Ġ controllers", "Ġcont rollers", "Ġcontrol lers", "Ġcontroller s", "Ġ setTitle", "Ġset Title", "N P", "a my", "am y", "f lat", "fl at", "( ui", "(u i", "0 69", "06 9", "_ document", "_d ocument", "_doc ument", "è ĥ½", "èĥ ½", "Ġ Coin", "ĠC oin", "ĠCo in", "ĠAd ams", "ĠAdam s", "ĠAda ms", "p tic", "pt ic", "Ġ productive", "Ġpro ductive", "Ġproduct ive", "Ġprod uctive", "Ġaccompl ished", "Ġaccomplish ed", "čĊ čĊčĊčĊ", "čĊčĊ čĊčĊ", "čĊčĊčĊ čĊ", "Ġde ferred", "Ġdefer red", "i entes", "ient es", "ien tes", "iente s", "Ġs inc", "Ġsi nc", "Ġsin c", "ol ars", "olar s", "ola rs", "Right arrow", "Ġvar iations", "Ġvari ations", "Ġvariation s", "( offset", "(o ffset", "(off set", "9 57", "95 7", ". LayoutInflater", ".Layout Inflater", "Ġ suspend", "Ġs uspend", "Ġsus pend", "Ġsusp end", "Ġpre vention", "Ġprevent ion", "Ġprev ention", "_ private", "_pr ivate", "_priv ate", "_ js", "_j s", "â ĺħ", "âĺ ħ", "Ġw ieder", "Ġwie der", "Ġwi eder", "at um", "atu m", "Ĵ Į", "Ġappear ances", "Ġappearance s", ". Document", ".D ocument", ".Doc ument", "Ġvalid ates", "Ġvalidate s", "Ġvalida tes", "c alendar", "cal endar", "} \";Ċ", "}\" ;Ċ", ". demo", ".d emo", ".de mo", "con ut", "co nut", "Ġcor rection", "Ġcorrect ion", "Ġcorre ction", "Ġcorr ection", "Ġ Deal", "ĠDe al", "Ġbatter ies", "Ġbatt eries", ". duration", ".d uration", ", \\", "_ marker", "_m arker", "_mark er", "_mar ker", "m ulti", "mul ti", "mult i", "Ġ halt", "Ġh alt", "Ġha lt", "Ġhal t", "Ġ cms", "Ġc ms", "Ġcm s", "Ġsh aped", "Ġshape d", "Ġsha ped", "B ro", "Br o", "re duce", "red uce", "Ġ ####", "Ġ# ###", "Ġ## ##", "Ġ### #", "C TOR", "CT OR", "Ġ Benef", "ĠB enef", "ĠBen ef", "ĠBene f", "Ġicon ic", "Ġic onic", "Ġp iano", "Ġpi ano", "Ġpian o", "Ġeffect iveness", "Ġeffective ness", "| .Ċ", "|. Ċ", "Ġ ajax", "Ġa jax", "Ġaj ax", "Ġv olumes", "Ġvol umes", "Ġvolume s", "Ġvolum es", "ภ¡", "Ġ cljs", "Ġcl js", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "a ths", "at hs", "ath s", "r aits", "ra its", "rait s", "rai ts", "å¤ §", "Ñ ĸ", "_ mult", "_m ult", "_mul t", "_mu lt", "Ġfasc inating", "A verage", "Ġp ré", "Ġpr é", "ĠChair man", ".find Element", "_ pin", "_p in", "_pi n", "Ġcom paring", "Ġcomp aring", "Ġcompar ing", "Ġdark ness", "- Fi", "-F i", "- server", "-s erver", "-ser ver", "Ġselect ing", "s terdam", "ster dam", "Ġ Parts", "ĠP arts", "ĠPart s", "ĠPar ts", "ĠPa rts", "FORM ATION", "FORMAT ION", "Ġn oting", "Ġnot ing", "Ġno ting", "Ġ pile", "Ġp ile", "Ġpi le", "Ġpil e", "o gs", "og s", "Ġ palette", "Ġp alette", "Ġpa lette", "Ġpal ette", "Ġpale tte", "_ do", "_d o", "it ize", "iti ze", "0 79", "07 9", "( )(", "() (", "Ġdef ining", "Ġdefin ing", "Ġ remainder", "Ġremain der", "Un its", "Unit s", "Uni ts", "_ TASK", "_T ASK", "_TA SK", "Http Client", "S ocial", "So cial", "Ġf undra", "Ġfun dra", "Ġfund ra", "N R", "c hest", "ch est", "che st", "ches t", "C urrency", "Curr ency", ". adapter", ".ad apter", "Ġd op", "Ġdo p", "un ting", "unt ing", "ANG UAGE", "\" He", "\"H e", "ĉ index", "ĉin dex", "_ package", "_p ackage", "_pack age", ". Icon", ".I con", "Ġre pet", "Ġrep et", "Ġrepe t", "m ass", "ma ss", "mas s", "= \".$", "=\" .$", "=\". $", "ĠS ud", "ĠSu d", "Ġ lid", "Ġl id", "Ġli d", "pro vince", "prov ince", "ì ľ", "G PIO", "GP IO", "Ð ļ", "Ġ MySQL", "ĠMy SQL", "Ġ docs", "Ġd ocs", "Ġdo cs", "Ġdoc s", "Ġ GA", "ĠG A", "Ġip sum", "Ġips um", "K ernel", "Ġac cepts", "Ġaccept s", "Ġf itting", "Ġfit ting", "Ġcu ando", "Ġd uplic", "Ġdup lic", "ĠBr other", "ĠBro ther", "ĠK le", "ĠKl e", "n ums", "num s", "nu ms", "Ġ morph", "Ġm orph", "Ġmor ph", "Ġ ########", "Ġ# #######", "Ġ## ######", "Ġ### #####", "Ġ#### ####", "Ġ##### ###", "Ġ CGPoint", "ĠCG Point", "< unsigned", "ä¾ ĭ", "ĠD uke", "ĠDu ke", ".set Bounds", "q s", "o ric", "or ic", "ori c", "j er", "je r", "Ġreg arded", "Ġregard ed", "Http Request", "Ġb onds", "Ġbo nds", "Ġbon ds", "Ġbond s", "Ġthorough ly", "en cent", "ence nt", "enc ent", "Ġhighlight ed", "Ġac res", "Ġacre s", "Ġwork place", "Ġ Lux", "ĠL ux", "ĠLu x", "Ġ quot", "Ġqu ot", "Ġquo t", "9 86", "98 6", ". inflate", ".in flate", ".inf late", "Ġd ocumented", "Ġdocument ed", "Ġadd iction", "Ġaddict ion", "Ġ mutation", "Ġm utation", "Ġmut ation", ". city", ".c ity", ".ci ty", "Ġbott les", "Ġbottle s", "Ġ Repository", "ĠRe pository", "ĠRepos itory", "o nn", "on n", "err no", "ARI ABLE", "åº ¦", "_ BEGIN", "_B EGIN", "_BE GIN", "g las", "gl as", "' })Ċ", "'} )Ċ", "'}) Ċ", "Ġ Massage", "ĠM assage", "ĠMass age", "ĠMas sage", "ĠW hit", "ĠWh it", "reg ex", "W A", "Ġout let", "- head", "-h ead", "-he ad", "Ġ expired", "Ġex pired", "Ġexp ired", "Ġexpire d", "Ġ Thai", "ĠT hai", "ĠTh ai", "/ include", "/in clude", "/inc lude", "g radient", "grad ient", "s canf", "scan f", "Ġs eam", "Ġse am", "Ġsea m", "w al", "wa l", "ĉ buf", "ĉb uf", "B earer", "Be arer", "Bear er", "Ġprec ious", "Ġprecio us", "i facts", "if acts", "ifact s", "ifa cts", "c oord", "co ord", "Ġexpl oration", "Ġexplo ration", "Ġexplor ation", ". getY", ".get Y", "( handle", "(h andle", "(hand le", "T opic", "To pic", "Top ic", "Ġ Vent", "ĠV ent", "ĠVen t", "ĠVe nt", "r hs", "rh s", "- -----Ċ", "-- ----Ċ", "---- --Ċ", "--- ---Ċ", "----- -Ċ", "------ Ċ", "Ġ Bright", "ĠB right", "ĠBr ight", "ĠBrig ht", "ĠBri ght", "Ġ guild", "Ġg uild", "Ġgu ild", "Ġgui ld", "m other", "mo ther", "mot her", "moth er", "st orm", "sto rm", "stor m", "Ġmunicip al", "Ġ ink", "Ġin k", "Ġi nk", ". TYPE", ".T YPE", "w l", ".. . < /", "_ ro", "_r o", "( (*", "(( *", "? ???", "?? ??", "??? ?", "_ vertex", "_ver tex", "_vert ex", "ke it", "ĠH alloween", "T I", "Ġ Va", "ĠV a", "_ car", "_c ar", "_ca r", "=\" {{$", "=\"{{ $", "=\"{ {$", "Ġrandom ly", "а ние", "ан ие", "ани е", "Ġsh ocked", "Ġshock ed", "ĠPok émon", "s ignal", "sign al", "sig nal", "Ġ SDK", "ĠS DK", "ĠSD K", "m iddleware", "middle ware", "Ġt reating", "Ġtr eating", "Ġtreat ing", "Ġtre ating", "Ġbur ned", "Ġburn ed", "De partment", "Dep artment", "Depart ment", "ĠS pect", "ĠSp ect", "ĠSpec t", "ĠSpe ct", "Ġ cliente", "Ġcl iente", "Ġclient e", "Ġcli ente", "Ġ Reddit", "ĠRe ddit", "ĠRed dit", "_ avg", "_a vg", "_av g", "Ġinst alling", "Ġinstall ing", "Ġinstal ling", "_ alpha", "_al pha", ", data", ",d ata", "Ġ setId", "Ġset Id", "Ġ ListView", "ĠList View", "( property", "(p roperty", "(pro perty", "(prop erty", "Ġcross ing", "Ġ Obj", "ĠO bj", "ĠOb j", "ĠW ard", "ĠWar d", "ĠWa rd", "ĠRedirect To", "Ġ Present", "ĠP resent", "ĠPres ent", "ĠPre sent", "Ġdr aws", "Ġdraw s", "Ġdra ws", "ched uled", "chedule d", "Ġlegisl ative", "Ġtw ist", "Ġ Stra", "ĠS tra", "ĠSt ra", "ĠStr a", "Ġ AFP", "ĠA FP", "ĠAF P", "ĠC hap", "ĠCh ap", "ĠCha p", "- pr", "-p r", ": CGRect", "Ġ ces", "Ġc es", "Ġce s", "R outes", "Route s", "Ro utes", "n of", "no f", "Ġ visa", "Ġv isa", "Ġvis a", "Ġvi sa", "Ġ TCP", "ĠT CP", "ĠTC P", "ĠE VEN", "ĠEV EN", "ĠEVE N", "iv ial", "ivia l", "ivi al", "Ġ Letter", "ĠL etter", "ĠLet ter", "R AY", "RA Y", "Ġ implode", "Ġim plode", "Ġimpl ode", ". eq", ".e q", "= '+", "=' +", "Ġmot ivated", "Ġmotiv ated", "Ġmotivate d", ". visible", ".v isible", ".vis ible", ". short", ".s hort", ".sh ort", "> manual", "Ġ Technical", "ĠTechn ical", "Ġcorpor ation", "Ġcorp oration", "Ġcorpo ration", "Ġ HW", "ĠH W", "an ka", "ank a", "T AIL", "TA IL", "is tas", "ist as", "ista s", "Ġper forms", "Ġperform s", "Ġperfor ms", "Ġ Behavior", "ĠBeh avior", ". For", ".F or", "_ ORDER", "_OR DER", "_ORD ER", "Ġ Kick", "ĠK ick", "ĠKi ck", "Ġ callbacks", "Ġcall backs", "Ġcallback s", "_ dr", "_d r", "u ego", "ue go", "h ub", "hu b", "uff icient", "s ky", "sk y", "Ġ bp", "Ġb p", "h table", "ht able", "hta ble", "Ġ ONLY", "ĠON LY", "ĠAUTH ORS", "ĠAUTHOR S", ". Argument", ".Arg ument", "\" };Ċ", "\"} ;Ċ", "Ġ Thunder", "ĠTh under", "ĠThu nder", "Ġ Kom", "ĠK om", "ĠKo m", ". Should", ".Sh ould", "A UTH", "AU TH", "AUT H", "a hu", "ah u", "_ payment", "_p ayment", "_pay ment", "Ġ starter", "Ġst arter", "Ġstart er", "Ġstar ter", "ìĦ ľ", "ìļ ©", "B log", "Bl og", "Blo g", ". patch", ".p atch", ".pat ch", "Ġgover ned", "Ġgovern ed", "as sy", "ass y", "- found", "-f ound", "Ġthe ater", "Ġtheat er", "ĠFont Weight", "Ġ Batman", "ĠBat man", "\" If", "\"I f", ". Random", ".R andom", "_ delta", "_d elta", "_del ta", "Ġ CE", "ĠC E", "Auth enticated", "Authenticate d", "Ġd rone", "Ġdr one", "Ġdro ne", "Ġc ous", "Ġco us", "Ġcou s", "r adius", "rad ius", "radi us", "M er", "Me r", "( None", "(N one", "Ġ NJ", "ĠN J", "_ headers", "_header s", "_head ers", "_he aders", "Ġ amer", "Ġa mer", "Ġam er", "py test", "Ġ Actions", "ĠA ctions", "ĠAct ions", "ĠAction s", "ĉ ĉĉĠĠĠĠ", "ĉĉ ĉĠĠĠĠ", "ĉĉĉ ĠĠĠĠ", "ĉĉĉĠĠĠ Ġ", "ĉĉĉĠ ĠĠĠ", "ĉĉĉĠĠ ĠĠ", "Ġ ett", "Ġe tt", "Ġet t", "Ġh oly", "Ġho ly", "Ġhol y", "Ġun comfort", "ĠN in", "ĠNi n", "Ġ Decimal", "ĠD ecimal", "ĠDe cimal", "ĠDec imal", "Ġ Messages", "ĠM essages", "ĠMessage s", "ĠMess ages", ". sender", ".s ender", ".se nder", ".send er", "] ])Ċ", "]] )Ċ", "]]) Ċ", "Ġem brace", "Ġemb race", "Ġembr ace", "Th ough", "Tho ugh", "/ sp", "/s p", "Ġcult ures", "Ġculture s", "Ġcul tures", "Ġhigh way", "t ar", "ta r", ". fail", ".f ail", ".fa il", "_ hidden", "_h idden", "_hi dden", "_hid den", "ĠcomponentDid Mount", "ĠW right", "ĠWr ight", "Ġj ag", "Ġja g", "_ il", "_i l", "../ ../../", "../../ ../", "i gu", "ig u", "F ood", "Foo d", "Fo od", "Ġ ace", "Ġa ce", "Ġac e", "Ġa ños", "Ġaño s", "Ġañ os", "U SD", "US D", "Ġmut ual", "Ġmu tual", "Log ic", "Ġt emple", "Ġtem ple", "Ġtemp le", "Ġtempl e", "Ġbrief ly", "Ġ Trip", "ĠT rip", "ĠTr ip", "ĠTri p", "class method", "default s", "Ġ chunks", "Ġch unks", "Ġchunk s", ", ,,,", ",, ,,", ",,, ,", "Ġ Reason", "ĠRe ason", "$ id", "$i d", "- ups", "-up s", "-u ps", "Ġ damn", "Ġda mn", "Ġdam n", "Ġtr ucks", "Ġtruck s", "Ġun limited", "Ġunl imited", "Ġsc ulpt", "Ġ Cards", "ĠC ards", "ĠCar ds", "ĠCard s", "Ġ autor", "Ġa utor", "Ġaut or", "Ġauto r", "Ġau tor", "Ġ Testing", "ĠT esting", "ĠTest ing", "ĠTes ting", "Ġd iese", "Ġdi ese", "Ġdie se", "Ġdies e", "sh ops", "shop s", "ç ´", "( payload", "(p ayload", "Ġ PATH", "ĠP ATH", "ĠPA TH", "ĠPAT H", "ĠMem orial", "ĠMemo rial", "ĠMemor ial", "Ġridic ulous", "e gree", "eg ree", "egr ee", "-w inning", "-win ning", "Ġre hab", "Ġreh ab", "Ġsophistic ated", "w pdb", "wp db", "ĉ path", "ĉp ath", "! \";Ċ", "!\" ;Ċ", "_ SYS", "_S YS", "_SY S", ". speed", ".s peed", ".sp eed", "Ġ soap", "Ġso ap", "s uffix", "W rap", "Wr ap", "Ġenh ancement", "Ġenhance ment", "à ī", "ú b", "Ġ playlist", "Ġplay list", "Ġmix ing", "Ġmi xing", "Ġmixin g", "ant idad", "anti dad", "=\" \";Ċ", "=\"\" ;Ċ", "Ġ Revision", "ĠRe vision", "ĠRev ision", "Ġ Beat", "ĠB eat", "ĠBe at", "ĠBea t", ". inc", ".in c", ".i nc", "- way", "-w ay", "e ncias", "enc ias", "encia s", "enci as", "u lers", "ul ers", "ule rs", "uler s", "C at", "Ca t", "i del", "id el", "ide l", "Ġ Ship", "ĠS hip", "ĠSh ip", "ĠShi p", ". setColor", ".set Color", "Ġthreat ening", "Ġthreaten ing", ". modules", ".module s", ".mod ules", "Ġafter wards", "Ġafterward s", "Ġ Dashboard", "ĠD ashboard", "ĠDash board", "Ċ ĠĊ", "S ignal", "Sign al", "Sig nal", "Ġ primer", "Ġpr imer", "Ġprim er", "Ġprime r", "Ġpri mer", "or neys", "orney s", "orne ys", "ici ary", "icia ry", "iciar y", "Ġ ligne", "Ġl igne", "Ġli gne", "Ġlig ne", "Ġlign e", "_ predict", "_p redict", "_pre dict", "_pred ict", "Ġa est", "Ġae st", "Ġaes t", "_ https", "_http s", "_ht tps", "> :", "Ġ Lex", "ĠL ex", "ĠLe x", "Ġren contres", "Ġrencont res", "Ġrencontre s", "Ġrencontr es", "eg ral", "egr al", "egra l", "s cala", "sc ala", "scal a", "_ family", "_f amily", "ÃŁ en", "ÃŁe n", "_ sym", "_s ym", "_sy m", "Ġunc ertainty", "Ġuncert ainty", "Ġuncertain ty", "Ġ VALUE", "ĠVAL UE", "Ġ };čĊčĊ", "Ġ} ;čĊčĊ", "Ġ};čĊ čĊ", "Ġ}; čĊčĊ", "Ġbr oader", "Ġbro ader", "Ġbroad er", "Ġh orses", "Ġhor ses", "Ġhorse s", "Ġhors es", "ãģ Ŀ", "Ġ Kal", "ĠK al", "ĠKa l", "o ba", "ob a", "_ INET", "_IN ET", "_I NET", "Ġ Kill", "ĠK ill", "ĠKi ll", "ĠKil l", "j query", "a mination", "am ination", "amin ation", "ami nation", "amina tion", "[ @\"", "[@ \"", "Ġm uj", "Ġmu j", "# ##Ċ", "## #Ċ", "### Ċ", "First OrDefault", "then Return", "C he", "Ch e", "/ footer", "/f ooter", "/foo ter", "Ġp arks", "Ġpar ks", "Ġpark s", "as je", "ĠG ulf", "ĠGu lf", "ĠGul f", "Ġmod est", "Ġmode st", "Ġmo dest", "Ġmodes t", ". Init", ".I nit", ".In it", "ï¼Ł ĊĊ", "ï¼ŁĊ Ċ", "Ġpros pects", "Ġprospect s", "Ġ svg", "Ġs vg", "Ġsv g", "Ġ åı", "Ġå ı", ". Dialog", ".D ialog", ".Di alog", ".Dial og", "_ NET", "_N ET", "_NE T", "Ġ (($", "Ġ( ($", "Ġ(( $", "Ġ ek", "Ġe k", "Ġ Warning", "ĠW arning", "ĠWar ning", "ĠWarn ing", "Ġ MK", "ĠM K", "< LM", "", ")= >", "Ġ Repair", "ĠRe pair", "ĠRep air", "_ BE", "_B E", "B rand", "Br and", "u art", "ua rt", "uar t", "p review", "pr eview", "pre view", "prev iew", "Ġiniti atives", "Ġinitiative s", "r unning", "run ning", "b ang", "ba ng", "ban g", "ĉ update", "ĉup date", "Ġ Coach", "ĠCo ach", "R ich", "Ġ youtube", "Ġy outube", "Ġyou tube", "Ġrit ual", "Ġri tual", "ap pa", "app a", "ĠRob inson", "ĠRobin son", "p recision", "pre cision", "prec ision", "//// ////////////////////////////////////////////////////////////////////////", "//////// ////////////////////////////////////////////////////////////////////", "//////////////// ////////////////////////////////////////////////////////////", "//////////////////////////////////////////////////////////////// ////////////", "//////////// ////////////////////////////////////////////////////////////////", "//////////////////////////////////////////////////////////////////////// ////", "//////////////////////////////////////////////////////////////////// ////////", "//////////////////////////////////////////////////////////// ////////////////", "= []Ċ", "=[ ]Ċ", "=[] Ċ", "Ġcelebr ated", "Ġcelebrate d", "O TO", "OT O", "Ġin clusion", "Ġincl usion", "Ġinclus ion", "J P", "' ;čĊčĊ", "'; čĊčĊ", "';čĊ čĊ", "Ġnot able", "Ġno table", "Ġnota ble", "( _.", "(_ .", "Man aged", "Manage d", "Mana ged", "Ġgu ides", "Ġguide s", "Ġguid es", "Ġgui des", "& nbsp", "ated Route", "Ġ Adjust", "ĠAd just", "ĠAdj ust", "Ġcol ored", "Ġcolor ed", "Ġcolore d", "_ scores", "_s cores", "_score s", "_sc ores", "Ġ Tesla", "ĠTe sla", "ĠTes la", "_ progress", "_pro gress", "_prog ress", ". inst", ".in st", ".i nst", ".ins t", "[ '_", "[' _", ". flags", ".f lags", ".fl ags", ".flag s", "Ġ fclose", "Ġf close", "Ġfc lose", "_ OPER", "_O PER", "_OP ER", "ż y", "_ note", "_n ote", "_no te", "_not e", "Ġtrans gender", "å ķ", "RI PT", "Ġab sent", "Ġabs ent", "Ġ amet", "Ġa met", "Ġam et", "Ġ operand", "Ġoper and", "Ġopera nd", "ë ©", "Ġ hood", "Ġh ood", "Ġho od", "to LowerCase", "a vo", "av o", "ĠC ircuit", "ĠCirc uit", "ĠL ind", "ĠLin d", "ĠLi nd", "-- }}Ċ", "= m", "Ġ suppress", "Ġsup press", "Ġsupp ress", "Ġ MAP", "ĠM AP", "ĠMA P", "i ang", "ia ng", "ian g", "- admin", "-ad min", "Ġ sidebar", "Ġs idebar", "Ġside bar", "Ġ Bu", "ĠB u", "Ġ Hex", "ĠH ex", "ĠHe x", ", F", "Ġ Signal", "ĠS ignal", "ĠSign al", "ĠSig nal", "Ġtrans parency", "ĠF ederation", "ĠFeder ation", "ĠFed eration", "/ V", "R eq", "Re q", "Ġ pulse", "Ġp ulse", "Ġpul se", "Ġpu lse", "Ġpuls e", "Ġt ends", "Ġte nds", "Ġten ds", "Ġtend s", "Number s", "Num bers", "% '", "Ġde port", "Ġdep ort", "d atas", "data s", "da tas", "dat as", "_ UINT", "_U INT", "_UI NT", "_ tra", "_t ra", "_tr a", "o ko", "ok o", "Ġ \"?", "Ġ\" ?", "com pet", "comp et", "so lete", "sole te", "sol ete", "un dry", "und ry", "Ġ overlap", "Ġover lap", "Ġoverl ap", "} `,Ċ", "}` ,Ċ", "}`, Ċ", ". ly", ".l y", "_ summary", "_sum mary", "Ġ Lost", "ĠL ost", "ĠLo st", "ĠLos t", ". Center", ".C enter", "Ġdis ability", ". Serialization", ".S erialization", ".Serial ization", "Ġ geom", "Ġge om", "Ġgeo m", "Ġ ?:", "Ġ? :", "Ġ Wo", "ĠW o", "Ġsh ipped", "Ġship ped", "Ĥ æķ°", "Ġu gly", "Ġug ly", "Ġugl y", "Ġexcit ement", "Ġex terior", "Ġext erior", "Ġexter ior", "Ġ checkout", "Ġcheck out", "Ġ kur", "Ġk ur", "Ġku r", ", D", "ĠAl aska", "ĠAla ska", "Ġsyn thetic", "Ġsynth etic", "Ġsynt hetic", "Ġ Budget", "ĠB udget", "ĠBud get", "Ġ Subscribe", "ĠSub scribe", "Ġ &Ċ", "Ġ& Ċ", "ÈĻ i", "Ġ Yu", "ĠY u", "ĉ query", "} .Ċ", "}. Ċ", "Ġtr aged", "Ġtra ged", "Ġtrag ed", "as sen", "ass en", "asse n", "Ġaccom modation", "Ġaccommod ation", "Ġphys ician", "Ġphysic ian", "Ġre named", "Ġren amed", "Ġrename d", "Ġt idak", "Ġtid ak", "z Äħ", "Ġ minus", "Ġm inus", "Ġmin us", "n ych", "ny ch", "0 97", "09 7", "_EX CEPTION", "th reads", "thread s", "Ġt ire", "Ġti re", "Ġtir e", "_ created", "_c reated", "_create d", "_cr eated", "_cre ated", "en sure", "ens ure", "Ġ worthy", "Ġw orthy", "Ġwor thy", "Ġworth y", "Ġexc use", "Ġ cloth", "Ġc loth", "Ġcl oth", "Ġclo th", "Ġclot h", ". parentNode", ".parent Node", "/ platform", "/pl atform", "ĠU FC", "ĠUF C", "Ġ Gtk", "ĠG tk", "un ny", "unn y", "Ġg ibt", "Ġgi bt", "Ġgib t", "ke ley", "kel ey", "h um", "hu m", "( tx", "(t x", "ĉ dev", "ĉd ev", "ĉde v", "Ġout fit", "Ġoutf it", "do ors", "door s", "Ġ fon", "Ġf on", "Ġfo n", "i cut", "ic ut", "v olatile", "vol atile", "Ġhom osex", "Ġhomo sex", "Max imum", "Ġex pend", "Ġexp end", "Ġ });ĊĊĊ", "Ġ} );ĊĊĊ", "Ġ});Ċ ĊĊ", "Ġ});ĊĊ Ċ", "Ġ}) ;ĊĊĊ", "Ġ}); ĊĊĊ", "E q", "on ders", "ond ers", "onder s", "onde rs", "de partment", "dep artment", "depart ment", "Ġ Physics", "ĠPh ysics", "ĠPhys ics", "\" });Ċ", "\"} );Ċ", "\"}) ;Ċ", "Ġpar ad", "Ġpara d", "Ġpa rad", ". Str", ".S tr", ".St r", "Ġs ele", "Ġse le", "Ġsel e", "IF IED", "IFI ED", "Ġdel ivers", "Ġdeliver s", "i van", "iv an", "iva n", "Ġrespons ibilities", "Ġadvoc ates", "Ġadvocate s", "è µ", "Ġ RID", "ĠR ID", "ĠRI D", ". parameters", ".param eters", ".parameter s", "M etrics", "Met rics", "Metric s", "ron ics", "ronic s", "Ġ UITableViewCell", "ĠUI TableViewCell", "ĠUITableView Cell", "A bsolute", "Abs olute", "ip se", "ips e", "y lum", "yl um", "ML Element", "MLE lement", "_ VALID", "_VAL ID", "< title", " \\<^", ">\\< ^", "Ġ ios", "Ġi os", "Ġio s", "s ound", "so und", "sou nd", "\" ];", "\"] ;", "Ġf reed", "Ġfr eed", "Ġfree d", "Ġfre ed", "r ottle", "rot tle", "rott le", "Ġ Lower", "ĠL ower", "ĠLo wer", "ĠLow er", "ĠLowe r", "[ count", "[c ount", "å Ŀ", "Ġp ale", "Ġpa le", "Ġpal e", "ĠW ayne", "ĠWay ne", "ĠWa yne", "e arth", "ear th", "_ categories", "_c ategories", "U CK", "UC K", ". metadata", ".m etadata", ".meta data", ".met adata", "Ġsum mon", "Ġsumm on", "H OME", "HO ME", "олÑĮ з", "Ġmanufact ured", "Ġmanufacture d", "Ġ dock", "Ġd ock", "Ġdo ck", "Ġdoc k", "Ġcompet itors", "Ġcompetitor s", "Ġcompetit ors", "_ MODEL", "_MODE L", "_MO DEL", "_MOD EL", "ok ia", "oki a", "Ġ Hey", "ĠH ey", "ĠHe y", "Î ¿", "Ġ backward", "Ġback ward", "ĠPO SS", "ĠPOS S", "r opa", "ro pa", "rop a", "Ġ cri", "Ġc ri", "Ġcr i", "_ OBJ", "_O BJ", "Trans port", "- high", "-h igh", "Ġerot ik", "Ġero tik", "_ slot", "_s lot", "_sl ot", "Ġ artic", "Ġar tic", "Ġart ic", "_ framework", "_f ramework", "_frame work", "_fr amework", "-s erif", "-se rif", "-ser if", "ĠSql DbType", "' )(", "') (", "+ \"/", "+\" /", "Ġw ore", "Ġwor e", "Ġwo re", "S il", "Si l", "Ġst oring", "Ġstor ing", "Ġsto ring", "Ġ Phase", "ĠPh ase", "u ant", "ua nt", "uan t", "Ġb ump", "Ġbu mp", "Ġbum p", "in ho", "inh o", "Ġd ign", "Ġdi gn", "Ġdig n", "Ġ backs", "Ġb acks", "Ġback s", "Ġba cks", "Ġbac ks", "q q", "( hash", "(h ash", "(has h", "Ġ geo", "Ġg eo", "Ġge o", "Ġt ender", "Ġte nder", "Ġten der", "Ġtend er", "L ogo", "Log o", "Lo go", "! )Ċ", "!) Ċ", "Ġ MX", "ĠM X", "Ġ Arthur", "ĠAr thur", "ĠArt hur", "ĠArth ur", "ess oa", "esso a", "_ Ch", "_C h", "Ġbed rooms", "Ġbedroom s", "=\"# \"><", "=\"#\" ><", "=\"#\"> <", "Ġ throat", "Ġth roat", "Ġthro at", "i nsic", "in sic", "ins ic", "insi c", ". integer", ".int eger", "Ġ primitive", "Ġpr imitive", "Ġprim itive", "Truth y", "Ġfacilit ate", "Ġfacil itate", "Ġcre ativity", "Ġcreat ivity", "Ġ DNS", "ĠD NS", "ĠDN S", "Ġ gra", "Ġg ra", "Ġgr a", "u ez", "ue z", "Ġcount less", "ĠP oland", "ĠPol and", "ĠPo land", "' M", "Ġ Dist", "ĠD ist", "ĠDis t", "ĠDi st", "Ġ vest", "Ġv est", "Ġve st", "Ġves t", "Ġcert ification", "Ġcertif ication", "á» ij", "h eld", "he ld", "hel d", "ext ensions", "extension s", "( static", "(st atic", "(stat ic", "Ġ grades", "Ġg rades", "Ġgr ades", "Ġgrad es", "Ġgrade s", "Ġgra des", "Ġ Uber", "ĠU ber", "ĠUb er", "ãģ Ł", "Ġ [])Ċ", "Ġ[ ])Ċ", "Ġ[] )Ċ", "Ġ[]) Ċ", "d atos", "da tos", "dat os", "dato s", "Ġ getData", "Ġget Data", "ĠCh arg", "ĠChar g", "ĠCha rg", "Ġ BS", "ĠB S", ".m icrosoft", ".micro soft", ". video", ".v ideo", ". direction", ".d irection", ".dir ection", ".di rection", ".direct ion", "-> {'", "->{ '", "l ua", "lu a", "a pest", "ap est", "ape st", "apes t", "Ġbo iler", "Ġboil er", "e rek", "er ek", "ere k", "Ġdec ides", "Ġdecide s", "Ġdecid es", ". jar", ".j ar", "I SC", "IS C", "Ġ Words", "ĠW ords", "ĠWord s", "ĠWor ds", "( CON", "(C ON", "EMPL ATE", "ree ze", "s hots", "sh ots", "shot s", "a pps", "ap ps", "app s", "un ted", "unt ed", "unte d", ". setName", ".set Name", ": :<", ":: <", "- bold", "-b old", "-bo ld", "ê ²", "å¯ Ĩ", "Long rightarrow", "Ġun fair", "Ġunf air", "Ġ earning", "Ġe arning", "Ġear ning", "Ġearn ing", "Ġ shelf", "Ġsh elf", "Ġshe lf", "Ġshel f", "UR EMENT", "URE MENT", "Ġ idle", "Ġi dle", "Ġid le", "_ MENU", "_M ENU", "_ME NU", ". Custom", ".C ustom", "A GER", "AG ER", "AGE R", "- \"", "_ switch", "_s witch", "_sw itch", "b ecause", "be cause", "bec ause", ") view", ")v iew", "m are", "ma re", "mar e", "_ condition", "_con dition", "_cond ition", "Ġ Starting", "ĠStart ing", "ĠStar ting", "M vc", "( pre", "(p re", "(pr e", "d ump", "du mp", "dum p", "_ LOCK", "_L OCK", "_LO CK", "_LOC K", "at etime", "ate time", ". callback", ".c allback", ".call back", "ĠC er", "ĠCe r", "o pol", "op ol", "opo l", "ib rary", "ibr ary", "Ġ reservation", "Ġres ervation", "Ġreserv ation", "Ġreserva tion", "ĉ ĉĉĉĉĉĉĊ", "ĉĉ ĉĉĉĉĉĊ", "ĉĉĉĉ ĉĉĉĊ", "ĉĉĉ ĉĉĉĉĊ", "ĉĉĉĉĉ ĉĉĊ", "ĉĉĉĉĉĉ ĉĊ", "ĉĉĉĉĉĉĉ Ċ", "l ector", "le ctor", "lect or", "lec tor", "grad uate", "Ġgener ous", "Ġgene rous", "Ġ ion", "Ġi on", "Ġio n", "r icao", "ri cao", "ric ao", "rica o", "m q", "_ complete", "_com plete", "_comp lete", "( cursor", "(c ursor", "Ġ FormControl", "ĠForm Control", ": center", ":c enter", "Ġsub stitute", "Ġsubstit ute", "Ġ Planning", "ĠPl anning", "ĠPlan ning", "Ġp ension", "Ġpens ion", "Ġrecommend ation", "Ġ Tags", "ĠT ags", "ĠTag s", "ĠTa gs", "Ġg ef", "Ġge f", "Ġ albums", "Ġalbum s", "Ġalb ums", "Ġ washing", "Ġw ashing", "Ġwash ing", "r oc", "ro c", "Ġtr ains", "Ġtrain s", "Ġtra ins", "Ġtrai ns", "a tings", "at ings", "ating s", "atin gs", "Ġex ponent", "Ġexp onent", "ack bar", "- ln", "-l n", "á g", ".Data Annotations", "Ġ EIF", "ĠE IF", "ĠEI F", "ĠMal aysia", "ĠMalays ia", "ĉ PORT", "ĉP ORT", "on us", "onu s", "Ġc lever", "Ġcl ever", "Ġcle ver", "Ġp eu", "Ġpe u", "> ĊĊĊĊ", ">Ċ ĊĊĊ", ">ĊĊ ĊĊ", ">ĊĊĊ Ċ", "Ġ Arguments", "ĠArg uments", "ĠArgument s", "Ġdeb ugging", "Ġdebug ging", "( right", "(r ight", "' D", "com pute", "comp ute", "comput e", "Ġfin est", "Ġfine st", "Ġfi nest", "Ġfines t", "OR AGE", "ORA GE", "Ġspect acular", "ph rase", "Ġin dia", "Ġind ia", "Ġlegend ary", "b irth", "bir th", "Ġ composite", "Ġcom posite", "Ġcomp osite", "Ġcompos ite", "Ġg rows", "Ġgr ows", "Ġgrow s", "Ġgro ws", "Ġ TD", "ĠT D", "Ġe pid", "Ġep id", "Ġlaunch ing", "] ][", "]] [", "Min utes", "Minute s", "Ġ Cha", "ĠC ha", "ĠCh a", "Ġclean ed", "Ġcle aned", "Ġwitness es", "u kan", "uk an", "uka n", "ĉ Type", "ĉT ype", "Ġh abe", "Ġhab e", "Ġha be", "par agraph", "para graph", "ĠJ Panel", "ĠJP anel", "ĠH ann", "ĠHa nn", "ĠHan n", "Ġvar ied", "Ġvari ed", "Ġva ried", "Ġ Pokemon", "ĠP okemon", "ĠPok emon", "ĠPoke mon", "ĠM UST", "ĠMU ST", "åĬ ¨", ". visibility", ".vis ibility", "op up", "^ [", ". expand", ".exp and", "Ġ \"',", "Ġ\" ',", "Ġ\"' ,", ".f asterxml", "_ auto", "_a uto", "_aut o", "Ġ Sheet", "ĠS heet", "ĠShe et", "m arker", "mark er", "mar ker", "Par cel", "e ws", "ew s", "Ġ Strategy", "ĠStr ategy", "ĠStrateg y", "- making", "-m aking", "Ġun ve", "Ġtr ailing", "Ġtrail ing", "Ġtra iling", "Ġtrai ling", "Ġcl icks", "Ġclick s", "Ġcli cks", "Ġclic ks", "Ġ GetComponent", "ĠGet Component", "ĉ content", "ĉc ontent", "ĉcon tent", "ĉcont ent", "IG ENCE", "ER NEL", "ERN EL", "NSMutable Array", "Ġb reat", "Ġbr eat", "Ġbre at", "Ġharm ful", "¶ Ī", "Ġbes ides", "Ġbeside s", "Ġb oring", "Ġbo ring", "Ġbor ing", "Ġbrut al", "Ġbru tal", "v ang", "va ng", "van g", "( parse", "(p arse", "(par se", "qu ick", "qui ck", "Ġ pytest", "Ġpy test", "Ġpyt est", "Ġswitch ing", "( )]Ċ", "() ]Ċ", "()] Ċ", "Ġ ìĦ", "Ġì Ħ", "L ER", "LE R", "ĉ font", "ĉf ont", "Ġ nett", "Ġn ett", "Ġne tt", "Ġnet t", ") ]ĊĊ", ")]Ċ Ċ", ")] ĊĊ", "( /\\", "(/ \\", "æŀ ľ", "to Array", "Ġb reed", "Ġbr eed", "Ġbre ed", "Ġbree d", "Ġ CAR", "ĠC AR", "ĠCA R", "Ġ Weapon", "ĠWe apon", "A bs", "Ab s", "t ot", "to t", "Ġ setName", "Ġset Name", "a ptive", "apt ive", "Ġ :,", "Ġ: ,", "Ġ escaped", "Ġesc aped", "Ġescape d", "Ġescap ed", "or den", "ord en", "orde n", "Ġ Pri", "ĠP ri", "ĠPr i", "th umbnail", "Ġde scriptions", "Ġdes criptions", "Ġdescription s", "Ġdescri ptions", "/ styles", "/st yles", "/style s", "Ġ PCI", "ĠP CI", "ĠPC I", "Ġ alphabet", "Ġal phabet", "Ġalpha bet", "Ġalph abet", "astic search", "astics earch", "N OTE", "NO TE", "NOT E", "Ġc ialis", "Ġci alis", "ĠGr iff", "ĠGri ff", "Ġp orque", "Ġpor que", "Ġprote ins", "Ġprotein s", "p lays", "pl ays", "play s", "pla ys", "Ġst ating", "Ġstat ing", "Ġsta ting", "Ġstati ng", "Ġim agination", "Ġimag ination", "Ġimagin ation", "Ġf acial", "Ġfa cial", "Ġfac ial", "ĠM echan", "ĠMe chan", "ĠMech an", "ĠMec han", "Ġarr anged", "Ġarrang ed", "Ġarrange d", "_ used", "_u sed", "_us ed", "_use d", "Ġarrang ements", "Ġarrangement s", "Ġarrange ments", "Ġ Pipe", "ĠP ipe", "ĠPi pe", "ĠPip e", "host name", "Ġpro vinc", "Ġprov inc", "T it", "Ti t", ".Flat Style", "Ġ Split", "ĠS plit", "ĠSp lit", "ĠSpl it", "Ġ Loader", "ĠL oader", "ĠLo ader", "ĠLoad er", ". cc", ".c c", "Ġ clinic", "Ġcl inic", "Ġclin ic", "Ġcli nic", "-------- --------------------", "---------------- ------------", "------------ ----------------", "------------- ---------------", "--------------- -------------", "-------------- --------------", "-------------------- --------", "Ġb aking", "Ġba king", "Ġbak ing", "Ġ ENT", "ĠE NT", "ĠEN T", "ne ath", "nea th", "ãĢģ ĊĊ", "ãĢģĊ Ċ", "A NE", "AN E", ".EntityFramework Core", "a ppers", "ap pers", "app ers", "apper s", "appe rs", ". ic", ".i c", "Ġ NgModule", "ĠNg Module", "Ġ FORM", "ĠF ORM", "ĠFOR M", "ĠFO RM", "Ġ ';", "Ġ' ;", "- profit", "-pro fit", "-prof it", "h w", "en emy", "ene my", "Ġ Eye", "ĠE ye", "ĠEy e", "Ġca ution", "Ġcaut ion", "t own", "to wn", "Ġur ged", "Ġurge d", "Ġurg ed", "Ġ Jimmy", "ĠJim my", "ynchron ous", "-s ized", "-size d", "m aking", "ma king", "mak ing", ", {", "] ',", "]' ,", "_ Object", "_O bject", "_Obj ect", "ah oma", "aho ma", "Ġact ivist", "Ġactiv ist", "IN VAL", "INV AL", "Ġ Commercial", "ĠCom mercial", "ĠComm ercial", "ĠOr lando", "( tab", "(t ab", "Ġ ب", "ĠØ ¨", "Al gorithm", "Ġher itage", "Get Mapping", "Ġfail ures", "Ġfailure s", "r ios", "ri os", "rio s", "at iva", "ati va", "ativ a", "Ġ tet", "Ġt et", "Ġte t", "Ġcar pet", "Ġcarp et", "( Z", "th ree", "thr ee", "Ġdis closure", "Ġdisc losure", ". ERROR", ".ERR OR", "_ called", "_c alled", "_call ed", "_cal led", "Ġd ial", "Ġdi al", "Ġdia l", "Ġoccas ional", "Ġoccasion al", ". Err", ".E rr", "Ġfun cion", "Ġfunc ion", "caff old", "caf fold", "Ġre leasing", "Ġrele asing", "ï¼ī ĊĊ", "ï¼īĊ Ċ", "_ Value", "_V alue", "_Val ue", "Ġ Vari", "ĠV ari", "ĠVar i", "ĠVa ri", "y ellow", "Ġstrugg les", "Ġstruggle s", ". cal", ".c al", ".ca l", "ĠDak ota", "ĉ close", "ĉc lose", "ĉcl ose", "Ġsand wich", "Ġ analytics", "Ġan alytics", "Ġanaly tics", "Ġanalytic s", "Ġ **)", "Ġ* *)", "Ġ** )", "& #", "Ġ Jos", "ĠJ os", "ĠJo s", "Ġpass ive", "AT TR", "ATT R", "Th rowable", "Throw able", "ĠM un", "ĠMu n", "Ġ Uint", "ĠU int", "ĠUi nt", "( disposing", "(dis posing", "a rak", "ar ak", "ara k", "Ġ Leaders", "ĠLe aders", "ĠLeader s", "ĠLead ers", "Ġaffect ing", "Ġitem View", "Ġe conomics", "Ġecon omics", "Ġeconomic s", "Ġeconom ics", "f v", "๠Ģ", ". rb", ".r b", "Ġ Overall", "ĠOver all", "Ġwealth y", "Ġev olved", "Ġevolve d", "n da", "nd a", "ĠH us", "ĠHu s", "re strict", "u men", "um en", "ume n", "ĠA gricult", "ĠAgr icult", "! ĊĊĊ", "!ĊĊ Ċ", "!Ċ ĊĊ", "Ġ expires", "Ġex pires", "Ġexp ires", "Ġexpire s", "Ġspokes person", "int erval", "inter val", "Ġ â", "Ġà ¢", "Ġ queen", "Ġqu een", "Ġque en", "( nil", "(n il", "i ngo", "in go", "ing o", "He ap", "Ù İ", "Ġcom plain", "Ġcomp lain", "Ġcompl ain", "S ym", "Sy m", "Ġ Clone", "ĠCl one", "ĠClo ne", "Ġ Ru", "ĠR u", "ĠW ILL", "ĠWI LL", "Ġ Crystal", "ĠCr ystal", "ĠCry stal", "/ content", "/c ontent", "/con tent", "i ngen", "in gen", "ing en", "inge n", "oint ment", "Last Name", "av icon", "avi con", "avic on", "Ġ IBM", "ĠI BM", "ĠIB M", "Ġ Dimension", "ĠD imension", "ĠDim ension", "a nh", "an h", "ici pants", "icip ants", "icipant s", "Ġ Anne", "ĠAn ne", "ĠAnn e", ". progress", ".pro gress", "Ġ algo", "Ġal go", "Ġalg o", "o bil", "ob il", "obi l", "Ġ Voice", "ĠV oice", "ĠVo ice", "Ġ FE", "ĠF E", "Ġ gli", "Ġg li", "Ġgl i", "Ġ ved", "Ġv ed", "Ġve d", "Ġpr events", "Ġpre vents", "Ġprevent s", "Ġprev ents", "\\ Column", "\\C olumn", "Ġ folk", "Ġf olk", "Ġfol k", "Ġfo lk", "e tti", "et ti", "ett i", "Ġ mn", "Ġm n", "Ġ CLASS", "ĠCL ASS", "Ġdis playing", "Ġdisplay ing", "Ġdispl aying", "ĠK l", "ĠF err", "ĠFe rr", "ĠFer r", "d uto", "du to", ". ib", ".i b", "Ġ dados", "Ġd ados", "Ġda dos", "Ġdad os", "Ġdado s", "' name", "'n ame", "'na me", "- space", "-s pace", "-sp ace", "Ġit alian", "Ġitalia n", "Ġ inverse", "Ġin verse", "Ġinv erse", "Ġinvers e", "Ġ dense", "Ġd ense", "Ġden se", "Ġdens e", "u ter", "ut er", "ute r", "Ġ IEnumerator", "ĠI Enumerator", "- sign", "-s ign", "Ġnation wide", "Ġ persona", "Ġperson a", "Ġpers ona", "Ġperso na", "Ġs olved", "Ġsol ved", "Ġsolve d", "Ġdram atically", "Ġdramatic ally", "Log out", "Logo ut", "Ġ grav", "Ġg rav", "Ġgr av", "Ġgra v", "Ġanal yses", "Ġanaly ses", "Ġanalys es", "Ġanalyse s", "ol lo", "oll o", "Ġ lamp", "Ġl amp", "Ġla mp", "Ġlam p", ". team", ".t eam", ".te am", "Ġ Erot", "ĠE rot", "ĠEr ot", "= [\"", "=[ \"", "Ġd ancing", "Ġdan cing", "Ġ ?>/", "Ġ? >/", "Ġ?> /", "Ġc ater", "Ġca ter", "Ġcat er", "Ġcate r", "f fe", "ff e", "Ġ Sha", "ĠS ha", "ĠSh a", "ĠB os", "ĠBo s", "ĠRE QUIRE", "Ġ Monster", "ĠMon ster", "ĠMons ter", "Ġ RB", "ĠR B", "Ġ IDE", "ĠI DE", "ĠID E", "Ġs uits", "Ġsu its", "Ġsuit s", "Ġsui ts", "Ġ formData", "Ġform Data", "( theta", "(th eta", "(the ta", "Ġs patial", "Ġsp atial", "Ġspat ial", "= NULL", "=N ULL", "Ġ SqlConnection", "ĠSql Connection", "Ġ à", "ĠV enez", "ĠVen ez", "ĠVe nez", "Ġ Morning", "ĠM orning", "ĠMor ning", "Ġpublic ations", "Ġpub lications", "Ġpublication s", "ĠNON INFRINGEMENT", "first Name", "u ds", "ud s", "W ould", "Wo uld", "_ HEAD", "_HE AD", "Ġinv ested", "Ġinvest ed", "Ġinve sted", "s table", "st able", "sta ble", "stab le", "f red", "fr ed", "fre d", "Ġcomm ander", "Ġcommand er", "Ġcomma nder", "Ġcommande r", "S ES", "SE S", "âĢĶ a", "an che", "anc he", "anch e", "Ġ Movement", "ĠM ovement", "ĠMo vement", "ĠMove ment", "ĠMov ement", "ë ³", "S uite", "Su ite", "Suit e", "Ġjur isdiction", "ë ¦¬", "ë¦ ¬", "Ġ Beth", "ĠB eth", "ĠBe th", "ĠBet h", "j Query", "ĠI sa", "ĠIs a", "Ġd ental", "Ġden tal", "Ġdent al", ", *", "Ġ Limit", "ĠL imit", "ĠLim it", "ĠLi mit", "il iation", "ili ation", "ilia tion", "= \"{", "=\" {", "b ast", "ba st", "bas t", "Ġt urb", "Ġtu rb", "Ġtur b", "i sy", "is y", "O OK", "OO K", "Ġadv ocate", "Ġadvoc ate", "i mag", "im ag", "ima g", "LE CTION", "LECT ION", "LEC TION", "л ÑĮ", "( category", "(c ategory", ". dec", ".d ec", ".de c", "Ġun iqu", "Ġuni qu", "Ġuniq u", "_ sn", "_s n", "Ġat tracted", "Ġattr acted", "Ġattract ed", "Ġ Ãī", "Ġà ī", "Ġ Running", "ĠR unning", "ĠRun ning", "_ edges", "_edge s", "_ed ges", "Ġ Disable", "ĠD isable", "ĠDis able", "_ AS", "_A S", "åĽ ¾", "Ġnetwork ing", "Ġnet working", "_ branch", "_br anch", "H aving", "Ha ving", "toBe Truthy", "G I", "Ġc amps", "Ġca mps", "Ġcamp s", "Ġcam ps", "s ep", "se p", "- part", "-p art", "-par t", "Ġ )ĊĊĊĊĊĊĊĊ", "Ġ) ĊĊĊĊĊĊĊĊ", "Ġ)Ċ ĊĊĊĊĊĊĊ", "Ġ)ĊĊ ĊĊĊĊĊĊ", "Ġ)ĊĊĊ ĊĊĊĊĊ", "ustr alia", "ustral ia", "Ġ Reports", "ĠRe ports", "ĠRep orts", "ĠReport s", "ĠRepo rts", "r ito", "ri to", "rit o", "Ġwa ist", "_ plus", "_p lus", "_pl us", "Ġ WW", "ĠW W", "- person", "-p erson", "-per son", "Ap ril", "Apr il", "Ġ sar", "Ġs ar", "Ġsa r", ". tar", ".t ar", ".ta r", "Ġagricult ural", "Ġagr icultural", "t ic", "ti c", "Ġ tcp", "Ġt cp", "Ġtc p", "Ġ setValue", "Ġset Value", "ag ento", "agent o", "agen to", "Ġ Appe", "ĠA ppe", "ĠApp e", "ĠAp pe", "p iler", "pi ler", "pile r", "C ADE", "CA DE", "CAD E", "Ġ anche", "Ġan che", "Ġanch e", "Ġanc he", "at cher", "atch er", "Ġc omics", "Ġcom ics", "Ġcomic s", "Ġ lbs", "Ġl bs", "Ġlb s", "_ segment", "_s egment", "_se gment", "_seg ment", "' ]=$", "'] =$", "']= $", "it ters", "itt ers", "itter s", "itte rs", "i cher", "ic her", "ich er", "iche r", "G INE", "GIN E", "GI NE", "Ġutil ize", "Ġutiliz e", "Ġ Cursor", "ĠC ursor", "ĠCurso r", "_ expression", "_ex pression", "_exp ression", "_expr ession", "Ġ dag", "Ġd ag", "Ġda g", "< long", " < ?=", "> x", ". Task", ".T ask", "m oney", "mon ey", "mo ney", "ib aba", "iba ba", "' });Ċ", "'} );Ċ", "'}) ;Ċ", "Ġ Specific", "ĠS pecific", "ĠSpec ific", "Ġ Linear", "ĠL inear", "ĠLine ar", "ĠLin ear", "ĠLi near", "_ OPT", "_O PT", "_OP T", "Hash Code", "( Player", "(P layer", ".Contains Key", "Ġ collapsed", "Ġc ollapsed", "Ġcoll apsed", "Ġcollapse d", "Ġcollaps ed", "trans parent", "_R ANGE", "View er", "( cfg", "(c fg", "(cf g", "Ġ sorting", "Ġs orting", "Ġsort ing", "Ġsor ting", "Ġinf ected", "Ġinfect ed", "Ġ Nach", "ĠN ach", "ĠNa ch", "Ġaccommod ate", ". elements", ".e lements", ".element s", ".el ements", ".elem ents", "_ PART", "_P ART", "_PA RT", "_PAR T", "Ġ Sexy", "ĠSe xy", "ĠSex y", "= get", "=g et", "( year", "(y ear", "Ġ xhr", "Ġx hr", ": ]", "ow ski", "ows ki", "Ġsum mar", "Ġsumm ar", "Ġ ¿", "Ġ ¿", "Ġ inte", "Ġin te", "Ġint e", "Ġi nte", "Ġ workflow", "Ġwork flow", "ĠTai wan", "v ersions", "vers ions", "version s", "åı ij", "Ġsur prisingly", "Ġsurprising ly", "Ġop tical", "Ġopt ical", "Ġoptic al", "Ġpro ces", "Ġproc es", "Ġdis agree", "Ġdisag ree", "Ġn uevo", "Ġnue vo", "Ġ CAM", "ĠC AM", "ĠCA M", "s orted", "sort ed", "le ases", "lease s", "lea ses", "is tle", "ist le", "I dent", "Id ent", "Ide nt", "ĉ event", "ĉe vent", "ĉev ent", "j ected", "ject ed", "jec ted", "Ch unk", "V ars", "Var s", "Va rs", ". provider", ".pro vider", "Ġproceed ings", "Ġproceeding s", "Ġ inclusive", "Ġin clusive", "Ġincl usive", "Ġinclus ive", "Ġart work", "end ants", "enda nts", "endant s", "ï¼ļ Ċ", "s een", "se en", "see n", "Ġ lig", "Ġl ig", "Ġli g", "Ġ makers", "Ġm akers", "Ġmake rs", "Ġma kers", "Ġmaker s", "Ġmak ers", "_ fun", "_f un", "_fu n", "Ġlength s", "Ġleng ths", "Path Variable", "[ item", "[i tem", "[it em", "ภµ", "D ead", "De ad", "FF FFFF", "FFFF FF", "FFF FFF", "Ġ Urban", "ĠUr ban", "ĠUrb an", "u ples", "up les", "uple s", "i chen", "ic hen", "ich en", "iche n", "( nullptr", "(null ptr", ". spec", ".s pec", ".sp ec", ", System", ",S ystem", "U RATION", "UR ATION", "URA TION", "( job", "(j ob", "å¼ ı", "Ġ tracker", "Ġtr acker", "Ġtrack er", "Ġtra cker", "Å Ļ", "Ġ MR", "ĠM R", "Ġ SQLite", "ĠSQL ite", "ĠSQ Lite", "Ġ dto", "Ġd to", "Ġdt o", "Ġ ;;Ċ", "Ġ; ;Ċ", "Ġ;; Ċ", "Ġ mint", "Ġm int", "Ġmin t", "Ġmi nt", "Ġ Introduction", "ĠInt roduction", "ĠIntro duction", "c ao", "ca o", "Ġquest ioned", "Ġquestion ed", "Ġquesti oned", "Ġf itted", "Ġfit ted", "Ġfitte d", "re vision", "rev ision", "s q", "Ġm ig", "Ġmi g", "_ units", "_un its", "_unit s", "_ async", "_a sync", "_as ync", "Ġf lick", "Ġfl ick", "} );ĊĊĊ", "});Ċ ĊĊ", "});ĊĊ Ċ", "}) ;ĊĊĊ", "}); ĊĊĊ", "Ġn otre", "Ġnot re", "Ġno tre", "} `,", "}` ,", "F ilters", "Filter s", "Fil ters", "Ġm undo", "Ġmu ndo", "Ġmund o", "Ġmun do", "_ days", "_d ays", "_day s", "_da ys", "Ġ frm", "Ġf rm", "Ġfr m", "u tc", "ut c", "Ġ vals", "Ġv als", "Ġval s", "Ġva ls", "e width", "ew idth", "Ġ Generator", "ĠG enerator", "ĠGener ator", "ĠGen erator", "Ġ Artist", "ĠArt ist", "Ġ IDs", "ĠI Ds", "ĠID s", "Ġ Articles", "ĠArt icles", "ĠArticle s", "re ater", "reate r", "reat er", "rea ter", "ĠComponent Fixture", ". =", "Ġ rou", "Ġr ou", "Ġro u", "- no", "-n o", ".b ukkit", "e gg", "eg g", "Ġ Diff", "ĠD iff", "ĠDi ff", "a tics", "at ics", "atic s", "ati cs", "Ñĥ Ñĩ", "âĢĶ ĊĊ", "Ġ Charlotte", "ĠChar lotte", "ĠCharl otte", "b ye", "by e", "Ġ });čĊčĊ", "Ġ} );čĊčĊ", "Ġ}) ;čĊčĊ", "Ġ});čĊ čĊ", "Ġ}); čĊčĊ", "ĠV ik", "ĠVi k", "ĠB row", "ĠBr ow", "ĠBro w", "Ġ lv", "Ġl v", "ĠG ib", "ĠGi b", "- wing", "-w ing", "-win g", "GL IGENCE", "( Il", "(I l", "ĠEngine er", ". Wait", ".W ait", "Ġ Pictures", "ĠP ictures", "ĠPicture s", "ĠPic tures", "Ġr het", "Ġrh et", "Ġrhe t", "Ġ thermal", "Ġth ermal", "Ġther mal", "Ġtherm al", "Ġp raise", "Ġpr aise", "Ġpra ise", "< >();ĊĊ", "<>();Ċ Ċ", "<>( );ĊĊ", "<> ();ĊĊ", "Ġ Spider", "ĠSp ider", "ĠSpi der", "P ause", "Pa use", "ĠB aker", "ĠBa ker", "ĠBak er", "ĠBake r", "Ġs lower", "Ġsl ower", "Ġslow er", "Ġslo wer", "Ġ }]Ċ", "Ġ} ]Ċ", "Ġ}] Ċ", "_ enqueue", "_en queue", "Ġdis appeared", "Ġdisappe ared", "Ġdisappear ed", "Ġ Ticket", "ĠT icket", "ĠTi cket", "ĠTick et", "ĠTic ket", "IN UX", "INU X", "_ LOCAL", "_LO CAL", "_LOC AL", "аÑģ Ñģ", "@Inject able", "comm unity", "G estureRecognizer", "Gesture Recognizer", "åĽ ½", "Ġs cales", "Ġsc ales", "Ġscale s", "Ġsca les", "Ġscal es", "Ġ -(", "Ġ- (", "/ '+", "/' +", "Ġ Sit", "ĠS it", "ĠSi t", "Ġexecutive s", "Ġexecut ives", "ar ding", "ard ing", "ardi ng", "ardin g", "Ġad vers", "Ġadv ers", "Ġback wards", "Ġbackward s", "ĉ context", "ĉcon text", "ĉcont ext", "ĠH amp", "ĠHam p", "ĠHa mp", "Ġ PF", "ĠP F", "Ġ Deck", "ĠD eck", "ĠDe ck", "ĠDec k", "Ġ Craig", "ĠC raig", "ĠCra ig", "A merican", "Americ an", "America n", "Ġ bell", "Ġb ell", "Ġbe ll", "Ġbel l", "Ġp rol", "Ġpro l", "Ġpr ol", "u fen", "uf en", "ufe n", "Ġ rng", "Ġr ng", "Ġrn g", "ar shal", "ars hal", "Ġ Simply", "ĠSim ply", "ĠSimpl y", "first name", "sh ore", "J uly", "Jul y", "Ju ly", "Ġm ortality", "Ġmort ality", "Ġmortal ity", "ĠâĨĴ ĊĊ", "H elpers", "Helper s", "Help ers", "Hel pers", "Ġ benchmark", "Ġb enchmark", "Ġbench mark", "e made", "em ade", "ema de", "Ġorgan isations", "Ġorganis ations", "Ġorganisation s", ".g son", ".gs on", "Ġ TextField", "ĠT extField", "ĠText Field", "Ġcivil ians", "Ġciv ilians", "Ġcivilian s", ". Arrays", ".Array s", ".Ar rays", "ĠMiss issippi", "Ġinter mediate", "Ġintermedi ate", "get User", "_ cluster", "_cl uster", "Rel ative", "fore ign", ".querySelector All", "Fore ignKey", "Foreign Key", "Ġreason ably", "- --------Ċ", "-- -------Ċ", "---- -----Ċ", "-------- -Ċ", "--- ------Ċ", "----- ----Ċ", "------ ---Ċ", "------- --Ċ", "--------- Ċ", "C ards", "Card s", "Car ds", "ĠK am", "ĠKa m", "Ġ Thor", "ĠT hor", "ĠTh or", "Ġ roller", "Ġr oller", "Ġro ller", "Ġroll er", "Ġrol ler", "- element", "-e lement", "-el ement", "Ġ Currency", "ĠC urrency", "d die", "dd ie", "AL LY", "ALL Y", "Ġ RA", "ĠR A", "Ġper met", "Ġperm et", "Ġperme t", "a aaa", "aa aa", "aaa a", "Ġhome work", "Ġhom ework", "ĠV it", "ĠVi t", "Ġm old", "Ġmo ld", "Ġmol d", "ĠF er", "ĠFe r", "[ start", "Ġstat istical", "Ġstatist ical", "Ġstatistic al", "Ġsc ary", "Ġsca ry", "Ġscar y", "_ HOME", "_H OME", ". Begin", ".B egin", ".Be gin", "Con struct", "o genic", "og enic", "ogen ic", "oge nic", "ĠDEAL INGS", "Ġtamb ién", "i xon", "ix on", "ixo n", ". ind", ".in d", ".i nd", "a cre", "ac re", "acr e", "Ġ transforms", "Ġtrans forms", "Ġtransform s", "ĠN ap", "ĠNa p", ". Block", ".B lock", ".Bl ock", "uss ia", "p iration", "pi ration", "pir ation", "ul ent", "ule nt", "ulen t", "Ġ ceil", "Ġc eil", "Ġce il", "Cl ause", "Cla use", "n aire", "na ire", "T ES", "TE S", "Ġn eat", "Ġne at", "S TD", "ST D", "Ġ RegExp", "ĠReg Exp", "per form", "perf orm", ": )", "Ġun ions", "Ġunion s", "Ġuni ons", "Ġs ublic", "Ġsub lic", "Ġw inds", "Ġwin ds", "Ġwind s", "Ġwi nds", "lo ating", "loat ing", "g lich", "gl ich", "gli ch", "Ġ pagination", "Ġp agination", "Ġpag ination", "Ġpagina tion", "S kill", "Sk ill", "App ly", "Ap ply", "Ġ Operator", "ĠO perator", "ĠOper ator", "ĠOp erator", "ĠOpera tor", "ist ogram", "isto gram", "Ġ qualities", "Ġqual ities", "Ġquali ties", "C ross", "Cr oss", "Cro ss", "Ġd ecom", "Ġde com", "Ġdec om", "Ġdeco m", "] ,\"", "], \"", "Ġ Juan", "ĠJ uan", "ĠJu an", ". modal", ".m odal", ".mod al", ".mo dal", ". Child", ".Ch ild", "Ġ Roger", "ĠR oger", "ĠRo ger", "ĠRog er", "STIT UTE", ":CGRect Make", "a lette", "al ette", "ale tte", "alet te", "Ġ sta", "Ġs ta", "Ġst a", "a side", "as ide", "asi de", "Ġ blur", "Ġbl ur", "Ġ Wa", "ĠW a", "if etime", "ife time", "r eed", "re ed", "ree d", "control s", "contr ols", "contro ls", "Ġ bins", "Ġb ins", "Ġbi ns", "Ġbin s", "Ġ пол", "Ġп ол", "Ġпо л", "* /,Ċ", "*/ ,Ċ", "*/, Ċ", "U IS", "UI S", "ĠR ou", "ĠRo u", "Ġ Demo", "ĠD emo", "ĠDe mo", "ĠDem o", "- awesome", "Ġ Chain", "ĠCh ain", "ĠCha in", "Ġh asta", "Ġhas ta", "Ġha sta", "Ġhast a", "ĠB art", "ĠBar t", "ĠBa rt", ". KEY", ".K EY", "Ġ vendors", "Ġv endors", "Ġvend ors", "Ġvendor s", "no follow", "nof ollow", "Ġ Dest", "ĠD est", "ĠDe st", "ĠDes t", "_ builder", "_b uilder", "_build er", "Ġarg ues", "Ġargue s", "_ answer", "_an swer", "_ans wer", "g oto", "go to", "got o", "Ġ RESULT", "ĠRES ULT", "Ġ MON", "ĠM ON", "ĠMO N", "Ġp oder", "Ġpo der", "Ġpod er", "Ġpode r", "o ons", "oo ns", "oon s", "_ CASE", "_C ASE", "_CA SE", "Ġrep lic", "Ġrepl ic", "Ġfin ancing", "Ġfinanc ing", "Ġfinan cing", "Ġ DATE", "ĠD ATE", "ĠDA TE", "ĠDAT E", "c ern", "ce rn", "cer n", "_ track", "_t rack", "_tr ack", "_tra ck", "t ies", "ti es", "tie s", "/ logo", "/l ogo", "/log o", "/lo go", "ĠNE GLIGENCE", "get Type", "> T", "b et", "be t", "g irl", "gi rl", "ĠINCIDENT AL", "- site", "-s ite", ". trigger", ".tr igger", "Ġ Lisa", "ĠL isa", "ĠLi sa", "ĠLis a", "_ inputs", "_in puts", "_input s", "_inp uts", "Ġrel atives", "Ġrelative s", "Ġrelativ es", "Ġrelat ives", "Logged In", "Con figure", "Config ure", "Conf igure", "I K", ". accept", ".ac cept", ".acc ept", "Re sume", "Res ume", "Ġ Draft", "ĠD raft", "ĠDr aft", "ĠDra ft", "Ġ *>(", "Ġ* >(", "Ġ*> (", "Ġ WA", "ĠW A", "ed ian", "edia n", "edi an", "er ness", "ern ess", "erne ss", "ernes s", "Ġ LayoutInflater", "ĠLayout Inflater", "* /čĊčĊ", "*/ čĊčĊ", "*/čĊ čĊ", "o thy", "ot hy", "oth y", "Ġoblig ation", "Ġobl igation", "Sub scribe", "Ġ thumbnail", "Ġth umbnail", "ex ist", "Ġins isted", "Ġinsist ed", "Ġ UICollectionView", "ĠU ICollectionView", "ĠUI CollectionView", "Ġ Angular", "ĠAng ular", "Ġtable ts", "Ġtab lets", "Ġtablet s", "Ġ Impact", "ĠImp act", "ãĢį ĊĊ", "ãĢįĊ Ċ", "a ho", "ah o", "Ġcharacter istic", "g d", "Ġ= ================================================", "Ġ================= ================================", "Ġ================================= ================", "o urt", "ou rt", "our t", "` .", "App ro", "Ap pro", "Co ordinate", "Coord inate", "Re member", "Rem ember", "Ġ marine", "Ġm arine", "Ġmar ine", "Ġma rine", "Ġmari ne", "Ġmarin e", "] =='", "]= ='", "]== '", "Ġ Administrator", "ĠAdmin istrator", "ĠAdministr ator", ". getDefault", ".get Default", ".getD efault", "Ġ forgot", "Ġf orgot", "Ġfor got", "Ġforg ot", "Ġ Structure", "ĠStruct ure", "V ue", "Vu e", "ar sing", "ars ing", "arsi ng", "m oment", "mo ment", "mom ent", "k w", "_ cursor", "_c ursor", "Att ack", "Ġath letic", "Ġdiagn osed", "Ġdiagnose d", "Ġ ende", "Ġe nde", "Ġen de", "Ġend e", "åĪ łéĻ¤", "H ouse", "Ho use", "Ġ PARAM", "ĠP ARAM", "ĠPA RAM", "ĠPAR AM", "ĠPARA M", "Ġ wiki", "Ġw iki", "Ġwi ki", "Ġwik i", "Ġ Opp", "ĠO pp", "ĠOp p", "Ġcons ervation", "Ġconserv ation", "Ġ snd", "Ġs nd", "Ġsn d", "_ tem", "_t em", "_te m", "sub str", "subst r", "subs tr", "ĠC ape", "ĠCap e", "ĠCa pe", ". sim", ".s im", ".si m", "U TION", "UT ION", "a nan", "an an", "ana n", "âĢĻ un", "Ġ gy", "Ġg y", "- work", "-w ork", "Ġcomp elling", "Ġcompel ling", "= '#", "=' #", "ĉ sub", "ĉs ub", "Ġ directories", "Ġdirect ories", "Ġdirector ies", "íĬ ¸", "Ġ touches", "Ġtouch es", "Ġtou ches", "out ines", "outine s", ". Collection", ".C ollection", ".Col lection", "s chedule", "sched ule", ". lat", ".l at", "Ġ Doctrine", "ĠDo ctrine", "C AA", "CA A", "Ġ Refer", "ĠRe fer", "ĠRef er", "Ġshift s", "Ġ likelihood", "Ġlik elihood", "pr eter", "pre ter", "pret er", "Ġ Female", "ĠF emale", "ĠFe male", "ĠFem ale", "Ġinter cept", "Ġ lou", "Ġl ou", "Ġlo u", "çĻ »", "Ġ rug", "Ġr ug", "Ġru g", "ĠC rown", "ĠCr own", "ĠCro wn", "ĠCrow n", "Ġ ****************************************************************************", "Ġ************************************************************************ ****", "Ġ************************************************************************** **", "Ġ**************************************************************** ************", "Ġ**** ************************************************************************", "Ġ******************************************************** ********************", "Ġ************************************************ ****************************", "- product", "-pro duct", "-produ ct", "Ġprompt ed", "u ngle", "un gle", "ung le", "d ocker", "do cker", "doc ker", "dock er", "Ġ Tu", "ĠT u", "Ġ Unique", "ĠUn ique", "ĠUni que", "_ Error", "_E rror", "_Err or", "u los", "ul os", "ulo s", "Ġ âĦ", "Ġâ Ħ", "Ġ (`", "Ġ( `", "G etting", "Get ting", "_ scal", "_s cal", "_sc al", "Ġ Enh", "ĠE nh", "ĠEn h", "ü t", "Ġsust ained", "Ġsustain ed", "Ġ patches", "Ġp atches", "Ġpat ches", "Ġpatch es", "Ġpros per", "ĠG aza", "ĠGa za", "ĠGaz a", "_ light", "_l ight", "_li ght", "Ġin cons", "Ġinc ons", "Ġincon s", "- -------Ċ", "-- ------Ċ", "---- ----Ċ", "-------- Ċ", "--- -----Ċ", "----- ---Ċ", "------ --Ċ", "------- -Ċ", "ĉ ĉĠĠĠĠĠĠ", "ĉĉ ĠĠĠĠĠĠ", "ĉĉĠĠĠ ĠĠĠ", "ĉĉĠ ĠĠĠĠĠ", "ĉĉĠĠ ĠĠĠĠ", "ĉĉĠĠĠĠ ĠĠ", "ĉĉĠĠĠĠĠ Ġ", "S F", "C N", ": \";Ċ", ":\" ;Ċ", "ĠColl ins", "( *)", "(* )", "Ġcomp ilation", "Ġcompil ation", "' ]čĊ", "'] čĊ", "Ġcon sequence", "Ġconsequ ence", "Ġconse quence", ", ...", ",. ..", "Ġ dm", "Ġd m", "Ġ BLOCK", "ĠB LOCK", "ĠBL OCK", "Cl uster", "Ġ ski", "Ġs ki", "Ġsk i", "( argc", "(arg c", "(ar gc", "T uple", "Tu ple", "Ġj oins", "Ġjoin s", "Ġjo ins", "ĠSher iff", "W ar", "Wa r", "in di", "ind i", "Ġcom mented", "Ġcomm ented", "Ġcomment ed", "H OST", "HO ST", "Ġ invitation", "Ġinv itation", "Ġinvit ation", "apan ese", "Ġper mits", "Ġpermit s", "Ġperm its", "preced ented", "_ zone", "_z one", "Ġ Amy", "ĠA my", "ĠAm y", "_ RD", "_R D", "Min imum", "Ġinv ocation", "Ġinvo cation", ". enable", ".e nable", ".en able", "i chten", "ich ten", "icht en", "ichte n", "- owned", "\" id", "_PO INTER", "_POINT ER", "F ac", "Fa c", "Ġspec ifications", "Ġspecific ations", "Ġspecification s", "Ġn omination", "Ġno mination", "Ġnom ination", "Ġnomin ation", "Ġ gp", "Ġg p", "< (", "Ġ robots", "Ġro bots", "Ġrob ots", "Ġrobot s", "Ġ Jerry", "ĠJ erry", "ĠJer ry", "Ġ holders", "Ġh olders", "Ġhold ers", "Ġholder s", "Ġhol ders", "Ġ wand", "Ġw and", "Ġwa nd", "Ġwan d", "c ms", "cm s", "Ġ }))Ċ", "Ġ} ))Ċ", "Ġ}) )Ċ", "Ġ})) Ċ", ". Toast", ".To ast", "ĠI List", "ĠIL ist", "B ased", "Base d", "Bas ed", "Ba sed", "z oom", "zo om", "/ style", "/st yle", "ĠB eck", "ĠBe ck", "ĠBec k", "M en", "Me n", "Ġcontrib uting", "Ġ undo", "Ġu ndo", "Ġun do", "Ġund o", "Ġ OH", "ĠO H", "Ġadd Object", "Ġe igen", "Ġei gen", "Ġeig en", "sign up", "éĶ Ļ", "Ġd istant", "Ġdis tant", "Ġdist ant", "Ġdi stant", "PAR ATOR", "Ġ Mari", "ĠM ari", "ĠMar i", "ĠMa ri", "Ġ má", "Ġm á", "E mp", "Em p", "ó s", "Ġ ìĪĺ", "Ġì Īĺ", "ĠìĪ ĺ", "e vt", "ev t", "+ j", "p ark", "par k", "pa rk", "Ġ Stay", "ĠSt ay", "ĠSta y", "ĠD un", "ĠDu n", "Ġs oy", "Ġso y", "> %", "az ines", "azine s", "azi nes", "Ġti empo", "( me", "(m e", "p resent", "pre sent", "pres ent", ". This", ".T his", ".Th is", "Ġed itors", "Ġedit ors", "Ġeditor s", "Ġedi tors", "F IELD", ". Work", ".W ork", "ĠUn iverse", "ĠUnivers e", "ĠUni verse", "ĠUniv erse", "Ġdr unk", "Ġdru nk", ". timer", ".t imer", ".time r", ".tim er", "Ġal tered", "Ġalt ered", "Ġalter ed", "Ġalte red", "ĠN ar", "ĠNa r", "ëł ¥", ". Active", ".Act ive", "id or", "ido r", "ç Ń", ".delta Time", "Ġawk ward", "& quot", "ĠS afari", "ĠSaf ari", "Ġt ricks", "Ġtr icks", "Ġtri cks", "Ġtrick s", "M ENTS", "MENT S", "div ision", "di vision", "Ġvar ying", "Ġva rying", "Ġvary ing", "ĠHigh way", "Ġphot ographer", "Ġphotograph er", "ĠSt ewart", "ĠSte wart", "Ġ lasting", "Ġl asting", "Ġlast ing", "Ġlas ting", ". Pre", ".P re", ".Pr e", ".amazon aws", "Ġ Luck", "ĠL uck", "ĠLuc k", "ĠLu ck", ". Description", ".D escription", ".De scription", ".Des cription", "Ġ Naz", "ĠN az", "ĠNa z", "n eg", "ne g", "Ġc ó", "<< \"\\", "<<\" \\", "Ġ Surv", "ĠS urv", "ĠSur v", "ĠSu rv", "Ġ Unc", "ĠU nc", "ĠUn c", "Rec ipe", ". BorderStyle", ".Border Style", "Ġmod ifications", "Ġmodification s", "Ġmodific ations", "- at", "-a t", "AT FORM", "h dr", "hd r", "a ko", "ak o", "Ġsub license", "Ġsublic ense", "Ġ Jump", "ĠJ ump", "ĠJu mp", "Ġbe im", "Ġbei m", "ĠMan hattan", ". bool", ".b ool", ".bo ol", "_ hw", "_h w", "ÑĤ ÑĮ", "B in", "Bi n", "Ġ gateway", "Ġg ateway", "Ġgate way", "\" \":", "\"\" :", "Ġ UIS", "ĠU IS", "ĠUI S", ": \"+", ":\" +", "- def", "-d ef", "-de f", "Ġ Regular", "ĠReg ular", "/ testing", "/t esting", "/test ing", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "string stream", "Ġdis par", "Ġdi spar", "Ġdisp ar", "Ġm obil", "Ġmo bil", "Ġmob il", "- read", "-r ead", "-re ad", "Ġ Adapter", "ĠAd apter", "ĠAda pter", "ĠAdapt er", "ĠCh ampions", "ĠChampion s", "ĠChamp ions", "Ġ scheduler", "Ġs cheduler", "Ġsched uler", "Ġschedule r", "Ġ kills", "Ġk ills", "Ġkill s", "Ġkil ls", "Ġ Multiple", "ĠM ultiple", "ĠMulti ple", "ĠMult iple", "ĠMultip le", "i rror", "ir ror", "Ġg ods", "Ġgo ds", "Ġgod s", "A DO", "AD O", "a kte", "ak te", "akt e", "Ġ Usuario", "ĠUs uario", ".c ircular", "Ġre cept", "Ġrec ept", "Ġrece pt", "Ġrecep t", "Ġ Expr", "ĠEx pr", "ĠExp r", "Ġelder ly", "Ġnice ly", "Ġnic ely", "Ġb este", "Ġbe ste", "Ġbest e", "Ġbes te", "W ant", "Wa nt", "Ġclass ical", "Ġclassic al", ". sprite", ".s prite", ".sp rite", "ob jc", "obj c", "ĠM ason", "ĠMa son", "ĠMas on", "Ġs istema", "Ġsist ema", "Ġsistem a", ". Black", ".B lack", ".Bl ack", "e so", "es o", "ĠZe it", "Ġ divid", "Ġd ivid", "Ġdi vid", "Ġdiv id", "Ġen ters", "Ġent ers", "Ġenter s", "_ subject", "_sub ject", "_su bject", "Ġ Planet", "ĠPlan et", "ĠPlane t", "ĠPla net", ". warning", ".w arning", ".warn ing", "Ġ Gram", "ĠG ram", "ĠGr am", "ĠGra m", "_ tokens", "_t okens", "_token s", "_tok ens", "Ġhouse holds", "Ġhousehold s", "_ customer", "_c ustomer", "_custom er", "_cust omer", "user Name", "c ross", "cr oss", "cro ss", "Ġp ione", "Ġpi one", "Ġass ists", "Ġassist s", "_ SM", "_S M", "i bo", "ib o", "Ġl oyal", "Ġlo yal", "Ġus eless", "Ġuse less", "# elif", "ĠUlt imate", "C ome", "Com e", "Co me", "g el", "ge l", "Ġd ich", "Ġdi ch", "Ġdic h", "x yz", "xy z", "i kel", "ik el", "ike l", "o bra", "ob ra", "_ scan", "_s can", "_sc an", "Ġ Interior", "ĠIn terior", "ĠInt erior", "ĠInter ior", "Ġ Nice", "ĠN ice", "ĠNic e", "ĠNi ce", "Ġp lac", "Ġpl ac", "Ġpla c", "ĉ target", "ĉt arget", "Ġv iral", "Ġvi ral", "Ġvir al", "as so", "ass o", "( )/", "() /", "u nde", "un de", "und e", "Ġ Adobe", "ĠAd obe", "O s", "vis ited", "visit ed", "Ġ OW", "ĠO W", "Ġ Feed", "ĠF eed", "ĠFe ed", "ĠFee d", "Ġ Sequence", "ĠSe quence", "ĠSequ ence", "Ġman ages", "Ġmanage s", "Ġmana ges", "in son", "ins on", "ĠLouis iana", "{ })", "{} )", "ĠH ab", "ĠHa b", "Ġ LD", "ĠL D", "Ġb ip", "Ġbi p", "p rites", "pr ites", "prite s", "prit es", "pri tes", "( elem", "(e lem", "(el em", "(ele m", ".h ibernate", "é lé", "él é", "Ġoh ne", "_ transaction", "_trans action", "Ġann unci", "P ublished", "Publish ed", "Ġ Honda", "ĠH onda", "ĠHon da", "ĠHo nda", "ĠHond a", "Ġ Tam", "ĠT am", "ĠTa m", "Ġ Packet", "ĠP acket", "ĠPac ket", "ĠPack et", "ĠPa cket", "_ selector", "_se lector", "_select or", "_sel ector", "Ġchalleng ed", "Ġchallenge d", "P rocessing", "Process ing", "- hover", "-h over", "Ġ trainer", "Ġtr ainer", "Ġtrain er", "Ġtra iner", "Ġtrai ner", "_ cancel", "_c ancel", "_can cel", "Ġ NSDictionary", "ĠNS Dictionary", "ab ric", "abr ic", "Ġ MLS", "ĠM LS", "ĠML S", "_ sensor", "_s ensor", "Ġsh rink", "Ġshr ink", "Ġshri nk", "Ġ FX", "ĠF X", "th reshold", "thresh old", "ĉ HX", "ĉH X", "- mark", "-m ark", "-mar k", "` .`", "`. `", "S cheme", "Sch eme", "( full", "(f ull", "_ writer", "_w riter", "_write r", "_wr iter", "Ġ Sys", "ĠS ys", "ĠSy s", "Ġf led", "Ġfl ed", "Ġfle d", "ĠC in", "ĠCi n", "- widget", "-w idget", "Ġ Previous", "ĠPre vious", "ĠPrev ious", "G ender", "Ge nder", "Gen der", "_ question", "_q uestion", "_qu estion", "_quest ion", "F eed", "Fe ed", "Fee d", "Ġsc rut", "Ġscr ut", "( prefix", "(p refix", "(pre fix", "(pref ix", "ãĢĤ ãĢĤ", "Ġin fections", "Ġinf ections", "Ġinfection s", "Ġinfect ions", "P arts", "Par ts", "Part s", "Pa rts", "Ġh ierarchy", "Ġhier archy", "_ DELETE", "_DE LETE", "Ġ Patient", "ĠP atient", "ĠPat ient", "_ pay", "_p ay", "_pa y", "Ġprom oted", "Ġpromote d", "Ġpromot ed", "Ġpromo ted", "Ġ ìĭ", "Ġì ĭ", "Ġcivil ian", "Ġciv ilian", "Ġagricult ure", "Ġagr iculture", "Ġ Piece", "ĠP iece", "ĠPi ece", "ĠPie ce", "Ġ stance", "Ġst ance", "Ġsta nce", "Ġstan ce", "uts che", "utsch e", "As sign", "Ass ign", ". ACTION", ".A CTION", ".AC TION", ".ACT ION", "F ig", "Fi g", "_ radius", "_r adius", "_rad ius", "_radi us", "Ġ Sync", "ĠS ync", "ĠSy nc", "ĠSyn c", "d ucer", "du cer", "duc er", "duce r", "f ailure", "fail ure", "en sed", "ens ed", "ense d", "p time", "pt ime", "B M", "_ datetime", "_d atetime", "_date time", "_dat etime", "qu ivo", "quiv o", "qui vo", "QUE UE", "èĢ ħ", "App ear", "Ap pear", "Appe ar", "Ġsum mit", "Ġsumm it", ": void", ":v oid", "Ġ vine", "Ġv ine", "Ġvi ne", "Ġvin e", "è ®¤", "è® ¤", "on ne", "onn e", "_ TRANS", "_TR ANS", "_TRAN S", "_TRA NS", ". green", ".g reen", ".gr een", "_ cc", "_c c", "Ġhung ry", "Ġ \">", "Ġ\" >", "( ));čĊčĊ", "() );čĊčĊ", "()) ;čĊčĊ", "());čĊ čĊ", "()); čĊčĊ", "Ex tract", "Ext ract", "Extra ct", "i zens", "iz ens", "ize ns", "izen s", "Ġ solver", "Ġs olver", "Ġsol ver", "Ġsolve r", "N otify", "Not ify", "Ġ english", "Ġeng lish", "Ġ Shopping", "ĠSh opping", "ĠShop ping", "ĠSho pping", "inter faces", "interface s", "R EQ", "RE Q", "Ġil leg", "Ġill eg", "Ġ UIImageView", "ĠUI ImageView", "ĠUIImage View", "Ġ disconnect", "Ġdis connect", "Ġ Until", "ĠUn til", "ĠUnt il", "ĠCons ervative", "ĠConserv ative", "@ Column", "Ġshift ed", "Ġ :čĊ", "Ġ: čĊ", "Ġf ich", "Ġfi ch", "Ġfic h", "Ġd la", "Ġdl a", "Ġs hoe", "Ġsh oe", "Ġsho e", "\" ),čĊ", "\") ,čĊ", "\"), čĊ", "ul arity", "ular ity", "_ RESP", "_RE SP", "_R ESP", "_RES P", "We ather", "UI Application", ". iterator", ".it erator", ".iter ator", "Ġ aging", "Ġa ging", "Ġag ing", ". Parent", ".P arent", ".Par ent", "o wie", "ow ie", "owi e", "( equal", "(e qual", "(eq ual", "Ġ Conv", "ĠCon v", "ĠCo nv", "/ default", "/d efault", "/de fault", "Ġme asuring", "Ġmeas uring", ". prev", ".p rev", ".pre v", ".pr ev", ". IsValid", ".Is Valid", ". Fat", ".F at", "Ġs Äĥ", "key words", "keyword s", "with out", "Ġs overe", "Ġso vere", "Ġex changes", "Ġexchange s", "Ġm elt", "Ġme lt", "Ġmel t", "Ġis lands", "Ġisland s", "Ġisl ands", "ĠInt egr", "Ġj umping", "Ġjump ing", "Ġ gle", "Ġg le", "Ġgl e", "Ġjournal ism", "Ġ dated", "Ġd ated", "Ġdate d", "Ġda ted", "Ġdat ed", "Local ized", "Ġ Refresh", "ĠRe fresh", "ĠRef resh", "P article", "Part icle", "Ġ aa", "Ġa a", "ĠST RICT", "ĠSTR ICT", "Ġb od", "Ġbo d", ". Process", ".P rocess", ".Pro cess", "_ AUTO", "_A UTO", "_AUT O", "Ġ Published", "ĠP ublished", "ĠPublish ed", "e very", "ever y", "ev ery", "eve ry", "Ġtechn ological", "Ġtechno logical", "Ġtechnolog ical", "l sx", "ls x", "Ġir rit", "Ġirr it", "Add itional", "Ġ delimiter", "Ġdel imiter", "Ġdelim iter", "_ language", "_l anguage", "- area", "-a rea", "-ar ea", "bo ys", "boy s", "Ġ Tube", "ĠT ube", "ĠTu be", "ĠTub e", "Ġ wat", "Ġw at", "Ġwa t", "Ġmechan ics", "Ġmechanic s", "_ owner", "_o wner", "_own er", "S pell", "Sp ell", "Spe ll", "Ġ Stories", "ĠSt ories", "ĠSto ries", ".Append Line", "T ableView", "Table View", "h em", "he m", "s tick", "st ick", "sti ck", "ol lower", "oll ower", "ollow er", "ollo wer", "I FF", "IF F", "Ġ UV", "ĠU V", "oll ision", "S UB", "SU B", "Ġcom parable", "Ġcompar able", "Ġd onde", "Ġdo nde", "Ġdon de", "s ales", "sa les", "sal es", "sale s", "ll vm", "Ġ }],Ċ", "Ġ} ],Ċ", "Ġ}] ,Ċ", "OTT OM", "Ġ Purpose", "ĠP urpose", "ĠPur pose", "L ab", "La b", "Ġinterview ed", "o is", "oi s", "a sil", "as il", "asi l", ". setId", ".set Id", "Ġ Instruction", "ĠIn struction", "- ->", "-- >", "Ġ Modified", "ĠMod ified", "ation ally", "ational ly", "Ġ Meeting", "ĠMe eting", "ĠMeet ing", "è¯ ¯", "# region", "Ġ routing", "Ġr outing", "Ġro uting", "Ġrout ing", "Ġrou ting", ". focus", ".f ocus", "ĠY outh", "ĠYou th", "ĠYo uth", "< D", "ĠN ag", "ĠNa g", "cont acts", "contact s", "Ġ forming", "Ġfor ming", "Ġform ing", "Ġ mie", "Ġm ie", "Ġmi e", "',[' ../", "Ġ BP", "ĠB P", "Ġapp et", "Ġap pet", "Ġappe t", "Ġ Teacher", "ĠT eacher", "ĠTe acher", "ĠTea cher", "ĠTeach er", "Ġ TP", "ĠT P", "Ġann ually", "Ġannual ly", "outed EventArgs", "Ġ Speaker", "ĠS peaker", "ĠSpe aker", "ĠSpeak er", "Ġ rename", "Ġre name", "Ġr ename", "Ġren ame", "C FG", "CF G", "(\" //", "(\"/ /", "æİ ¥", "/ pages", "/p ages", "/page s", "Ġpr és", "Ġpré s", "Ġ Spell", "ĠS pell", "ĠSp ell", "ĠSpe ll", ". Allow", ".Al low", ".All ow", "ĠINT ERRU", "ĠINTER RU", "Ġ (#", "Ġ( #", "âĢĻ ĊĊ", "âĢĻĊ Ċ", "_ Generic", "_G eneric", ". imshow", ".im show", "_ tim", "_t im", "_ti m", "- face", "-f ace", "( &(", "(& (", "ati num", "atin um", "Ġrevolution ary", "Ġ Hours", "ĠH ours", "ĠHo urs", "ĠHour s", "ĠHou rs", "r ain", "ra in", "rai n", "Ġany time", "Ġ abb", "Ġa bb", "Ġab b", ". jsp", ".j sp", ".js p", "S crollView", "Scroll View", "Ġ Truth", "ĠTr uth", "ĠTru th", "Ġ anticipated", "Ġanticip ated", "Ġanticipate d", "Ġantic ipated", "Ġ accent", "Ġacc ent", "Ġac cent", ". checked", ".check ed", "Ġspec ifies", "Ġ caf", "Ġc af", "Ġca f", "Ġcell padding", "Ġ cooked", "Ġco oked", "Ġcook ed", "Ġ Hugh", "ĠH ugh", "ĠHu gh", "pe ek", "pee k", "_ RATE", "_R ATE", "_RA TE", "Ġd orm", "Ġdo rm", "Ġdor m", "/ čĊ", "IV ITY", ". Controller", ".Cont roller", ".Control ler", "( part", "(p art", "(par t", "(pa rt", ". constraint", ".con straint", "Ġin vasion", "Ġinv asion", "M OVE", "MO VE", "MOV E", "Ġgl uc", "Ġglu c", "l ename", "le name", "len ame", "lena me", "Ġ amen", "Ġa men", "Ġam en", "eng lish", "engl ish", "ĠSw itzerland", "\" ;ĊĊĊ", "\";Ċ ĊĊ", "\";ĊĊ Ċ", "\"; ĊĊĊ", "p est", "pe st", "pes t", ". collect", ".c ollect", ".col lect", ".coll ect", "N ib", "Ni b", "Ġ Dict", "ĠD ict", "ĠDi ct", "Ġ Emb", "ĠE mb", "ĠEm b", "( subject", "(sub ject", "Ġout rage", "Ġoutr age", "Ġoutra ge", "Ġdec iding", "Ġdecid ing", "Ġsent enced", "Ġsentence d", "F echa", "Fe cha", "\" A", "Ġ quer", "Ġqu er", "Ġque r", "Ġq uer", "Ġfont Family", "Ġqu adr", "Ġquad r", "Ġqua dr", "- Y", "_ CACHE", "_C ACHE", "_CA CHE", "Ġan alyzed", "Ġanaly zed", "Ġanalyze d", "Ġg aining", "Ġgain ing", "Ġga ining", "Ġ Against", "ĠAgain st", "ĠS oul", "ĠSo ul", "ĠSou l", "t au", "ta u", "Ġlight weight", "Ġ TF", "ĠT F", "Ġ Effects", "ĠE ffects", "ĠEffect s", "ĠEff ects", ". Types", ".T ypes", ".Type s", ". addClass", ".add Class", "Ġv egan", "Ġve gan", "Ġveg an", "é ģ", ". '\"", ".' \"", "Ġ Explorer", "ĠEx plorer", "ĠExpl orer", "ĠExplore r", ". detect", ".d etect", ".det ect", ". shift", ".s hift", ".sh ift", "Ġoblig ations", "Ġobligation s", "last Name", "Ġassoci ations", "Ġassociation s", "Ġassoc iations", "ĠTime Span", "un ter", "unt er", "unte r", "Ġ Fresh", "ĠF resh", "ĠFr esh", "ĠFre sh", "ĠFres h", "Com patible", "Compat ible", "P ub", "Pu b", "id ges", "idge s", ". option", ".op tion", ".o ption", ".opt ion", "v ari", "var i", "va ri", ". hashCode", ".hash Code", "Ġ geb", "Ġg eb", "Ġge b", ". section", ".s ection", ".se ction", ".sec tion", "- not", "-n ot", "-no t", "Ġ Submit", "ĠSub mit", "T N", "reg istry", "registr y", "regist ry", "_ media", "_m edia", "_me dia", "_med ia", "Ġn aj", "Ġna j", "f ft", "ff t", "Ġ mate", "Ġm ate", "Ġmat e", "Ġma te", "- third", "-th ird", "Ġp ockets", "Ġpocket s", "e sta", "es ta", "est a", "Ġb ent", "Ġbe nt", "Ġben t", "ĠN ord", "ĠNo rd", "ĠNor d", "Ġretail ers", "Ġretailer s", "ĠM orris", "ĠMor ris", "ĠMorr is", ". \"\"\"ĊĊ", ".\"\" \"ĊĊ", ".\"\"\"Ċ Ċ", ".\"\"\" ĊĊ", "W rong", "Wr ong", "Ġ ÅĽ", "ĠÅ Ľ", "R ay", "Ra y", ". ec", ".e c", "Ġ Bind", "ĠB ind", "ĠBi nd", "ĠBin d", "_ HAND", "_H AND", "( non", "(n on", "(no n", "is Valid", "Ġsimilar ly", "_ LIMIT", "_L IMIT", "Ġd ynamics", "Ġdynamic s", "Ġdynam ics", "Ġdyn amics", "Ġdist inction", "Ġdistinct ion", "ãģ Ĩ", "< N", "Ġ orth", "Ġor th", "Ġort h", "Ġ Toyota", "ĠToy ota", "Ġ Kate", "ĠK ate", "ĠKat e", "ĠKa te", "Ġ LS", "ĠL S", "o rie", "or ie", "ori e", "ĠSpring s", "ĠSpr ings", "Ġf reak", "Ġfre ak", "last name", "_ MULT", "_M ULT", "_MUL T", "- step", "-s tep", "-st ep", "-ste p", "\" (", "A DDR", "AD DR", "ADD R", "Ġentert aining", "Ġentertain ing", "_ CONF", "_CON F", "_CO NF", "Ġ decoded", "Ġde coded", "Ġdec oded", "Ġdecode d", "Ġdeco ded", "Ġst reak", "Ġstre ak", "Ġwait ed", "Ġwa ited", "Ġnot ified", "ro duced", "rodu ced", "rod uced", "roduce d", "vis ual", ". LayoutParams", ".Layout Params", "æ °", "e sian", "es ian", "esi an", "f its", "fit s", "fi ts", "s pring", "sp ring", "spr ing", "ĠBer nie", "ĠBern ie", "User Defaults", "Ġpe dest", "Ġped est", "Ap pearance", "Appear ance", "Ġ Wiki", "ĠW iki", "ĠWi ki", "ĠWik i", "ĠNOT ICE", "Ġ ssh", "Ġs sh", "Ġss h", "Ġdur ante", "Ġ Zip", "ĠZ ip", "ĠZi p", "ı r", "ĠN ATO", "ĠNAT O", "ĠNA TO", "Ġtw elve", "Ġr oyal", "Ġro yal", "Ġroy al", "ï ¸", "Ġ merchant", "Ġm erchant", "Ġmer chant", "Ġmerch ant", "ĠF urniture", "ĠFurn iture", "' ]),Ċ", "'] ),Ċ", "']) ,Ċ", "']), Ċ", ", X", "Ġ folders", "Ġf olders", "Ġfolder s", "Ġfol ders", "Ġfold ers", "Ġ Gate", "ĠG ate", "ĠGa te", "ĠGat e", "ĉ func", "ĉf unc", "ĉfun c", "p ick", "pi ck", "pic k", "_ usuario", "_us uario", "ĠV erm", "ĠVer m", "ĠVe rm", "m ention", "ment ion", "men tion", "ur pose", "Ġ alerts", "Ġal erts", "Ġalert s", "Ġale rts", "x ious", "xi ous", "_ sig", "_s ig", "_si g", "Ġ Fu", "ĠF u", "Ġ (:", "Ġ( :", "Ġd umb", "Ġdu mb", "Ġdum b", "åħ ³", "Ġaccur ately", "Ġaccurate ly", "éĩ į", "R B", "- screen", "-s creen", "-sc reen", "Ġ VER", "ĠV ER", "ĠVE R", "j our", "jo ur", "Ġrom ance", "Ġroman ce", "Ġroma nce", "uc ceed", "ucc eed", ". choice", ".ch oice", "Ġad ip", "_ dims", "_d ims", "_dim s", "_di ms", "Serial izable", "ãĤ ĭ", ". job", ".j ob", "Ġ prog", "Ġp rog", "Ġpro g", "Ġpr og", "u char", "uch ar", "uc har", "ucha r", "Ġg ently", "Ġgent ly", "Ġ RSS", "ĠR SS", "ĠRS S", "ict ured", "icture d", "_ENABLE D", "ĉ label", "ĉl abel", "aw ks", "awk s", "Ġ Ensure", "ĠEn sure", "ĠEns ure", "re member", "rem ember", "ìł ķ", "Ġtrans mit", "{ {$", "{{ $", ". Transaction", ".Trans action", "ur se", "urs e", "_ relative", "_rel ative", "Ġs ized", "Ġsize d", "Ġsi zed", "Ġsiz ed", "Ġ XX", "ĠX X", "ĠPr incess", "ĠPrince ss", "Ġ Larry", "ĠL arry", "ĠLar ry", "Ġp ró", "Ġpr ó", "Ġ ÑģÑĤÑĢ", "ĠÑģ ÑĤÑĢ", "ĠÑģÑĤ ÑĢ", "Ġs isters", "Ġsi sters", "Ġsister s", "Ġsist ers", "Ġsis ters", "e struct", "estr uct", "Ġ checkpoint", "Ġcheck point", ": length", ":len gth", ":l ength", "Ġ Carlos", "ĠCar los", "ĠCarl os", "ĠCarlo s", "/ icon", "/i con", "/ic on", "_ TARGET", "_T ARGET", "T okens", "Token s", "Tok ens", "Ġpat ience", "Ġ Selected", "ĠSe lected", "ĠSelect ed", "ĠSel ected", "q ty", "qt y", ".show Message", "Ġwild life", "Ġ Props", "ĠP rops", "ĠPro ps", "ĠPr ops", "ĠProp s", "b m", "- arrow", "-ar row", "Ġ parcel", "Ġpar cel", "Ġparc el", "Ġparce l", "f irebase", "fire base", "ĠBen jamin", "c esso", "cess o", "ces so", ". tim", ".t im", "ĠG arc", "ĠGar c", "ĠGa rc", ". any", ".a ny", ".an y", "ĠHOW EVER", "ĠK o", "Ġgrab bed", "_ frames", "_f rames", "_frame s", "_fr ames", "Ġobject AtIndex", "ĠADV ISED", "Ġsu bur", "Ġsub ur", "ĉ GL", "ĉG L", "Ġ })}Ċ", "Ġ} )}Ċ", "Ġ}) }Ċ", "- length", "-l ength", "-le ngth", "-len gth", "ìĭ ľ", "ĠPot ter", "_ buff", "_b uff", "_buf f", ". gui", ".g ui", "Ġ Encoding", "ĠEn coding", "ĠEnc oding", "E lect", "El ect", "Ele ct", "- message", "-m essage", "Ġ �", "Ġ ÈĻi", "Ġ ArgumentNullException", "ĠArgument NullException", "а ÑĨи", "Ġmin imize", "Ġminim ize", "Ġrespond ing", "$_ ['", "Ġ Individual", "ĠInd ividual", "á c", "Ġ INTER", "ĠIN TER", "ĠINT ER", "Ġmast urb", "Ġmastur b", "Ġ Bin", "ĠB in", "ĠBi n", "( '$", "(' $", "ëĵ ľ", "Ġopen ly", "Ġ ><", "Ġ> <", "Ġ unto", "Ġun to", "Ġunt o", "olog ically", "ological ly", "ologic ally", "Ġ Mul", "ĠM ul", "ĠMu l", "VID IA", "Ġs lim", "Ġsl im", "ĠCommission er", "( on", "(o n", "Ġunder neath", "/ db", "/d b", "v ote", "vo te", "( Message", "(M essage", "ĠP ope", "ĠPop e", "ĠPo pe", "D efined", "Def ined", "Define d", "Ġ swift", "Ġsw ift", "u rf", "ur f", "Ġadapt ed", "Ġadap ted", "S EL", "SE L", "Ġre venues", "Ġrevenue s", "Ġreven ues", "Ġdi vine", "Ġdiv ine", "= y", "G radient", "Grad ient", "_ act", "_a ct", "_ac t", "Ġ/* !<", "Ġ/*! <", "Ġ polygon", "Ġp olygon", "Ġpoly gon", "Ġ FDA", "ĠF DA", "ĠFD A", "ĠC arr", "ĠCar r", "ĠCa rr", "a tables", "at ables", "ata bles", "atab les", "atable s", "( stdout", "(std out", "Ġref riger", "Ġrefr iger", "Ġco ordin", "Ġcoord in", "avor ites", "avorite s", "avo rites", "ÑĪ и", "Ġcompass ion", "ĠPOSS IBILITY", "- secondary", "-second ary", "ur acy", "ura cy", "Ġcom promise", "Ġcomp romise", "Ġcomprom ise", "_ AV", "_A V", "_ os", "_o s", "Ġbe side", "Ġbes ide", "ĥ Ŀ", "Ġ ln", "Ġl n", ". plugins", ".pl ugins", ".plugin s", "Cap acity", "a lah", "al ah", "ala h", ". bin", ".b in", ".bi n", "Ġ CRC", "ĠC RC", "ĠCR C", "_ balance", "_b alance", "_bal ance", "Ġflex Direction", "Ġam bit", "Ġamb it", "Ġ nickname", "Ġn ickname", "Ġnick name", "ĠFor ces", "ĠForce s", "C LE", "CL E", "Ġ Shell", "ĠS hell", "ĠSh ell", "ĠShe ll", "ĠShel l", "Ġs ail", "Ġsa il", "Ġsai l", "Ġ Writer", "ĠW riter", "ĠWrite r", "ĠWr iter", "Ġ Alice", "ĠA lice", "ĠAl ice", "ĠAli ce", "ĠAlic e", "d w", "ĠInd ians", "ĠIndia ns", "ĠIndian s", "ĠIndi ans", "ĠMar shall", "ĠMars hall", "ĠMarshal l", "ĠMarsh all", "_ SRC", "_S RC", "_SR C", "Ġ normalized", "Ġnormal ized", "Ġnormalize d", "ĠJ ag", "ĠJa g", "ãĤ Ĵ", "ze it", "r pc", "rp c", "ÃŃ c", ". inline", ".in line", "Ġtr avers", "Ġtra vers", "Ġtrav ers", "_ numeric", "_n umeric", "_num eric", "_numer ic", "Ġ utilities", "Ġutil ities", "Ġut ilities", "Ġe vac", "Ġev ac", "IN PUT", "ĉ register", "ĉreg ister", "M X", "ĠCamp bell", "Ġ datasets", "Ġd atasets", "Ġdata sets", "Ġdataset s", "Ġdatas ets", "Ġdem anded", "Ġdemand ed", "Ġdemande d", "Ġinitial State", "g an", "ga n", "Ġ ei", "Ġe i", "Un expected", "- web", "-w eb", "-we b", "t rait", "tr ait", "tra it", ", Y", "Ġ Todd", "ĠT odd", "ĠTo dd", "ĠTod d", "Ġs keleton", "Ġske leton", "Ġ optimize", "Ġopt imize", "Ġoptim ize", "ç ¬¬", "ç¬ ¬", "Ġ Upon", "ĠU pon", "ĠUp on", "ĠSt Object", "Ġap lic", "Ġapl ic", ". ' P", "v ron", "vr on", "vro n", ". UN", ".U N", "Ġp ainter", "Ġpaint er", "Ġpain ter", "Ġpa inter", "izar re", "Ġ lav", "Ġl av", "Ġla v", "Ġ pom", "Ġp om", "Ġpo m", "p reg", "pr eg", "pre g", "= function", "=f unction", "( serial", "(s erial", "(se rial", "if ica", "ific a", "ifi ca", "u ming", "um ing", "umin g", "umi ng", "åľ °", "ãģ Ĥ", "- op", "-o p", "U CH", "UC H", "ĠH end", "ĠHe nd", "ĠHen d", ". propTypes", ".prop Types", "Ġ yo", "Ġy o", "Ġr outines", "Ġrout ines", "Ġroutine s", "Ġc aring", "Ġcar ing", "Ġca ring", "S em", "Se m", "Ġres erves", "Ġreserve s", "Ġreserv es", "Ġprior ities", "Ġpriorit ies", "red its", "redit s", "redi ts", "I STR", "IS TR", "IST R", "Content Type", "ĠS chw", "ĠSc hw", "ĠSch w", "/ media", "/m edia", "/me dia", "Ġ estr", "Ġe str", "Ġes tr", "Ġest r", "Ġclim bing", "Ġclimb ing", "- week", "-we ek", "cher che", "s ensor", "To Array", "ĠMont real", "Ġclo uds", "Ġcloud s", "ĠInject able", "ĠR ice", "ĠRic e", "ĠRi ce", "Ġpropag anda", "_ provider", "_pro vider", "_prov ider", "Ġin door", "Ġind oor", "Ġindo or", "Ġin aug", "Ġdipl om", "Ġdip lom", "Ġm essaging", "Ġmess aging", "_ mut", "_m ut", "_mu t", "å ¦Ĥ", "å¦ Ĥ", "Ġ kw", "Ġk w", "O NS", "ON S", "a rians", "ar ians", "ari ans", "arian s", "aria ns", "R PC", "RP C", ") ]čĊ", ")] čĊ", "- ray", "-r ay", "-ra y", "ĠS or", "ĠSo r", "m all", "ma ll", "mal l", "Ġmarket place", "Ġ vtk", "Ġv tk", "Ġvt k", "M a", "o gan", "og an", "oga n", "i gi", "ig i", "Ġs ponsored", "Ġspons ored", "Ġsponsor ed", "Ġ Dani", "ĠD ani", "ĠDan i", "ĠDa ni", ".S EVER", ".SE VER", "> '.$", ">' .$", ">'. $", "m ultipart", "multi part", "multip art", "ĠW ol", "ĠWo l", "Ġ tableName", "Ġtable Name", "Ġ Username", "ĠUser name", "Back groundColor", "Background Color", "Ġf right", "Ġfr ight", "Ġfri ght", "_ EMAIL", "_E MAIL", "_EM AIL", "Sept ember", "Sep tember", "_ vals", "_v als", "_val s", "_va ls", "op ia", "opi a", "Ġsp otted", "Ġspot ted", "- Ch", "-C h", "Ġ dataSource", "Ġdata Source", "/ \"Ċ", "/\" Ċ", "е кÑĤ", "ек ÑĤ", "Ġ RequestMethod", "ĠRequest Method", "Ġ Replace", "ĠRe place", "ĠRep lace", "- do", "-d o", "a hn", "ah n", "ĠPh D", "] .ĊĊ", "]. ĊĊ", "].Ċ Ċ", "N ON", "NO N", "g ement", "ge ment", "gem ent", "geme nt", "Ġ Thr", "ĠT hr", "ĠTh r", "Ġquiet ly", "Ġtor ture", "Ġtort ure", "Ġte as", "Ġtea s", "Ġ CY", "ĠC Y", "Ġ atr", "Ġa tr", "Ġat r", "de velopment", "dev elopment", "develop ment", "- detail", "-d etail", "-de tail", "-det ail", "Ġl ighter", "Ġlight er", "Ġarg uing", "Ġdes erves", "Ġdeserve s", "Ġdeser ves", "Ġcur riculum", "_ CONTEXT", "_CON TEXT", "_CONT EXT", "ÅĤ y", "H ITE", "HI TE", "ĉ ID", "ĉI D", "/ uploads", "/upload s", "/up loads", "Ġt its", "Ġtit s", "Ġti ts", "r eo", "re o", "_ drop", "_d rop", "_dr op", ". UTF", ".U TF", "Ġ pickup", "Ġpick up", "Ġpic kup", "Ġgro cery", "Ġ Pure", "ĠP ure", "ĠPur e", "ĠPu re", "Ġeas iest", "P hil", "Ph il", "Phi l", ". feature", ".f eature", ".fe ature", "( \"*", "(\" *", "Ġinvest or", "Ġinve stor", "t ok", "to k", "Ġ jar", "Ġj ar", "Ġja r", "L os", "Lo s", "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", ". queue", ".q ueue", "- speed", "-s peed", "-sp eed", "-spe ed", "M al", "Ma l", "um blr", "umb lr", "Ġ CONST", "ĠCON ST", "ĠCO NST", "ĠCONS T", "Ġ HRESULT", "ĠH RESULT", "ĠD ance", "ĠDan ce", "ĠDa nce", "( filePath", "(file Path", "Ġattribute d", "Ġattrib uted", "ॠį", "ĠB und", "ĠBu nd", "ĠBun d", "c oins", "co ins", "coin s", "Ġs ão", "Ġ pir", "Ġp ir", "Ġpi r", "person al", "pers onal", "persona l", "Ġpr elim", "Ġpre lim", "Ġpro pose", "Ġprop ose", "Ġpropos e", "Ġ TL", "ĠT L", "] ])", "]] )", "Ġ Subscription", "ĠSub scription", "ĠK re", "ĠKr e", ", len", ",l en", ". FirstOrDefault", ".First OrDefault", ") --", ")- -", "_ products", "_product s", ".Get Bytes", "S hip", "Sh ip", "Ġ encrypt", "Ġen crypt", "Ġenc rypt", "Ġ SG", "ĠS G", "ĠM yst", "ĠMy st", "h ir", "hi r", "Ġ iterate", "Ġit erate", "Ġiter ate", "Ġint end", "Ġinte nd", ".mock ito", "Ġch apters", "Ġchapter s", "Ġchap ters", "( angle", "(a ngle", "(an gle", "(ang le", "ĠV lad", "è® ¾", "' .ĊĊ", "'. ĊĊ", "'.Ċ Ċ", "Response Body", "ĠA bd", "ĠAb d", "de al", "dea l", "Ġbar riers", "Ġbarrier s", "Ġbarr iers", "- outline", "-out line", "b ill", "bi ll", "bil l", "ĠF alls", "ĠFall s", "ĠFal ls", "_ second", "_se cond", "_sec ond", ". include", ".in clude", ".inc lude", ". ceil", ".c eil", ".ce il", "Ġ occupation", "Ġoccup ation", "ph ony", "phon y", ".move To", "Ġ Jennifer", "ĠJ ennifer", "ĠJenn ifer", "A STER", "AS TER", "AST ER", "ASTE R", "; \"><", ";\" ><", ";\"> <", "Ġ Enabled", "ĠEn abled", "ĠEnable d", "Ġ terminate", "Ġter minate", "Ġterm inate", "Ġtermin ate", "Ġ Io", "ĠI o", "l ations", "lation s", "lat ions", "ĠTHE ORY", "Ġear liest", "Ġ rack", "Ġr ack", "Ġrac k", "Ġra ck", "Ġ Scar", "ĠS car", "ĠSc ar", "sh ake", "sha ke", "c hip", "ch ip", "chi p", "Ġ uv", "Ġu v", "Ġall iance", "п иÑģ", "пи Ñģ", "ĠGOOD S", "z ione", "zi one", "zion e", "Ġ VI", "ĠV I", "Ġ {-", "Ġ{ -", "Ġfil tering", "Ġfilter ing", "Ġfilt ering", "Ġmis con", "Ġmisc on", ".Dock Style", "Ġb ush", "Ġbu sh", "Ġbus h", "Ġj unk", "Ġju nk", "Ġjun k", "æ Į", "Ġ QUE", "ĠQ UE", "ĠQU E", "Ġ hooks", "Ġh ooks", "Ġhook s", "Ġho oks", "Ġf irmware", "Ġfirm ware", "Ġ middleware", "Ġm iddleware", "Ġmiddle ware", "d ic", "di c", "ĠOak land", "Ġarr ives", "Ġarrive s", "Ġarriv es", "P ayload", "Pay load", "p ixel", "pix el", "] |", "Ġ startDate", "Ġstart Date", ". PRO", ".P RO", ".PR O", "_ audio", "_a udio", "Ġmid field", "igid body", "ĠSw iss", "Ġ Clip", "ĠC lip", "ĠCl ip", "ĠCli p", "Ġ Dump", "ĠD ump", "ĠDu mp", "ĠDum p", "Ġ TextBox", "ĠText Box", "Ġ geh", "Ġg eh", "Ġge h", "y ield", "yi eld", "o ds", "od s", "Ġrefer endum", "Back end", "Ġ Cream", "ĠC ream", "ĠCr eam", "ĠCre am", "Ġd ominated", "Ġdo minated", "Ġdom inated", "Ġdomin ated", "Ġdominate d", "Ġdomina ted", "Ġ Archive", "ĠA rchive", "ĠArch ive", "ĠArc hive", "Ġr iders", "Ġrid ers", "Ġride rs", "Ġri ders", "Ġrider s", ".prepare Statement", "Ġqu ando", "Ġquand o", "Ġqua ndo", "Ġquan do", "Ġ chef", "Ġch ef", "Ġche f", "w iki", "wi ki", "wik i", "i nel", "in el", "ine l", "am pling", "amp ling", "(\" \\\\", "(\"\\ \\", "Ġs ag", "Ġsa g", "_ proxy", "_pro xy", "_pr oxy", "ãģ ķ", "p do", "pd o", ". getElementsByTagName", ".get ElementsByTagName", ".getElementsBy TagName", "Ġdemon stration", "Ġdemonstr ation", "Ġ NPC", "ĠN PC", "ĠNP C", "Ġ archivo", "Ġarch ivo", "en dance", "end ance", "enda nce", "Ġefficient ly", "( actual", "(ac tual", "(act ual", ". tableView", ".t ableView", ".table View", "Ġm ush", "Ġmus h", "Ġmu sh", "Ġb ears", "Ġbe ars", "Ġbear s", "_ threads", "_th reads", "_thread s", "j as", "ja s", "ah un", "ahu n", "Ġne ural", "Ġneu ral", "Ġneur al", "Ġdesign ing", "ĠG DP", "ĠGD P", "Ġlif ted", "Ġlift ed", "çĽ ®", "Ġ Joint", "ĠJ oint", "ĠJo int", "ĠJoin t", "ĠJoi nt", "Ġ Include", "ĠIn clude", "ĠInc lude", "ĠG iants", "ĠGi ants", "ĠGiant s", "ĠGian ts", "ĠGia nts", "Ġwithdraw al", "Ġ Rent", "ĠR ent", "ĠRe nt", "ĠRen t", "n ative", "nat ive", "Ġ Seek", "ĠSe ek", "ĠSee k", "g ression", "gr ession", "gress ion", "_ CPU", "_C PU", "_CP U", "\\ S", "Ġ Shield", "ĠSh ield", "ĠShi eld", "Ġs olic", "Ġso lic", "Ġsol ic", "Ġ boom", "Ġb oom", "Ġbo om", "Ġboo m", "yect o", "Ġmanufact ure", "Ġ âĢĭ", "ĠâĢ ĭ", "Ġ bbox", "Ġb box", "Ġbb ox", "Ġearth qu", "oll ectors", "ollect ors", "olle ctors", ":@\" %", ":@ \"%", "Ġ loops", "Ġl oops", "Ġlo ops", "Ġloop s", "J e", "al king", "alk ing", "Ġ Whats", "ĠWh ats", "ĠWhat s", "ĠBo ys", "ĠBoy s", ". book", ".b ook", ".bo ok", "AR GE", "ARG E", "_ pixel", "_p ixel", "_pix el", "Ġsus pects", "Ġsusp ects", "Ġsuspect s", "Î ¹", "u sp", "us p", "Ġ BMW", "ĠB MW", "ĠBM W", "ie ces", "iece s", "iec es", "( person", "(p erson", "(per son", "å¼ Ģ", "é »", "ĠP odcast", "ĠPod cast", "Ġ bou", "Ġb ou", "Ġbo u", "( Item", "(I tem", "(It em", "à »", "( Input", "(In put", "Http Get", "Ġ burg", "Ġb urg", "Ġbu rg", "Ġbur g", ") ^", "BO ARD", "* /,", "*/ ,", "Ġ gulp", "Ġg ulp", "Ġgu lp", "Ġgul p", "ĠB enn", "ĠBe nn", "ĠBen n", "Ġde cks", "Ġdec ks", "Ġdeck s", ". statusCode", ".status Code", "Ġ acute", "Ġac ute", "Ġh ug", "Ġhu g", "u gu", "ug u", "Ġ pled", "Ġp led", "Ġpl ed", "Ġple d", ", \"%", ",\" %", "h ape", "ha pe", "hap e", "Ġз ап", "Ġза п", "ĠM aine", "ĠMain e", "ĠMa ine", "ĠMai ne", ". real", ".re al", "Ġd alam", "Ġda lam", "Ġdal am", "Ġ Minor", "ĠMin or", "ĠMi nor", ". Float", ".F loat", "d isp", "dis p", "di sp", "Ġ tl", "Ġt l", "Ġen count", "Ġenc ount", "= >$", "=> $", "Ġ fg", "Ġf g", "t ees", "te es", "tee s", "ĠRe comm", "ĠRec omm", "ĠReco mm", "ä l", "Ġ chemistry", "Ġchem istry", "B locks", "Block s", "Bl ocks", "Bloc ks", "Blo cks", "O ID", "OI D", "Ġf orex", "Ġfor ex", "Ġfore x", "Ġfo rex", "Ġ Append", "ĠApp end", "ĠAp pend", "ĠAppe nd", "Ġ {*", "Ġ{ *", "Ġ Supply", "ĠS upply", "ĠSup ply", "CG Float", "( bl", "(b l", "Ġ ate", "Ġa te", "Ġat e", "ad ora", "ado ra", "ador a", "Ġg ust", "Ġgu st", "Ass oci", "Assoc i", "> .Ċ", ">. Ċ", "F ETCH", ". serial", ".s erial", ".se rial", ".ser ial", "widget s", "wid gets", "ard less", "i efs", "ie fs", "ief s", "_ FULL", "_F ULL", "ern etes", "ernet es", "erne tes", "Ġ Pred", "ĠP red", "ĠPr ed", "ĠPre d", "Ø Ń", "äº ĭ", "ub ernetes", "ubern etes", "Ġ Laura", "ĠL aura", "ĠLa ura", "ĠLaur a", "ĠLau ra", "Ġl abeled", "Ġlabel ed", "Ġlab eled", "High light", "Ġanno ying", "Ġannoy ing", "/ update", "/up date", "( description", "(d escription", "(de scription", "(des cription", "Ġint imid", "Ġintim id", "$ c", "\" )))Ċ", "\") ))Ċ", "\")) )Ċ", "\"))) Ċ", ". AP", ".A P", "Ġ[ ]*", "Ġ[] *", "Ġ EXIT", "ĠEX IT", ". Host", ".H ost", "Ġ OPEN", "ĠOP EN", ". sendMessage", ".send Message", "_ camera", "_c amera", "_cam era", "_ tile", "_t ile", "_ti le", "Ġth erm", "Ġthe rm", "Ġther m", "onom ous", "Ġdis adv", "Ġn aar", "Ġna ar", "index Of", "Ġ PP", "ĠP P", ". protocol", ".prot ocol", ".proto col", "A FE", "AF E", "Ġ textures", "Ġtext ures", "Ġtexture s", "Ġtex tures", "Ġtextu res", "######## ########################################", "################ ################################", "################################ ################", "######################################## ########", "######################## ########################", "um bai", "umb ai", "umba i", ". stats", ".st ats", ".stat s", "Ġ GE", "ĠG E", "Ġ ie", "Ġi e", "Ġ STD", "ĠS TD", "ĠST D", "ĠM ann", "ĠMan n", "ĠMa nn", ". reflect", ".ref lect", "K B", "Ġd ive", "Ġdi ve", "Ġdiv e", ". wav", ".w av", "/* ----------------------------------------------------------------", "/*------------------------------------------------ ----------------", "/ settings", "/s ettings", "/set tings", ".l ifecycle", ".life cycle", "Ġda ughters", "Ġdaughter s", "o rus", "or us", "oru s", "u ber", "ub er", "ube r", "N ING", "NI NG", "s tri", "st ri", "str i", "Ġ Tip", "ĠT ip", "ĠTi p", "Ġ zn", "Ġz n", "Ġsw itched", "Ġswitch ed", "i net", "in et", "ine t", "uff y", "uf fy", "ĠTransport ation", "( conf", "(con f", "(co nf", "f rica", "fr ica", "Ġ XL", "ĠX L", "Ġ Lead", "ĠL ead", "ĠLe ad", "_ percent", "_per cent", "_perc ent", "< Map", " __", "->_ _", "per missions", "perm issions", "permission s", "ĠD etermine", "ĠDetermin e", ". Man", ".M an", ".Ma n", "Ġadv ances", "Ġadvance s", ". InputStream", ".Input Stream", "Ġstrong est", "Ġstron gest", "Ġe Bay", "Ġ# -", "Ġ dirname", "Ġdir name", "Ġ SMS", "ĠS MS", "ĠSM S", "Ġmed ications", "Ġmedic ations", "Ġmedication s", "Ġam ended", "Ġamen ded", "Ġamend ed", "Ġchurch es", "ĠIm perial", "ĠImp erial", "ĠImper ial", "$ row", "$r ow", "ĠMad ison", "Ġ Insp", "ĠIn sp", "ĠIns p", "Ġaff air", "Ġaf fair", "Ġpsych ology", "Ġpsycho logy", "v h", "Ġ severity", "Ġse verity", "Ġsever ity", "âĢ IJ", "Ġst rips", "Ġstr ips", "Ġstri ps", "Ġstrip s", "A H", "vert ising", "vertis ing", "Ġc onse", "Ġcon se", "Ġcons e", "IM AGE", "IMA GE", "Ġ Stats", "ĠSt ats", "ĠStat s", "ĠSta ts", "ĉ sc", "ĉs c", ". Cursor", ".C ursor", "Ġ freeze", "Ġf reeze", "Ġfree ze", "s son", "ss on", "( xml", "(x ml", "Ġ Susan", "ĠS usan", "ĠSus an", "ĠSu san", ". tile", ".t ile", "e ded", "ed ed", "ede d", "ĠĠ ĠĠĉĉĉ", "ĠĠĠĠ ĉĉĉ", "ĠĠĠ Ġĉĉĉ", "ĠĠĠĠĉ ĉĉ", "ĠĠĠĠĉĉ ĉ", "u elle", "ue lle", "uel le", "uell e", "ĠMitch ell", "b ased", "base d", "ba sed", "bas ed", "Oper and", "Opera nd", "½ æķ°", "Ġ FF", "ĠF F", "ĉ strcpy", "ĉstr cpy", "ou nces", "oun ces", "ounc es", "ounce s", "il do", "ild o", ".execute Query", "Ġappro aching", "Ġapproach ing", "Ġ Seven", "ĠS even", "ĠSe ven", "ĠSev en", "Ġ nuts", "Ġn uts", "Ġnut s", "Ġnu ts", "Ġ ric", "Ġr ic", "Ġri c", "ass ignment", "assign ment", "Ġ calculator", "Ġcal culator", "Ġcalcul ator", "Ġcalc ulator", "ĠMur phy", "ĠB ou", "ĠBo u", "í Ħ", "Ġ butt", "Ġb utt", "Ġbut t", "Ġbu tt", "Ġ ticks", "Ġt icks", "Ġti cks", "Ġtick s", "Ġtic ks", "Project s", "Proj ects", "i lib", "il ib", "ili b", ".text Color", "m ov", "mo v", "_ logo", "_l ogo", "_log o", "_lo go", "( template", "(t emplate", "(temp late", "Ġ INIT", "ĠIN IT", "Ġ imageView", "Ġimage View", "s criptions", "script ions", "scri ptions", "scription s", "OR ITY", "Con sumer", "Cons umer", "Consum er", "Ġun precedented", "Ġtour ist", "Ġtou rist", "Ġ bron", "Ġb ron", "Ġbr on", "Ġbro n", "Ġcon tractor", "Ġcontract or", "Ġcontr actor", "Ġcontra ctor", "Ġ licence", "Ġli cence", "Ġlic ence", "Ġ Nam", "ĠN am", "ĠNa m", "æ ¯", "( transform", "(trans form", "_ ATT", "_A TT", "_AT T", "P ref", "Pr ef", "Pre f", "Ġ Gam", "ĠG am", "ĠGa m", "Ġvess els", "Ġvessel s", "Ġh av", "Ġha v", "L ater", "La ter", "Lat er", "Late r", ". ToLower", ".To Lower", "Ġ urls", "Ġurl s", "Ġur ls", "Ġbreak down", "Ġpen alties", "Ġpenal ties", "Ġf oster", "Ġfo ster", "Ġfost er", "Ġfos ter", "Ġ UE", "ĠU E", "Ġc lue", "Ġcl ue", "c omed", "com ed", "co med", "come d", "åIJį 称", "- main", "-m ain", "Ġ pts", "Ġp ts", "Ġpt s", "Ġco unted", "Ġcount ed", "Ġcoun ted", "i cts", "ic ts", "ict s", "/ post", "/p ost", "Ġ getattr", "Ġget attr", "Ġ ping", "Ġp ing", "Ġpi ng", "Ġpin g", "AN CEL", "ANCE L", "ANC EL", "Ġ pec", "Ġp ec", "Ġpe c", "Ñħ од", "Ñħо д", "an tom", "ant om", "anto m", "Ġ Blueprint", "ĠBlue print", "ĠEvent Emitter", "Ġ lä", "Ġl ä", "æ ²", "Ġst raw", "Ġstr aw", "Ġstra w", "( comp", "(c omp", "(com p", "(co mp", "' une", "'un e", "'u ne", "> N", "- client", "-c lient", "-cl ient", "-cli ent", "es Module", "- base", "-b ase", "Ġret reat", "Ġretr eat", "_ simple", "_s imple", "_sim ple", "ĉ ĉĉĉĉĉĠ", "ĉĉ ĉĉĉĉĠ", "ĉĉĉĉ ĉĉĠ", "ĉĉĉ ĉĉĉĠ", "ĉĉĉĉĉ ĉĠ", "ĉĉĉĉĉĉ Ġ", "f ee", "fe e", "' )čĊčĊ", "') čĊčĊ", "')čĊ čĊ", "Control Item", "Ġsub scribers", "Ġsubscri bers", "Ġsubscribe rs", "Ġsubscriber s", "p lease", "pl ease", "ple ase", "Ġ Eff", "ĠE ff", "ĠEf f", "Ġp ound", "Ġpo und", "Ġpou nd", "Ġ Bytes", "ĠBy tes", "ĠByte s", "ĠT ea", "ĠTe a", "_ activity", "_act ivity", "_activ ity", "Ġmax im", "Ġmaxi m", "Ġ opcode", "Ġop code", "Ġopc ode", "B SD", "BS D", ". constant", ".con stant", ".const ant", ".cons tant", "; }", "omb res", "ombre s", "Ġcare ers", "Ġcareer s", ") .ĊĊĊĊ", "). ĊĊĊĊ", ").ĊĊ ĊĊ", ").Ċ ĊĊĊ", ").ĊĊĊ Ċ", "Ġsp reading", "Ġspread ing", "Ġspre ading", "- expanded", "-exp anded", "-expand ed", "Ġ Ord", "ĠO rd", "ĠOr d", "am arin", "ama rin", "amar in", "Ġmob ility", "Ġmobil ity", "Un fortunately", "a kk", "ak k", "N L", "_ redirect", "_re direct", "_red irect", "Ġ PG", "ĠP G", "Ġ Sensor", "ĠS ensor", "ĠSens or", "b ol", "bo l", "t ap", "ta p", "_ MEMORY", "_MEM ORY", "Ġ UIAlert", "ĠUI Alert", "pl itude", "plit ude", "We bsite", "Web site", "Ġ Logo", "ĠL ogo", "ĠLog o", "ĠLo go", "l ove", "lo ve", "lov e", "[ ind", "[i nd", "[in d", "Ġalto gether", "Ġwonder ed", "Ġ esper", "Ġes per", "Ġesp er", "ĠLib eral", "ĠLiber al", "Ġ oss", "Ġo ss", "Ġos s", "Ġe lit", "Ġel it", "Ġst iff", "Ġstif f", "od ox", "odo x", "_ mentions", "_m entions", "_ment ions", "ĠDou glas", "ĠDoug las", "_ pid", "_p id", "_pi d", "Ġ CK", "ĠC K", "ĠinitWith Frame", ". blog", ".b log", ".bl og", "p kg", "pk g", "ang hai", "QUI RED", "QUIRE D", "u u", "Ġ mkdir", "Ġm kdir", "Ġmk dir", "AT AL", "ATA L", "Ġu nh", "Ġun h", "i nces", "in ces", "ince s", "inc es", "s th", "st h", "Ġhypo thesis", "Ġhypoth esis", "Ġc ata", "Ġca ta", "Ġcat a", "Ġ TB", "ĠT B", "Ġ Clar", "ĠC lar", "ĠCl ar", "ĠCla r", "Ġpre decess", "Ġpred ecess", "Ġsit uated", "Ġsitu ated", "- world", "-w orld", ") )/", ")) /", "Ġhead lines", "Ġheadline s", ". stat", ".s tat", ".st at", "Ġout break", "s path", "sp ath", "spa th", "_ FLAGS", "_FLAG S", "ĠServlet Exception", "S un", "Su n", "F ROM", "FR OM", "Ġ Dir", "ĠD ir", "ĠDi r", "ãĥ» ãĥ»ãĥ»", "ãĥ»ãĥ» ãĥ»", "_ coord", "_c oord", "_co ord", "ĠOp tim", "ĠOpt im", "M onitor", "Mon itor", ". bit", ".b it", ".bi t", "X XX", "XX X", "Ġto das", "Ġtod as", "Ġtoda s", "f eld", "fe ld", "fel d", "ÑĢ и", "i mir", "im ir", "imi r", "Ġpolit ically", "Ġpolitical ly", "Ġpolitic ally", "Ġm olecular", "Ġmolec ular", "Ġmole cular", "Ġtr aded", "Ġtrad ed", "Ġtrade d", "Ġtra ded", "Ġ {{$", "Ġ{ {$", "Ġ{{ $", "ĠSw edish", "ĠSwe dish", "Ġ'@ /", "_ REAL", "_RE AL", "Ġ warehouse", "Ġw arehouse", "Ġware house", "t oday", "to day", "tod ay", ", L", "o rp", "or p", "< section", " false", ">f alse", "Ġ spa", "Ġs pa", "Ġsp a", "Ġ Near", "ĠN ear", "ĠNe ar", "ì ķ", "Ġint rig", "Ġintr ig", "_ members", "_m embers", "_mem bers", "_member s", "w ave", "wa ve", "wav e", "Ġanal ysts", "Ġanaly sts", "Ġanalyst s", "Ġanalys ts", "_ OS", "_O S", "e din", "ed in", "edi n", "Ġ Fri", "ĠF ri", "ĠFr i", "Ġret rieved", "Ġretrie ved", "Ġretrieve d", "Ġretr ieved", "Reg ular", "_ obs", "_o bs", "_ob s", "EX PORT", "EXP ORT", "' )}}\"", "') }}\"", "')}} \"", "')} }\"", "\" class", "\"c lass", "__ ((", "__( (", "b ucket", "bu cket", "Ġ stro", "Ġs tro", "Ġst ro", "Ġstr o", "Ġ Patch", "ĠP atch", "ĠPat ch", "y stick", "yst ick", "ys tick", "ful ness", "a pos", "ap os", "apo s", "D a", "ĉ ĉĉĉĉĠĠĠ", "ĉĉ ĉĉĉĠĠĠ", "ĉĉĉĉ ĉĠĠĠ", "ĉĉĉ ĉĉĠĠĠ", "ĉĉĉĉĉ ĠĠĠ", "ĉĉĉĉĉĠ ĠĠ", "ĉĉĉĉĉĠĠ Ġ", "Ġen rich", "Ġenr ich", "un ordered", "h ole", "ho le", "hol e", "C ong", "Con g", "Co ng", "< Product", "ĠC urt", "ĠCur t", "ĠCu rt", "( the", "(t he", "(th e", "_ lower", "_l ower", "_lo wer", "_low er", "Ġavoid ing", "Ġ buzz", "Ġb uzz", "Ġbu zz", "Ġbuz z", "Ġv iable", "Ġvi able", "Ġvia ble", "u ba", "ub a", "- is", "-i s", "a rel", "ar el", "are l", "Ġ acted", "Ġact ed", "Ġac ted", "- details", "-d etails", "-de tails", "-detail s", "-det ails", "ภĩ", "Ġ Theory", "ĠThe ory", "ĠTheo ry", "ĠP un", "ĠPu n", "Ġ Anonymous", "ĠAn onymous", ".. .\"Ċ", "... \"Ċ", "...\" Ċ", "è res", "ère s", "åı ¯", "Ġ Vision", "ĠV ision", "ĠVis ion", "_ sem", "_s em", "_se m", "a sha", "as ha", "ash a", "Ġcelebr ity", "Ġ endDate", "Ġend Date", "Ġ populate", "Ġpop ulate", "Ġpopul ate", "Ġc uis", "Ġcu is", "Ġcui s", "q uant", "qu ant", "qua nt", "quan t", "f loor", "fl oor", "flo or", "Ġglobal ly", "Ġglob ally", "Ġc ruise", "Ġcru ise", "Ġcruis e", "ĠStan ley", "Ġb ikes", "Ġbi kes", "Ġbike s", "Ġbik es", ". getConnection", ".get Connection", "Ġpoor ly", "_ other", "_o ther", "_ot her", "am ping", "amp ing", ". \");ĊĊ", ".\" );ĊĊ", ".\");Ċ Ċ", ".\") ;ĊĊ", ".\"); ĊĊ", "o di", "od i", "_ ADMIN", "_A DMIN", "_AD MIN", ". colors", ".color s", ".col ors", "ĠG aming", "ĠGa ming", "ĠGam ing", "> ';ĊĊ", ">' ;ĊĊ", ">';Ċ Ċ", ">'; ĊĊ", "STR UCT", "STRU CT", "Q R", "I Ds", "ID s", "( arguments", "(arg uments", "(argument s", "_ aux", "_a ux", "( Event", "(E vent", "_ PRIVATE", "_PR IVATE", "_PRIV ATE", "ĠT rek", "ĠTr ek", "ĠTre k", "Ġ downloads", "Ġdown loads", "Ġdownload s", "m utable", "mut able", "mu table", "_ STRUCT", "_STR UCT", "( wx", "(w x", "Ġ domains", "Ġdom ains", "Ġdomain s", "Ġdoma ins", "js px", "jsp x", "ĠVi agra", "ĠVia gra", "Command s", "Comm ands", "J s", ". cfg", ".c fg", ".cf g", "Content Pane", "Ġ EditText", "ĠEdit Text", "à¥į à¤", "At tach", "Att ach", "Ġ ARM", "ĠA RM", "ĠAR M", "pos itive", "posit ive", "Ġ Generated", "ĠG enerated", "ĠGener ated", "ĠGenerate d", "ĠGene rated", "Ġse ized", "Ġseiz ed", "Ġsei zed", "Ġseize d", "= :", "Ġ electronics", "Ġelect ronics", "Ġelectronic s", "Ġelectron ics", "Ġ AppComponent", "ĠApp Component", "/ ',Ċ", "/' ,Ċ", "/', Ċ", ". equalsIgnoreCase", ".equals IgnoreCase", "Do ctrine", "d isk", "dis k", "di sk", "Ġ Political", "ĠPol itical", "ĠPolit ical", "C HO", "CH O", "< F", "ĉ height", "ĉh eight", "Ġ Bug", "ĠB ug", "ĠBu g", ". le", ".l e", "i kh", "ik h", "Ġ milliseconds", "Ġm illiseconds", "Ġmill iseconds", "Ġmilli seconds", "Ġconst itu", "Ġconstit u", "m ag", "ma g", ". nl", ".n l", "- range", "-r ange", "-ra nge", "ang gal", "' ,[", "', [", "r opolitan", "ropol itan", "Ġ Ãľ", "Ġà ľ", "Ġ UC", "ĠU C", ". desc", ".d esc", ".de sc", ".des c", "- LAST", "-L AST", "f stream", "fst ream", "i bil", "ib il", "ibi l", "Ġf ier", "Ġfi er", "Ġfie r", "V ERY", "VER Y", "VE RY", "Ġ ë³", "Ġë ³", "I RT", "IR T", "_ UI", "_U I", "( abs", "(a bs", "(ab s", "Ġk nees", "Ġkn ees", "Ġkne es", "Ġknee s", "Ġr ookie", "Ġro okie", "Ġ Vac", "ĠV ac", "ĠVa c", "a rena", "ar ena", "are na", "aren a", "comm end", "- \\", "ĠSUB STITUTE", "S oft", "So ft", "Ġpart ir", "Ġpar tir", "Ġparti r", "we alth", "è¦ ģ", "( dataset", "(d ataset", "(data set", "(dat aset", "(datas et", "Ġ Climate", "ĠCl imate", "ĠClim ate", "ĠCli mate", "- show", "-s how", "-sh ow", "Ġrel iability", "Ġreli ability", "_ chunk", "_ch unk", "ä» £", "_ stock", "_st ock", "ĠEX EMPLARY", "ï ¸ı", "ï¸ ı", "Ġ vÃŃ", "Ġv ÃŃ", "Ġsm iled", "Ġsmile d", "Ġdr ill", "Ġdri ll", ". Function", ".F unction", ".Func tion", "Ġ SI", "ĠS I", "Ġre gression", "Ġreg ression", "Ġregress ion", "- X", "Ġ Jar", "ĠJ ar", "ĠJa r", "p ref", "pr ef", "pre f", "ĉ success", "ĉs uccess", "ĠH itler", "ĠHit ler", "Ġinst inct", "Ġfem mes", "Ġfemme s", "Ġ lover", "Ġl over", "Ġlo ver", "Ġlove r", "Ġlov er", "< Ċ", "Ġmulti plier", "Ġmultip lier", "r il", "ri l", "Re size", "Res ize", "Ġ Authorization", "ĠAuthor ization", "ĠK an", "ĠKa n", "Dispatch ToProps", "Ġc rops", "Ġcr ops", "Ġcro ps", "Ġcrop s", "t okens", "token s", "tok ens", "e cn", "ec n", "ent ially", "ential ly", "enti ally", "ĠINTERRU PTION", "f ake", "fa ke", "fak e", "Un defined", "Und efined", "Ġ AK", "ĠA K", "Ġ TestCase", "ĠTest Case", "Ġ rab", "Ġr ab", "Ġra b", "Ġ torrent", "Ġt orrent", "Ġtor rent", "Ġ Ot", "ĠO t", "B ars", "Bar s", "Ba rs", "Ġ lecture", "Ġl ecture", "Ġlect ure", "Ġen jo", "Ġrespond s", "Ġresp onds", "Ġ indexed", "Ġindex ed", "Ġinde xed", "Of Work", "_ chain", "_ch ain", ") )->", ")) ->", "))- >", "Ġ Beauty", "ĠBe auty", "ĠBeaut y", "ĠBea uty", "ĠBeau ty", "Ġ` <", "Ġtouch ing", "Ġtou ching", "Ġ |--", "Ġ| --", "Ġ|- -", "ĉ flag", "ĉf lag", "normal ize", "Ġt rapped", "Ġtr apped", "Ġtra pped", "Ġtrap ped", "Ġestablish ing", "/ build", "/b uild", "A J", "f y", "- react", "-re act", "a vn", "av n", "RI PTION", "RIPT ION", "Ġk ut", "Ġku t", "Ġ Fashion", "ĠF ashion", "Ġ Inform", "ĠIn form", "ĠInfo rm", "ĠInf orm", "c urities", "cur ities", "< byte", "{Ċ", "Ġ= >{Ċ", "Ġ=> {Ċ", "Ġgar lic", "Ġ repr", "Ġre pr", "Ġrep r", "Ġre plies", "Ġrep lies", "Ġrepl ies", "( prop", "(p rop", "(pro p", "(pr op", "Ġspirit s", "Ġspir its", "Ġins pire", "Ġinspir e", "Ġinsp ire", "Ġbase ment", "Ġbas ement", ". reject", ".re ject", "Ġ hints", "Ġh ints", "Ġhint s", "Ġhi nts", "Ġhin ts", "Ġpol ling", "Ġpoll ing", "ĉ ĠĊ", "ĉĠ Ċ", "_ rating", "_r ating", "_ra ting", "_rat ing", "Ġc ath", "Ġca th", "Ġcat h", "a vier", "av ier", "avi er", "Ġ compressed", "Ġcom pressed", "Ġcomp ressed", "Ġcompr essed", "Ġcompress ed", "Ġ VS", "ĠV S", "] '", "Ġjud icial", "ĠT rend", "ĠTr end", "ĠTre nd", "tr aining", "tra ining", "train ing", "EST AMP", "ogn ition", "Ä ģ", "S ENT", "SE NT", "SEN T", "v entions", "vent ions", "vention s", "Ġconsult ant", "Ġconsulta nt", "Ġconsul tant", "u mph", "um ph", "ump h", "Ġ userService", "Ġuser Service", ", NULL", ",N ULL", "k h", "D ear", "De ar", "_ BAD", "_B AD", "it ations", "itation s", "itat ions", "Ġmet aph", "Ġmeta ph", "' é", "and ise", "andi se", "- font", "-f ont", ". chart", ".c hart", ".ch art", ".char t", "Ġ sg", "Ġs g", "_ Controller", "_Control ler", ". jpeg", ".j peg", ".jp eg", "Ġ ULONG", "ĠU LONG", "ĠUL ONG", "ĉ game", "ĉg ame", "( ss", "(s s", "ĠM aj", "ĠMa j", "ĉ go", "ĉg o", "Ġ Sad", "ĠS ad", "ĠSa d", "ĠB erg", "ĠBe rg", "ĠBer g", "Ġ Mine", "ĠM ine", "ĠMin e", "ĠMi ne", "P ack", "Pa ck", "Ġres istant", "Ġresist ant", "Ġ ROM", "ĠR OM", "ĠRO M", "Ġ peg", "Ġp eg", "Ġpe g", "ĠSt anford", "ĠStan ford", "Ġ Yahoo", "ĠY ahoo", "ĠYa hoo", "ĠYah oo", "Ġ scaled", "Ġs caled", "Ġsc aled", "Ġscale d", "Ġsca led", "Ġscal ed", "Ġ lan", "Ġl an", "Ġla n", "= []", "=[ ]", "\" /> < /", "Ġ plots", "Ġp lots", "Ġpl ots", "Ġplot s", "Ġplo ts", ". *Ċ", ".* Ċ", "Ġtr aveled", "Ġtravel ed", "Ġtra veled", "Ġtrav eled", "ĠO scar", "ĠOs car", "ĠOsc ar", "V L", "Ġl inking", "Ġlink ing", "Ġlin king", "Ġt ires", "Ġti res", "Ġtire s", "Ġtir es", "Ġ' *'", "Ġ'* '", "ĠBuffer ed", "ĠBuff ered", "e ri", "er i", "Ġ ****", "Ġ* ***", "Ġ** **", "Ġ*** *", "Ġover look", "Ġoverl ook", ". Non", ".N on", ".No n", "Ġr és", "Ġré s", "Ġe gy", "Ġeg y", "å° ı", "Ġatt acker", "Ġattack er", "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", ". sync", ".s ync", ".syn c", ".sy nc", "AS CADE", "ASC ADE", "G round", "Gr ound", "Gro und", "Ġ decay", "Ġdec ay", "Ġ Ton", "ĠT on", "ĠTo n", "Ġjew elry", "Ġjewel ry", "Ġb ypass", "Ġby pass", "Ġm embr", "Ġmem br", "Ġmemb r", "R NA", "RN A", "< System", " ččĊ", "Ġs ud", "Ġsu d", "ĉ background", "ĉback ground", "Ġsch olars", "Ġscholar s", "-m uted", "a rá", "ar á", "Ġ =====", "Ġ= ====", "Ġ== ===", "Ġ=== ==", "Ġ==== =", "Ġ ____", "Ġ_ ___", "Ġ__ __", "Ġ___ _", "C reat", "Cre at", "Cr eat", "e never", "en ever", "ene ver", "/ wp", "/w p", "Ġ VPN", "ĠV PN", "ĠVP N", "Error Code", ") ],Ċ", ")] ,Ċ", ")], Ċ", "( builder", "(b uilder", "(build er", "Ġ Enemy", "ĠEn emy", "S ensor", "u sa", "us a", "Ġtr iggers", "Ġtrigger s", "Ġtrig gers", "Ġplay offs", "Ġplayoff s", "_ REQ", "_RE Q", "_R EQ", "Ġ (~", "Ġ( ~", "ĠB arry", "ĠBar ry", "ĠBarr y", "Ġperman ently", "Ġpermanent ly", "Ġ RUN", "ĠR UN", "ĠRU N", "Ġb ure", "Ġbu re", "Ġbur e", ".Fatal f", ".Fat alf", "Ġch ick", "Ġchi ck", "Ġchic k", "ĉ panic", "ĉp anic", "p si", "ps i", "o ka", "ok a", "éĢ ī", "> [", "Ġunder stands", "Ġunderstand s", "Ġunderst ands", "Ġ Junior", "ĠJun ior", "ĠJuni or", "Ġ INFO", "ĠIN FO", "ĠINF O", "= mysqli", "=m ysqli", "=mysql i", "us tain", "ust ain", "usta in", "- source", "-s ource", "s erv", "se rv", "ser v", "Ġ CREATE", "ĠC REATE", "ĠCRE ATE", ". au", ".a u", "Ġs ells", "Ġsell s", "Ġsel ls", "Ġ ĠĊĠĠĊ", "ĠĠ ĊĠĠĊ", "ĠĠĊ ĠĠĊ", "E urope", "Euro pe", "z w", "p reh", "pr eh", "pre h", "ĠN SA", "ĠNS A", "Ġ xy", "Ġx y", "ภ´", "Ġ Beyond", "ĠB eyond", "ĠBey ond", "In stead", "Inst ead", "Non Query", "Ġa rise", "Ġar ise", "Ġavoid ed", ". emplace", ".em place", ".emp lace", "_ models", "_model s", "_mode ls", "_mod els", "} ),Ċ", "}) ,Ċ", "}), Ċ", "Ġ hid", "Ġh id", "Ġhi d", "Ġ &_", "Ġ& _", ". points", ".p oints", ".point s", ".po ints", ".poi nts", ". getWidth", ".get Width", ". Exec", ".Ex ec", ".E xec", "Ġ ////", "Ġ// //", "Ġ/ ///", "Ġ/// /", "Ġ Sessions", "ĠS essions", "ĠSession s", ". ..\\", ".. .\\", "... \\", "ĠCol omb", "ĠColo mb", "Ġacceler ation", "Ġaccel eration", "re store", "rest ore", "Ġ ile", "Ġi le", "Ġil e", "o bic", "ob ic", "obi c", "< Node", " }Ċ", ">} Ċ", "pl aint", "plain t", "pla int", "get Text", "Ġindividual ly", "Ġindivid ually", "Ġ checkbox", "Ġcheck box", "U Y", "ĠL amb", "ĠLa mb", "ĠLam b", "Ġdys function", "ĠL ar", "ĠLa r", "à °", "Ġ Creating", "ĠC reating", "ĠCr eating", "ĠCre ating", "ĠCreat ing", "' );ĊĊĊ", "') ;ĊĊĊ", "');Ċ ĊĊ", "');ĊĊ Ċ", "'); ĊĊĊ", "\" They", "\"The y", "\"T hey", "l ocations", "loc ations", "location s", "_ CORE", "_C ORE", "_CO RE", "_COR E", "Inter action", "umbn ails", "umbnail s", "Ġ Partner", "ĠP artner", "ĠPart ner", "b rit", "br it", "Ġl esser", "Ġless er", "Ġles ser", "Ġ Slot", "ĠS lot", "ĠSl ot", "ĠSlo t", "set Attribute", "Ġ Wave", "ĠW ave", "ĠWa ve", ". po", ".p o", "/ store", "/st ore", "Ġb rowsing", "Ġbrows ing", "Ġbrow sing", "_ pd", "_p d", "s ume", "sum e", "su me", "s ed", "se d", "C urve", "Cur ve", "Cu rve", "Ġpl asma", "Ġsusp icious", "ìĿ ¸", "Ġ Bah", "ĠB ah", "ĠBa h", "Ġ Explicit", "ĠExp licit", "ĠExpl icit", "_ CC", "_C C", ".Client Size", "\\ View", "\\V iew", "Ġsub stit", "Ġsubs tit", "Ġsubst it", "l oon", "lo on", "loo n", "Ġ GAME", "ĠG AME", "ĠGA ME", "ĠGAM E", "ĠB rid", "ĠBr id", "ĠBri d", "Ľ 建", "_ User", "_U ser", "Ġs quares", "Ġsqu ares", "Ġsquare s", "f one", "fo ne", "fon e", "Ġsa cred", "Ġsac red", "Ġsacr ed", "ug hs", "ugh s", "] interface", "Ġ Throw", "ĠTh row", "ĠThr ow", "ĠK irk", "ĠKir k", "ĠKi rk", "Ġem pire", "Ġemp ire", "Ġempir e", "Ġass essed", "Ġassess ed", "Ġasses sed", "T ax", "Ta x", "ĠHe aven", "- buffer", "-b uffer", "_ STATIC", "_ST ATIC", "_STAT IC", "é né", "én é", "-b ordered", "-border ed", "Ġp unct", "Ġpun ct", "( mode", "(m ode", "(mod e", "Ġke ine", "Ġkein e", "S ent", "Se nt", "Sen t", "Ġ Calcul", "ĠCal cul", "ĠCalc ul", "ĠE ve", "ĠEv e", "Ġsty lish", "Ġstyl ish", "Ġo ils", "Ġoil s", "Ġoi ls", ". TestCase", ".Test Case", "Ġtrad emark", "Ġtrade mark", "Ġliter ary", "Ġlite rary", "Ġconcentr ations", "Ġconcentration s", "Ġ Relations", "ĠRe lations", "ĠRel ations", "ĠRelation s", "( Class", "(C lass", "(Cl ass", "Ġ stdin", "Ġst din", "Ġstd in", "Ġv æ", "back up", "bac kup", ". VERSION", ".V ERSION", ".AutoScale Dimensions", "st arter", "start er", "star ter", "Transaction al", "- panel", "-p anel", "-pane l", "St udio", "k c", "ĠCh amber", "ĠCham ber", "ĠS piel", "ĠSp iel", "ĠSpi el", "Ġ rho", "Ġr ho", "Ġrh o", "ا ÙĦ", "ا٠Ħ", "! '", ". Attributes", ".At tributes", ".Attribute s", "Ġmurder ed", "apeut ic", "Ġint imate", "Ġintim ate", "Ġ textField", "Ġt extField", "Ġtext Field", "ĠBuff alo", "d ummy", "dum my", "\" %", "ĠLib erty", "ĠLibert y", "ĠLiber ty", "o bar", "ob ar", "oba r", "Ġ Tank", "ĠT ank", "ĠTa nk", "ĠTan k", "Ġ Popular", "ĠPop ular", "er visor", "erv isor", "ĠIn iti", "ĠInit i", "ĠIni ti", "ĠM all", "ĠMal l", "ĠMa ll", "Ġ Prior", "ĠP rior", "ĠPr ior", "ĠPri or", "C AP", "CA P", "ĠC lay", "ĠCl ay", "ĠCla y", "Ġ Certificate", "ĠC ertificate", "ĠCert ificate", "ĠCertif icate", ". Lock", ".L ock", ".Lo ck", "- strip", "-s trip", "-st rip", "-str ip", "-dr iven", "-drive n", "/ all", "/a ll", "/al l", "ĠMessageBox Buttons", "ĠMessageBoxButton s", "_ SECRET", "_SE CRET", "_SEC RET", "_ pb", "_p b", "Ġ rats", "Ġr ats", "Ġrat s", "Ġra ts", "ा à¤", "Ġ nt", "Ġn t", ". Router", ".R outer", ".Route r", "_ topic", "_t opic", "_to pic", "_top ic", "Ġt ennis", "Ġten nis", "Ġ PUBLIC", "ĠP UBLIC", "ĠPUB LIC", "ĠActiv atedRoute", "Ġ ',Ċ", "Ġ' ,Ċ", "Ġ', Ċ", "Ġcost ume", "Ġj okes", "Ġjo kes", "Ġjoke s", ". Handle", ".H andle", ".Hand le", "ĉ byte", "ĉb yte", "Ġfl avors", "Ġflavor s", "Ġflav ors", "( cc", "(c c", "Ġperson as", "Ġpers onas", "Ġpersona s", "Ġperso nas", "ĉ image", "ĉi mage", "ĉim age", "ĠN azi", "ĠNa zi", "ĠNaz i", "Ġ grammar", "Ġgram mar", "Ġgramm ar", "Ġú lt", "Ġval ve", "Ġ vic", "Ġv ic", "Ġvi c", "Ġ Rachel", "ĠR achel", "ĠRac hel", "ĠRach el", "_ invalid", "_in valid", "P refs", "Pr efs", "Pre fs", "Pref s", "std int", "stdin t", "( route", "(r oute", "(ro ute", "Ġ htmlspecialchars", "Ġhtml specialchars", "Ġpe oples", "Ġpeople s", "p line", "pl ine", "Ġ nv", "Ġn v", "Ġ Quant", "ĠQ uant", "ĠQu ant", "o ppers", "op pers", "opp ers", "opper s", "Ġ currentUser", "Ġcurrent User", "ĠC atal", "ĠCa tal", "ĠCat al", "ĠCata l", "Ġre conc", "Ġrecon c", "Ġreco nc", "Ġcon junction", "Ġconj unction", "l x", "am burg", "amb urg", "Ġinflu ential", "d anger", "da nger", "dan ger", "in ders", "ind ers", "inder s", "inde rs", "Ġ %@\",", "Ġ% @\",", "Ġ%@ \",", ". configuration", ".config uration", "o some", "os ome", "oso me", ". identity", ".id entity", ".ident ity", ".ide ntity", "Ġ picker", "Ġp icker", "Ġpick er", "Ġpi cker", "Ġpic ker", "n ost", "no st", "nos t", "ĠDI Y", "Aug ust", "a blo", "ab lo", "abl o", "Le af", "ĠR eco", "ĠRe co", "ĠRec o", "c ko", "ck o", "D OC", "DO C", "ĠH erm", "ĠHe rm", "ĠHer m", ": any", ":a ny", "Ġ Interview", "ĠInt erview", "ĠInter view", "Ġ Tex", "ĠT ex", "ĠTe x", "x fe", "xf e", "( work", "(w ork", "Ġle ap", "He ading", "Head ing", "Ġ quarters", "Ġqu arters", "Ġquarter s", "Ġquar ters", "Ġquart ers", "\\ Bundle", "r eb", "re b", "Per haps", "ĠG mbH", "B irth", "Bir th", "ĉ sum", "ĉs um", "ĠWat son", ". nil", ".n il", "ç ¡", "{ }ĊĊ", "{} ĊĊ", "{}Ċ Ċ", "ic aid", "ica id", "G etter", "Get ter", "\" name", "Ġ \"čĊ", "Ġ\" čĊ", "_ none", "_n one", "_no ne", "_non e", "z m", "ac ute", "u esto", "ue sto", "ues to", "uest o", "Ġs ous", "Ġso us", "Ġsou s", "Ġre build", "Ġreb uild", "Ġnews papers", "Ġnewsp apers", "Ġnewspaper s", "Ġ Haz", "ĠH az", "ĠHa z", "Ġ kits", "Ġk its", "Ġkit s", "Ġki ts", "i fo", "if o", "Bl ur", "Ġsu ited", "Ġsuit ed", "Ġsuite d", "Ġsui ted", "- In", "-I n", "à ¯", "Ġ Keith", "ĠKe ith", "ĠNor way", "IN IT", "INI T", "ire ccion", "i eties", "ie ties", "iet ies", "_ usage", "_u sage", "_us age", "Ġ Doug", "ĠD oug", "ĠDo ug", "ĠDou g", "r ise", "ri se", "ris e", "Ġtr illion", "im ited", "imit ed", "imi ted", "Ġ REL", "ĠR EL", "ĠRE L", "a lic", "al ic", "ali c", "Ġcritic ized", "Ġcriticize d", "the orem", "Ġc ease", "Ġce ase", "Ġside w", "Ġsid ew", "ĠT erry", "ĠTer ry", "ĠTerr y", "Ġsubs idi", "Ġsubsid i", "Ġfirm ly", "Ġ aws", "Ġa ws", "Ġaw s", "Ġh ott", "Ġhot t", "Ġho tt", "Ġd ressing", "Ġdress ing", "b adge", "ba dge", "bad ge", "Ġ Applications", "ĠApp lications", "ĠApplication s", "ĠAppl ications", "è¿ ĶåĽŀ", "è¿Ķ åĽŀ", "Ġlaugh ed", "Ġh obby", "Ġhob by", "Ġmus icians", "Ġmusic ians", "Ġmusician s", "Ġ *.", "Ġ* .", ". placeholder", ".place holder", "Ġc ounters", "Ġcount ers", "Ġcoun ters", "Ġcounter s", "ĠCap itol", "S DK", "SD K", "Ġh elmet", "Ġhel met", "Ġhelm et", "and box", "q uit", "qu it", "qui t", "Ġcriminal s", "Ġcrim inals", "Ġteen ager", "Ġteenage r", "( update", "(up date", "G l", ". selection", ".s election", ".se lection", ".select ion", ".sel ection", "Ġdis charge", "Ġpres enting", "Ġpresent ing", "ufact urer", "_ UNKNOWN", "_UN KNOWN", "Ġst ressed", "Ġstr essed", "Ġstress ed", "å Ļ¨", "åĻ ¨", "Pro to", "Pr oto", "Prot o", "_ correct", "_c orrect", "_cor rect", "_corr ect", "h aus", "ha us", "Ġre nov", "Ġren ov", "Ġfire arms", "Ġfirearm s", "Ġtechn ically", "Ġtechnical ly", "- browser", "-b rowser", "Ġc andy", "Ġcan dy", "Ġcand y", "St roke", "Str oke", "Ġ executor", "Ġexec utor", "Ġexecut or", "Ġocc urrence", "Ġoccur rence", "Ġ IPv", "ĠIP v", "_ INTERFACE", "_INTER FACE", "Ġ Retrieve", "ĠRe trieve", "ĠRet rieve", "ĠRetrie ve", ". bad", ".b ad", ".ba d", "Ex change", "Nav bar", "Ġ Kid", "ĠK id", "ĠKi d", "(get ApplicationContext", "_ STOP", "_S TOP", "_ST OP", "Ġ Boss", "ĠB oss", "ĠBo ss", "ĠBos s", "List eners", "Listener s", "Listen ers", "Ġsh ooter", "Ġshoot er", "Ġsho oter", "ĠA lb", "ĠAl b", "ä ch", "Ġ pix", "Ġp ix", "Ġpi x", ". keyCode", ".key Code", "al one", "alo ne", "alon e", "Ġabs urd", "Ġ Cum", "ĠC um", "ĠCu m", "ĠNewton soft", "i kt", "ik t", "Ġlaugh ing", "Ġcapital ism", "ree Node", "T x", "_ QUERY", "_QU ERY", ". Sleep", ".S leep", "( login", "(log in", "(lo gin", "Web Element", "Ġcelebr ating", "Ġ deprecated", "Ġde precated", "Ġdep recated", "Ġm aar", "Ġma ar", "Ġart istic", "Ġartist ic", "_ASS OC", "_AS SOC", "ĠBorder Radius", "ĉ wp", "ĉw p", "Ġsurv ivors", "Ġsurviv ors", "Ġsurvivor s", "In ner", "- red", "-r ed", "-re d", "Ġprosec ution", "_ pp", "_p p", "( \"$", "\"=> $", "Ġ comma", "Ġcom ma", "Ġco mma", "Ġcomm a", "un checked", "g raphics", "graph ics", "graphic s", "r ors", "ro rs", "ror s", "G ROUND", "GR OUND", "( public", "(p ublic", "(pub lic", "Ġcustom ized", "Ġcustomize d", "ĠArk ansas", "Ġ Rew", "ĠR ew", "ĠRe w", "Ġ expiration", "Ġex piration", "Ġexp iration", "× ķ", "ĠC ul", "ĠCu l", "Ġn ons", "Ġno ns", "Ġnon s", ". Filter", ".F ilter", "Ġsen ator", "_ definition", "_def inition", "ash ington", "ashing ton", "y mph", "ym ph", "/ J", "Ġ fuse", "Ġf use", "Ġfu se", "Ġfus e", "ra mid", "ram id", "Ġ Supplier", "ĠS upplier", "ĠSup plier", "Ġ autocomplete", "Ġaut ocomplete", "Ġauto complete", "Ġ }),", "Ġ} ),", "Ġ}) ,", ". \"ĊĊĊ", ".\" ĊĊĊ", ".\"ĊĊ Ċ", ".\"Ċ ĊĊ", "_ functions", "_function s", "_fun ctions", "ĉ to", "ĉt o", ". eval", ".e val", ".ev al", "ĠT Object", "ĠTO bject", "Re ferences", "Reference s", "Refer ences", "Ġh eated", "Ġhe ated", "Ġheat ed", "H AL", "HA L", "Ġ ))}Ċ", "Ġ) )}Ċ", "Ġ)) }Ċ", "} $", "ĠB arr", "ĠBar r", "ĠBa rr", "_ UNIT", "_UN IT", "+ $", "Ġ getValue", "Ġget Value", "i ped", "ip ed", "ipe d", "ch ied", "chie d", "chi ed", "( vm", "(v m", "c ue", "cu e", "_ integer", "_int eger", "_ course", "_c ourse", "_co urse", "th ird", "Ġre vised", "Ġrev ised", "Ġrevis ed", "Ġrevise d", "* */Ċ", "** /Ċ", "_ DIRECT", "_D IRECT", "_DIR ECT", "_DI RECT", "Out Of", "( \"(", "(\" (", "Ġ Feel", "ĠF eel", "ĠFe el", "ĠFee l", "Ġre ass", "Ġ subtitle", "Ġsub title", "Ġsubt itle", "p eri", "pe ri", "per i", "n f", "Ġenjoy s", "Ġenjo ys", "Ġtreat s", "Ġtre ats", ") this", ")t his", "- tabs", "-t abs", "-tab s", "an cers", "ance rs", "anc ers", "ancer s", "Ġ continent", "Ġcont inent", "Ġcontin ent", "Ġcar dio", "Ġcard io", "Ġcardi o", "S er", "Se r", ". question", ".q uestion", ".qu estion", ".quest ion", "Ġph rases", "Ġphrase s", "Valid ators", "Validator s", "Ġpop ul", "Ġ lÃŃ", "Ġl ÃŃ", "s ong", "so ng", "son g", "_ INTERNAL", "_IN TERNAL", "_INTER NAL", "Ġadv iser", "Ġadvis er", "Ġadvise r", "Ġp uzz", "Ġpu zz", "Ġamb itious", "Ġambit ious", "ĠT ob", "ĠTo b", "Ġ DP", "ĠD P", "Ġpres idency", "Ġsur render", "Ġsurre nder", "Ġw atches", "Ġwatch es", "Ġwat ches", "_ binary", "_b inary", "_bin ary", "Ġ Soon", "ĠS oon", "ĠSo on", "Ġcan ada", "(\" \")Ċ", "(\"\" )Ċ", "(\"\") Ċ", "] ='", "]= '", "Ġ Brandon", "ĠBr andon", "ĠBrand on", "ĠBra ndon", "ĠBran don", "e psilon", "eps ilon", "r w", ". addChild", ".add Child", ". Copy", ".C opy", ".Co py", "Pr incipal", "Ph otos", "Photo s", "Phot os", "Ġmargin al", "Ġmarg inal", "Ġb asics", "Ġbas ics", "Ġbasic s", "e ing", "ei ng", "ein g", "M ust", "Mu st", "Mus t", "_ String", "_S tring", "_Str ing", "_St ring", "Ġ ole", "Ġo le", "Ġol e", "M agento", "Mag ento", ". customer", ".c ustomer", ".custom er", "( prev", "(p rev", "(pr ev", "(pre v", "ภ¥", "Ġlo yalty", "Ġloyal ty", "C og", "Co g", "Ġ protocols", "Ġprot ocols", "Ġprotocol s", "Ġproto cols", "Ġ Companies", "ĠCom panies", "Ġthe oretical", "Ġtheoret ical", "Ġtheor etical", "Ġaccess ing", "Ġacces sing", "Ġ Zen", "ĠZ en", "ĠZe n", ". ones", ".on es", ".o nes", ".one s", "att ice", "atti ce", "_ world", "_w orld", "z es", "ze s", "Ġtatto o", "Ġtat too", "Ġm enos", "Ġme nos", "Ġmen os", "Ġmeno s", "Ġ intersect", "Ġinter sect", "Ġinters ect", "\" ];ĊĊ", "\"] ;ĊĊ", "\"];Ċ Ċ", "\"]; ĊĊ", "be lie", "bel ie", "Ġ inactive", "Ġin active", ".read line", "-label led", ". done", ".d one", ".do ne", "lic kr", "lick r", "Ġ WORK", "ĠW ORK", "Ġder ivative", "Ġderiv ative", "Ġd atabases", "Ġdata bases", "Ġdatabase s", "Ġdatab ases", "âĤ Ĥ", "Ġ sx", "Ġs x", ". isArray", ".is Array", "Ġ ys", "Ġy s", "Ġp ada", "Ġpa da", "Ġpad a", "Ġ Bullet", "ĠB ullet", "ĠBul let", "ĠBull et", "(` /", "is Active", "Ġ CGSize", "ĠCG Size", "( equalTo", "(equal To", "ĠColum bus", "Ġm arry", "Ġmar ry", "D EV", "DE V", "_ limits", "_l imits", "_limit s", "_li mits", "_lim its", "r ones", "ro nes", "ron es", "rone s", "I AS", "IA S", "Ġ tau", "Ġt au", "Ġta u", "m ino", "min o", "mi no", "_ Write", "_W rite", "ĠW ine", "ĠWin e", "ĠWi ne", "Ġ [['", "Ġ[ ['", "Ġ[[ '", "Ġ Pull", "ĠP ull", "ĠPu ll", "ĠPul l", "ri ters", "rit ers", "rite rs", "riter s", "r ients", "ri ents", "rie nts", "rient s", "rien ts", "Ġsh ifting", "Ġshift ing", "u pp", "up p", "_ TIMER", "_T IMER", "_TIME R", "_TIM ER", "_TI MER", "Ġ Conditions", "ĠCondition s", "ĠCond itions", "Ạ¥", "Ġ Orders", "ĠOr ders", "ĠOrder s", "ĠOrd ers", "Ġ Strength", "ĠSt rength", "ĠStr ength", "ĠStre ngth", "æī Ģ", "Ġval idity", "Ġvalid ity", "Ġf ot", "Ġfo t", "e tur", "et ur", "etu r", "Ġ bolt", "Ġb olt", "Ġbo lt", "Ġbol t", "åĨ ħ", "Ġ Along", "ĠA long", "ĠAl ong", "ĠAlo ng", "o shi", "os hi", "osh i", "Ġassum ptions", "Ġassumption s", "Ġmag azines", "Ġmagazine s", "_ SPI", "_S PI", "_SP I", "Ġp unt", "Ġpun t", "Ġpu nt", "_ PRODUCT", "_PRO DUCT", "_PROD UCT", "Ġ relay", "Ġre lay", "Ġr elay", "Ġrel ay", "Ġ Javascript", "ĠJ avascript", "ĠJava script", ". te", ".t e", "- es", "-e s", "Ġ widgets", "Ġwidget s", "Ġwid gets", "( fs", "(f s", "< Item", " \";", ">\" ;", "at ching", "atch ing", "Ġ Knowledge", "ĠK nowledge", "ĠKnow ledge", "ĉ The", "ĉT he", "; margin", ";m argin", "less ness", "o pard", "op ard", "opa rd", "u matic", "um atic", "uma tic", "umat ic", "( )));čĊ", "() ));čĊ", "()) );čĊ", "())) ;čĊ", "())); čĊ", "Ġf als", "Ġfa ls", "Ġfal s", "( cache", "(c ache", "(ca che", "Type Id", "éĢ ļ", "_ choice", "_ch oice", "ĠG oth", "ĠGo th", "ĠGot h", "Ġ Sites", "ĠS ites", "ĠSi tes", "ĠSite s", "ĠSit es", "M G", "_ border", "_b order", "Ind ices", "Com parer", "Comp arer", "Compar er", "Compare r", "ĠRe distribution", "ĠRed istribution", "ĠRedis tribution", "Ġclose t", "Ġclos et", "Ġclo set", "Ġvers atile", "Ġversa tile", "In puts", "Input s", "**** ****************", "******** ************", "**************** ****", "****** **************", "************ ********", "************** ******", "Ġob esity", "qu iz", "qui z", "g ra", "gr a", "( global", "(g lobal", "åĬ ¡", "Ġ collector", "Ġcol lector", "Ġcoll ector", "Ġcollect or", "Ġcolle ctor", "Ġ kor", "Ġk or", "Ġko r", "o vable", "ov able", "ova ble", "A DC", "AD C", "Ġ EventHandler", "ĠEvent Handler", ". nc", ".n c", "Ġplay back", "ient os", "ien tos", "iento s", "_ perm", "_p erm", "_per m", "_pe rm", "_ WARNING", "_W ARNING", "_WARN ING", "ĠOlymp ics", "ĠOlympic s", ". norm", ".n orm", ".no rm", "Ġ Broadcast", "ĠB roadcast", "ĠBroad cast", "_ small", "_s mall", "_sm all", "d rive", "dr ive", ". iloc", ".i loc", ".il oc", "Ġ typed", "Ġt yped", "Ġtype d", "Ġtyp ed", "Ġty ped", "M EM", "ME M", "_ cons", "_c ons", "_con s", "_co ns", "D METHOD", "DM ETHOD", "Ġ lun", "Ġl un", "Ġlu n", ". distance", ".d istance", ".di stance", ".dist ance", "( par", "(p ar", "(pa r", "p oon", "po on", "Ġ bast", "Ġb ast", "Ġbas t", "Ġba st", "act ivities", "activ ities", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", ": čĊčĊ", ":čĊ čĊ", "S ER", "SE R", ") &&", ")& &", "_ lst", "_l st", "_ls t", "ĠPol ish", "ĠPo lish", "Ġkn ocked", "Ġknock ed", "Ġfrustr ation", "au kee", "Ġph osph", "iqu id", "iq uid", "_ coeff", "_c oeff", "_co eff", "_coef f", "æŃ ¤", "L atest", "La test", "Lat est", "Late st", "ĠD ust", "ĠDu st", "T ipo", "Tip o", "Ti po", "Ġmain tains", "Ġmaint ains", "Ġmaintain s", "Ġ marsh", "Ġmar sh", "Ġmars h", "inc inn", "inci nn", "l bl", "lb l", "C are", "Car e", "Ca re", "Ġneighborhood s", "_ gpio", "_g pio", "_gp io", "ĠAr senal", "ĠArs enal", "D em", "De m", "ĠW he", "ĠWh e", "_ hook", "_h ook", "Ġl dc", "Ġld c", "ĠHar per", "ĠBer keley", "Ġgrad uated", "Ġgraduate d", "Ġgradu ated", "Per cent", "Ġarr iving", "Ġarriv ing", "Ġ Adventure", "ĠAd venture", "ĠAdvent ure", "( scope", "(s cope", "(sc ope", "( '*", "(' *", "qu arter", "ĠM arie", "ĠMar ie", "ĠMa rie", "ĠMari e", "Spe aking", "Speak ing", "_ codegen", "_code gen", "_cod egen", "Ġim mun", "Ġimm un", "c aster", "ca ster", "cast er", "cas ter", "ãĤ Į", "åķ Ĩ", "Ġ Dimensions", "ĠDim ensions", "ĠDimension s", ". record", ".re cord", ".rec ord", "Ġ texto", "Ġtext o", "Ġtex to", "Ġ Michelle", "ĠMich elle", "ĠMichel le", "ĠMiche lle", "P ending", "Pen ding", "( by", "(b y", "_ PAR", "_P AR", "_PA R", "u cht", "uch t", "uc ht", "b ee", "be e", ". Thread", ".T hread", ".Th read", "am pire", "amp ire", "k now", "kn ow", "Ġ Clinical", "ĠClin ical", "ĠClinic al", "Ġmargin Bottom", "Ġd istinguish", "Ġdistingu ish", ". Full", ".F ull", ". undefined", ".un defined", "ĠSequ elize", "#### ########################################################################", "################ ############################################################", "################################################################ ############", "############ ################################################################", "################################################ ############################", "######################################################################## ####", "############################ ################################################", "############################################################ ################", "Ġ educated", "Ġeduc ated", "Ġeducate d", "_ OVER", "_O VER", "åº ı", "Ġ ÂłĠÂł", "ĠÂł ĠÂł", "ĠÂłĠ Âł", "_ each", "_e ach", "Ġ urge", "Ġur ge", "Ġurg e", "de part", "dep art", "Ġdon ors", "Ġdonor s", "Ġ Au", "ĠA u", "Ġb illions", "Ġbill ions", "Ġbillion s", "Ġbelong ing", "_ age", "_a ge", "_ag e", "_ Int", "_I nt", "_In t", "Ġsub stances", "Ġsubstance s", "Ġsubst ances", "m achine", "ma chine", "mach ine", "! !!ĊĊ", "!! !ĊĊ", "!!! ĊĊ", "!!!Ċ Ċ", "Ġjson ify", "ib bean", "Ġ Cad", "ĠC ad", "ĠCa d", "Ġ endTime", "Ġend Time", "Ġc ycling", "Ġcy cling", "Ġcycl ing", "Ġcyc ling", "Ġ UITextField", "ĠUI TextField", "ĠUIT extField", "Ġle verage", "Ġlever age", "Ġleve rage", "Ġvan illa", "e at", "ea t", "L aunch", "La unch", "( pt", "(p t", "st ates", "state s", "stat es", "sta tes", "Ġ Controls", "ĠControl s", "ĠContr ols", "Ġ Respons", "ĠRes pons", "ĠResp ons", "Ġ Jake", "ĠJ ake", "ĠJa ke", "ĠJak e", "Ġa sleep", "Ġas leep", "fort unate", ".next Line", "Size Mode", "ì Ŀ¼", "ìĿ ¼", "Testing Module", "G erman", "Ger man", "ĠInvest ig", ". reverse", ".re verse", ".rev erse", "Ġ BACK", "ĠB ACK", "ĠBA CK", "( DateTime", "(Date Time", "Ġnon profit", "Ġ Expect", "ĠEx pect", "ĠExp ect", "Ġt anto", "Ġtan to", "Ġtant o", "' ]),", "'] ),", "']) ,", "ĉ the", "ĉt he", "ĉth e", "M ultiple", "Multi ple", "Mult iple", "Multip le", "(get Activity", "_ WAIT", "_W AIT", "Ġj á", "de cor", "dec or", "lev ance", "Ġ GitHub", "ĠGit Hub", "m ination", "min ation", "mi nation", "mina tion", "_ quantity", "_qu antity", "_quant ity", ". Scanner", ".Sc anner", ".Scan ner", "ĠL ion", "ĠLi on", "éĶĻ 误", "Ġ dre", "Ġd re", "Ġdr e", "Ġtan tra", "Ġtant ra", "Ġtantr a", "Ġ contentType", "Ġcontent Type", "Ġ fid", "Ġf id", "Ġfi d", "_ alt", "_a lt", "_al t", "NS IndexPath", "- pl", "-p l", "åĮ ĸ", "Ġantib iot", "t ables", "table s", "ta bles", "tab les", "tabl es", "a cial", "ac ial", "aci al", "acia l", "Ġ Registry", "ĠReg istry", "ĠRegistr y", "Ġo live", "Ġol ive", "Ġoli ve", "i gers", "ig ers", "ige rs", "iger s", "Ġ subscriber", "Ġsub scriber", "Ġsubscri ber", "Ġsubscribe r", "_ pres", "_p res", "_pr es", "_pre s", "Ġ Syntax", "ĠS yntax", "ĠSy ntax", "ĠSyn tax", "Ġl overs", "Ġlo vers", "Ġlove rs", "Ġlover s", "Ġlov ers", ". Byte", ".B yte", ".By te", "ol ders", "old ers", "older s", "_ forward", "_for ward", "al ways", "C aption", "Cap tion", "Ca ption", "Capt ion", "P riv", "Pr iv", "Pri v", "ĠT ampa", "ĠTam pa", "is ateur", "-labelled by", "Ġ ToString", "ĠTo String", "Ġ ìĤ¬", "Ġì Ĥ¬", "ĠìĤ ¬", "Ġinit iated", "Ġiniti ated", "Ġinitiate d", "W F", "Ġinstitution al", "in ject", "Ġ Scr", "ĠS cr", "ĠSc r", "Ġ doctrine", "Ġdo ctrine", "Ġdoctr ine", "Ġsp acious", "Ġspac ious", "i sure", "is ure", "isu re", "Ġ Ana", "ĠA na", "ĠAn a", "\" time", "ess aging", "essa ging", "Ġ cid", "Ġc id", "Ġci d", "ĠN an", "ĠNa n", "Ġin complete", "Ġincom plete", "T AG", "TA G", "- build", "-b uild", "Dec ember", "Ġres idual", "Ġresid ual", "( PDO", "(P DO", "Ġ Listen", "ĠL isten", "ĠList en", "ĠLi sten", "ĠLis ten", "ĠListe n", "Ġ glyph", "Ġg lyph", "Ġgly ph", "Ġg aps", "Ġgap s", "Ġga ps", "n ea", "ne a", ". Rect", ".R ect", ".Re ct", ".Rec t", "Ġs au", "Ġsa u", "ĠPhoto graph", "ĠPhot ograph", "Ġ executable", "Ġexec utable", "Ġexecut able", "Ġ Expert", "ĠEx pert", "ĠExp ert", "Co routine", "Cor outine", "_ sizes", "_s izes", "_size s", "_si zes", "Ġ NL", "ĠN L", ". isValid", ".is Valid", ") ;}Ċ", "); }Ċ", ");} Ċ", "- reg", "-r eg", "-re g", "Ġc iting", "Ġcit ing", "Ġci ting", "c wd", "cw d", "ĠOtt awa", "ĠB att", "ĠBa tt", "ĠBat t", "Ġrenew able", "Ġprelim inary", "Ġas ylum", "Ġw rist", "Ġwr ist", "Ġutil iz", "Ġut iliz", "Ġdet ention", "F ast", "Fa st", "Ġ ange", "Ġa nge", "Ġan ge", "Ġang e", "incinn ati", "Ġste ering", "Ġsteer ing", "Ġ NaN", "ĠN aN", "ĠNa N", "i osity", "ios ity", "/ page", "/p age", "Ġ è¿", "Ġè ¿", "ster ol", "ste rol", "Ġdis g", "Ġdi sg", "( DB", "(D B", "Ġ DESCRIPTION", "ĠDE SCRIPTION", "ĠDESC RIPTION", "Ġ _$", "Ġ_ $", "Ġob stacle", "Ġobst acle", "Ġb izarre", "Ġex traction", "Ġext raction", "Ġextra ction", "Ġextract ion", "Ġextr action", "_ expected", "_ex pected", "_exp ected", "_expect ed", "Ġ loses", "Ġl oses", "Ġlo ses", "Ġlos es", "Ġlose s", "Ġ Celebr", "ĠCele br", "Ġhtml For", "Ġexp loit", "Ġexpl oit", "Ġexplo it", "олÑĮз ов", "X YZ", "XY Z", "Ġm agnet", "Ġmag net", "Ġmagn et", "am ped", "amp ed", "Ġ atoms", "Ġat oms", "Ġatom s", "S ources", "Source s", "pect ives", "pective s", "Ñģ ли", "Ñģл и", "Ġ= čĊ", "Ġd are", "Ġda re", "Ġdar e", "ĠW alter", "ĠWal ter", "ĠWalt er", "Ġ brightness", "Ġb rightness", "Ġbright ness", "Ġ annotations", "Ġan notations", "Ġannotation s", "Ġannot ations", "ë ı", "is ke", "isk e", "S chedule", ". images", ".image s", ".im ages", ".imag es", "ross o", "ros so", "Ġ \"..", "Ġ\" ..", "Ġ\". .", "g amma", "ga mma", "gam ma", "Ġin structor", "Ġinstr uctor", "Ġinstruct or", "Ġ overwrite", "Ġover write", "- am", "-a m", "Ġdevast ating", "ĠSaint s", "ĠSa ints", "ĠSai nts", "Ġ hs", "Ġh s", "Ġbon uses", "Ġbonus es", "$ output", "$out put", "i jd", "ij d", "(Action Event", "m onitor", "mon itor", "Ġmatt ress", "Jan uary", ". jp", ".j p", "Ġcar acter", "Ġcara cter", "Ġcaract er", "Ġim pose", "Ġimp ose", "_ rest", "_re st", "_r est", "_res t", "Ġ Signature", "ĠSign ature", "ĠSig nature", "Ġcoron avirus", "ãģ Ĭ", "_ compare", "_com pare", "_comp are", "Me asure", "it ated", "ita ted", "itate d", "itat ed", "e lijk", "el ijk", "eli jk", "i gos", "ig os", "igo s", "e sar", "es ar", "esa r", "Ġr ushed", "Ġru shed", "Ġrush ed", "Ġrus hed", "m etry", "me try", "met ry", "_SE PARATOR", "_ WE", "_W E", "_ ATTRIBUTE", "_ATTR IBUTE", "_ATTRIB UTE", "Ġ yaml", "Ġy aml", "Ġya ml", "Ġ specs", "Ġsp ecs", "Ġspec s", "Ġspe cs", "ĠR ah", "ĠRa h", "ph eric", "pher ic", "phe ric", "ĠIn vestment", "ĠInvest ment", "ä ll", "äl l", "Ġappe aling", "Ġappeal ing", "Ġ viewport", "Ġview port", "ç ©", "Ġ marginLeft", "Ġmargin Left", "Ġ subtract", "Ġsub tract", "Ġsubt ract", "Ġ EDIT", "ĠED IT", "ĉ ArrayList", "ĉArray List", "gr ading", "grad ing", "gra ding", "Ġ Failure", "ĠF ailure", "ĠFail ure", "as per", "asp er", "E EK", "EE K", "( now", "(n ow", "(no w", "< object", "Ġ Alignment", "ĠAl ignment", "ĠAlign ment", "ple ado", "q tt", "qt t", "( ERROR", "(ERR OR", "Ġ INVALID", "ĠIN VALID", "Ġ userid", "Ġuse rid", "Ġuser id", "r aises", "ra ises", "raise s", "rais es", "rai ses", "I DI", "ID I", "Ġv ariance", "Ġvar iance", "Ġvari ance", "Ġ Nil", "ĠN il", "ĠNi l", "/ delete", "/de lete", "_ MAIN", "_M AIN", "_MA IN", ". Token", ".T oken", ".To ken", ". Category", ".C ategory", "> )Ċ", ">) Ċ", "C ollision", "Coll ision", "Ġ Greater", "ĠG reater", "ĠGreat er", "ĠGre ater", "ĠR acing", "ĠRa cing", "ĠRac ing", "a lan", "al an", "ala n", "Ġmon etary", "Ġmonet ary", ", new", ",n ew", "Ġ Sorry", "ĠS orry", "ĠSor ry", ". Enable", ".E nable", ".En able", "Ġ Instantiate", "ĠIn stantiate", "ĠInstant iate", "o llen", "ol len", "oll en", "olle n", "ë© ´", "Ġ Calling", "ĠC alling", "ĠCal ling", "ĠCall ing", "_ hour", "_h our", "A DA", "AD A", "Ġs hy", "Ġsh y", ") **", ")* *", "Ġ ==>", "Ġ= =>", "Ġ== >", "Ġe special", "Ġes pecial", "Ġesp ecial", "Ġespec ial", "Ġ interpreted", "Ġinterpret ed", "Ġinterpre ted", "! =\"", "!= \"", "Ġph armacy", "Ġpharm acy", "Ġpharmac y", ". single", ".s ingle", ".sin gle", ".si ngle", "ĠC ialis", "ĠCi alis", "Ġp aras", "Ġpar as", "Ġpara s", "Ġpa ras", ". toUpperCase", ".to UpperCase", "Ġ Demon", "ĠD emon", "ĠDe mon", "ĠDem on", "ĠDemo n", "Pr ime", "Prim e", "Pri me", "Ġrank ings", "Ġranking s", "Add ing", "Ad ding", "_ HASH", "_H ASH", "_HAS H", "Ġ Exam", "ĠEx am", "Ú ©", "ĠV ictor", "ĠVi ctor", "ĠVict or", "ĠVic tor", "Ok ay", "\" ];čĊ", "\"] ;čĊ", "\"]; čĊ", "Ġ fortune", "Ġfort une", "Ġ FETCH", "ĠF ETCH", "exp and", ". Interop", ".Inter op", "Ġb arn", "Ġbar n", "Ġba rn", "æ ¶Ī", "æ¶ Ī", "ue vo", "Ġspec ulation", "âĶĢâĶĢ âĶĢâĶĢ", "Ġ Nu", "ĠN u", "ĠBl ues", "ĠBlue s", "ĠBlu es", "( fname", "(f name", "(fn ame", "Ġin habit", "Ġinhab it", "Ġinh abit", "Ġ\\ \"%", "Ġ\\\" %", "C ES", "CE S", "ul ario", "ular io", "ula rio", "_ cr", "_c r", "Ġ validated", "Ġvalid ated", "Ġvalidate d", "Ġvalida ted", "Ġmid night", "an king", "ank ing", "anki ng", "Ġincor porate", "Ġincorpor ate", "Ġpur suit", "Ġpurs uit", "E XP", "EX P", "pr ime", "prim e", "pri me", "P id", "Pi d", "- US", "-U S", "ĠN urs", "ĠNu rs", "ĠNur s", "Ġ Wheel", "ĠW heel", "ĠWh eel", "ĠWhe el", "é ĺ", "Ġ inp", "Ġin p", "Ġi np", "Ġsupport ive", ". member", ".m ember", ".mem ber", "Ġ Shot", "ĠS hot", "ĠSh ot", "ĠSho t", ". CheckBox", ".Check Box", "Ġaff irm", "Ġaf firm", "T or", "To r", "Full Year", "Ġconsider ably", "c redentials", "cred entials", "credential s", "_ opts", "_op ts", "_o pts", "_opt s", "R oll", "Ro ll", "Rol l", "( round", "(r ound", "(ro und", "Ġc oment", "Ġcom ent", "Ġco ment", "Ġcome nt", "_ UART", "_U ART", "Ġext ending", "Ġextend ing", "R G", "result ado", "i tu", "it u", ". getSession", ".get Session", ".getS ession", "Ġat traction", "Ġatt raction", "Ġattr action", "Ġattract ion", "& D", "$ html", "$h tml", "Ġ Jessica", "ĠJess ica", "Ġ Associate", "ĠAssoci ate", "ĠAssoc iate", "a ñ", "_ ed", "_e d", "ĠL ag", "ĠLa g", "Ġorig ins", "Ġorigin s", "( ))->", "() )->", "()) ->", "add EventListener", "IA LOG", "IAL OG", "åIJ ¦", ". Compare", ".Com pare", ".Comp are", "Al bum", "ĠK u", "< Q", "ar gest", "arg est", "arge st", "arges t", "Ġpro long", "Ġprol ong", "Ġconfig urations", "Ġconfiguration s", "Ġconfigur ations", "Ġacc identally", "Ġaccident ally", "Ġaccidental ly", "_ photo", "_ph oto", "Ġ' ';čĊ", "Ġ'' ;čĊ", "Ġ''; čĊ", "Ġ verse", "Ġv erse", "Ġver se", "Ġvers e", "B ob", "Bo b", "Ġf arming", "Ġfar ming", "Ġfarm ing", "d elivery", "del ivery", "deliver y", "ĠM ack", "ĠMac k", "ĠMa ck", "Ġuse Selector", ".bootstrap cdn", "ke eping", "keep ing", "kee ping", "e ny", "en y", ". upload", ".up load", "Ġ METHOD", "ĠM ETHOD", "ĠMETH OD", "c reator", "cre ator", "creat or", "< _", "ĠE aster", "ĠEast er", "ĠEa ster", ". --", ".- -", "UI Button", "ãĤ ī", "om eters", "ome ters", "omet ers", "ometer s", "Ġ shine", "Ġsh ine", "Ġshin e", "Ġh ogy", "Ġho gy", "Ġhog y", "\\ s", "Ġh arness", "Ġhar ness", ". Cell", ".C ell", "Ġ lifting", "Ġl ifting", "Ġlif ting", "Ġlift ing", "Ġcomb ines", "Ġcombine s", "Ġcombin es", "Ġ Occup", "ĠOcc up", "ĠOc cup", "ex clude", "exc lude", "p atial", "pat ial", "Ġre spir", "Ġres pir", "Ġresp ir", "_ fit", "_f it", "Ġf ifty", "Ġfi fty", "Ġfif ty", "ĠM ol", "ĠMo l", "Ġt uned", "Ġtu ned", "Ġtun ed", "Ġtune d", "-d imensional", "Ġ qs", "Ġq s", "Ġt ops", "Ġto ps", "Ġtop s", "> \";ĊĊ", ">\";Ċ Ċ", ">\" ;ĊĊ", ">\"; ĊĊ", "quis ite", "qui site", "ch annels", "chan nels", "channel s", "/ res", "/r es", "/re s", "Ġ Analytics", "ĠAn alytics", "ĠAnaly tics", ".app compat", "/ to", "/t o", "Ġon Error", "( attr", "(at tr", "(att r", "I RM", "IR M", "Ġrag az", "- as", "-a s", ". Second", ".Se cond", "ori ented", "orient ed", "Ġd onn", "Ġdo nn", "Ġdon n", "Ġlight ning", "f id", "fi d", "ĠP le", "ĠPl e", "ãģ¾ ãģĻ", "t ro", "tr o", ". True", ".Tr ue", "O bservable", "Observ able", "× Ļ", "um bing", "umb ing", "Ġpros pective", "Ġprospect ive", "- filter", "-f ilter", "Ġpurs uant", "( points", "(p oints", "(point s", "(po ints", ". Bind", ".B ind", "Ġp alm", "Ġpa lm", "Ġpal m", "clear fix", "ö s", "ĠG onz", "ĠGo nz", "ĠGon z", "Ġwe aken", "Ġweak en", "D rive", "Dr ive", "en ido", "eni do", "l ld", "ll d", "o box", "ob ox", "obo x", "an ean", "ane an", "G ot", "Go t", "ä¿ Ŀ", "Reg ex", "æ ĥ", "Ġsa lad", "Ġsal ad", "Ġsala d", "as sis", "ass is", "assi s", "\" net", "inherit Doc", "Ġ RV", "ĠR V", "qu ier", "qui er", "Ġ clazz", "Ġcl azz", "Ġcla zz", "ı ÅŁ", "oster one", "oste rone", "Ġair line", "Ġairl ine", ".list dir", "Ġdown loading", "Ġdownload ing", "ĠP alm", "ĠPal m", "ĠPa lm", "w aukee", "& lt", ". BL", ".B L", "_ INLINE", "_IN LINE", "of fs", "off s", "< <(", "<< (", "_ news", "_n ews", "_new s", "_ne ws", "Ġch ase", "Ġcha se", "/ ><", "/> <", "Ġe uros", "Ġeu ros", "Ġeuro s", "ĠEgypt ian", "ĠSt ainless", "_ BOOL", "_BO OL", "Ġ Guild", "ĠG uild", "ĠGu ild", "ĠGui ld", "ĠGuil d", "ĠD ynam", "ĠDy nam", "ĠDyn am", "[ indexPath", "[index Path", "Ġ ï", "Ġmem orable", "Ġmemor able", "ĠCh ampion", "ĠChamp ion", "Resource Manager", ". Login", ".Log in", ".Lo gin", "Ġ Former", "ĠFor mer", "ĠForm er", "y ped", "ype d", "yp ed", "Ġl leg", "Ġll eg", "Ġlle g", "; \",", ";\" ,", "D WORD", "DW ORD", "Ġtax i", "Ġta xi", "Ġb ombs", "Ġbomb s", "Ġbom bs", "r ah", "ra h", ". tags", ".t ags", ".tag s", ".ta gs", "_ tests", "_t ests", "_test s", "_te sts", "s tones", "st ones", "ston es", "stone s", "sto nes", "âĢĿ )", "[ g", "r type", "rt ype", "Ġ vu", "Ġv u", "Ġhost ile", "Ġho stile", "Ġhos tile", "Ch ars", "Char s", "Cha rs", "ĠPatri ots", "ĠPatriot s", "/ status", "/s tatus", "/st atus", "/stat us", "< B", "Ġ Income", "ĠIn come", "ĠInc ome", "ĠD ad", "ĠDa d", "Ġpat rol", "_ CHANGE", "_CH ANGE", "_CHAN GE", "Ġup graded", "Ġupgrade d", "Ġ china", "Ġch ina", "Ġchi na", "Ġchin a", "set q", "Start ed", "Star ted", ".U ndef", ".Un def", "Ġ checksum", "Ġcheck sum", "Ġchecks um", "Ġfrustr ated", "{ o", "Ġe nf", "Ġen f", "Ġ woods", "Ġw oods", "Ġwood s", "Ġwo ods", "Ġwoo ds", "Ġ Anyone", "ĠAny one", "En code", "Enc ode", "ĠQt Widgets", "a reas", "are as", "area s", "Ġsh eer", "Ġshe er", "s ki", "sk i", "end point", "_ Test", "_T est", "S oup", "So up", "Sou p", "~~~~~~~~ ~~~~~~~~", "( files", "(f iles", "(file s", "(fi les", "(fil es", "ĉ ĉĉĉĉčĊ", "ĉĉ ĉĉĉčĊ", "ĉĉĉĉ ĉčĊ", "ĉĉĉ ĉĉčĊ", "ĉĉĉĉĉ čĊ", ". spark", ".s park", ".sp ark", "Ġvalue d", "Ġval ued", "Ġvalu ed", "Ġ %Ċ", "Ġ% Ċ", ". controls", ".control s", "ĠXCTAssert Equal", "Ġf ame", "Ġfam e", "Ġfa me", "ĠR ic", "ĠRi c", "D OT", "DO T", "ĠAlbert a", "ĠAlb erta", "ä½ ¿", "o sal", "os al", "osa l", ".Web Controls", "Ġ ------------", "Ġ- -----------", "Ġ-- ----------", "Ġ---- --------", "Ġ--- ---------", "Ġ----- -------", "Ġ---------- --", "Ġ------ ------", "Ġ-------- ----", "Ġ------- -----", "Ġ--------- ---", "Ġ----------- -", "Ġ Mis", "ĠM is", "ĠMi s", "Ġ SYS", "ĠS YS", "ĠSY S", "Non null", "= item", "=i tem", "Ġ expire", "Ġex pire", "Ġexp ire", "De code", "Dec ode", "_ operation", "_op eration", "_o peration", "_oper ation", "Ġ Validator", "ĠValid ator", ". CENTER", ".C ENTER", "uff s", "uf fs", "* m", "Ġa vant", "Ġav ant", "Ġava nt", "Ġavan t", "æ¬ ¡", "âĢľ You", ". permission", ".per mission", ".perm ission", ".. .)", "... )", "Ġ Lic", "ĠL ic", "ĠLi c", "_ coords", "_co ords", "_coord s", ". nombre", ".n ombre", ".nom bre", "c lo", "cl o", ". Internal", ".In ternal", ".Int ernal", ".Inter nal", "Ġ Cho", "ĠC ho", "ĠCh o", "_ sw", "_s w", "ĉ Il", "ĉI l", "c lk", "cl k", "Ġ castle", "Ġc astle", "Ġcas tle", "Ġcast le", "( layer", "(l ayer", "p it", "pi t", "Ġ guided", "Ġgu ided", "Ġguide d", "Ġguid ed", "Ġgui ded", "Ġ âĸĪ", "Ġâĸ Ī", "Ġsup erb", "Ġsuper b", "Ġsup plements", "Ġsupplement s", "Ġsuppl ements", "Ġsupp lements", "_ cent", "_c ent", "_ce nt", "Ġ peek", "Ġpe ek", "Ġpee k", "IN ARY", "INA RY", ". ContentAlignment", ".Content Alignment", "f alls", "fall s", "fal ls", "\" ));", "\") );", "\")) ;", "W all", "Wal l", "Wa ll", ") .čĊ", "). čĊ", "Ġ Danny", "ĠD anny", "ĠDan ny", "ĠDann y", "irm ingham", "IAL IZ", "( create", "(c reate", "\" In", "\"I n", "Service Provider", "Ġpr iced", "Ġprice d", "Ġpri ced", "m acro", "ma cro", "mac ro", "a mac", "am ac", "ama c", ". box", ".b ox", ".bo x", "- ---Ċ", "-- --Ċ", "---- Ċ", "--- -Ċ", "ãĥ «", "Ġ Suit", "ĠS uit", "ĠSu it", "u rst", "ur st", "urs t", "b ru", "br u", "ourn als", "ournal s", "num ero", "numer o", "_ _()Ċ", "__ ()Ċ", "__( )Ċ", "__() Ċ", "D as", "Da s", "ĠM itt", "ĠMi tt", "ĠMit t", "u der", "ud er", "ude r", "? \\", "f u", "[ B", "Ġ: )ĊĊ", "Ġ:) ĊĊ", "Ġ:)Ċ Ċ", "( inter", "(int er", "(in ter", "br ains", "bra ins", "brain s", "Ġatt itudes", "Ġattitude s", "Ver ify", "Ġsign atures", "Ġsignature s", "ack Bar", "Ġ gd", "Ġg d", "J ack", "Ja ck", "Jac k", ". cat", ".c at", ".ca t", "Ġ zz", "Ġz z", "w arf", "wa rf", "war f", "F TER", "FT ER", "\" );ĊĊĊ", "\");Ċ ĊĊ", "\") ;ĊĊĊ", "\");ĊĊ Ċ", "\"); ĊĊĊ", "A live", "Al ive", "Ali ve", "I CLE", "IC LE", "Ġ Whatever", "ĠWh atever", "ĠWhat ever", "Ġ outlined", "Ġout lined", "Ġoutline d", "s prite", "sp rite", "spr ite", "е в", "еР²", "_ AB", "_A B", "_ DEPTH", "_DE PTH", "Ġcr ushed", "Ġcrush ed", "Ġcru shed", "Ġcrus hed", "a aa", "aa a", "( ev", "(e v", "æľ º", "An ti", "Ant i", "I CO", "IC O", "is EqualTo", "isEqual To", ". sun", ".s un", "i culo", "ic ulo", "s ale", "sa le", "sal e", "_ hex", "_h ex", "_he x", "Ġ Vk", "ĠV k", "ap tor", "apt or", "Un ion", "Uni on", "Ġ Discount", "ĠDis count", "ĠDisc ount", "ĠDisco unt", "l ista", "li sta", "list a", "lis ta", ".Undef Or", "Ġ automation", "Ġa utomation", "Ġautom ation", "Ġautomat ion", "N or", "No r", "å¯ ¹", "åı Ĥæķ°", "åıĤ æķ°", "Ġre flex", "Ġref lex", "Ġrefl ex", "ĠLa ure", "ĠLaur e", "ĠLau re", ".showMessage Dialog", ". temp", ".t emp", ".te mp", ".tem p", "Ġ akan", "Ġa kan", "Ġak an", "Ġaka n", "Ġ_ _____", "Ġ__ ____", "Ġ___ ___", "Ġ____ __", "Ġ_____ _", ".Is True", "A RED", "AR ED", "ARE D", "a gle", "ag le", "E nergy", "En ergy", "Ġquant ities", "âĢĻ é", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "Ġcitizens hip", "Ġcitizen ship", "m outh", "mo uth", "Ġin appropriate", "Ġ Outdoor", "ĠOut door", "White Space", "An onymous", "lo ads", "load s", "webElement Properties", "T en", "Te n", "Ġacc idents", "Ġaccident s", "Ġ advertisement", "Ġad vertisement", "Ġadvertis ement", "Ġadvertise ment", "ĠY emen", "ĠYe men", "( call", "(c all", "(cal l", "(ca ll", "Ġsl avery", "Ġslave ry", "Ġsla very", "Ġslav ery", "Ñģ п", "ĠL am", "ĠLa m", "_ BITS", "_B ITS", "_BIT S", "o mega", "om ega", "ome ga", "ĠO le", "ĠOl e", "Ġkid n", "Ġki dn", "_ An", "_A n", "ĠR aid", "ĠRa id", "ĠRai d", "C reation", "Cre ation", "Creat ion", "s aved", "save d", "sa ved", "sav ed", "Ġpro port", "Ġprop ort", "Ġpropor t", "W ARNING", "WAR NING", "WARN ING", "\\ P", "Ġ pwd", "Ġp wd", "Ġpw d", "Data Reader", "is cher", "isc her", "ische r", "isch er", "ad eon", "ade on", "Ġ Predict", "ĠP redict", "ĠPre dict", "ĠPred ict", "Ġreason ing", "Ġdestroy ing", "H el", "He l", "* d", "ĠLeg isl", "_ Pr", "_P r", "ĉ ĉĉĠĠĠĠĠĠĠ", "ĉĉ ĉĠĠĠĠĠĠĠ", "ĉĉĉ ĠĠĠĠĠĠĠ", "ĉĉĉĠĠĠ ĠĠĠĠ", "ĉĉĉĠ ĠĠĠĠĠĠ", "ĉĉĉĠĠ ĠĠĠĠĠ", "ĉĉĉĠĠĠĠ ĠĠĠ", "ĉĉĉĠĠĠĠĠ ĠĠ", "ĉĉĉĠĠĠĠĠĠ Ġ", "Ġsym path", "Ġsymp ath", "Ġch ess", "Ġche ss", "Ġ mam", "Ġm am", "Ġma m", ": hover", ":h over", "Ġcon verts", "Ġconvert s", "Ġconv erts", "Ġconver ts", "Ġp ela", "Ġpe la", "Ġpel a", "Ġpro gression", "Ġprogress ion", "Ġprog ression", "Ġ\" _\"", "Ġ\"_ \"", "ĠG ill", "ĠGi ll", "ĠGil l", "ĉ show", "ĉs how", "ĉsh ow", "Ġsupposed ly", "ac curacy", "acc uracy", "accur acy", "e lin", "el in", "eli n", "Ġunf olding", "Ġunfold ing", "Ġ Hyper", "ĠH yper", "ĠHy per", "ĠHyp er", "Ġw anna", "Ġwann a", "Ġwan na", "Ġ ups", "Ġu ps", "Ġup s", "( #", "ĠC riminal", "ĠCr iminal", "( Point", "(P oint", "at Lng", "act ly", "Ġcontract ors", "Ġcontr actors", "Ġcontractor s", "Ġcontra ctors", "' ]}", "'] }", "draul ic", "ód igo", "Ġ TT", "ĠT T", "Ġ Wide", "ĠW ide", "ĠWi de", "ĠWid e", "Ġ ARG", "ĠA RG", "ĠAR G", "_ ic", "_i c", "FLAG S", "S chool", "Sch ool", "Ġcl earing", "Ġclear ing", "Ġcle aring", "- being", "-b eing", "-be ing", "={ [", ", const", "man ent", "Over lay", "( '\"", "(' \"", "éĩ ı", "Ġ Timestamp", "ĠT imestamp", "ĠTime stamp", "Ġm ailing", "Ġma iling", "Ġmail ing", "Ġmai ling", "Ġ Cake", "ĠC ake", "ĠCa ke", ". That", ".T hat", ".Th at", "Ġmed itation", "q p", "Ġ empresa", "Ġemp resa", "Ġempres a", "ĠL ions", "ĠLi ons", "ĠLion s", "Ġw eld", "Ġwe ld", "Ġwel d", "Ġ LinkedIn", "ĠLinked In", "Ġc ush", "Ġcu sh", "Ġcus h", "Ġ genome", "Ġge nome", "Ġgen ome", "Ġgenom e", ". IndexOf", ".Index Of", "a gain", "ag ain", "aga in", "Ġ fallback", "Ġf allback", "Ġfall back", "Ġc amping", "Ġcamp ing", "Ġcam ping", "r edd", "re dd", "red d", "-strip ed", "-str iped", "Ġ dv", "Ġd v", "Fe bruary", "Feb ruary", "Ġ Proxy", "ĠPro xy", "ĠPr oxy", "u sk", "us k", "Ġd iesel", "Ġdi esel", "Ġdie sel", "Ġdies el", "Ġdiese l", "W RITE", "WR ITE", "RE AK", "REA K", "L orem", "Lo rem", ". Invoke", ".In voke", ".Inv oke", "- div", "-d iv", "-di v", "Inter ceptor", "Ġ DH", "ĠD H", "i ales", "ial es", "ia les", "iale s", "Ġvill ages", "Ġvillage s", "Ġvilla ges", "Ø ´", "Ġ ENV", "ĠE NV", "ĠEN V", "S ys", "Sy s", ". XR", ".X R", "Ġpo em", "à Ĥ", "c ade", "ca de", "cad e", "p lots", "pl ots", "plot s", "Ġ {(", "Ġ{ (", ". git", ".g it", "/ svg", "/s vg", "n cmp", "nc mp", "Ġ Äį", "ĠÄ į", "a ines", "ain es", "ai nes", "aine s", "åĩ ½æķ°", "åĩ½ æķ°", "Ġ ()ĊĊ", "Ġ( )ĊĊ", "Ġ() ĊĊ", "Ġ()Ċ Ċ", "op sis", "ops is", "Ġ Relationship", "ĠRel ationship", "ĠRelations hip", "ĠRelation ship", "_ aut", "_a ut", "Ġ Bomb", "ĠB omb", "ĠBo mb", "ĠBom b", "ĉ com", "ĉc om", "* sizeof", "*size of", "off icial", "_ payload", "_p ayload", "_pay load", "ĉ ĉĉĉĉĠĠ", "ĉĉ ĉĉĉĠĠ", "ĉĉĉĉ ĉĠĠ", "ĉĉĉ ĉĉĠĠ", "ĉĉĉĉĉ ĠĠ", "ĉĉĉĉĉĠ Ġ", ". manager", ".m anager", ".man ager", ".manage r", "Ġ Around", "ĠA round", "ĠAr ound", "ĉ send", "ĉs end", "ĉse nd", "Ġ Exercise", "ĠEx ercise", "Ġ Billy", "ĠB illy", "ĠBill y", "ĠBil ly", "i vi", "iv i", "Ġne eding", "Ġneed ing", "_ urls", "_url s", "_ur ls", "_ tasks", "_t asks", "_task s", "ĠH em", "ĠHe m", "Ġ tearDown", "Ġte arDown", "Ġtear Down", "en crypt", "enc rypt", ". tie", ".t ie", "Ġ asm", "Ġa sm", "Ġas m", "I CH", "IC H", "ĠCGRect Make", "ìĦ ±", "u long", "ul ong", "ulo ng", "Ġ itr", "Ġit r", "Ġi tr", "Ġ GST", "ĠG ST", "ĠGS T", "Ġoffer ings", "Ġoffering s", "r obe", "ro be", "rob e", "E EE", "EE E", "oper ators", "operator s", "_ PROP", "_P ROP", "_PRO P", "_PR OP", "in dent", "ind ent", "inde nt", "inden t", "A DE", "AD E", "o rf", "or f", "ë IJ", "Ġbl essed", "Ġbless ed", "v ascular", "vas cular", "Ġcon oc", "Ġco noc", "H appy", "Ha ppy", "B ridge", "Br idge", "il itation", "ilit ation", "j oint", "join t", "jo int", "Ġ Administr", "ĠAdmin istr", "- transform", "-trans form", "Ġmean time", "Ġmeant ime", "/ K", "ĠBed room", "Ġr igid", "Ġrig id", "Ġri gid", "Ġb rowsers", "Ġbrowser s", "Ġbrows ers", "Ġbrowse rs", "EM PTY", "EMP TY", ". Serialize", ".S erialize", ".Serial ize", "_ ED", "_E D", "Ġst itch", "Ġ jan", "Ġj an", "Ġja n", "el lt", "ell t", "Ġ brace", "Ġb race", "Ġbr ace", "Ġbra ce", "Ġt rails", "Ġtr ails", "Ġtrail s", "Ġtra ils", "Ġtrai ls", "p ublished", "publish ed", "å¯Ĩ çłģ", "} ')Ċ", "}' )Ċ", "}') Ċ", "Ġac ids", "Ġacid s", "Ġ !!!", "Ġ! !!", "Ġ!! !", "_ direct", "_d irect", "_dir ect", "_di rect", "> ());Ċ", ">( ));Ċ", ">() );Ċ", ">()) ;Ċ", "a jÄħ", "aj Äħ", "_O CC", "_OC C", "Ġplan ets", "Ġplane ts", "Ġplanet s", "Ġpla nets", "æ Ł¥", "æŁ ¥", "ĠDub lin", "Ġ serie", "Ġs erie", "Ġse rie", "Ġser ie", "Ġseri e", ". printf", ".print f", "de ep", "dee p", "` )", "Ġ \\$", "Ġ\\ $", "Ġ μ", "ĠÎ ¼", "_ VIDEO", "_V IDEO", "end ors", "endor s", "endo rs", "Ġ Crypto", "ĠC rypto", "ĠCrypt o", "ĠCry pto", "F ar", "Fa r", ". Transparent", ".Trans parent", ". TR", ".T R", "i asm", "ia sm", "ias m", "_ training", "_tr aining", "_train ing", "_tra ining", "Ġte aches", "Ġteach es", "Ġtea ches", "ĠB elt", "ĠBe lt", "ĠBel t", "Ġlimit ing", "Ġlim iting", "ĠK ath", "ĠKat h", "ĠKa th", "Ġ IndexPath", "ĠIndex Path", "Ġachie vements", "Ġachieve ments", "Ġachievement s", "Ġse rá", "Ġser á", "interop Require", "Ġd isse", "Ġdis se", "Ġdi sse", "Ġdiss e", ". If", ".I f", "ar ming", "arm ing", "uls ion", "P o", "_ DETAIL", "_DE TAIL", "_DET AIL", "Prot otype", "Proto type", "Ġ CAL", "ĠC AL", "ĠCA L", "Ġag rees", "Ġagre es", "Ġagree s", "Ġagr ees", ". vo", ".v o", ".Execute NonQuery", "Ġ Topic", "ĠT opic", "ĠTo pic", "ĠTop ic", "Ġ' {}", "Ġ'{ }", "A rm", "Ar m", "Ġ ecc", "Ġe cc", "Ġec c", "M ag", "Ma g", "Ġ serialized", "Ġs erialized", "Ġser ialized", "Ġserial ized", "Ġserialize d", "ĉ conn", "ĉc onn", "ĉcon n", "c ached", "ca ched", "cache d", "cac hed", "= tf", "=t f", "Ġ ByteArray", "ĠByte Array", "prot obuf", "proto buf", "var char", "ĉ ASSERT", "ĉA SSERT", "Ġ liste", "Ġl iste", "Ġli ste", "Ġlist e", "Ġlis te", "_ trigger", "_tr igger", "_tri gger", "· ¸", "F eel", "Fe el", "Fee l", "T ahoma", "Ġ Lik", "ĠL ik", "ĠLi k", "Ġ structured", "Ġstruct ured", "Ġstructure d", "erg us", ". Initial", ".In itial", ".Init ial", "_ ge", "_g e", "cl js", ". contact", ".cont act", "Ġand ere", "Ġan dere", "Ġander e", "$ stmt", "_ CURRENT", "_C URRENT", "Ġ Discover", "ĠDis cover", "ĠDisc over", "ĠDisco ver", "$ res", "$r es", "form atter", "for matter", "format ter", "H a", "van gst", "vang st", "Ġe merge", "Ġem erge", "Ġemerg e", "ãĢĤ âĢĿ", "ĠC abinet", "ĠCabin et", "ĠCab inet", "- square", "-s quare", "éĥ ¨", "Ġ rage", "Ġr age", "Ġra ge", "Ġrag e", "Ġ AJ", "ĠA J", "Ġ VT", "ĠV T", "sh adow", "ĠFa ith", "e names", "en ames", "ename s", "ena mes", "p retty", "pr etty", "pre tty", "pret ty", "h asil", "ha sil", "has il", "p arty", "par ty", "part y", "Ġ varchar", "Ġvar char", "Ġf otos", "Ġfo tos", "Ġfoto s", "Ġfot os", "Ġa lum", "Ġal um", "ĠBel gium", "ĠBelg ium", ". ylabel", ".y label", "Ġ dej", "Ġd ej", "Ġde j", "_ numbers", "_num bers", "_number s", "Ġ hu", "Ġh u", ".set Adapter", "Ġ Usually", "ĠUs ually", "( sample", "(s ample", ". Shared", ".Sh ared", "Ġbo oked", "Ġbook ed", "Ġboo ked", "Ġ> >=", "Ġ>> =", "Ġmin erals", "Ġmineral s", "Ġminer als", "\" > < ?=", "\">", "'] )->", "']) ->", "p rog", "pr og", "pro g", "b oo", "bo o", "_ md", "_m d", "_ pack", "_p ack", "_pa ck", "( express", "(ex press", "(exp ress", "(expr ess", "u tz", "ut z", "\\ Auth", ", id", ",i d", "ĠCh ile", "ĠChi le", "act ice", "actic e", "Ġrec ruitment", "Ġrecruit ment", "Ġ poses", "Ġp oses", "Ġpos es", "Ġpo ses", "Ġpose s", "Ġvulner ability", "inst anc", "o rum", "or um", "oru m", "d ess", "de ss", "des s", "Ġ xl", "Ġx l", "%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%", "( fig", "(f ig", "(fi g", "Ġde leting", "Ġdel eting", "Ġdelet ing", "Ġdele ting", ". del", ".d el", ".de l", ") ')Ċ", ")' )Ċ", "Ġ Weekly", "ĠWeek ly", "? ??", "?? ?", "( strcmp", "(str cmp", "s mith", "sm ith", "Ġpurs uing", "- so", "-s o", "Ġ Apps", "ĠA pps", "ĠApp s", "ĠAp ps", "/ 'Ċ", "/' Ċ", "Ġde cis", "Ġdec is", "F ORE", "FO RE", "FOR E", "Every one", "Ġl anes", "Ġla nes", "Ġlane s", "Ġlan es", "V irtual", "Vir tual", ". attach", ".at tach", ".att ach", "( Log", "(L og", "ĠMed icaid", "ĠMedic aid", "( Path", "(P ath", "ĠTur ner", "ĠTurn er", "/ application", "/app lication", "/ap plication", "Ġ portrait", "Ġport rait", "Ġpor trait", "Ġop pose", "Ġopp ose", "Ġoppos e", "check out", "Ġfin ishes", "Ġfinish es", "_ ME", "_M E", "Bar rier", "S ong", "So ng", "Son g", "V AR", "VA R", "Ear lier", "r ella", "re lla", "rel la", "rell a", "Ġh ast", "Ġhas t", "Ġha st", "a zar", "az ar", "aza r", "Ġp ulls", "Ġpull s", "Ġpul ls", "n gx", "ng x", "Ġins piring", "Ġinspir ing", "Ġinsp iring", "Ñĥ Ñİ", "- direction", "-d irection", "-direct ion", "-dir ection", "-di rection", "Ġexplos ive", "Ġ createdAt", "Ġcreated At", "s to", "st o", "Ġw heat", "Ġwh eat", "Ġwhe at", "Ġ Built", "ĠB uilt", "ĠBu ilt", "' ai", "'a i", "Ġ tracked", "Ġtr acked", "Ġtrack ed", "h ammad", "ham mad", "RowAt IndexPath", "_ heap", "_he ap", "D ue", "Du e", "Ġconn ects", "Ġconnect s", ". publish", ".p ublish", ".pub lish", "e mu", "em u", "Ġb ullets", "Ġbul lets", "Ġbull ets", "Ġbullet s", "B AR", "BA R", "o late", "ol ate", "ola te", "Ġintern ally", "Ġinternal ly", "Ġc atching", "Ġcatch ing", "Ġcat ching", "- password", "-p assword", "-pass word", "ou ched", "ouch ed", "æĢ §", "e ous", "eo us", "Ġx range", "Ġxr ange", "Q uality", "Qu ality", "Qual ity", "v v", "Man age", "Ma nage", "Mana ge", "( ($", "(( $", "ace ments", "ac ements", "acement s", "ĠBr others", "ĠBro thers", "ĠBrother s", "Ġ HEAD", "ĠHE AD", "Ġ Unsupported", "ĠUn supported", "ĠUns upported", "s an", "sa n", "e si", "es i", "* **Ċ", "** *Ċ", "*** Ċ", "Ġadapt ation", "Ġ Worker", "ĠWork er", "ĠWor ker", "' ]/", "'] /", ".save fig", "( trans", "(t rans", "(tr ans", "Ø ¬", "n ee", "ne e", "C orrect", "Cor rect", ".. .\")Ċ", "... \")Ċ", "...\" )Ċ", "Ġsubmit ting", "- path", "-p ath", "ĉ last", "ĉl ast", "is san", "iss an", "issa n", ". xlabel", ".x label", "Ġ Separ", "ĠS epar", "ĠSe par", "ĠSep ar", "/ no", "/n o", "_ best", "_b est", "_be st", "ĠM ills", "ĠMill s", "ĠMil ls", "_ sock", "_s ock", "_so ck", "_soc k", "( flag", "(f lag", "(fl ag", "Ġdest inations", "Ġdestination s", "Ġdestin ations", "em ption", "emp tion", "empt ion", "Ġ FAIL", "ĠF AIL", "ĠFA IL", "å ĴĮ", "åĴ Į", "Ġ rp", "Ġr p", "f act", "fa ct", "fac t", "ĉ len", "ĉl en", "D AY", "DA Y", "Ġse iz", "Ġsei z", "_ dst", "_d st", "_ds t", "l ip", "li p", ". Linear", ".L inear", ".Line ar", "Ġ Basket", "ĠB asket", "ĠBas ket", "$ t", "$ i", "- brand", "-b rand", "-br and", "Ġ Neil", "ĠN eil", "ĠNe il", "Ġ Eq", "ĠE q", "Ġt hou", "Ġth ou", "Ġtho u", "o gene", "og ene", "ogen e", "oge ne", "Ġscholar ship", "Ġscholars hip", "æĽ ´", "Ġs wo", "Ġsw o", "ag inator", "agina tor", "e ni", "en i", "( book", "(b ook", "Ġ blink", "Ġb link", "Ġbl ink", "Ġbli nk", "t hus", "th us", "Ġ cancellationToken", "Ġc ancellationToken", "Ġcancell ationToken", "Ġcancellation Token", "ĠPalestin ians", "ĠPalestinian s", "Ġprof itable", "Ġprofit able", "Ġback pack", "en son", "ens on", "enso n", "< Long", " < /", "_ WORD", "_W ORD", "\\M igrations", "\\Migration s", "Ġ ENABLE", "ĠEN ABLE", "_PARAM ETER", "ĠB ishop", "ĠBi shop", "ĠBis hop", ". subject", ".sub ject", "il las", "ill as", "illa s", ". matrix", ".m atrix", ".mat rix", "urre nces", "urrenc es", "urr ences", "urrence s", "* y", "Ġcost ly", "Ġ Chuck", "ĠCh uck", "ĠChu ck", "Ġc loses", "Ġcl oses", "Ġclose s", "Ġclos es", "Ġclo ses", "ĠM ight", "ĠMi ght", "ĠMig ht", "- store", "-st ore", "Ġ mall", "Ġm all", "Ġma ll", "Ġmal l", "i eten", "ie ten", "iet en", "iete n", ". Abs", ".A bs", ".Ab s", "Ġcou pled", "Ġcouple d", "Ġcoup led", ". basic", ".b asic", ".ba sic", "Ġ ::::::::", "Ġ: :::::::", "Ġ:: ::::::", "Ġ::: :::::", "M aker", "Make r", "Ma ker", "c annot", "can not", "Ġ ach", "Ġa ch", "Ġac h", "ĠE li", "ĠEl i", "âĪ Ĵ", "o rna", "or na", "orn a", "Ġ cps", "Ġc ps", "Ġcp s", "Ġthere of", "Ġther eof", "Ġ@ {", "Ġ NSMutableArray", "ĠNSMutable Array", "Î ½", "pro ductive", "product ive", "prod uctive", "S quare", "tem pts", "temp ts", "tempt s", "Ġelim inated", "Ġeliminate d", "Ġelimin ated", "< M", "Ġcons ervatives", "Ġconserv atives", "Ġconservative s", "ĠS urg", "ĠSur g", "ĠSu rg", ". par", ".p ar", ".pa r", "ĠB uch", "ĠBu ch", "* b", "F ort", "For t", "Fo rt", "Col our", "Ġ Chi", "ĠC hi", "ĠCh i", "e dic", "ed ic", "edi c", "> true", "ĠN YC", "ĠNY C", "Ġb ored", "Ġbo red", "Ġbor ed", "Ġbore d", "Ġ Detect", "ĠD etect", "ĠDet ect", "Ġapp ar", "Ġap par", "Ġje ans", "Ġjean s", "Ġ Tak", "ĠT ak", "ĠTa k", "I OD", "IO D", "ĠH orse", "ĠHor se", "( FILE", "(F ILE", "( ?", "r ique", "ri que", "opt imizer", "optim izer", "optimize r", "n at", "na t", "lo ys", "loy s", "ĉ Token", "ĉT oken", "oub ted", "u ess", "ue ss", "ues s", "oc oa", "oco a", "Data Member", "_ POWER", "_P OWER", "_PO WER", "class List", "Push Button", "Ġ WiFi", "ĠWi Fi", ". Stream", ".St ream", ".Str eam", ". guild", ".g uild", ".gui ld", "Ġn og", "Ġno g", "ĠPort ugal", "ĠPortug al", "ĠUn ter", "ĠUnt er", "Pr imitive", "Prim itive", "b oss", "bo ss", "bos s", "ĠDe utsch", "Ġer otic", "Ġerot ic", "Ġero tic", "Ġ strconv", "Ġstr conv", ".Try Parse", "Ġ grams", "Ġg rams", "Ġgr ams", "Ġgram s", "Ġgra ms", ". Success", ".S uccess", "_ pk", "_p k", "ĠHar vey", "-m inded", "-min ded", ". country", ".c ountry", ".count ry", "[ ]\"", "[] \"", "Ġ angel", "Ġan gel", "Ġang el", "Ġange l", "Ġbe ats", "Ġbeat s", "ĠV or", "ĠVo r", "i lio", "il io", "ili o", ". master", ".m aster", ".mas ter", ".ma ster", "s omething", "some thing", "som ething", "Ġ PACK", "ĠP ACK", "ĠPA CK", "ĠPAC K", "( if", "(i f", "Request Body", "Ġ antes", "Ġan tes", "Ġant es", "Ġante s", "/ widget", "/w idget", "Ġ modo", "Ġm odo", "Ġmod o", "Ġmo do", "Ġ AW", "ĠA W", "f inder", "find er", "fin der", "fi nder", "Ġ optimized", "Ġopt imized", "Ġoptim ized", "Ġoptimize d", "Ġmiss iles", "Ġmissile s", "N B", "ĉ internal", "ĉint ernal", "ĉin ternal", "ĉinter nal", "t ex", "te x", "ĠS ri", "ĠSr i", "Ġdam aging", "Ġ Mais", "ĠM ais", "ĠMa is", "ĠMai s", "- Allow", "-Al low", "Ġ Zh", "ĠZ h", "- alt", "-a lt", "-al t", "Ġ ));ĊĊ", "Ġ) );ĊĊ", "Ġ)) ;ĊĊ", "Ġ));Ċ Ċ", "Ġ)); ĊĊ", "è ī", "Ġinflu ences", "Ġinfluence s", "Ġc atal", "Ġca tal", "Ġcat al", "Ġcata l", "_ REGISTER", "_REG ISTER", "ĠAPI s", "ĠAP Is", "-cent ury", "Ġ biology", "Ġb iology", "Ġbi ology", "Ġbio logy", "Ġ Actual", "ĠAct ual", "ĠAc tual", "Ġ heels", "Ġhe els", "Ġheel s", "TR ACE", "TRA CE", "_ DIG", "_D IG", "_DI G", "D ataset", "Data set", "Dat aset", "Datas et", "ĠM atter", "ĠMat ter", "ĠMatt er", "ĠMatte r", "Ġ classifier", "Ġclass ifier", ".w ikipedia", "ĠRo gers", "ĠRog ers", "ĠRoger s", "Ġdon ated", "Ġdonate d", "raw ler", "rawl er", "e nen", "en en", "ene n", "Ġcas inos", "Ġcasino s", "Ġcasi nos", "or tal", "ort al", "orta l", "Ġp rive", "Ġpr ive", "Ġpriv e", "Ġpri ve", "s pe", "sp e", "du cers", "duc ers", "duce rs", "ducer s", ". ep", ".e p", "Ġgr asp", "Ġgra sp", "Ġgras p", "ac ji", "Ġd airy", "Ġda iry", "Ġdai ry", "Ġdair y", "Ġb uses", "Ġbu ses", "Ġbus es", ". comm", ".c omm", ".com m", ".co mm", ". ins", ".in s", ".i ns", "Ġ IRS", "ĠI RS", "ĠIR S", "Ġ Beer", "ĠB eer", "ĠBe er", "ĠBee r", "a dc", "ad c", "o ard", "oa rd", "_ MET", "_M ET", "_ME T", "Ġ' +'", "Ġ'+ '", "r ans", "ra ns", "ran s", "Ġk inda", "Ġkind a", "Ġki nda", "Ġkin da", "Ġ âĶĤ", "ĠâĶ Ĥ", "ĠM aur", "ĠMa ur", "а г", "аР³", "Ġband width", "i bus", "ib us", "ibu s", "Ġ Different", "ĠD ifferent", "( mat", "(m at", "Ġ Resume", "ĠRe sume", "ĠRes ume", "_ UNS", "_U NS", "_UN S", "est ablish", "Ġfon ction", "Sub scription", "_ company", "_com pany", "_comp any", "Ġ lightly", "Ġl ightly", "Ġlight ly", ". confirm", ".con firm", ".conf irm", ". yaml", ".y aml", "Ġ Boost", "ĠBo ost", "ĠBoo st", "Com merce", "Comm erce", "- template", "-t emplate", "-temp late", "_ DELAY", "_DE LAY", "_DEL AY", "Ġ HI", "ĠH I", "Ġn avig", "Ġna vig", "Ġnav ig", "( Sender", "(S ender", "Ġ HS", "ĠH S", "_ \"+", "_\" +", "Ġ REQUEST", "ĠRE QUEST", "ĠREQ UEST", "Ġ wifi", "Ġw ifi", "Ġwi fi", "= \"\"Ċ", "=\" \"Ċ", "=\"\" Ċ", "] )->", "]) ->", "])- >", "Ġr ope", "Ġro pe", "Ġrop e", "Ġvi olated", "Ġviol ated", "Ġviolate d", "Ġgl ance", "ĠK urd", "ĠKur d", "ĠKu rd", "Ġ è®", "Ġè ®", "d eck", "de ck", "dec k", "Ġ ISBN", "ĠIS BN", "Ġin fect", "Ġinf ect", "Ġ Foo", "ĠF oo", "ĠFo o", "Ġ getter", "Ġg etter", "Ġget ter", "Ġ tener", "Ġt ener", "Ġte ner", "Ġten er", "a ppe", "ap pe", "app e", ". hh", ".h h", "_ hot", "_h ot", "< AM", " \".$", ">\" .$", ">\". $", "Ġre lies", "Ġrel ies", "Ġreli es", "Ġrelie s", "( Console", "Int ernational", "Inter national", "Intern ational", "- >{$", "-> {$", "->{ $", "M id", "Mi d", "Ġdis sert", "Ġdiss ert", "Ġdisse rt", "d ds", "dd s", "Ġdeposit s", "Ġdepos its", "ĉ driver", "ĉd river", "# ga", "#g a", "p rising", "pr ising", "pri sing", "print ln", "Ġp resenter", "Ġpres enter", "Ġpresent er", "Ġpresente r", "Ġm ines", "Ġmin es", "Ġmi nes", "Ġmine s", "C SS", "CS S", "Ġ Dual", "ĠD ual", "ĠDu al", "( !(", "(! (", "Ġk am", "Ġka m", "Ġ isLoading", "Ġis Loading", "Ġ Protect", "ĠProt ect", "ĠProte ct", ". upper", ".u pper", ".up per", "a rium", "ar ium", "ari um", "] :ĊĊĊ", "]: ĊĊĊ", "]:Ċ ĊĊ", "]:ĊĊ Ċ", "Y ii", "- shirt", "-sh irt", "Ġ IMAGE", "ĠIM AGE", "_ colors", "_color s", "_col ors", "Ġ urgent", "Ġur gent", "Ġurge nt", "Ġurg ent", ". Container", ".Cont ainer", "! (Ċ", "!( Ċ", "S aturday", "Ġsoc ieties", "Ġsoci eties", "Ġ Than", "ĠT han", "ĠTh an", "Ġ Cod", "ĠC od", "ĠCo d", "= @", "Ġ attachments", "Ġattach ments", "Ġattachment s", ". mobile", ".m obile", ".mob ile", "Ġs pite", "Ġsp ite", "Ġspi te", "Ġspit e", "Ġ bounce", "Ġb ounce", "Ġbo unce", "Ġbou nce", "r awl", "ra wl", "raw l", "instance type", "instanc etype", "ĠTr uck", "ĠTru ck", "Ġmanip ulation", "( Config", "- inst", "-in st", "-i nst", "-ins t", "Ġ stor", "Ġs tor", "Ġst or", "Ġsto r", "it ution", "itu tion", "Preferred Gap", "Ġmain AxisAlignment", "Ġlist ened", "Ġlisten ed", "Ġliste ned", "'' 'ĊĊ", "'''Ċ Ċ", "''' ĊĊ", "ott age", "otta ge", "- project", "-pro ject", ". APPLICATION", ".AP PLICATION", "ĉ root", "ĉr oot", "Ġw hit", "Ġwh it", "Ġ bilder", "Ġb ilder", "Ġbi lder", "Ġbil der", "Ġbild er", "Ġ ker", "Ġk er", "Ġke r", "Ġappl iances", "Ġappliance s", "ro wave", "row ave", "ìĿ Ģ", "em atics", "ema tics", "ematic s", "emat ics", "Ġ Org", "ĠO rg", "ĠOr g", "o ping", "op ing", "opi ng", "_ SEARCH", "_SE ARCH", "Ġc ham", "Ġch am", "Ġcha m", "add ContainerGap", "Ġ ().", "Ġ( ).", "Ġ() .", "Ġ Arrow", "ĠAr row", "ĠArr ow", "Il legal", "Ill egal", "Current ly", "Curr ently", "Ġ usa", "Ġu sa", "Ġus a", "Ġpass words", "Ġpassword s", "Ġre nown", "Ġren own", "a vern", "av ern", "ave rn", "aver n", "ĠE vil", "ĠEv il", "Ġ concat", "Ġcon cat", "Ġconc at", "Ġd uo", "Ġdu o", "Ġ vale", "Ġv ale", "Ġval e", "Ġva le", "Ġ Bean", "ĠB ean", "ĠBe an", "ĠBea n", "Ġind icators", "Ġindic ators", "Ġindicator s", "Ġindica tors", "c math", "cm ath", "ĠP ump", "ĠPu mp", "Nov ember", "ific ant", "ifi cant", "ifica nt", "_ DOMAIN", "_DO MAIN", "_DOM AIN", "re gar", "reg ar", "rega r", "Ġ Portal", "ĠP ortal", "ĠPort al", "ĠPor tal", "\" $", "Ġ formerly", "Ġformer ly", "\" ]:Ċ", "\"] :Ċ", "\"]: Ċ", "Ġ Visibility", "ĠVis ibility", ".getElementsBy ClassName", "_ RED", "_RE D", "_R ED", "Ġch ampions", "Ġchampion s", "Ġchamp ions", "à ´", "Val or", "Va lor", "_ es", "_e s", "* a", "- repeat", "-re peat", "B and", "Ban d", "Ba nd", ". stage", ".st age", "Ġbure auc", "Ġbureau c", "C nt", "e ten", "et en", "ete n", "- function", "-f unction", "Ġm uito", "Ġmu ito", "Ġmuit o", "P ID", "PI D", "_ editor", "_e ditor", "_edit or", "_ed itor", "Ġcr ashed", "Ġcrash ed", "Ġcra shed", "d ead", "de ad", "dea d", "k at", "ka t", "a gh", "ag h", "Ġ EXT", "ĠE XT", "ĠEX T", "as ser", "ass er", "asse r", "- small", "-s mall", "-sm all", "Ġre aliz", "Ġreal iz", "( Entity", "(E ntity", "ú s", "Ġ Actually", "ĠAct ually", "ĠActual ly", "Ġ Elite", "ĠE lite", "ĠEl ite", "ĠEli te", "Ġ helm", "Ġh elm", "Ġhe lm", "Ġhel m", "( nonatomic", "(non atomic", "a sher", "as her", "ash er", "Comm unity", "all eng", "alle ng", "allen g", "i ry", "ir y", "ĠG rowth", "ĠGrow th", "Ġs ue", "Ġsu e", "Ġf requencies", "Ġfrequ encies", "_ descriptor", "_des criptor", ". Attribute", ".At tribute", "Ġrec ipients", "Ġrecipient s", "Ġrecip ients", "_ NS", "_N S", "/ \"+", "/\" +", "i ban", "ib an", "iba n", "Ġ athlete", "Ġath lete", "Ġ Ign", "ĠI gn", "ĠIg n", "_ DMA", "_D MA", "_DM A", "( ds", "(d s", "Ġ Requirements", "ĠRequire ments", "ĠRequirement s", "A DI", "AD I", "e rez", "er ez", "ere z", "\\ Admin", "br aska", "bra ska", "bras ka", "ĠR ust", "ĠRu st", "ĠRus t", "Re lation", "Rel ation", "C OD", "CO D", "Ġ VERSION", "ĠV ERSION", "ĠVER SION", "e mma", "em ma", "emm a", ") ){", ")) {", ". Duration", ".D uration", "Ġ Camb", "ĠC amb", "ĠCam b", "ĠCa mb", "- logo", "-l ogo", "-lo go", "-log o", "Ġread able", "Ġcre ators", "Ġcreat ors", "Ġcreator s", "Ġcrea tors", "( )];Ċ", "() ];Ċ", "()] ;Ċ", "Up Down", "- half", "-h alf", ".get Month", ".getM onth", "( sf", "(s f", "P ic", "Pi c", "Ġh unger", "Ġhun ger", "Ġhung er", "Ġhu nger", ". tx", ".t x", "Ġex ceeded", "Ġexceed ed", "Ġexce eded", "_ seed", "_s eed", "_se ed", "( ^", "_ sk", "_s k", ". perform", ".per form", "Ġ >::", "Ġ> ::", "Ġ mongo", "Ġm ongo", "Ġmon go", "Ġmo ngo", "Ġmong o", "= float", "=f loat", "bind Param", "S mart", "Sm art", "i fa", "if a", "Ġse curities", "Ġsec urities", "Ġpre jud", "Ġ ,\"", "Ġ, \"", "Ġcor ps", "Ġcorp s", "Ġv ra", "Ġvr a", "ama care", "amac are", "i terr", "it err", "ite rr", "iter r", "( Media", "(M edia", "(Me dia", "u che", "uch e", "uc he", "Ġ cob", "Ġc ob", "Ġco b", "Ġl iber", "Ġli ber", "Ġlib er", ". geometry", ".ge ometry", ".geom etry", ".geo metry", "L ocator", "Loc ator", "Ġsl iding", "Ġslid ing", "Ġs urgical", "Ġsurg ical", "_ CUR", "_C UR", "Ġcon sect", "Ġcons ect", "Ġconsec t", "Ġconse ct", "[ *", "ĠRe sort", "ĠRes ort", "St ub", "_ DOUBLE", "_DO UBLE", "Ġ Soph", "ĠS oph", "ĠSo ph", "Ġelect oral", "_ disable", "_d isable", "_dis able", "Ġ Ñģо", "ĠÑģ о", "ĠLight ning", "Ġ mentions", "Ġm entions", "Ġmention s", "Ġment ions", "o cy", "oc y", "Ġle aked", "Ġleak ed", "Ġrelax ing", "P resenter", "Pres enter", "Present er", "v sp", "vs p", "Ġg uilt", "Ġgu ilt", "Ġgui lt", "=- =-", ". reply", ".re ply", ".rep ly", "Ġ Mirror", "ĠM irror", "ĠMir ror", "ĠMi rror", "C amp", "Ca mp", "Cam p", "Ġ+#+ #+#+", "Ġ+#+#+#+ #+#+", ". Author", ".A uthor", ".Auth or", "Ġ directive", "Ġdirect ive", "Ġdir ective", "- hook", "-h ook", "íĦ °", "} ĊĊĊĊĊ", "}Ċ ĊĊĊĊ", "}ĊĊ ĊĊĊ", "}ĊĊĊ ĊĊ", "}ĊĊĊĊ Ċ", "@ pytest", "_ rand", "_r and", "_ra nd", "m is", "mi s", "Ġcolor ful", "u je", "uj e", "l asses", "lass es", "las ses", "Ġ Classes", "ĠC lasses", "ĠCl asses", "ĠClass es", "ĠClasse s", ". have", ".h ave", "% ),", "%) ,", "é¢ ĺ", "Ġdistur bing", "Ġdisturb ing", "sub string", "substr ing", "subst ring", "subs tring", "ĠK oh", "ĠKo h", "In vest", "Inv est", "p urchase", "Ġrec ycling", "Ġrecycl ing", "Ġ ART", "ĠA RT", "ĠAR T", "ier archy", "Ġ fps", "Ġf ps", "Ġfp s", ". checkBox", ".check Box", "íķ ´", "_ material", "_m aterial", "_mat erial", "du cation", "duc ation", "Ġ fw", "Ġf w", "u dit", "ud it", "udi t", "Ġreview ing", "Ġ Sid", "ĠS id", "ĠSi d", "S yntax", "Sy ntax", "Syn tax", "Ġ Written", "ĠW ritten", "ĠWr itten", "ar gar", "arg ar", "arga r", "U ME", "UM E", "/ q", "Class ifier", "Off icial", "Ġj azz", "Ġja zz", "Ġjaz z", "Ġ omega", "Ġo mega", "Ġom ega", "Ph ysics", "Phys ics", "Ġl ugar", "Ġlu gar", "Ġlug ar", "_access or", "_acc essor", ". commands", ".command s", ".comm ands", "Ab ility", "Ġ Batch", "ĠB atch", "ĠBat ch", "R AM", "RA M", "Ġenc ounters", "Ġencounter s", "Ġencount ers", ". Qu", ".Q u", "B YTE", "BY TE", "Ġ Distribution", "ĠD istribution", "ĠDis tribution", "ĠDistrib ution", "Ġ uso", "Ġu so", "Ġus o", "ĠRe covery", "ĠRec overy", "ĠReco very", "ĠRecover y", "ap proved", "appro ved", "approve d", "Ġden ial", "/ share", "/s hare", "/sh are", "Link edList", "Linked List", ") čĊčĊčĊ", ")čĊ čĊčĊ", ")čĊčĊ čĊ", "u ddy", "ud dy", "udd y", "Ġf ines", "Ġfin es", "Ġfine s", "Ġfi nes", "Ġ ry", "Ġr y", "Un icode", "Uni code", "ĉ render", "ĉr ender", "ĉre nder", "Ġprem ises", "Ġpremise s", "Ġpremi ses", "Ġ pon", "Ġp on", "Ġpo n", "ali ases", "alias es", "alia ses", "/ Foundation", "/F oundation", "c uda", "cu da", "ĠC ock", "ĠCo ck", "ĠCoc k", ", :)", ",: )", "( folder", "(f older", "Ġm éd", "Ġmé d", "d rag", "dr ag", "dra g", "Ġtal ents", "Ġtalent s", "Ġtale nts", "Ġ ĠĠĊĊ", "ĠĠ ĠĊĊ", "ĠĠĠ ĊĊ", "ĠĠĠĊ Ċ", "е ÑģÑĤв", "еÑģÑĤ в", "m ob", "mo b", ".y ml", "Ġ aster", "Ġa ster", "Ġas ter", "Ġast er", "Ġdis cre", "Ġdisc re", "go al", "ĠG TX", "ĠGT X", "Ġ SUCCESS", "ĠS UCCESS", "Ġ LONG", "ĠL ONG", "ĠLO NG", "( find", "(f ind", "(fin d", "(fi nd", "Ġ singular", "Ġs ingular", "Ġsing ular", "_ sz", "_s z", "ĠEth ereum", "ĠEther eum", ". .Ċ", ".. Ċ", "Ġir res", "Ġirre s", "Ġirr es", "' )){Ċ", "') ){Ċ", "')) {Ċ", "Ġmin isters", "Ġminister s", "Ġmini sters", "Ġminist ers", "St eps", "Step s", "Ste ps", "iver sal", "ivers al", "Ġ Nevertheless", "ĠNever theless", "- led", "-l ed", "-le d", "Ġ( %)", "Ġ(% )", "ç¡ ®", "Ġ timezone", "Ġtime zone", "Ġstr anger", "Ġstrange r", "Ġstrang er", "Ġstran ger", "Ġstra nger", "( render", "(r ender", "(re nder", "Ġsh util", "Ġshut il", "Ġ mph", "Ġm ph", "Ġmp h", "Ġt rio", "Ġtr io", "Ġtri o", "p py", "pp y", "Ġpred omin", "Ġ endors", "Ġend ors", "ĠRuss ians", "ĠRussia ns", "ĠRussian s", "ĉ row", "ĉr ow", "Ġ wizard", "Ġw izard", ". serialize", ".s erialize", ".serial ize", "Ġcompl ained", "Ġcomplain ed", "Ġs ido", "Ġsi do", "Ġsid o", "Ġdel ighted", "Ġdelight ed", "- me", "-m e", "ĠR av", "ĠRa v", "H uman", "Hum an", "Hu man", "a days", "ad ays", "ada ys", "aday s", "re cv", "rec v", "Work ing", "J ump", "Ju mp", "Ġ Ã¥r", "ĠÃ¥ r", "Ġ Automatic", "ĠAuto matic", "ĠAut omatic", "ĠAutom atic", "_ Base", "_B ase", "æł ¼", "aur ants", "aurant s", "aura nts", " ¯", "æ ¸", "(C Type", "I FI", "IF I", "( amount", "(a mount", "(am ount", "Ġbel ieving", "Ġbelie ving", "= mysql", "=m ysql", "=my sql", "Ġ fir", "Ġf ir", "Ġfi r", "Ġrest oration", "Ġresto ration", "er eco", "ere co", "Ð ¢", "_ '+", "_' +", "Ġe book", "Ġeb ook", "Ġde bris", "Ġdeb ris", "( inputs", "(input s", "(in puts", "(inp uts", "AY OUT", "Ġscre aming", "Ġscream ing", "a via", "av ia", "avi a", "l ander", "land er", "la nder", "lan der", "Ġdist ress", "Ġdi stress", "Ġdistr ess", "Ġas sembled", "Ġassemble d", "Ġ Avoid", "ĠA void", "ĠAv oid", "( thread", "(t hread", "(th read", "Ġ RPC", "ĠR PC", "ĠRP C", "_ EXIT", "_EX IT", "( queue", "(q ueue", "и ÑģÑĤ", "иÑģ ÑĤ", "D ll", "Ġsk ull", "Ġsku ll", "_ pub", "_p ub", "ch ez", "che z", "m inate", "min ate", "mina te", "en sen", "ens en", "ense n", "Ġins ane", "Ġinsan e", "b ounds", "bo unds", "bound s", "bou nds", "ĠR osen", "ĠRo sen", "ĠRose n", "ĠRos en", "Ġcondition ing", "process ed", "proc essed", "v ideos", "vid eos", "video s", "vide os", "f our", "fo ur", ". Conv", ".Con v", ".Co nv", "| ;Ċ", "Person al", "Pers onal", "Persona l", "cer pt", ":UIControlState Normal", "Ġd oses", "Ġdo ses", "Ġdos es", "Ġdose s", "ĠK arl", "ĠKar l", "ĠKa rl", "ĠF requ", "ĠFr equ", "ĠFre qu", ". BASE", ".B ASE", "Ġ Vote", "ĠV ote", "ĠVo te", "Ġcon current", "Ġconc urrent", "ĠMessageBox Icon", "Ġ Ãĸ", "Ġà ĸ", "ĠDu bai", "ĠDub ai", "Ġ Retail", "ĠR etail", "ĠRe tail", "ĠRet ail", ": number", ":n umber", ":num ber", "Ġ Observer", "ĠOb server", "ĠObserv er", "ĠObs erver", "Ġ BigInteger", "ĠB igInteger", "ĠBig Integer", "ĠBigInt eger", "_ origin", "_or igin", "_orig in", "_ori gin", "_ WORK", "_W ORK", "F rames", "Frame s", "Fr ames", "Fra mes", "Ġnot ably", ". âĢľ", "Ġt ropical", "Ġtrop ical", "Ġn iche", "Ġni che", "Ġnic he", "Ġnich e", "a mina", "am ina", "amin a", "ami na", ". sys", ".s ys", ".sy s", "( tokens", "(t okens", "(token s", "(tok ens", "mod ify", "o sit", "os it", "osi t", "st rom", "str om", "stro m", "ĠC omics", "ĠCom ics", "ĠComic s", "O PTION", "OP TION", "OPT ION", "T icket", "Tick et", "Ti cket", "Ġf actories", "Ġfact ories", "Ġfactor ies", "Ġfacto ries", "Ġdis put", "Ġdisp ut", "_ File", "_F ile", "ĠF inn", "ĠFin n", "ĠFi nn", "e ee", "ee e", "ĠDis cord", "ĠDisc ord", "ĠDisco rd", "_ money", "_m oney", "_mon ey", "_mo ney", ". tpl", ".t pl", ".tp l", "_ safe", "_s afe", "_sa fe", "L B", "Ġg lut", "Ġgl ut", "Ġglu t", "J K", ". flow", ".f low", ".fl ow", "- cont", "-c ont", "-con t", "-co nt", "g os", "go s", "Ġhor izon", "ĠR ush", "ĠRu sh", "ĠRus h", ": :*", ":: *", "P ipe", "Pi pe", "u lla", "ul la", "ull a", "b orough", "bo rough", "bor ough", "boro ugh", "he imer", "heim er", "hei mer", "( move", "(m ove", "( Text", "(T ext", "} );čĊčĊ", "}) ;čĊčĊ", "}); čĊčĊ", "});čĊ čĊ", "w elcome", "wel come", "Ġ Components", "ĠCom ponents", "ĠComponent s", "ĠComp onents", "Ġgovern ance", "c losed", "cl osed", "close d", "clo sed", "ĉ margin", "ĉm argin", "Ġla undry", "Ġ Terminal", "ĠTerm inal", "ĠTermin al", "iz ards", "izar ds", "izard s", ". âĢĶ", ". remote", ".rem ote", ". radius", ".r adius", ".rad ius", "ĠQue bec", "Ġ dh", "Ġd h", "T ech", "Te ch", "ĠM ist", "ĠMi st", "ĠMis t", "s eller", "se ller", "sel ler", "sell er", "_ literal", "_l iteral", "_lite ral", "_lit eral", "Ġgen ius", "Ġ brains", "Ġbr ains", "Ġbrain s", "Ġbra ins", "g em", "ge m", "Ġ Measure", "ĠMe asure", "Ġcat ast", "Ġcata st", "r ance", "ra nce", "ran ce", ". TextField", ".T extField", ".Text Field", "Ġcon suming", "Ġcons uming", "Ġconsum ing", "Ġ'\\ ''", "Ġ'\\' '", "oubted ly", "Ġ Certain", "ĠC ertain", "ĠCert ain", "ĠCer tain", "E v", "er ti", "ert i", "b eing", "be ing", "bei ng", "Ex perience", "Ġ //[", "Ġ// [", "Ġ/ /[", "ĠAr abic", "ĠArab ic", "ĠAra bic", "ĠC rist", "ĠCr ist", "ĠCri st", "Ġ Azure", "ĠA zure", "ĠAz ure", "Ġ hora", "Ġh ora", "Ġhor a", "Ġho ra", "l adesh", "lad esh", "\\ Blueprint", "d ar", "da r", ". rel", ".re l", ".r el", "Ġsup rem", "ĠRe agan", "Ġ Attributes", "ĠAt tributes", "ĠAttribute s", "- sidebar", "-s idebar", "-side bar", "Ġuse Styles", "ĠA irlines", "ĠAir lines", "Ġh ills", "Ġhill s", "Ġhil ls", "/x html", "v inc", "vin c", "vi nc", "_ mock", "_m ock", "_mo ck", "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠP ill", "ĠPi ll", "ĠPil l", ".Layout Style", "ĠComm ander", "ĠCommand er", "] <", "sign ature", "sig nature", "Ġ{ }čĊ", "Ġ{} čĊ", "Ġhat red", "Ġ ëĭ", "Ġë ĭ", "ole sterol", "Ġ ********", "Ġ* *******", "Ġ** ******", "Ġ*** *****", "Ġ**** ****", "Ġ***** ***", "ancel lor", "ancell or", "c rop", "cr op", "cro p", "T IM", "TI M", "ĉ ĉĊĊ", "ĉĉ ĊĊ", "ĉĉĊ Ċ", "ys qli", "ysql i", "u itive", "uit ive", "ĉ unset", "ĉun set", "_ sel", "_s el", "_se l", "Ġ menus", "Ġm enus", "Ġmen us", "Ġmenu s", "t ick", "ti ck", "tic k", "Ġcon stitute", "Ġconstit ute", "Ġconstitu te", "Ġ Elements", "ĠE lements", "ĠEl ements", "ĠElement s", "ĠEle ments", "ĠElem ents", "Ġ Redis", "ĠR edis", "ĠRe dis", "ĠRed is", "ag gio", "agg io", "aggi o", "_ fp", "_f p", "_ depend", "_d epend", "_de pend", "_dep end", "e mas", "em as", "ema s", "C AST", "CA ST", "CAS T", "o range", "or ange", "ora nge", "oran ge", "orang e", "j on", "jo n", "Ġ Emily", "ĠEm ily", "ĠEmil y", "Ġpot atoes", "Ġpotato es", "Ġre ceptor", "Ġrecept or", "Ġrecep tor", "Ġ Electronic", "ĠElect ronic", "ĠElectro nic", "ĠElectron ic", "Ġ Lights", "ĠL ights", "ĠLight s", "Ġcomb ining", "Ġcombin ing", "Ġ Someone", "ĠSome one", "Ġ######## .", "ĠT OD", "ĠTO D", "/ show", "/s how", "/sh ow", "X d", ". \"'", ".\" '", "a fx", "af x", "Ġtr agic", "Ġtrag ic", "St yled", "Style d", "Ġ Marco", "ĠMar co", "ĠMarc o", "G allery", "d ale", "da le", "dal e", ".âĢĿ ĊĊĊĊ", ".âĢĿĊĊ ĊĊ", ".âĢĿĊ ĊĊĊ", "é rie", "ér ie", "éri e", "/ service", "/s ervice", "äº Ĩ", "Ġ ambient", "Ġamb ient", "_ SETTINGS", "_SET TINGS", "_SETTING S", ". Adapter", ".Ad apter", "l ene", "le ne", "len e", "Ġtravel s", "Ġtrav els", "Not ice", "Ġc leans", "Ġclean s", "Ġcle ans", "ĠF em", "ĠFe m", "c hair", "ch air", "cha ir", "chai r", "Ñĥ н", "/ my", "/m y", "_ bad", "_b ad", "ĠE conomics", "ĠEcon omics", "ĠEconomic s", "ĠEconom ics", "I SA", "IS A", "_ CNT", "_C NT", "_CN T", "( Menu", "(M enu", "(Me nu", "äº İ", "ĠR idge", "ĠRid ge", "ĠRi dge", "Ġlength y", "Ġleng thy", "D ot", "Do t", "Ġj umps", "Ġjump s", "Ġju mps", "Ġ hey", "Ġh ey", "Ġhe y", "$ pdf", "$p df", "Ġ worm", "Ġw orm", "Ġwor m", "Ġwo rm", "Ġ sut", "Ġs ut", "Ġsu t", "Ġ sher", "Ġs her", "Ġsh er", "Ġshe r", "i amo", "ia mo", "iam o", "Ġ Calc", "ĠC alc", "ĠCal c", "ĠCa lc", "t rieve", "tr ieve", "trie ve", "tri eve", "Ġc ops", "Ġco ps", "Ġcop s", "ĠCh rom", "ĠChr om", "Ġ regulated", "Ġreg ulated", "Ġregul ated", "Ġregulate d", "reat ment", "Ġ Higher", "ĠHigh er", "o ks", "ok s", "Ġde ze", "Ġdez e", "LOC ATION", "ongs To", "Ġ finite", "Ġf inite", "Ġfin ite", "Ġfi nite", "Ġv aries", "Ġvar ies", "Ġvari es", "Ġva ries", "Ġposition ed", "Ġposit ioned", "' il", "'i l", "éĩ ij", "Ġh ike", "Ġhi ke", "Ġhik e", "( done", "(d one", "(do ne", "play list", "Ġ ada", "Ġa da", "Ġad a", "Ġcoast al", "ĠN ancy", "ĠNa ncy", "ĠNan cy", ".DateTime Field", "Cpp CodeGen", "Ġ Similarly", "ĠSimilar ly", "r eur", "re ur", "reu r", "Ġ Contr", "ĠCon tr", "ĠCont r", "Ġ Hidden", "ĠH idden", "ĠHi dden", "Ġ Beta", "ĠB eta", "ĠBe ta", "ĠBet a", "at ched", "atch ed", "_ install", "_inst all", ". Output", ".Out put", "Look up", "ĠRich mond", "qu ared", "quare d", "qua red", "Ġm anga", "Ġman ga", "Ġma nga", "Ġmang a", "- controls", "-control s", "ĠBer nard", "ĠBern ard", "L arge", "Ġs lices", "Ġsl ices", "Ġslice s", "Ġslic es", "Ġoff ence", "Ġoffen ce", "ĠM ega", "ĠMe ga", "ĠMeg a", "Ġ estar", "Ġe star", "Ġes tar", "Ġest ar", "Ġesta r", "Ġj oints", "Ġjoin ts", "Ġjo ints", "Ġjoint s", "Ġ summ", "Ġs umm", "Ġsu mm", "Ġsum m", "_ platform", "_pl atform", "B uff", "Buf f", "Bu ff", ".add Subview", "Ġret ained", "Ġretain ed", "L etter", "Let ter", ". dim", ".d im", ".di m", "Ġess ere", "Ġesse re", "ĠS caffold", "EX PECT", "EXP ECT", "ĉ RE", "ĉR E", ". longitude", ".long itude", "ü nd", "ün d", "Ġstat ue", ". addWidget", ".add Widget", "ĠCar ibbean", "add PreferredGap", "il de", "ild e", "UI Label", "UIL abel", "ĠOp port", "ĠOpp ort", "Ġim perial", "Ġimp erial", "Ġimper ial", "Ġimpe rial", "urs ion", "Ġman date", "Ġmand ate", "Ġprom otional", "Ġpromot ional", "Ġpromotion al", "Ġ vk", "Ġv k", "ia ÅĤ", "Ġp yl", "Ġpy l", "Ġ Creation", "ĠC reation", "ĠCre ation", "ĠCreat ion", "о зд", "оз д", "Ġsim pler", "Ġsimple r", "Ġsimp ler", "Ġsimpl er", ". what", ".w hat", ".wh at", "Ġ Recent", "ĠRe cent", "ĠRec ent", "ĠRece nt", "St orm", ". quantity", ".qu antity", ".quant ity", "Ġ Lov", "ĠL ov", "ĠLo v", "\" -", "ub bles", "ubble s", "ubb les", "_ notification", "_not ification", "( world", "(w orld", "ur ger", "urg er", "urge r", "* (-", "*( -", ": \"Ċ", ":\" Ċ", "h m", "an ship", "ans hip", "Ġ Almost", "ĠAl most", "Ġmotor cycle", "_ fee", "_f ee", "_fe e", "Ġabs orb", "Ġabsor b", "ĠVin cent", "ĠVince nt", "Ġs ounded", "Ġso unded", "Ġsound ed", "ÃŃ st", "ÃŃs t", "Ġpharm aceutical", "h tag", "ht ag", "hta g", "ĠK indle", "ĠKind le", "ĠKin dle", "ital ize", "ĠEm peror", "ous tic", "oust ic", "Ġspecial ists", "Ġspecialist s", "åħ ¬", "Border Style", "/ \\", "RE LATED", "REL ATED", "(' ,',", "(', ',", "(',' ,", "( expr", "(ex pr", "(exp r", "Ġ ht", "Ġh t", "åį Ī", "_ Create", "_C reate", "Ġs pecially", "Ġspec ially", "Ġspecial ly", "Ġspeci ally", "Ġ [];čĊ", "Ġ[ ];čĊ", "Ġ[] ;čĊ", "Ġ[]; čĊ", "Ġ heel", "Ġh eel", "Ġhe el", "Ġs ept", "Ġse pt", "Ġsep t", "_ arch", "_a rch", "_ar ch", "_arc h", "( initial", "(in itial", "(init ial", "% .ĊĊ", "%. ĊĊ", "%.Ċ Ċ", "\\\" ,\\\"", "\\\", \\\"", "\\\",\\ \"", "Ġdisc usses", "Ġdiscuss es", "Ġ upt", "Ġu pt", "Ġup t", "Ġ[ &", "Ġm anus", "Ġman us", ". hand", ".h and", "Ġ MAIN", "ĠM AIN", "ĠMA IN", "ĠDen mark", "Ġ ],čĊ", "Ġ] ,čĊ", "Ġ], čĊ", "Ġcr yst", "Ġcry st", "Ġn ack", "Ġna ck", "Co ords", "Coord s", "_ inner", "_in ner", "Ġmid st", "Ġmi dst", "Ġa wake", "Ġaw ake", "Ġ Ðŀ", "ĠÐ ŀ", "- break", "-b reak", "-bre ak", "ÃŃ vel", "ÃŃv el", "_ PASS", "_P ASS", "_PA SS", "Ġ Params", "ĠPar ams", "ĠParam s", "ĠPa rams", "ĠPara ms", "Ġd etr", "Ġde tr", "Ġdet r", "Ġsp ider", "Ġspi der", "Ġ Concept", "ĠCon cept", "ĠConc ept", "ĠConce pt", "Ġ prend", "Ġp rend", "Ġpr end", "Ġpre nd", "CH ED", "CHE D", ". Exit", ".Ex it", ".E xit", "Ġpop ulated", "Ġpopulate d", "Ġpopul ated", "Ġvirt ue", "_ SESSION", "_SE SSION", "Ġnou vel", "Ġnouve l", "o auth", "oa uth", "Ġд аннÑĭ", "Ġдан нÑĭ", "r ink", "ri nk", "rin k", ". HeaderText", ".Header Text", "atur ated", "atura ted", "atu rated", "Ġe rst", "Ġer st", "Ġers t", "Ġ åħ", "Ġå ħ", "ॠĩ", "_ visible", "_v isible", "_vis ible", "e yer", "ey er", "eye r", "Ġ liable", "Ġl iable", "Ġli able", "Ġlia ble", "Ġd ebe", "Ġde be", "Ġdeb e", "Ġ bw", "Ġb w", "{- #", "_ WIN", "_W IN", "d fs", "df s", "H over", "Ho ver", "Ġ PUT", "ĠP UT", "ĠPU T", "- angle", "-a ngle", "-an gle", "Ġn oble", "Ġno ble", "Ġnob le", "Ġtr aces", "Ġtra ces", "Ġtrace s", "en cv", "enc v", "Ġ userData", "Ġuser Data", "_ ins", "_in s", "_i ns", "ĠS uz", "ĠSu z", "Ġnews letters", "Ġnewsletter s", "ĠM odi", "ĠMod i", "ĠMo di", "Ġentreprene urs", "Ġentrepreneur s", "Ġ tribute", "Ġtrib ute", "Ġrum ors", "Ġrumor s", "Ġ rr", "Ġr r", "Ġ Quarter", "ĠQu arter", "ĠQuart er", "ĠQuar ter", "ê³ ł", "Ġ feeds", "Ġfe eds", "Ġfeed s", "Ġfee ds", "ó g", "Ġen velope", "Ġenv elope", "Ġenvelop e", "Ġ lear", "Ġl ear", "Ġle ar", "Ġk ø", "de veloper", "develop er", "S imilar", "Sim ilar", ": \")Ċ", ":\" )Ċ", ":\") Ċ", "sub scription", "subs cription", "Mod ifier", "it alic", "ital ic", "ita lic", "Ġn asty", "Ġna sty", "Ġnas ty", "Ġnast y", "Ġ termination", "Ġter mination", "Ġterm ination", "Ġtermin ation", "Ġch arming", "Ġchar ming", "Ġcharm ing", "Ġ âŁ", "Ġâ Ł", "t ons", "ton s", "to ns", ". trace", ".t race", ".tr ace", "h ots", "ho ts", "hot s", "Ġ UR", "ĠU R", "M ont", "Mon t", "Mo nt", "Ġjust ified", "ĠG ang", "ĠGa ng", "ĠGan g", "i nea", "in ea", "ine a", "Ġb og", "Ġbo g", "( ap", "(a p", "_ $", "Ġcont amin", "Ġconta min", ". Dot", ".D ot", ".Do t", "ĉ Debug", "( exports", "(ex ports", "(exp orts", "Ġ paired", "Ġp aired", "Ġpair ed", "Ġpa ired", "Ġpai red", "Ġ Assignment", "ĠAss ignment", "ĠAssign ment", "Ġauto mobile", "Ġautom obile", "ĵ į", "Ġph ases", "Ġphase s", "Ġpha ses", "v w", "@ SuppressWarnings", "= \\", "r ant", "ra nt", "ran t", "- ed", "-e d", "ĉ await", "ĉa wait", "Ġcert ificates", "Ġcertificate s", "Ġcertif icates", "' >\"", "'> \"", "Ġint act", "C TRL", "CT RL", "CTR L", "M ike", "Mi ke", "g regation", "greg ation", "AT TERN", "ATT ERN", "ATTER N", "Ġre public", "Ġrep ublic", "_ upper", "_u pper", "_up per", "ili ary", "iliar y", "ilia ry", "Ġcom putation", "Ġcomp utation", "Ġcomput ation", "h ire", "hi re", "hir e", "ĠS hin", "ĠSh in", "ĠShi n", "_ ANY", "_A NY", "_AN Y", "Ġ Manufacturer", "ĠMan ufacturer", "ĠManufact urer", "ĠC arm", "ĠCar m", "ĠCa rm", "Ġbear ings", "Ġbearing s", "_ comb", "_c omb", "_com b", "_co mb", "c ad", "ca d", "ur istic", "Ġwh olesale", "Ġwhole sale", "Ġwholes ale", "Ġd onor", "Ġdo nor", "Ġdon or", ". interfaces", ".inter faces", ".interface s", "pr esso", "press o", "pres so", "Ġ Brun", "ĠB run", "ĠBr un", "ĠBru n", "- close", "-c lose", "-cl ose", "p rove", "pr ove", "pro ve", "prov e", "_ SK", "_S K", "ĉ frame", "ĉf rame", "ĉfr ame", "et ros", "etro s", "etr os", "ĠP ain", "ĠPa in", "ĠPai n", "_ EXP", "_E XP", "_EX P", "Ġ LT", "ĠL T", "_ fs", "_f s", ". datas", ".d atas", ".data s", ".dat as", ".da tas", "ĉ ss", "ĉs s", "v oir", "vo ir", "Ġ Axis", "ĠA xis", "ĠAx is", "M ajor", "= \"<", "=\" <", "[ h", "Ġprof ess", "Ġprofes s", "ig rate", "igr ate", "( score", "(s core", "(sc ore", "Key word", "\" os", "ĠĠ ĠĠĉĊ", "ĠĠĠĠ ĉĊ", "ĠĠĠ ĠĉĊ", "ĠĠĠĠĉ Ċ", "an alysis", "analy sis", "anal ysis", "Ġre play", "Ġrep lay", "Ġrepl ay", ". pass", ".p ass", ".pa ss", "\\ d", "t ls", "tl s", "Ġsan ct", ". light", ".l ight", ".li ght", "_ mobile", "_m obile", "_mob ile", "Ñģ ÑĤÑĮ", "ÑģÑĤ ÑĮ", "ĉ total", "ĉt otal", "ĉto tal", "u ity", "ui ty", "uit y", "Ġ paused", "Ġpa used", "Ġpause d", "Ġpau sed", "N AS", "NA S", "Ġen core", "Ġenc ore", "l oe", "lo e", "Ġ-* -ĊĊ", "Ġ-*- ĊĊ", "Ġ-*-Ċ Ċ", ". high", ".h igh", "am pler", "amp ler", "ample r", "Ġ Secure", "ĠS ecure", "ĠSec ure", "Ġf ragments", "Ġfra gments", "Ġfrag ments", "Ġfragment s", "_ vel", "_v el", "_ve l", "ill ary", "illa ry", "ĠS tein", "ĠSt ein", "ĠSte in", "ĠD awn", "ĠDa wn", "ĠDaw n", "Ġmax imize", "Ġmaxim ize", "ภ¢", "Ġ /^", "Ġ/ ^", "Ġcontin ually", "Ġcontinu ally", "Ġcontinual ly", "Ġsh adows", "Ġshadow s", "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠI ActionResult", "Ġinform ación", "C HECK", "CHE CK", ". SelectedItem", ".Selected Item", "b undle", "ol ley", "oll ey", "olle y", "< Int", "<", "\\\" ><", "\\\"> <", "Ġ trajectory", "Ġtra jectory", "_ ring", "_r ing", "Ġhydro gen", "Ġhydr ogen", "t ron", "tr on", "tro n", "Ġstat ute", "Ġ conditional", "Ġcondition al", "Ġcond itional", "Ġt ray", "Ġtr ay", "Ġtra y", "- school", "-s chool", "( widget", "(w idget", "$ config", "$con fig", "Ġrequest ing", "Ġrequ esting", ". uint", ".ui nt", ".u int", "e ton", "et on", "eto n", "br ities", "brit ies", "Of Type", "A DMIN", "AD MIN", "ADM IN", "p redict", "pre dict", "pred ict", "Ġg egen", "Ġge gen", "Ġgeg en", "ĠH app", "ĠHa pp", "OC UMENT", "Ġ Apart", "ĠA part", "ĠAp art", "Ġ -----", "Ġ- ----", "Ġ-- ---", "Ġ---- -", "Ġ--- --", "r oe", "ro e", "u ide", "ui de", "uid e", "just ify", "ĠS quad", "ĠSqu ad", "Ġprof es", ". bot", ".b ot", ".bo t", "_ currency", "_c urrency", "_curr ency", "i nnen", "in nen", "inn en", "inne n", "ĠM umbai", "ĠMum bai", "Ġ Numbers", "ĠNumber s", "ĠNum bers", "avana ugh", "agn itude", "âĢľ There", "âĢľThe re", "= http", "=h ttp", "çī ĩ", "Ġ vb", "Ġv b", "+ '{{$", "\"> {{$", "\">{{ $", "\">{ {$", "Ġ inode", "Ġin ode", "Ġi node", "s il", "si l", "Ġh ace", "Ġha ce", "Ġhac e", "Ġsever ely", "Ġsevere ly", "Ġ Overview", "ĠOver view", "ĠOv erview", "Ġsp raw", "Ġspr aw", "Ġbe aches", "Ġbeach es", ": left", ":l eft", "· »", "( ${", "($ {", "Ġ FIRST", "ĠF IRST", "ĠFIR ST", "ĠS pa", "ĠSp a", "- ass", "-a ss", "-as s", "Ġb aise", "Ġba ise", "Ġbais e", "Ġ NODE", "ĠN ODE", "ĠNO DE", "Ġ Pizza", "ĠP izza", "ĠPi zza", "P et", "Pe t", "( seq", "(s eq", "(se q", "\\ \">Ċ", "\\\" >Ċ", "\\\"> Ċ", "CppMethod Pointer", "Ġ vp", "Ġv p", "Ġ ia", "Ġi a", "_ seconds", "_se conds", "_sec onds", "_second s", "e met", "em et", "eme t", "/ blob", "/b lob", "/bl ob", "_TH RESH", ".. .čĊ", "... čĊ", "D est", "De st", "Des t", "Ġ NH", "ĠN H", ". dataSource", ".data Source", "it és", "ité s", "Ġ Jak", "ĠJ ak", "ĠJa k", "s ell", "se ll", "sel l", "Ġwork shops", "Ġworkshop s", "< u", "Ġr ivals", "Ġrival s", "Ġri vals", "Ġriv als", "ĠEX ISTS", "h om", "ho m", "- token", "-t oken", "-to ken", "com patible", "compat ible", ".J Panel", "Ġphys icians", "Ġphysician s", "Ġphysic ians", "ar tin", "art in", "arti n", "Ġdes irable", "Ġdistinct ive", ". Dep", ".D ep", ".De p", "g id", "gi d", "il iate", "ili ate", "ilia te", ", max", ",m ax", "Ġprem iere", "Ġpremier e", "Ġpremi ere", "Ġq Debug", "Ġadvoc acy", "Ġwh isper", "P t", "Ġun changed", "_ qty", "_q ty", "请 æ±Ĥ", "Se ason", "Sea son", "ave length", "avel ength", "ĠP ul", "ĠPu l", "Ġd ÃŃa", "ĠdÃŃ a", "'] ]],Ċ", "']] ],Ċ", "a lis", "al is", "ali s", "( \"&", "(\" &", "b oro", "bo ro", "bor o", "Ġ bm", "Ġb m", "Ġ Radi", "ĠR adi", "ĠRa di", "ĠRad i", "w rong", "wr ong", "Ġ Going", "ĠGo ing", "ime Type", "i ji", "ij i", "- feedback", "-fe edback", "-feed back", "Ġ Names", "ĠN ames", "ĠName s", "ĠNa mes", "ĠNam es", "ĠB apt", "ĠBa pt", "Ġpro bable", "Ġprob able", "Ġ Ether", "ĠE ther", "ĠEth er", "ĠEt her", "Ġ Politics", "ĠPol itics", "ĠPolit ics", "_ protocol", "_prot ocol", "_proto col", "l ining", "li ning", "lin ing", "S at", "Sa t", "Ġcor rel", "Ġcorre l", "Ġcorr el", ". Primary", ".Pr imary", "( nullable", "(null able", "RI ORITY", "Ġcol oring", "Ġcolor ing", "Ġutil izing", "Ġutiliz ing", "d as", "da s", "Ġex ported", "Ġexp orted", "Ġexport ed", "Ġcar riers", "Ġcarrier s", "Ġcarr iers", "Con v", "Co nv", ". editor", ".e ditor", ".ed itor", ".edit or", "i ó", "( handles", "(h andles", "(handle s", "(hand les", "Ġapprec iation", ". import", ".im port", ".imp ort", "ĠA ustria", "ĠAust ria", "ĠAustr ia", "Ġ Strip", "ĠS trip", "ĠSt rip", "ĠStr ip", "i light", "il ight", "ili ght", "ilig ht", "Ġappropri ately", "Ġappropriate ly", "ĠP rest", "ĠPr est", "ĠPres t", "ĠPre st", "Ġ Wir", "ĠW ir", "ĠWi r", "Ġ UIApplication", "ĠUI Application", "al chemy", "Ġ Mob", "ĠM ob", "ĠMo b", "Ġ Determin", "ĠD etermin", "ĠDe termin", "ergus on", "register ed", "regist ered", "_ convert", "_con vert", "_conv ert", "ĠVlad imir", "ĠVladim ir", ".Show Dialog", "ref lect", "Ġs hook", "Ġsh ook", "Ġsho ok", "Ġas sure", "Ġass ure", "Ġ Often", "ĠO ften", "ĠOf ten", "Ġcivil ization", "Ġv ocabulary", "Ġvocab ulary", "fore ground", "Ġ Scope", "ĠS cope", "ĠSc ope", "ĠSco pe", "Ġun wanted", "Ġunw anted", "act ing", "ac ting", "Ġ ([]", "Ġ( []", "Ġ([ ]", "Ġm arking", "Ġmark ing", "Ġmar king", ". original", ".origin al", ".or iginal", ".orig inal", "Ġ MOVE", "ĠM OVE", "ĠMO VE", "ĠMOV E", "Ġsp orting", "Ġsport ing", "Ġspor ting", "ce ptions", "ception s", "cept ions", "NS Number", "S izes", "Size s", "Si zes", "Ġpro vincial", "Ġprovinc ial", "Ġprovincia l", "_ Trans", "_T rans", "_Tr ans", "Ġproble matic", "Ġproblem atic", "Ġproblema tic", "Ġprobl ematic", "d igit", "di git", "dig it", "Ġ Emma", "ĠE mma", "ĠEm ma", "ĠEmm a", "l ocks", "lo cks", "lock s", "loc ks", "ĠC rew", "ĠCr ew", "ĠCre w", "i ba", "ib a", "' ):", "') :", "i sha", "is ha", "ish a", "Ġm amm", "Ġma mm", "Ġmam m", "Ġocc ured", "Ġoccur ed", "w cs", "wc s", "( rule", "(r ule", "Ġmerch andise", "es pecially", "ĠT win", "ĠTw in", "Ġn aming", "Ġna ming", "Ġnam ing", "Ġs log", "Ġsl og", "Ġslo g", "Ġimpro ves", "Ġimprove s", "Ġimpr oves", "Ġimprov es", "Ġad her", ": text", ":t ext", ".h adoop", "_ HTTP", "_HT TP", ". toList", ".to List", ". disabled", ".dis abled", ".disable d", "Ġl enses", "Ġlen ses", "Ġlens es", ". ini", ".in i", ".i ni", "Ġ Rare", "ĠR are", "ĠRa re", "Ġ Ubuntu", "ĠUb untu", "Ġsc ram", "Ġscr am", "o lation", "ol ation", "ola tion", "t itulo", "tit ulo", "Every thing", "Ġnod ded", "icht ig", "_ constant", "_con stant", "_const ant", "_cons tant", "z c", "l ift", "li ft", "lif t", "Ġ Notify", "ĠN otify", "ĠNot ify", "o ndo", "on do", "ond o", "Ġ INF", "ĠI NF", "ĠIN F", "( \"+", "(\" +", "ĠK az", "ĠKa z", "Ġd read", "Ġdr ead", "Ġdre ad", ". mapper", ".m apper", ".map per", ".ma pper", "l eur", "le ur", "ĠCom ey", "ĠCo mey", "ĠCome y", "Ġ NB", "ĠN B", "i cers", "ic ers", "ice rs", "icer s", ". Push", ".P ush", "Ġ Hack", "ĠH ack", "ĠHa ck", "ĠBrazil ian", "ĠBraz ilian", "_ prod", "_p rod", "_pro d", "_pr od", "Ġ //ĊĊ", "Ġ// ĊĊ", "Ġ/ /ĊĊ", "Ġ//Ċ Ċ", "Ġb icycle", "Ġbi cycle", "Ġbicy cle", "Ġbic ycle", "Ġun available", "Ġuna vailable", "Ġadoles cent", "b lk", "bl k", "Ġmit ig", "_ blue", "_b lue", "_bl ue", "ì ĺ", "fade In", "Ġ Utilities", "ĠUtil ities", "ĠUt ilities", "Ġ MN", "ĠM N", "; k", "< style", "- status", "-s tatus", "-st atus", "-stat us", "i ndo", "in do", "ind o", "Ġin nings", "Ġinn ings", "Ġinning s", "Ġg j", "Ġ| |=", "Ġ|| =", ". eu", ".e u", ": Number", ":N umber", "Ġc uisine", "Ġcu isine", "Ġcuis ine", "ĠURL s", "i ek", "ie k", "Ġw ires", "Ġwire s", "Ġwir es", "Ġwi res", "ĉ ps", "ĉp s", "i eg", "ie g", ". mk", ".m k", "so ap", "Ġsome time", "Ġsom etime", "Ġs tap", "Ġst ap", "Ġsta p", "_ series", "_s eries", "_se ries", "_ser ies", ". Target", ".T arget", "æ º", ". destination", ".d estination", ".dest ination", "OUN TER", "OUNT ER", "R aises", "Ra ises", "Raise s", "& A", "Ġsmart phones", "Ġsmartphone s", "NI Env", ". sdk", ".s dk", ".sd k", "Ġhel icopter", "Ġhelicopt er", "Ġim pe", "Ġimp e", "Ġ Birth", "ĠB irth", "ĠBir th", "A U", "b readcrumbs", "breadcrumb s", "co ords", "coord s", "Ġexpl ored", "Ġexplo red", "Ġexplore d", "Ġexplor ed", "Ġ lod", "Ġl od", "Ġlo d", "Ġ Ip", "ĠI p", "g able", "ga ble", "i ane", "ia ne", "ian e", "Ġart ifacts", "Ġartifact s", "Box Layout", "ا ر", "Ø§Ø ±", "list ener", "listen er", "lis tener", "liste ner", ". cart", ".c art", ".ca rt", ".car t", "ĠH uff", "ĠHu ff", "ĠHind u", "ĠHin du", "ĠData Types", "ĠDataType s", "Ġ Drupal", "ĠDr upal", "IGN ORE", "Ġoff sets", "Ġoffset s", "Ġoffs ets", "Ġ RTC", "ĠR TC", "ĠRT C", "- login", "-lo gin", "-log in", "æ ®", "Ġ QObject", "ĠQ Object", "Ġprosec utor", "R ock", "Ro ck", "_ chat", "_c hat", "_ch at", "W ay", "Wa y", "ì ²", "Ġneg lig", "Ġd ude", "Ġdu de", "; <", "Ġde legates", "Ġdelegate s", "Ġdeleg ates", "_ failed", "_f ailed", "_fail ed", "_fa iled", "/ dev", "/d ev", "/de v", "/ work", "/w ork", "( New", "(N ew", "e table", "et able", "eta ble", "( )\"", "() \"", "( Icons", "(I cons", "Ġp ork", "Ġpo rk", "Ġpor k", "ĠModel AndView", "Ġ VIP", "ĠV IP", "ĠVI P", "ĠK or", "ĠKo r", "m ix", "mi x", "Ġ oxid", "Ġox id", "Ġ SCREEN", "ĠS CREEN", "ĠSC REEN", "Ġ Fourth", "ĠFour th", "/ \",Ċ", "/\" ,Ċ", "/\", Ċ", "Ġ tee", "Ġt ee", "Ġte e", "ĠSte vens", "ĠSteve ns", "ĠSteven s", "t icks", "ti cks", "tic ks", "tick s", "Ġp ledge", "Ġpl edge", "Ġple dge", "Ġpled ge", "ib bon", "Ġ Loan", "ĠLo an", "Ġ neo", "Ġn eo", "Ġne o", "n umpy", "num py", "Ġ SharedPreferences", "ĠShared Preferences", "- oriented", "ĠLogger Factory", "Ġ GraphQL", "ĠGraph QL", "z enia", "ze nia", "zen ia", "\" _", "W omen", "Wo men", ". cast", ".c ast", ".ca st", "Ġdeliber ately", "Ġdeliberate ly", "+ b", "Ġ Arn", "ĠA rn", "ĠAr n", "font Size", "Ġ maze", "Ġm aze", "Ġma ze", "Ġmaz e", "Ġbl amed", "Ġblame d", "Ġbla med", ". mas", ".m as", ".ma s", "} )čĊ", "}) čĊ", "eler ik", "ele rik", "eleri k", "Ġsc anning", "Ġscan ning", "ĠWork shop", "ĠWorks hop", "Ġf inden", "Ġfind en", "Ġfin den", "Ġfinde n", "Ġc aut", "Ġca ut", "UI Font", "( return", "(r eturn", "(re turn", "(ret urn", "a lin", "al in", "ali n", "c astle", "cast le", "cas tle", "//// ////////////////////////////////////////////////////////////////////", "//////// ////////////////////////////////////////////////////////////////", "//////////////// ////////////////////////////////////////////////////////", "//////////////////////////////////////////////////////////////// ////////", "//////////// ////////////////////////////////////////////////////////////", "//////////////////////////////////////////////////////////////////// ////", "//////////////////////////////////////////////////////// ////////////////", "//////////////////////////////////////////////////////////// ////////////", "Ġincent ive", "Ġincentiv e", "o path", "op ath", "opa th", "b lob", "bl ob", "blo b", "Ġcigaret te", "Ġcigar ette", "Ġf ertil", "Ġfer til", "Ġfert il", "* /ĊĊĊ", "*/ ĊĊĊ", "*/Ċ ĊĊ", "*/ĊĊ Ċ", "Ġ Shar", "ĠS har", "ĠSh ar", "ĠSha r", "Ċ ĠĠĠĠĠĠĊ", "Ġunc ertain", "Ġuncert ain", "ĠS ton", "ĠSt on", "ĠSto n", "Oper ations", "Operation s", "ĠSp encer", "Ġde fin", "Ġdef in", "Ġ Solo", "ĠS olo", "ĠSo lo", "ĠSol o", "o nest", "on est", "one st", "ones t", "·» åĬł", "Ġu omo", "Ġuom o", "G ive", "Gi ve", "Ġden tro", "Ġdent ro", "; padding", ";p adding", "ent ai", "enta i", "Ġ Cars", "ĠC ars", "ĠCar s", "ĠCa rs", "Ġenthus iasm", "Ġenthusi asm", "Ġ Operating", "ĠOper ating", "ĠOpera ting", "S kip", "Sk ip", "par ation", "pa ration", "para tion", "Ġprot ects", "Ġprote cts", "Ġprotect s", "Ġre ver", "Ġr ever", "Ġrev er", "Ġreve r", "d g", "ĠC incinnati", "Ġconsect etur", "Ġm uss", "Ġmus s", "Ġmu ss", "employ ed", "a uses", "au ses", "ause s", "aus es", "in kle", "ink le", ". Values", ".Value s", ".Val ues", "£ ¼", "l ov", "lo v", "_ WARN", "_W ARN", "Ġ bookmark", "Ġbook mark", "Ġ Apollo", "ĠA pollo", "ĠAp ollo", ". axis", ".a xis", ".ax is", "Ġm ét", "Ġmé t", "Ġop ener", "Ġopen er", "Ġt umor", "Ġtu mor", "Ġtum or", "d an", "da n", "Ġelement ary", "Ġsk ipped", "Ġskip ped", "Ġski pped", "ĠK er", "ĠKe r", "as ia", "asi a", "_ resp", "_re sp", "_r esp", "_res p", "Ġde mol", "Ġdem ol", "Ġdemo l", "ĠCan adians", "ĠCanadian s", "Ġt astes", "Ġtaste s", "Ġtas tes", "Ġtast es", "U Integer", "UInt eger", "Ġ' ${", "Ġ'$ {", ". aws", ".a ws", ".aw s", "R OID", "RO ID", "ROI D", "r ians", "ri ans", "ria ns", "rian s", "M Q", "ord able", "orda ble", "Ġcou sin", "Ġcous in", "Prop agation", "( Session", "(S ession", "p halt", "ph alt", "pha lt", "U LD", "UL D", "Ġ Scalar", "ĠS calar", "ĠSc alar", "ĠScala r", "ĠScal ar", "Ġblood y", "Ġblo ody", "Ġ à¦", "Ġà ¦", ". mask", ".m ask", ".mas k", ".ma sk", ", q", "Ġ Units", "ĠUn its", "ĠUnit s", "ĠUni ts", "Ġcent res", "Ġcentre s", "Ġcentr es", "Ġcen tres", "Ġ Prim", "ĠP rim", "ĠPr im", "ĠPri m", ". ]ĊĊ", ".] ĊĊ", "ĠSh aw", "ĠSha w", "P rom", "Pro m", "Pr om", "Ġ Thought", "ĠTh ought", "ĠThough t", "ĠThou ght", "Check er", "Che cker", "_ outputs", "_out puts", "_output s", "( chan", "(c han", "(ch an", "E INVAL", "Ġ bob", "Ġb ob", "Ġbo b", "_ cmp", "_c mp", "_cm p", "P ed", "Pe d", "Ġmat rices", "Ġvrou wen", "Ġvrouw en", "Ġgenu inely", "Ġgenuine ly", "high light", "( display", "(d isplay", "(dis play", ") !=", ")! =", "Ġde licate", "Ġdel icate", "Ġdelic ate", "ĠL uther", "ĠLu ther", "ĠM iles", "ĠMil es", "ĠMi les", "ĠMile s", "Ġ userID", "Ġuser ID", "% =", "at eurs", "ate urs", "ateur s", "_ BUF", "_B UF", "_BU F", "- ------Ċ", "-- -----Ċ", "---- ---Ċ", "--- ----Ċ", "----- --Ċ", "------ -Ċ", "------- Ċ", "im itives", "imit ives", "imitive s", "Ġsh elves", "Ġshel ves", "s low", "sl ow", "_ information", "_in formation", "L EG", "LE G", "W r", ". forms", ".for ms", ".form s", "c eland", "ce land", "cel and", "cela nd", "/ un", "/u n", ": &", ". âĢĻĊĊ", ".âĢĻ ĊĊ", "= \"%", "=\" %", "Ġp rost", "Ġpro st", "Ġpr ost", "Ġpros t", "Ġ fontsize", "Ġfont size", "Ġfonts ize", "u ción", "uc ión", "uci ón", "g etic", "get ic", "ge tic", "a mt", "am t", "= \".", "=\" .", "De cor", "Dec or", "B rit", "Br it", "Ġ\" \").", "Ġ\"\" ).", "Ġ\"\") .", "Ġf ounding", "Ġfound ing", "Ġfo unding", ". FileName", ".File Name", "Ġ Tier", "ĠT ier", "ĠTi er", "ĠTie r", "Ġdis close", "Ġdisc lose", "á m", ". syn", ".s yn", ".sy n", ". ViewHolder", ".View Holder", "lic ant", "li cant", "lica nt", "_ stage", "_st age", "_sta ge", "Mon day", "Ġ deserialize", "Ġde serialize", "Ġdes erialize", "t alk", "ta lk", "tal k", "Ġtrad itionally", "Ġtraditional ly", "Ġtradition ally", "æĢ ģ", "Ø ®", "L EX", "LE X", "Ġ eh", "Ġe h", "ĉ ROM", "ĉR OM", "Ġ {})Ċ", "Ġ{ })Ċ", "Ġ{} )Ċ", "Ġ{}) Ċ", "Question s", "Quest ions", "n cpy", "nc py", "Ġfix ing", "Ġfi xing", "к Ñĥ", "_ Key", "_K ey", ": x", "Ġ STRING", "ĠST RING", "ĠSTR ING", "ĠÑĦ ай", "ĉ left", "ĉl eft", "ĠB ench", "ĠBen ch", "el lij", "ell ij", "elli j", "UR RED", "URRE D", "Ġ Diagram", "ĠDi agram", "ĠDia gram", "} catch", "/ time", "/t ime", "Ġ Missing", "ĠM issing", "ĠMiss ing", "ĠMis sing", "db name", "Ġs ore", "Ġso re", "Ġsor e", "ĠW alt", "ĠWal t", "ĠWa lt", "ug ging", "ugg ing", "re present", "rep resent", "Ġ GS", "ĠG S", "ne ys", "ney s", "ĉ page", "ĉp age", "Ġvol can", "( btn", "(b tn", "(bt n", "Ġexceed s", "Ġexce eds", "Ġ erg", "Ġe rg", "Ġer g", "Ġpi lots", "Ġpil ots", "Ġpilot s", "ĠS ed", "ĠSe d", "ers ions", "ersion s", "Ġp atron", "Ġpat ron", "Ġpa tron", "R V", "/ top", "/t op", "/to p", ". asset", ".as set", "_ cross", "_c ross", "_cr oss", ". Editor", ".E ditor", ".Edit or", ".Ed itor", ". tb", ".t b", "Ġwel coming", "S CREEN", "SC REEN", ") findViewById", "C oder", "Code r", "Co der", "Cod er", " \",Ċ", ">\" ,Ċ", ">\", Ċ", "_ Pin", "_P in", "u ese", "ue se", "ues e", "Ġ overrides", "Ġover rides", "Ġoverride s", "_ ready", "_re ady", "_read y", "Ad vanced", "Adv anced", "Advance d", "Ġ opi", "Ġo pi", "Ġop i", "- cart", "-c art", "-car t", "-ca rt", "(\" /\",", "(\"/ \",", "ĠD eb", "ĠDe b", "C RY", "CR Y", "Ġ Vertical", "ĠVer tical", "ĠVert ical", "Ġ OVER", "ĠO VER", "ĠOV ER", "Ġ Corporate", "ĠCor porate", "ĠCorpor ate", "ĠCorp orate", "Ġ\" \";", "Ġ\"\" ;", "Ġstep ping", "Ġste pping", "e j", "Ġaccus ations", "Ġaccusation s", "Ġo raz", "Ġor az", "Ġora z", "_ tail", "_t ail", "_ta il", "Ġin duced", "Ġind uced", "Ġindu ced", "Ġinduce d", "Ġ elastic", "Ġe lastic", "Ġel astic", "Ġelast ic", "Ġbl own", "Ġblow n", "Ġblo wn", ", //", ",/ /", "Ġback grounds", "Ġbackground s", "âĢĻ une", "âĢĻun e", "- sdk", "-s dk", "Ġset Interval", "Ġincent ives", "Ġincentive s", "Ġincentiv es", "Ġveget able", "Ġveg etable", "_ On", "_O n", "exp anded", "expand ed", "p ix", "pi x", "_ shader", "_sh ader", "_sha der", "ĠSP DX", "ĠSPD X", "@ example", "Ġ Wrapper", "ĠW rapper", "ĠWr apper", "ĠWrap per", ". Zero", ".Z ero", "Pos itive", "Ġ spinner", "Ġsp inner", "Ġspin ner", "Ġin vented", "Ġinv ented", "Ġinvent ed", "ĠG ates", "ĠGa tes", "ĠGate s", "ĠGat es", "о ÑĤоÑĢ", "оÑĤ оÑĢ", "оÑĤо ÑĢ", "Ġcompar isons", "Ġcomparison s", "è ·", ". primary", ".pr imary", "data Provider", "add itional", "ĉ options", "ĉo ptions", "ĉopt ions", "ĉoption s", "s napshot", "snap shot", ".set Horizontal", "Ġ\" {}", "Ġ\"{ }", "ĠF isher", "ĠFish er", "ĠFi sher", "h alten", "hal ten", "halt en", "< Type", "", "Ġ) ->", "Ġ Registered", "ĠRegister ed", "IN ED", "INE D", "k al", "ka l", "par ison", "Ġobj eto", "Ġobjet o", "V i", "m anda", "man da", "ma nda", "mand a", "Ġren ewed", "Ġrenew ed", "ĠS of", "ĠSo f", "e ssel", "es sel", "ess el", "esse l", ".nd array", "Ġ crap", "Ġc rap", "Ġcr ap", "Ġcra p", "ç® ¡", ".abs path", ".ab spath", "( up", "(u p", "Ġclear ance", "Ġ TW", "ĠT W", "_ COPY", "_C OPY", "_CO PY", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĉ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠĠ Ġĉ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĉ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĉ", "Ġfor ests", "Ġfore sts", "Ġforest s", "Ġfores ts", "Ġarg uably", "Ġ ASS", "ĠA SS", "ĠAS S", "h ey", "he y", "a mel", "am el", "ame l", "_ fore", "_f ore", "_for e", "ĠSouth east", "ĠSou theast", "Ġab used", "Ġabuse d", "Ġpract icing", "ake dirs", "aked irs", "ä¸ »", "_ resources", "_re sources", "_res ources", "_resource s", "Ġ pond", "Ġp ond", "Ġpo nd", "Ġpon d", ". Fixed", ".F ixed", "Last Error", "ĠPsych ology", "ĠPsycho logy", "Ġ\" //", "Ġ\"/ /", "! :", "Re usable", "Ġ mensaje", "Ġm ensaje", "Ġmens aje", "Ġro spy", "Ġros py", "Ġ bour", "Ġb our", "Ġbo ur", "Ġbou r", "Ġvar ieties", "Ġvari eties", "Ġem path", "Ġemp ath", "( ({", "(( {", "_ org", "_or g", "_o rg", "Ġ Mes", "ĠM es", "ĠMe s", "Ġ Magento", "ĠM agento", "ĠMag ento", "IST ORY", "Un less", "Ġh j", "ĠD uty", "ĠDu ty", "ĠDut y", "J un", "Ju n", ", size", ",s ize", "Ġpaint ings", "Ġpain tings", "Ġpainting s", "Ġd ispens", "Ġdisp ens", "d art", "da rt", "dar t", "Ġbehavior al", "Ġ rpc", "Ġr pc", "Ġrp c", "c alculate", "cal culate", "calc ulate", "calcul ate", "f ruit", "fr uit", "_ mm", "_m m", "ĉ pthread", "ĉp thread", "ĉpt hread", "Max Length", "Ġc urrencies", "Ġcurr encies", "_ capacity", "_cap acity", "ĠO z", "Ġfire arm", "Ġco efficient", "Ġcoeff icient", "Ġbank ruptcy", "Ġbankrupt cy", "w art", "wa rt", "war t", "Ġfat igue", "A VA", "AV A", "Ġe spa", "Ġes pa", "Ġesp a", "_ pc", "_p c", "Ġ Quotes", "ĠQu otes", "ĠQuote s", "_ LIGHT", "_L IGHT", "Ġ Tickets", "ĠT ickets", "ĠTicket s", "ĠTick ets", "Ġre lates", "Ġrel ates", "Ġrelate s", "Ġrelat es", "Ġpublish ers", "Ġpublisher s", "Ġun locked", "Ġunlock ed", "Ġunl ocked", "Ġ //----------------------------------------------------------------", "Ġ// ----------------------------------------------------------------", "Ġ//------------------------------------------------ ----------------", "Ġ//-------------------------------- --------------------------------", "Ġ//---------------- ------------------------------------------------", "Ġ InterruptedException", "ĠInterrupt edException", "Ġout look", "r n", "Ġreb els", "Ġrebel s", "W ritten", "Wr itten", "Ġa sian", "Ġas ian", "Ġasi an", "Ġasia n", "ot to", "ott o", "Ġ ĉĉĉĉ", "Ġĉ ĉĉĉ", "Ġĉĉ ĉĉ", "Ġĉĉĉ ĉ", "_ gpu", "_g pu", "_gp u", "T xt", "Tx t", ". ImageView", ".Image View", "Ġs uis", "Ġsu is", "Ġsui s", "_ tables", "_t ables", "_table s", "_tab les", "_ta bles", ". RecyclerView", ".Rec yclerView", "Ġwhat soever", "è ģ", "] ++;Ċ", "assert True", "_ verify", "_ver ify", "ĠR ivers", "ĠRiver s", "ĠRiv ers", "ĠRi vers", "Ġ ][", "Ġ] [", "J et", "Je t", "id ian", "idi an", "idia n", "S ibling", "Si bling", "Ġ genres", "Ġgen res", "Ġgenre s", ". Access", ".A ccess", ".Ac cess", ".Acc ess", "O PS", "OP S", "Ġtr ivial", "Ġtrivia l", "ภª", "a len", "al en", "ale n", "в ед", "ве д", "ĠS word", "ĠSw ord", "Ġscrut iny", "Ġscrutin y", "( cb", "(c b", "Ġ commerce", "Ġcom merce", "Ġcomm erce", "Ġcommerc e", "Ġguarante es", "Ġguarantee s", "_ adv", "_a dv", "_ad v", "Ġ LET", "ĠL ET", "ĠLE T", "re cio", "rec io", "Ġh ilar", "Ġhi lar", "Ġhil ar", "Ġback yard", "ãĢ ı", "Ġillustr ated", "Ġillustrate d", "Ġillust rated", "/ vendor", "/v endor", ". Util", ".U til", "Ġ wow", "Ġw ow", "Ġwo w", "LO Y", "Ġ Marshal", "ĠM arshal", "ĠMar shal", "ĠMars hal", "ĠMarsh al", "\" >'.$", "\"> '.$", "\">' .$", "\">'. $", "ĠB ak", "ĠBa k", "Ġ modifiers", "Ġmod ifiers", "Ġmodifier s", "d ictionary", "ĠS tre", "ĠSt re", "ĠStr e", "m ultiple", "mult iple", "multi ple", "multip le", "\" )),", "\") ),", "\")) ,", "ĠC ort", "ĠCo rt", "ĠCor t", "' ]\").", "'] \").", "( admin", "(ad min", "Ġ Creator", "ĠC reator", "ĠCre ator", "ĠCreat or", "In ternet", "Int ernet", "Inter net", "Intern et", "( ms", "(m s", "l ogy", "lo gy", "log y", "DECL ARE", "Ġ Marcus", "ĠMar cus", "ĠMarc us", "< <<<", "<< <<", "<<< <", "ãģ ł", "_ my", "_m y", "( inst", "(i nst", "(in st", "(ins t", "Ġsc iences", "Ġscience s", "Ġsci ences", "N DER", "ND ER", ". enter", ".en ter", ".ent er", "Ġ itu", "Ġit u", "Ġi tu", "Ġbe have", "Ġbeh ave", "P an", "Pa n", "om bies", "omb ies", "ombie s", "= '<", "=' <", "' ));čĊ", "') );čĊ", "')) ;čĊ", "')); čĊ", "Ġ MENU", "ĠM ENU", "ĠME NU", "ĠMEN U", "Ġ Workers", "ĠWork ers", "ĠWorker s", "ĠWor kers", ".No Error", "Ġ bindings", "Ġbin dings", "Ġbind ings", "Ġbinding s", "Ġdis abilities", "{ \\", "ĠM unicip", "ĠMun icip", "Ġ cores", "Ġc ores", "Ġco res", "Ġcor es", "Ġcore s", "ur ple", "ĠN okia", "us ions", "usion s", "usi ons", "Ġ Fitness", "ĠF itness", "ĠFit ness", ". handleChange", ".handle Change", "Ġ javascript", "Ġj avascript", "Ġjav ascript", "Ġjava script", "ìļ Ķ", "( dec", "(d ec", "(de c", "Ġ packing", "Ġp acking", "Ġpack ing", "Ġpac king", "- depend", "-d epend", "-de pend", "Ġtrans cript", "Ġtran script", "z eros", "ze ros", "zer os", "zero s", "_ alert", "_al ert", "? \",Ċ", "?\" ,Ċ", "?\", Ċ", "l ibs", "li bs", "lib s", "± оÑĤ", "Ġ |ĊĊ", "Ġ| ĊĊ", "Ġ|Ċ Ċ", "tr ained", "tra ined", "train ed", "ĠG ent", "ĠGe nt", "ĠGen t", "ĠR ab", "ĠRa b", "x p", "_ configuration", "_config uration", "å¤ ©", "_ accept", "_ac cept", "_acc ept", ".rec yclerview", ": url", "ĠMu hammad", "ĠMuham mad", "Ġpriv ileges", "Ġprivile ges", "Ġprivilege s", "_ bank", "_b ank", "u ku", "uk u", "w allet", "wall et", "wal let", "Ġ ROOT", "ĠR OOT", "ĠRO OT", "Ġenc uent", "? family", "?f amily", "ĉ position", "ĉp osition", "ĉpos ition", "Ġ cg", "Ġc g", "Ġpre cip", "Ġprec ip", "method s", "meth ods", "_ fast", "_f ast", "_fa st", "in crement", "inc rement", "incre ment", "incr ement", "ĠT iger", "ĠTi ger", "ĠTig er", "_OCC URRED", "qu ip", "qui p", "Ġ HAS", "ĠH AS", "ĠHA S", "_ dom", "_d om", "_do m", "Ġw reck", "Ġwr eck", "Ġwre ck", "b j", "Ġd ern", "Ġde rn", "Ġder n", "Ġorg ans", "Ġorgan s", ". entries", ".en tries", ".ent ries", "Ġ _('", "Ġ_ ('", "Ġ_( '", "r amento", "ram ento", "Ġ Jamie", "ĠJam ie", "ĠJa mie", "Ġ punk", "Ġp unk", "Ġpun k", "Ġpu nk", "I PP", "IP P", "Ġprogram a", "Ġprog rama", "Ġat tain", "Ġatt ain", "Ġpro ves", "Ġpr oves", "Ġprov es", "Ġprove s", "/ sign", "/s ign", "Ġanswer ing", "Ġl adder", "Ġlad der", "**** ************************", "******** ********************", "**************** ************", "************************ ****", "******************** ********", "************ ****************", "************** **************", "ĠW almart", "ĠWal mart", "Ġ CONTENT", "ĠCONT ENT", "d uctor", "du ctor", "duct or", "duc tor", "Ġver bal", "Ġverb al", "Ġ PID", "ĠP ID", "ĠPI D", "c rypto", "crypt o", "cry pto", "_CALL BACK", "Ġ= ================================", "Ġ================= ================", "Ġp otent", "Ġpo tent", "Ġpot ent", "Ġsh orts", "Ġshort s", "Ġsho rts", ". Uri", ".U ri", ". uniform", ".un iform", ".uni form", "; border", ";b order", "Ġ Wer", "ĠW er", "ĠWe r", "Ġher ein", "Ġhere in", "l la", "ll a", "ĠI hr", "ĠIh r", "P ixmap", "Pix map", "l iteral", "lit eral", "lite ral", "liter al", "! )ĊĊ", "!) ĊĊ", "!)Ċ Ċ", "g eneric", "gen eric", "gener ic", "gene ric", "r ust", "ru st", "rus t", "_ scripts", "_s cripts", "_script s", "o sto", "os to", "ost o", "it us", "itu s", "ĠCoal ition", "Ġre mot", "Ġrem ot", "de ploy", "dep loy", "ĠE agle", "ĠEag le", "ĠEa gle", "ãĢģ ãĢĮ", "Ġimport ante", "Ġimportant e", "ĉ object", "ĉo bject", "ĉobj ect", "ĉob ject", "Ġseason al", "Ġseas onal", "n ej", "ne j", "ai du", "aid u", "Bind View", "ĠSi erra", "- bg", "-b g", "Ġmake Styles", "[ offset", "[o ffset", "G ames", "Game s", "Gam es", "Ga mes", "Ġhorm one", "AR IO", "ARI O", "he ads", "head s", "hea ds", "( select", "(s elect", "(se lect", "(sel ect", "Ġ Started", "ĠStart ed", "ĠStar ted", "@ param", "_ decl", "_de cl", "_dec l", "_ blog", "_b log", "_bl og", "Ġa ño", "Ġañ o", "\\ Api", "ĠMil waukee", "Pro vid", "Pr ovid", "Prov id", "An imated", "Anim ated", "Animate d", "Ġco oler", "Ġcool er", "Ġ Seed", "ĠS eed", "ĠSe ed", "ĠSee d", ". Edit", ".E dit", ".Ed it", "Ï Ħ", "Ġ Taking", "ĠT aking", "ĠTa king", "ĠTak ing", "Ġborder Color", "-f ounder", "-found er", ".Logger Factory", "Ġ\" \"ĊĊ", "Ġ\"\" ĊĊ", "Ġ\"\"Ċ Ċ", "A LT", "AL T", "Ġ Late", "ĠL ate", "ĠLa te", "ĠLat e", "EDI ATE", "EDIA TE", "Ġ );ĊĊĊ", "Ġ) ;ĊĊĊ", "Ġ);Ċ ĊĊ", "Ġ);ĊĊ Ċ", "Ġ); ĊĊĊ", "a fa", "af a", "Ġc ancellation", "Ġcancel lation", "Ġcancell ation", "Ġcanc ellation", "A tom", "At om", "ĠB irmingham", "emp resa", "empre sa", "H EMA", "HE MA", "a scal", "as cal", "asc al", "asca l", "Ġup side", "Ġups ide", ". Version", ".V ersion", "Ġ Folder", "ĠF older", "ĠFo lder", "ĠFol der", "ĠFold er", "Ġ Eight", "ĠE ight", "ĠEig ht", "Ġ Vintage", "ĠV intage", "Ġ AppDelegate", "ĠApp Delegate", "ĠPre vention", "ĠPrevent ion", "ĠPrev ention", ". separator", ".s eparator", ".se parator", "S TM", "ST M", "( room", "(r oom", "(ro om", "g enerator", "gen erator", "gener ator", "Ġc attle", "Ġcat tle", "ĉ Z", "Ġ Particle", "ĠP article", "ĠPart icle", "ĠParti cle", "' };Ċ", "'} ;Ċ", "Ġne ighbours", "Ġneighb ours", "Ġneighbour s", "ĠState less", "ĠStat eless", "Ġ altitude", "Ġalt itude", "Ġs aint", "Ġsa int", "Ġsai nt", "о бав", "об ав", "оба в", "Ġcon vinc", "Ġconv inc", "Ġconvin c", "Ġ Contents", "ĠCont ents", "ĠContent s", "ĠConte nts", "Ġje une", "Ġjeu ne", "( ts", "(t s", "S erialization", "Serial ization", "( collection", "(c ollection", "(col lection", "(coll ection", "ĠJ azz", "ĠJa zz", "Ġ Dod", "ĠD od", "ĠDo d", "ĠR och", "ĠRo ch", "ĠRoc h", "a cio", "ac io", "aci o", "comm ended", "commend ed", "DE FINE", "DEF INE", ".on load", "Ġspecial ty", "Ġspeci alty", "PL ACE", "_ MOVE", "_M OVE", "_MO VE", "_MOV E", "Ġaccount able", "Re uters", "Ġf icken", "Ġfi cken", "Ġfic ken", "Ġfick en", "Ġde pr", "Ġdep r", "W ow", "Wo w", "V oid", "Vo id", ". space", ".s pace", ".sp ace", "ภĹ", "Ġt q", "Ġ Pets", "ĠP ets", "ĠPe ts", "ĠPet s", "< $", "( Current", "(C urrent", "ber ries", "plan ation", "pla nation", "Ġlist Of", "Ġ Thu", "ĠT hu", "ĠTh u", "Ġ PRINT", "ĠPR INT", "ĠPRI NT", "Ġm ismo", "Ġmis mo", "Ġmism o", "Ġ doi", "Ġd oi", "Ġdo i", "c hk", "ch k", "Ġ Unicode", "ĠUn icode", "ĠUni code", "( role", "(r ole", "(ro le", "Ġvir gin", "< Point", "

-->Ċ", ">- ->Ċ", ">-- >Ċ", "V ol", "Vo l", "ĠS SD", "ĠSS D", ") )),", ")) ),", "))) ,", ". Optional", ".Option al", "Ġn urses", "Ġnur ses", "Ġnurs es", "Ġnurse s", "Ġ orb", "Ġo rb", "Ġor b", "_ pe", "_p e", ") ;čĊčĊčĊ", ");čĊ čĊčĊ", "); čĊčĊčĊ", ");čĊčĊ čĊ", "pl aced", "place d", "pla ced", "es ser", "ess er", "esse r", "Ġther apeutic", "Ġwh itespace", "Ġwhite space", "Ġwhites pace", "Ġ aston", "Ġa ston", "Ġas ton", "Ġast on", "Success ful", "Ġp raised", "Ġpr aised", "Ġpraise d", "Ġpra ised", "ĠW es", "ĠWe s", "Ġe ighth", "Ġeight h", "i ral", "ir al", "ira l", "Ġv rouw", "Ġvrou w", "Ġ faction", "Ġf action", "Ġfact ion", "Ġfa ction", "Ġfac tion", "_ bias", "_b ias", "_bi as", "Ġ witch", "Ġw itch", "Ġwit ch", "Ġ npc", "Ġn pc", "Ġnp c", "( sb", "(s b", "ĠRod rig", "ĠRodr ig", "_ big", "_b ig", "_bi g", "D ependency", "Dep endency", "ĠAb raham", "ar di", "ard i", "C AR", "CA R", "n os", "no s", "Ġab undance", "Ġabund ance", "Ġnut rients", "Ġnutrient s", "in stein", "ins tein", "inst ein", ". Vert", ".V ert", ".Ver t", "Ġ ISS", "ĠI SS", "ĠIS S", "< U", "Ġs ums", "Ġsu ms", "Ġsum s", "_ hist", "_h ist", "_hi st", "Ġfar mer", "Ġfarm er", "Ġ Abr", "ĠA br", "ĠAb r", "S hot", "Sh ot", "Ġ BadRequest", "ĠBad Request", "Ġh ass", "Ġhas s", "Ġha ss", "Ġ Rails", "ĠR ails", "ĠRa ils", "ĠRail s", "ĠRai ls", "Ġaff iliated", "Ġaffili ated", "Ġaffiliate d", "æĿ ¥", "Ġe rf", "Ġer f", "I NF", "IN F", "Ġ ViewHolder", "ĠView Holder", "m ini", "min i", "mi ni", "ĠR oth", "ĠRo th", "ĠRot h", "Ġfaith ful", "ĠPhill ips", "ĠPhillip s", "AN DOM", "AND OM", "] .[", "]. [", "_ PAY", "_P AY", "_PA Y", "ĠAr ctic", "ĠArc tic", "f aker", "fa ker", "fake r", "fak er", "D igit", "Di git", "Dig it", "M ale", "Ma le", "Mal e", "std err", "se ys", "sey s", "Ġ Å¡", "ĠÅ ¡", "_ remote", "_rem ote", "l ique", "li que", "liqu e", "Ġin def", "Ġi ndef", "Ġind ef", "Ġinde f", "ĠIndust ries", "i tra", "it ra", "itr a", "_ pairs", "_p airs", "_pair s", "_pa irs", "< iostream", " D", "Ġs ervlet", "Ġserv let", "bast ian", "Ġ >&", "Ġ> &", "S ID", "SI D", "_ clk", "_c lk", "_cl k", "Ġdi visions", "Ġdiv isions", "Ġdivision s", "Ġdivis ions", "} ',Ċ", "}' ,Ċ", "}', Ċ", "Ġd ildo", "Ġdil do", "Ġpar ade", "Ġpara de", "Ġpa rade", "Ġparad e", "m ajor", "maj or", "Ġa board", "Ġab oard", "; ++", "Ġ fusion", "Ġf usion", "Ġfus ion", "\" },{\"", "\"} ,{\"", "\"}, {\"", "ĠDialog Result", "ĉ arr", "ĉa rr", "ĉar r", "- em", "-e m", "_ nr", "_n r", "( handler", "(h andler", "(handle r", "(hand ler", ". NET", ".N ET", ".Xtra Reports", "ĠSh ah", "ĠSha h", "Ġ Brief", "ĠB rief", "ĠBr ief", "ĠBri ef", "- ,", "Ġ precio", "Ġp recio", "Ġpre cio", "Ġprec io", "ĉ ĉĉĠĠĠĠĠĠ", "ĉĉ ĉĠĠĠĠĠĠ", "ĉĉĉ ĠĠĠĠĠĠ", "ĉĉĉĠĠĠ ĠĠĠ", "ĉĉĉĠ ĠĠĠĠĠ", "ĉĉĉĠĠ ĠĠĠĠ", "ĉĉĉĠĠĠĠ ĠĠ", "ĉĉĉĠĠĠĠĠ Ġ", "Ġ tant", "Ġt ant", "Ġta nt", "Ġtan t", "ĠGr ande", "ĠGrand e", "ĠGran de", "ĠGra nde", "/ xml", "/x ml", "_ ICON", "_I CON", "_IC ON", "ĠR etro", "ĠRe tro", "ĠRet ro", "un que", "Ġn ag", "Ġna g", "to Fixed", "X L", "Ġ declaring", "Ġdecl aring", "Ġdeclar ing", "Ġ Concrete", "ĠCon crete", "ĠConc rete", "Ġ Amazing", "ĠAm azing", "ĠAma zing", "ĉprint k", "Ġdeb ates", "Ġdebate s", "D ATED", "DA TED", "DATE D", "DAT ED", "Ġa esthetic", "Ġaest hetic", "Ġaes thetic", "eme tery", "emet ery", "Routing Module", "ĠNash ville", "W AYS", "WA YS", "WAY S", "Ġ wolf", "Ġw olf", "Ġwo lf", "Ġwol f", "Ġob servers", "Ġobs ervers", "Ġobserv ers", "Ġobserver s", "Ġobserve rs", "O TA", "OT A", "an son", "ans on", "Ġ ea", "Ġe a", "Ġgreen house", "ĵį ä½ľ", "Ġst air", "Ġsta ir", "Ġimm igrant", "Ġimmigr ant", "_ apply", "_app ly", "_ap ply", "pe are", "pear e", "ĠB loomberg", "ĠBloom berg", "_ PLAYER", "_PL AYER", "_PLAY ER", "R esp", "Re sp", "Res p", "æŃ £", "Choose r", "Cho oser", "Ġ ICollection", "ĠI Collection", "ĠIC ollection", "P eter", "Pe ter", "Pet er", "Err o", "Er ro", ".detect Changes", "M aps", "Map s", "Ma ps", "Ġ squeeze", "Ġs queeze", "Ġsqueez e", "ĠH omes", "ĠHome s", "ĠHom es", "ĠHo mes", "weg ian", "Ġformat ting", "Ġnegot iate", "u ld", "ul d", "ĠN ep", "ĠNe p", "Ġ QB", "ĠQ B", "Ġeconom ies", "Ġec onomies", "Ġ */,", "Ġ* /,", "Ġ*/ ,", "Ġred und", "Ġredu nd", "ĠA ber", "ĠAb er", "ĠAbe r", ".IsNullOr WhiteSpace", "yc led", "ycle d", "ycl ed", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "_ Sh", "_S h", "Ġs kept", "Ġsk ept", "Ġske pt", "Ġre created", "Ġrec reated", "Ġrecre ated", "Ġrecreate d", "Ġ getType", "Ġget Type", "Ġm argins", "Ġmargin s", "Ġmarg ins", "Ġcolon ial", "ch arts", "char ts", "cha rts", "chart s", "/ /@", "// @", "Ġ processors", "Ġprocess ors", "Ġprocessor s", "Ġprocesso rs", "è¯ ´", "b atis", "bat is", "æĦ ı", "at orio", "ator io", "ato rio", "atori o", "ment ioned", "mention ed", "P atient", "Pat ient", "Ġp rey", "Ġpr ey", "Ġpre y", "Check box", "_ xpath", "_x path", ". skip", ".s kip", ".sk ip", "ĠMor mon", "ĠMorm on", "ĠMemory Stream", "CR EMENT", "CRE MENT", "Ġ ku", "Ġk u", "m eld", "me ld", "mel d", "\\ Data", "\\D ata", "Ġ Kernel", "ĠK ernel", "ĠKer nel", "ĠKern el", "i ltr", "il tr", "ilt r", "éĢ ģ", "( profile", "(pro file", "(pr ofile", "C arbon", "Car bon", "R OLE", "RO LE", "ROL E", "( pl", "(p l", "] *(", "]* (", ". memory", ".m emory", ".mem ory", ".memo ry", "Ġme dal", "Ġmed al", "Ġ advisor", "Ġad visor", "Ġadv isor", "Ġadvis or", "it ät", "itä t", "Ġ hdr", "Ġh dr", "Ġhd r", "ie rung", "ier ung", "Ġ Provides", "ĠPro vides", "ĠProvid es", "ĠProvide s", "ĠProv ides", "( alpha", "(al pha", "Ġteen agers", "Ġteenager s", "Ġteenage rs", "- parser", "-p arser", "-par ser", "-parse r", ". LatLng", ".L atLng", "] ()Ċ", "]( )Ċ", "]() Ċ", "Ġfel ony", "Ġfelon y", "ĉĉĉ ĊĉĉĉĊ", "ĉĉĉĊ ĉĉĉĊ", "B OOK", "BO OK", "Ġ slash", "Ġs lash", "Ġsl ash", "Ġsla sh", "Ġ clearfix", "Ġclear fix", "ĠPro phet", "ĠProp het", "å® ¹", "right ness", "- fi", "-f i", ". kind", ".k ind", "er ton", "ert on", "erto n", "J im", "Ġmanip ulate", "Ġ worksheet", "Ġwork sheet", "Ġworks heet", "o lin", "ol in", "oli n", "st ars", "star s", "sta rs", "Ġ artifact", "Ġart ifact", "_ EMPTY", "_EM PTY", "_EMP TY", "ĉ main", "ĉm ain", "--- ---------- ';", ">' ;", "Ġexp ressing", "Ġexpress ing", "Ġexpres sing", "Ġ IQ", "ĠI Q", "Ġ Fact", "ĠF act", "ĠFac t", "ĠFa ct", "/************************************************************************ *******Ċ", "/**************************************************************************** ***Ċ", "_ mass", "_m ass", "_ma ss", "_mas s", ") ):", ")) :", "Ġcon dom", "Ġcond om", "Ġcondo m", "Ġcreate State", "ome town", "omet own", "Ġi rr", "Ġir r", "Ġ >(", "Ġ> (", "> B", "it eration", "ite ration", "iter ation", "ãĥ ª", "Ġsh irts", "Ġshirt s", "ou nty", "ount y", "oun ty", "- >$", "-> $", "_ SIGN", "_S IGN", "_SIG N", "_SI GN", "ĠD ale", "ĠDa le", "ĠDal e", "Ġ jj", "Ġj j", "E asy", "F re", "Fr e", "Ġ Ny", "ĠN y", "Ġ chlor", "Ġch lor", "m atched", "match ed", "mat ched", "ĠG erm", "ĠGe rm", "ĠGer m", "- UA", "-U A", "ĠN athan", "ĠNa than", "ĠNat han", "ĠNath an", "e ducation", "educ ation", "edu cation", "- yard", "-y ard", "- che", "-c he", "-ch e", "h ouses", "ho uses", "house s", "hou ses", "r itional", "rit ional", "rition al", "Ġpro ximity", "Ġprox imity", "Ġdie sem", "Ġdies em", "Ġdiese m", "áºŃ p", "Ġd rought", "Ġdr ought", "Ġdro ught", ". audio", ".a udio", ".au dio", "Ġ Leo", "ĠL eo", "ĠLe o", "Ġfavor able", "Ġfav orable", "in ch", "inc h", "ĠD aw", "ĠDa w", "r ibly", "rib ly", "_ student", "_st udent", "id able", "ida ble", "O VE", "OV E", "Ġl acks", "Ġla cks", "Ġlack s", "Ġlac ks", "oun cing", "ounc ing", ". business", ".b usiness", ".bus iness", "Ġ reopen", "Ġre open", "m aybe", "may be", "_ GLOBAL", "_G LOBAL", "Ġd resses", "Ġdr esses", "Ġdress es", "ĠEd wards", "ĠEdward s", "ens ible", "ensi ble", "Ġ Hardware", "ĠH ardware", "ĠHard ware", "Ġ Excellent", "ĠEx cellent", "ĠExcell ent", "ĠTime Unit", "CT IONS", "CTION S", "Ġs chedules", "Ġsched ules", "Ġschedule s", "Ġ segue", "Ġse gue", "Ġseg ue", "Ġsegu e", "Open s", "Op ens", "am men", "amm en", "- Identifier", "Ġst aring", "Ġstar ing", "Ġsta ring", "Ġhapp ily", "ĠH ob", "ĠHo b", "' _", "Ġ \");", "Ġ\" );", "Ġ\") ;", "ament os", "amento s", "amen tos", "et ched", "etch ed", "etc hed", "Ġ/ >}Ċ", "Ġ/> }Ċ", "Ġ/>} Ċ", ". Users", ".User s", ".Use rs", "Ġint errupted", "Ġinter rupted", "Ġinterrupt ed", "Cont acts", "Contact s", "Conta cts", "Ġ registro", "Ġreg istro", "Ġregistr o", "Ġregist ro", "in burgh", "C HA", "CH A", "_ imp", "_i mp", "_im p", "p his", "ph is", "phi s", "s ay", "sa y", "Ġret ailer", "Ġretail er", ". NODE", ".N ODE", ".NO DE", "/ maps", "/m aps", "/map s", "_ LAST", "_L AST", "_LA ST", "Ġ Charge", "ĠCh arge", "ĠChar ge", "ĠCharg e", "_ guard", "_g uard", "C ollider", "Col lider", "Coll ider", "ĠStateless Widget", "\" :[\"", "\": [\"", "\":[ \"", "(\" ../../", "(\"../ ../", "(\".. /../", "i oxide", "iox ide", "ioxid e", "ĠS und", "ĠSun d", "ĠSu nd", "Ġ' ';", "Ġ'' ;", "un set", "uns et", "add Widget", "л Ñİ", "el les", "ell es", "elle s", "al ker", "alk er", "A rc", "Ar c", "Ġd educt", "Ġde duct", "Ġded uct", "G UILayout", "GUI Layout", "ĠV illa", "ĠVi lla", "ĠVill a", "ĠVil la", "Ġfor bidden", "Ġforb idden", "Ġforbid den", "_ where", "_w here", "_wh ere", "Ġ \\/", "Ġ\\ /", "ĠT ib", "ĠTi b", "_ AX", "_A X", "] čĊčĊ", "]čĊ čĊ", "Ġ Bir", "ĠB ir", "ĠBi r", "Ġb end", "Ġbe nd", "Ġben d", "Ġ MAKE", "ĠM AKE", "ĠMA KE", "ĠMAK E", "Ġ MET", "ĠM ET", "ĠME T", "Ġf utures", "Ġfuture s", "Ġfut ures", "Ġfu tures", "Ġ weighted", "Ġwe ighted", "Ġweight ed", "Ġweigh ted", "\"\" \"čĊ", "\"\"\" čĊ", "Ġ authorize", "Ġauthor ize", "( program", "(p rogram", "(pro gram", "(pr ogram", "(prog ram", "} ,{\"", "}, {\"", "},{ \"", "Ġcoeff icients", "Ġcoefficient s", "ê s", "Per Page", "ĠB athroom", "ĠBath room", "ĠPublish ing", "G PL", "GP L", "Ġsub missions", "Ġsubmission s", "Ġ NUMBER", "ĠNUM BER", "ĠNU MBER", "j Äħ", "Ġadd itionally", "Ġadditional ly", "Ġaddition ally", "em pre", "emp re", "ĠS hel", "ĠSh el", "ĠShe l", "o typ", "ot yp", "oty p", "S olution", "Sol ution", "Ġth under", "Ġthu nder", "_ ec", "_e c", "Ġ ĊĠĠĠĠĊ", "ĠĊ ĠĠĠĠĊ", "ĠF ellow", "ĠFel low", "ĠFell ow", "Ġk ay", "Ġka y", "Ġnew State", "ONT AL", "Im plementation", "Implement ation", ". Look", ".L ook", ".Lo ok", "Ġ ents", "Ġe nts", "Ġen ts", "Ġent s", "Ġl ors", "Ġlo rs", "Ġlor s", "Ġ BIG", "ĠB IG", "ĠBI G", "f ab", "fa b", "Ġaverage d", "Ġaver aged", "Ġ Feedback", "ĠFe edback", "ĠFeed back", "ĠW ells", "ĠWell s", "ĠWel ls", "Ġm artial", "Ġmart ial", "Ġind ul", "Ġindu l", "ĠComm unist", "ĠCommun ist", "ĠF orex", "ĠFor ex", "ĠFore x", "ĠFo rex", "ĠAgricult ure", "ĠAgr iculture", "\" [", "Ġqu ar", "Ġq uar", "Ġqua r", "ĠK ont", "ĠKon t", "ĠKo nt", "ĉ view", "ĉv iew", ". Bytes", ".Byte s", ".By tes", "d esktop", "des ktop", "desk top", "Ġ Makes", "ĠM akes", "ĠMake s", "ĠMa kes", "ĠMak es", "akes peare", ". Nullable", ".Null able", "Ġspot light", "V B", "o wy", "ow y", "( torch", "(t orch", "(to rch", "t ridge", "tr idge", "tri dge", "_ bounds", "_b ounds", "_bound s", "_bo unds", "Ġapolog ize", ". addItem", ".add Item", "an td", "ant d", "* );Ċ", "*) ;Ċ", ", u", "( gen", "(g en", "ç» ĵ", "re ator", "reat or", "rea tor", "ĠC ord", "ĠCo rd", "ĠCor d", "o upper", "ou pper", "oup per", ". metro", ".m etro", ".me tro", ".met ro", "Ġ ew", "Ġe w", "Ġ WORD", "ĠW ORD", ". After", ".A fter", "Ġdet ained", "Ġdetain ed", "ĠH ammer", "ĠHam mer", "ĠHamm er", "ex isting", "exist ing", "Ġ ost", "Ġo st", "Ġos t", "Ġmon ument", "- custom", "-c ustom", "User ID", "Ġ Nom", "ĠN om", "ĠNo m", "Ġre jection", "Ġreject ion", "Ġrej ection", "( dim", "(d im", "(di m", "Ġ singleton", "Ġs ingleton", "Ġsingle ton", "Ġsing leton", "ĉ die", "ĉd ie", "ar iance", "ari ance", "arian ce", "aria nce", "re ports", "rep orts", "report s", "repo rts", "] !=", "e lda", "el da", "eld a", "Ġpreval ence", "_ regs", "_re gs", "_reg s", ". \".", ".\" .", "Ġfemin ist", "Code c", "Co dec", "Cod ec", "Ġ **Ċ", "Ġ* *Ċ", "Ġ** Ċ", "( labels", "(label s", "_ MARK", "_M ARK", "_MA RK", "FA ILED", "FAIL ED", "Ġadminister ed", "W N", "ĠĠ ĠĠĠĠĠĠĉĉ", "ĠĠĠĠ ĠĠĠĠĉĉ", "ĠĠĠĠĠĠĠĠ ĉĉ", "ĠĠĠĠĠĠĠ Ġĉĉ", "ĠĠĠĠĠ ĠĠĠĉĉ", "ĠĠĠĠĠĠ ĠĠĉĉ", "ĠĠĠĠĠĠĠĠĉ ĉ", "Ġ noun", "Ġn oun", "Ġno un", "Ġnou n", "w ig", "wi g", "Ġg otta", "Ġgot ta", "Ġ rif", "Ġr if", "Ġri f", "- im", "-i m", "ĠPaul o", "ĠPa ulo", "ĠCommand Type", "] ))ĊĊ", "]) )ĊĊ", "])) ĊĊ", "]))Ċ Ċ", "- zero", "-z ero", "Tr aining", "Train ing", "Tra ining", "Ġ lord", "Ġl ord", "Ġlo rd", "Ġlor d", "_ art", "_a rt", "_ar t", "re ddit", "red dit", "redd it", "C ert", "Ce rt", "Ġp eso", "Ġpe so", "Ġpes o", "R ot", "Ro t", "Ġen danger", "Ġend anger", ". dr", ".d r", "user Info", "u nts", "un ts", "unt s", "n v", "ĠTr ailer", "ĠTra iler", "ĠTrail er", "- first", "-f irst", "-fi rst", "( make", "(m ake", "Ġbenef ici", "- black", "-b lack", "-bl ack", "i ÃŁ", "Ġund oubtedly", "Ġm ex", "Ġme x", "ĠAn cient", "ĠAnc ient", "( as", "(a s", "Ġdes cent", "Ġdesc ent", "P ick", "Pic k", "Pi ck", "Ġrep lica", "Ġrepl ica", "Ġreplic a", "$ obj", "$o bj", "ä hr", "äh r", "Ġar rows", "Ġarr ows", "Ġarrow s", "f ty", "ft y", "ĠLib ya", "u ga", "ug a", "ch arged", "char ged", "charge d", "charg ed", "T ur", "Tu r", "Ġh omic", "Ġhom ic", "Ġho mic", "is sen", "iss en", "isse n", "Ġ Fake", "ĠF ake", "ĠFa ke", "ĠFak e", "Ġbe ers", "Ġbeer s", "Ġbee rs", "Ġsc attered", "Ġscatter ed", "( Time", "(T ime", "UT IL", "Ġbureauc r", "Ġbureau cr", "/ plain", "/p lain", "/pl ain", "Ġst icking", "Ġstick ing", "F AIL", "FA IL", "ĠC ovid", "ĠCo vid", "ĠCov id", "Th ird", "_ present", "_p resent", "_pre sent", "_pres ent", "ĠP ierre", "ĠPi erre", "ĠPier re", "Ġ ëª", "Ġë ª", "Ġ[ ...]ĊĊ", "Ġ[... ]ĊĊ", "Ġ[...] ĊĊ", "P rob", "Pro b", "Pr ob", "Ġ Traffic", "ĠTra ffic", "ĠTraff ic", "i cao", "ic ao", "ica o", "do ctor", "doc tor", "Ġ ),ĊĊ", "Ġ) ,ĊĊ", "Ġ),Ċ Ċ", "Ġ), ĊĊ", "T abs", "Tab s", "Ta bs", "a lu", "al u", "ï¼ļ âĢľ", "Ġin herent", "Ġinher ent", "_ No", "_N o", "r itis", "rit is", "Ġ Proof", "ĠP roof", "ĠPro of", ". basename", ".b asename", ".base name", "ä¼ ļ", "Ġc him", "Ġch im", "Ġchi m", "Ġ Protected", "ĠProt ected", "ĠProtect ed", "c rit", "cri t", "cr it", "Ġp rone", "Ġpro ne", "Ġpr one", "Ġpron e", "Ġ кон", "Ġк он", "Ġко н", "Ġ Heroes", "ĠHer oes", "ĠHero es", "Ġan xious", "Ġanx ious", "Ġ anos", "Ġa nos", "Ġan os", "Ġano s", "Ġweek ends", "Ġweekend s", "Ġs ext", "Ġse xt", "Ġsex t", "Ġre ducer", "Ġred ucer", "Ġredu cer", "Ġreduce r", "= UTF", "h alf", "ha lf", "hal f", "ĠS aw", "ĠSa w", ". mm", ".m m", "Ġn ueva", "Ġnu eva", "Ġnue va", ".current Target", ". lua", ".l ua", ".lu a", "_EXT ENSION", "ĉ reg", "ĉr eg", "ĉre g", "Ġ Ctrl", "ĠC trl", "ĠCt rl", "_ align", "_al ign", "accept able", "Ġr ushing", "Ġrush ing", "f rac", "fr ac", "fra c", "Ġbo asts", "Ġboast s", "Ġboa sts", "F ive", "Fi ve", " ±", "Ġ Temperature", "ĠT emperature", "ĠTem perature", "ĠTemper ature", "> ):", ">) :", "Ġch arter", "Ġchar ter", "Ġchart er", "RE ATED", "REATE D", "REAT ED", "REA TED", "Ġsub jected", "Ġsubject ed", "Ġsubj ected", "Ġ opc", "Ġo pc", "Ġop c", "health y", "使 çĶ¨", "ĠScient ific", "Ġ frau", "Ġfr au", "Ġfra u", "ri ages", "ria ges", "riage s", "ภĶ", ". inventory", ".in ventory", "at ionale", "ation ale", "ational e", "M ad", "Ma d", "min utes", "minute s", "> >();Ċ", ">> ();Ċ", ">>( );Ċ", ">>() ;Ċ", "Ġ Env", "ĠE nv", "ĠEn v", "Ġrecord ings", "Ġrecording s", "Ġsusp icion", "sql ite", "sq lite", "ĉ read", "ĉr ead", "ĉre ad", "ãģ ¦", "Ġwor ries", ".put String", "ĠSh anghai", "( uid", "(u id", "(ui d", "r er", "re r", "ĠvÃŃ de", "\" ):", "\") :", "Ġmethod ology", "Ġк оÑĤоÑĢ", "Ġко ÑĤоÑĢ", "ĠкоÑĤ оÑĢ", "c cc", "cc c", "av ad", "ava d", "Ġin duction", "Ġind uction", "Ġindu ction", "ĉ Thread", "ĉT hread", ", string", ",s tring", ",str ing", ",st ring", "ạ i", "neh men", "u ition", "ui tion", "uit ion", "Ġ* __", "Ġ*_ _", ".e mf", ".em f", "Ġ ìľ", "Ġì ľ", "/ themes", "/th emes", "/theme s", "/the mes", "Ġ Nine", "ĠN ine", "ĠNi ne", "ĠNin e", ". One", ".On e", ".O ne", "Ġ Embed", "ĠEm bed", "ĠEmb ed", "Ġf az", "Ġfa z", "u ations", "uation s", "uat ions", "Ġprivate ly", "Ġpriv ately", "Ġprivat ely", "Ġ ling", "Ġl ing", "Ġli ng", "Ġlin g", "[ F", "u shi", "us hi", "ush i", "Ġlaunch es", "( KEY", "(K EY", "G MT", "GM T", "Ġa iming", "Ġaim ing", "Ġai ming", "pat ible", "ĠB iden", "ĠBi den", "ĠBid en", "i w", "Ġ Degree", "ĠD egree", "ĠDe gree", "ĠDeg ree", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "Ġ$ ('<", "Ġ$( '<", "Ġ$(' <", "á rios", "ário s", "ár ios", "to UpperCase", "ìł ľ", "Ġ EUR", "ĠE UR", "ĠEU R", "Ġovers ight", "Ġtable sp", "Ġtables p", "Up dates", "Update s", ".m akedirs", ".make dirs", "Ġ humidity", "Ġh umidity", "Ġhum idity", "Ġhumid ity", "/ template", "/t emplate", "/temp late", "Al ways", "( IS", "(I S", "_ cert", "_c ert", "_ce rt", "D ig", "Di g", "Ġunder way", "or ton", "ort on", "ĠHur ricane", "Ġsp ends", "Ġspe nds", "Ġspend s", "Ġ Segment", "ĠS egment", "ĠSe gment", "ĠSeg ment", "Ġ flies", "Ġf lies", "Ġfl ies", "Ġ Toggle", "ĠT oggle", "ĠL ynch", "ĠLyn ch", "Ġs enses", "Ġsense s", "Ġsens es", "Ġsen ses", "ĠK os", "ĠKo s", "set Enabled", "ist ically", "istic ally", "istical ly", "Ġ tester", "Ġt ester", "Ġte ster", "Ġtest er", "Ġtes ter", "Ġteste r", "Ġadministr ators", "Ġadministrator s", "Ġt agged", "Ġtag ged", "Ð ĵ", "Ġ shortcut", "Ġshort cut", "Ġ Resolution", "ĠRe solution", "ĠRes olution", "Ġsuper vision", "Ġsuperv ision", "ĠAsh ley", "Tr acking", "Track ing", "ul atory", "ulator y", "an del", "and el", "ande l", "i sten", "is ten", "ist en", "iste n", "Ġun re", "Ġunr e", "( diff", "(d iff", "(di ff", "AN TS", "ANT S", "Ġr ider", "Ġrid er", "Ġride r", "Ġri der", "Ġs Äħ", ". Series", ".S eries", ".Se ries", "_ orders", "_order s", "_or ders", "_ord ers", "ORIZ ONTAL", "Ġret ention", "ãĢĤ čĊčĊ", "\"> čĊčĊ", "\">čĊ čĊ", "Ġdi agonal", "Ġdiag onal", "Ġdiagon al", "ĠC ancellationToken", "_ Internal", "_In ternal", "_Int ernal", "_Inter nal", "Ġr uin", "Ġru in", ". Qt", ".Q t", "ocr atic", "ocrat ic", "T el", "Te l", "Ġ Answers", "ĠAn swers", "ĠAnswer s", "ĠAns wers", "m atic", "ma tic", "mat ic", "Ġ xp", "Ġx p", "a tem", "at em", "ate m", "_ jobs", "_j obs", "_job s", "_ any", "_a ny", "_an y", "Ġsen iors", "Ġsenior s", "Ġseni ors", "Ġland mark", "ĠQ List", "Ġman eu", "Ġmane u", "ot ify", "oti fy", "/ \";Ċ", "/\" ;Ċ", "/ server", "/s erver", "ĠPhil osoph", "u tenant", "ut enant", "ute nant", "uten ant", "( io", "(i o", "h z", "Ġ authenticated", "Ġauth enticated", "Ġauthentic ated", "Ġauthenticate d", "d v", "- Compatible", "Origin ally", "Original ly", "Orig inally", ", function", ",f unction", "ãĢĤ čĊ", "ĠRepresent ative", "as ily", "asi ly", "asil y", "irc uit", ". dt", ".d t", "( math", "(m ath", "(mat h", ". Marshal", ".M arshal", ".Mar shal", "[ ,", "Ġ Cities", "ĠC ities", "ĠCit ies", "ĠCi ties", "_ turn", "_t urn", "| )Ċ", "Ġ cantidad", "Ġc antidad", "Ġcant idad", "al ter", "alt er", "alte r", "ĉ ui", "ĉu i", "ĠNe braska", "Ġsk irt", "Ġski rt", ". bg", ".b g", "Shared Preferences", "( style", "(st yle", "Ġg rief", "Ġgr ief", "Ġgri ef", "g ew", "ge w", "Ġsaf eg", "Ġsafe g", "o lang", "ol ang", "ola ng", "olan g", "_ lists", "_l ists", "_list s", "_li sts", "ì Ľ", "Ġgran ite", "Ġgra nite", "Ġhot test", "Ġhott est", ". jdbc", ".j dbc", ".jd bc", ". Customer", ".C ustomer", ".Custom er", "Ġ âī¤", "Ġâī ¤", "Ġw aar", "Ġwa ar", "_ scene", "_s cene", "_sc ene", "+ '/", "+' /", "ĠJ TextField", "ĠJText Field", "ĠJT extField", "Ġs eating", "Ġse ating", "Ġsea ting", "Ġseat ing", "Ġw ears", "Ġwe ars", "Ġwear s", "Ġ` /", "C ases", "Case s", "Ca ses", "Cas es", "Ġ Youtube", "ĠY outube", "ĠYou tube", "ı m", "Ġb alcon", "Ġbal con", ", G", "Meta Data", "Met aData", "- price", "-p rice", "-pr ice", "S CR", "SC R", "Un ity", "Unit y", "Uni ty", "Ġtr unk", "={ `${", "={` ${", "Ġearth quake", "Ġearthqu ake", "P artial", "Part ial", "Ġ subst", "Ġsu bst", "Ġsub st", "Ġsubs t", "Ġel imin", "Ġelim in", "=\" '.", "=\"' .", "//* [@", "//*[ @", "Ġsup ervisor", "Ġsuper visor", "Ġsuperv isor", "vr olet", "vro let", "_ article", "_art icle", "Ġ pane", "Ġp ane", "Ġpa ne", "Ġpan e", "b io", "bi o", "Ġmot ors", "Ġmo tors", "Ġmotor s", "Ġmoto rs", "N M", "F rank", "Fr ank", "Fran k", "Fra nk", "Ġon ion", "- word", "-w ord", "Item ClickListener", "ItemClick Listener", "Ġ brit", "Ġb rit", "Ġbr it", "Ġbri t", "end encies", "enden cies", "Com puter", "Comp uter", "Compute r", "Comput er", "_ running", "_r unning", "_run ning", "( day", "(d ay", "(da y", "- he", "-h e", "( named", "(n amed", "(name d", "ĠS ach", "ĠSa ch", "ĠSac h", "о Ñĩ", "c ampaign", "camp aign", ". Abstract", ".A bstract", ".Ab stract", ".Abs tract", "( wrapper", "(w rapper", ". pay", ".p ay", ".pa y", "Ġ uw", "Ġu w", "G eo", "Ge o", "r ails", "ra ils", "rai ls", "rail s", "/ select", "/s elect", "/se lect", "i chte", "ic hte", "ich te", "icht e", "s ons", "so ns", "son s", "E VENT", "EV ENT", "Ġal iment", "Ġali ment", "Pro viders", "Provider s", "Provid ers", "Provide rs", "Prov iders", "A wait", "Aw ait", "_INTER VAL", ". off", ".of f", ".o ff", "Ġgl uten", "Ġglut en", "Ġglu ten", "_ cloud", "_c loud", "_cl oud", "Ġ wen", "Ġw en", "Ġwe n", ". extract", ".ex tract", ".ext ract", ".extra ct", "ĉ button", "ĉb utton", "/ MM", "/M M", "P arty", "Par ty", "Part y", "Ġdem ographic", "Ġdemo graphic", "_ errno", "_err no", "Ġh iking", "Ġhi king", "Ġhik ing", "(' ')Ċ", "\", @\"", "Ġ wit", "Ġw it", "Ġwi t", "r á", "ol ogie", "olog ie", "olo gie", "ologi e", "Ġ Styles", "ĠSt yles", "ĠStyle s", "ĠSty les", "ĠBrowser Module", ". RequestMapping", ".Request Mapping", "ic ans", "ica ns", "ican s", "P AGE", "PA GE", "c reation", "cre ation", "creat ion", "ĠF erguson", "u ded", "ud ed", "ude d", "num bers", "number s", "Ġ GTK", "ĠG TK", "ĠGT K", "Ġpresent ations", "Ġpresentation s", "ĠB obby", "ĠBob by", "_ span", "_s pan", "_sp an", "e style", "est yle", "esty le", "Ġillegal ly", "Ġilleg ally", "ab ela", "abel a", "abe la", "Ġbattle field", "cap acity", "t error", "ter ror", "te rror", "terr or", "] \");Ċ", "]\" );Ċ", "]\") ;Ċ", "Ġwar rior", "le ader", "lead er", "lea der", "Ġ DBG", "ĠD BG", "ĠDB G", "Ġ Revenue", "ĠRe venue", "Ġvi gil", "Ġvig il", "Ġcounter parts", "Ġcounterpart s", "( Error", "(E rror", "AC TER", "ACT ER", "Ġhe eft", "Ġse lections", "Ġselect ions", "Ġselection s", "Ġsel ections", "Ġsele ctions", "ze ug", "t om", "to m", "- two", "-t wo", "-tw o", ". ;Ċ", ".; Ċ", "_ statement", "_st atement", "_state ment", "_stat ement", "_sta tement", "ĠA id", "ĠAi d", "ĠV ul", "ĠVu l", "_ rgb", "_r gb", "_rg b", "Ġpr izes", "Ġpri zes", "Ġprize s", "Ġ editable", "Ġed itable", "Ġedit able", "Ġedi table", "ĉ form", "ĉf orm", "ĉfor m", "ın ı", ". decor", ".de cor", ".dec or", "D emo", "De mo", "Dem o", "l ices", "lic es", "li ces", "lice s", "Ġen ctype", "Ġenc type", "rat ulations", "Ġ ROS", "ĠR OS", "ĠRO S", "_ chars", "_ch ars", "_char s", "ĠJ ahr", "ĠJa hr", "ĠJah r", "p artial", "part ial", "Ñĥ ÑĤ", "Ġ Receive", "ĠRe ceive", "ĠRece ive", "ĠL ands", "ĠLa nds", "ĠLand s", "ĠLan ds", "AP TER", "APT ER", "Ġch opped", "Ġcho pped", "Ġchop ped", ". .\"", ".. \"", "Ġ Analy", "ĠAn aly", "ĠAnal y", "ĠAna ly", "Ġ UID", "ĠU ID", "ĠUI D", "ĠR adeon", "ĠRad eon", "ĠB ee", "ĠBe e", "Ġu nm", "Ġun m", "> M", ".find all", "Token izer", "Ġ WHAT", "ĠWH AT", "Ġ sj", "Ġs j", "D rawing", "Draw ing", "E ss", "Es s", "O ND", "ON D", "Ĭ ¶", "( packet", "(p acket", "(pa cket", "(pack et", "âĢĶ but", "Inv ocation", "ĠN uclear", "ĠNu clear", "? ;Ċ", "Ġgr andes", "Ġgrand es", "Ġgran des", "Ġgrande s", "Ġ Crypt", "ĠC rypt", "ĠCry pt", "r emark", "re mark", "rem ark", "rema rk", "Ġ' ../../../../", "Ġ'../ ../../../", "Ġ'../../ ../../", "Ġ'../../../ ../", "Ġin ability", "m agic", "mag ic", "c ats", "ca ts", "cat s", "Ġ simulate", "Ġsim ulate", "Ġsimul ate", ": ${", ":$ {", "in flate", "inf late", "Ġ ener", "Ġe ner", "Ġen er", ": NO", ":N O", "i ples", "ip les", "iple s", "ipl es", "Ġme rit", "Ġmer it", "Ġ Rated", "ĠR ated", "ĠRa ted", "ĠRate d", "ĠRat ed", "Ġg lue", "Ġgl ue", "Ġglu e", "/ blog", "/b log", "/bl og", "Ġ gren", "Ġg ren", "Ġgr en", "Ġgre n", "Ġthr illed", "Ġthrill ed", ". CH", ".C H", "un can", "unc an", "unca n", "Ġ PRIMARY", "ĠPR IMARY", "ĠPRI MARY", "Ġper sec", "Ġpers ec", "Ġperse c", "Ġfe ared", "Ġfear ed", ". MIN", ".M IN", "ĠThe ater", "é Ĵ", "ateg orie", "ategor ie", "ategori e", "atego rie", "æ® µ", "Ġappet ite", "s quare", "squ are", "ĠAlex and", "ĠAlexa nd", ". UserId", ".User Id", "_ gt", "_g t", "_ enter", "_en ter", "_ent er", "Ġgrad uates", "Ġgraduate s", "Ġgradu ates", "Fragment Manager", "Author ize", "-N LS", "( My", "(M y", "Ġtri umph", "Ġtrium ph", "us ting", "ust ing", "ustin g", "_PARAM S", "_PAR AMS", "Char acters", "Character s", "(: ,:,", "(:, :,", "_ BUILD", "_B UILD", "_BU ILD", "M Hz", "MH z", "Ġw ashed", "Ġwas hed", "Ġwa shed", "Ġwash ed", "Ġun cle", "Ġunc le", "St eve", "Ste ve", "ar down", "ard own", "ardo wn", "< stdio", " ${", ">$ {", "_ confirmation", "_confirm ation", "Ġt rophy", "Ġtr ophy", "Ġtro phy", "Ġtrop hy", "W orks", "Work s", "ĠElect ronics", "ĠElectronic s", "ĠElectron ics", "ĠMediterr anean", "_ metrics", "_m etrics", "_metric s", "_met rics", "Ġann ouncing", "Ġannounc ing", "Ġ DAY", "ĠD AY", "ĠDA Y", "_ proto", "_pro to", "_pr oto", "_prot o", "Ġ pear", "Ġp ear", "Ġpe ar", "Ġpea r", "base Url", "ĉ ĉĉĉĉĉĉĉĊ", "ĉĉ ĉĉĉĉĉĉĊ", "ĉĉĉĉ ĉĉĉĉĊ", "ĉĉĉ ĉĉĉĉĉĊ", "ĉĉĉĉĉ ĉĉĉĊ", "ĉĉĉĉĉĉ ĉĉĊ", "ĉĉĉĉĉĉĉĉ Ċ", "ĉĉĉĉĉĉĉ ĉĊ", "Ġco ordination", "Ġcoord ination", "Ġcoordin ation", ": N", ". animate", ".an imate", ".anim ate", "ĠC otton", "ĠCot ton", "_ hit", "_h it", "_hi t", "â ľ", "Ġj etzt", "Ġjet zt", "i fter", "if ter", "ift er", "( fields", "(f ields", "(field s", "own load", "ific acion", "ifica cion", ". cuda", ".c uda", "ĠL iu", "ĠLi u", "> equals", ">e quals", "Ġ Ace", "ĠA ce", "ĠAc e", "ÑĢ ам", "ÑĢаР¼", "ÑĢа м", "ĠSup erman", "ĠSuper man", "ĠGar cia", "ĠGarc ia", "Ġarr ests", "Ġarrest s", "a gar", "ag ar", "aga r", "Ġ {})", "Ġ{ })", "Ġ{} )", "Ġ macros", "Ġmac ros", "Ġmacro s", "ro upe", "rou pe", "roup e", "ê tre", "êt re", "Ġtw isted", "Ġtwist ed", "str uments", "strument s", "stru ments", "_ (\"", "_( \"", "_ vertices", "_vert ices", "Ġ Transition", "ĠT ransition", "ĠTrans ition", "ĠTransit ion", "и к", "[ max", "[m ax", "m ind", "min d", "mi nd", "Ġ accessToken", "Ġaccess Token", "Ġun le", "Ġunl e", "m us", "mu s", "c op", "co p", "Ġ Factor", "ĠF actor", "ĠFac tor", "ĠFa ctor", "ĠFact or", "Ġcon ced", "Ġconc ed", "Ġconce d", "Ġre tr", "Ġr etr", "Ġret r", ".l inalg", ".lin alg", "- slider", "-s lider", "-slide r", "-sl ider", "o bl", "ob l", "_Static Fields", "Ġz ombie", "s elling", "sel ling", "sell ing", "Ġ chap", "Ġc hap", "Ġch ap", "Ġcha p", "Ġsh aking", "Ġsha king", "Ġ Translate", "ĠTrans late", "ĠAm sterdam", "Ġ ETH", "ĠE TH", "ĠET H", "_ EXTERN", "_EX TERN", "_EXT ERN", "k d", "_ disc", "_d isc", "_dis c", "_di sc", "Ġprec eding", "Ġpreced ing", "Ġ prix", "Ġp rix", "Ġpr ix", "Ġpri x", "Object Name", "_ modified", "_mod ified", "ard ware", "Ġ?> \">", "Ġ?>\" >", "Ġ DW", "ĠD W", "` ${", "Ġ?> \">\" >\"> \">< ?", "u yen", "uy en", "uye n", "Ġd onna", "Ġdon na", "Ġdonn a", "Ġx si", "Ġxs i", "Ġ$ \"{", "Ġ$\" {", "Ġ Drawing", "ĠD rawing", "ĠDraw ing", "ĠDra wing", ", nil", ",n il", "Ġ onder", "Ġo nder", "Ġon der", "Ġonde r", "B G", "O bserv", "Ob serv", "Obs erv", "Ġconsider ations", "Ġconsideration s", "bo at", "boa t", "ĠB anks", "ĠBank s", "ĠBan ks", "Ġin dict", "Ġind ict", "Ġindic t", ", I", "ĠB lu", "ĠBl u", "( version", "(v ersion", "cl iente", "client e", "cli ente", "o lan", "ol an", "ola n", "L ESS", "LE SS", "LES S", "assert Same", "_ void", "_v oid", "ĠW AS", "ĠWA S", "ĉ enum", "ĉe num", "ĉen um", "Ġm ixer", "Ġmix er", "E W", "a ffe", "af fe", "aff e", "Ġblow job", "t extField", "text Field", "Ġimm ense", "_ repo", "_re po", "_rep o", "Ġ globals", "Ġg lobals", "Ġglobal s", "Ġglob als", "ant ages", "anta ges", "antage s", ". today", ".t oday", ".to day", "Th ursday", "ĠB rig", "ĠBr ig", "ĠBri g", "{ })Ċ", "{} )Ċ", "{}) Ċ", "Ġ Imagine", "ĠIm agine", "ĠImag ine", "( GPIO", "(G PIO", "Ġ esto", "Ġe sto", "Ġes to", "Ġest o", "Ġ Province", "ĠPro vince", "ĠProv ince", "ĠM ental", "ĠMen tal", "ĠMent al", "_ cells", "_c ells", "_cell s", "ĠJul ian", "ĠJu lian", "ĠJulia n", "ĠJuli an", ". Screen", ".S creen", ".Sc reen", "Ġc andle", "Ġcan dle", "Ġcand le", "Ġm onde", "Ġmon de", "Ġmo nde", "Ġmond e", "Ġv erg", "Ġver g", "Ġve rg", "it erals", "iter als", "iteral s", "- layout", "-l ayout", "G uest", "Gu est", "Ġv ind", "Ġvi nd", "Ġvin d", "Ġ Echo", "ĠE cho", "ĠEc ho", "' )}", "') }", "Ġ mann", "Ġm ann", "Ġman n", "Ġma nn", "_ BOOLEAN", "_BO OLEAN", "h ap", "ha p", "Ġnight mare", "U GH", "UG H", "Ġnon etheless", "Ġnone theless", "Ġ athe", "Ġa the", "Ġat he", "Ġath e", "ĠH olland", "ĠHol land", "ĠHo lland", "ĠHoll and", "Ġ Born", "ĠB orn", "ĠBo rn", "ĠBor n", "\\ ORM", "a nut", "an ut", "_ levels", "_level s", "Ġpet ite", "Ġpetit e", "- art", "-a rt", "-ar t", "_ SHOW", "_S HOW", "_SH OW", "number Of", "_ thumbnail", "_th umbnail", "a mins", "am ins", "amin s", "ami ns", "Ġ Defines", "ĠDef ines", "ĠDefine s", "Ġ\" =", ". StatusCode", ".Status Code", "Ġdign ity", "ĠB ike", "ĠBi ke", "ĠBik e", ".New Line", "ĠG las", "ĠGl as", "( logger", "(log ger", "(lo gger", "Ġc atches", "Ġcatch es", "Ġcat ches", "v otes", "vo tes", "vote s", "Ġexam ining", "/ register", "/reg ister", "Ġspec ifying", "Ġspecify ing", "_ fixed", "_f ixed", "_fix ed", "Ġdraw ings", "Ġdrawing s", "Th reshold", "A x", "Ġ Architecture", "ĠArch itecture", "ĠArchitect ure", "( pid", "(p id", "(pi d", "W ire", "Wir e", "Wi re", "( cont", "(c ont", "(con t", "(co nt", "l ane", "la ne", "lan e", "L ists", "List s", "Li sts", "Ġs print", "Ġsp rint", "Ġspr int", "Ġgrand father", "_ AG", "_A G", "Ġs cheduling", "Ġsched uling", "CL US", "CLU S", "at urity", "atur ity", "Ġ locking", "Ġl ocking", "Ġloc king", "Ġlock ing", "[ size", "[s ize", "_ styles", "_st yles", "_style s", "Ġ wb", "Ġw b", "-- >ĊĊ", "-->Ċ Ċ", "--> ĊĊ", "Ġsp inning", "Ġspin ning", "_ pending", "_p ending", "_pen ding", "Match ers", "Mat chers", "Matcher s", ". Keys", ".Key s", "Ġ PV", "ĠP V", "en us", "enu s", "ant is", "anti s", "Ġ discard", "Ġd iscard", "Ġdis card", "Ġdisc ard", "Ġ haul", "Ġh aul", "Ġha ul", "Ġem pir", "Ġemp ir", "Ġpath way", "Ġo ak", "Ġoa k", "м ен", "ме н", "-in duced", "-ind uced", "Ġim pair", "Ġimp air", "ĠCal gary", ".is Hidden", "d z", "_ include", "_in clude", "_inc lude", "Ġ gm", "Ġg m", "Ġ' ('", "Ġ'( '", "P Y", "uggest ions", "uggestion s", "Ġcom modity", "Ġcommod ity", "c ro", "cr o", "/ sub", "/s ub", "Ġ getInstance", "Ġget Instance", "Ġ Legacy", "ĠLeg acy", "ĠK il", "ĠKi l", "B al", "Ba l", "( short", "(s hort", "(sh ort", "In form", "Info rm", "Inf orm", "+ x", "* r", "Ġ Hopefully", "ĠHope fully", "ĠHop efully", "o rate", "or ate", "ora te", "Ġm achen", "Ġma chen", "Ġmach en", "Ġmac hen", "Ġtreat y", "Ġtre aty", "ĠO ri", "ĠOr i", ". public", ".p ublic", ".pub lic", "- horizontal", "-h orizontal", "Ġt actic", "Ġta ctic", "Ġtact ic", "Ġtac tic", "Ġb ord", "Ġbo rd", "Ġbor d", "w ares", "ware s", "wa res", "war es", "Ġ ammo", "Ġa mmo", "Ġam mo", "Ġ Lists", "ĠL ists", "ĠList s", "ĠLi sts", "ĠLis ts", "Ġequ ations", "Ġeq uations", "Ġequation s", "/ her", "/h er", "/he r", "ĠN SW", "ĠNS W", "B ounding", "Bo unding", "Bound ing", "_ Collections", "_C ollections", "Ġ avail", "Ġa vail", "Ġav ail", "Ġava il", ". DropDown", ".Drop Down", "è °", "Ġ hh", "Ġh h", "Ġl Ãł", ". pb", ".p b", "Ġmem orial", "Ġmemor ial", "Ġmemo rial", "Ġmemoria l", "Ġ ATTR", "ĠAT TR", "ĠATT R", "Ġexhaust ed", "Ġt sp", "Ġts p", "ĉ redirect", "ĉre direct", "Ġlike wise", "Ġlik ewise", "S TER", "ST ER", "STE R", "L java", "Ġcondem ned", "Ġcondemn ed", "oca ust", "( strict", "(str ict", "Ġex empt", "Ġexem pt", "Ġexemp t", "Ġ sms", "Ġs ms", "Ġsm s", "Ġex agger", "S YS", "SY S", "Ġl ounge", "Ġlo unge", "Ġlou nge", "Ġloung e", ": ^", "Ġt odd", "Ġto dd", "Ġtod d", "d eb", "de b", "at orial", "ator ial", "ato rial", "atori al", "atoria l", "ĠPort er", "ĠPor ter", "Ġt uition", "Ġtu ition", "Ġex empl", "Ġexem pl", "Ġexe mpl", "Ġexemp l", "Ġ paren", "Ġp aren", "Ġpar en", "Ġpa ren", "Ġpare n", ".line To", "Ġkid ney", "Ġki dney", "Ġkidn ey", "Ġ ça", "Ġç a", "Ġc ui", "Ġcu i", "ï¼Į 请", "X C", "Ġmo ż", "Ġn ominated", "Ġno minated", "Ġnom inated", "Ġnomin ated", "Ġnominate d", "l ung", "lu ng", "lun g", "Im Gui", "Ġ Buzz", "ĠB uzz", "ĠBu zz", "Ġst ereo", "Ġste reo", "Ġster eo", "Ġstere o", "p ortal", "port al", "por tal", "res as", "resa s", "Ġ klass", "Ġk lass", "Ġkl ass", "Ġkla ss", "Ġklas s", "Ġd rafted", "Ġdraft ed", "Ġproject ile", "/g pl", "( parameters", "(param eters", "(parameter s", "* )Ċ", "*) Ċ", "Ġass isted", "Ġassist ed", "Ġ NSInteger", "ĠNS Integer", "s itemap", "site map", "sit emap", ": nth", ":n th", ". Views", ".View s", ".Argument Parser", "Ġ meer", "Ġm eer", "Ġme er", "Ġmee r", "z ier", "zi er", "zie r", "Ġ Dig", "ĠD ig", "ĠDi g", "Ċ", ")} >Ċ", ")}> Ċ", "Ġp lag", "Ġpl ag", "Ġpla g", "p ine", "pi ne", "pin e", "Ġblank et", "Ġ : -", "Ġ lcd", "Ġl cd", "Ġlc d", "- --------------", "-- -------------", "---- -----------", "-------- -------", "--- ------------", "------------ ---", "----- ----------", "---------- -----", "------ ---------", "----------- ----", "------------- --", "------- --------", "--------- ------", "-------------- -", "( \"\"", "(\" \"", "Ġt actical", "Ġtact ical", "Ġtactic al", "Ġtac tical", "ĠR onald", "ĠRon ald", "ex tr", "ext r", "ĠF est", "ĠFe st", "Ġf uer", "Ġfu er", "Ġfue r", "- navigation", "-n avigation", "-nav igation", "Ġ kb", "Ġk b", "g host", "gh ost", "Ġ handleChange", "Ġhandle Change", "_ cls", "_c ls", "_cl s", "( )!=", "() !=", "Com parator", "Compar ator", ". vm", ".v m", "ĠC ox", "ĠCo x", "_ review", "_re view", "_r eview", "_rev iew", "/ @", "_ cookie", "_c ookie", "_co okie", "Ġrecogn ised", "Ġrecognise d", "l dap", "ld ap", "lda p", "Th reads", "Thread s", "ĠS exual", "ĠSex ual", "ĠB earing", "ĠBe aring", "ĠBear ing", "ĠBea ring", "( SQL", "(S QL", "Ġ xr", "Ġx r", "Ġt high", "Ġth igh", "Ġthi gh", "URL Connection", "ĠS UV", "ĠSU V", "Ġm Context", "Ġinc idence", "Ġincid ence", "Ġ Este", "ĠE ste", "ĠEs te", "ĠEst e", ". sup", ".s up", "_ te", "_t e", "( EXIT", "(EX IT", "C MD", "CM D", "/ \">", "/\" >", "Al most", "Ġ Une", "ĠU ne", "ĠUn e", "Ġand eren", "Ġandere n", "Ġander en", "Ġ Singleton", "ĠS ingleton", "ĠSing leton", "ĠSingle ton", "Ġb ore", "Ġbo re", "Ġbor e", "Th ink", "Thin k", "Ġn arc", "Ġna rc", "Ġnar c", "] initWith", "]init With", "_ shop", "_s hop", "_sh op", "( strategy", "(str ategy", "! ',", "!' ,", "her its", "herit s", "Ġ Desk", "ĠD esk", "ĠDe sk", "ĠDes k", "_ machine", "_m achine", "_ma chine", ".n etty", ".net ty", ".ne tty", "ı nda", "ın da", "ınd a", "= <", "Ġ QR", "ĠQ R", "Ġ Sidebar", "ĠS idebar", "ĠSide bar", ".split Container", "Ġon Success", "Ġ monkey", "Ġmon key", "Ġmonk ey", "En joy", "( nodes", "(n odes", "(node s", "(no des", "pect rum", "Ġ (*(", "Ġ( *(", "Ġ(* (", "ĉ UINT", "ĉU INT", "ĉUI NT", ", height", ",h eight", "ĠNetwork s", "ĠNet works", ". tail", ".t ail", ".ta il", ".l inspace", ".lin space", "Ġ \"...", "Ġ\" ...", "Ġ\". ..", "Ġ\".. .", "L isten", "List en", "Li sten", "Æ ¡", ". Channel", ".Ch annel", "- defined", "-d efined", "-def ined", "Re peat", "Rep eat", "ad just", "adj ust", "E RM", "ER M", "_ application", "_app lication", "_ap plication", ".assert NotNull", ".assertNot Null", "- stream", "-st ream", "-str eam", "Ġ rabbit", "Ġr abbit", "Ġrab bit", "Ġposition ing", "Ġ woke", "Ġw oke", "Ġwo ke", "Ġf ing", "Ġfin g", "Ġfi ng", "Ġmulti player", "Ġmultip layer", "Ġregister ing", "Ġregist ering", "un til", "unt il", "Ã¥ n", "( ::", "(: :", "uss ions", "ussion s", "Ġpot ato", "Ġ Equals", "ĠE quals", "ĠEqu als", "ĠEqual s", ". Sup", ".S up", "/ apache", "/ap ache", "Ġ (=", "Ġ( =", ". \")", ".\" )", ". ptr", ".p tr", ".pt r", "Ġ Speech", "ĠS peech", "ĠSpe ech", ". clip", ".c lip", ".cl ip", ".cli p", "ĠGab riel", "ĠGabri el", "Ġmus ician", "Ġmusic ian", "/ issues", ". shop", ".s hop", ".sh op", "Ġ Hier", "ĠH ier", "ĠHi er", "_ RET", "_RE T", "_R ET", "_ bucket", "_b ucket", "ãĥ ¡", "a vs", "av s", "Ġ roz", "Ġr oz", "Ġro z", "f lower", "fl ower", "flow er", "flo wer", "Write Barrier", "ĠM ilan", "ĠMil an", "ĠMi lan", "Ġlegisl ature", "ĠD oll", "ĠDo ll", "ĠDol l", "Ġpro ving", "Ġpr oving", "Ġprov ing", ".concat enate", "âķ IJ", "Ġg char", "Ġgc har", "cdn js", "b les", "ble s", "bl es", "Ġ Listing", "ĠL isting", "ĠList ing", "ĠLis ting", "л о", ".xr Label", "ĠS ak", "ĠSa k", "just ice", "ju stice", "ĠVal entine", "ĠValent ine", "un less", "Ġp iger", "Ġpi ger", "Ġpig er", "Ġpige r", "( run", "(r un", "Ġtest ified", "A NA", "AN A", "ĠRe moves", "ĠRem oves", "ĠRemove s", ") )));Ċ", ")) ));Ċ", "))) );Ċ", ")))) ;Ċ", "rec ated", "ĠRuntime Method", "Ġcon qu", "ãĤ ¢", "Ġt issues", "Ġtissue s", "a iler", "ail er", "ai ler", "é té", "ét é", "- Star", "-S tar", "-St ar", "Ġfl ames", "Ġflame s", "Ġflam es", "Ġfla mes", ". setIcon", ".set Icon", "Ġsup ern", "Ġsuper n", "Ġv agina", "Ġvag ina", "- variable", "-var iable", "Ġwell ness", "C UR", "CU R", "Ġb elle", "Ġbe lle", "Ġbel le", "Ġbell e", ". getRequest", ".get Request", "Ġp oco", "Ġpo co", "Ġpoc o", "b enh", "be nh", "ben h", "a gens", "ag ens", "age ns", "agen s", "Ġs pill", "Ġsp ill", "Ġspi ll", "Ġ Jur", "ĠJ ur", "ĠJu r", "Ġ dispatcher", "Ġdispatch er", "Ġdisp atcher", "н ого", "но го", "ног о", "e monic", "em onic", "emo nic", "emon ic", "( dirname", "(dir name", "Ġ ÐĶ", "ĠÐ Ķ", "Ġp asse", "Ġpass e", "Ġpas se", "Ġpa sse", "Ġg anz", "Ġga nz", "Ġgan z", "r icing", "ri cing", "ric ing", "E U", "Ġmuj eres", "Ġmujer es", "es sen", "ess en", "esse n", ". attribute", ".at tribute", ".attrib ute", "j j", "ĉ ĉĠĊ", "ĉĉ ĠĊ", "ĉĉĠ Ċ", "[ ^", "Ġ strtolower", "Ġstr tolower", "Ġstrtol ower", "lex er", "ect ar", "ec tar", "ecta r", "h otel", "ho tel", "hot el", ". square", ".s quare", "Ġr all", "Ġra ll", "Ġl owered", "Ġlow ered", "Ġlower ed", "handle d", "hand led", "M arket", "Mark et", "Mar ket", "Ġ Uses", "ĠU ses", "ĠUs es", "ĠUse s", "i vas", "iv as", "iva s", ". Business", ".B usiness", ".Bus iness", "ãģĹ ãģ¦", "ãģĹãģ ¦", "D IV", "DI V", "Ġw asted", "Ġwas ted", "Ġwa sted", "Ġwaste d", "Ġwast ed", "Ġa voir", "Ġav oir", "ê m", "_ ACCOUNT", "_AC COUNT", "_ACC OUNT", ". et", ".e t", "ĉ SDL", "ĉS DL", "k ap", "ka p", "Ġ fox", "Ġf ox", "Ġfo x", "up pet", "upp et", "uppe t", "{ },Ċ", "{} ,Ċ", "{}, Ċ", "\" ,'", "\", '", "F avorite", "P END", "PE ND", "Ġ AES", "ĠA ES", "ĠAE S", "} ),", "}) ,", "Ġde duction", "Ġded uction", "Ġdeduct ion", "Ġpol ÃŃt", "Ġcomponent Will", "ĠT elerik", "ĠTele rik", "_ SELF", "_SE LF", "_SEL F", "Ġm use", "Ġmus e", "Ġmu se", "C raft", "Cr aft", "Ġ dens", "Ġd ens", "Ġde ns", "Ġden s", "ठ¿", "( tp", "(t p", "Ġt asty", "Ġta sty", "Ġtas ty", "Ġtast y", "Ġ balances", "Ġbalance s", "Ġbal ances", "Ġded ication", "Ġdedic ation", "Ġdedi cation", "ĠWall ace", "ĠWal lace", "Ġun law", "Ġunl aw", "\\ \">\\", "\\\" >\\", "\\\"> \\", "Ġm um", "Ġmu m", "- update", "-up date", "e mente", "em ente", "ement e", "eme nte", "emen te", "Ġs oda", "Ġso da", "Ġsod a", "Re public", "Rep ublic", "as mine", "asm ine", "é ric", "ér ic", "éri c", "( Status", "(S tatus", "ĠJson Convert", "Ġ Disk", "ĠD isk", "ĠDis k", "ĠDi sk", ". Redirect", ".Re direct", ".Red irect", "Ġfil ming", "Ġfilm ing", "/ mol", "/m ol", "R o", "Ġ ville", "Ġv ille", "Ġvi lle", "Ġvill e", "Ġvil le", "Ġtr abaj", "Ġtrab aj", "Ġs ynthesis", "Ġsyn thesis", "Ġsynth esis", "Ġsynthes is", "r ega", "re ga", "reg a", "Ġ rl", "Ġr l", "S cheduler", "Schedule r", "ISH ED", "current User", "( errors", "(err ors", "(error s", "(er rors", "' h", "_ bot", "_b ot", "_bo t", "x imo", "xi mo", "Ġ USART", "ĠUS ART", "ĠUSA RT", "_ super", "_s uper", "_sup er", "_su per", "_ DECREF", "_DEC REF", "н ой", "но й", "_ ROW", "_R OW", "_RO W", "Ġprom otes", "Ġpromote s", "Ġpromot es", "Ġpromo tes", "Ġ TA", "ĠT A", "Ġh oras", "Ġhor as", "Ġho ras", "Ġhora s", "ĠRep resents", "ĠRepresent s", "Ġ nameof", "Ġname of", "Ġnam eof", "Ġ Exc", "ĠE xc", "ĠEx c", "ĠGar age", "ĠGa rage", "Ġse ine", "Ġsein e", "Ġsei ne", ", #", "Ġh erb", "Ġhe rb", "Ġher b", "/ resources", "/re sources", "/res ources", "/resource s", "Ġple aded", "Ġplea ded", "Ġplead ed", ".r adioButton", ".radio Button", "Ġ æĺ", "Ġæ ĺ", "O ps", "Op s", "ĠN est", "ĠNe st", "ĠNes t", "c string", "cs tring", "ĠDef ence", "Ġref ere", "Ġrefer e", "_ leaf", "_le af", "Ġreve lation", "Ġrevel ation", "ë §", ".execute Update", "_W ORLD", "Ġexp ans", "(\" \\\"", "(\"\\ \"", "j ab", "ja b", "Ġdoub ts", "Ġdoubt s", "Ġ Geometry", "ĠGe ometry", "ĠGeo metry", "Ġintrodu ces", "Ġintroduce s", "Ġsen ators", "Ġsenator s", "Ġc anal", "Ġcan al", "Ġca nal", ". helper", ".h elper", ".help er", "ĠB iology", "ĠBi ology", "ĠBio logy", "ĠBiol ogy", "_ SENS", "_S ENS", "_SE NS", ". previous", ".pre vious", ".prev ious", "- touch", "-t ouch", "-to uch", "a bit", "ab it", "abi t", "Ġimp acted", "Ġimpact ed", "Ġbr ackets", "Ġbracket s", ". direct", ".d irect", ".dir ect", ".di rect", "ac cum", "acc um", "Ġtest osterone", "ĉ action", "ĉa ction", "ĉac tion", "ĉact ion", "Ġ Chance", "ĠCh ance", "ĠCha nce", "ĠChan ce", "Ġpe aks", "Ġpeak s", "Ġpea ks", "CppCodeGen WriteBarrier", "Ġun belie", "Ġunbe lie", "_ press", "_p ress", "_pr ess", "_pre ss", "_pres s", ". Rel", ".R el", ".Re l", "ang led", "angle d", "angl ed", "/ templates", "/t emplates", "/template s", "/temp lates", "-- >čĊ", "--> čĊ", "l ime", "li me", "lim e", "Ġsufficient ly", "_ nt", "_n t", "Exp and", ".is file", "Ġ isEmpty", "Ġis Empty", "Ġ qt", "Ġq t", "Ġmul her", "a cob", "ac ob", "aco b", "Ge orge", "å¸ ¸", "Ġas sim", "Ġass im", "a so", "as o", "Ġcompr ised", "Ġcomprise d", "O V", "( CONFIG", "(CON FIG", "ĉ writer", "ĉw riter", "ĉwrite r", "Ġd esp", "Ġde sp", "Ġdes p", "Ġten ure", "( cr", "(c r", ". pool", ".p ool", ".po ol", "ĠB rend", "ĠBr end", "ĠBre nd", "ĠBren d", "Ġc ensor", "( timeout", "(time out", "Ġp lea", "Ġpl ea", "Ġple a", ". Wrap", ".W rap", "Ġt ightly", "Ġtight ly", "Ġ Were", "ĠW ere", "ĠWe re", "ĠWer e", "Ġ Ignore", "ĠI gnore", "ĠIgn ore", "ĠIg nore", "a bei", "ab ei", "abe i", "Ġbr idges", "Ġbridge s", "Ġbrid ges", "Ġcondem n", "Ġsimp licity", "Ġsimpl icity", "Ġrout inely", "Ġroutine ly", "Ġbl acks", "Ġblack s", "Ġbla cks", "j b", "ĠP it", "ĠPi t", "U tf", "Ut f", "Ġ /Ċ", "Ġ/ Ċ", "r eload", "re load", "rel oad", "Ġset Object", "/ global", "/g lobal", "Ġf atty", "Ġfa tty", "Ġfat ty", "Ġfatt y", "Ġs ocks", "Ġso cks", "Ġsock s", "Ġsoc ks", "Could n", "Ġerot isk", "æĿ ¡", "Ġ Pressure", "ĠPres sure", "ĠPress ure", "ĠM az", "ĠMa z", "n pos", "np os", "to lower", "tol ower", "Ġ EQ", "ĠE Q", "ut eur", "ute ur", "Ġ Moment", "ĠM oment", "ĠMo ment", "ĠMom ent", "Ġ eta", "Ġe ta", "Ġet a", "{{ --", "Ġ graphs", "Ġgraph s", "Ġgrap hs", "ĠG uar", "ĠGu ar", "r ine", "ri ne", "rin e", "( --", "(- -", "Ġ HttpStatus", "ĠHttp Status", "( student", "(st udent", "* np", "*n p", "Ġrail way", "Ġas ynchronous", "_ vm", "_v m", "' ],'", "'] ,'", "'], '", ", text", ",t ext", "m erchant", "mer chant", "( Guid", "(G uid", "ĠG ra", "ĠGr a", "ix er", "ixe r", "fetch All", ". addListener", ".add Listener", "f lip", "fl ip", "* $", "> (),", ">( ),", ">() ,", "Ġsun light", "as signed", "ass igned", "assign ed", "Ġ abc", "Ġa bc", "Ġab c", "Ġ COLUMN", "ĠC OLUMN", "ĠðŁĻĤ ĊĊ", ") ...", "). ..", ").. .", "Ġ ensemble", "Ġen semble", "Ġens emble", "Ġ newline", "Ġnew line", "_S INGLE", "i edad", "ie dad", "ied ad", "Ġd arker", "Ġdark er", "Ġdar ker", "or map", "orm ap", "Ġ lion", "Ġl ion", "Ġli on", "pl its", "plit s", "Ġillustr ation", "Ġillust ration", "Ġ IEEE", "ĠI EEE", "ĠIE EE", "Ġv ista", "Ġvis ta", "Ġvi sta", "ous ands", "ousand s", "* ******", "** *****", "**** ***", "****** *", "*** ****", "***** **", "ĠTom my", "Ġ hue", "Ġh ue", "Ġhu e", "S el", "Se l", "Ġ aura", "Ġa ura", "Ġau ra", "Ġaur a", "ĠThe rapy", "ĠTher apy", "Ġan imator", "Ġanim ator", ". constraints", ".con straints", ".constraint s", "Ġv ague", "Ġva gue", "Ġvag ue", "(\" \")", "(\"\" )", "Ġvill ain", "Ġvil lain", "Ġvilla in", "Ġbless ing", "Ġstring Builder", "Ġ Misc", "ĠM isc", "ĠMi sc", "ĠMis c", "Ġ DIR", "ĠD IR", "ĠDI R", "f ax", "fa x", "- node", "-n ode", "-no de", "Ġ Walking", "ĠW alking", "ĠWalk ing", "ĠWal king", "Ġ AU", "ĠA U", "s ess", "se ss", "ses s", "Ġgr ill", "Ġgri ll", "VERT ISE", "ĠF oods", "ĠFood s", "ĠFo ods", "ĠFoo ds", "Ġt ournaments", "Ġtour naments", "Ġtournament s", "à ĵ", "Ġ Marsh", "ĠMar sh", "ĠMars h", "Ġw onders", "Ġwon ders", "Ġwonder s", "Long itude", ".Command Text", "= input", "=in put", "_ encoder", "_e ncoder", "_en coder", "_encode r", "_enc oder", "page Size", "Ġ getState", "Ġget State", "> >Ċ", ">> Ċ", ". grey", ".g rey", ".gr ey", "p od", "po d", "Ġread ings", "Ġreading s", "Ġre consider", "Start up", "Ġex cer", "Ġexc er", "Ġexce r", ". balance", ".b alance", "_ cycle", "_c ycle", "_ Time", "_T ime", "LO CAL", "LOC AL", "Ġ EFI", "ĠE FI", "ĠEF I", "ĠR eyn", "ĠRe yn", "ĠRey n", ".set Foreground", "b yn", "by n", "Ġdis connected", "Ġdisconnect ed", "ACT IVE", "Ġ embedding", "Ġembed ding", "ic kers", "ick ers", "icker s", "Ġsurround ings", "Ġsurrounding s", "* c", "Ġgar ant", "Ġga rant", "Ġ bf", "Ġb f", "Ġ wipe", "Ġw ipe", "Ġwi pe", "Ġ ä¸ĭ", "Ġä¸ ĭ", "_ TRA", "_T RA", "_TR A", "ad ox", "ado x", "ç ķ", "Ġs ucks", "Ġsu cks", "Ġsuc ks", "Ġsuck s", "Ġ Songs", "ĠS ongs", "ĠSon gs", "ĠSong s", "ĠAssoci ates", "ĠAssociate s", "ĠAssoc iates", "ĠB ald", "ĠBa ld", "ĠBal d", "ĠB rett", "ĠBr ett", "ĠBre tt", "ĠBret t", "ven ile", "Ġ vt", "Ġv t", "Ġin ade", "Ġre signed", "Ġres igned", "Ġresign ed", "ĠGl enn", "ĠGlen n", "ĠGle nn", ". pattern", ".p attern", ".pat tern", ".Data Bind", "Ñĥ м", "Layout Inflater", "c het", "ch et", "che t", "ĠTest ament", ". ms", ".m s", "Ġp av", "Ġpa v", "Ġ ReactDOM", "ĠReact DOM", "ur dy", "urd y", "A DATA", "AD ATA", "ADA TA", "M u", "/ actions", "/a ctions", "/action s", "Ġ Js", "ĠJ s", "_ extract", "_ex tract", "_ext ract", "_extra ct", "Ġ Bring", "ĠB ring", "ĠBr ing", "ĠBri ng", ": id", ":i d", "st rt", "str t", "i vation", "iv ation", "iva tion", "Ġout right", "Ġou tright", "Ġoutr ight", "a zu", "az u", "loy ment", "и Ñı", "al do", "ald o", "Ġ Publisher", "ĠP ublisher", "ĠPublish er", "E ducation", "Educ ation", "P alette", "Pal ette", "Pa lette", "Pale tte", "_ drv", "_d rv", "_dr v", "Ġ ($(", "Ġ( $(", "Ġ($ (", "ĠA nda", "ĠAn da", "ĠAnd a", "Ġrem edy", "Ġremed y", "Ġin consistent", "Ġincons istent", "Ġinconsist ent", "t ection", "te ction", "tec tion", "Ġreg ulators", "Ġregul ators", "Ġregulator s", "Ġshort est", "( pair", "(p air", "(pa ir", "Ġ Installation", "ĠInstall ation", "Ġdef endants", "Ġdefend ants", "Ġdefendant s", "Ġ ();", "Ġ( );", "Ġ() ;", "- large", "-l arge", "M el", "Me l", "Ġthreat en", "н Ñı", "Ġfet ish", "ot ine", "oti ne", "_ dic", "_d ic", "_di c", "Ġ <$", "Ġ< $", "Ġst agger", "Ġsta gger", "Ġstag ger", "s pi", "sp i", "$ response", "$res ponse", "S erv", "Se rv", "Ser v", "- born", "-b orn", "-bo rn", "j os", "jo s", "ĉ img", "ĉi mg", "ĉim g", "ĉ WHERE", "ĉW HERE", "_ lt", "_l t", "å½ ĵ", ". cost", ".c ost", ".co st", ".cos t", "Ġ Tue", "ĠT ue", "ĠTu e", ". labels", ".label s", ".lab els", "Ġ LV", "ĠL V", "wcs store", "ĠJ esse", "ĠJes se", "ĠJe sse", "ĠJess e", "ภ«", "T rade", "Tr ade", "Trad e", "Tra de", "Ġpredecess or", "ë Ĥ", "f inally", "fin ally", "final ly", "_ general", "_g eneral", "_gen eral", "_gene ral", "_gener al", "oggle r", "ogg ler", "_ REGION", "_REG ION", "n ement", "ne ment", "nem ent", "Ġb logger", "Ġblog ger", "Ġblo gger", "ĠHar bor", "Ġ Dataset", "ĠD ataset", "ĠData set", "ĠDat aset", "[ w", "Ġattend ees", "Ġattendee s", ". ico", ".i co", ".ic o", "max imum", ". Unlock", ".Un lock", "_ SYNC", "_S YNC", "_SY NC", "_SYN C", "ág ina", "Ġd owns", "Ġdown s", "Ġdow ns", "ĠW ii", "ĠWi i", "] )/", "]) /", "Ġk icking", "Ġkick ing", "un ication", "unic ation", "uni cation", "Ġ DAC", "ĠD AC", "ĠDA C", "Ġ IDS", "ĠI DS", "ĠID S", "ĠR ental", "ĠRen tal", "ĠRent al", "Ġ currentTime", "Ġcurrent Time", "Ġvacc ines", "Ġvaccine s", "ĠD evil", "ĠDe vil", "ĠDev il", "Ġn ors", "Ġno rs", "Ġnor s", "_ mouse", "_m ouse", "_mo use", "ur rection", "urre ction", "urr ection", "urrect ion", "( no", "(n o", "Ġ >čĊ", "Ġ> čĊ", "Ġag gression", "Ġaggress ion", "Ġagg ression", "Ġbre eding", "Ġbreed ing", "Ġbree ding", ". symbol", ".s ymbol", ".sym bol", "i man", "im an", "ima n", "Absolute Path", "Ġ WHO", "ĠW HO", "ĠWH O", "_ flush", "_f lush", "_fl ush", "- root", "-r oot", "-ro ot", "a rna", "ar na", "arn a", "& M", "Ġf athers", "Ġfa thers", "Ġfather s", "Ġ Rocket", "ĠR ocket", "ĠRock et", "ĠRo cket", "ĠRoc ket", "i veau", "ive au", "Ġw ander", "Ġwa nder", "Ġwand er", "Ġwan der", "Ġcom pos", "Ġcomp os", "ĠWar rior", "Ġ Seat", "ĠS eat", "ĠSe at", "ĠSea t", "ĠCl inic", "ĠClin ic", "ĠCli nic", "_ invoice", "_in voice", "_inv oice", "( dispatch", "(dis patch", "Product o", "at uring", "atur ing", "atu ring", "oss ier", "ĠM AY", "ĠMA Y", "Ġd agger", "Ġda gger", "Ġdag ger", "Ġsan itized", "Ġsanit ized", "Ġsanitize d", "Ġ RFC", "ĠR FC", "ĠRF C", "Ġp roph", "Ġpro ph", "Ġpr oph", "Ġprop h", "Ġu rine", "Ġur ine", "Ġuri ne", "Ġgr ind", "Ġgri nd", "Ġgrin d", "Ġ Expanded", "ĠExp anded", "ĠExpand ed", "des cripcion", "- fw", "-f w", "ĠK erry", "ĠKer ry", "ĠKerr y", "= name", "=n ame", "Ġ chk", "Ġc hk", "Ġch k", "Ġn ationally", "Ġnational ly", "Ġnation ally", "Ġt hee", "Ġth ee", "Ġthe e", "I nc", "In c", "Ġ ?>>", "Ġ? >>", "Ġ?> >", ". RadioButton", ".R adioButton", ".Http ServletResponse", ".HttpServlet Response", "/ Y", "ĉ field", "ĉf ield", "ĉfi eld", "Ġ homme", "Ġhom me", "y per", "ype r", "yp er", "Ph ysical", "Phys ical", "= v", "Ġd riv", "Ġdr iv", "Ġdri v", "Ġ Errors", "ĠError s", "ĠEr rors", "ĠErr ors", "Ġc Äĥ", "De ath", "Ġ WINDOW", "ĠW INDOW", "Ġpo et", "Ġ Sharp", "ĠSh arp", "ĠSha rp", "ĠShar p", "Ġ Immutable", "ĠIm mutable", "ĠImm utable", "ĉ create", "ĉc reate", "Ġge ht", "Ġgeh t", "ĠRe form", "ĠRef orm", "a iser", "ai ser", "ais er", "aise r", "Ġ Initialization", "ĠInitial ization", "Ġimm unity", "Ġimmun ity", ". compose", ".com pose", ".comp ose", "Ġlate ncy", "Ġlat ency", "Ġlaten cy", "ĠLeban on", "ĠPar ad", "ĠPa rad", "ĠPara d", "Ġf uels", "Ġfuel s", "Ġfu els", "Ġfue ls", "ĠEx hib", "c oh", "co h", "% \">Ċ", "%\" >Ċ", "%\"> Ċ", "Ġ CLI", "ĠC LI", "ĠCL I", ") initWith", ")init With", "- Za", "-Z a", "_ CLEAR", "_C LEAR", "_CL EAR", "re gn", "reg n", "Ġfin ances", "Ġfinance s", "Ġfinanc es", "Ġfinan ces", ". standard", ".st andard", "_ CATEGORY", "_C ATEGORY", ". library", ".l ibrary", ".lib rary", "Ġtravel ers", "Ġtraveler s", "_ wp", "_w p", "Ġ Evaluation", "ĠE valuation", "ĠEval uation", "ĠEvalu ation", "start ing", "star ting", "Ġ )),Ċ", "Ġ) ),Ċ", "Ġ)) ,Ċ", "ep isode", "Ġ Variant", "ĠV ariant", "ĠVar iant", "ĠVari ant", "Ġ daemon", "Ġda emon", "ĠJ ulia", "ĠJul ia", "ĠJu lia", "ĠJuli a", "Ġ NR", "ĠN R", "Ġd oubles", "Ġdouble s", "Ġdoub les", "Ġdou bles", "< v", "/ runtime", "/r untime", "/run time", "Ġ interpreter", "Ġinter preter", "Ġinterpret er", "Ġinterpre ter", "Ġ INDEX", "ĠIN DEX", "ĠIND EX", "ĠHol mes", "_ DIM", "_D IM", "_DI M", "Ġp addle", "Ġpad dle", "Ġpadd le", "_ example", "_ex ample", "_exam ple", "Ġ foreground", "Ġfore ground", ". routes", ".r outes", ".route s", ".ro utes", "Ġs owie", "Ġso wie", "Ġsow ie", "S UCCESS", "Ġ CDC", "ĠC DC", "ĠCD C", "Ġ BD", "ĠB D", "_ -", "as ured", "asure d", "asu red", "W riting", "Wr iting", "Ġ currentPage", "Ġcurrent Page", "( answer", "(ans wer", "(an swer", "Ġ ASCII", "ĠA SCII", "ĠASC II", "à ¨", "Ġsocial ly", "Ġsoc ially", "Ġsoci ally", "y yy", "yy y", "ĠSpecial ist", "( customer", "(c ustomer", "(custom er", "ist ani", "istan i", "ista ni", "k est", "ke st", "kes t", "ĠM ak", "ĠMa k", "Ġt ho", "Ġth o", ". pt", ".p t", "( comment", "(com ment", "(comm ent", "Ġ Converter", "ĠCon verter", "ĠConvert er", "g am", "ga m", "b ins", "bin s", "bi ns", ". tele", ".t ele", ".te le", ".tel e", "ĠVeter ans", "ĠVeteran s", "_ ALLOC", "_AL LOC", "_ALL OC", "олÑĮзов аÑĤ", "inn amon", "; width", "o hl", "oh l", "Ġf antas", "Ġfan tas", "Ġfant as", "Ġs ung", "Ġsu ng", "Ġsun g", "ĉ K", "( Json", "(J son", "Ġneighbour hood", "Ġv ow", "Ġvo w", "Ġs ins", "Ġsi ns", "Ġsin s", "on acci", "ona cci", "Ġ epochs", "Ġepoch s", "im agen", "image n", "ima gen", "imag en", ". Change", ".Ch ange", ".my batis", "Se ek", "See k", "W ER", "WE R", "管 çIJĨ", "Ġinter ess", "Ġinte ress", "Ġinteres s", "_ Event", "_E vent", "ed erland", "eder land", "Ġterr itor", "Ġci udad", "uc ked", "uck ed", "Ġsn ack", "Ġsna ck", "Ġtrans ported", "Ġtransport ed", "Ġtransporte d", "Ġ Manifest", "ĠMan ifest", "Ġ DAT", "ĠD AT", "ĠDA T", "_ theta", "_th eta", "_the ta", "Ġw ont", "Ġwon t", "Ġwo nt", ". ĊĊĊĊĊĊĊĊĊĊ", ".ĊĊ ĊĊĊĊĊĊĊĊ", ".Ċ ĊĊĊĊĊĊĊĊĊ", ".ĊĊĊĊ ĊĊĊĊĊĊ", ".ĊĊĊ ĊĊĊĊĊĊĊ", ".ĊĊĊĊĊĊ ĊĊĊĊ", ".ĊĊĊĊĊĊĊĊ ĊĊ", ".ĊĊĊĊĊ ĊĊĊĊĊ", "Ĭ¶ æĢģ", "ĠE pic", "ĠEp ic", "D eck", "De ck", "Dec k", "l tra", "lt ra", "ltr a", "_ ZERO", "_Z ERO", "Ġ[ ];", "Ġ[] ;", "/ scripts", "/s cripts", "/script s", "Ġ --------------------------------------------------------------------------------", "Ġ---------------------------------------------------------------- ----------------", "Ġ---- ----------------------------------------------------------------------------", "Ġ---------------- ----------------------------------------------------------------", "Ġ------------------------------------------------ --------------------------------", "Ġ-------------------------------- ------------------------------------------------", "Ġ---------------------------------------------------------------------------- ----", "Ġ---------- ----------------------------------------------------------------------", "Ġ------------------------------------------------------------ --------------------", "Ġ------------------------------------------------------------------------- -------", "æĥ ħ", "Ġ weed", "Ġw eed", "Ġwe ed", "Ġwee d", "N BC", "NB C", "Ġr aped", "Ġrap ed", "Ġra ped", "Ġrape d", "Ġ Gateway", "ĠG ateway", "ĠGate way", "ĠGat eway", "[ M", "Ġ Timeout", "ĠTime out", "ench mark", ". ViewModel", ".View Model", "Ġporn os", "Ġpor nos", "Ġporno s", "Ġ Ya", "ĠY a", "th ritis", "thr itis", "ĠFl ynn", "ĠFly nn", "Ġ mega", "Ġm ega", "Ġme ga", "Ġmeg a", "a cin", "ac in", "aci n", "Ġtr ibal", "Ġtri bal", "Ġtrib al", ". apple", ".app le", ".ap ple", "Ġ Blo", "ĠB lo", "ĠBl o", "â n", "i bi", "ib i", "r ov", "ro v", "ĠL ives", "ĠLive s", "ĠLi ves", "ĠLiv es", "^ .", "get Request", "Ġ Establish", "ĠEst ablish", "cont ainers", "container s", "contain ers", "Ġst arring", "Ġstar ring", "Ġcele brities", "Ġcelebr ities", "Ġ Relative", "ĠRel ative", "ĠHe ights", "ĠHeight s", "Ġtq dm", "ĠNorth west", "i vic", "iv ic", "ivi c", "ĉ cl", "ĉc l", "Ġautom otive", "ent ric", "entr ic", "Ġ fortunate", "Ġfort unate", "Ġfire place", "Ġfi replace", "se ud", "n ickname", "nick name", "; s", "_ CAL", "_C AL", "_CA L", "h alt", "ha lt", "hal t", "( ns", "(n s", "_ deleted", "_de leted", "_delete d", "_del eted", "De velopment", "Dev elopment", "Develop ment", "m ovies", "movie s", "mov ies", "Ġid entities", "Ġident ities", "Ġprompt ly", "ا ÙĨ", "ا٠Ĩ", "Ġ ante", "Ġa nte", "Ġan te", "Ġant e", "Ġ\" ','", "Ġ\"' ,'", "Ġ\"', '", "åı £", "imp se", "imps e", "Ġy ap", "Ġya p", "Type Name", "Ġb itch", "Ġbit ch", "Ġassoci ates", "Ġassociate s", "Ġassoc iates", "HE ME", "- empty", "-em pty", "Ġ ت", "ĠØ ª", "ol vers", "olve rs", "olver s", "olv ers", "Ġp istol", "Ġpist ol", "Ġpis tol", "Sc oped", "Scope d", "ag ner", "agn er", "agne r", "' ]=='", "'] =='", "']= ='", "']== '", "Ġ IMP", "ĠI MP", "ĠIM P", "e xc", "ex c", "Ġo mitted", "Ġom itted", "Ġomit ted", "Ġmind set", "Ġminds et", "Ġ [](", "Ġ[ ](", "Ġ[] (", "Ġ orn", "Ġo rn", "Ġor n", "_ CAM", "_C AM", "_CA M", "A vg", "Av g", "Localized String", "ĠN atur", "ĠNa tur", "ĠNat ur", "Ġ composer", "Ġcom poser", "Ġcomp oser", "Ġcompose r", "Ġcompos er", "Ġ Playing", "ĠPl aying", "ĠPlay ing", "ĠPla ying", "Ġover d", "Ġov erd", "_ utf", "_u tf", "_ut f", ". sk", ".s k", "ĠF ol", "ĠFo l", "$ page", "$p age", ", Object", ",O bject", "Ġb ees", "Ġbe es", "Ġbee s", "al ary", "ala ry", "alar y", "b ullet", "bul let", "bull et", "_ library", "_l ibrary", "_lib rary", "O ffer", "Of fer", "Off er", "loc ated", "locate d", "Ġ (_,", "Ġ( _,", "Ġ(_ ,", "âĢľ He", "Ġ Owners", "ĠOwn ers", "ĠOwner s", "ĠOw ners", ") ).Ċ", ")) .Ċ", ")). Ċ", "Ġb ri", "Ġbr i", ". Admin", ".Ad min", "k tion", "kt ion", "лÑİ Ñĩ", "Ġerot ici", "Ġerotic i", "Cancel led", "Ġ agr", "Ġa gr", "Ġag r", "re views", "review s", "_ dma", "_d ma", "_dm a", "R ICT", "RI CT", "RIC T", "Ġ gfx", "Ġg fx", "Ġgf x", "m pi", "mp i", "p po", "pp o", "Ġ //@", "Ġ// @", "Ġ/ /@", "Ġ uppercase", "Ġupper case", "Ġcomm itting", "Ġcommit ting", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "User Data", "Ġv ai", "Ġva i", "ĉ sort", "ĉs ort", "Ġcongr at", "Ġcong rat", "Ġd ioxide", "Ġdi oxide", "д а", ". area", ".a rea", ".ar ea", ".are a", "ĠJosh ua", "ĠJos hua", "ĠK och", "ĠKo ch", "_ break", "_b reak", "a zure", "az ure", "azu re", "is tical", "ist ical", "istic al", "isti cal", "istica l", "_AL PHA", "_ views", "_view s", "_vi ews", "Ġelim inating", "Ġelimin ating", "O MB", "OM B", "e numer", "en umer", "enu mer", "enum er", "ĠH ydro", "ĠHy dro", "( *(", "(* (", "ERT ICAL", "Ġinev itably", "Ġst ole", "Ġsto le", "Ġstol e", "- east", "-e ast", "i eron", "ie ron", "ier on", "iero n", "Ġ linger", "Ġl inger", "Ġli nger", "Ġlin ger", "Ġling er", "/ doc", "/d oc", "/do c", "Å º", "Ġ Already", "ĠAl ready", "as io", "asi o", "Ġ --Ċ", "Ġ- -Ċ", "Ġ-- Ċ", "Ġ abbrev", "Ġabb rev", "Ġ Atom", "ĠA tom", "ĠAt om", "h im", "hi m", "Ġ INSERT", "ĠINS ERT", "s un", "su n", "âĻ ª", "CON NECT", "CONN ECT", "er ator", "era tor", "ĠM anning", "ĠMan ning", "ĠMann ing", "Ġ :(", "Ġ: (", "g as", "ga s", "= >'", "=> '", "Ġquery set", "; }čĊ", ";} čĊ", "Ġ Population", "ĠPop ulation", "uted String", "res ident", "resi dent", "_ FONT", "_F ONT", "Ġ Respond", "ĠRes pond", "ĠResp ond", "Ġobsc ure", "Ġ observable", "Ġo bservable", "Ġobserv able", "ĠContrib utors", "ĠContributor s", "k on", "ko n", "ĠM usk", "ĠMus k", "ĠMu sk", "ex ao", "ĠT ub", "ĠTu b", "Boot Application", "S OR", "SO R", ". Horizontal", ".H orizontal", ". findBy", ".find By", ". power", ".p ower", ".pow er", ".po wer", "Ġpositive ly", "Ġposit ively", "ven ience", "ĠJ ong", "ĠJo ng", "ĠJon g", "Ġwh istle", "Ġ знаÑĩ", "Ġз наÑĩ", "Ġзна Ñĩ", "Ġзн аÑĩ", "Ġl ending", "Ġlen ding", "Ġlend ing", "Ġdestruct ive", "Ġ onDelete", "Ġon Delete", "author ization", "() ;?>", "(); ?>", "_ original", "_or iginal", "_origin al", "_orig inal", "sc ience", "sci ence", "a tra", "at ra", "atr a", "?, ?,", "Ġ Asc", "ĠA sc", "ĠAs c", "Ġconvin cing", "Ġconvinc ing", "$ a", "or gen", "org en", "orge n", "_ Date", "_D ate", "Ġ Provide", "ĠPro vide", "ĠProvid e", "ĠProv ide", "Ġlon ely", "Ġlone ly", ") 'Ċ", ")' Ċ", "ex change", "; ?>Ċ", ";?> Ċ", ". fast", ".f ast", ".fa st", "S amples", "Sample s", "Sam ples", "L ondon", "Lo ndon", "Lon don", "' ])čĊ", "'] )čĊ", "']) čĊ", "Ġ Ionic", "ĠI onic", "ĠIo nic", "ĠIon ic", "Ġp esso", "Ġpes so", "ĠKn ights", "ĠKnight s", "ĠR af", "ĠRa f", "_ attrs", "_at trs", "_attr s", "_att rs", "Ġrepe al", "> Main", ">M ain", "Ġ Ordered", "ĠOrder ed", "ĠOrd ered", "_ New", "_N ew", "=\" \"> < /", "url patterns", "ATION AL", "ATIO NAL", "pe ech", "pee ch", "ĠId aho", "Ġpr incess", "Ġprince ss", "Ġprin cess", "Ġprinc ess", "Ġprinces s", "Ġ Customers", "ĠCustom ers", "ĠCustomer s", "ĠCust omers", "a ways", "aw ays", "away s", "awa ys", "a db", "ad b", "ĠBry ant", "ĠBryan t", "n once", "no nce", "non ce", "Ġad ul", "Ġ` `(", "Ġ`` (", "Ġafter math", "= dict", "=d ict", "text Box", "Ġs perm", "Ġsp erm", "Ġspe rm", "Ġsper m", "Ġc ough", "Ġco ugh", "Ġcou gh", "H or", "Ho r", "âĢĻ S", ".Component ResourceManager", "Ġreg ulator", "Ġregul ator", "Ġpartner ships", "Ġpartners hips", "Ġpartnership s", "/ projects", "/project s", "t rys", "tr ys", "try s", "ĠL aser", "ĠLa ser", "ĠLas er", "⣠©", "ĠF unk", "ĠFun k", "ĠFu nk", "Ġun conscious", "Ġuncon scious", "Ġc rust", "Ġcr ust", "Ġcru st", "Ġcrus t", "Ġ Teams", "ĠTe ams", "ĠTeam s", "ĠTea ms", "Ġ Banner", "ĠB anner", "ĠBan ner", "ĠH oney", "ĠHon ey", "ĠHo ney", "l ems", "le ms", "lem s", "Ġmax Width", "Pointer Exception", "fade Out", "- St", "-S t", "Ġstr angers", "Ġstrange rs", "Ġstranger s", "Ġstrang ers", "Ġstran gers", "_ GO", "_G O", "W ritable", "Wr itable", "_ Info", "_In fo", ". NonNull", ".Non Null", "an notations", "annot ations", "annotation s", "Ġ GD", "ĠG D", "Ġendorse d", "Ġendors ed", "ĉToken Name", "Ġ Depending", "ĠDe pending", "ĠDep ending", "ĠDepend ing", "YN AM", "Ġ Meteor", "ĠM eteor", "ĠMet eor", "Ġ Increase", "ĠIn crease", "ĠIncre ase", ". Many", ".M any", ".Man y", ".Ma ny", "= =(", "== (", ". UUID", ".U UID", "_ KERNEL", "_K ERNEL", "Ġvid é", "Ġ pq", "Ġp q", "ĠQt Gui", "Ġ Various", "ĠV arious", "ĠVar ious", "ĠVa rious", "ĠVari ous", "Ġ john", "Ġj ohn", "Ġjo hn", "_ patch", "_p atch", "_pat ch", "Ġt outes", "Ġto utes", "Ġtou tes", "Ġtout es", "Ġtoute s", "Ġ Fail", "ĠF ail", "ĠFa il", "Ġsurv iving", "Ġsurviv ing", "( \"${", "(\" ${", "(\"$ {", "Ġ ĠĠĠĠĠĠčĊ", "ĠĠ ĠĠĠĠĠčĊ", "ĠĠĠĠ ĠĠĠčĊ", "ĠĠĠ ĠĠĠĠčĊ", "ĠĠĠĠĠĠĠ čĊ", "ĠĠĠĠĠ ĠĠčĊ", "ĠĠĠĠĠĠ ĠčĊ", "Ġ imageUrl", "Ġimage Url", ". wordpress", ".word press", "s ources", "source s", "ĉgl Vertex", "âĢĻ a", "Ġes col", "Ġesc ol", "R ARY", "RA RY", "RAR Y", "Ġ Snake", "ĠSn ake", "Ġqu int", "Ġq uint", "Ġqui nt", "Ġl asts", "Ġla sts", "Ġlast s", "Ġlas ts", "ĠHar mon", "ĠHarm on", "Ġ coil", "Ġc oil", "Ġco il", "Ġcoi l", "Ġexplo itation", "Ġexploit ation", "l een", "le en", "lee n", "' >\";Ċ", "'> \";Ċ", "'>\" ;Ċ", "Ġ SERVER", "ĠS ERVER", "ĠSER VER", "Ġ HEADER", "ĠHE ADER", "ĠHEAD ER", "_ velocity", "_v elocity", "_vel ocity", "Ġ Invoke", "ĠIn voke", "ĠInv oke", ". timestamps", ".timestamp s", "Ġs ulf", "Ġsu lf", "Ġsul f", "I QUE", "IQ UE", "Ġinhabit ants", "ph ins", "phi ns", "phin s", "a zzo", "az zo", "azz o", "Ġ mono", "Ġm ono", "Ġmon o", "Ġmo no", "L egend", "Le gend", "Leg end", "Ġ nonce", "Ġn once", "Ġno nce", "Ġnon ce", "I FE", "IF E", "; \";Ċ", ";\" ;Ċ", "- create", "-c reate", "\" \",Ċ", "\"\" ,Ċ", "\"\", Ċ", "per mit", "perm it", "ĠIm migration", "ĠImm igration", "Ġ pathname", "Ġpath name", "ff ective", "ffect ive", "âĻĢ âĻĢ", "Ġex ams", "Ġexam s", "- event", "-e vent", "-ev ent", "-even t", "ĠT ill", "ĠTi ll", "ĠTil l", "[ mid", "[m id", "F IX", "FI X", "; color", ";c olor", "( Order", "_ traits", "_t raits", "_tr aits", "_tra its", "_trait s", "Ġ orderBy", "Ġorder By", "Ġs unt", "Ġsu nt", "Ġsun t", "ĠNich olas", "Ø ²", "Ġs unny", "Ġsun ny", "i ners", "in ers", "ine rs", "iner s", "Ġaccess ibility", "Ġ HB", "ĠH B", ". comp", ".c omp", ".com p", ".co mp", "ĉ op", "ĉo p", "Ġminor ities", "eth eus", "ethe us", "Ġcollabor ative", "p rit", "pr it", "pri t", "H IR", "HI R", "Ġwr aps", "Ġwrap s", "ĉ draw", "ĉd raw", "g od", "go d", "Ġ IX", "ĠI X", ". apps", ".app s", ".ap ps", ".a pps", "Ġ NM", "ĠN M", "Ġir relevant", "Ġirre levant", "ĠT igers", "ĠTi gers", "ĠTiger s", "ĠTig ers", "Ġ diag", "Ġd iag", "Ġdi ag", "Ġdia g", "G V", "ĠAccess ories", "k ont", "ko nt", "kon t", "Ġs implify", "Ġsimp lify", "Ġsimpl ify", "Ġ Favorite", "ĠF avorite", "ĠFavor ite", "_ tools", "_t ools", "_to ols", "_tool s", "( []);Ċ", "([ ]);Ċ", "([] );Ċ", "Ġt owers", "Ġto wers", "Ġtow ers", "Ġtower s", "B es", "Be s", "Ġ hunter", "Ġh unter", "Ġhun ter", "Ġhunt er", "Ġs alon", "Ġsa lon", "Ġsal on", "( buff", "(b uff", "(buf f", "ĉ debug", "ĉde bug", "Ġmal ware", "M oving", "Mo ving", "Mov ing", "- options", "-o ptions", "-option s", "-opt ions", ") +'", ")+ '", "ĠL OVE", "ĠLO VE", "_S OCKET", "_SO CKET", "_ fin", "_f in", "ĠDel aware", "Ġsher iff", "- invalid", "-in valid", "Ġ FULL", "ĠF ULL", "ĠFU LL", "Ġ под", "Ġп од", "Ġпо д", "e las", "el as", "ela s", "\" strings", "ĠRepresent atives", "ĠRepresentative s", "s urface", "sur face", "surf ace", "res olved", "resolve d", "ht docs", ") ):čĊ", ")) :čĊ", ")): čĊ", "Ġpress ures", "Ġpressure s", "Ġno rms", "Ġnor ms", "Ġnorm s", "Ġ pla", "Ġp la", "Ġpl a", "Ġ surname", "Ġs urname", "Ġsur name", "Ġ postal", "Ġpos tal", "Ġpost al", "Ġpo stal", "Ġ Depart", "ĠDe part", "ĠDep art", "Ġs laughter", "Ġsla ughter", "or ida", "ori da", "Ġhe bben", "Ġheb ben", "Ġd esar", "Ġde sar", "Ġdes ar", "comp act", "_ LANG", "_L ANG", "_LA NG", "åIJ Ī", "o poly", "op oly", "opol y", "opo ly", "_ rad", "_r ad", "_ra d", "ĠST DMETHOD", "ĠSTD METHOD", "L azy", "La zy", "Ġ ĠĠĉ", "ĠĠ Ġĉ", "ĠĠĠ ĉ", ".. .,", "... ,", "( web", "(w eb", "Ġ Pont", "ĠP ont", "ĠPo nt", "ĠPon t", "Ġet was", "Ġetwa s", "Ġup ward", "_ hat", "_h at", "Ġ ],ĊĊ", "Ġ] ,ĊĊ", "Ġ],Ċ Ċ", "Ġ], ĊĊ", "Ġ baseUrl", "Ġbase Url", "Ġwor rying", "Ġworry ing", "- addon", "-add on", "-ad don", "( getClass", "(get Class", "S PI", "SP I", "Ġcapt uring", ") },Ċ", ")} ,Ċ", ")}, Ċ", "E ffects", "Effect s", "Eff ects", "Ġcompet ent", "Ġcompete nt", "Ġf oul", "Ġfo ul", "Ġfou l", "Ġsubs cribing", "Ġsubscri bing", "Ġ OBJECT", "ĠO BJECT", "ĠOBJ ECT", "ĠOB JECT", "IX EL", "b ucks", "bu cks", "( edge", "(e dge", "(ed ge", "( pass", "(p ass", "(pa ss", "ĠPeter son", "ĠPet erson", "ĠPeters on", "Ġbo obs", "Ġboo bs", "Ġboob s", "Ġ Delay", "ĠD elay", "ĠDe lay", "ĠDel ay", "_ square", "_s quare", "e lim", "el im", "eli m", "o ters", "ot ers", "ote rs", "oter s", "_ PC", "_P C", "% E", "on click", "Ġ SVG", "ĠS VG", "ĠSV G", "Ġt opped", "Ġto pped", "Ġtop ped", "Ġtopp ed", "Ġf ist", "Ġfi st", "Ġfis t", "s mart", "sm art", "ĠR alph", "( owner", "(o wner", "j ours", "jo urs", "jour s", "Ġbro nze", "Ġbron ze", "Ġ ArgumentException", "ĠArgument Exception", "( original", "(origin al", "(orig inal", "(or iginal", "_ SCALE", "_S CALE", "_SC ALE", "_ cp", "_c p", "Ġrecomm ends", "Ġrecommend s", ".set Style", "S ure", "Sur e", "Su re", "L AND", "LA ND", "LAN D", "Ġre peating", "Ġrep eating", "Ġrepe ating", "Ġrepeat ing", "M att", "Mat t", "Ma tt", ". Visibility", "Ġenter prises", "Ġenterprise s", ". Setup", ".Set up", "( scene", "(s cene", "(sc ene", "ĠRe active", "ĠReact ive", "ur ge", "urg e", "b w", ". Put", ".P ut", "p ersist", "pers ist", ". cookie", ".c ookie", ".co okie", "ĠA udi", "ĠAud i", "ĠAu di", "` s", "s upplier", "sup plier", "( Form", "(F orm", " ¡", "_ so", "_s o", "Į Ģ", "ĠLeg ion", "t te", "tt e", "N d", "L oss", "Lo ss", "Los s", "( attrs", "(at trs", "(attr s", "(att rs", ". scatter", ".sc atter", "Ġg room", "Ġgr oom", "Ġgro om", "Ġgl impse", "Ġglimps e", "Ġn ails", "Ġna ils", "Ġnail s", "Ġcum ulative", "Ġf azer", "Ġfa zer", "Ġfaz er", "_ services", "_s ervices", "_service s", "_serv ices", ". Num", ".N um", "ib ilit", "ibil it", "ibi lit", "ibili t", "_ resolution", "_re solution", "_res olution", "Ġ Tx", "ĠT x", "um inium", "umin ium", "o pa", "op a", ". schedule", ".s chedule", "sm tp", "ภķ", "ur ry", "urr y", "ü k", "g oog", "go og", "goo g", "_ signature", "_sign ature", "_sig nature", ". into", ".in to", ".int o", "Ġ Steps", "ĠSt eps", "ĠSte ps", "ĠStep s", "Ġhome owners", "Ġhomeowner s", "Ġ NSURL", "ĠNS URL", "ĠP AC", "ĠPA C", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĊĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĊĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĊĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĊĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĊĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĊĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĊĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĊĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĊĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĊ Ċ", "> ')Ċ", ">' )Ċ", ">') Ċ", "e nh", "en h", "Ġin cap", "Ġinc ap", "$ MESS", "Ġm oins", "Ġmo ins", "Ġmoi ns", "Ġ Fi", "ĠF i", "Ġoff season", "pr essions", "press ions", "pression s", "> .. < /", "Ġpro vinces", "Ġprov inces", "Ġprovince s", "Ġprovinc es", "_ RAW", "_R AW", "_RA W", "\\ App", "Ġprostit uer", "Ġprostitu er", "_ gain", "_g ain", ".t encent", "ff ects", "ffect s", "ffe cts", "( pk", "(p k", "s ku", "sk u", "Ġ usable", "Ġus able", "Ġusa ble", "ER VED", "ERV ED", "ERVE D", "Ġant enna", "Ġantenn a", "h ea", "he a", "p list", "pl ist", "_ PLUGIN", "_PL UGIN", "Ñģ л", ". lookup", ".look up", "á» ģ", "Ġen larg", "Ġp iss", "Ġpi ss", "Ġpis s", "H am", "Ha m", "i map", "im ap", "ima p", "Ġ invalidate", "Ġin validate", "Ġinvalid ate", "Ġs ilk", "Ġsi lk", "Ġsil k", "=\"# \">Ċ", "=\"#\" >Ċ", "=\"#\"> Ċ", "ĠGr ass", "ĠGra ss", "Ġ Goal", "ĠGo al", "ĠGoa l", "_ pdf", "_p df", "_pd f", "Hand lers", "Handler s", "Handle rs", "Ġst acks", "Ġstack s", "Ġsta cks", ". getFullYear", ".get FullYear", "= [];Ċ", "=[ ];Ċ", "=[] ;Ċ", "è½ ¦", ", V", "( split", "(s plit", "(sp lit", "Ñĥн к", "Ġbake ca", "Ġbak eca", "Ġ~ /.", "Ġ~/ .", "p ez", "pe z", "t ails", "ta ils", "tail s", "ĠG len", "ĠGl en", "ĠGle n", "Ġ setImage", "Ġset Image", "Ġ Comic", "ĠC omic", "ĠCom ic", "ĠCo mic", "B LOCK", "BL OCK", "ĉ This", "ĉT his", "o ader", "oad er", "oa der", "Ġcapital ist", "Ġcapita list", "Ġcapit alist", "_ STEP", "_ST EP", "( Boolean", "Ġ Correct", "ĠC orrect", "ĠCor rect", "r ina", "ri na", "rin a", "Ġconc aten", "Ġconcat en", "å® ŀ", "( ):ĊĊ", "() :ĊĊ", "():Ċ Ċ", "(): ĊĊ", "Ġun anim", "Ġuna nim", "l li", "ll i", "al ars", "ala rs", "alar s", "- ne", "-n e", "Ġd ivor", "Ġdi vor", "Ġdiv or", "ĠKick starter", "] ._", "]. _", "< number", " * * < /", ": d", "m di", "md i", "bind Value", "Ġ Decision", "ĠDe cision", "ĠDec ision", "Return Value", ", index", ",in dex", "x fc", "xf c", "Ġse rum", "Ġser um", "get Field", "Connection String", "- object", "-o bject", "-ob ject", ". recv", ".re cv", ".rec v", "Ġunder graduate", "Ġundergrad uate", ". Infrastructure", ".Inf rastructure", "ĠK ab", "ĠKa b", "Ġadv isory", "Ġadvis ory", "Ġadvisor y", "- tree", "-t ree", "-tr ee", "Ġ mue", "Ġm ue", "Ġmu e", "in form", "info rm", "inf orm", ". embed", ".em bed", "Ġ errorCode", "Ġerror Code", "m icro", "mi cro", "mic ro", "Ġsp arked", "Ġspark ed", "Ġspar ked", "Ġimage ry", "Ġimag ery", "con c", "co nc", "_ missing", "_m issing", "_miss ing", "Ġsur plus", "K S", "ĉR THOOK", "ĉRT HOOK", "T ell", "Te ll", "Tel l", "r ium", "ri um", "Ġ Radius", "ĠR adius", "ĠRad ius", "ĠRadi us", "r ika", "ri ka", "rik a", "los ion", "ĠH ern", "ĠHe rn", "ĠHer n", "G amma", "Gam ma", "Ga mma", "Ġ Fee", "ĠF ee", "ĠFe e", "Ġ Named", "ĠN amed", "ĠName d", "ĠNa med", "ĠNam ed", "ĠCan yon", "Ġ JSONArray", "ĠJSON Array", "Ġz wei", "Ġzw ei", "Ġzwe i", "Ġ SSH", "ĠS SH", "ĠSS H", "Ġser vant", "Ġserv ant", "co al", "Ġden ying", "Ġdeny ing", "Ġs plits", "Ġsplit s", "Ġspl its", "In correct", "Inc orrect", "Ġt ox", "Ġto x", "ĠAnal yst", "ĠAnaly st", "Ġacc red", "Ġac cred", "Ġaccr ed", "u ble", "ub le", "ubl e", "Ġ wt", "Ġw t", "Ġ Trial", "ĠT rial", "ĠTr ial", "ĠTri al", ". extension", ".ext ension", "Ġ Career", "ĠCar eer", "ĠCare er", "Ġsec uring", "ĠL il", "ĠLi l", "Ġpro jections", "Ġproject ions", "Ġproj ections", "Ġprojection s", "Ġproje ctions", "Ġy east", "Ġye ast", "M ade", "Ma de", "Mad e", "Ġfound ations", "Ġfoundation s", "ac ific", "aci fic", ". volume", ".v olume", ".vol ume", "Ġmir rors", "Ġmirror s", "#### ############################################################################", "######## ########################################################################", "################ ################################################################", "################################ ################################################", "################################################################ ################", "################################################ ################################", "############################################################################ ####", "######################################################################## ########", "######################################## ########################################", "######################## ########################################################", "######################################################## ########################", "Ġvi olate", "Ġviol ate", "ars ers", "arse rs", "arser s", "Ġs ocio", "Ġso cio", "Ġsoc io", "Ġsoci o", "Ġtk inter", "Ġ LINK", "ĠL INK", "ĠLI NK", "ĠLIN K", ". getSize", ".get Size", ".getS ize", "Ġ Whole", "ĠW hole", "ĠWh ole", "ĠWho le", ")view DidLoad", "ĉ done", "ĉd one", "ĉdo ne", "ude au", "\\ \"> < /", "And rew", "Andre w", "e rb", "er b", "Ġf ö", ". cluster", ".cl uster", "Ġdis course", "Ġdisc ourse", "Ġdiscour se", "Ġdisco urse", "_DE FIN", "_DEF IN", "Ġpued en", "Ġpu eden", "Ġpuede n", "Ġ LOW", "ĠL OW", "ĠLO W", ". av", ".a v", "Ġpr eca", "Ġpre ca", "Ġprec a", "Ġ quo", "Ġqu o", "Ġq uo", "Ġve loc", "Ġvel oc", ", ''", ",' '", "Ġ xyz", "Ġx yz", "Ġxy z", "ĉ padding", "ĉp adding", "Ġtom atoes", "Ġtomato es", "ĠB ent", "ĠBe nt", "ĠBen t", "_ curr", "_c urr", "_cur r", "_cu rr", "NS Date", "Ġ getCurrent", "Ġget Current", "ĠgetC urrent", "Ġ [`", "Ġ[ `", "Wed nesday", ". Bar", ".B ar", "Ġ Vous", "ĠV ous", "ĠVo us", "i nz", "in z", "ĠQu inn", "ĠQui nn", "ex cel", "exc el", "d os", "do s", "Ġout dated", "O UTH", "OUT H", "OU TH", "Ġ Maker", "ĠM aker", "ĠMake r", "ĠMa ker", "ĠMak er", "ep endency", "epend ency", "Ġd ull", "Ġdu ll", "Ġdul l", "ĠW inn", "ĠWin n", "ĠWi nn", "o ge", "og e", "cl ave", "cla ve", "Ġ nova", "Ġn ova", "Ġno va", "Ġnov a", "Ġ aval", "Ġa val", "Ġav al", "Ġava l", "C apt", "Cap t", "Ca pt", "ĠSp otify", "ĠSpot ify", "Ġj ul", "Ġju l", ") tableView", ")t ableView", "Ġf ilenames", "Ġfile names", "Ġfil enames", "Ġfilename s", "Ġesk ort", "åij ¨", "Ġsk ew", "Ġske w", "t erior", "ter ior", "te rior", "teri or", "Ġfin anc", "Ġfinan c", "Ġ tabla", "Ġtab la", "Ġta bla", "ĠU IB", "ĠUI B", "Ġ ():", "Ġ( ):", "Ġ() :", "ĠD ocker", "ĠDo cker", "ĠDoc ker", "ĠDock er", "per centage", "percent age", "Me et", "i chi", "ic hi", "ich i", "Ġinter im", "Ġinte rim", "Ġ' ='", "Ġ'= '", ". JSONObject", ".JSON Object", "( fid", "(f id", "(fi d", "Ġd ownt", "Ġdown t", "Ġdow nt", "Ġtrans ient", "ĠSt eph", "ĠSte ph", "ĠStep h", "Ġignor ance", "Ġ Codes", "ĠC odes", "ĠCo des", "ĠCode s", "ĠCod es", "= '',", "=' ',", "='' ,", "Ġ ICE", "ĠI CE", "ĠIC E", "Ġtran qu", "Ġ Extended", "ĠExt ended", "ĠExtend ed", "Ġ mund", "Ġm und", "Ġmu nd", "Ġmun d", "Ġ HOME", "ĠH OME", "ĠHO ME", "Ġkil ometers", "Ġkilomet ers", "Ġ imagen", "Ġim agen", "Ġimage n", "Ġimag en", "Ġima gen", "o ux", "ou x", "( sz", "(s z", "You ng", "Yo ung", "uff ed", "uf fed", "Ġ Wake", "ĠW ake", "ĠWa ke", "ĠWak e", "Ġa ide", "Ġaid e", "Ġai de", "P ROC", "PR OC", "PRO C", "ĠR at", "ĠRa t", "ĠL ith", "ĠLi th", "ĠLit h", "b art", "bar t", "ba rt", "Ġ Arrange", "ĠAr range", "ĠArr ange", "p rompt", "prom pt", "Ð £", "( ct", "(c t", "Ġ Interval", "ĠInt erval", "ĠInter val", "d ept", "de pt", "dep t", "D aniel", "Dan iel", "Dani el", "Ġ fills", "Ġf ills", "Ġfil ls", "Ġfill s", ". tensor", ".t ensor", "( trim", "(t rim", "(tr im", "Ġje alous", "F eb", "Fe b", "\\ Common", "Ġamendment s", "Ġamend ments", "_ operator", "_op erator", "_o perator", "_oper ator", "_ customize", "_custom ize", "Ġ ]]", "Ġ] ]", "Ġ bn", "Ġb n", "Ġdis appointment", "Ġdisappoint ment", "Ġmill enn", ". when", ".w hen", ".wh en", "Ġob ey", "Ġobe y", "Ġoff enders", "Ġoffender s", "Ġoffend ers", "Ġoffen ders", "W ild", "Wil d", "Wi ld", "Ġcell For", "Ġappar atus", ". after", ".a fter", ".af ter", "Ġ EPS", "ĠE PS", "ĠEP S", "Ġad orable", "ope rand", "oper and", "( listener", "(list ener", "ve al", "Ġ )(", "Ġ) (", "Ġcardio vascular", "uplic ates", "uplicate s", "r istol", "rist ol", "ris tol", "Ġref uses", "Ġrefuse s", "( QWidget", "(Q Widget", "Ġel emento", "Ġelement o", "Ġelem ento", "Number Of", ". delay", ".d elay", ".de lay", ".del ay", ". groups", ".g roups", ".group s", "\" >'+", "\"> '+", "\">' +", "åĿ Ģ", "ace ncy", "ac ency", "acen cy", "( URL", "(U RL", "_ half", "_h alf", "_hal f", "= l", "Ġlist View", "( section", "(s ection", "(se ction", "(sec tion", ". toArray", ".to Array", "+ /", "ĠRodrig uez", "i stream", "ist ream", "istr eam", "Ġelig ibility", ": :-", ":: -", ". newInstance", ".new Instance", "P B", "Ġ Assets", "ĠAs sets", "ĠAss ets", "ĠAsset s", "Ġ Composite", "ĠCom posite", "ĠComp osite", "ĠL abs", "ĠLa bs", "ĠLab s", "ĠH amas", "ĠHam as", "ĠHa mas", "++ );Ċ", "++) ;Ċ", "Ġ blk", "Ġb lk", "Ġbl k", "Ġ Neo", "ĠN eo", "ĠNe o", "L uc", "Lu c", "@ login", "Ġun aware", "Ġuna ware", ". met", ".m et", ".me t", "_ RELEASE", "_RE LEASE", "( ST", "(S T", "AM IL", "AMI L", "r ike", "ri ke", "rik e", "Ġ (){Ċ", "Ġ( ){Ċ", "Ġ() {Ċ", "Ġ(){ Ċ", "( sprintf", "(s printf", "Ġ Accounts", "ĠAc counts", "ĠAccount s", "Ġ VIEW", "ĠV IEW", "ĠVI EW", "Ġ Aj", "ĠA j", "ãĤ °", "Ġwh isk", "Ġ idi", "Ġi di", "Ġid i", "Ġr ode", "Ġro de", "Ġrod e", "Ġ ihn", "Ġi hn", "Ġih n", "ĠElement ary", "Q ty", "Qt y", "Ġintrig uing", "Ġ å¤", "Ġå ¤", "J obs", "Job s", "Jo bs", "ĉ offset", "ĉo ffset", "ĠAh med", "ĠTal iban", "Ġ èİ·åıĸ", "Ġè İ·åıĸ", "Ġin jected", "Ġinj ected", "Ġinject ed", ". Authentication", ".Auth entication", "_ linear", "_l inear", "_line ar", "_lin ear", "_li near", ". Decimal", ".D ecimal", ".De cimal", ".Dec imal", "Ġapp les", "Ġap ples", "Ġappl es", "Ġapple s", "Ġshare holders", "Ġshareholder s", "Ġb aked", "Ġba ked", "Ġbake d", "Ġbak ed", ". diff", ".d iff", ".di ff", "ĠE ddie", "ĠEd die", "o kers", "ok ers", "oke rs", "oker s", "Ġconfront ed", "vo ices", "voice s", "Ġt us", "Ġtu s", "Ġ Spin", "ĠS pin", "ĠSp in", "ĠSpi n", "N ODE", "NO DE", "_ Un", "_U n", "C TX", "CT X", "/ google", "/g oogle", "/go ogle", "T emperature", "Tem perature", "Ġ' ').", "Ġ'' ).", "Ġ'') .", "Ġmagn ificent", "Ġ startIndex", "Ġstart Index", "semb les", "sem bles", "semble s", "sembl es", "Any one", "z k", "e hen", "eh en", "ĠD ame", "ĠDa me", "ĠDam e", ". strict", ".str ict", "Ġre places", "Ġrep laces", "Ġrepl aces", "Ġreplace s", "Ġline back", "Ġpush es", "Ġpus hes", "Ġche ek", "ĠS hi", "ĠSh i", "_ BYTES", "_BY TES", "_BYTE S", "R EA", "RE A", "ả n", "_CON NECTION", "_CONNECT ION", "G ateway", "Gate way", "ĠTr avis", "ĠTra vis", "ĠTrav is", "Ġ AX", "ĠA X", "Ġ Basically", "ĠBasic ally", "ĠBas ically", "Ġ Upgrade", "ĠUp grade", "à ª", "th emes", "the mes", "theme s", "them es", "er mo", "erm o", "k or", "ko r", "F emale", "Fe male", "_ attach", "_at tach", "_att ach", "ĠìĤ¬ ìļ©", "Ġ poz", "Ġp oz", "Ġpo z", "= =============Ċ", "== ============Ċ", "==== ==========Ċ", "======== ======Ċ", "=== ===========Ċ", "============ ==Ċ", "============= =Ċ", "=========== ===Ċ", "============== Ċ", "========= =====Ċ", "========== ====Ċ", "====== ========Ċ", "===== =========Ċ", "======= =======Ċ", "( symbol", "(s ymbol", "(sym bol", "Ġ Sector", "ĠS ector", "ĠSe ctor", "ĠSec tor", "ĠSect or", "__ )ĊĊ", "__) ĊĊ", "__)Ċ Ċ", "_ padding", "_p adding", "_pad ding", "ï¼ļ \"", "Ġ fabs", "Ġf abs", "Ġfa bs", "Ġfab s", "Ġr anged", "Ġrange d", "Ġran ged", "Ġrang ed", "set Name", "Ġp error", "Ġper ror", "Ġpe rror", "â Ĺ", "ĠFile Reader", "Ġf ulfilled", "Ġful filled", "Ġfulfill ed", "Ġfulfil led", "_ Current", "_C urrent", "Ġdo minate", "Ġdom inate", "Ġdomin ate", "Ġdomina te", "Ġsm ugg", "Post Mapping", "_ force", "_f orce", "_for ce", "Ġb loc", "Ġbl oc", "Ġblo c", "ĠG iant", "ĠGi ant", "ĠGian t", "ĠGia nt", "( video", "(v ideo", "Ġ CU", "ĠC U", "System Service", "Ġ elf", "Ġe lf", "Ġel f", "Ġkont akt", "ë ª", "k ees", "ke es", "kee s", "g tk", "gt k", "Ġparam Int", "Ġ markup", "Ġmark up", "Ġmar kup", "u ales", "ual es", "ua les", "uale s", "Ġaccount ed", "Ġgang bang", "RY PT", "Ġ Wrong", "ĠW rong", "ĠWr ong", "Ġ credited", "Ġcr edited", "Ġcred ited", "Ġcredit ed", "Ġ MESSAGE", "ĠM ESSAGE", "Ġf laws", "Ġfl aws", "Ġflaw s", "Ġfla ws", "Ġb bw", "Ġbb w", "Ġmet abolic", "Ġmetab olic", "Ġmetabol ic", "ĠO EM", "ĠOE M", "/ event", "/e vent", "(C ollectors", "mon ton", "mo nton", "mont on", "monto n", "ap pear", "app ear", "appe ar", "Ġop ted", "Ġopt ed", "Ġc heat", "Ġch eat", "Ġche at", "Ġd av", "Ġda v", "Ġ Proceed", "ĠPro ceed", "ĠProc eed", "Ġ ê¸", "Ġê ¸", "an ked", "ank ed", "anke d", "и з", "an sk", "ans k", "Ġ Hang", "ĠH ang", "ĠHa ng", "ĠHan g", "ĠC ler", "ĠCl er", "ĠCle r", "Ġdis gu", "Ġdisg u", "Ġc map", "Ġcm ap", ". cljs", ".cl js", "Ġa ument", "Ġau ment", "l ez", "le z", "Ġ Joined", "ĠJ oined", "ĠJo ined", "ĠJoin ed", "ĠJoi ned", "_ received", "_re ceived", "_receive d", "Ġa erial", "Ġaer ial", "Ġae rial", "o tel", "ot el", "ote l", "Ġg reet", "Ġgre et", "\" s", "Ġ Genesis", "ĠGen esis", "ĠGene sis", "ĠCal if", "ĠCa lif", "pan ion", "Ġtail ored", "Ġtailor ed", "m apping", "ma pping", "map ping", "and Expect", ". track", ".t rack", ".tr ack", "at omy", "ato my", "atom y", "ĠO w", "ul lah", "ull ah", "ulla h", ". Yes", ".Y es", "Ġ SimpleName", "ĠSimple Name", "d bh", "db h", "' en", "'e n", "Ġn onsense", "Ġnon sense", "Ġnons ense", "Ġphilosoph ical", "( getContext", "(get Context", "Ġis so", "Ġiss o", "Ġ ACE", "ĠA CE", "ĠAC E", "start Date", "Ġb ÄĻd", "ĠAUTH OR", "ĠG lobe", "ĠGl obe", "ĠGlo be", "ĠGlob e", "Ġin sects", "Ġins ects", "Ġinsect s", "Ġinse cts", "_ Al", "_A l", "ush ing", "ushi ng", "è® °", "/ Home", "/H ome", "ĠLocal Date", "ne eded", "need ed", "nee ded", "hes ive", "Ġ illusion", "Ġill usion", "äº Į", "Ġt rat", "Ġtr at", "Ġtra t", "x o", "/ detail", "/d etail", "/de tail", "_ MATCH", "_M ATCH", "_MAT CH", "Ġbroad band", "Ġ wal", "Ġw al", "Ġwa l", "ĠIllegal StateException", "IRE CTION", "IRECT ION", "Ġnor theast", "Ġnorth east", "es ium", "esi um", "Ġ Cliente", "ĠCl iente", "ĠClient e", "ĠCli ente", "ul ance", "ula nce", "ulan ce", "n ty", "nt y", "Ġt ecn", "Ġte cn", "Ġtec n", "Device s", "Dev ices", "Ġgr ains", "Ġgrain s", "Ġgra ins", "ĠO g", "Ġ SEL", "ĠS EL", "ĠSE L", "ud iant", "udi ant", "Ġ ++;Ċ", "Ġ++ ;Ċ", "Ġexplan ations", "Ġexplanation s", "o cco", "oc co", "occ o", "Ġd iets", "Ġdi ets", "Ġdie ts", "Ġdiet s", "Ġco hort", "Ġcoh ort", "( controller", "(cont roller", "(control ler", ". Iterator", ".It erator", ".Iter ator", "- rich", "-r ich", "ro cess", "roc ess", "G D", "Ġcar bohydr", "Ġ fried", "Ġf ried", "Ġfr ied", "Ġfri ed", "ĠEm ployment", "ĠEmp loyment", "ĠEmploy ment", "ìŀ ¥", "ĠLeon ard", "ĠLeo nard", "_ ${", "_$ {", "qu ares", "quare s", "qua res", "Ġcompan ions", "Ġcompanion s", "Ġp aris", "Ġpar is", "Ġpa ris", "Ġpari s", "Ġst imulation", "Ġstim ulation", "ĠZ oo", "ĠZo o", "Ġre levance", "Ġrelev ance", "Ġ Colour", "ĠCol our", "ĠColo ur", "Ġs pear", "Ġsp ear", "Ġspe ar", "ot ional", "otion al", "oti onal", "Ġ Lite", "ĠL ite", "ĠLi te", "ĠLit e", "ĠK osten", "ĠKo sten", "ĠKos ten", "Ġ ó", "Ġà ³", "_ attachment", "_att achment", "_attach ment", "orph ic", "orp hic", "Ġda mit", "Ġdam it", "Ġ dlg", "Ġd lg", "Ġdl g", "Ġth rive", "Ġthr ive", "CH ANGE", "CHAN GE", "Ġ Apparently", "ĠApp arently", "Ġa tual", "Ġat ual", "Ġro oted", "Ġroot ed", "( images", "(image s", "(im ages", "a wi", "aw i", "ar iat", "ari at", "aria t", "Ġch erry", "Ġcher ry", "ST ATIC", "STAT IC", "m nt", "mn t", "Ġ UserId", "ĠUser Id", "il let", "ill et", "ille t", "ĠHis panic", "ĠHispan ic", "Ġ nak", "Ġn ak", "Ġna k", "Ġcent ro", "Ġcentr o", "Ġcen tro", "Ġ dims", "Ġd ims", "Ġdi ms", "Ġdim s", "_ initialize", "_initial ize", "ı k", "ĠCent ers", "ĠCenter s", "R EN", "RE N", "Ġevolution ary", "Ġ Topics", "ĠTo pics", "ĠTop ics", "ĠTopic s", "_ damage", "_d amage", "_da mage", "e mer", "em er", "eme r", "Ġr und", "Ġrun d", "Ġru nd", "Ġpun ished", "Ġpunish ed", "Ġc ubic", "Ġcu bic", "Ġcub ic", "f air", "fa ir", "[ ];ĊĊ", "[] ;ĊĊ", "[];Ċ Ċ", "Ġin stantiate", "Ġinstant iate", "Ġover see", "Ġovers ee", "Ġoverse e", "- delete", "-de lete", "-del ete", "unt eer", "unte er", "start Time", "Ġ Pipeline", "ĠP ipeline", "ĠPipe line", "ĠPip eline", "_ GAME", "_G AME", "ĠC ir", "ĠCi r", "ĉ Null", "ĉN ull", ". Formatting", ".Format ting", "uc umber", "ĠR ide", "ĠRid e", "ĠRi de", "Ġz oo", "Ġzo o", "Ġ checker", "Ġcheck er", "Ġche cker", "åIJ Į", "= C", "Ġg rit", "Ġgr it", "Ġgri t", "\" );//", "\") ;//", "\"); //", "_ xy", "_x y", "Ġ Declaration", "ĠDe claration", "Ġ callable", "Ġcall able", "F oo", "Fo o", "Ġ ListItem", "ĠList Item", "Ġin accur", "m lin", "ml in", "ĉ Data", "ĉD ata", "Ġev olving", "a wan", "aw an", "awa n", "Ġc afe", "Ġca fe", "Ġcaf e", "f olk", "fo lk", "fol k", "_ IDX", "_ID X", "_I DX", "Ġ Anything", "ĠAny thing", "ĠPalest ine", "ĠPalestin e", "Ġ GridView", "ĠGrid View", "Ġcol ony", "Ġcolon y", "ĠGerman s", "ĠGer mans", "ĠGerm ans", "( +", ". pid", ".p id", ".pi d", ". jsx", ".j sx", ".js x", "ĠSup erior", "ĠSuper ior", "Christ ian", "ĠL ect", "ĠLe ct", "ĉ Game", "ĉG ame", "Ġinstrument al", "An imations", "Animation s", "Anim ations", "д ал", "да л", "ĠM oses", "ĠMo ses", "ĠMos es", "ĉĉ čĊĉĉčĊ", "ĉĉčĊ ĉĉčĊ", "z s", "k te", "kt e", "ä¸ ļ", "_ DIST", "_D IST", "_DIS T", "_DI ST", "b itmap", "bit map", "d B", "Ġp ersistence", "Ġpers istence", "Ġpersist ence", "ÑĢ оÑģ", "ÑĢо Ñģ", "$ l", "B ron", "Br on", "Bro n", "Ġ {|", "Ġ{ |", "_ chart", "_c hart", "_ch art", "_char t", "Ġ Consum", "ĠCon sum", "ĠCons um", "Ġh emp", "Ġhe mp", "Ġhem p", "Ġ \"))Ċ", "Ġ\" ))Ċ", "Ġ\") )Ċ", "Ġ\")) Ċ", "Ġatt ackers", "Ġattack ers", "Ġattacker s", "Ġknowledge able", "Ġc et", "Ġce t", "Ġvir uses", "Ġvirus es", "' I", "Ġpitch er", "Ġpit cher", "Ġswe eping", "Ġsweep ing", "= list", "=l ist", "apt ops", "aptop s", ". depth", ".de pth", ".dep th", "Ġinstruct ed", "Ġ Rus", "ĠR us", "ĠRu s", "benh avn", "Ġ ин", "Ġи н", "S ports", "Sp orts", "Sport s", "Spo rts", "Ġon set", "Ġons et", "æĿ ĥ", ". RED", ".R ED", ".RE D", "_ si", "_s i", "ĠP ST", "ĠPS T", ". onChange", ".on Change", "> tag", ">t ag", "ĠR oh", "ĠRo h", "_ character", "_char acter", "ĠL aws", "ĠLa ws", "ĠLaw s", "Ġ Bachelor", "ĠB achelor", "ĠBach elor", "_ swap", "_s wap", "_sw ap", ".re activex", "Ġreward ing", "Ġrew arding", "M edium", "Med ium", "- [", "Ġ Recently", "ĠRec ently", "ĠRecent ly", "J oint", "Join t", "Jo int", "part ition", "Ġ Minutes", "ĠMin utes", "ĠMinute s", "Ġ indo", "Ġin do", "Ġi ndo", "Ġind o", "Ġabsor bed", "Ġabsorb ed", "Ġ GN", "ĠG N", "_ IND", "_IN D", "_I ND", "Ġs aber", "Ġsa ber", "Ġsab er", "Ġsabe r", "S pawn", "Sp awn", "out puts", "output s", "ĠJeff rey", "Ġmed ieval", "Ġmedi eval", "h ed", "he d", "G uide", "Gui de", "Guid e", "Gu ide", "Ġpsych o", "Ġpsy cho", "Ġg lam", "Ġgl am", "E lim", "El im", "äd chen", "_ plain", "_p lain", "_pl ain", "Ġ Sau", "ĠS au", "ĠSa u", "- four", "-f our", "Ġanaly zing", "QUE RY", "QU ERY", "Ġtom ato", "_ buttons", "_button s", "_but tons", "V EN", "VE N", ". setStatus", ".set Status", ". Url", ".U rl", "+ ĊĊ", "+Ċ Ċ", "Ġcompl aining", "Ġcomplain ing", "d egree", "de gree", "deg ree", "conf irmed", "confirm ed", "Ġsu bt", "Ġsub t", "p arsed", "par sed", "parse d", "pars ed", "Ġt orque", "Ġtor que", "Ġtrouble d", "Ġtroub led", "Ġtrou bled", "Ġ TARGET", "ĠT ARGET", "ĠTAR GET", "Ġtrad emarks", "Ġtrade marks", "Ġtrademark s", "Ġ Coordinate", "ĠCo ordinate", "ĠCoord inate", "ĠV iv", "ĠVi v", "Ġ //}ĊĊ", "Ġ// }ĊĊ", "Ġ//}Ċ Ċ", "Ġapr ès", ". getPosition", ".get Position", ".getP osition", "( KeyCode", "(Key Code", "ĠSil va", "Ġ meteor", "Ġm eteor", "Ġmet eor", "Ġendorse ment", "Ġendors ement", "Over view", "Ġ Poss", "ĠP oss", "ĠPo ss", "ĠPos s", ". Inject", ".In ject", "Ġeven ly", "Ġ visualization", "Ġvisual ization", "Ġ wchar", "Ġw char", "Ġwc har", "ĠH DMI", "ĠHD MI", "Ġf unct", "Ġfun ct", "Ġfunc t", "ick name", "',' ','", "','', '", "Ġfor wards", "Ġforward s", "Managed Object", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĉ server", "ĉs erver", "ĠOut look", "ĠChron icle", "ĠChronic le", "Ġdub bed", "Ġd ok", "Ġdo k", "ĠW ear", "ĠWe ar", ". AL", ".A L", "p aren", "par en", "pare n", "pa ren", ". Interface", ".Inter face", "Inter faces", "Interface s", ". cod", ".c od", ".co d", "Ġd ib", "Ġdi b", ".Global ization", "ĠAc ademic", "ĠAcad emic", "ĠAcadem ic", "Ġas sms", "Ġass ms", "A utom", "Auto m", "Aut om", "Au tom", "Ġ lw", "Ġl w", "Ġ NW", "ĠN W", "Ġ&& čĊ", "Ġproble ma", "Ġproblem a", "Ġprobl ema", "ĠManufact uring", "l imits", "li mits", "lim its", "limit s", "- mobile", "-m obile", "Ġ filme", "Ġfil me", "Ġfilm e", "/ map", "/m ap", "Ġd oit", "Ġdo it", "Ġdoi t", "ĠI nk", "ĠIn k", "Ġs ued", "Ġsu ed", "Ġsue d", ". arr", ".a rr", ".ar r", "Ġunder min", "Ġ Proc", "ĠP roc", "ĠPro c", "ĠPr oc", "croll View", "_ _$", "__ $", "Ġside walk", "Ġsidew alk", "( that", "(t hat", "(th at", "ภ·", "[ q", "gram mar", "Ġt ë", "q uito", "qu ito", "quit o", "qui to", "Ġsp iral", "Ġspir al", "Ġspi ral", "ext ended", "extend ed", "Ġf ocal", "Ġfoc al", "Ġfo cal", "Ġdig ging", "p as", "pa s", "ĠT all", "ĠTal l", "ĠTa ll", ". proxy", ".pro xy", ".pr oxy", "i tures", "it ures", "iture s", "itu res", "itur es", "T RACT", "TR ACT", "TRA CT", "Ġ Realm", "ĠRe alm", "ĠReal m", "Ġf eder", "Ġfe der", "Ġfed er", "Ġ oriented", "Ġorient ed", "Ġori ented", "Ġ Alternative", "ĠAltern ative", "ĠAlter native", "Ġ owe", "Ġo we", "Ġow e", "Ġs ourced", "Ġsource d", "Ġsour ced", "in ker", "ink er", "inke r", ". det", ".d et", ".de t", "S ep", "Se p", "Ġ Qui", "ĠQ ui", "ĠQu i", "ĠPal mer", "ĠPalm er", "( _,", "(_ ,", "s amples", "sample s", "sam ples", "samp les", "o yer", "oy er", "ul lan", "ull an", "ulla n", "q uez", "qu ez", "que z", "Ed ges", "Edge s", "Ġsh out", "Ġsho ut", "Ġ Achie", "ĠA chie", "ĠAch ie", "Ġ haar", "Ġh aar", "Ġha ar", "_ Construct", "_Con struct", "Ġprem ature", "Ġre vert", "Ġrev ert", "Ġreve rt", "Ġrever t", "' ).Ċ", "') .Ċ", "'). Ċ", "Ġs chn", "Ġsc hn", "Ġsch n", "filter ed", "fil tered", "filt ered", "null ptr", "S aved", "Save d", "Sa ved", "it ecture", "itect ure", "C LA", "CL A", "Ġ vl", "Ġv l", "s tell", "st ell", "ste ll", "ĉ Me", "ĉM e", "ĠL ip", "ĠLi p", "n ational", "nat ional", "nation al", "Ġwh olly", "Ġspr ings", "Ġspring s", ". Timer", ".T imer", ".Time r", "ĉ src", "ĉs rc", "e lsen", "el sen", "else n", "els en", "åħ ¶", "Ġcommunic ating", "Ġ Quiz", "ĠQu iz", "ĠQui z", "Ġt eng", "Ġte ng", "Ġten g", "Ġg ez", "Ġge z", "Ġ Outside", "ĠOut side", "ĠOuts ide", ". Sign", ".S ign", "( cs", "(c s", "Ġdisp utes", "Ġdispute s", "Ġdisput es", "ĠWe iss", "ĠWei ss", "an nes", "ann es", "anne s", "> No", ">N o", "ĠB ach", "ĠBa ch", "ĠBac h", ".remove All", "re fer", "ref er", "/ dashboard", "/d ashboard", "Ġ Ajax", "ĠA jax", "ĠAj ax", "Index Changed", "Ġ Weak", "ĠWe ak", "' \"Ċ", "'\" Ċ", "Ġs ights", "Ġsight s", "Ġsigh ts", "access Token", "ĠJ oi", "ĠJo i", "( domain", "(d omain", "(dom ain", "(do main", "ĉ cv", "ĉc v", "Ġcontin uation", "Ġcontinu ation", "Ġcontinua tion", "Ġp lum", "Ġpl um", "Ġplu m", "a dir", "ad ir", "adi r", ". setMessage", ".set Message", "Ġ ï¼Į", "Ġï¼ Į", "Ġsw allow", "Ġswal low", "ĠL amp", "ĠLa mp", "ĠLam p", "Ġ qw", "Ġq w", "Ġ uu", "Ġu u", "C oin", "Co in", "u bic", "ub ic", "ubi c", "ĠDe als", "ĠDeal s", "r ace", "ra ce", "rac e", "Ġdict ator", "Ġm eme", "Ġme me", "Ġmem e", "turn ed", "tur ned", "ĠJul ie", "ĠJu lie", "ĠJuli e", ".grid Column", "Ġp uppy", "Ġpup py", "Ġpu ppy", "Ġp am", "Ġpa m", "Ġ ){čĊ", "Ġ) {čĊ", "Ġ){ čĊ", "Ġinv iting", "Ġinvit ing", "Ġf rench", "Ġfr ench", "Ġfren ch", "v im", "vi m", "Ġwr apping", "Ġwrap ping", "Ġ#- }Ċ", "( [-", "([ -", "Ear ly", "Ġsh iny", "Ġshin y", ". faces", ".f aces", ".face s", ".fac es", ".fa ces", "Ġre bell", "Ġreb ell", "Ġrebel l", "abc def", "abcd ef", "ä lt", "äl t", "Ġest imation", "Ġestim ation", "ph ys", "phy s", "los ures", "losure s", "_ REL", "_RE L", "_R EL", "Ġex clusion", "Ġexclus ion", "Ġexcl usion", "ĠSk ype", "ĠSky pe", "we ise", "wei se", "weis e", "- stop", "-s top", "-st op", "no thing", "ĠE gg", "ĠEg g", "is ors", "iso rs", "isor s", "Rich ard", "Ġcounsel ing", "Ġcom mem", "Ġcomm em", "Ġcomme m", "ĠQ MessageBox", "ĠSy nd", "ĠSyn d", "ĠF rost", "ĠFr ost", "ĠFro st", "ĠCom petition", "ĠCompet ition", "ĠA wake", "ĠAw ake", "Ġ ted", "Ġt ed", "Ġte d", "ic iones", "ici ones", "icio nes", "icion es", "ĠDev Components", "VERTISE MENT", "o tti", "ot ti", "ott i", ". runner", ".r unner", ".run ner", "Ġunique ly", "Ġuniqu ely", "Ġuniq uely", ". flag", ".f lag", ".fl ag", "ĉ rs", "ĉr s", "_ generic", "_g eneric", "_gen eric", "_gene ric", "_gener ic", "Ġ` ``Ċ", "Ġ`` `Ċ", "Ġ``` Ċ", "ACH INE", "ACHI NE", "Ġm ein", "Ġme in", "( Application", "(App lication", "( br", "(b r", "Ġrat ios", "Ġratio s", ": ,", "ĠX CTest", "ĠXCT est", "ĠXC Test", "ustain able", "- www", "-w ww", "it les", "itle s", "_ TEMP", "_T EMP", "_TE MP", "_TEM P", "Ġs yst", "Ġsys t", "Ġsy st", "umeric UpDown", "ĉ assertTrue", "ĉassert True", "Ġ wf", "Ġw f", ". peek", ".pe ek", "ĠB ulg", "ĠBul g", "ĠBu lg", "Ġterr ifying", ". MODE", ".M ODE", ".MOD E", "Ġ GW", "ĠG W", "á r", "Ġ fic", "Ġf ic", "Ġfi c", "Ġcommit ments", "Ġcommitment s", "- tech", "-t ech", "-te ch", "Ġ Liquid", "ĠL iquid", "ĠLiqu id", "o pez", "op ez", "ope z", "z heimer", "a ña", "añ a", "- media", "-m edia", "-me dia", "-med ia", "( animated", "(an imated", "_ goal", "_go al", "Ġg um", "Ġgu m", "y stone", "yst one", "ys tone", ". SET", ".S ET", ".SE T", "ĠW end", "ĠWe nd", "ĠWen d", "set CellValue", "Ġ msgs", "Ġmsg s", "Ġms gs", "c ash", "ca sh", "cas h", "AL LOC", "ALL OC", "/ aws", "/a ws", "Ġmicro wave", "Ġmic rowave", ". Pointer", ".Point er", "ĉ Console", "ĉCon sole", "_ sorted", "_s orted", "_sort ed", "ĠF ilip", "ĠFil ip", "ĠFi lip", "P rod", "Pro d", "Pr od", "Ġ// !<", "Ġ//! <", "in group", "ing roup", "Ġ ks", "Ġk s", "_T RI", "_TR I", "Ġteas poon", "Ġ ATT", "ĠA TT", "ĠAT T", "Ġre covering", "Ġrecover ing", "Ġ GLOBAL", "ĠG LOBAL", ". Par", ".P ar", "Ġ/ >;Ċ", "Ġ/> ;Ċ", "Ġmar ble", "ul ators", "ula tors", "ulator s", "Ġ Cycle", "ĠC ycle", "ĠCy cle", "ĠCycl e", "ĠCyc le", "Ġher bs", "Ġherb s", "_ metric", "_m etric", "_met ric", ") !", "_C LOCK", "_CL OCK", "_ Button", "_B utton", "H arry", "Har ry", "è¿ Ľ", "Ġstr ains", "Ġstrain s", "Ġstra ins", "Ġ AppBar", "ĠApp Bar", "Ġ Chan", "ĠC han", "ĠCh an", "ĠCha n", "/ video", "/v ideo", "Ġ bam", "Ġb am", "Ġba m", ". Progress", ".Pro gress", "$ f", "l emen", "le men", "lem en", "leme n", "Ġir regular", "ĠD uncan", "ĠDun can", "ĠM int", "ĠMin t", "ĠMi nt", "- video", "-v ideo", "ঠ¾", "ó wn", "ów n", "Ġ EMPTY", "ĠEM PTY", "ĠEMP TY", "Ġst acked", "Ġstack ed", "Ġ HA", "ĠH A", "_ cut", "_c ut", "_cu t", "Ġwhere in", "ĠW ays", "ĠWay s", "ĠWa ys", "( counter", "(c ounter", "(count er", "(co unter", "è¯ ķ", "Form Group", "Ġb lew", "Ġbl ew", "Ġble w", "c ourses", "co urses", "course s", "cour ses", "Ġ productos", "Ġproduct os", "Ġproducto s", "r ys", "ry s", "Ġ Restr", "ĠR estr", "ĠRe str", "ĠRes tr", "ĠRest r", "Ġst yling", "Ġsty ling", "Ġstyl ing", "> s", "Ġp iv", "Ġpi v", "Ġit ertools", "Ġiter tools", "get Repository", "Ġ Ik", "ĠI k", "_ devices", "_device s", "_dev ices", "lay ui", "Ġhalf way", "Ġfran ç", "Ġt uning", "Ġtu ning", "Ġtun ing", "O A", "_ Node", "_N ode", "_No de", "ar de", "ard e", "Ġf ierce", "Ġfi erce", "Ġfier ce", "Ġfierc e", "l icted", "lic ted", "lict ed", "# čĊ", "Ġbreak through", "ĠE rik", "ĠEr ik", "Ġb ride", "Ġbr ide", "Ġbri de", "Ġbrid e", "Ġ .\"", "Ġ. \"", "c ulus", "cul us", "cu lus", "in side", "ins ide", "insi de", "ĠIndian apolis", "Ġ EE", "ĠE E", "Ġy og", "Ġyo g", "ur ret", "urre t", "urr et", ". fs", ".f s", ". grad", ".g rad", ".gr ad", "_ cards", "_c ards", "_card s", "_car ds", "_ accuracy", "_ac curacy", "_acc uracy", "_e pi", "_ep i", "qu eda", "que da", "/ org", "/or g", "/o rg", "é ªĮ", "éª Į", "Ġcom pte", "Ġcomp te", "Ġcompt e", ") )[", ")) [", "Out side", "G reater", "Great er", "Gre ater", "Ġ Renderer", "ĠRender er", ". actor", ".a ctor", ".ac tor", ".act or", "Account s", "Ac counts", "I dle", "Id le", "_ hours", "_h ours", "_hour s", "er ner", "ern er", "erne r", "J oined", "Join ed", "Jo ined", "Ġme nj", "Ġmen j", "re quires", "require s", "requ ires", "Ġ OPER", "ĠO PER", "ĠOP ER", ".remove Child", "ĉ sp", "ĉs p", "Ġ esse", "Ġe sse", "Ġes se", "Ġess e", "r ift", "ri ft", "rif t", "x FE", "xF E", "ĠSh akespeare", "____ ________", "________ ____", "Ġbudget s", "Ġbud gets", "Model State", "fill able", "- component", "-com ponent", "-comp onent", "o cos", "oc os", "oco s", "Ġ BUTTON", "ĠB UTTON", "ĠBUT TON", "/ io", "/i o", ", out", ",o ut", "s ms", "sm s", "Th omas", "Tho mas", "ĠAr med", "ĠArm ed", "re sume", "res ume", "Ġrot ating", "Ġ Vault", "ĠV ault", "ĠVa ult", "Ġs eus", "Ġse us", "Ġseu s", ". (*", ".( *", "Ġa mino", "Ġam ino", "Ġami no", "Ġ[ ]);ĊĊ", "Ġ[] );ĊĊ", "Ġ[]) ;ĊĊ", "Ġ[]);Ċ Ċ", "Ġprov oc", "n ox", "no x", ". GetEnumerator", ".Get Enumerator", "= ======Ċ", "== =====Ċ", "==== ===Ċ", "=== ====Ċ", "====== =Ċ", "===== ==Ċ", "======= Ċ", "æĸ Ļ", "_ scroll", "_s croll", "_sc roll", "_scr oll", "Ġfil med", "Ġfilm ed", "Ġfilme d", "ĠS oci", "ĠSo ci", "ĠSoc i", "g ap", "ga p", "g ro", "gr o", "V ote", "Vo te", "\" But", "\"B ut", "_ RC", "_R C", "An imal", "Anim al", " Ģ", "ib ile", "ibil e", "ibi le", "Ġaw aken", "Ġawake n", "o rest", "or est", "ore st", "ores t", "in ja", "ĠI van", "ĠIv an", "( Command", "Ġ *****", "Ġ* ****", "Ġ** ***", "Ġ*** **", "Ġ**** *", "Î ·", "Ġkv inder", "Ġkvin der", "Ġkvinde r", "/ helpers", "/h elpers", "/help ers", "/helper s", "_ cases", "_c ases", "_case s", "_ca ses", "t g", "ìĦ ¸", "Register ed", "ĉ pass", "ĉp ass", "_ digits", "_d igits", "_digit s", "Ġcon tour", "Ġcont our", "Ġinf ants", "Ġinfant s", "Ġjust ification", "Ġ Fortunately", "ĠFort unately", "Cont r", "Con tr", "ĠonCreate View", "_ SAMPLE", "_S AMPLE", "_SAMPL E", "Ġallow Null", "Ġn ud", "Ġnu d", "Ġf etched", "Ġfetch ed", "Ġfet ched", "_ equ", "_e qu", "_eq u", "Ġ Unable", "ĠU nable", "ĠUn able", "ĠUna ble", "= \\\"\"", "=\\\" \"", "=\\ \"\"", "> {Ċ", ">{ Ċ", "Ġcommit tees", "Ġcommittee s", "ist ema", "iste ma", "istem a", "+ \".", "+\" .", "ÃŃ an", "ÃŃa n", "m ant", "man t", "ma nt", "Ġsou theast", "Ġsouth east", "ï¼Į Ċ", "dialog s", "dia logs", "PRO JECT", "ch arger", "char ger", "charge r", "charg er", "- port", "-p ort", "-po rt", "( uuid", "(u uid", ". export", ".ex port", ".exp ort", "S ix", "Si x", "Ġ RP", "ĠR P", "P rem", "Pr em", "Pre m", "Ġcon science", "Ġconsc ience", "Ġmargin Right", "_ distribution", "_d istribution", "_dis tribution", "y aml", "ya ml", "res izing", "resi zing", "D ock", "Do ck", "Doc k", "Ġ Locations", "ĠL ocations", "ĠLocation s", "ĠLoc ations", "G Y", "S eed", "Se ed", "See d", "B UFFER", "BUF FER", "BUFF ER", "os sip", "oss ip", "u llen", "ul len", "ull en", "ulle n", "Th ings", "Thing s", "Thin gs", "- self", "-s elf", "-se lf", ". poll", ".p oll", ".po ll", ".pol l", "PL AYER", "PLAY ER", "Ġ å®", "Ġå ®", "G ROUP", "Ġ Away", "ĠA way", "ĠAw ay", "Ġg ospel", "x fd", "xf d", "M ary", "Mar y", "Ma ry", "Ġ Portable", "ĠPort able", "ĠPor table", "T URE", "TU RE", "Ġutil is", "Ġut ilis", "Ġse it", "Ġsei t", "Ġ strand", "Ġs trand", "Ġst rand", "Ġstr and", "Ġstran d", "Ġstra nd", "Ġtrans c", "Ġtran sc", "Ġ (^", "Ġ( ^", "ĠAl fred", "ĠAlf red", ". mem", ".m em", ".me m", ". circle", ".c ircle", "Ġ ~/", "Ġ~ /", "for cing", "forc ing", "Ġ riot", "Ġr iot", "Ġri ot", "Ġrio t", "p rox", "pr ox", "pro x", "TH ON", "iz ación", "iza ción", "Ġ NI", "ĠN I", "r ost", "ro st", "ros t", "Ġdis pro", "Ġdisp ro", "_ instances", "_in stances", "_instance s", "_inst ances", "ï¼Į âĢľ", "ograph er", "ogra pher", "en das", "end as", "enda s", "ĠIs aac", "ĠIsa ac", "ĠP ine", "ĠPin e", "ĠPi ne", "/ dis", "/d is", "Ġcolor With", "it erate", "ite rate", "iter ate", "_ stride", "_st ride", "_str ide", "Ġp unto", "Ġpun to", "Ġpunt o", ". EventArgs", ".Event Args", "( center", "(c enter", "Ġneighb oring", "Ġneighbor ing", "ĠPr ison", "ĠPri son", "Ġ Messenger", "ĠM essenger", "ĠMess enger", "Ġepid emic", "Ġepidemi c", "d ao", "da o", "_ complex", "_com plex", "_comp lex", "Ġg ravel", "Ġgr avel", "Ġgrave l", "Ġgra vel", "Ġgrav el", "_D IP", "_DI P", "é ment", "ém ent", "ĠA ri", "ĠAr i", "_ bitmap", "_b itmap", "_bit map", ". quit", ".q uit", ".qu it", "( valid", "(val id", "(va lid", "Ġ pend", "Ġp end", "Ġpe nd", "Ġpen d", "Ġrespir atory", "Ġre bound", "Ġreb ound", "Default Value", "ãĥ Ń", "Ġcom mits", "Ġcomm its", "Ġcommit s", ". tests", ".t ests", ".test s", ".te sts", "_ fr", "_f r", "i tet", "it et", "ite t", ". sf", ".s f", "Ġspace craft", "c ritical", "cri tical", "cr itical", "crit ical", "Ġde pressed", "Ġdep ressed", "Ġdepr essed", "Ġdepress ed", "ĠAny Object", "Ġu nb", "Ġun b", "Ġdis cern", "Ġdisc ern", "( mysql", "(m ysql", "(my sql", "L atin", "La tin", "Lat in", "ĠB og", "ĠBo g", "ĠWild life", "To File", "ToF ile", "i oxid", "iox id", "@ RestController", "Ġ\" $(", "Ġ\"$ (", "Ġ <<\"", "Ġ< <\"", "Ġ<< \"", "Ġdef ects", "Ġdefe cts", "Ġdefect s", "Ġ datum", "Ġd atum", "Ġdat um", "h in", "hi n", "Ġreal izar", "Ġrealiz ar", "Ġrealiza r", "any ahu", "anya hu", "Ġ Sig", "ĠS ig", "ĠSi g", "@ Data", "ad aptive", "ada ptive", "adapt ive", "ĠC atherine", ". cr", ".c r", "Ġ COOKIE", "ĠCO OKIE", "Ġ pictured", "Ġp ictured", "Ġpicture d", "Ġpict ured", "ĠF ighter", "ĠFight er", "Query able", "Ġ Anyway", "ĠAny way", "ĠGL FW", "_ namespace", "_n amespace", "_name space", "_names pace", "_ ft", "_f t", "Ġ ])", "Ġ] )", "O rganization", "Organ ization", "Ġconstit utes", "Ġconstitu tes", "Ġconstitute s", "Ġqu and", "Ġqua nd", "Ġquan d", "( chunk", "(ch unk", "\" />čĊ", "\"/ >čĊ", "\"/> čĊ", "ĠL akes", "ĠLa kes", "ĠLake s", "ĠLak es", "main window", "Car thy", "Cart hy", "s pin", "sp in", "spi n", "( csv", "(c sv", "(cs v", ": red", ":r ed", "- commerce", "-com merce", "-comm erce", "ภ¹", "Ġdis covering", "Ġdiscover ing", "Ġ eco", "Ġe co", "Ġec o", "_ fac", "_f ac", "_fa c", "ince ton", "inc eton", "ĠGreen s", "ĠGre ens", "ĠGree ns", "j wt", "Ø µ", "ĠBron cos", "Ġ Goods", "ĠG oods", "ĠGo ods", "ĠGood s", "( GTK", "(G TK", "Ġ returnValue", "Ġreturn Value", "Ġsi empre", "Ġne utr", "Ġneu tr", "Ġneut r", "w ent", "we nt", "wen t", "ĠN atal", "ĠNa tal", "ĠNat al", "Ġenthusi astic", "Ġenthusiast ic", "á» į", "F N", "/ database", "/d atabase", "/data base", "/dat abase", "C atalog", "Cat alog", "Ġb run", "Ġbr un", "Ġbru n", "ĠK ash", "ĠKa sh", "ĠKas h", "_ Pl", "_P l", "isc rim", ", width", ",w idth", "Ġin mates", "Ġinmate s", "Ass ignment", "Assign ment", "ĠH aven", "ĠHave n", "ĠHa ven", "ĠHav en", "Ġplay ground", "ex am", "@ Controller", "ul iar", "uli ar", "ulia r", ". getParent", ".get Parent", ".getP arent", "Ġ \";ĊĊ", "Ġ\" ;ĊĊ", "Ġ\";Ċ Ċ", "Ġ\"; ĊĊ", ": size", ":s ize", "iss ors", "issor s", "Ġf is", "Ġfi s", "Ġ alc", "Ġa lc", "Ġal c", "ens ation", "ensa tion", "ĠN ixon", "ĠNi xon", "Ġ mighty", "Ġmight y", "- str", "-s tr", "-st r", "_ special", "_s pecial", "_sp ecial", "_spec ial", "_ ADC", "_A DC", "_AD C", "Ġ Twig", "ĠT wig", "ĠTw ig", "um bling", "umb ling", "- address", "-add ress", "-ad dress", "Ġher oin", "Ġhero in", "Y TE", "YT E", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "F riend", "Fri end", "Ġ ave", "Ġa ve", "Ġav e", "Ġ PNG", "ĠP NG", "ĠPN G", "ĠKur dish", "ĠKurd ish", "DataSet Changed", "Ġbl ades", "Ġblade s", "Ġbla des", "b ral", "br al", "bra l", "S team", "St eam", "Ste am", "Ġs igu", "Ġsi gu", "Ġsig u", "IRT UAL", "a cos", "ac os", "aco s", "U DP", "UD P", "( database", "(d atabase", "(data base", "(dat abase", "h ec", "he c", "Ġ Strings", "ĠString s", "ĠStr ings", "_ scalar", "_s calar", "_sc alar", "_scal ar", "ĉ desc", "ĉd esc", "ĉdes c", "ĉde sc", "Ġ TLS", "ĠT LS", "ĠTL S", "; \"Ċ", ";\" Ċ", "ĠCor byn", "Simple Name", "u ell", "ue ll", "uel l", "Ġ Entre", "ĠEn tre", "ĠEnt re", "ĠEntr e", "ell ites", "ellite s", "elli tes", "- place", "-p lace", "-pl ace", "Ġfrank ly", "ĠE rf", "ĠEr f", "C EL", "CE L", "Ġpa ÃŃs", "Ġh edge", "Ġhe dge", "Ġhed ge", "Ġ latent", "Ġla tent", "Ġlate nt", "Ġlat ent", "Ġlaten t", "Ġ IRQ", "ĠIR Q", "ĠH erald", "ĠHer ald", "ĠHera ld", "Ġ Prec", "ĠP rec", "ĠPr ec", "ĠPre c", "ë³ ´", ". TEXT", ".T EXT", "S alary", "Sal ary", "Ġaut umn", "Ġtr avail", "Ġtra vail", "Ġtrav ail", "Ġtrava il", ". Sum", ".S um", "Ġc ared", "Ġcar ed", "Ġca red", "Ġcare d", "M or", "Mo r", "Ġint uitive", "Ġintuit ive", "Ġj ournals", "Ġjournal s", "_ IT", "_I T", "Ġ Trou", "ĠT rou", "ĠTr ou", "ĠTro u", "ä¼ ł", "Has ColumnName", "Com posite", "Comp osite", "Ġsp ice", "Ġspi ce", "_ disk", "_d isk", "_dis k", "_di sk", "_CODE S", "_CO DES", "_COD ES", "Ġ Introduced", "ĠInt roduced", "ĠIntro duced", "i ona", "ion a", "io na", "Ġn uestra", "Ġnue stra", "Ġnuest ra", "Ġnues tra", "o ct", "oc t", "ĠĠĠĠ ĊĠĠĠĠĊĠĠĠĠĊ", "ĠĠĠĠĊ ĠĠĠĠĊĠĠĠĠĊ", "ĠĠĠĠĊĠĠĠĠĊ ĠĠĠĠĊ", "( parameter", "(param eter", "(para meter", "Ġst udios", "Ġstud ios", "Ġstudio s", "Ġstudi os", "Ġ projectId", "Ġproject Id", "Ġbd sm", ".Sql Client", "im izer", "imize r", "imi zer", "imiz er", "Ġ CARD", "ĠC ARD", "ĠCA RD", "ĠCAR D", "+ t", "a an", "aa n", ". sol", ".s ol", ".so l", "_ Adjust", "_Ad just", "Ġright eous", "Ġ Logging", "ĠLog ging", ". filters", ".f ilters", ".filter s", ".fil ters", "_ TAB", "_T AB", "_TA B", "ĉ sys", "ĉs ys", "rop hic", "roph ic", "o therapy", "other apy", "Ġ Browse", "ĠB rowse", "ĠBrow se", "key board", "R ON", "RO N", "+ \\", "r opped", "ro pped", "rop ped", "ropp ed", "Ġext ensively", "Ġextensive ly", "f k", "Ġ lime", "Ġl ime", "Ġli me", "Ġlim e", "y ears", "year s", "ye ars", "E xc", "Ex c", "Ġs ph", "Ġsp h", "Ġch eating", "Ġche ating", "Ġcheat ing", "an dro", "and ro", "andr o", "ÃŃ o", "Ġpr ince", "Ġpri nce", "Ġprin ce", "Ġprinc e", "o ire", "oi re", "oir e", "Ġ Destination", "ĠD estination", "ĠDest ination", "ĠCon verts", "ĠConvert s", "ĠConv erts", "Ġup stream", "o led", "ol ed", "ole d", "Ġserv ants", "Ġservant s", "Ġ semantic", "Ġsem antic", "Ġcr unch", "Ġeven tual", "Ġevent ual", "r unner", "run ner", "/ error", "/e rror", "S pin", "Sp in", "Spi n", "Ġsecret ly", "Ġ assemble", "Ġas semble", "Ġass emble", ". Person", ".P erson", ".Per son", "end error", "ender ror", "ende rror", "_ <", "Ġp endant", "Ġpend ant", "S leep", "ĠChem istry", "Ġboss es", "Ġbos ses", "l k", ") )),Ċ", ")) ),Ċ", "))) ,Ċ", "))), Ċ", "Block ly", "DE VICE", "DEV ICE", "Ġreflect ing", "Ġ ample", "Ġam ple", "Ġampl e", "Ġamp le", "M illiseconds", "Mill iseconds", "ĠPres idential", "ĠPresident ial", "Ġ usuarios", "Ġus uarios", "Ġusuario s", "Ġusu arios", "Ġ NZ", "ĠN Z", "Ġ Salary", "ĠS alary", "ĠSal ary", "ĠSala ry", "ĠA manda", "ĠAm anda", "ĠAma nda", "_ np", "_n p", "j ury", "ju ry", "jur y", "Ġk ön", "Ġkö n", "Ġther apist", "Ġtherap ist", "Ġhom osexual", "Ġhomosex ual", "Ġhomo sexual", "ĠDr ake", "ĠDra ke", "- window", "-w indow", "Ġ Located", "ĠLoc ated", "ĠLocate d", ". Driver", ".D river", "Ġ VIDEO", "ĠV IDEO", "ĠVID EO", "Ġmer chants", "Ġmerch ants", "Ġmerchant s", "ĠC hest", "ĠCh est", "ĠChe st", "ĠChes t", "- lock", "-l ock", "-lo ck", "/ php", "/p hp", "/ph p", "Ġmil ano", "Ġmilan o", "_ STYLE", "_ST YLE", "ar ger", "arg er", "arge r", "i dea", "id ea", "ide a", "G UID", "GUI D", "GU ID", "ad vanced", "adv anced", "advance d", "me al", "Options ItemSelected", "= '%", "=' %", "ĠC ham", "ĠCh am", "ĠCha m", ": data", ":d ata", "( stat", "(s tat", "(st at", "Will Appear", "Ġinf ormal", "Ġinform al", "a ji", "aj i", "Ġre productive", "Ġrepro ductive", "Ġ CAS", "ĠC AS", "ĠCA S", "ãģ £", "F UNC", "FUN C", "FU NC", "ĠR uth", "ĠRu th", "ĠRut h", ") +(", ")+ (", "CON ST", "CO NST", "CONS T", "Ġ Fans", "ĠF ans", "ĠFa ns", "ĠFan s", "Ġ groupId", "Ġgroup Id", "x ffffffff", "xf fffffff", "xff ffffff", "xffff ffff", "xffffff ff", "Ġs ampler", "Ġsample r", "Ġsam pler", "Ġsamp ler", "Ġ}} \">", "Ġ}}\" >", ". the", ".t he", ".th e", "Ġh ollow", "Ġhol low", "W AY", "WA Y", "Ġ Faculty", "ĠFac ulty", "Attrib utedString", "Ġ Looks", "ĠL ooks", "ĠLo oks", "ĠLook s", "ĠR ex", "ĠRe x", "j k", "ĠM IL", "ĠMI L", "Ġ bard", "Ġb ard", "Ġbar d", "Ġba rd", ". Long", ".L ong", ".Lo ng", "Ġli vest", "Ġlive st", "Ġlives t", "Ġliv est", "Ġs kal", "Ġsk al", "Ġska l", "ic ism", "ici sm", "M AIN", "MA IN", "Ġmuch o", "Ġmu cho", "Ġmuc ho", "B ODY", "BO DY", "Ġ ese", "Ġe se", "Ġes e", "ĉ use", "ĉu se", "ĉus e", "F oot", "Foo t", "Fo ot", ". SQLException", ".SQL Exception", "Ġin heritance", "Ġinherit ance", "re ceived", "receive d", "rece ived", "Ġp utas", "Ġput as", "Ġpu tas", "Ġputa s", "e dis", "ed is", "edi s", "a lsa", "al sa", "als a", "Ġ ErrorMessage", "ĠError Message", "Bo oking", "Book ing", "Ġ tract", "Ġt ract", "Ġtr act", "Ġtra ct", "a cz", "ac z", "ĠC ant", "ĠCan t", "ĠCa nt", "_ regex", "_reg ex", "Ġide ological", "Ġj ihad", "Ġji had", "Ġjih ad", "h os", "ho s", "/ sys", "/s ys", "co lm", "col m", "( pool", "(p ool", "(po ol", "Ġest án", "Ġestá n", "Ġ Pending", "ĠP ending", "ĠPen ding", "ĠPend ing", "em ás", "Ġktó ry", ") );ĊĊĊ", ")) ;ĊĊĊ", "));Ċ ĊĊ", "));ĊĊ Ċ", ")); ĊĊĊ", "trans actions", "transaction s", "Ġw ield", "Ġwie ld", "Ġwi eld", "Ġwiel d", "i tere", "it ere", "ite re", "iter e", "er ture", "ert ure", "_ ss", "_s s", "Ġstretch ing", "Ġstret ching", "Ġpr isoner", "Ġprison er", "Ġpris oner", ".Read All", "Ġb esch", "Ġbe sch", "Ġbes ch", "-- ;čĊ", "--; čĊ", "Ġcr isp", "Ġcri sp", "Ġcris p", "_ SCAN", "_S CAN", "_SC AN", "Ġ ae", "Ġa e", "Str ict", "ĠMin neapolis", "ĠBo eing", "a ris", "ar is", "ari s", "r ek", "re k", "_ pipe", "_p ipe", "_pi pe", "Ġpri ests", "Ġpriest s", "( EIF", "(E IF", "eh icles", "ehicle s", "Ġ Interactive", "ĠInter active", "b etween", "bet ween", "ĉNull Check", "ĠBl air", "Ġ Lt", "ĠL t", "_ inline", "_in line", "eth yl", " ¼", "_ packages", "_p ackages", "_package s", "_pack ages", "Ġbar rels", "Ġbarrel s", "Ġbarr els", "_ he", "_h e", "Ġ regexp", "Ġreg exp", "Ġregex p", "_ pts", "_p ts", "_pt s", "_ Handler", "_H andler", "_Handle r", "ing ular", "ingu lar", "ĠN issan", "ĠR anch", "ĠRan ch", "Ġper ch", "Ġpe rch", "Ġperc h", "Un supported", "S mith", "Sm ith", "ĠLeg ends", "ĠLegend s", "M i", "Ġ gf", "Ġg f", "st eder", "ste der", "sted er", "Ġac quiring", "Ġacqu iring", "Ġs imulator", "Ġsim ulator", "Ġsimul ator", "( ),\"", "() ,\"", "(), \"", "re ceive", "rece ive", "Ġin place", "Ġinp lace", "A CTION", "AC TION", "ACT ION", "Ġ WebDriver", "ĠWeb Driver", "file system", "files ystem", "< Order", "l open", "lo pen", "lop en", "lope n", "Ġ HEIGHT", "ĠHE IGHT", ".set Border", "į °", "__ [\"", "__[ \"", "Ġ clamp", "Ġc lamp", "Ġcl amp", "Ġclam p", "Ġcla mp", "Seg oe", "b ands", "ba nds", "ban ds", "band s", "to List", "am ba", "amb a", "> '+Ċ", ">' +Ċ", ">'+ Ċ", "Ġ credible", "Ġcred ible", "a mat", "am at", "ama t", "pl aying", "play ing", "pla ying", ".setImage Resource", "q uel", "qu el", "que l", "Ġpo dr", "Ġpod r", "ge om", "geo m", "E k", "ĠQ atar", "Ġg eld", "Ġge ld", "Ġgel d", "? ',Ċ", "?' ,Ċ", "?', Ċ", "Ġc yl", "Ġcy l", "( ax", "(a x", "Ġ WI", "ĠW I", "ur ally", "ural ly", "ĠBr asil", "ĠBra sil", "ĠBras il", "Ġs enza", "Ġsen za", "a ley", "al ey", "ale y", "o nen", "on en", "one n", "Ġ bah", "Ġb ah", "Ġba h", "Ġm olecule", "Ġmolec ule", "R ad", "Ra d", "è¿ °", "AN CH", "ANC H", "- background", "-back ground", "- agent", "-a gent", "-ag ent", "-age nt", "Ġprol ifer", ": boolean", "Ġt ide", "Ġti de", "Ġtid e", "erial izer", "erialize r", "_ ;čĊ", "_; čĊ", "F ee", "Fe e", "* *)", "** )", "er gy", "erg y", "ĠH onor", "ĠHon or", "ĠHo nor", ". Logging", ".Log ging", "i ris", "ir is", "iri s", "Ġunder mine", "Ġundermin e", "ĠD y", "Ġt yr", "Ġty r", "Ġ deque", "Ġde que", "Ġd amer", "Ġda mer", "Ġdam er", "Ġdame r", "( [])Ċ", "([ ])Ċ", "([] )Ċ", ".layout ControlItem", ".layoutControl Item", "p eated", "pe ated", "peat ed", "C AN", "CA N", "ra gments", "rag ments", "ragment s", "L and", "La nd", ") ]);Ċ", ")] );Ċ", ")]) ;Ċ", "ĠS ah", "ĠSa h", "Ġ DECL", "ĠDE CL", "ĠDEC L", "With in", "Wi thin", "Ġ Namespace", "ĠN amespace", "ĠName space", "ĠNames pace", "an other", "ano ther", "semb ling", "sem bling", "sembl ing", ". describe", ".de scribe", ".des cribe", "Con sum", "Cons um", "Ġ Fear", "ĠF ear", "ĠFe ar", "g iven", "gi ven", "give n", "O range", "Or ange", "< boolean", " This", ">T his", "Ġdata Index", "Ġprint able", "Ġprin table", "ĠE yes", "ĠEye s", "ĠEy es", "_ targets", "_target s", "_tar gets", "( Py", "(P y", ". over", ".o ver", ".ov er", "Ġ bru", "Ġb ru", "Ġbr u", "am pton", "amp ton", "Ġplaint iff", "< Key", " );Ċ", ">) ;Ċ", "in vest", "inv est", ". *ĊĊ", ".* ĊĊ", ".*Ċ Ċ", "Ġt élé", "Ġté lé", "Ġsu perf", "Ġsuper f", "Ġ cascade", "Ġc ascade", "Ġcas cade", "Ġcasc ade", "D TD", "DT D", "Ġv ivid", "Ġvi vid", "Ġviv id", "Ġsubsid ies", "Ġsubsidi es", "ĠH ass", "ĠHas s", "ĠHa ss", "Ġcol laps", "Ġcoll aps", "Ġcer amic", "{ }\".", "{} \".", "ĠLeak age", "- trash", "-tr ash", "-tra sh", "c ollapsed", "coll apsed", "collapse d", "- social", "-s ocial", "-so cial", "ĠC had", "ĠCh ad", "ĠCha d", "Ġinc lined", "Ġincl ined", "Ġ sto", "Ġs to", "Ġst o", "Ġstory board", ". payment", ".p ayment", ".pay ment", "stack overflow", "ĠRa iders", "ĠRaid ers", "ĠRaider s", "ĠRai ders", "Ġ #'", "Ġ# '", "ol icies", "olic ies", "oli cies", "ìľ¼ ë¡ľ", "e map", "em ap", "ema p", "Ġ kj", "Ġk j", "Ġ quota", "Ġqu ota", "Ġquot a", "Ġquo ta", "ĠGar dens", "ĠGarden s", "ĠGard ens", "ë² Ī", "ĠAng els", "ĠAnge ls", "ĠAngel s", "Ġ oft", "Ġo ft", "Ġof t", "Ġlower case", "Ġ iParam", "Ġi Param", "ĠiP aram", "Ġche apest", "Ġcheap est", "un ta", "unt a", "_ pkt", "_p kt", "_pk t", "ic ators", "ica tors", "icator s", "Ġ leurs", "Ġl eurs", "Ġle urs", "Ġleur s", "Ġdecre ases", "Ġdecrease s", "ĉ define", "ĉdef ine", "ĉde fine", "P REC", "PR EC", "PRE C", "am mers", "amm ers", "ammer s", "Ġ PreparedStatement", "ĠPre paredStatement", "ĠPrepared Statement", "( direction", "(d irection", "(dir ection", "(di rection", "Ġcr ews", "Ġcre ws", "Ġcrew s", "ar ked", "ark ed", "ĠMem phis", "Ġ Sell", "ĠS ell", "ĠSe ll", "ĠSel l", "G TK", "GT K", "Ġ maid", "Ġm aid", "Ġma id", "Ġmai d", ": disable", ":d isable", "éĽ Ĩ", "ĠP f", "Ġal beit", "op enh", "open h", "ope nh", "?> \">Ċ", "?>\" >Ċ", ". getSource", ".get Source", ".getS ource", "( scale", "(s cale", "(sc ale", "D u", "ĠP IL", "ĠPI L", "_ refresh", "_re fresh", "_ref resh", "Ġb ets", "Ġbe ts", "Ġbet s", "( car", "(c ar", "(ca r", "ĠV on", "ĠVo n", "| --------------------------------------------------------------------------Ċ", "ĠG rat", "ĠGr at", "ĠGra t", "M uch", "Mu ch", "( Dialog", "(D ialog", ".stop Propagation", "Ġ tek", "Ġt ek", "Ġte k", "Ġex its", "Ġexit s", "' ],$", "'] ,$", "'], $", "Ġ phoneNumber", "Ġphone Number", "u cs", "uc s", "e cimal", "ec imal", "eci mal", "- -------------", "-- ------------", "---- ----------", "-------- ------", "--- -----------", "------------ --", "----- ---------", "---------- ----", "------ --------", "----------- ---", "------------- -", "------- -------", "--------- -----", "i np", "in p", ".po jo", "Ġcor pus", "Ġcorp us", "Ġpractition ers", "Ġpractitioner s", ". pic", ".p ic", ".pi c", "\" testing", "Ġstring By", ". NotNull", ".Not Null", "Ġ rang", "Ġr ang", "Ġran g", "Ġra ng", ". Dynamic", ".D ynamic", "_ Render", "_R ender", "_Re nder", "а ÑĤа", "аÑĤ а", "Wait ing", "Wa iting", "Ġ Wik", "ĠW ik", "ĠWi k", "Ġoverwhel med", "Ġoverwhelm ed", "% \">", "%\" >", "Ġ AE", "ĠA E", "} }>Ċ", "}} >Ċ", "}}> Ċ", "u w", "_ typ", "_t yp", "_ty p", "Ġ buckets", "Ġb uckets", "Ġbucket s", "Ġbuck ets", "Ġg reeting", "Ġgre eting", "Ġgreet ing", "Ġ laughter", "Ġla ughter", "Ġlaugh ter", "Ġant agon", "ugg estion", "uggest ion", "- email", "-e mail", "-em ail", "ĉ top", "ĉt op", "ĉto p", "Ġ eros", "Ġe ros", "Ġer os", "Ġero s", "_ tri", "_t ri", "_tr i", "Ġiss uing", "Ġissu ing", "Ġ há", "Ġh á", "Ġis olate", "Ġisol ate", "Ġiso late", "Over flow", ", E", "Ġnut ritional", "Ġnutrition al", "Ġnutrit ional", "ĠAbb ott", "Ġ nf", "Ġn f", ". touch", ".t ouch", ".to uch", ".fetch all", "_ zip", "_z ip", "\" )}Ċ", "\") }Ċ", "\")} Ċ", "Ġ amat", "Ġa mat", "Ġam at", "Ġama t", "Ġ Cisco", "ĠC isco", "Ġn Ã¥", "P LEX", "PL EX", "PLE X", "Ġ sei", "Ġs ei", "Ġse i", "f oto", "fo to", ". toJson", ".to Json", "å¤ ļ", "ĠK lein", "ĠKle in", "ĠKl ein", "Ġ libc", "Ġli bc", "Ġlib c", "Ġm iners", "Ġmin ers", "Ġmi ners", "Ġmine rs", "Ġminer s", "å ¢", "- print", "-p rint", "-pr int", "ĠP ride", "ĠPr ide", "ĠPri de", "T odos", "To dos", "Todo s", "Ġ masked", "Ġmask ed", "Ġmas ked", "Ġ setData", "Ġset Data", "Ġtele fon", "Ġtel efon", "Ġun happy", "Ġunh appy", "Ġ Tables", "ĠT ables", "ĠTable s", "ĠTab les", "ĠTa bles", "g eb", "ge b", "( debug", "(de bug", "_ allowed", "_all owed", "_allow ed", "- access", "-a ccess", "-ac cess", "Ġlog istics", "Ġlogistic s", "Ġ gems", "Ġg ems", "Ġge ms", "Ġgem s", "ĠM ature", "ĠMat ure", "ĠMa ture", "Ġ rsp", "Ġr sp", "Ġrs p", "Ġ Alle", "ĠA lle", "ĠAl le", "ĠAll e", ". getBytes", ".get Bytes", ".getBy tes", "\\ web", "ynchron ized", "ynchronize d", "Par agraph", "Para graph", "Ġth rottle", "Ġthr ottle", "Ġthrott le", ". sqlite", ".sql ite", "cons ulta", "consult a", "ĠS eah", "ĠSe ah", "ĠSea h", "C e", "Ġsub mar", "E RE", "ER E", "V ous", "Vo us", "Ġ reddit", "Ġre ddit", "Ġred dit", "Ġredd it", "Ġsql alchemy", "- mile", "-m ile", "oc ide", "oci de", "P our", "Po ur", "} }\">Ċ", "}} \">Ċ", "}}\" >Ċ", "st ead", "ste ad", "Ġ @(", "Ġ@ (", "Ġ [])", "Ġ[ ])", "Ġ[] )", "Ġ Ads", "ĠA ds", "ĠAd s", "Ġover load", "Ġoverl oad", "r idden", "ri dden", "rid den", "ĠDe sert", "ĠDes ert", "Ġ Wrap", "ĠW rap", "ĠWr ap", "ĠPortug uese", "e tz", "et z", "ĉ first", "ĉf irst", "ĉfi rst", "Ġm ilestone", "Ġmil estone", "Ġmiles tone", "Ġmile stone", "æĹ ł", "Ñĥ Ñī", "( success", "(s uccess", "< Vector", " \")Ċ", ">\" )Ċ", ">\") Ċ", "ĠD ollar", "ĠDol lar", "ĠDoll ar", "Ġ emoji", "Ġem oji", "Ġemo ji", "Car ousel", "- player", "-p layer", "-play er", "-pl ayer", "Ġadjust ing", "Ġadj usting", "Ġj uga", "Ġju ga", "Ġjug a", "allenge s", "alleng es", "allen ges", "g ene", "ge ne", "gen e", "(body Parser", "lo pedia", "lop edia", "lope dia", "Ġ Behind", "ĠBe hind", "ĠBeh ind", "Ġslee ves", "Ġsleeve s", "Ġdrag ging", "ĠChe vrolet", "Ġ biz", "Ġb iz", "Ġbi z", "iv ities", "ivi ties", "Ġ Frequency", "ĠF requency", "ĠFrequ ency", ", char", ",c har", ",ch ar", ". WHITE", ".W HITE", "_ preview", "_p review", "_pr eview", "_pre view", "_prev iew", ") ';Ċ", ")' ;Ċ", "_ ax", "_a x", "I ONS", "ION S", "IO NS", ". cpu", ".c pu", ".cp u", ". inputs", ".in puts", ".input s", "U BE", "UB E", "_ feed", "_f eed", "_fe ed", "_fee d", "ĠSup plement", "! ).", "!) .", "e sus", "es us", "Ġ UDP", "ĠU DP", "ĠUD P", "Ġmicro phone", "Ġconf irms", "Ġconfirm s", ".is NotEmpty", "\" :\"\",Ċ", "\": \"\",Ċ", "\":\" \",Ċ", "\":\"\" ,Ċ", "_ SCREEN", "_S CREEN", "_SC REEN", "ĉ expected", "ĉex pected", "ĉexpect ed", "ĉexp ected", "+-+- +-+-", "ĠH ait", "ĠHa it", "ĠHai t", "fast call", "Ġdep ict", "v b", "_ picture", "_p icture", "_pic ture", "ĉ description", "ĉd escription", "ĉdes cription", "ĉde scription", "ĠW ife", "ĠWi fe", "u ci", "uc i", "Ġv icious", "Ġvic ious", "ä» ĸ", "u eba", "ue ba", "Ġset User", "ãģ ¡", "Ġd iving", "Ġdi ving", "Ġdiv ing", "Ġop era", "Ġoper a", "user content", "a rah", "ar ah", "ara h", ") },", ")} ,", "y un", "yu n", "v elt", "ve lt", "vel t", "Ġun covered", "Ġuncover ed", "Ġ hips", "Ġh ips", "Ġhi ps", "Ġhip s", "Ġosc ill", "Ġassert ing", "Ġ Xi", "ĠX i", ". restore", ".re store", ".rest ore", "k ea", "ke a", "Ġsp elling", "Ġspell ing", "Ġspel ling", "Ġ derive", "Ġde rive", "Ġder ive", "Ġderiv e", "ab we", "ĠD ow", "ĠDo w", ". setType", ".set Type", "_ vs", "_v s", "Ġc ozy", "Ġco zy", "Ġcoz y", ". categories", ".c ategories", "O rg", "Or g", "_ mgr", "_m gr", "Ġd ungeon", "Ġdung eon", "collection View", "Ġ Blank", "ĠBl ank", "ac ias", "aci as", "acia s", "ä ä", "_ cleanup", "_c leanup", "_clean up", "_ACT IVITY", "_ACTIV ITY", "Ġtri angles", "Ġtriangle s", "Ġtriang les", ". MenuItem", ".Menu Item", "Ġ iphone", "Ġi phone", "Ġip hone", "Ġ Won", "ĠW on", "ĠWo n", "] ]ĊĊ", "]] ĊĊ", "]]Ċ Ċ", "Ġ Comparison", "ĠCom parison", "ĠCompar ison", ". Doc", ".D oc", ".Do c", "Ġ canonical", "Ġcan onical", "Ġcanon ical", "ĠSu dan", "ĠSud an", "' ){", "') {", "Up Inside", "b uiltin", "built in", "E NCY", "EN CY", "ENC Y", "x be", "xb e", "Ġch uck", "Ġchu ck", "Ġcontrad ict", "Ġcontra dict", "Ġnu estro", "Ġnue stro", "Ġnuest ro", "Ġnues tro", "Ġarchitect ural", "ĠF ib", "ĠFi b", "Ġcomp ares", "Ġcompar es", "Ġcompare s", "* k", "C fg", "çĦ ¡", "n ten", "nt en", "nte n", "M atches", "Match es", "Mat ches", "Ġ DOWNLOAD", "ĠDOWN LOAD", "_HANDLE R", "_HAND LER", "man agement", "manage ment", "mana gement", "[ S", "E NG", "EN G", "ÂĢ Â", "f ang", "fa ng", "fan g", "Ġsl ipped", "Ġslip ped", "ĠL anka", "ĠLan ka", "esc aping", "Ġtack les", "Ġtackle s", "ĠPe dro", "ĠPed ro", ". Prop", ".P rop", ".Pro p", ".Pr op", ". ''", ".' '", ". Generated", ".G enerated", ".Generate d", ".New Guid", "at rigesimal", "il lon", "ill on", "illo n", "Ġstat istic", "Ġstatist ic", "s pecies", "sp ecies", "spec ies", "spe cies", "h olding", "hold ing", "hol ding", "Dr upal", "Ġfundament ally", "Ġfundamental ly", "Ġbond age", "Ġres olutions", "Ġresolution s", "Inline Data", "\\ Type", "es tion", "est ion", "esti on", ". wrap", ".w rap", ".wr ap", "Ġwar riors", "Ġwarrior s", "Ġ LOCAL", "ĠLO CAL", "ĠLOC AL", "A rchive", "Arch ive", "Arc hive", "Ġembr aced", "Ġembrace d", "á» §", ". Ver", ".V er", "ĠAff ordable", "ole sale", "oles ale", "Ġ Applied", "ĠApp lied", "ĠAp plied", "ĠAppl ied", "Ġ Conversion", "ĠCon version", "ĠConv ersion", "ĠConvers ion", "m ega", "me ga", "meg a", "_ cam", "_c am", "_ca m", "Ġcer emon", "Ġcere mon", "a urus", "au rus", "aur us", "ĠV olk", "ĠVol k", "ĠVo lk", ". opens", ".open s", ".op ens", "/ about", "/a bout", "Ġ Std", "ĠS td", "ĠSt d", "j ournal", "jo urnal", "jour nal", "( )){čĊ", "() ){čĊ", "()) {čĊ", "()){ čĊ", ", \"\\", ",\" \\", "( Arrays", "(Array s", "ĠD ense", "ĠDen se", "ase ña", "än ner", "änn er", "/ stat", "/s tat", "/st at", "user Data", "Ġg erman", "Ġger man", "Ġgerm an", "Ġ tz", "Ġt z", "w orthy", "worth y", "wort hy", "wor thy", "Format Exception", "ph erd", "pher d", "phe rd", "Ġsm iles", "Ġsmile s", "Ġ Whenever", "ĠWh enever", "ĠWhen ever", "ĠWhe never", "( adapter", "(ad apter", ".bad logic", "Ġbrief ing", ". GridColumn", ".Grid Column", "- char", "-c har", "-ch ar", "d imension", "dim ension", "ĠC opper", "ĠCo pper", "ĠCop per", "ĠCopp er", "Ġn inth", "Ġni nth", "Ġnin th", "Ġ' {{", "Ġ'{ {", "Ġ rav", "Ġr av", "Ġra v", "_ Table", "_T able", "_Tab le", "Ġderiv atives", "Ġderivative s", "Ġ Raise", "ĠR aise", "ĠRa ise", "ĠRai se", "ĠF ut", "ĠFu t", "ar mor", "arm or", "- padding", "-p adding", "-pad ding", "Ġre min", "Ġr emin", "Ġrem in", "ĉ style", "ĉst yle", "Ġ Membership", "ĠMember ship", "ĠMembers hip", "Ġsp reads", "Ġspread s", "Ġspre ads", "Ġg alleries", "Ġgall eries", "ĠClark e", "ĠClar ke", "Ġcon ception", "Ġconcept ion", "Ġconce ption", "min ute", "Ġab usive", "_ adj", "_a dj", "_ad j", "Ġterr ific", "Ġo vert", "Ġover t", "Ġov ert", "our cing", "Ġ entrada", "Ġent rada", "Ġentr ada", "Ġentra da", "level s", "lev els", "Ġcrit ique", "Ġres pects", "Ġrespect s", "Ġresp ects", "ĠM MA", "ĠMM A", "i ene", "ie ne", "ien e", "Ġen caps", "Ġenc aps", "ĠRay mond", "Div ider", "Di vider", "i vable", "iv able", "iva ble", "b az", "ba z", "Ġ@ _;Ċ", "Ġ@_ ;Ċ", "ĠCl aire", "ĠCla ire", "ĠClair e", "Ġur ging", "Ġurg ing", "C EE", "CE E", "Ġtrans former", "Ġtransform er", "dis cord", "disc ord", "ĠJ ourney", "t os", "to s", "Ġcompet itions", "Ġcompetition s", "Ġcompetit ions", "Ġ OBJ", "ĠO BJ", "ĠOB J", "ĠB is", "ĠBi s", "Ġrelax ation", "i dy", "id y", "_ INSTANCE", "_IN STANCE", "_INST ANCE", "Ġ Pref", "ĠP ref", "ĠPr ef", "ĠPre f", "d ados", "da dos", "dad os", "ici encies", "ĠMedia Query", "Ġ Cube", "ĠC ube", "ĠCub e", "ĠCu be", "Ġ Strange", "ĠSt range", "ĠStr ange", "ĠStra nge", "g pu", "gp u", "( days", "(d ays", "(day s", "(da ys", "_ InitStruct", "_Init Struct", "Ġf ingerprint", "Ġfinger print", "e mat", "em at", "ema t", "ĠG ecko", "ĠGe cko", "Ġ rails", "Ġr ails", "Ġrail s", "Ġra ils", "ĠL um", "ĠLu m", "s traction", "st raction", "str action", "stract ion", "stra ction", "ig ung", "igu ng", "( movie", "(m ovie", "_ dictionary", "_d ictionary", "_ interrupt", "_int errupt", "_inter rupt", "Ġ QC", "ĠQ C", "i ked", "ik ed", "ike d", "append Child", "rec ipient", "r é", "V e", "Ġt owel", "Ġto wel", "Ġtow el", ".last IndexOf", "Ġplace bo", "Ġplac ebo", "Ġ Wie", "ĠW ie", "ĠWi e", ". esp", ".e sp", ".es p", "( Debug", "oper ative", "Ġde ceased", "Ġdece ased", "& id", "ĉ mutex", "ĉm utex", "e lic", "el ic", "eli c", "Ġb apt", "Ġba pt", "ĉ čĊčĊ", "ĉčĊ čĊ", "Ġfar ther", "Ġfart her", "H alf", "Ha lf", "Hal f", ". disable", ".d isable", ".dis able", ".menu Strip", "le ccion", "lec cion", "Ġ resultCode", "Ġresult Code", "Ġc ans", "Ġcan s", "Ġca ns", "- election", "-e lection", "-elect ion", "-el ection", "f emale", "fe male", "_ FIX", "_F IX", "aus ible", "Ġ POWER", "ĠP OWER", "ĠPO WER", "ĠPOW ER", "Ġre construction", "Ġrecon struction", "Ġreconstruct ion", "Ġsc ans", "Ġscan s", "Ġsca ns", ".Xtra Bars", "âĢĺ s", "Re moved", "Rem oved", "Remove d", "Ġpara graphs", "Ġparagraph s", "_ margin", "_m argin", "_mar gin", "Ġl ymph", "Ġly mph", "Ġ bos", "Ġb os", "Ġbo s", "l ington", "ling ton", "ĠBapt ist", "Ġadvertis ements", "Ġadvertisement s", "Ġadvertise ments", "Ġ Manage", "ĠMan age", "ĠMa nage", "ĠMana ge", "/ yyyy", "/y yyy", "I OUS", "IO US", "EN CES", "ENCE S", "ENC ES", "ĠF iction", "ĠFi ction", "ĉ menu", "ĉm enu", "ĉme nu", "ĠFile OutputStream", "o van", "ov an", "ova n", "ĠF eng", "ĠFe ng", "ĠFen g", "Ġsk ipping", "Ġskip ping", "Ġski pping", "get Class", "getC lass", "an ni", "ann i", "Ġre bounds", "Ġreb ounds", "Ġrebound s", "Ġpublic ity", "Ġpub licity", "Ġpubli city", "Ġin gres", "Ġing res", "Ġingr es", "us ement", "use ment", "Ġthought ful", ". Chart", ".C hart", ".Ch art", ".Char t", "Ġh atte", "Ġha tte", "Ġhat te", "pass port", "pas sport", "Ġhook ed", "Ġho oked", "Ġ Lens", "ĠL ens", "ĠLe ns", "ĠLen s", "Ġflag ship", "Ġflags hip", "Ġs tip", "Ġst ip", "Ġ GEN", "ĠG EN", "ĠGE N", "Ġcl ues", "Ġclue s", "i pv", "ip v", "ĠR ise", "ĠRi se", "ĠRis e", "ĠG ew", "ĠGe w", "table name", "tab lename", "tabl ename", "Ġfore most", "_ validate", "_valid ate", "_ analysis", "_an alysis", "o lla", "ol la", "oll a", "Ġqual ifications", "Ġqualification s", "Ġd istributions", "Ġdistrib utions", "Ġdistribution s", "ĠF lower", "ĠFl ower", "ĠFlo wer", "ĠFlow er", "Ġt ense", "Ġten se", "Ġtens e", "Ġthank ful", "Ġcl utch", "Ġun ified", "ro ads", "road s", "Ġs iti", "Ġsit i", "Ġsi ti", "Ġst all", "Ġsta ll", "Ġstal l", "_P RIORITY", "_PRI ORITY", "c stdlib", "_ USERNAME", "_USER NAME", ". bytes", ".by tes", ".byte s", "? page", "?p age", "er malink", "erm alink", "ermal ink", "ĠVe get", "ĠVeg et", "/v nd", "- author", "-a uthor", "-auth or", "-aut hor", ". NONE", ".N ONE", ".NO NE", "ĠCon current", "ĠConc urrent", "ĠC ry", "ĠCr y", "Ġst arters", "Ġstart ers", "Ġstar ters", "Ġstarter s", "Ġ Interaction", "ĠInter action", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "Ġ LEVEL", "ĠLE VEL", "E ll", "El l", "Ġ comboBox", "Ġcom boBox", "Ġcombo Box", "ĠThe resa", "ĠTh eresa", "ĠThere sa", "ĠTher esa", "t ek", "te k", "_ Handle", "_H andle", "Ġ aby", "Ġa by", "Ġab y", ".g dx", ", end", ",e nd", ",en d", "( Local", "(L ocal", "O l", "kn ife", "a rial", "ar ial", "ari al", "aria l", "ĠH off", "ĠHo ff", "ĠHof f", "Ġprostituer ade", "Do ctor", "Doc tor", "In stances", "Instance s", "Inst ances", ". SetValue", ".Set Value", "ĉ from", "ĉf rom", "ĉfr om", "Ġlux urious", "In dent", "Ind ent", "Al locator", "All ocator", "Alloc ator", "_ DRAW", "_D RAW", "_DR AW", "(\" ,\",", "(\", \",", "(\",\" ,", "ĠF rances", "ĠFr ances", "ĠFrance s", "ĠFranc es", "ĠFra nces", "ĠFran ces", "Ġ groupBox", "Ġgroup Box", "( schema", "(s chema", "Print f", "O RIES", "OR IES", "- gradient", "-g radient", "Ġre put", "Ġrep ut", "a rin", "ar in", "ari n", "_ DONE", "_D ONE", "_DO NE", "in cre", "inc re", "incr e", "ig nty", "ign ty", "Ġex ert", "Ġexe rt", "Ġ -.", "Ġ- .", "/ App", "/A pp", "- through", "-th rough", "Ġdec lining", "Ġdecl ining", "Ġdes sert", "Ġdess ert", "Ġinc umb", "Ġ designation", "Ġdesign ation", ". PORT", ".P ORT", ".PO RT", ", strong", ",str ong", ",st rong", "Ġ sandbox", "Ġs andbox", "Ġsand box", "Ġw ines", "Ġwin es", "Ġwine s", "Ġwi nes", "ĠP av", "ĠPa v", "$ str", "$s tr", "ask ell", "Ġh ö", "Ġ PY", "ĠP Y", "Get Instance", "Text Input", "game Object", "/ events", "/e vents", "/event s", "created At", "Ġlocal Var", "Ġ WHITE", "ĠW HITE", "ĠWH ITE", "p ered", "pe red", "per ed", "i lege", "ile ge", "eff icient", ", color", ",c olor", ",col or", "c ate", "ca te", "cat e", "ĠC afe", "ĠCa fe", "ĠCaf e", "Ġsimilar ities", "Ġp umps", "Ġpump s", "Ġpu mps", "ĠHun gary", "ĠHung ary", ". Username", ".User name", "Ġs kate", "Ġsk ate", "Ġska te", "Ġtouchdown s", "Ġacceler ate", "Ġaccel erate", "ĠH elen", "ĠHe len", "ĠHel en", "O MEM", "OM EM", "OME M", "ĠK un", "ĠKu n", "_ vol", "_v ol", "Ġ findAll", "Ġfind All", "ĠMens chen", "a head", "ah ead", ") ;\"", "); \"", "k ommen", "kom men", "Ġposs essed", "Ġpossess ed", ".arg max", ". transition", ".t ransition", ".trans ition", "A RP", "AR P", "OL UME", "OLUM E", "( script", "(s cript", "Ġ Ðĺ", "ĠÐ ĺ", "Ġ Finding", "ĠF inding", "ĠFin ding", "ĠFind ing", "o nces", "on ces", "once s", "I o", "B old", "Bo ld", "Ġrenew al", "_D IALOG", "Ġdis reg", "IN TERN", "INT ERN", "INTER N", "Ġt oute", "Ġto ute", "Ġtou te", "Ġtout e", "Ġelect r", "Ġele ctr", "ĠG ross", "ĠGr oss", "ĠGro ss", "ĠGros s", "ĉ true", "ĉtr ue", ". Fields", ".F ields", ".Field s", "Ġ WIDTH", "ĠW IDTH", "ĠD ent", "ĠDe nt", "ĠDen t", "Ġ Ãģ", "Ġà ģ", "NS Notification", "Ġ aos", "Ġa os", "Ġao s", "Ġme lee", "Ġmel ee", ". Validation", ".Valid ation", "Ġ DEC", "ĠD EC", "ĠDE C", "- dependent", "-depend ent", "Ġsu ic", "Ġsui c", "T raits", "Tr aits", "Tra its", "Trait s", "$ message", "$m essage", "Ġ Dear", "ĠD ear", "ĠDe ar", "ĉ FILE", "ĉF ILE", "l anguages", "language s", ". Prot", ".P rot", ".Pro t", ".Pr ot", ". addr", ".add r", ".ad dr", "- generation", "-g eneration", "-gen eration", "I CON", "IC ON", "ICO N", "Ġtrans plant", "- description", "-d escription", "-de scription", "-des cription", "Ġch asing", "Ġcha sing", "Ġch ees", "Ġche es", "Ġ }*/Ċ", "Ġ} */Ċ", "T rad", "Tr ad", "Tra d", "qu eries", "que ries", "quer ies", "/ widgets", "/widget s", "sub package", "Ġe spec", "Ġes pec", "Ġesp ec", "Ġcr acked", "Ġcrack ed", "Ġcompet itor", "Ġcompetit or", "P urchase", "- team", "-t eam", "-te am", "ole cular", "olec ular", "or Thunk", "& P", "Ġrel ent", "Ġrele nt", "/ #{", "/# {", "Ġ productId", "Ġproduct Id", "Ġ è¾", "Ġè ¾", "ĠL av", "ĠLa v", "Ġ Alter", "ĠAl ter", "ĠAlt er", ". Mode", ".M ode", ".Mod e", "AD IO", "ADI O", "g rp", "gr p", "æ ·»åĬł", "æ·» åĬł", "Q uit", "Qu it", "Qui t", "Ġdep ths", "Ġdepth s", "Ġdept hs", "- category", "-c ategory", "Ġ DATABASE", "ĠD ATABASE", "ĠDATA BASE", "S PELL", "SP ELL", "ĠF alcon", "ĠFal con", "ĠQString List", "Ġ ''.", "Ġ' '.", "Ġ'' .", "ĠIn stitution", "ĠInst itution", "ĠInstit ution", "ĠInstitut ion", "d amage", "da mage", "dam age", "az or", "azo r", "bel ongsTo", "belongs To", "ver ages", "verage s", "Ġ NONE", "ĠN ONE", "ĠNO NE", "ĠNON E", "ip pets", "ipp ets", "ippet s", ", \\Ċ", ",\\ Ċ", "Ġfoot print", "_ archive", "_a rchive", "_arch ive", "_arc hive", "n ak", "na k", ". getField", ".get Field", "Ġ Reflection", "ĠRef lection", "ĠReflect ion", "Ġ ']", "Ġ' ]", "ĠH BO", "ĠHB O", "_ discount", "_dis count", "_disc ount", "Ġin cest", "Ġinc est", "Ġince st", "ĠD odge", "ĠDo dge", "ĠDod ge", "ĠW ade", "ĠWa de", ". NO", ".N O", "\" encoding", "Ġ Blockchain", "ĠBlock chain", "Ġlaws uits", "Ġlawsuit s", "Ġ Maint", "ĠM aint", "ĠMain t", "ĠMa int", "ĠMai nt", "ch ten", "cht en", "chte n", "Ġét ait", "Ġktó re", "_ ctl", "_c tl", "_ct l", "( timer", "(t imer", "(time r", "(ti mer", "B attle", "Bat tle", "i zo", "iz o", "ay ed", "aye d", "I OR", "IO R", "ĠGlas gow", "Ġs ynth", "Ġsy nth", "Ġsyn th", "Ġsynt h", "_ logs", "_l ogs", "_log s", "_lo gs", ". pose", ".p ose", ".pos e", ".po se", "_Adjust orThunk", "( (&", "(( &", "Ġun sure", "Ġuns ure", "Ġunsur e", "y state", "yst ate", "íķĺ ëĬĶ", "O ULD", "OU LD", ". ng", ".n g", "Ġdefault dict", "work space", "works pace", "Ġselect ive", "Ġsel ective", "Picker Controller", "YNAM IC", ". methods", ".method s", "Ġpath ways", "Ġpathway s", "Ġ Few", "ĠF ew", "ĠFe w", "K G", "C RYPT", "CRY PT", "follow ing", "ĠD LC", "ĠDL C", "ĠS ara", "ĠSar a", "ĠSa ra", "Ġ preset", "Ġp reset", "Ġpre set", "Ġpres et", "e structor", "estr uctor", "estruct or", "ĠK urt", "ĠKur t", "ĠKu rt", "Ġair plane", "Ġ omp", "Ġo mp", "Ġom p", "Ġ Parents", "ĠPar ents", "ĠParent s", "ĠParen ts", "ĠPare nts", "ĠMart inez", "ĠMartin ez", ". complete", ".com plete", ".comp lete", "Ġbroad ly", "Ġs care", "Ġsc are", "Ġsca re", "Ġscar e", "ĠM é", "Ġelim ination", "Ġelimin ation", "Ġp oured", "Ġpo ured", "Ġpour ed", "Ġpou red", "/ sw", "/s w", "Ġcom un", "Ġco mun", "Ġm asc", "Ġma sc", "Ġmas c", "ĠOrgan ic", "ĠOrg anic", "Ġ StringUtils", "ĠString Utils", "ĠStringUtil s", "il ateral", "ilate ral", "ilater al", "Ġreluct ant", "- age", "-a ge", "-ag e", "Ġ nz", "Ġn z", ". \"\\", ".\" \\", "Ġpast or", "Ġpas tor", "Ġpa stor", "a lez", "al ez", "ale z", "Ġe fect", "Ġef ect", "p rov", "pr ov", "pro v", "/ init", "/i nit", "/in it", "Ġp enn", "Ġpe nn", "Ġpen n", "u nds", "un ds", "und s", "Ġ ssize", "Ġs size", "Ġss ize", "Ġ Proj", "ĠP roj", "ĠPro j", "ĠPr oj", "b asename", "base name", "bas ename", "Ġsh ells", "Ġshell s", "Ġshel ls", "ĠN eck", "ĠNe ck", "ĠNec k", "ĠEn forcement", "v ided", "vid ed", "vi ded", "vide d", "s town", "st own", "sto wn", "S phere", "Sp here", "$ r", "us sen", "uss en", "a fil", "af il", "afi l", "Ġ Telegram", "ĠTele gram", "Ġanaly tical", "Ġanalytic al", "н Ñĭе", "нÑĭ е", "us ually", "usu ally", "usual ly", "x n", "Ġhistor ian", "Ġhist orian", "Ġhistoria n", "Ġhisto rian", "ĠGreg ory", "ol ph", "Ġ Una", "ĠU na", "ĠUn a", "Ġcon tributes", "Ġcontrib utes", "Ġcontribute s", "% -", "anti ago", "ÑĢ ед", "ÑĢе д", ". region", ".reg ion", "Ġab rupt", "ĠUnsupported OperationException", "Ġ TASK", "ĠT ASK", "ĠTA SK", "ĠTAS K", "_ finish", "_f inish", "_fin ish", "Ġnot orious", "Ġ Vs", "ĠV s", "Ġ MQ", "ĠM Q", "Ġs unset", "Ġsun set", "Ġun acceptable", "ar cer", "arc er", "Ġill umin", "Ġillum in", "ĠO rb", "ĠOr b", "Ġ bh", "Ġb h", "E ste", "Est e", "Es te", "_ dispatch", "_dis patch", "_disp atch", "Ġr ipped", "Ġrip ped", "Ġri pped", "Ġtou jours", "Ġ Parcel", "ĠPar cel", "_ ll", "_l l", ". userName", ".user Name", ". classes", ".c lasses", ".class es", ".cl asses", "S OURCE", "( Number", "(N umber", "е лÑı", "ел Ñı", "Ġhead phones", "Ġheadphone s", "( side", "(s ide", "(si de", "(sid e", "con stitution", "const itution", "an nah", "ann ah", "anna h", "čĊ ĠĠĠĠĠĠĠĠčĊ", "Ġcl iff", "Ġcli ff", "- ref", "-r ef", "-re f", "Ġmost rar", "Ġmo strar", "Ġmostr ar", "Ġmostra r", "ĠP owell", "ĠPo well", "ĠPow ell", "+ y", "Ġ BG", "ĠB G", "_ fragment", "_f ragment", "_fr agment", "_frag ment", ". Port", ".P ort", "Ġreal izing", "Ġrealiz ing", "param ref", "Ġh ometown", "Ġhome town", "@ Table", "+ \" --}}Ċ", ">-- }}Ċ", "F rench", "Fr ench", "Entity Manager", "Ġ Plain", "ĠP lain", "ĠPl ain", "ĠPla in", "//// ////////////////////////////////////////////////////////////////", "//////// ////////////////////////////////////////////////////////////", "//////////////// ////////////////////////////////////////////////////", "//////////////////////////////////////////////////////////////// ////", "//////////// ////////////////////////////////////////////////////////", "//////////////////////////////////////////////////////// ////////////", "//////////////////////////////////////////////////////////// ////////", "//////////////////////////////////////////////////// ////////////////", " ³", "( RE", "(R E", "c apt", "ca pt", "cap t", "Ġ organisms", "Ġorgan isms", "Ġorganis ms", "Ġorganism s", "Ġ jets", "Ġj ets", "Ġje ts", "Ġjet s", "o location", "ol ocation", "olo cation", "ĠApp RoutingModule", "Ġgl orious", "Ġglo rious", "Ġglor ious", "æľ į", "Ġdisc arded", "Ġdiscard ed", "ĉ ĉĉĉĠĠĠĠĠ", "ĉĉ ĉĉĠĠĠĠĠ", "ĉĉĉĉ ĠĠĠĠĠ", "ĉĉĉ ĉĠĠĠĠĠ", "ĉĉĉĉĠ ĠĠĠĠ", "ĉĉĉĉĠĠĠ ĠĠ", "ĉĉĉĉĠĠ ĠĠĠ", "ĉĉĉĉĠĠĠĠ Ġ", "ĠArn old", "l ug", "lu g", "Ġp arl", "Ġpar l", "Ġpa rl", "Ġhorm ones", "Ġhormone s", "Ġ mah", "Ġm ah", "Ġma h", "ĠS onic", "ĠSo nic", "ĠSon ic", "Ġorgan izers", "Ġorganiz ers", "Ġorganize rs", "Ġorganizer s", "_ PLATFORM", "_PL ATFORM", ". inv", ".in v", ".i nv", "Ġch ord", "Ġcho rd", "Ġchor d", "vent ional", "vention al", "ĉ of", "ĉo f", "Ep isode", ". Enum", ".E num", ".En um", "un kt", "unk t", "ĠD h", "ĠJ ared", "ĠJa red", "ĠJar ed", "ĠN ak", "ĠNa k", "Ġint ends", "Ġinte nds", "Ġintend s", "End ian", "Ġa ustralia", "_ cv", "_c v", "( resolve", "(res olve", "(re solve", "Ġclin ics", "Ġclinic s", "l iked", "li ked", "like d", "lik ed", "ASH INGTON", "in ha", "inh a", "' *", "Ġ NP", "ĠN P", "_ beh", "_b eh", "_be h", "Ġ hf", "Ġh f", "Ġw ür", "c ategoria", "$ form", "$f orm", "Ġsub way", "Ġ isActive", "Ġis Active", "pop ular", "C our", "Co ur", "Cou r", "Ġ cooldown", "Ġco oldown", "Ġcool down", "Ġa insi", "Ġain si", "Ġ GLuint", "ĠGL uint", "e real", "ere al", "erea l", "Ġarray Of", "Ġh atch", "Ġhat ch", "= =========", "== ========", "==== ======", "======== ==", "=== =======", "========= =", "====== ====", "===== =====", "======= ===", "r esses", "res ses", "ress es", "resse s", "_ PP", "_P P", ". ^", "_ decay", "_dec ay", "ĠB less", "ĠBl ess", "ĠBle ss", "m etrics", "met rics", "metric s", "ĠCOPY ING", "ĠDump ster", "ĠJos é", "ĠDesign s", "< Void", "<", "Ġ? ><", "Ġ?> <", "Ġ \"}Ċ", "Ġ\" }Ċ", "Ġ\"} Ċ", "time zone", "Ġ eer", "Ġe er", "Ġee r", "max cdn", "Ġ ESC", "ĠE SC", "ĠES C", "ig aret", "iga ret", "igar et", "_ connected", "_connect ed", "_conn ected", "_ reverse", "_re verse", "_rev erse", "Ġquestion able", "ĠU SC", "ĠUS C", "Ġtu tti", "Ġtut ti", "Ġ dropout", "Ġdrop out", "Ġ Activities", "ĠAct ivities", "ĠActiv ities", "ĠW inds", "ĠWin ds", "ĠWi nds", "ĠWind s", "' )));Ċ", "') ));Ċ", "')) );Ċ", "'))) ;Ċ", "Ġcon gest", "Ġcong est", "ÄŁ ı", "Ġprolong ed", "è¿ Ļ", "ĠCross AxisAlignment", "L EEP", "LE EP", "LEE P", "Ġ VALID", "ĠVAL ID", "ĠG az", "ĠGa z", "Ġ dependence", "Ġdepend ence", "ĠP rix", "ĠPr ix", "ĠPri x", ".Compiler Services", "j ump", "ju mp", "Ġst rat", "Ġstr at", "Ġstra t", "c irc", "ci rc", "cir c", "Ġ CUSTOM", "ĠC USTOM", "x aa", "xa a", "Ġ bmp", "Ġb mp", "Ġbm p", "Ġb ureau", "Ġbu reau", "Ġbure au", "Ġw aren", "Ġwar en", "Ġwa ren", "Ġware n", "N X", "( Window", "(W indow", "ĠChrist ie", "ĠChris tie", "_ FE", "_F E", "Ġ tn", "Ġt n", "Ġ Omega", "ĠO mega", "ĠOm ega", "communic ations", "communication s", "Home Page", "com pletion", "comp letion", "Ġsupply ing", "Ġsuppl ying", "Ġsupp lying", "YPE S", "YP ES", "á vel", "áv el", "åĪ ¶", "( click", "(c lick", "(cl ick", "(cli ck", "\\ Contracts", "/ questions", "/question s", "Ġ ez", "Ġe z", "A MS", "AM S", ". mesh", ".m esh", ".me sh", "Ġ' \\Ċ", ">\\ Ċ", "R obot", "Rob ot", "Ro bot", "Json Object", "Ġ DF", "ĠD F", "Ġ Processor", "ĠProcess or", "ĠProc essor", "_ should", "_sh ould", ". protobuf", ".prot obuf", ".proto buf", "- users", "-user s", "-use rs", "-us ers", "Ġemb ry", "Ġembr y", "F ONT", "FO NT", "Ġstart ups", "Ġstartup s", "Ġ DataSource", "ĠData Source", ") #", "u ros", "ur os", "uro s", "_ Color", "_C olor", "Ġst andalone", "Ġstand alone", "} [", "j d", "Ġfor give", "Ġforg ive", "Ġ ngx", "Ġn gx", "Ġng x", "Ġ Generally", "ĠGener ally", "ĠGeneral ly", "Ġconfig urable", "Ġconfigur able", "/ order", "/or der", "Ġ vas", "Ġv as", "Ġva s", "' )\";Ċ", "') \";Ċ", "')\" ;Ċ", "Ġ RR", "ĠR R", "ĠT roy", "ĠTr oy", "ĠTro y", "Ġcomprom ised", "Ġcompromise d", "ĠS wan", "ĠSw an", "int endent", "C entral", "Cent ral", "_ keeper", "_k eeper", "_ke eper", "_keep er", "Ġar quivo", "Ġ ReadOnly", "ĠRead Only", "_ curve", "_c urve", "_cur ve", "_cu rve", "k v", "en tin", "ent in", "enti n", "è ±", "Ġ Ey", "ĠE y", ".im read", "ĠP am", "ĠPa m", "i ffe", "if fe", "iff e", "at ivity", "ativ ity", "x bc", "xb c", "Ġ grim", "Ġg rim", "Ġgr im", "Ġgri m", "- filled", "-f illed", "-fill ed", "name se", "names e", "nam ese", "' ]:", "'] :", "Ġ aur", "Ġa ur", "Ġau r", "ĠGi bson", "ĠGib son", ". MouseEvent", ".Mouse Event", "Ġl ado", "Ġla do", "Ġlad o", "ava doc", "avad oc", "Ġf amil", "Ġfam il", "Ġfa mil", "Ġ Moder", "ĠM oder", "ĠMod er", "ĠMo der", "ĠMode r", "f ps", "fp s", "ãĢĢ ãĢĢ", "- example", "-ex ample", "ĠAl zheimer", "Ġ Utf", "ĠU tf", "ĠUt f", "_ arguments", "_arg uments", "_argument s", "Con clusion", "text Content", "rem aining", "remain ing", "rema ining", "Ġinterrupt s", "Ġ Backup", "ĠBack up", "ĠBac kup", "ĠM ong", "ĠMon g", "ĠMo ng", "Ġre ceptors", "Ġrecept ors", "Ġreceptor s", "Ġrecep tors", "h istor", "hi stor", "hist or", "his tor", ".cor outines", "Ġsh outed", "Ġshout ed", "Ġsho uted", "Al arm", "Ġcomb ust", "Ġg rote", "Ġgr ote", "Ġgro te", "ult ural", "ultur al", "( ids", "(i ds", "(id s", "---- ----------------------------------------------------------------------------", "---------------- ----------------------------------------------------------------", "-------------------------------- ------------------------------------------------", "---------------------------------------------------------------- ----------------", "------------------------------------------------ --------------------------------", "---------- ----------------------------------------------------------------------", "---------------------------------------------------------------------------- ----", "---------------------------------------------------------------------- ----------", "ipl inary", "iplina ry", "O pts", "Op ts", "Opt s", "ĠY ale", "ĠYa le", "local Storage", "Ġequ ival", "Ġequiv al", "ĠF leet", "ĠFle et", "\\ b", "* pi", "*p i", "ĠQ Label", "æ ¡", "Ġ vx", "Ġv x", "Ġ ACL", "ĠA CL", "ĠAC L", "Ġsu cesso", "Ġsuc esso", "Ġsucess o", "Ġ perc", "Ġp erc", "Ġper c", "Ġpe rc", "ĠN otre", "ĠNo tre", "ĠNot re", "Ġan arch", "Ġana rch", "R ing", "s pb", "sp b", "Ġ strpos", "Ġstr pos", "st ores", "store s", "sto res", "stor es", "ĠMap le", "ĠMa ple", "( MainActivity", "(Main Activity", "(\" \"))", "(\"\" ))", "(\"\") )", "Ġview Holder", "Qu ad", "Ġig ual", "ors che", "orsch e", ". margin", ".m argin", ".mar gin", "Ġin die", "Ġind ie", "Ġfr anc", "Ġfra nc", "Ġfran c", "ĠForm Builder", "ĠPart icip", "ĠParti cip", ". flash", ".f lash", ".fl ash", "Ġ storms", "Ġstorm s", "Ġstor ms", "Ġsto rms", "U lt", "Ul t", "Ġ fen", "Ġf en", "Ġfe n", "[ new", "[n ew", "E ver", "Ev er", "= \"Ċ", "=\" Ċ", "Ġ localized", "Ġlocal ized", "Ġlocalize d", "_ follow", "_f ollow", "Ġ nave", "Ġn ave", "Ġna ve", "Ġnav e", "Ġdom inance", "Ġdomin ance", "Ġdomina nce", "( tile", "(t ile", "(ti le", "J ournal", "Jo urnal", "Ġ VC", "ĠV C", "Ġpen etration", "Ġpenet ration", "Ġpenetr ation", "ï¼ ķ", "Ġcom partment", "Ġcomp artment", "Ġcompart ment", "Ġb ids", "Ġbi ds", "Ġbid s", "Form atted", "Format ted", "**** **/ĊĊ", "****** /ĊĊ", "*** ***/ĊĊ", "***** */ĊĊ", "******/ ĊĊ", "******/Ċ Ċ", "( city", "(c ity", "(ci ty", "âĢĶ it", "[ C", "Ġuse Callback", "a ub", "au b", ") ?.", ")? .", "Ġ VAR", "ĠV AR", "ĠVA R", "ĠSe bastian", "ĠSebast ian", "ĠM oss", "ĠMo ss", "ĠMos s", "Ġabund ant", "G reg", "Gr eg", "Gre g", "ÑĤ а", "_ ci", "_c i", "Ġb ibli", "Ġbib li", "C RM", "CR M", "Ġ Attempt", "ĠAt tempt", "ĠAtt empt", "is me", "ism e", "d ash", "da sh", "das h", "ãĢ İ", "_ mu", "_m u", ".Formatting Enabled", "Ind eed", "- direct", "-d irect", "-dir ect", "-di rect", "Ġs ucking", "Ġsuc king", "Ġsuck ing", "Ġp ne", "Ġpn e", "ocab ulary", "ĠP ackers", "ĠPac kers", "ĠPack ers", ". Navigation", ".N avigation", ".Nav igation", "Ġp ied", "Ġpie d", "Ġpi ed", "cri bing", "ĠSt uart", ".To Double", "Ġ Secondary", "ĠSecond ary", "S aving", "Sa ving", "ĠD ut", "ĠDu t", "ĠM add", "ĠMad d", "ĠMa dd", "M agic", "Mag ic", ", H", ".document Element", "Ġ BST", "ĠB ST", "ĠBS T", "Ġdif fers", "Ġdiffer s", "Ġdiff ers", "Ġmore over", "_ nd", "_n d", "SE ARCH", "п ÑĢав", "пÑĢа в", "пÑĢ ав", "æ ´", "to Match", "Ġde creasing", "Ġdecre asing", "- member", "-m ember", "am pus", "amp us", "( boost", "D aily", "Da ily", "Data GridView", "Ġ HttpContext", "ĠHttp Context", "Ġh ipp", "Ġhi pp", "Ġhip p", "_ workers", "_work ers", "_worker s", "- language", "-l anguage", "é ĵ", "Ġcons isted", "Ġconsist ed", "a thing", "ath ing", "athi ng", "ĠMer cury", "ĠMerc ury", "$ content", "$c ontent", "$con tent", "Ġpract iced", "Ġpractice d", "Ġ Modules", "ĠMod ules", "ĠModule s", "_ DAY", "_D AY", "_DA Y", "Ġweakness es", "ĠL odge", "ĠLo dge", "ĠLod ge", "Ġ nar", "Ġn ar", "Ġna r", "Ġ Mate", "ĠM ate", "ĠMat e", "ĠMa te", "Ġ jp", "Ġj p", "ĠHttp Headers", "Ġs mo", "Ġsm o", "Ġ TOKEN", "ĠT OKEN", "ĠTO KEN", "ĠTOK EN", "] )(", "]) (", "Ġa qui", "Ġaqu i", "sw agen", "Ġ srv", "Ġs rv", "Ġsr v", "ĉ ans", "ĉa ns", "ĉan s", "A round", "Ar ound", "ĠMan uel", "Ġfiction al", "Ġfict ional", "Ġ IMG", "ĠI MG", "ĠIM G", "Ġ .'", "Ġ. '", "Ġ Berry", "ĠB erry", "ĠBer ry", "Ġwall paper", "s exual", "sex ual", "i ero", "ie ro", "ier o", "Ġ çļĦ", "ìĨ Į", "Backing Field", "ĠAd rian", "ĠAdri an", "BASE PATH", "Ġre peats", "Ġrepe ats", "Ġrepeat s", "Ġbl ues", "Ġblue s", "Ġun predict", "Ġunp redict", "_ coll", "_c oll", "_col l", "_co ll", "st acle", "sta cle", "Ġ Tumblr", "ĠT umblr", "Ġ Elf", "ĠE lf", "ĠEl f", "Ġass urance", "Ġc ensus", "Ġcen sus", "Ġ IMPORT", "ĠIM PORT", "ĠIMP ORT", "E NDER", "EN DER", "END ER", "a nos", "an os", "ano s", "Ġ =(", "Ġ= (", "ĠEl lis", "ĠEll is", "ĠElli s", "\" ĊĊĊĊ", "\"Ċ ĊĊĊ", "\"ĊĊ ĊĊ", "\"ĊĊĊ Ċ", ". win", ".w in", "Ġ Above", "ĠA bove", "ĠAb ove", "a lon", "al on", "alo n", "_ tick", "_t ick", "_ti ck", "Ġrepresent ations", "Ġrepresentation s", "Ġ æķ", "Ġæ ķ", "w id", "wi d", "ĠA rms", "ĠAr ms", "ĠArm s", "L ista", "List a", "Li sta", "_ failure", "_f ailure", "_fail ure", "_ cm", "_c m", ".Flat Appearance", "Ġth rone", "Ġthr one", "Ġthro ne", "P atch", "Pat ch", "ĠV oy", "ĠVo y", "en gl", "eng l", "Ġnegot iating", "> `", "Ġshoot s", "Ġsho ots", "Ġ FPS", "ĠF PS", "ĠFP S", ". Year", ".Y ear", "ĠK iss", "ĠKi ss", "ĠKis s", "en ción", "enc ión", "enci ón", "re eting", "ree ting", "reet ing", "From File", "Ġresign ation", "Ø ·", "Ġt wins", "Ġtw ins", "Ġtwin s", "Æ° ợ", "Æ°á» £", "Ġge bru", "Ġgeb ru", ". getContent", ".get Content", ".getC ontent", ". Tree", ".T ree", ".Tr ee", "Ġ Employees", "ĠEmployee s", "ĠEmploy ees", "ĠF IFA", "ĠFI FA", "Ġc ertainty", "Ġcert ainty", "Ġcertain ty", "( Cl", "(C l", "Ġ totals", "Ġtot als", "Ġtotal s", "ed itable", "edit able", "edi table", "ॠĢ", ". Reporting", ".Report ing", "M as", "Ma s", "qu iet", "qui et", ". rules", ".r ules", ".ru les", ".rule s", "Ġ VO", "ĠV O", "con exion", ", K", "Ġ allocator", "Ġal locator", "Ġall ocator", "Ġalloc ator", "ĠPow der", "\\ Repository", "B eat", "Be at", "_ tipo", "_t ipo", "_tip o", "_ti po", "Ġ[ '',", "Ġ[' ',", "_ INTR", "_IN TR", "_INT R", "Ġ <<<", "Ġ< <<", "Ġ<< <", "< hr", " \");čĊ", ">\" );čĊ", ">\") ;čĊ", "drop IfExists", "ĠB eg", "ĠBe g", "_ HAL", "_H AL", "Ġcross AxisAlignment", "Ġ Evidence", "ĠE vidence", "ĠEv idence", "Ġpec uliar", "Ġin stitute", "Ġinstit ute", "ve is", "Ġ fft", "Ġf ft", "Ġff t", "à ģ", "Ġzo ekt", "Ġzoek t", "an aly", "ana ly", "anal y", "ĠHome land", "ĠHom eland", "Ġpen etr", "Ġpenet r", "udden ly", "ĉ element", "ĉe lement", "ĉel ement", "ĉelem ent", "ĠB ren", "ĠBr en", "ĠBre n", "ĠTr udeau", "ĠCub an", "ĠCu ban", "ĠCuba n", "j am", "ja m", "us lim", "_ ev", "_e v", "Ġs tems", "Ġst ems", "Ġste ms", "Ġstem s", "} %", "Ŀ å§ĭ", "Ġbr anding", "Ġbrand ing", "Ġbran ding", "Ġcorrespond ence", ". jquery", ".j query", "¢ åįķ", "ĠRe ads", "ĠRead s", "(Http StatusCode", "(HttpStatus Code", "as sin", "ass in", "assi n", "( slot", "(s lot", "(sl ot", "ĠGrad uate", "// /<", "/// <", "Ġinformation s", "Ġinform ations", "Ġinformat ions", "EN ABLE", "ENA BLE", "Ġp uis", "Ġpu is", "Ġ finder", "Ġf inder", "Ġfind er", "Ġfin der", "Ġfi nder", "Ġfinde r", "ĠB ris", "ĠBr is", "ĠBri s", "Ġnett steder", "_ mid", "_m id", "_mi d", "Ġ ogs", "Ġo gs", "Ġog s", "ĠSter ling", "Ġar rog", "Ġarr og", "str ftime", "| ĊĊ", "|Ċ Ċ", "Ġ vox", "Ġv ox", "Ġvo x", "Ġ Regardless", "ĠReg ardless", "Ġ eso", "Ġe so", "Ġes o", "Ġ Comfort", "ĠCom fort", ".Boolean Field", "Ġ uh", "Ġu h", "A CY", "AC Y", "Ġsque ez", "ĠV ic", "ĠVi c", "con tro", "cont ro", "contr o", ". lo", ".l o", "Ġ ire", "Ġi re", "Ġir e", "ĠCom edy", "ĠCome dy", "ë ¶", "Ġorig inated", "Ġorigin ated", "Ġoriginate d", "Ġ shipment", "Ġsh ipment", "Ġship ment", "| max", "|m ax", "_ guid", "_g uid", "_gui d", "le vation", "lev ation", "н аÑı", "на Ñı", "( undefined", "(un defined", "Ġ DDR", "ĠD DR", "ĠDD R", "Ġshoot ings", "Ġshooting s", "ĠLat ino", "ĠLatin o", "END OR", "Ġaver aging", "Ġgre eted", "Ġgreet ed", "Ġthe aters", "Ġtheater s", "Ġtheat ers", "о е", "оРµ", "Ġ dB", "Ġd B", "Ġ gst", "Ġg st", "Ġgs t", "Ġde finite", "Ġdef inite", "Ġdefinit e", "Ġdefin ite", ". Storage", ".St orage", ". her", ".h er", ".he r", "Ġa fore", "Ġaf ore", "Ġ Reality", "ĠRe ality", "ĠReal ity", "ĠG ods", "ĠGod s", "ĠGo ds", "v ersed", "ver sed", "vers ed", "verse d", "Ġhand some", "Ġhands ome", "Ġ excluding", "Ġex cluding", "Ġexcl uding", "( ad", "(a d", "Qu otes", "Quote s", "Ġ Scheme", "ĠS cheme", "ĠSch eme", "ĠSche me", "? q", "ĠT amil", "ĠTa mil", "ĠTam il", "T icks", "Tick s", "Ti cks", "Ġ pest", "Ġp est", "Ġpe st", "Ġpes t", "' n", "Ġporn ography", "_ modal", "_m odal", "_mod al", "_mo dal", "Ġ ----------", "Ġ- ---------", "Ġ-- --------", "Ġ---- ------", "Ġ--- -------", "Ġ----- -----", "Ġ------ ----", "Ġ-------- --", "Ġ------- ---", "Ġ--------- -", "Ġd isposable", "Ġdis posable", "Ġdispos able", "F REE", "FR EE", "Ġsh ark", "Ġsha rk", "Ġshar k", "C HE", "CH E", "Ġdep icted", "Ġdepict ed", "Ġdemonstr ations", "Ġdemonstration s", "ĠK illed", "ĠKill ed", "ĠKil led", "Ġ RULE", "ĠR ULE", "ĠRU LE", "Ġobs essed", "Ġobsess ed", "Ġs implified", "Ġsimpl ified", "Post al", "Pos tal", "Po stal", "Ġconcept ual", "Ġ pst", "Ġp st", "Ġps t", "L as", "La s", "_ PROJECT", "_PRO JECT", "uc ceeded", "ucceed ed", "o lu", "ol u", "ÄŁ i", "Ġpersonal ities", "Ġ reshape", "Ġre shape", "Ġres hape", "Ġresh ape", "Ġen closed", "Ġenc losed", "ĉ ptr", "ĉp tr", "ĉpt r", "Ġt utorials", "Ġtutorial s", "Ġtutor ials", "Ġexpl oded", "Ġexplo ded", "Ġexplode d", "_ DIRECTORY", "_DIRECT ORY", "åĨħ 容", "Ġc anon", "Ġcan on", "Ġca non", "Ġrecogn ise", "P AD", "PA D", "Ġ Approx", "ĠApp rox", "ĠAp prox", "ĠAppro x", "Ġ Restore", "ĠRe store", "ĠRest ore", "Ġ Important", "ĠImport ant", "Ġhe avier", "Ġheav ier", ". Sequential", ".Se quential", "E arth", "Ear th", "ĠM ilk", "ĠMil k", "ĠMi lk", ".set Request", ". tem", ".t em", ".te m", "Ġre construct", "Ġrecon struct", "Ġskept ical", "Ġskeptic al", "_ Private", "_Pr ivate", "B UF", "BU F", "q ua", "qu a", ": a", "Ġ sek", "Ġs ek", "Ġse k", "Ġd well", "Ġdw ell", "o ssa", "os sa", "oss a", "Ġreward ed", "Ġrew arded", "и й", "( topic", "(t opic", "(to pic", "(top ic", "_ partition", "_part ition", "Ġ__ ________________", "Ġ______ ____________", "Key words", "Keyword s", "ĠFr anco", "ĠFranc o", "ĠFran co", "L ite", "Li te", "Lit e", "Ġn aken", "Ġna ken", "Ġnak en", "Ġ за", "Ġз а", "O BJECT", "OB JECT", "OBJ ECT", "Ġcraft s", "Ġcra fts", "Ġ Swap", "ĠS wap", "ĠSw ap", ".X na", ". Connect", ".Con nect", ".Conn ect", "Ġbalcon y", "( real", "(re al", "ĠBar nes", "ĠBarn es", "b ir", "bi r", "Ġ Twenty", "ĠTw enty", "ĠTwe nty", "a yan", "ay an", "aya n", "at ars", "ata rs", "atar s", "ĠPro pel", "ĠProp el", "ĠIh nen", "Up grade", "Ġc urb", "Ġcur b", "Ġcu rb", "- second", "-se cond", "Ġn eph", "Ġne ph", "Ġnep h", ". pres", ".p res", ".pre s", ".pr es", "ìŀ ħ", ". seq", ".s eq", ".se q", "Ġp added", "Ġpad ded", "Ġpadd ed", "\" ?", "j l", "ãĥ ¬", "' ) a", "Co ordinates", "Coordinate s", "Ġen acted", "Ġenact ed", "EN TS", "ENT S", "Ġ lac", "Ġl ac", "Ġla c", ". final", ".f inal", ".fi nal", ".fin al", "ĠPhp Storm", "c alled", "cal led", "call ed", "Ġin quiries", ". middleware", ".m iddleware", ".middle ware", "ĠD owntown", "ĠDown town", "/ ';Ċ", "/' ;Ċ", "Ġkil omet", "ac cel", "acc el", "Ġqu ien", "Ġq uien", "Ġqui en", "w string", "ws tring", "set Data", "Ġman era", "Ġmane ra", "Ġmod ular", "r imp", "ri mp", "rim p", "Ġtar iffs", "Ġtariff s", "Ġtarif fs", "âĢĻ il", "âĢĻi l", "_TH ROW", "/ color", "/c olor", "/co lor", "Ġ HTMLElement", "ĠHT MLElement", "ĠHTML Element", "Ġc arro", "Ġcar ro", "Ġcarr o", "Ġpr ere", "Ġpre re", "Ġprer e", "Ġplot ting", "Ġ Positive", "ĠPos itive", "ĠM achines", "ĠMachine s", "ĠMach ines", "O TES", "OT ES", "OTE S", "á» Ľ", "ple asant", "Ġ alte", "Ġa lte", "Ġal te", "Ġalt e", "Ġa inda", "Ġai nda", "Ġain da", "th ese", "the se", "thes e", "Ġ cors", "Ġc ors", "Ġco rs", "Ġcor s", "i pay", "ip ay", "ipa y", "ĠAdv isory", "ĠAdvis ory", "ĠAdvisor y", "ĠRub io", "ĠRu bio", "j q", "Ġl imestone", "Ġlim estone", "Ġlime stone", "Ġdet ached", "Ġdetach ed", "设 ç½®", "t enant", "te nant", "ten ant", "Ġ Depth", "ĠDe pth", "ĠDep th", "ĠDept h", "a lore", "al ore", "alo re", "ĠÑģÑĤ ÑĢок", "ĠÑģÑĤÑĢ ок", "ĠÑģÑĤÑĢо к", "Ġ FORE", "ĠF ORE", "ĠFOR E", "ĠFO RE", "ĠL ay", "ĠLa y", "p resentation", "present ation", ") ');Ċ", ")' );Ċ", ".sub plots", ".subplot s", "Ï ĥ", "N OW", "NO W", "G ar", "Ga r", "h andles", "handle s", "hand les", "a bra", "ab ra", "abr a", "put ies", "pu ties", "ĠElect rical", "ĠElectric al", "M iddle", "Mid dle", "r opic", "ro pic", "rop ic", "Ġ JD", "ĠJ D", "Ġ Dyn", "ĠD yn", "ĠDy n", "ĠB ristol", "ĠBr istol", "ĠBris tol", "ĠMc Carthy", "ĠMcCart hy", "Ġstr iker", "Ġstri ker", "Ġstrike r", "Ġenum erable", "Ġenumer able", "ĠE van", "ĠEv an", "ĠEva n", ". defaults", ".default s", "qu ences", "que nces", "quence s", ") ||", ")| |", "ĉ token", "ĉt oken", "ĉto ken", "â Ĺı", "âĹ ı", "- dropdown", "-d ropdown", "-drop down", "ST ORE", "Ġ Graphic", "ĠG raphic", "ĠGraph ic", "( pp", "(p p", "Ex pl", "Exp l", "Ġup wards", "Ġupward s", "ĠD istributed", "ĠDistrib uted", "Ġ WEB", "ĠW EB", "ĠWE B", "J er", "Je r", "is NaN", "çĶŁ æĪIJ", "> R", "üss en", "üs sen", "e fs", "ef s", "Ġun cover", "Ġunc over", "Ġl ud", "Ġlu d", ". calculate", ".c alculate", ".cal culate", ".calc ulate", "Ġ intptr", "Ġint ptr", "Ġmidfield er", ". Headers", ".Header s", ".He aders", ".Head ers", "Ġ mf", "Ġm f", "e ref", "er ef", "ere f", ". Metro", ".M etro", ".Me tro", "Ġ Speaking", "ĠSpe aking", "ĠSpeak ing", ": b", "Ġcryptoc urrencies", "Ġd emons", "Ġde mons", "Ġdem ons", "Ġdemon s", "Ġdemo ns", "ĉ EXPECT", "Ġw icked", "y outube", "you tube", "youtu be", ": Int", ":I nt", "ĠH indi", "ĠHind i", "ĠHin di", "Ġ CAT", "ĠC AT", "ĠCA T", "Ġ ع", "ĠØ ¹", "r ar", "ra r", "o more", "om ore", "omo re", "omor e", "/ per", "/p er", "/ license", "/lic ense", "/l icense", "Ġre im", "Ġa waiting", "Ġawait ing", "Ġle thal", "Ġlet hal", "Ġleth al", "Ġ EF", "ĠE F", "r ounded", "ro unded", "round ed", "ĠPl atinum", "Ġв Ñģе", "ĠвÑģ е", ". coords", ".co ords", ".coord s", ". Device", ".D evice", ".De vice", ".Dev ice", "/ item", "/i tem", "Ġ Wenn", "ĠW enn", "ĠWe nn", "ĠWen n", "compile Components", "ĠK inder", "ĠKind er", "ĠKi nder", "ĠKin der", ".remove Item", "Ġ anda", "Ġa nda", "Ġand a", "Ġan da", "b nb", "bn b", "Ġ pra", "Ġp ra", "Ġpr a", "( transaction", "(trans action", "Ġembarrass ing", "ĉ BOOL", ".content View", "Ġevent data", "at ore", "ator e", "ato re", "Ġprovided In", "ir ma", "irm a", "Ġz ona", "Ġzo na", "_ HW", "_H W", "æ Ļ", "Ġst ove", "Ġsto ve", "Ġcounter part", "_ Product", "_Pro duct", "_MAN AGER", "Ġinf ring", "Ġinfr ing", "Ġ ERA", "ĠE RA", "ĠER A", "_ party", "_p arty", "_part y", "_par ty", "Ñ ij", "Ġin ici", "Ġi nici", "Ġini ci", "_ Request", "_Re quest", "Ġmir acle", "Ġmirac le", "Ġcancel Button", "S py", "Sp y", "at ó", "Ġpol ish", "Ġpo lish", "Ġpolis h", "ĠNic ole", "ĠNi cole", "ĠNico le", "ĠNicol e", ". displayName", ".display Name", "\\ Requests", "\\Request s", "Ġuse History", "Router Module", "Ġst ared", "Ġstar ed", "Ġsta red", "Ġstare d", "I DER", "ID ER", "IDE R", "Ñĥнк ÑĨи", "Ġ nota", "Ġn ota", "Ġnot a", "Ġno ta", "$ arr", "$a rr", "$ar r", "pec ified", "Ġt opp", "Ġto pp", "Ġtop p", "_DR IVER", "_DRIVE R", "/ ng", "/n g", "å ł", "_ tm", "_t m", "% timeout", "< s", "Ġ (*)", "Ġ( *)", "Ġ(* )", "Ġ HttpRequest", "ĠHttp Request", "_ TRACK", "_TR ACK", "_TRA CK", "( note", "(n ote", "(not e", "(no te", "Ġ Explore", "ĠExp lore", "ĠExpl ore", "_ serv", "_s erv", "_se rv", "_ser v", "Ġ ç»", "Ġç »", "B inder", "Bind er", "Bin der", "Bi nder", "+ \",", "+\" ,", ". att", ".a tt", ".at t", "ĠEth i", "ĠEt hi", "Ġc ódigo", "= '\\", "=' \\", ". lines", ".l ines", ".line s", ".li nes", ".lin es", "( Of", "(O f", "å° Ĩ", "miss ible", "Ġ vé", "Ġv é", "Ġac oustic", "Ġcraft ing", "n it", "ni t", ". ba", ".b a", "ĠLuc y", "ĠLu cy", "Ġi Pod", "ĠiP od", "Ġpup ils", "Ġpupil s", "- max", "-m ax", "_ wr", "_w r", "( cp", "(c p", "Ġ REPORT", "ĠRE PORT", "ĠREP ORT", "Ġ dns", "Ġd ns", "Ġdn s", "Ġ References", "ĠRe ferences", "ĠReference s", "ĠRefer ences", "Ġunder taken", "Ġundert aken", "Ġundertake n", "Ġkø benhavn", "Ġ chai", "Ġc hai", "Ġch ai", "Ġcha i", "ĠC roat", "ĠCro at", "_ Log", "_L og", "r owned", "row ned", "rown ed", "_ med", "_m ed", "_me d", "ĉ date", "ĉd ate", "# __", "Ġcost umes", "Ġcostume s", "Ġ Requires", "ĠRe quires", "ĠRequire s", "aff le", "ç Ĭ¶æĢģ", "çĬ¶ æĢģ", "-S emit", "-Se mit", "ela ide", "еÑĤ од", "Ġp estic", "Ġpes tic", "Ġpest ic", "Ġ dra", "Ġd ra", "Ġdr a", "D OCUMENT", "DOC UMENT", "Ġ ...čĊ", "Ġ... čĊ", "Ġ.. .čĊ", "} `}Ċ", "}` }Ċ", "}`} Ċ", "ĠA uction", "ĠAu ction", "Ġ Dock", "ĠD ock", "ĠDo ck", "ĠDoc k", "xxxx xxxx", "( getString", "(get String", "ħ į", "Ġborder Width", "ĠM achinery", "ĠMachine ry", "ĠMach inery", "Ġpredict able", "Ġpredic table", ". SH", ".S H", "Ġam plitude", "Ġampl itude", ".for Root", "I Navigation", "IN avigation", "Table Model", "at trib", "att rib", "attr ib", "Ġmaneu ver", "Ġexc av", "B ERS", "BER S", "BE RS", "Ġd apat", "Ġda pat", "Ġdap at", "Ġinstall ations", "Ġinstallation s", "Ġinstal lations", ". Async", ".A sync", ".As ync", "Ġ rays", "Ġr ays", "Ġra ys", "Ġray s", "= âĢĿ", "; ččĊ", ". crypto", ".c rypto", "_ dbg", "_d bg", "_db g", "Ġ Enumerable", "ĠEnum erable", "Of Size", "_ epochs", "_epoch s", "m w", "M ENU", "ME NU", "out line", "ĠP apers", "ĠPa pers", "ĠPaper s", "ĠPap ers", "= ===========Ċ", "== ==========Ċ", "==== ========Ċ", "======== ====Ċ", "=== =========Ċ", "============ Ċ", "=========== =Ċ", "========= ===Ċ", "========== ==Ċ", "====== ======Ċ", "===== =======Ċ", "======= =====Ċ", "Ġuniform s", "Ġuni forms", "ĠG ig", "ĠGi g", "- package", "-p ackage", "-pack age", "ĠJ enkins", "ĠJen kins", "Ġ HomePage", "ĠHome Page", ". isSelected", ".is Selected", "Ġmechan ic", "Ġmech anic", "M K", "Ġ Sounds", "ĠS ounds", "ĠSo unds", "ĠSou nds", "ĠSound s", "//---------------------------------------------------------------- -------------Ċ", "//---------------------------------------------------------------------------- -Ċ", "Ġresearch ing", "Ġ infos", "Ġin fos", "Ġinfo s", "Ġinf os", "o graphics", "og raphics", "ograph ics", "ographic s", "er set", "ers et", "erse t", "([ '/", "([' /", "ĠTim ber", ". agent", ".a gent", ".ag ent", ".age nt", ".to JSON", "_ commands", "_command s", "_comm ands", "p aring", "par ing", "pa ring", "_ adjust", "_ad just", "_adj ust", ". nome", ".n ome", ".no me", ".nom e", "( glm", "(g lm", "(gl m", "Status Bar", "file path", "? âĢĻ", "Ġdet ective", "Ġdetect ive", "Ġuns erer", "Ġunser er", "Ġunsere r", "ĠTi bet", "ĠTib et", "EN DED", "END ED", "( seed", "(s eed", "(se ed", "Ġsne ak", "Ġa mor", "Ġam or", "Ġamo r", "=\" //", "=\"/ /", "ĠPan thers", "ĠPanther s", "all ax", "alla x", "ĠL IVE", "ĠLI VE", "ĉ DWORD", "ĉD WORD", "] =-", "]= -", "Ġt ornado", "Ġtorn ado", "/ min", "/m in", "Ġl ungs", "Ġlung s", "Ġlun gs", "- current", "-c urrent", "-cur rent", "Ġ Booking", "ĠBo oking", "ĠBook ing", "ĠBoo king", "åĪĹ è¡¨", "Ġenjoy ment", "ठ°", "J A", "t yped", "type d", "ty ped", "typ ed", ". Btn", ".B tn", "f at", "fa t", "u gal", "ug al", "uga l", "Ġ Shares", "ĠSh ares", "ĠShare s", "ĠSha res", "ĠShar es", "Ġdis gr", "Ġdisg r", "Ġ BAR", "ĠB AR", "ĠBA R", "Ġ FOX", "ĠF OX", "ĠFO X", "Op code", "Ġ Sz", "ĠS z", "key down", "iction aries", "Ġdet ailing", "Ġdetail ing", "} ))Ċ", "}) )Ċ", "})) Ċ", "Ġ pok", "Ġp ok", "Ġpo k", "Ġdemonstr ating", "Ġ notation", "Ġn otation", "Ġnot ation", "Ġnota tion", "l ayers", "la yers", "lay ers", "layer s", "@ if", "ĠN PR", "ĠNP R", ".strict Equal", "Ġ Recipes", "ĠRec ipes", "ĠRecipe s", ". Tensor", ".T ensor", "Ġliqu or", "Ġdeb ts", "Ġdebt s", ". endsWith", ".end sWith", ".ends With", "W heel", "Wh eel", ". Pos", ".P os", "C SV", "CS V", "$ arity", "$ar ity", "Ġun stable", "Ġuns table", "Ġunst able", "( loss", "(l oss", "(lo ss", "EN SOR", "ENS OR", "Ġel even", "Ġele ven", "Ġelev en", "ĠL opez", "ĠLo pez", "ĠHop kins", "c onom", "con om", "co nom", "cono m", "ĠS eth", "ĠSe th", "ĠSet h", "Ġpo ems", "Ġpoem s", "Q uant", "Qu ant", "Ġg sl", "Ġgs l", "Ġsy rup", "Ġs ibling", "Ġsi bling", "Ġc ass", "Ġca ss", "Ġcas s", "- vous", "-v ous", "ö t", "_P ATTERN", "_ SECTION", "_SE CTION", "_SEC TION", "est imated", "estimate d", "up grade", ". mongodb", ".m ongodb", ".mongo db", "ĠBo at", "_ CTX", "_C TX", "_CT X", "Ġfetch ing", "Ġfet ching", "u stin", "us tin", "ust in", "p iel", "pi el", "pie l", "M arg", "Mar g", "Ma rg", "Ref lection", "Reflect ion", "Ġ duct", "Ġd uct", "Ġdu ct", "ĠMunicip al", "Ġ bx", "Ġb x", ". GetCurrent", ".Get Current", "m link", "ml ink", "mlin k", "ĠAccount ing", "ĠGen eva", "ĠGene va", "_ Pos", "_P os", "Ġp asser", "Ġpass er", "Ġpas ser", "Ġpasse r", "Ġhear ings", "Ġhearing s", "com pan", "comp an", "Ġfrag ile", "Initial izer", "Initialize r", "w alker", "walk er", "wal ker", ". Material", ".M aterial", "ĠH unting", "ĠHun ting", "ĠHunt ing", "try side", "trys ide", "Ġ kat", "Ġk at", "Ġka t", "Ġcl erk", "Ġcle rk", "Ġcler k", "á Ł", "do ing", "doi ng", "ĉ group", "ĉg roup", "ĉgr oup", "Ġsan ction", "Ġsanct ion", ". lb", ".l b", "Ġ Lazy", "ĠL azy", "ĠLa zy", "ĠLaz y", "Ġ Constraint", "ĠCon straint", "ĠConstr aint", "P agination", "Pag ination", "Ġpou vez", "ĠInd icates", "M ER", "ME R", "Ġc ours", "Ġco urs", "Ġcour s", "Ġcou rs", "Ġy early", "Ġyear ly", "Ġg rosse", "Ġgro sse", "Ġgross e", "Ġgros se", "abb rev", "abbr ev", "Ġ DON", "ĠD ON", "ĠDO N", "Ġpro ceeded", "Ġproceed ed", "ent lich", "Ġ propertyName", "Ġproperty Name", "ĠTe aching", "ĠTea ching", "ĠTeach ing", "st adt", "sta dt", "stad t", "Ġc utoff", "Ġcut off", "or ners", "orn ers", "orne rs", "Ġa frica", "Ġaf rica", "Ġafr ica", "Ġ renders", "Ġr enders", "Ġrender s", "Ġren ders", "Ġrend ers", "ĠYan kees", "ĠYankee s", "Ġ Toolbar", "ĠTool bar", "s paces", "sp aces", "space s", "spa ces", ".fill Style", "Ġseg undo", "Ġsegu ndo", "_ strlen", "_st rlen", "_str len", ". Firebase", ".F irebase", ".Fire base", "å¤ Ħ", "Ġmention ing", "\\ (", "ĠVal ve", "S etter", "Set ter", "Ġsp ans", "Ġspan s", "Ġspa ns", "ĠAl cohol", "Ġ Letters", "ĠLet ters", "ĠLetter s", "\\ xe", "\\x e", "Ġ TK", "ĠT K", "_ BLE", "_B LE", "_BL E", ". getResult", ".get Result", "< Player", "

\"", "=> \"", "t lement", "tle ment", "tl ement", "$ (\"", "$( \"", "From String", "ĠB ild", "ĠBi ld", "ĠBil d", "Ġcon ventions", "Ġconv entions", "Ġconvent ions", "Ġconvention s", "_ native", "_n ative", "_nat ive", "Ġ Inspector", "ĠIns pector", "ĠInsp ector", "ĠP ist", "ĠPi st", "ĠPis t", "u bar", "ub ar", "uba r", "Ġ regs", "Ġre gs", "Ġreg s", "ĠP ilot", "ĠPi lot", "ĠPil ot", "T hus", "Th us", "Thu s", "> '+", ">' +", "Ġ cela", "Ġc ela", "Ġce la", "Ġcel a", ". news", ".n ews", ".new s", ".ne ws", "( Product", "L iving", "Li ving", "Liv ing", "R ussia", "Russ ia", "Ġ facet", "Ġf acet", "Ġfac et", "Ġface t", "e tical", "et ical", "etic al", "eti cal", "Ġ[ '$", "Ġ[' $", "/ [", "Ġ Dire", "ĠD ire", "ĠDi re", "ĠDir e", "Ġg ases", "Ġgas es", "Ġga ses", "ĠIN FORMATION", "Ġ Eat", "ĠE at", "ĠEa t", "ĠFor ums", "ĠForum s", "Ġ Characters", "ĠChar acters", "ĠCharacter s", "_ met", "_m et", "_me t", "Ġ ìĭľ", "Ġìĭ ľ", "Ġk ings", "Ġking s", "Ġkin gs", "a chie", "ach ie", "achi e", "Ġ Lambda", "ĠL ambda", "ĠLamb da", "Ġt imers", "Ġtime rs", "Ġtim ers", "Ġti mers", "Ġtimer s", "ĠL ighting", "ĠLight ing", "ĠCas ey", "ĠCase y", "ĠCa sey", "ad dir", "add ir", "an dex", "and ex", "ande x", ". answer", ".an swer", "Ġ Hip", "ĠH ip", "ĠHi p", "ĠPr incip", "Start Date", "Ġ ãĢĮ", "ĠãĢ Į", "t res", "tr es", "tre s", "Ġ &#", "Ġ& #", ".Max Value", "ĠPro blems", "ĠProblem s", "ĠProb lems", "ĠProble ms", "Ġ latex", "Ġla tex", "Ġlate x", "Ġlat ex", "Of Class", "ĠL ynn", "ĠLy nn", "ĠLyn n", "/ /'", "// '", "Ġvoy age", "Ġsh uttle", "Ġshut tle", "ĠR oller", "ĠRo ller", "ĠRoll er", "ĠRol ler", "ĠRuntime Error", "u ya", "uy a", "D ic", "Di c", "ĉ builder", "ĉb uilder", "ĉbuild er", "Ġbul lying", "Ġbull ying", "Ġbully ing", "Ġsimple st", "Ġsimp lest", "Ġsimpl est", "Ġsimples t", ". called", ".c alled", ".call ed", ".cal led", "Ġ LR", "ĠL R", "Ġmor ality", "Ġmoral ity", "Ġst urdy", "tr acking", "track ing", ". swagger", ".sw agger", "_ BIND", "_B IND", "_BIN D", "I TOR", "IT OR", "ITO R", "-url encoded", "Ġ Ñħ", "ĠÑ ħ", "ĠTr inity", "Ġtr aps", "Ġtra ps", "Ġtrap s", "Ġ |-", "Ġ| -", "Ġ setText", "Ġset Text", "Ġbar gain", "Ġbarg ain", "Ġbr akes", "Ġbra kes", "Ġbrake s", ". getCode", ".get Code", ".g etCode", ".getC ode", "Ġm igrate", "Ġmigr ate", "Ġmig rate", "Ġ ribbon", "Ġr ibbon", "Ġrib bon", ") return", ")r eturn", "Ġ charger", "Ġch arger", "Ġchar ger", "Ġcharg er", "Ġcharge r", "a com", "ac om", "aco m", "ADI US", "ĠAmb assador", "- after", "-a fter", "Ġ anni", "Ġan ni", "Ġann i", "ĉ spin", "ĉs pin", "ĉsp in", "Con cept", "ĠHend erson", "Ġ HOST", "ĠH OST", "ĠHO ST", ". rank", ".r ank", ".ra nk", "ĠNorth east", "ĠNor theast", "Ġber lin", "Ġre quis", "Ġreq uis", "Ġrequ is", ". feed", ".f eed", ".fe ed", "Ġsource Mapping", "ĠRen contre", ". ajax", ".a jax", "nest js", "Ġt rek", "Ġtr ek", "Ġtre k", "ĠN acional", "Ġ& [", "Ġpay able", "or tex", "ort ex", "orte x", "Ġ dept", "Ġd ept", "Ġde pt", "Ġdep t", "field Name", "Ġcomp letes", "Ġcomple tes", "Ġcomplet es", "Ġcompl etes", "Ġcomplete s", "ĠR VA", "ĠRV A", "Ġon ions", "Ġonion s", "al ignment", "align ment", "Form ats", "Format s", "Ġ' {$", "Ġ'{ $", "Hash Set", "ĠB od", "ĠBo d", ".Invariant Culture", "Ġsett lements", "Ġsettlement s", "Ġsettle ments", "Ġ hydr", "Ġhy dr", ". updated", ".update d", ".up dated", "v enth", "ve nth", "vent h", "ven th", "( seconds", "(se conds", "(second s", "(sec onds", "=\" /\"", "=\"/ \"", "Ġweb page", "( ĊĊ", "(Ċ Ċ", "Ġ tir", "Ġt ir", "Ġti r", "Ġt oes", "Ġto es", "Ġtoe s", "ĠB rick", "ĠBr ick", "ĠBri ck", "Ġamb ition", "Ġambit ion", "P ot", "Po t", "= max", "=m ax", "E TIME", "ET IME", "Ġde pot", "Ġdep ot", "c alls", "cal ls", "call s", "ĠNor wegian", "` :", "Ġ burger", "Ġb urger", "Ġbur ger", "Ġburg er", "Ġburge r", "Ġprofessor s", "Ġprofess ors", "Ġ Allocate", "ĠAl locate", "ĠAll ocate", "ĠAlloc ate", "-third s", "- chart", "-c hart", "-ch art", "-char t", "Ġ ford", "Ġf ord", "Ġfor d", "Ġfo rd", "* N", ".k otlin", "Ġpaper work", "Ġ DEVICE", "ĠDE VICE", "ĠDEV ICE", "% @\",", "%@ \",", "re spect", "res pect", "resp ect", "( mp", "(m p", "é «ĺ", "é« ĺ", "- if", "-i f", "Ġcush ion", "o bot", "ob ot", "obo t", "Ġp arc", "Ġpar c", "Ġpa rc", "S PACE", "SP ACE", "SPA CE", "ĠNet anyahu", "Ġself ish", "Ġsel fish", "f eat", "fe at", "fea t", "Ġ clientes", "Ġcl ientes", "Ġclient es", "Ġcli entes", "Ġcliente s", "- tools", "-t ools", "-to ols", "-tool s", "-too ls", "Ġp orch", "Ġpo rch", "Ġpor ch", "Ġ jq", "Ġj q", ". verbose", ".ver bose", "Ġlib erals", "Ġliberal s", "Ġliber als", "] )ĊĊĊ", "]) ĊĊĊ", "])Ċ ĊĊ", "])ĊĊ Ċ", "p ies", "pi es", "pie s", "Not Blank", "( term", "(t erm", "(te rm", "È Ľi", "ÈĽ i", "_ Params", "_Param s", ". normalize", ".normal ize", "B ullet", "AS IC", "ASI C", "( hex", "(h ex", "_ cliente", "_cl iente", "_client e", "_cli ente", "+ ,", "_ DI", "_D I", "Ġforth coming", "} \")]Ċ", "}\" )]Ċ", "}\") ]Ċ", "s eo", "se o", "U m", "> Name", ">N ame", "Ġcomfort ably", "irect ional", "irection al", "W ITH", "WI TH", "/ pr", "/p r", "Ġ Poor", "ĠP oor", "ĠPo or", "ĠV itamin", "ĠVit amin", "ĠVita min", "v ic", "vi c", "G H", "Ġprior it", "Ġprio rit", "Ġ NN", "ĠN N", "Ġ Closed", "ĠC losed", "ĠCl osed", "ĠClose d", "ĠClo sed", "¤ í", "Ġ isOpen", "Ġis Open", "\\ Console", "And Feel", ". SUCCESS", ".S UCCESS", "_OPER ATION", "p olation", "po lation", "pol ation", "ĠT as", "ĠTa s", "p sz", "ps z", "> '.", ">' .", "C URRENT", "V endor", "host s", "ho sts", "hos ts", "ĠE rd", "ĠEr d", ">tag ger", ">t agger", "ĠsourceMapping URL", "Ġmar athon", "_ closed", "_c losed", "_cl osed", "_close d", "Ġex emption", "Ġexem ption", "Ġexempt ion", "Ġexemp tion", "Ġrecogn izes", "Ġrecognize s", "ide show", "ides how", "' $", "(' /');Ċ", "('/ ');Ċ", "('/') ;Ċ", "m its", "mit s", "mi ts", "wa rz", "war z", "ĠCh erry", "ĠCher ry", "µ ¬", "n or", "no r", "p orte", "port e", "por te", "Ġ wl", "Ġw l", "_ backup", "_back up", ".get Boolean", ". getResource", ".get Resource", "Ġdefinit ive", "Ġdefin itive", ". EditText", ".Edit Text", "Ġ sÃŃ", "Ġs ÃŃ", ". CONT", ".C ONT", ".CON T", ".CO NT", "Ġ PLAYER", "ĠPL AYER", "ĠPLAY ER", ". cards", ".c ards", ".card s", ".car ds", "ĠSh ore", "ĠSho re", "(' /')Ċ", "('/ ')Ċ", "('/') Ċ", "cl uir", "Web Driver", "( month", "(m onth", "(mon th", "- release", "-r elease", "-re lease", "-rel ease", "Ġins pector", "Ġinspect or", "Ġinsp ector", "å £", "Ġ NF", "ĠN F", "_ clip", "_c lip", "_cl ip", "_cli p", "å ŃIJ", "åŃ IJ", "Ġinter acting", "Ġinteract ing", ". tmp", ".t mp", ".tm p", "Ġ '''ĊĊ", "Ġ'' 'ĊĊ", "Ġ'''Ċ Ċ", "Ġ''' ĊĊ", "Ġ dee", "Ġd ee", "Ġde e", "Ġf rost", "Ġfr ost", "Ġfro st", "\" ]))Ċ", "\"] ))Ċ", "\"]) )Ċ", "\"])) Ċ", "Ġ Places", "ĠP laces", "ĠPl aces", "ĠPlace s", "ĠPla ces", "Th rows", "Throw s", "Thr ows", "f ork", "fo rk", "for k", "/ day", "/d ay", "i Phone", "Ġ MIC", "ĠM IC", "ĠMI C", "Ġf olding", "Ġfol ding", "Ġfold ing", "Ġcr ore", "Ġcro re", "ĠCh iefs", "ĠChief s", "ĠChi efs", "pher ical", "pheric al", "phe rical", "( price", "(p rice", "(pr ice", ".Write String", "Ġex iting", "Ġexit ing", "] ',Ċ", "]', Ċ", "]' ,Ċ", "ight ing", "igh ting", "Ing redient", "( vertex", "(ver tex", "Ġ scrollView", "Ġs crollView", "Ġscroll View", "h f", ": new", ":n ew", "S EN", "SE N", "s ector", "se ctor", "sec tor", "sect or", "Ġs pins", "Ġsp ins", "Ġspin s", "Ġspi ns", "Ġ Scheduler", "ĠS cheduler", "ĠSchedule r", "o techn", "ot echn", "ote chn", "otech n", "otec hn", "sem icolon", "semi colon", "Font OfSize", "ĠSpecific ally", "fl amm", ". ObjectId", ".Object Id", "Ġc onta", "Ġcon ta", "Ġcont a", "_ permissions", "_per missions", "_perm issions", "_permission s", "ĉ FROM", "ĉF ROM", "I CODE", "IC ODE", "ICO DE", "/ kg", "/k g", "ĠHot els", "ĠHotel s", "- med", "-m ed", "-me d", "ĠD in", "ĠDi n", "Ġn avy", "Ġna vy", "Ġnav y", "get Param", "Ġm end", "Ġme nd", "Ġmen d", "Ġportray ed", "ĠMet ropolitan", "P ainter", "Paint er", "Pa inter", "Ġref erral", "Ġrefer ral", "_ good", "_g ood", "_go od", "Ġmar vel", "os aic", "osa ic", "> (&", ">( &", ". ur", ".u r", "Ġes tos", "Ġest os", "Ġesto s", "Will iam", "Ġtim ber", "Ġquel ques", "Ġquelque s", "Ġ Documents", "ĠDocument s", "ĠDoc uments", ".X aml", "Ġb atches", "Ġbatch es", "Ġbat ches", "éģ ĵ", "Ġ Released", "ĠRe leased", "ĠRelease d", "T ail", "Ta il", "Tai l", "CO OKIE", "h eid", "he id", "hei d", "_ station", "_st ation", "_stat ion", "_sta tion", "Ġ Via", "ĠV ia", "ĠVi a", "S ale", "Sal e", "Sa le", "Ġ Repeat", "ĠRe peat", "ĠRep eat", "Ġpro min", "Ġpr omin", "Ġprom in", "ĠZ o", "- forward", "-for ward", "Ġ Ion", "ĠI on", "ĠIo n", "it ary", "ita ry", "itar y", "Ġj us", "Ġju s", "- request", "-re quest", "Ġproud ly", "Ġ Streaming", "ĠStream ing", "ĠStre aming", "( MouseEvent", "(Mouse Event", "ĠS print", "ĠSp rint", "ĠSpr int", "_ rotation", "_r otation", "_rot ation", "Re positories", "Ġt art", "Ġta rt", "Ġtar t", "ĠÑģ в", "Ġm appings", "Ġmapping s", "è ª", "C u", "C ycle", "Cy cle", "Ġb un", "Ġbu n", "ĉ lua", "ĉl ua", "ãĥ ī", "Ġ( (!", "Ġ(( !", "Ġcollect ively", "Ġcollective ly", "Ġ Cond", "ĠC ond", "ĠCon d", "ĠCo nd", "Ġws zyst", "Ġwsz yst", "( lib", "(l ib", "(li b", "openh agen", "_ skip", "_s kip", "_sk ip", ".Column Header", "é Ĥ", "perience d", "peri enced", "ı è¿°", "_ props", "_p rops", "_pro ps", "_pr ops", "_prop s", "Ġcon trace", "Ġcont race", "Ġcontr ace", "Ġcontra ce", "Ġmatch up", "ab etic", "abe tic", "abet ic", ". members", ".m embers", ".member s", ".mem bers", "R ECT", "RE CT", "REC T", "( dat", "(d at", "(da t", "Ġs og", "Ġso g", "re nom", "ren om", "reno m", "_ Method", "_M ethod", "Custom ers", "Customer s", "ful lname", "full name", "Z N", "r etry", "re try", "ret ry", "Ġ kap", "Ġk ap", "Ġka p", "ĠN eu", "ĠNe u", "è Ĭ", "add Child", "will Return", "_ permalink", "_p ermalink", "_per malink", "_perm alink", "Ġenerg etic", "Ġener getic", "ĠW et", "ĠWe t", "ĠM orr", "ĠMo rr", "ĠMor r", "Ġ gcd", "Ġg cd", "Ġgc d", "co unts", "count s", "cou nts", ", type", ",t ype", "d ig", "di g", "( Login", "(Log in", "Ġcr acks", "Ġcrack s", "Ġcra cks", "Ġb acterial", "Ġbacter ial", "Ġbacteria l", "ĠM eat", "ĠMe at", "ĠArm strong", "ĠBro nze", "ĠBron ze", "Ġapprox imate", "_ dirs", "_d irs", "_dir s", "_di rs", "l iga", "li ga", "lig a", "ÅĤ ad", "ÅĤa d", "Ġkind ness", "Ġ contre", "Ġcon tre", "Ġcont re", "Ġcontr e", "ĠE VERY", "ĠEV ERY", "ĠEVER Y", "ĠEVE RY", "M ET", "ME T", "Ġann ouncements", "Ġannounc ements", "Ġannouncement s", "Ġannounce ments", "g pio", "gp io", "ĠWaitFor Seconds", "ĠPhoto shop", "ĠPhotos hop", "Ġdis contin", "/ dd", "/d d", "Ġtop ology", "Ġtopo logy", "an ical", "ani cal", "anic al", ". interface", ".inter face", "auc oup", ". HashSet", ".Hash Set", "ARI ANT", "( routes", "(r outes", "(route s", "(ro utes", "ĠT eh", "ĠTe h", "Ġh ype", "Ġhy pe", "Ġhyp e", "] \").", "]\" ).", "]\") .", "Ġs lam", "Ġsl am", "Ġsla m", "Ġbr oth", "Ġbro th", "- inter", "-in ter", "-int er", "ĠR id", "ĠRi d", "- manager", "-m anager", "-man ager", "Cancel ar", "Ġ Pagination", "ĠP agination", "ĠPag ination", "Ġsound track", "Ġpos terior", "Ġpost erior", "Ġposter ior", "Ġposte rior", "Ġsc rub", "Ġscr ub", "c reating", "cre ating", "cr eating", "creat ing", "- *", "ir teen", "irt een", ". dy", ".d y", ".s ymmetric", ".sym metric", "Ġ \"\".", "Ġ\" \".", "Ġ\"\" .", "= ==============", "== =============", "==== ===========", "======== =======", "=== ============", "============ ===", "============= ==", "=========== ====", "============== =", "========= ======", "========== =====", "====== =========", "===== ==========", "======= ========", "Ġch assis", "ĠnumberOf Rows", "De veloper", "Develop er", "_ bins", "_b ins", "_bin s", "_bi ns", "Ġ OUR", "ĠO UR", "ĠOU R", "ri eb", "rie b", "P ros", "Pro s", "Pr os", "Ġ wiÄĻ", "Ġw iÄĻ", "Ġwi ÄĻ", "\" d", "Ġasync io", "ze igen", "_ spi", "_s pi", "_sp i", ". ALL", ".A LL", ".AL L", "Ġscre ws", "Ġscr ews", "Ġscrew s", "Ch inese", "Ġ apiKey", "Ġapi Key", "Ġun successful", "ĠSea hawks", "ĠSeah awks", "O RG", "OR G", "ç« ł", "Ġprofession ally", "Ġprofessional ly", "Ġ Coupon", "ĠC oupon", "ĠCo upon", "ĠCou pon", "åŃĹ æ®µ", "Con vention", "Conv ention", "Ġpol ym", "Ġpoly m", "æī ĭ", "Ġsal vation", "Ġsalv ation", "Ġengine ered", "Ġengineer ed", "ĠW rest", "ĠWr est", "Ġ GCC", "ĠG CC", "ĠGC C", "Ġwar mer", "Ġwarm er", "Layout Constraint", "Ġag grav", "Ġagg rav", "S cripts", "Script s", "vent ure", "ven ture", "Ġrefriger ator", "Ġinnov ations", "Ġinnovation s", "Ġ Runner", "ĠR unner", "ĠRun ner", "N IC", "NI C", "ĠRoll ing", "ĠRol ling", "Control Events", "Ġlo os", "p ac", "pa c", "ĉ panel", "ĉp anel", "e fe", "ef e", "ĠBudd ha", "ĠBuddh a", "- -------------Ċ", "-- ------------Ċ", "---- ----------Ċ", "-------- ------Ċ", "--- -----------Ċ", "------------ --Ċ", "----- ---------Ċ", "---------- ----Ċ", "------ --------Ċ", "----------- ---Ċ", "------------- -Ċ", "------- -------Ċ", "--------- -----Ċ", "-------------- Ċ", "åº ĵ", "(for Key", "Ġl umin", "Ġlu min", "Ġlum in", "Ġ (?", "Ġ( ?", "ĠA IDS", "ĠAI DS", ", user", ",u ser", "im ientos", "imiento s", "content Type", "ant lr", "é ¦", "ĠW elt", "ĠWe lt", "ĠWel t", "P roduction", "Pro duction", "Product ion", "Produ ction", "Prod uction", "m ight", "mi ght", "ĠV II", "ĠVI I", "\" ,(", "\", (", "Ġobs erving", "Ġobserv ing", "Ġdeliber ate", "( control", "(c ontrol", "(cont rol", "Ġwith d", "Ġwit hd", "Ġse mana", "Ġsem ana", "ST ACK", "STA CK", "u chen", "uch en", "uc hen", "uche n", "N ice", "Ni ce", "Nic e", "ĠDeutsch land", "Ġ Specifies", "ĠSpec ifies", "d ma", "dm a", "iz io", "izi o", "ĠF acts", "ĠFac ts", "ĠFa cts", "ĠFact s", "_ popup", "_p opup", "_pop up", "ĠDirect ors", "ĠDirector s", "ĠDir ectors", "ĠDire ctors", "{ :", "[ R", "ĠÑį леменÑĤ", "ĠÑįлем енÑĤ", "Ġ plat", "Ġp lat", "Ġpl at", "Ġpla t", "Ġdirect ing", "ä¸ ī", "ĠGil bert", "âĢ¦ .ĊĊ", "âĢ¦. ĊĊ", ". qml", ".q ml", "Ġthere after", "Ġdis position", "Ġdisp osition", "Ġdispos ition", "Ġdisposit ion", "d raft", "dr aft", "dra ft", "Ġs urgeon", "Ġsurg eon", "Ġsurge on", "ĠIns ider", "ĠInside r", "Bl end", "ĠT rev", "ĠTr ev", "ĠTre v", "tr insic", "tri nsic", "To pics", "Top ics", "Topic s", "r ieve", "ri eve", "rie ve", "_ FILENAME", "_FILE NAME", "Ġaut res", "Ġau tres", "Ġautre s", "J ose", "Jo se", "Jos e", "Pro ducer", "Produ cer", "Prod ucer", "e rus", "er us", "eru s", "Ġpe tit", "Ġpet it", "Ġ NEXT", "ĠN EXT", "ĠNE XT", "Ġ Filters", "ĠF ilters", "ĠFilter s", "ĠFil ters", "Ġrep licate", "Ġrepl icate", "Ġreplic ate", "Ġreplica te", "\" ]).", "\"] ).", "\"]) .", "Ġl enders", "Ġlen ders", "Ġlend ers", "Ġlender s", "] \",Ċ", "]\", Ċ", "]\" ,Ċ", "; charset", "Cpp Object", "Ġfl oral", "Ġflo ral", "Ġflor al", "Ġflora l", "Ġ Tipo", "ĠT ipo", "ĠTi po", "ĠTip o", "Ġcirc uits", "Ġcircuit s", "e asy", "ea sy", "(& $", "it ta", "itt a", "er yl", "ery l", "_ COMMON", "_COM MON", "_COMM ON", "' }}>Ċ", "'} }>Ċ", "'}} >Ċ", "'}}> Ċ", "-b acked", "-back ed", "( variable", "(var iable", "( Index", "(In dex", "Ġ voir", "Ġv oir", "Ġvo ir", "Ġvoi r", "_ locations", "_l ocations", "_location s", "_loc ations", "++ ){", "++) {", "ĠLouis ville", "Ġgr atitude", "Ġgrat itude", ".Mock ito", "ĠP owers", "ĠPower s", "ĠPo wers", "ĠPow ers", "i eurs", "ie urs", "ieu rs", "ieur s", "Ġge ographic", "Ġgeo graphic", "r ale", "ra le", "ral e", "Ġc ra", "Ġcr a", "ĠSp urs", "ipher text", "iph ertext", "AC ION", "- common", "-com mon", "-comm on", "Ġvict ories", "ĠF inals", "ĠFin als", "ĠFinal s", ". shuffle", ".sh uffle", "- million", "-m illion", "_ PROC", "_P ROC", "_PRO C", "_PR OC", "as sume", "ass ume", "Ġ ils", "Ġi ls", "Ġil s", "D BC", "DB C", "Boot Test", "Ġl avor", "Ġla vor", "Ġlav or", ". testing", ".t esting", ".test ing", ". ast", ".as t", ".a st", "\" ]/", "\"] /", "m oid", "mo id", "Ġ qualification", "Ġqual ification", "g esch", "ge sch", "ges ch", "ĉ put", "ĉp ut", "Ġair ports", "Ġairport s", "J I", "T eacher", "Te acher", "_ uniform", "_un iform", "Ġ nama", "Ġn ama", "Ġna ma", "Ġnam a", "ĠB ast", "ĠBa st", "ĠBas t", "e rtype", "er type", "ert ype", "erty pe", "c apture", "cap ture", "capt ure", "get All", "ĠReyn olds", "o oled", "ool ed", "oo led", ". comments", ".com ments", ".comment s", ".comm ents", "Ġ chin", "Ġc hin", "Ġch in", "Ġchi n", ") .*", "). *", "Ġ или", "Ġи ли", "t gl", "tg l", "u dos", "ud os", "udo s", "Ġd ÃŃas", "ĠdÃŃa s", "ĠdÃŃ as", "c hai", "ch ai", "cha i", ". program", ".p rogram", ".pro gram", ".pr ogram", "Ġ psz", "Ġp sz", "Ġps z", "ĉ icon", "ĉi con", "p hil", "ph il", "phi l", "ent ral", "entr al", "_W RAP", "_WR AP", "o vi", "ov i", "Ġnost alg", "In finity", "Inf inity", "ĉ yield", "ĉy ield", "Ġvit amins", "Ġvitamin s", "Ġvita mins", "Ġvitam ins", "Qu aternion", "S ink", "Si nk", "Sin k", "_ goods", "_g oods", "_go ods", "_good s", "Ġ ........", "Ġ. .......", "Ġ... .....", "Ġ.. ......", "Ġ.... ....", "Ġ..... ...", "Ġ...... ..", "ĠW ings", "ĠWin gs", "ĠWing s", "ur idad", "uri dad", "- story", "-st ory", "\" ])ĊĊ", "\"] )ĊĊ", "\"]) ĊĊ", "\"])Ċ Ċ", "idel ity", "Type Def", "G tk", "Ġ íĮ", "Ġí Į", "_ Main", "_M ain", "Ġ chez", "Ġch ez", "Ġche z", "ĠR aven", "ĠRa ven", "ĠRav en", "Ġpay roll", "Ġfreel ance", "L LU", "LL U", "ĠM end", "ĠMe nd", "ĠMen d", "e day", "ed ay", "eda y", "Api ModelProperty", ".Form BorderStyle", "Ġeconom ist", "stan bul", "Ġfr eight", "Ġfre ight", "Ġfrei ght", "- Agent", "-A gent", "( meta", "(m eta", "(me ta", "Ġsym metry", "Ġ' ..", "Ġ'. .", ". Calendar", ".C alendar", "- aut", "-a ut", "-au t", "g f", "p ent", "pe nt", "pen t", "yc lopedia", "Ġw ishing", "Ġwish ing", "Ċ ĊĊĊĊĊĊĊĊĊĊĊ", "ĊĊ ĊĊĊĊĊĊĊĊĊĊ", "ĊĊĊĊ ĊĊĊĊĊĊĊĊ", "ĊĊĊ ĊĊĊĊĊĊĊĊĊ", "ĊĊĊĊĊĊ ĊĊĊĊĊĊ", "ĊĊĊĊĊĊĊĊ ĊĊĊĊ", "ĊĊĊĊĊ ĊĊĊĊĊĊĊ", "ĊĊĊĊĊĊĊĊĊĊ ĊĊ", "ĊĊĊĊĊĊĊ ĊĊĊĊĊ", "ĊĊĊĊĊĊĊĊĊ ĊĊĊ", "ĊĊĊĊĊĊĊĊĊĊĊ Ċ", "Ġgentle man", "Ġ ê³", "Ġê ³", "= #", "Ġlect ures", "Ġlecture s", "âĢľ In", "âĢľI n", "Ġ! _", "Ġ hb", "Ġh b", "Ġ Vendor", "ĠV endor", "ĠVend or", "Rec ently", "Recent ly", "_ notes", "_n otes", "_no tes", "_not es", "_note s", "æıIJ 示", "\" My", "\"M y", "Headers Height", "_ SO", "_S O", "Ġunw illing", "Ġsuper hero", "g io", "gi o", "p sy", "ps y", "Ġ Peer", "ĠP eer", "ĠPe er", "ĠPee r", "j avax", "java x", "jav ax", "& apos", "&a pos", "ĠCr isis", "ĠCri sis", "ord inal", "ordin al", "Mem cpy", "++++++++ ++++++++", "- val", "-v al", "Ġwork book", "- ap", "-a p", "= k", "Ġmetal lic", "Ġmetall ic", "_ peer", "_p eer", "_pe er", "By PrimaryKey", "_ SD", "_S D", "u ator", "ua tor", "uat or", "_SH ADER", "_SHA DER", ") Math", ". Transform", ".Trans form", "Ġc ows", "Ġco ws", "Ġcow s", "P hi", "Ph i", "ĠC lem", "ĠCl em", "ĠCle m", "( _(\"", "(_ (\"", "ĠL ud", "ĠLu d", "- delay", "-d elay", "-de lay", "-del ay", "ĠSe curities", "ĠSec urities", "ĠOrth odox", "Sym fony", "( report", "(re port", "(repo rt", "(rep ort", "Ġent ertain", "Ġenter tain", "Ġentert ain", "E PS", "EP S", "iz oph", "izo ph", "ex ual", "I RD", "IR D", "ä» İ", "Ġl ith", "Ġli th", "Ġlit h", "Ġ sanitize", "Ġs anitize", "Ġsan itize", "Ġsanit ize", "Ġfem inine", "Ġfemin ine", "IS BN", ". authentication", ".auth entication", "_ pipeline", "_p ipeline", "_pipe line", "/ constants", "/con stants", "Ġ CONF", "ĠCON F", "ĠCO NF", "Ġlu cr", "Ġluc r", "r icia", "ri cia", "ric ia", ".t tf", ".tt f", ". setContent", ".set Content", "Ġ stan", "Ġs tan", "Ġst an", "Ġsta n", "or ean", "ore an", "orea n", "ĠL loyd", ".raw Value", "Ġ gor", "Ġg or", "Ġgo r", "ĠBr owns", "ĠBrown s", "ĠBrow ns", "Re gression", "Reg ression", "Ġl owering", "Ġlow ering", "Ġlower ing", "na issance", "Ġbl ows", "Ġblow s", "Ġblo ws", "Ġam azed", "Ġama zed", "Ġun related", "Ġunre lated", "Re views", "Review s", "Ġ ruby", "Ġr uby", "Ġrub y", "Ġru by", "Ġ Modifier", "ĠMod ifier", "Ġg iants", "Ġgi ants", "Ġgiant s", "Ġgia nts", "Ġgian ts", ". thread", ".t hread", ".th read", "Ġcon tainment", "Ġcont ainment", "Ġcontain ment", "ĠStart Coroutine", "u mat", "um at", "uma t", "o release", "or elease", "ore lease", "ĠR andy", "ĠRand y", "ĠRan dy", "@ endif", "@end if", "D igest", "Di gest", "Dig est", "Ġsub urban", "Ġsubur ban", "Ġsuburb an", "= \");Ċ", "=\" );Ċ", "Ġ annonce", "Ġan nonce", "Ġann once", "Ġanno nce", "Ġannon ce", ". variable", ".var iable", "\\ Foundation", "\\F oundation", "Ġ acre", "Ġa cre", "Ġac re", "V an", "Va n", "Ġt uples", "Ġtu ples", "Ġtuple s", "Ġtup les", "d ns", "dn s", "Ġ Standing", "ĠSt anding", "ĠStan ding", "ĠStand ing", "_ large", "_l arge", "Ġ boxing", "Ġbo xing", "Ġbox ing", "Support ActionBar", "ĠFort une", "ĠR um", "ĠRu m", "_ multiple", "_m ultiple", "_multi ple", "_mult iple", "_multip le", "arch ical", "Ġ fwrite", "Ġf write", "Ġfw rite", "_ quote", "_qu ote", "Ġfoo lish", "Ġfool ish", "Ġcom prising", "Ġcomp rising", "Ġcompr ising", "Ġ оп", "Ġо п", "- selected", "-se lected", "-select ed", "v f", "m aid", "ma id", "mai d", "N ama", "Na ma", "Nam a", "( datetime", "(d atetime", "(date time", "(dat etime", "Ġindirect ly", "g art", "ga rt", "gar t", "fix tures", "fixture s", "c hos", "ch os", "cho s", "ĠH alo", "ĠHa lo", "ĠHal o", "Ġre curring", "Ġrec urring", "Ġrecur ring", "- news", "-n ews", "-new s", "-ne ws", "v il", "vi l", "ĠNurs ing", "ĠNur sing", "- produ", "-p rodu", "-pro du", "-pr odu", "Ġ HQ", "ĠH Q", "\\Http Foundation", "en ci", "enc i", "a uen", "au en", "Ġ vy", "Ġv y", "ocr acy", "Ġde legation", "Ġdeleg ation", "Ġas phalt", "Ġasp halt", "Ġset Selected", "k ok", "ko k", "/ rest", "/r est", "/re st", "/res t", "m etics", "me tics", "met ics", "metic s", "Ġ NSDate", "ĠNS Date", "Ġtravel led", "Ġtrav elled", "Ġrec ib", "Ġ mime", "Ġm ime", "Ġmi me", "Ġmim e", "CL IENT", "CLI ENT", "Ġ GU", "ĠG U", "Ġ HANDLE", "ĠH ANDLE", "ĠHAND LE", "/ Q", "[ z", "Ġboth ered", "Ġbother ed", "ĠBB Q", "ç as", "ça s", "_ examples", "_ex amples", "_example s", "_exam ples", "_ FIN", "_F IN", "Ġwhite Color", "Ġastr onom", "Ġastro nom", "- dir", "-d ir", "-di r", "Ġsovere ign", "Ġb reeze", "Ġbree ze", "Ġ inning", "Ġin ning", "Ġinn ing", "ĠEd monton", "g li", "gl i", ".blog spot", "j sx", "js x", "Ġver sa", "Ġve rsa", "Ġvers a", "ĠMoh ammed", ". Job", ".J ob", "-t oggler", "-toggle r", "Ġп олÑĮзоваÑĤ", "ar don", "ard on", "ardo n", "Ġnew born", "Ġn aval", "Ġna val", "Ġnav al", "not eq", "note q", "Ġt umblr", "Ġtum blr", "Ġh entai", "ĠTyp ically", "ĠTypical ly", "Ġl oot", "Ġlo ot", ". Sprite", ".S prite", ".Sp rite", "F light", "Fl ight", "Ġw avelength", "Ġwave length", "- sk", "-s k", "ĠE lle", "ĠEl le", "ĠEll e", "_ exports", "_ex ports", "_exp orts", "_export s", "Ġ Ñı", "ĠÑ ı", "Ġ IH", "ĠI H", "izoph ren", "Ġ íģ", "Ġí ģ", "_ primary", "_pr imary", "_pri mary", "_prim ary", "Ġm ois", "Ġmo is", "Ġmoi s", "Ġ BN", "ĠB N", "Ġsystem ic", "Ġsyst emic", "Ġdifer entes", "Ġdiferente s", "IN CT", "INC T", "Ġ' 'ĊĊ", "Ġ'' ĊĊ", "Ġ''Ċ Ċ", "$ q", "Widget Item", "c lide", "cl ide", "cli de", "clid e", "$ file", "$f ile", "L emma", "Le mma", "/ table", "/t able", "/tab le", "a grid", "ag rid", "agr id", "ĠMongo DB", "i nte", "in te", "int e", "Ġapp rent", "ÂŃ ing", "ÂŃi ng", ". Db", ".D b", "Ġ ÃĤ", "Ġà Ĥ", "h ammer", "ham mer", "=' ';Ċ", "='' ;Ċ", "Ġbr okers", "Ġbro kers", "Ġbroke rs", "Ġbroker s", "i tlement", "it lement", "itle ment", "semb lies", "sembl ies", "E le", "El e", "{ x", "Ġ lastname", "Ġlast name", "< -", "Ġ flatten", "Ġfl atten", "Ġflat ten", "Ġflatt en", "_ band", "_b and", ". Root", ".R oot", ".read FileSync", ".readFile Sync", "= =====", "== ====", "==== ==", "=== ===", "===== =", ". rx", ".r x", "? čĊ", "Ġmetaph or", "T i", "c onte", "con te", "cont e", "co nte", "Ġ debit", "Ġde bit", "Ġdeb it", "Ġcon tempt", "Ġcont empt", "Cpp Type", "æĶ ¯", "Form Field", "r atio", "rat io", "os opher", "osoph er", "oso pher", "Ġim plant", "Ġimp lant", "Ġimpl ant", "P URE", "PU RE", "PUR E", "Ġ alta", "Ġal ta", "Ġalt a", "_ management", "_man agement", "_manage ment", "Ġre fine", "Ġref ine", "Ġrefin e", "Ġ CheckBox", "ĠCheck Box", "ĠCh arl", "ĠChar l", "ĠCha rl", "- version", "-v ersion", "-vers ion", "cond itional", "condition al", "ven ues", "venue s", "Ġrifle s", "Ġrif les", "Ġoff spring", "Ġoffs pring", "Ġm illing", "Ġmill ing", "Ġmil ling", "Ġmilli ng", "Ġsharp ly", "Ġshar ply", "Ġunder water", "( origin", "(orig in", "(or igin", "_ Control", "_C ontrol", "Ġ .$", "Ġ. $", "Pl ugins", "Plugin s", "Plug ins", "Ġd rying", "Ġdr ying", "Ġdry ing", "Ġillustr ates", "Ġillustrate s", "Ġillust rates", "- u", "Ġveget arian", "n pc", "np c", "He art", "; ',Ċ", ";' ,Ċ", ";', Ċ", "com ma", "co mma", "comm a", "t eenth", "te enth", "tee nth", "teen th", "a san", "as an", "asa n", "/ spec", "/s pec", "/sp ec", "_ moves", "_m oves", "_move s", "_mov es", "_mo ves", "- margin", "-m argin", "-mar gin", "Ġ ingen", "Ġin gen", "Ġi ngen", "Ġing en", "Âł ³³", "³³ Âł", "Ġpro jet", "Ġproj et", "Ġproje t", "Ġo tra", "Ġot ra", "Ġ bras", "Ġb ras", "Ġbr as", "Ġbra s", ". utc", ".u tc", ".ut c", "Ġsl ept", "Ġsle pt", "= sub", "=s ub", "ab ilit", "abil it", "abi lit", "p oster", "pos ter", "post er", "po ster", "Ġ sdk", "Ġs dk", "Ġsd k", "ounc ill", "ouncil l", "Ġ wd", "Ġw d", "Pre paredStatement", "ĠD rum", "ĠDr um", "( attribute", "(at tribute", "Ġ Ethernet", "ĠEth ernet", "ĠEther net", "ĉ DB", "ĉD B", "Cal ifornia", "c ube", "cu be", "[ I", ". Created", ".C reated", ".Create d", "Ġ HM", "ĠH M", "Ġtr acing", "Ġtra cing", "Forms Module", "- you", "-y ou", ". currency", ".c urrency", ".curr ency", "fe eding", "feed ing", "fee ding", "Ġ tbody", "Ġt body", "Ġtb ody", "L i", "a ccion", "ac cion", "acc ion", "acci on", "n as", "na s", "Ġtr ouver", "Ġtrou ver", "Ġtrouve r", "N ONE", "NO NE", "NON E", "\" },čĊ", "\"} ,čĊ", "\"}, čĊ", "Ġ ftp", "Ġf tp", "Ġft p", "With Identifier", "p olate", "po late", "pol ate", "File Info", "Ġpurs ued", "Ġpursue d", "ĠĠĠĠ čĊĠĠĠĠčĊ", "ĠĠĠĠčĊ ĠĠĠĠčĊ", "DE SCRIPTION", "DESC RIPTION", "} */Ċ", "}* /Ċ", "From Nib", "Ġdec orative", "Ġdecor ative", "_ SSL", "_S SL", "_SS L", "( chat", "(c hat", "(ch at", "T LS", "TL S", "Ġsur prises", "Ġsurpr ises", "Ġsurprise s", "al culate", "alc ulate", "Ġ Splash", "ĠS plash", "ĠSp lash", "ĠSpl ash", "( Configuration", "(Config uration", "Ġ SEM", "ĠS EM", "ĠSE M", "im son", "ims on", "/ library", "/lib rary", "/l ibrary", "< Double", "", "Ġ} }>", "Ġ}} >", "G ED", "GE D", "f aq", "fa q", "Ġoption ally", "Ġoptional ly", "_ Dis", "_D is", "Ġ Successful", "ĠSuccess ful", "ĠC ensus", "Ġinc arcer", "_ CARD", "_C ARD", "_CA RD", "_CAR D", "Ġav iation", "Ġavi ation", "ĠG ym", "ĠGy m", "Author ity", ". Bean", ".B ean", ".Be an", "sh ader", "sha der", "shade r", "Not Exist", "_ TextChanged", "_Text Changed", "Ġ STOP", "ĠS TOP", "ĠST OP", "( team", "(t eam", "(te am", "\" H", "w g", "Ġgr inder", "Ġgri nder", "Ġgrind er", "Ġgrin der", "Ġ stripe", "Ġst ripe", "Ġstr ipe", "Ġstri pe", "Ġstrip e", "Ġp reservation", "Ġpres ervation", "Cl aim", "Cla im", "aver sal", "avers al", "w arehouse", "ware house", "target s", "tar gets", "T rust", "Tr ust", "Ġal lev", "Ġall ev", "Ġalle v", ", www", ",w ww", "ou sse", "ous se", "_ chan", "_c han", "_ch an", "_ Size", "_S ize", "s ystems", "sys tems", "system s", "Ġob jection", "Ġobject ion", "Ġobj ection", "ĠK ane", "ĠKa ne", "ĠKan e", "Ġcor ros", "Ġcorr os", "Ġcorro s", "Ġ DSL", "ĠD SL", "ĠDS L", "Ġ ua", "Ġu a", "Ġ MH", "ĠM H", "ĠStr ategic", "ĠStrateg ic", "_ tcp", "_t cp", "_tc p", "Ġ ê°Ĵ", "Ġê° Ĵ", "Ġborrow ed", "Ġborr owed", "ĠA ch", "ĠAc h", "ĉ command", "ĉcom mand", "Ġ gps", "Ġg ps", "Ġgp s", "le ston", "les ton", "lest on", "ich ever", "iche ver", "Ġ UA", "ĠU A", "Ġassault ed", "Ġspecial izes", "Ġspecialize s", "ĉ search", "ĉs earch", "ĉse arch", "H otel", "Hot el", "Ho tel", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠčĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠčĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠčĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠčĊ", "Ġ Pitch", "ĠP itch", "ĠPit ch", "Ġ Ùģ", "ĠÙ ģ", "READ Y", "REA DY", "Ġpar ental", "Ġparent al", "Ġparen tal", "Ġg éné", "Ġgé né", "Ġgén é", "Ġdonn ées", "Ġde tain", "Ġdet ain", "T ARGET", "Ġprotagon ist", "Ġclear Interval", "Ġ IconButton", "ĠIcon Button", "ĠGet All", "Type Info", "E H", "âĢľ They", "âĢľThe y", "Ġ{ [", "Ġg ag", "Ġga g", "Ġ Ú©", "Ġ Dropdown", "ĠD ropdown", "ĠDrop down", ". free", ".f ree", ".fr ee", ".fre e", "g one", "go ne", "gon e", "i mens", "im ens", "ime ns", "imen s", "Ġin stal", "Ġins tal", "Ġinst al", "ĉ curl", "ĉc url", "ĉcur l", "_ CAN", "_C AN", "_CA N", "Ġ Bone", "ĠB one", "ĠBo ne", "ĠBon e", "ï¼ Ķ", "on yms", "ony ms", "onym s", "- government", "-g overnment", ".binding Navigator", "Ġ Dans", "ĠD ans", "ĠDan s", "ĠDa ns", "ĠMc L", "( en", "(e n", "> (_", ">( _", "ÐĴ Ñĭ", ".* ;čĊ", "= j", "- cor", "-c or", "-co r", "S on", "So n", ".ToolStrip Item", "- around", "-a round", "-ar ound", "_ XML", "_X ML", "end Date", "Ġ slack", "Ġs lack", "Ġsl ack", "Ġsla ck", "Ġrot ated", "Ġrotate d", "Ġno qa", "Ġc ottage", "Ġcott age", "Ġencontr ar", "_ skill", "_s kill", "_sk ill", "hou ette", "! čĊ", ". weather", ".we ather", "Ġemphas ized", "Ġemphasize d", "å® ¶", "ĠÑģ пиÑģ", "ĠÑģп иÑģ", "Ġ Compiler", "ĠC ompiler", "ĠCom piler", "ĠComp iler", "ĠCompile r", "( android", "(and roid", "Ġ âĢº", "ĠâĢ º", ". turn", ".t urn", "Ġsup pression", "Ġsuppress ion", "Ġsupp ression", "_ calls", "_c alls", "_call s", "_cal ls", "Ġ *@", "Ġ* @", "( strlen", "(str len", "(st rlen", ". hex", ".h ex", ".he x", "ĠB ills", "ĠBill s", "ĠBil ls", "Ġ RSA", "ĠR SA", "ĠRS A", "Ï Ĥ", "Ġ Escape", "ĠE scape", "ĠEs cape", "ĠEsc ape", "ement ia", "Ġ frontend", "Ġfront end", "Ġp int", "Ġpi nt", "Ġpin t", "_ exc", "_e xc", "_ex c", "z zo", "zz o", "[ ],Ċ", "[] ,Ċ", "[], Ċ", "Ġ\"' ,'\"", "Ġ\"', '\"", "Ġ\"',' \"", ". Environment", ".En vironment", "Ġafore mentioned", "Ġend ure", "prot otype", "proto type", "ther apy", "the rapy", "s si", "ss i", "D eg", "De g", "_ plugins", "_pl ugins", "_plugin s", ". userInfo", ".user Info", "Pr inter", "Print er", "Ġ PROGRAM", "ĠPRO GRAM", "Ġru ins", "Ġruin s", "Ġemp irical", "Ġempir ical", "Ġ crawl", "Ġc rawl", "Ġcr awl", "Ġcraw l", "Ġcra wl", "ĠBo iler", "- comment", "-com ment", "-comm ent", ". subplot", ".sub plot", "_ et", "_e t", "Ġ' .',", "Ġ'. ',", "Ġ'.' ,", "min or", "mi nor", "mino r", "ĠCustom s", "ĠCust oms", "Ġ yaw", "Ġy aw", "Ġya w", "under line", "Ġ Como", "ĠC omo", "ĠCom o", "ĠCo mo", "( ('", "(( '", "( mean", "(m ean", "(me an", "Ġch aque", "Ġcha que", "Ġ Blocks", "ĠB locks", "ĠBl ocks", "ĠBlock s", "ĠBlo cks", "ĠBloc ks", ". rad", ".r ad", ".ra d", "ilib rium", "Ġ webdriver", "Ġweb driver", "Ġmel hor", "d ana", "da na", "dan a", "ĠAb use", "ĠAbu se", "ĠSouth west", "Ġ Paren", "ĠP aren", "ĠPar en", "ĠPa ren", "ĠPare n", "PERT IES", "ĉ IL", "ĉI L", "Ġs cream", "Ġsc ream", "Ġscre am", "Ġscr eam", "v u", "Ġin comes", "Ġinc omes", "Ġincome s", "Ġincom es", "Ġ nim", "Ġn im", "Ġni m", "Ġ lace", "Ġl ace", "Ġla ce", "Ġlac e", "Ġcompens ate", "Re verse", "Rev erse", "D at", "Da t", "_ attack", "_att ack", "Ġn our", "Ġno ur", "Ġnou r", "a chen", "ac hen", "ach en", "ache n", "c ek", "ce k", "< Func", " \"+", ">\" +", "Ġ tokenizer", "Ġtoken izer", "Ġtokenize r", "Ġsovere ignty", "Ġsovereign ty", "ĠP ence", "ĠPe nce", "ĠPen ce", "( )\");Ċ", "() \");Ċ", "()\" );Ċ", "Ġpesso as", "Ġpessoa s", ". Ge", ".G e", "Ġ Included", "ĠIn cluded", "ĠInclude d", "Ġ pagina", "Ġp agina", "Ġpag ina", "Ġex posing", "Ġexp osing", "Ġexpos ing", "Ġexpo sing", "е ÑĪ", "_ SCRIPT", "_SC RIPT", "/ $',", "/$ ',", "Th umbnail", "× Ķ", "webElement X", "webElementX paths", "press ure", "pres sure", "ĠC urry", "ĠCur ry", "_ CP", "_C P", "OL UTION", "I LES", "IL ES", "ILE S", "prot ect", "o ola", "ool a", "oo la", "Work space", "Works pace", "{ };Ċ", "{} ;Ċ", "Ġ UNS", "ĠU NS", "ĠUN S", "Ġsymp athy", "Ġsympath y", "r oker", "ro ker", "roke r", "rok er", "Ġre model", "Ġr emodel", "Ġrem odel", "ĉ cell", "ĉc ell", "Ġa top", "Ġat op", ". FullName", ".Full Name", "Ġf aut", "Ġfa ut", "ĠE asily", "_ dynamic", "_d ynamic", "_dyn amic", "Ġfr amed", "Ġframe d", "Ġfra med", "Ġfram ed", "Ġm otive", "Ġmot ive", "Ġmotiv e", "è· ¯", "s am", "sa m", "Ġ marca", "Ġmar ca", "Ġmarc a", "ĠText EditingController", "Ġd estructor", "Ġde structor", "Ġdestruct or", "c ream", "cre am", "cr eam", "Ġr ude", "Ġru de", "Ġrud e", "Ġ Bold", "ĠB old", "ĠBo ld", "ĠBol d", "ĠInd igenous", "Ġ gens", "Ġg ens", "Ġge ns", "Ġgen s", "Ġrel acion", "( system", "(s ystem", "(sys tem", "Ġ UIFont", "ĠUI Font", "ĠUIF ont", "_ charge", "_ch arge", "_char ge", "U STER", "US TER", "UST ER", "E V", ". Namespace", ".N amespace", ".Name space", ".Names pace", "Ġmer ger", "Ġmerge r", "Ġmerg er", "Ġ calloc", "Ġc alloc", "Ġcall oc", "Ġcal loc", "g ang", "ga ng", "gan g", "Bad Request", "Ġs per", "Ġsp er", "Ġspe r", "- design", "-d esign", "-de sign", "-des ign", "Ġ âĩ", "Ġâ ĩ", "C han", "Ch an", "Cha n", "Ġorg anism", "Ġorgan ism", "Ġorganis m", ", )", "= id", "=i d", "_ plane", "_p lane", "_pl ane", "_plan e", "Ġ Cases", "ĠC ases", "ĠCas es", "ĠCase s", "ĠCa ses", "el fast", "elf ast", "ĠLegisl ature", "ĠF aker", "ĠFa ker", "ĠFake r", "ĠFak er", "Ġinv oking", "Ġinvo king", "- utils", "-util s", "( ).'", "() .'", "(). '", ". face", ".f ace", ".fac e", ".fa ce", "Ġguard ian", "my Modal", "Ġ clipboard", "Ġclip board", "ĠA TM", "ĠAT M", "Ġpe as", "Ġpea s", "ĠS ylv", "ĠSy lv", ". calc", ".c alc", ".ca lc", ".cal c", "Ġ Contacts", "ĠCont acts", "ĠContact s", "int Value", "Ġmod ifying", "Ġmodify ing", "ĠB arb", "ĠBar b", "ĠBa rb", ". loss", ".l oss", ".lo ss", "_ percentage", "_per centage", "_percent age", "As ked", "Ask ed", "( lst", "(l st", "(ls t", "ateg orical", "ategor ical", "ategori cal", "atego rical", "- files", "-f iles", "-file s", "-fi les", "ĠRom ania", "ĠRo mania", "ĠRoman ia", "ĠRoma nia", ". Ac", ".A c", "Ġ hai", "Ġh ai", "Ġha i", "Ġ Flying", "ĠF lying", "ĠFl ying", "ĠFly ing", "Ġ ż", "ĠÅ ¼", "j p", "ĠTr ainer", "ĠTra iner", "ĠTrain er", ". arc", ".a rc", ".ar c", "_ deg", "_d eg", "_de g", "Ġtrace back", "Or Fail", "F LOW", "FL OW", ". old", ".o ld", ".ol d", "o ya", "oy a", "g mt", "gm t", "is empty", "Ġvacc ination", "Ġ obsolete", "Ġob solete", "recogn ized", "Ġru ined", "Ġruin ed", "ĠR ein", "ĠRe in", "ĠRei n", "Ġ Tracking", "ĠTr acking", "ĠTrack ing", "x fb", "xf b", "ا ÛĮ", "Ġv ære", "Ġvæ re", "Ġbr yster", "Ġ ITS", "ĠI TS", "ĠIT S", "Ġdes tiny", "Ġdest iny", "Ġdestin y", "Ġs wear", "Ġsw ear", "Ġswe ar", "Ġre des", "Ġr edes", "Ġred es", "Ġrede s", "Ġ clf", "Ġc lf", "Ġcl f", "Ġfl ipped", "Ġflip ped", "ĉ head", "ĉh ead", "B luetooth", "Bl uetooth", "Ġ Overrides", "ĠOver rides", "ĠOverride s", ": Boolean", "_ =", "_ lr", "_l r", "s pawn", "sp awn", "spa wn", ": index", "VAL UES", "VALUE S", "is key", "isk ey", "iske y", "? \");Ċ", "?\" );Ċ", ".syn thetic", "Ġ Checking", "ĠCheck ing", "struct ures", "structure s", "i ping", "ip ing", "ipi ng", "Ġvoc als", "Ġvocal s", "- Up", "-U p", "ĠManufact urers", "ĠManufacturer s", "ĠMar riage", "ĠMarr iage", "代 çłģ", "Ġgar ner", "Ġgarn er", "_ Client", "_C lient", "_Cl ient", "par allel", "paralle l", "RI END", "Ġvine gar", "se gue", "seg ue", "J B", "Ġcont acting", "Ġcontact ing", "ĠCar roll", "ĠCarr oll", "Ġout reach", "Ġoutr each", "t ensor", "_ variant", "_v ariant", "_var iant", "Ġt heat", "Ġth eat", "Ġthe at", "l icable", "lic able", "lica ble", "{ |", "t iny", "ti ny", "tin y", "_ letter", "_l etter", "Ġp encil", "Ġpen cil", "Ġpenc il", "HeadersHeight SizeMode", "il tro", "ilt ro", "iltr o", ".auto configure", ". drag", ".d rag", ".dr ag", ". useState", ".use State", "Ġ BMI", "ĠB MI", "ĠBM I", "h int", "hi nt", "hin t", "Com pile", "Comp ile", "* \\", "en ary", "ena ry", "Ġ lvl", "Ġl vl", "Ġlv l", ". Cache", ".C ache", "+ =\"", "+= \"", "_ tv", "_t v", "ruit ment", "Ġf read", "Ġfr ead", "Ġfre ad", "Art icles", "Article s", "f ila", "fi la", "fil a", "Ġpack aged", "Ġpackage d", "âĺ Ĩ", "AT HER", "ATH ER", "ĠPl anned", "ĠPlan ned", "s cheme", "sch eme", "Ġdi ary", "Ġdia ry", "Ġoff enses", "Ġoffense s", "Ġoffen ses", "/ F", "Ġ Stick", "ĠS tick", "ĠSt ick", "Ġc erc", "Ġce rc", "Ġcer c", "ĠS lee", "ĠSl ee", "ĠSle e", "ĉ ĉĠĠĠĠĠĠĠĠ", "ĉĉ ĠĠĠĠĠĠĠĠ", "ĉĉĠĠĠ ĠĠĠĠĠ", "ĉĉĠ ĠĠĠĠĠĠĠ", "ĉĉĠĠ ĠĠĠĠĠĠ", "ĉĉĠĠĠĠĠĠĠ Ġ", "ĉĉĠĠĠĠ ĠĠĠĠ", "ĉĉĠĠĠĠĠ ĠĠĠ", "ĉĉĠĠĠĠĠĠ ĠĠ", "< Image", "", ";' >", "ĉ col", "ĉc ol", "V G", "_ boolean", "_bool ean", "_bo olean", "re cent", "rec ent", "rece nt", "Ġ *)ĊĊ", "Ġ* )ĊĊ", "Ġ*) ĊĊ", "Ġ*)Ċ Ċ", "ĠRain bow", "om men", "omm en", "Ġl ur", "Ġlu r", "Ġop pression", "Ġopp ression", "Ġoppress ion", "(\" ,\");Ċ", "(\", \");Ċ", "(\",\" );Ċ", "ĠFac ility", "DEF INED", "DEFINE D", "Ġn eon", "Ġne on", "Ġneo n", "Ġoff ender", "Ġoffend er", "Ġoffen der", "A FP", "AF P", "Ġ Cleaning", "ĠC leaning", "ĠClean ing", "ĠCle aning", "[ ]):", "[] ):", "[]) :", "Ġund ocumented", ". Repositories", ".Re positories", "ĠG uitar", "ĠGu itar", "ĠGui tar", "аÑģÑģ ив", "S kills", "Sk ills", "Skill s", "Ġtest imon", "Ġtestim on", "rypt ography", "ĠAm ber", "ĠAmb er", "ĠSt alin", "ĠSta lin", "Ġl one", "Ġlo ne", "Ġlon e", "Ġap enas", "Ġape nas", "Ġdi eses", "Ġdie ses", "Ġdies es", "Ġdiese s", "Ġ Arduino", "ĠAr duino", "è½ ¬", "= =-", "== -", "_ Act", "_A ct", "Ġ coded", "Ġc oded", "Ġco ded", "Ġcode d", "Ġcod ed", "âĸ ł", "am burger", "amb urger", "amburg er", "- links", "-l inks", "-link s", "Ġar mour", "Ġarm our", ". High", ".H igh", "get Content", "getC ontent", "s tag", "st ag", "sta g", "Ġ heck", "Ġh eck", "Ġhe ck", "Ġ ìĹĨ", "ĠìĹ Ĩ", "ĠMc Connell", "ĠCon cert", "ĠConc ert", "ĠConce rt", "Ġ Alloc", "ĠAl loc", "ĠAll oc", "ä re", "är e", ". replaceAll", ".replace All", "Ġpart itions", "Ġpartition s", "r ott", "ro tt", "rot t", "ĠF le", "ĠFl e", "_ TREE", "_T REE", "_TR EE", "reason able", "Ġ Reporting", "ĠRep orting", "ĠReport ing", "Ġbillion aire", "s cores", "sc ores", "score s", "m ins", "min s", "mi ns", "- eye", "-e ye", "M ORE", "MO RE", "ab ort", "abor t", "abo rt", "ĠS WT", "ĠSW T", "Ġin verted", "Ġinvert ed", "Ġ Teachers", "ĠTe achers", "ĠTeacher s", "ĠTea chers", "ĠTeach ers", "; n", "Ġ astro", "Ġa stro", "Ġas tro", "Ġast ro", "Ġastr o", "н ов", "но в", "а ниÑĨ", "ан иÑĨ", "ани ÑĨ", "product o", "c ountries", "count ries", "ĠO wen", "ĠOw en", "Ġcont amination", "Ġcontamin ation", "Ġconta mination", "Ġv ibe", "Ġvi be", "Ġvib e", "ĠE lli", "ĠEl li", "ĠEll i", ". script", ".s cript", "ĠO live", "ĠOl ive", "ĠOliv e", "D MA", "DM A", "v ier", "vi er", "vie r", ": semicolon", "- module", "-m odule", "-mod ule", "g ressive", "gress ive", "a gu", "ag u", "_ players", "_p layers", "_pl ayers", "_player s", "_play ers", "Ġresult ados", "Ġresultado s", "start ed", "star ted", "scroll Top", "= ====", "== ===", "==== =", "=== ==", "Ġweigh ing", "Ġ[ [[", "Ġ[[ [", "z ahl", "za hl", "( NS", "(N S", "Ġ Assertion", "ĠAssert ion", "le ague", "lea gue", ".set TextColor", ".setText Color", "ĉ Message", "ĉM essage", "Ġm oms", "Ġmom s", "Ġmo ms", "_ AF", "_A F", ". wh", ".w h", "A LS", "AL S", "Ġaut re", "Ġau tre", "] ĊĊĊĊ", "]Ċ ĊĊĊ", "]ĊĊ ĊĊ", "]ĊĊĊ Ċ", ". opacity", ".op acity", "ĠBudd hist", "ĠBuddh ist", "Ġde af", "ĠOrgan isation", "( Global", "(G lobal", "en sch", "ens ch", "Ġhead ache", "ĠA lien", "ĠAl ien", "ĠAli en", "_ inode", "_in ode", "_i node", "ĠSt ark", "ĠStar k", "ĠSta rk", "Ġ æī", "Ġæ ī", "-l nd", "-ln d", "o ref", "or ef", "ore f", "_ feat", "_f eat", "_fe at", "Ġpedest rian", "Ġnom inal", "Ġnomin al", "Ġball oon", "Ġbal loon", "Ġballo on", "Ġ sprites", "Ġs prites", "Ġsp rites", "Ġspr ites", "Ġsprite s", "Prototype Of", "ĠA post", "ĠAp ost", "Ġ FEATURE", "ĠF EATURE", "ĠFE ATURE", "O H", "Ġre cess", "Ġr ecess", "Ġrec ess", "Ġrece ss", "ĠD onna", "ĠDon na", "con sumer", "cons umer", "consum er", "consume r", "$ GLOBALS", "ĠG IF", "ĠGI F", "- frame", "-f rame", "-fr ame", "In icio", "Ini cio", "Ġpass ages", "Ġpassage s", "Date String", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", ". byte", ".b yte", ".by te", "B ug", "Bu g", "initial izer", "initialize r", "p kt", "pk t", "od ium", "odi um", "Ġ DER", "ĠD ER", "ĠDE R", ". ops", ".op s", ".o ps", "l eri", "le ri", "ler i", "Ġgift ed", "Ġgif ted", "Ġ detach", "Ġde tach", "Ġdet ach", "ter rain", "terra in", "terr ain", "el ters", "elt ers", "elter s", "ãģ ı", ". loader", ".l oader", ".load er", ".lo ader", "ĠN GO", "ĠNG O", "str ncmp", "K h", "( fontSize", "(font Size", "r ocket", "ro cket", "rock et", "roc ket", "Ġpreced ent", "ĠAur ora", "Ġ Experiment", "ĠEx periment", "i sphere", "is phere", "isp here", "En coded", "Enc oded", "Encode d", "Ġ âĢĵĊĊ", "ĠâĢĵ ĊĊ", "ĠâĢĵĊ Ċ", "Ġpy ramid", "ĠAnn iversary", "o fil", "of il", "ofi l", "ë Ł", "( plugin", "(pl ugin", "C oeff", "Co eff", "Ġco operate", "Ġcooper ate", "Ġcoop erate", "Ġpredomin antly", "Ġpredominant ly", "I SM", "IS M", "Ph rase", "_ DEFINE", "_DE FINE", "_DEF INE", "_DEFIN E", "F lip", "Fl ip", "AMIL Y", "AMI LY", "ĠMark ets", "ĠMarket s", "Ġ StreamReader", "ĠStream Reader", "Ġ Combine", "ĠC ombine", "ĠCom bine", "ĠComb ine", "Ġmanus cript", "z za", "zz a", ", tp", ",t p", "Wh atever", "What ever", "IT ICAL", "igh bour", "ighb our", "Data Provider", ". Texture", ".Text ure", "priv acy", ". SDK", ".S DK", "Ġre charge", "Ġ cpp", "Ġc pp", "Ġcp p", "Ġ CFG", "ĠC FG", "ĠCF G", "( holder", "(h older", "( py", "(p y", "m ot", "mo t", "Ġsa voir", "Ġsav oir", "ĠR osa", "ĠRo sa", "ĠRos a", "ĠP Cs", "ĠPC s", "Ġ íĻ", "Ġí Ļ", ".her oku", ".hero ku", "Ġf ren", "Ġfr en", "Ġfre n", "ĠR iley", "ĠRi ley", "a gate", "ag ate", "aga te", "Ġs ond", "Ġso nd", "Ġson d", ". xlsx", ".x lsx", ".xls x", "Ġh acked", "Ġhack ed", "Ġhac ked", "st ad", "sta d", "G i", "Ġs anity", "Ġsan ity", "Ġsanit y", "ĠSql DataAdapter", ".. .\",", "... \",", "...\" ,", "ĠP ussy", "Ġ ****************", "Ġ** **************", "Ġ**** ************", "Ġ******** ********", "Ġhass le", "_P ARENT", "_PAR ENT", "ĠU AE", "ĠUA E", "Ġbegin ners", "Ġbeginner s", "( Client", "(C lient", "(Cl ient", "Ġstat istically", "Ġstatist ically", "Ġstatistical ly", "Ġstatistic ally", ". hour", ".h our", "e delta", "ed elta", "Ġ traction", "Ġt raction", "Ġtr action", "Ġtra ction", "Ġtract ion", "u elve", "uel ve", "a rat", "ar at", "ara t", "Ġsa una", "Ġsau na", "IN VALID", "INVAL ID", "Ġindict ment", "AL LE", "ALL E", "Ġd issent", "Ġdis sent", "Ġdiss ent", "Ġdisse nt", "Ġ Typography", "ĠTyp ography", "Ġintent ional", "Ġintention al", "s it", "si t", "ĠAn imals", "ĠAnimal s", "ĠAnim als", "Ġcoun tryside", "Ġcountry side", "Ġ uart", "Ġu art", "Ġua rt", "} \\\"", "}\\ \"", "Ġseam less", "¾ 示", "Ġ autos", "Ġa utos", "Ġaut os", "Ġauto s", "Ġau tos", "Ġ\" '\";Ċ", "Ġ\"' \";Ċ", "Ġ\"'\" ;Ċ", "F lush", "Fl ush", "AN NOT", "ANN OT", "Ġal gebra", "Ġalg ebra", "as soc", "ass oc", "asso c", "ĠW aters", "ĠWater s", "ĠWat ers", "ĠWa ters", "Ġprepar ations", "Ġpreparation s", "r onym", "ro nym", "ron ym", "[, ]", "S ans", "San s", "Sa ns", "Ġarm ies", "i peg", "ip eg", "ipe g", "Ġcre amy", "Ġcream y", "Ġcrea my", ". art", ".a rt", ".ar t", "e tre", "et re", "etr e", "Ġ Animated", "ĠAn imated", "ĠAnim ated", "Ġun pleasant", "e mean", "em ean", "eme an", "g reat", "gr eat", "gre at", "i Äħ", "Ġ Earlier", "ĠEar lier", "ĠEarl ier", "Ġc hic", "Ġch ic", "Ġchi c", "Ġpres erving", "( exec", "(e xec", "(ex ec", "ĠInvest igation", "ĠInvestig ation", "ĉ GPIO", "ĉG PIO", "Ġrig orous", "Ġrigor ous", "i jo", "ij o", "= num", "=n um", "Ġ toolStrip", "Ġt oolStrip", "Ġtool Strip", ") set", ")s et", "+ \"&", "+\" &", "Ġ Acceler", "ĠAcc eler", "Ġdevelop mental", "Ġdevelopment al", "is posable", "Ġflaw ed", "Ġfla wed", "r ene", "re ne", "ren e", "Up dating", "Ġwatch dog", "Ġden ominator", "Ġdenom inator", "Ġdenomin ator", "Ġsubur bs", "Ġsuburb s", "Ġ ...)", "Ġ... )", "Ġ.. .)", "Ġconv ictions", "Ġconviction s", "Ġconvict ions", "c losure", "clo sure", ". IP", ".I P", "Ġtrans lates", "Ġtransl ates", "Ġtranslate s", ".s wt", ".sw t", ". Trace", ".T race", ".Tr ace", "Ġmet tre", ". isEnabled", ".is Enabled", "Ġ Effective", "ĠE ffective", "ĠEffect ive", "ĠEff ective", ". toInt", ".to Int", "Ġen chant", "Ġench ant", "Ġst unned", "Ġstun ned", "Ġ poi", "Ġp oi", "Ġpo i", "/ code", "/c ode", "/co de", "a dm", "ad m", ".data binding", ".datab inding", ".databind ing", "Ġ Lorem", "ĠL orem", "ĠLo rem", "ĠLore m", "ĠLor em", "________________________________ ________________________________", "Ġ ledger", "Ġled ger", "Ġledge r", "Ġc ara", "Ġcar a", "Ġca ra", "ĠG ir", "ĠGi r", "Ġw aits", "Ġwait s", "Ġwa its", "U no", "Un o", "Ġ cwd", "Ġc wd", "Ġcw d", "è¾ ij", "ĠT Result", "Ġre jo", "Ġrej o", "Ġe mitted", "Ġem itted", "Ġemit ted", "ĠWest minster", "ä¸Ģ 个", "n ek", "ne k", "_T is", "Ġen act", "ĉ with", "ĉw ith", "or gia", "org ia", "Ġj ue", "Ġju e", "Per form", "S PATH", "SP ATH", "SPA TH", ". topic", ".t opic", ".to pic", ".top ic", "ĠD aten", "ĠDate n", "ĠDa ten", "ĠDat en", "Ạ§", "Ġsit io", "Ġsiti o", "_ MM", "_M M", "\" So", "\"S o", "b ial", "bi al", "bia l", "Ġ scoped", "Ġsc oped", "Ġscope d", "Ġsco ped", "Ġscop ed", "Re quires", "Require s", "Ġ TOTAL", "ĠT OTAL", "ĠCh ancellor", "( contents", "(content s", "(cont ents", "Ġste alth", "Ġsteal th", "dev ices", "device s", "- pass", "-p ass", "il ih", "ili h", "ĠMal colm", "ĠDe pot", "ĠDep ot", "Ġcon figur", "Ġconfig ur", "a ussian", "aus sian", "auss ian", "_ constraint", "_con straint", "в еÑĤ", "ве ÑĤ", "G RA", "GR A", "Ġ Rates", "ĠR ates", "ĠRa tes", "ĠRate s", "ĠRat es", ".dataGridView TextBoxColumn", "ĠN obel", "ĠNo bel", "ĠNob el", "i tics", "it ics", "iti cs", "itic s", "Ġignor ant", "Ġ Reporter", "ĠRe porter", "ĠReport er", "ĠEb ola", "Ġ Shock", "ĠSh ock", "ĠSho ck", "_ relation", "_re lation", "_rel ation", "ĠN inja", "ĠNin ja", ") c", "Ġ ticker", "Ġt icker", "Ġti cker", "Ġtick er", "Ġtic ker", ". isChecked", ".is Checked", "ĠSup pliers", "ĠSupplier s", "ĠR apid", "ĠRa pid", "ĠRap id", "Level s", "âĤ¬ âĦ¢", "ĉ queue", "ĉq ueue", "Ġ chop", "Ġc hop", "Ġch op", "Ġcho p", "Ġ Unix", "ĠUn ix", "ĠUni x", "re ject", "rej ect", "- calendar", "-c alendar", "-cal endar", "( sort", "(s ort", "(so rt", "è ne", "èn e", "erc icio", "Ġh ect", "Ġhe ct", "CALL TYPE", "r oupon", "ro upon", "rou pon", "roup on", "Ġrent als", "Ġrental s", "author s", "auth ors", "{ name", "{n ame", "ĠF IFO", "ĠFI FO", "Ġ lassen", "Ġl assen", "Ġlas sen", "Ġ Nous", "ĠN ous", "ĠNo us", "ĠNou s", "Ġsn apped", "Ġsnap ped", "Ġsna pped", "Ġf ertility", "Ġfer tility", "Ġfert ility", "Ġfertil ity", "\" log", "cl icked", "click ed", "Ġplan ting", "Ġplant ing", "Ġ gb", "Ġg b", "/ output", "/out put", "PE AT", "Ġ categoria", "Ġc ategoria", "Ġcategor ia", "Ġ bach", "Ġb ach", "Ġba ch", "Ġbac h", "Prof essor", "i nth", "in th", "int h", "\" ]čĊ", "\"] čĊ", "Rec order", "Record er", "s erde", "ser de", "Ġ Transmission", "ĠTrans mission", "t rad", "tr ad", "tra d", "Ġtur bo", "Ġturb o", "_ VERTEX", "_VER TEX", "\\ Event", "\\E vent", "il ver", "Ġbod ily", "Ġ Sources", "ĠS ources", "ĠSource s", "ĠSour ces", "Ġkill ings", "Ġkilling s", "Ġkil lings", ".xr TableCell", "Ġfol ded", "Ġfold ed", "/ legal", "/l egal", "u ner", "un er", "une r", "ĠR ifle", "ĠRif le", "ĠM IDI", "ĠMI DI", "ĠMID I", "_Selected IndexChanged", ".Size Type", "Ġ WebSocket", "ĠWeb Socket", "Ġse leccion", "Ġsele ccion", "S and", "San d", "Sa nd", "ot ros", "otr os", "Ġen vision", "Ġenv ision", "Ġenvis ion", "/ etc", "/e tc", "ĠMel issa", "S pot", "Sp ot", "Spo t", "н ое", "но е", "_ ARM", "_A RM", "_AR M", "At tempt", "Att empt", "Ġ BI", "ĠB I", "ãģ Ķ", "Ġ DU", "ĠD U", "Ġback lash", "st ride", "str ide", "stri de", "/ classes", "/c lasses", "/class es", "/cl asses", "Ġtext Color", "_ staff", "_st aff", "_sta ff", "ob lin", "obl in", "ag enta", "agent a", "agen ta", ". collections", ".c ollections", ".col lections", ".collection s", ".collect ions", ".coll ections", "il lage", "ill age", "illa ge", "' čĊčĊ", "'čĊ čĊ", "fl atten", "flat ten", "_ sales", "_s ales", "_sale s", "_sal es", "_sa les", "_ MASTER", "_M ASTER", "_MA STER", "T W", "_ da", "_d a", "P itch", "ph ies", "phi es", "Ġz ombies", "Ġzombie s", "Ġ VERY", "ĠV ERY", "ĠVER Y", "ĠVE RY", "ĠPh armacy", "ĠPharm acy", "ĠPharmac y", "ĠPharma cy", "Ġprogress Bar", "Ġhas htag", "Ġhash tag", "S idebar", "Side bar", "@ stop", "@s top", "( pc", "(p c", "ол ж", "M AKE", "MA KE", "ĠC oron", "ĠCo ron", "ĠCor on", "Ġkv inner", "Ġkvin ner", "Ġkvinn er", "Ġkvinne r", "ĠM aid", "ĠMa id", "ĠMai d", "b ob", "bo b", ". titleLabel", ".title Label", "Ġsuccess es", "Ġsucc esses", "Ġsucces ses", "ĠDem ocracy", "ĠDemocr acy", "ĠS urgery", "ĠSurg ery", "ĠSurge ry", "Ġco ugar", "Ġcou gar", "Ġ curso", "Ġcur so", "Ġcurs o", "Ġl oro", "Ġlo ro", "Ġlor o", "ist ency", "iste ncy", "isten cy", "Sen ior", "æ k", "Ġ AAA", "ĠA AA", "ĠAA A", "Ġ BOOK", "ĠB OOK", "ĠBO OK", "к о", "W STR", "WS TR", "Ġ */,Ċ", "Ġ* /,Ċ", "Ġ*/ ,Ċ", "Ġ*/, Ċ", "o yal", "oy al", "oya l", ". vector", ".v ector", ".vec tor", "Ġ SPEC", "ĠS PEC", "ĠSP EC", "ĠSPE C", "S SF", "SS F", "Ġcomp uls", "ĠAppe als", "ĠAppeal s", "ĠW inston", "ĠWin ston", "ĠWins ton", "ĠMock ito", "con trib", "cont rib", "contr ib", "contri b", ". available", ".a vailable", ".av ailable", "entity Manager", "a rias", "ar ias", "ari as", "aria s", "_ sale", "_s ale", "_sal e", "_sa le", "_ rs", "_r s", "Ġde coding", "Ġdec oding", "Ġdeco ding", "Ġ locator", "Ġl ocator", "Ġloc ator", "ol ith", "oli th", "olit h", "Ġ kol", "Ġk ol", "Ġko l", "Ġ ascii", "Ġasc ii", "ĠR ut", "ĠRu t", "/ interface", "ĉ ĉĉĉĉĉĠĠĠ", "ĉĉ ĉĉĉĉĠĠĠ", "ĉĉĉĉ ĉĉĠĠĠ", "ĉĉĉ ĉĉĉĠĠĠ", "ĉĉĉĉĉ ĉĠĠĠ", "ĉĉĉĉĉĉ ĠĠĠ", "ĉĉĉĉĉĉĠ ĠĠ", "ĉĉĉĉĉĉĠĠ Ġ", "Ġ Numer", "ĠN umer", "ĠNum er", "ĠNu mer", ". flip", ".f lip", ".fl ip", "- del", "-d el", "-de l", "Ġbol ster", "Ġbols ter", "on omic", "ono mic", "onom ic", "Ġ zm", "Ġz m", "L G", "Find By", "Ġ adaptive", "Ġad aptive", "Ġadapt ive", "Ġada ptive", "l oo", "lo o", "Ġ vue", "Ġv ue", "Ġvu e", "( reverse", "(re verse", "_ canvas", "_c anvas", "_can vas", ". roles", ".r oles", ".role s", ".ro les", "ific ado", "ifica do", "ven ient", "\" As", "\"A s", "Ġ Entr", "ĠEn tr", "ĠEnt r", "al igned", "align ed", "Ġbere its", "/ //ĊĊ", "// /ĊĊ", "/// ĊĊ", "///Ċ Ċ", ".g wt", ". employee", ".e mployee", "_ cli", "_c li", "_cl i", "Ġanticip ate", "éĻ IJ", "Ġp ik", "Ġpi k", "Ġmush rooms", "Ġmushroom s", "( tt", "(t t", "Ġ oma", "Ġo ma", "Ġom a", "ĠSan chez", "_ google", "_g oogle", "_go ogle", ". Valid", ".Val id", "Ġ FileName", "ĠFile Name", "iv ative", "k ed", "ke d", "- war", "-w ar", "Ġm aturity", "Ġmat urity", "и д", "Ġ miner", "Ġm iner", "Ġmin er", "Ġmi ner", "Ġmine r", "Re ducers", "Reduc ers", "Reducer s", "Reduce rs", "Ġ LatLng", "ĠL atLng", "ĠLat Lng", "_ STD", "_S TD", "_ST D", "D igits", "Digit s", "Dig its", "C alc", "Cal c", "Ca lc", "- upload", "-up load", "Ġhand ic", "Ġhan dic", "ี à¹Ī", "eg rated", "egr ated", "egrate d", "egra ted", "Ġ STM", "ĠS TM", "ĠST M", "C lients", "Client s", "Cl ients", "Cli ents", "ĠTur bo", "S YNC", "SY NC", "Ġphot ographers", "Ġphotograph ers", "Ġphotographer s", ". Out", ".O ut", ". character", ".char acter", "B UILD", "BU ILD", ". unlock", ".un lock", "Ġar ises", "Ġarise s", "Ġ Commands", "ĠComm ands", "ĠCommand s", "(\" \");čĊ", "(\"\" );čĊ", "(\"\") ;čĊ", "(\"\"); čĊ", "_ FORE", "_F ORE", "_FOR E", "; ',", ";' ,", "+ \"'", "+\" '", ". Images", ".Image s", ".Im ages", "\" ){", "\") {", "ĠM eyer", "ĠMe yer", "ĠMey er", "Ġneg atively", "Ġnegative ly", "Ġ DLL", "ĠD LL", "ĠDL L", "Ġ exe", "Ġe xe", "Ġex e", "Ġdef iciency", "Ġwild ly", "- switch", "-s witch", "-sw itch", "con struction", "construct ion", "Ġexception ally", "Ġexceptional ly", "ĠL iz", "ĠLi z", "/ java", "/j ava", "/jav a", "Ġthe irs", "Ġtheir s", "ĠCon temporary", "ĠCont emporary", "l is", "li s", ".fill Rect", "ĠN FC", "ĠNF C", "Ġre he", "Ġreh e", "( numbers", "(num bers", "(number s", "Ġr aster", "Ġra ster", "Ġras ter", "Ġrast er", "Ġfig uring", "Ġfigur ing", "Ġshow c", "Ġsho wc", "ĠJ ill", "ĠJi ll", "Ġar cade", "Ġarc ade", "ĠConstruct s", "m dl", "md l", "( '|", "(' |", "Ġident ifiers", "Ġidentifier s", "Ġ stellar", "Ġst ellar", "( Connection", "Ġ\" {{", "Ġ\"{ {", "y or", "yo r", "( mysqli", "(m ysqli", "(mysql i", "Ġd ove", "Ġdo ve", "Ġdov e", "Of Birth", ". disconnect", ".dis connect", "_ hi", "_h i", "Ġzw ischen", "ĠGr und", "i ros", "ir os", "iro s", "_ Array", "_A rray", ". onclick", ".on click", "an som", "ans om", "An swers", "Answer s", "Ans wers", "ĉ remove", "ĉre move", "F a", "Ġh urry", "Ġhur ry", "- inf", "-in f", "-i nf", "Ġ getClass", "Ġget Class", "ĠgetC lass", "ĠReg ulation", "Ġ FLAGS", "ĠFLAG S", "m isc", "mi sc", "mis c", "K en", "Ke n", "_ heading", "_head ing", "_he ading", "G Hz", "GH z", "- entry", "-en try", "Ġbi ography", "S ig", "Si g", "- mf", "-m f", "W atcher", "Watch er", "Wat cher", "âĢľ A", "} px", "Ġsp icy", "Ġspi cy", "_ sq", "_s q", "L ost", "Lo st", "Los t", "( track", "(t rack", "(tr ack", "а ли", "ал и", "Desc ending", "< bits", " ((", ">( (", "s urvey", "sur vey", "Ġ íĺ", "Ġí ĺ", ".. .')Ċ", "... ')Ċ", "...' )Ċ", "Ġ Divider", "ĠDi vider", "ĠDiv ider", "ĠDivide r", "o sl", "os l", "_ CANCEL", "_C ANCEL", "_CAN CEL", "_ prepare", "_pre pare", "_prep are", "s tin", "st in", "sti n", "ĠHe ath", "ĠHeat h", ". PrimaryKey", ".Primary Key", "Ġ âĨIJ", "ĠâĨ IJ", "ĠLocal DateTime", "ĠLocalDate Time", "Ġco operative", "Ġcooper ative", "L earning", "Le arning", "Learn ing", ". enqueue", ".en queue", "Ġ goog", "Ġg oog", "Ġgo og", "Ġgoo g", "Ġ Regression", "ĠRe gression", "ĠReg ression", "i mates", "im ates", "imate s", "ima tes", "imat es", "Ġvoy eur", "Ġ Drink", "ĠD rink", "ĠDr ink", "p lug", "pl ug", "Ġl ender", "Ġle nder", "Ġlen der", "Ġlend er", "m ana", "man a", "ma na", "Ġperson nes", "Ġpersonne s", "Ġpersonn es", "yp se", "yps e", "Ġ unlink", "Ġun link", "Ġunl ink", "ĠRa vens", "ĠRav ens", "ĠRaven s", "Ġh urd", "Ġhur d", "Ġhu rd", "Ġperiod ically", "Ġperiodic ally", "AR GS", "ARG S", "Ġ GH", "ĠG H", "char acters", "character s", ".. .\"ĊĊ", "... \"ĊĊ", "...\" ĊĊ", "...\"Ċ Ċ", "- establish", "Ġ dn", "Ġd n", "( condition", "(con dition", "(cond ition", "Ġ Gravity", "ĠGr avity", "Ġes tas", "Ġest as", "Ġesta s", "_ focus", "_f ocus", "C reature", "Cre ature", "Cr eature", "Creat ure", "( site", "(s ite", "(si te", "Ġc arr", "Ġcar r", "Ġca rr", "Ġ RL", "ĠR L", "Ġ RI", "ĠR I", "ĠM oto", "ĠMo to", "ĠMot o", "A SF", "AS F", "Ġ Luckily", "ĠLuck ily", "ĉ Route", "ĉR oute", "Ġ entropy", "Ġent ropy", "Ġentr opy", "( \",\"", "(\" ,\"", "(\", \"", "C ollect", "Col lect", "Coll ect", "( contact", "(cont act", "ĠFlor ence", "ĠFlo rence", "Ġpremium s", "Ġpremi ums", "Ġl ifecycle", "Ġlife cycle", "Ġlif ecycle", "Ġb ans", "Ġban s", "Ġba ns", "x ef", "xe f", "Web Kit", "Ġ Floating", "ĠF loating", "ĠFloat ing", "ĠFlo ating", "Ġ cosa", "Ġc osa", "Ġco sa", "Ġcos a", "S pecific", "Spec ific", "ĠLo ans", "ĠLoan s", "b read", "br ead", "bre ad", "Ġdes criptors", "Ġdescriptor s", "Ġ{ :.", "Ġ{: .", "TH READ", "ĠT rent", "ĠTr ent", "ĠTre nt", "Ġs cop", "Ġsc op", "Ġsco p", "Q A", "ĠAn tar", "ĠAnt ar", "p el", "pe l", "_ difference", "_d ifference", "_diff erence", "_ changes", "_ch anges", "_change s", "_chan ges", "( ...)", "(... )", "(.. .)", "Ġ Rotation", "ĠR otation", "ĠRot ation", "ĠL GPL", "ĠLG PL", "Ġ JUST", "ĠJ UST", "( Task", "(T ask", "_ subset", "_sub set", "_subs et", "Ġ TRANS", "ĠTR ANS", "ĠTRAN S", "åĬ Ľ", "ĠS cout", "ĠSc out", "ĠSco ut", "- popup", "-p opup", "-pop up", "Ġsm oked", "Ġsmoke d", "Ġsmo ked", "_ Class", "_C lass", "_Cl ass", "Ġturn over", "Ġturno ver", "br akk", "bra kk", "ĠRock y", "ĠRo cky", "ĠRoc ky", "t as", "ta s", ".Regular Expressions", "ĠElli ott", "ĠElliot t", "Ġ Spinner", "ĠSp inner", "ĠSpin ner", "DUCT ION", "DU CTION", "Ġl ibre", "Ġli bre", "Ġlib re", "Ġlibr e", "Ġmol to", "Ġmolt o", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", "Ġ FTP", "ĠF TP", "ĠFT P", "m peg", "mp eg", "( features", "(f eatures", "(feature s", "(fe atures", "Ġb ald", "Ġbal d", "Ġba ld", "ĠV id", "ĠVi d", "Ġsh outing", "Ġshout ing", "Ġsho uting", "L int", "Li nt", "Lin t", "Ġ sockets", "Ġs ockets", "Ġsocket s", "Ġsock ets", "Ġp row", "Ġpro w", "Ġpr ow", "Ġnou velle", "Ġnouvel le", "Ġnouve lle", "is card", "isc ard", "ĠS ponsor", "Ġ consulta", "Ġcons ulta", "Ġconsult a", "Ġconsul ta", ") ));", ")) );", "))) ;", "Ind ian", "India n", "ĠR aspberry", "Ġteam mate", "Ġ JWT", "ĠJ WT", "ĠJW T", "ĠG hana", "ĠGh ana", "Ġ cakes", "Ġc akes", "Ġca kes", "Ġcake s", "pr imer", "prim er", "prime r", "pri mer", "form a", "for ma", "erg arten", "_ Manager", "_M anager", "_Man ager", "Ġpre season", "G AME", "GA ME", "| \"", "ĠB rock", "ĠBr ock", "ĠBro ck", "Ġocc upy", "Ġoccup y", "Ġdecor ations", "Ġdecoration s", "á nd", "án d", "Ġ cot", "Ġc ot", "Ġco t", "Ġp aran", "Ġpar an", "Ġpara n", "Ġpa ran", "D isk", "Dis k", "Di sk", "r emain", "re main", "rem ain", "rema in", "> ?", "St rong", "Str ong", "Ġf rance", "Ġfr ance", "Ġfra nce", "Ġfran ce", "Ġfranc e", "ĠE ra", "ĠEr a", "- cr", "-c r", ".Buffer edReader", ".Buffered Reader", "ĠParad ise", "ĠV AT", "ĠVA T", "ĠAn ders", "ĠAnd ers", "Ġl imb", "Ġli mb", "Ġlim b", "amp oo", "ampo o", "Ġimper ative", "UT ILITY", "UTIL ITY", "Ġ Recognition", "ĠRec ognition", "ĠRecogn ition", "Ġragaz ze", "Ġp ops", "Ġpop s", "Ġpo ps", "y press", "yp ress", "Ġemb argo", "Ġembar go", "// {Ċ", "Ġs yll", "Ġsy ll", "P TR", "PT R", "åŃĺ åľ¨", "Ġdid nt", "Ġdidn t", "M ailer", "Mail er", "Ma iler", "Ġacad emics", "Ġacademic s", "ĠFr auen", "ĠFra uen", "ĠFrau en", "ne ider", "- rel", "-r el", "-re l", "Ġrain bow", "( In", "(I n", "Ġs liced", "Ġsl iced", "Ġslice d", "Ġslic ed", "= ============Ċ", "== ===========Ċ", "==== =========Ċ", "======== =====Ċ", "=== ==========Ċ", "============ =Ċ", "============= Ċ", "=========== ==Ċ", "========= ====Ċ", "========== ===Ċ", "====== =======Ċ", "===== ========Ċ", "======= ======Ċ", "( send", "(s end", "(se nd", "NSMutable Dictionary", "v os", "vo s", "( package", "(p ackage", "(pack age", "Ġord inance", "Ġordin ance", "view er", "vie wer", "ĠSan tos", "ĠSant os", "ĠSanto s", "- selling", "-s elling", "Ġ gov", "Ġg ov", "Ġgo v", "et tle", "ett le", "Ġfound ers", "Ġfo unders", "Ġfounder s", "Ġw aking", "Ġwa king", "s lashes", "sl ashes", "slash es", "-p ound", "-po und", "re cht", "rec ht", "rech t", "ا ت", "Ø§Ø ª", ". onClick", ".on Click", "Ġn ord", "Ġno rd", "Ġnor d", "st änd", "_ when", "_w hen", "_wh en", "U TERS", "UT ERS", "UTE RS", "i cc", "ic c", "Ġcaps ule", "ĠW id", "ĠWi d", "M arc", "Mar c", "Ma rc", "ภ¸", "r ored", "ro red", "ror ed", "U GE", "UG E", "LO UD", "Ġ Audit", "ĠA udit", "ĠAud it", "ĠAu dit", "ĠAudi t", "ip ients", "ipient s", "ipi ents", "op ian", "opia n", "opi an", "ĠS ue", "ĠSu e", "Ġwur den", "Ġwurde n", ". Helpers", ".H elpers", ".Helper s", ".Help ers", "Ġf actions", "Ġfact ions", "Ġfa ctions", "Ġfaction s", "[ np", "[n p", "- than", "-t han", "-th an", "Ġre co", "Ġr eco", "Ġrec o", "Ġ kas", "Ġk as", "Ġka s", "Ġ cmds", "Ġcmd s", "Ġcm ds", "/ network", "/n etwork", "/net work", "x bf", "xb f", "get Color", "getC olor", "Ġ biased", "Ġbi ased", "Ġbias ed", "ĠL ak", "ĠLa k", "D atas", "Data s", "Da tas", "Dat as", "v ents", "ve nts", "vent s", "ven ts", "Ġ ë²", "Ġë ²", "_ PS", "_P S", ". Validate", ".Valid ate", "Inv oker", "Invoke r", "Ġne uen", "Ġneu en", "Ġneue n", "Ġju venile", "Ġjuven ile", "V ISION", "VI SION", "VIS ION", "Ġde vote", "Ġdev ote", "Ġ linha", "Ġl inha", "Ġlin ha", "Ġlinh a", "Ġdiscount ed", "Ġdisco unted", "\\ Config", "Ġworth while", "Ġskin ny", "Ġ Courses", "ĠC ourses", "ĠCo urses", "ĠCour ses", "ĠCourse s", "le ys", "ley s", "ĠMort gage", "K evin", "Ke vin", "Ġann ounces", "Ġannounc es", "Ġannounce s", "] )*", "]) *", "res ervation", "Ġ æķ°", "Ġæķ °", "Ġprejud ice", "ĠString Comparison", "Ġbe ard", "Ġbear d", "- win", "-w in", "ĠS ão", "ĉ ms", "ĉm s", "j al", "ja l", "Ġ Earn", "ĠE arn", "ĠEar n", "ĠEa rn", "_ ports", "_p orts", "_port s", "_po rts", "_por ts", "Ġ Nombre", "ĠN ombre", "ĠNom bre", "_ COR", "_C OR", "_CO R", "Ġ BUILD", "ĠB UILD", "ĠBU ILD", ". sound", ".s ound", ".so und", "Y ellow", "Ġlineback er", "Ġchar itable", "Ġcha ritable", "j ug", "ju g", "_NON NULL", "ĠD ental", "ĠDen tal", "ĠDent al", "\" >${", "\"> ${", "\">$ {", "ĉ match", "ĉm atch", "ĉmat ch", "R ussian", "Russia n", "Russ ian", "Rus sian", "Ġver sch", "Ġvers ch", "Ġp inned", "Ġpin ned", "Ġadopt ing", "Options Menu", "P ag", "Pa g", "Ġpair ing", "Ġpa iring", "Ġpai ring", "Ġt read", "Ġtr ead", "Ġtre ad", "erc ises", "ercise s", "Ġ Spread", "ĠS pread", "ĠSp read", "ĠSpr ead", ") i", "Ġ BAD", "ĠB AD", "ĠBA D", "_ tf", "_t f", "UI ImageView", "UIImage View", "pop ulate", "b ab", "ba b", "Ġ Ïĥ", "ĠÏ ĥ", "[ ++", "Ġopi oid", "Ġ ##Ċ", "Ġ# #Ċ", "Ġ## Ċ", "d type", "dt ype", "ĠSt arts", "ĠStart s", "ĠStar ts", "ĠSta rts", "(' /')", "('/ ')", "Ġperson als", "Ġpersonal s", "Ġpersona ls", "- market", "-m arket", "-mark et", "-mar ket", "Ġredund ant", "ĠEss ential", "Ġsc rapy", "Ġscr apy", "Ġscrap y", "Ġ им", "Ġи м", "a cl", "ac l", "Ġ crear", "Ġc rear", "Ġcr ear", "Ġcre ar", "Ġcrea r", "ĠB end", "ĠBe nd", "ĠBen d", "Ġrel ieve", "Ġreli eve", "Ġrelie ve", "- room", "-r oom", "-ro om", "w ife", "wi fe", "Ġv Ãł", "ĠQ Point", "Ġqu asi", "Ġqua si", "Ġ methodName", "Ġmethod Name", "\\ xc", "\\x c", "ĠP eru", "ĠPer u", "ĠPe ru", "/ The", "/T he", ". orm", ".o rm", ".or m", "Ġ viz", "Ġv iz", "Ġvi z", "/ pdf", "/p df", "Loc ated", "Ġconfront ation", "ĠChampionship s", "ĠChampions hips", "ĠChampion ships", "Ġhy pert", "Ġhyp ert", "Ġhyper t", "Ġhype rt", "Ġ dj", "Ġd j", "Ġ UserInfo", "ĠUser Info", "Ġ åĪĽå»º", "ĠåĪ Ľå»º", "\\ xb", "\\x b", "( sim", "(s im", "(si m", "Ġ ==Ċ", "Ġ= =Ċ", "Ġ== Ċ", "Ġst aging", "Ġsta ging", "Ġstag ing", "Ġdr astically", "Ġdrastic ally", "åŃ ¦", "l ords", "lor ds", "lord s", ". less", ".l ess", ".le ss", "вед иÑĤе", "Ġ Bucket", "ĠB ucket", "ĠBuck et", "ĠBu cket", "ĠM am", "ĠMa m", ". term", ".t erm", ".te rm", "_ pi", "_p i", "c zy", "cz y", ". pub", ".p ub", "p recio", "pre cio", "prec io", "preci o", "ĠV irt", "ĠVir t", "ĠVi rt", "Ġ roman", "Ġr oman", "Ġro man", "Ġrom an", "Ġroma n", "i tat", "it at", "ita t", "L ex", "Le x", "_ infos", "_in fos", "_info s", "_inf os", "Ä °", ". other", ".o ther", ".ot her", "VE LO", "VEL O", "Ġ ponder", "Ġp onder", "Ġpo nder", "Ġpon der", "Ġpond er", "Ġh anno", "Ġhan no", "Ġhann o", "( Page", "(P age", "d oi", "do i", "Ġpol ite", "Ġpo lite", "Ġpolit e", "Ġprogram mer", "Ġprogramme r", "Ġprogramm er", "D ies", "Die s", "Di es", "$ d", "Ġre plication", "Ġrep lication", "Ġrepl ication", "Ġreplic ation", "Ġreplica tion", "add Column", "fr ican", "frica n", "Ġl eng", "Ġle ng", "Ġlen g", "b eer", "be er", "bee r", "o it", "oi t", "Ġw asting", "Ġwas ting", "Ġwast ing", "y lim", "yl im", "me asure", "N eg", "Ne g", "Ġpart ie", "Ġpar tie", "Ġparti e", ". console", ".con sole", ".cons ole", "ĠGu inea", "ĠGui nea", "T EL", "TE L", "_ fact", "_f act", "_fac t", "_fa ct", ". chunk", ".ch unk", "Ġl ent", "Ġle nt", "Ġlen t", "Ġ aller", "Ġa ller", "Ġal ler", "Ġall er", "Ġalle r", "Ġ à¤ķ", "Ġठķ", "_ idle", "_id le", "_i dle", "Ġad missions", "Ġadm issions", "Ġadmission s", "JSON Array", "Ġv ibration", "Ġvibr ation", "Ġvib ration", ". helpers", ".h elpers", ".helper s", ".help ers", "å¤ ĸ", "Ġ hen", "Ġh en", "Ġhe n", "j ohn", "jo hn", "Ġ ìĥĿ", "Ġì ĥĿ", "Ġìĥ Ŀ", "Ġjud gement", "Ġjudge ment", "Ġg een", "Ġge en", "Ġgee n", "t erra", "ter ra", "terr a", "^ {", "ĠI z", "Ġc â", "in stances", "instance s", "inst ances", "instanc es", "Ġthreat ens", "Ġthreaten s", "Ġm üssen", "Kind OfClass", "Ġstoryt elling", "_ demo", "_d emo", "_de mo", "_dem o", "r ias", "ri as", "ria s", "Priv acy", "h ift", "hi ft", "ĠY i", "es or", "eso r", "íķ ł", "ens itivity", ". Writer", ".W riter", ".Write r", "ภĤ", "D istrict", "Di strict", ".get JSONObject", "Im pro", "Imp ro", "(get Resources", "Ġ SPELL", "ĠS PELL", "ĠSP ELL", "ĠSPE LL", "ro duce", "rodu ce", "rod uce", "Ġsl owed", "Ġslow ed", "Ġslo wed", "Ġ linewidth", "Ġline width", "Ġlin ewidth", "Ġhon esty", "Ġhonest y", "Ġho nesty", "Ġhone sty", "Ġ Coord", "ĠC oord", "ĠCo ord", "ĠF ork", "ĠFor k", "ĠFo rk", "ĠDispatch Queue", "ĠCl iff", "ĠCli ff", "ĠW iring", "ĠWi ring", "ĠWir ing", "_TIM ESTAMP", "ol lah", "oll ah", "olla h", "a void", "av oid", "avo id", "++ ];Ċ", "++] ;Ċ", "sem antic", "- css", "-c ss", "Ġv eto", "Ġve to", "Ġvet o", "ĠM err", "ĠMe rr", "ĠMer r", "Ġlegisl ators", "C EEDED", "CEE DED", "CEED ED", "Ġquestion naire", "ĠP ills", "ĠPill s", "ĠPil ls", "C alculate", "Cal culate", "Calc ulate", "Calcul ate", "( core", "(c ore", "(co re", "(cor e", "' e", "Ġdis like", "Ġ Preferences", "ĠP references", "ĠPre ferences", "ĠPreference s", "ĠPrefer ences", "_ EXTERNAL", "_EX TERNAL", "_EXTERN AL", "è° ĥ", "Ġd odge", "Ġdo dge", "Ġdod ge", "æľį åĬ¡", ". names", ".n ames", ".name s", ".draw Image", "_ prom", "_p rom", "_pro m", "_pr om", "uck land", "Ġ<$ >", "ı z", "/ site", "/s ite", "é¡ ¹", "r ophe", "ro phe", "rop he", "roph e", "Ġcomp elled", "Ġcompel led", "Ġl aptops", "Ġlaptop s", "Ġ uni", "Ġu ni", "Ġun i", "C LOSE", "CL OSE", "Ġcasual ties", "Ġ Uniform", "ĠUn iform", "ĠUni form", "Term inal", ". \",\"", ".\" ,\"", ".\", \"", "D AT", "DA T", "( TreeNode", "(T reeNode", "(Tree Node", "ĠGand hi", "( stmt", "(st mt", "A XB", "AX B", "* M", "Ġumb rella", "an imal", "ani mal", "anim al", "Ġ grpc", "Ġg rpc", "Ġgr pc", "Ġgrp c", "Ġwhere by", "Ġfloat s", "Ġflo ats", "ĉ arg", "ĉa rg", "ĉar g", "Ġ dbg", "Ġd bg", "Ġdb g", "Ġexceed ing", "Ġexce eding", "Event Type", ".SaveChanges Async", "Ġ {{{", "Ġ{ {{", "Ġ{{ {", "Ġ owed", "Ġo wed", "Ġow ed", "Ġowe d", "ahren heit", "Ġ ì§", "Ġì §", "Ġequ ipo", "Ġequip o", "u rai", "ur ai", "ura i", "Ġi dol", "Ġid ol", "] \")Ċ", "]\" )Ċ", "]\") Ċ", "_ major", "_m ajor", "Ġentire ty", "inger print", "ç os", "ço s", "/ account", "/a ccount", "/ac count", "ĉ right", "ĉr ight", "urs os", "ĠE DT", "ĠED T", "_ INSERT", "_INS ERT", "Ġsh ining", "Ġshin ing", "Ġ< :", "Edge Insets", "Ġcolon ies", ". IM", ".I M", "ĉ Ġĉ", "ĉĠ ĉ", "R OAD", "RO AD", "C CCC", "CC CC", "CCC C", "pl acing", "pla cing", "Ġget Activity", "em acs", "ema cs", "' %(", "'% (", ". clicked", ".cl icked", ".click ed", "Ġ Them", "ĠT hem", "ĠThe m", "ĠTh em", "is ia", "isi a", "Bus car", "Bu scar", ". rename", ".re name", ".r ename", "Ġo ath", "Ġoat h", "Ġoa th", "Ġafter ward", "ĠU FO", "ĠUF O", "A PS", "AP S", "ĠJackson ville", ". some", ".s ome", ".so me", "Conf irmed", "Confirm ed", ". scan", ".s can", ".sc an", "ig Integer", "Decor ator", "sh ield", "shi eld", "ress ive", ". did", ".d id", ".di d", "请 è¾ĵåħ¥", "Ġsh utter", "Ġshut ter", "D am", "Da m", "Ġpar enting", "Ġparent ing", "Ġparen ting", "ey ed", "eye d", "$ item", "$i tem", "- develop", "-de velop", "-dev elop", "-devel op", "Ġex tracts", "Ġextra cts", "Ġextract s", "Ġextr acts", "Ġdecentral ized", "ĠE lsa", "ĠEl sa", "_ spin", "_s pin", "_sp in", "_spi n", "] )+", "]) +", "- initial", "-in itial", "-init ial", "Ġmult itude", "Ġmultit ude", "Ġsens ory", "Ġsensor y", "Ġ MODEL", "ĠMO DEL", "ĠMOD EL", "ĠMODE L", "Ġsaf eguard", "Ġsafe guard", "Ġsafeg uard", "ì ¹", "Ġhun ters", "Ġhunt ers", "Ġhunter s", "Ġ Tiny", "ĠT iny", "ĠTi ny", "ĠTin y", "I NO", "IN O", "dec orate", "decor ate", "Ġ NoSuch", "ĠNo Such", "H o", "( Response", "Ġr uler", "Ġrule r", "Ġru ler", "ĉ short", "ĉs hort", "ĉsh ort", "Ġ caster", "Ġc aster", "Ġca ster", "Ġcas ter", "Ġcast er", "Ġcaste r", "Ġ clientId", "Ġclient Id", "Ġ pdb", "Ġp db", "Ġpd b", "ëı Ħ", "i tic", "it ic", "iti c", "Ġ GameState", "ĠGame State", "Ġnew Item", ") ĊĊĊĊĊĊ", ")Ċ ĊĊĊĊĊ", ")ĊĊ ĊĊĊĊ", ")ĊĊĊ ĊĊĊ", ")ĊĊĊĊ ĊĊ", ")ĊĊĊĊĊ Ċ", "o uis", "ou is", "oui s", "n oc", "no c", ". BLACK", ".BL ACK", "_ VECTOR", "_V ECTOR", "_VEC TOR", "_VE CTOR", "---------- ();", ">( );", ">() ;", ".get P", "an ye", "any e", "Ġne uron", "Ġneuro n", "Ġneu ron", "Ġneur on", "i fold", "if old", "ifo ld", "Ġ Known", "ĠK nown", "ĠKn own", "ĠKnow n", "Bit coin", "Any way", "ay ette", "aye tte", "ayet te", "Ġ' ['", "Ġ'[ '", "Ãł nh", "Ãłn h", "m gr", "mg r", "Ġcor related", "Ġcorre lated", "Ġcorrel ated", "Ġcorrelate d", "Ġn ause", "Ġna use", "Ġnau se", "Ġmental ity", "Ġment ality", "has Many", "Ġ FG", "ĠF G", "am pie", "amp ie", "I TU", "IT U", "F s", ". Sp", ".S p", "_ between", "_b etween", "_bet ween", "Dep endencies", "o ug", "ou g", "Place holder", "= text", "=t ext", "Ġ Managing", "ĠMan aging", "ĠMana ging", "ocal ypse", "åĮ Ĺ", "_ mag", "_m ag", "_ma g", "f ld", "fl d", "â ij", "C AM", "CA M", "Ġ Helpers", "ĠH elpers", "ĠHelp ers", "ĠHelper s", "ĠHel pers", "Ġd ost", "Ġdo st", "Ġdos t", "/ out", "/o ut", "Ġassass ination", "Ġassassin ation", ". getImage", ".get Image", "ĠK enny", "ĠKen ny", "ĠKenn y", ". ')ĊĊ", ".' )ĊĊ", ".')Ċ Ċ", ".') ĊĊ", ") {//", "){ //", "ĠR anger", "ĠRange r", "ĠRa nger", "ĠRan ger", "Ġg ek", "Ġge k", "Ġsince re", "Ġsinc ere", "Ġsincer e", "< Value", "čĊ", "/> čĊ", ".get Resources", ".getResource s", "Ġl ump", "Ġlu mp", "Ġlum p", "_ consts", "_con sts", "_const s", "_cons ts", "( ext", "(e xt", "(ex t", "ĉ dir", "ĉd ir", "â Ŀ", "Ġpadding Top", "Ġob session", "Ġobs ession", "Ġobsess ion", "Ġb anning", "Ġban ning", "ĠApp Module", "Ġp artisan", "Ġpart isan", "Ġparti san", "Ġcatalog ue", "Ġcata logue", "Ġcatal ogue", "Ġmin ors", "Ġminor s", "Ġp itches", "Ġpitch es", "Ġpit ches", "we ep", "Ġunder take", "Ġundert ake", "Ġth emed", "Ġthe med", "Ġthem ed", "Ġtheme d", "a udit", "au dit", "aud it", "audi t", ". scrollTop", ".scroll Top", ".scrollTo p", "Ġ rer", "Ġre r", "Ġr er", "Ġsym ptom", "Ġsympt om", "Ġsymp tom", "Ġopen ings", "Ġopening s", ". blocks", ".b locks", ".bl ocks", ".block s", "open id", "ope nid", "Ġa ssh", "Ġas sh", "Ġass h", "- save", "-s ave", "ĠP ig", "ĠPi g", "Ġre gain", "Ġreg ain", "Ġin icial", "Ġini cial", "Ġinici al", "/ favicon", "/f avicon", "ĉ exp", "ĉe xp", "ĉex p", "Ġsp ices", "Ġspi ces", "Ġspice s", "i ska", "is ka", "isk a", "cl aims", "claim s", "cla ims", "m ak", "ma k", "definition s", "Ġcorrespond ent", "ĠCann abis", "_ _,Ċ", "__ ,Ċ", "__, Ċ", "ĠL ucky", "ĠLuc ky", "ĠLu cky", "ĠLuck y", "ĠG aussian", "ĠGa ussian", "ĠGauss ian", "Ġ Nearly", "ĠN early", "ĠNear ly", "C AD", "CA D", "' ]]Ċ", "'] ]Ċ", "']] Ċ", "Ġadequate ly", "Ġadequ ately", "Ġ TITLE", "ĠT ITLE", "constitution al", "- mm", "-m m", "_ override", "_over ride", "Ġ blas", "Ġb las", "Ġbl as", "Ġbla s", ".ready State", "Ġrem inis", "Ġremin is", "Ġrein forced", "Ġreinforce d", "ĠColl abor", "Ġdecor ating", "Ġdeco rating", "Ġb achelor", "Ġbach elor", "ERRU PT", "Ġup right", "ip ation", "ipa tion", "ĠN oble", "ĠNo ble", "ĠNob le", "Ġvalue ForKey", "Ġset Loading", ". Ignore", ".I gnore", "å ģ", "G lobals", "Global s", "ĠM ent", "ĠMe nt", "ĠMen t", "AS SES", "ASS ES", "Ġlim bs", "Ġlimb s", "Ġ HUD", "ĠH UD", "in ci", "inc i", ". iv", ".i v", "ĠQ ModelIndex", "F use", "Fu se", "Ġpe dal", "Ġped al", "_F REQ", "_FR EQ", "_FRE Q", "( verbose", "(ver bose", "Ġlong itud", "ĠCh arter", "ĠChar ter", "ĠChart er", "ê ·¸", "ê· ¸", "Ġ bundles", "Ġb undles", "Ġbund les", "Ġbundle s", ". ignore", ".i gnore", "um bo", "umb o", "E MA", "EM A", ". ......", ".. .....", "... ....", ".... ...", "..... ..", "...... .", "s x", ". Card", ".C ard", ".Car d", "Ġhe ute", "Ġst eer", "Ġste er", "j umlah", "Ġ {_", "Ġ{ _", "_ Checked", "_Check ed", "Ġ fax", "Ġf ax", "Ġfa x", "ĠG ust", "ĠGu st", "ĠGus t", "itch ens", "itchen s", "Ġ ))ĊĊ", "Ġ) )ĊĊ", "Ġ)) ĊĊ", "Ġ))Ċ Ċ", "Ġremark ably", "/ XML", "/X ML", "- remove", "-re move", "_ bt", "_b t", "Ġinc ub", ". package", ".p ackage", ".pack age", ".current Thread", "ĠHigh lander", "ĠHighland er", ". side", ".s ide", ".sid e", ".si de", "s plash", "sp lash", "spl ash", "Ġ ici", "Ġi ci", "Ġic i", "= D", "Ġp uck", "Ġpu ck", "Ġball ots", "Ġbal lots", "Ġballot s", "Ġballo ts", "Ġhuge ly", "Ġhug ely", "c oeff", "co eff", "coef f", "coe ff", "Ġ pData", "Ġp Data", ". COLUMN", ".C OLUMN", "ĠHe aling", "ĠHeal ing", "Ġ ordin", "Ġor din", "Ġord in", "! ),", "!) ,", "Ġ' ',čĊ", "Ġ'' ,čĊ", "Ġ'', čĊ", "( md", "(m d", "ĠS ask", "ĠSa sk", "ĠSas k", "< strong", "Ġsurv ivor", "Ġsurviv or", ". series", ".s eries", ".se ries", ".ser ies", "Ġcaffe ine", "Ġ `(", "Ġ` (", ".TRA ILING", "_ Input", "_In put", "( \"^", "(\" ^", "z d", "& );Ċ", "&) ;Ċ", "Ġ Ping", "ĠP ing", "ĠPin g", "ĠPi ng", "Ġ voucher", "Ġv oucher", "Ġvo ucher", "Ġvou cher", ". rating", ".r ating", ".ra ting", "-sh irts", "-shirt s", "ĠRetrie ves", "ĠRetrieve s", ".al ibaba", "Or acle", "_ MOV", "_M OV", "_MO V", "Old Data", "Ġ /*čĊ", "Ġ/ *čĊ", "Ġ/* čĊ", "Ġ gboolean", "Ġg boolean", "Ġ= >čĊ", "Ġ=> čĊ", "Ġ rá", "Ġr á", "Ġbl unt", "ĠImage Icon", "i fik", "if ik", "ifi k", "R TC", "RT C", "Ġfi bers", "Ġfib ers", "Ġfiber s", "Ġto ile", "Ġtoi le", ". sent", ".s ent", ".se nt", "ĠPy Qt", "$ app", "$a pp", "Ġm edio", "Ġme dio", "Ġmed io", "Ġmedi o", "Ġgrant ing", "Ġgran ting", "Ġts lint", "Ġtsl int", "ĠM ö", "(fig size", "Ġhur ricane", "Ġl ifes", "Ġlife s", "Ġlif es", "Ġ ÃĦ", "Ġà Ħ", "rocess ing", "_ standard", "_st andard", "_stand ard", "- option", "-o ption", "-op tion", "-opt ion", "' )))", "') ))", "')) )", "Ġvac ant", "Ġva cant", "å· ¥", "ĠH ollow", "ĠHol low", "ĠHoll ow", "handle Change", "Ġ divider", "Ġdi vider", "Ġdiv ider", "Ġdivide r", "Ġdivid er", "ĠEngine ers", "ĠEngineer s", "Ġs vens", "Ġsv ens", "Ġsve ns", "Ġcom pliant", "Ġcompl iant", "t anggal", "Ġ Credits", "ĠC redits", "ĠCredit s", "ĠEm irates", "Rule Context", "Ġreal ization", "Ġrealiz ation", "Ġrealiza tion", "Ġdis tracted", "Ġdistr acted", "Ġdistract ed", "] +=", "]+ =", "Ġau gment", "Ġaug ment", "ĠD w", "o tp", "ot p", "or rent", "orr ent", "orre nt", "Ed itar", "Edit ar", ". stock", ".st ock", "St udy", "p ections", "pe ctions", "pect ions", "pection s", "Ġ GameManager", "ĠGame Manager", "= cut", "=c ut", "Ġf lock", "Ġfl ock", "Ġflo ck", "ĠRom ans", "ĠRo mans", "ĠRoman s", "ĠRoma ns", "t hem", "th em", "the m", "- hop", "-h op", "Ġscreen shots", "Ġscreens hots", "Ġscreenshot s", "Ġ /*!Ċ", "Ġ/* !Ċ", "Ġ/*! Ċ", "Ġcon versions", "Ġconv ersions", "Ġconvers ions", "Ġconversion s", "Ġnormal ization", "( configuration", "(config uration", "Ġa eros", "Ġaer os", "Ġae ros", "_ security", "_s ecurity", "_se curity", "_sec urity", "! 'Ċ", "!' Ċ", "B onus", "Bon us", "ĠDR IVER", "ĠDRIVE R", "ĉ Date", "ĉD ate", "t ie", "ti e", "ĠWy oming", "St and", "Stan d", "i tre", "it re", "itr e", "Ġsh oppers", "Ġshop pers", "Ġsho ppers", "Ġshopper s", "Ġdisadv antage", "Ġl iking", "Ġli king", "Ġlik ing", "ç¬ ij", "Ġunderstand able", "S EE", "SE E", "Ġh oy", "Ġho y", "Ġnine te", "Ġni nete", "Ġnin ete", "Ġcon fer", "Ġconf er", "Ġ nowrap", "Ġno wrap", "Ġnow rap", "ĠV ern", "ĠVer n", "ĠVe rn", ", čĊčĊ", ",čĊ čĊ", "ime step", "imes tep", "imest ep", "Layout Manager", "à ·", "ĉ wait", "ĉw ait", "P LETED", "PLE TED", "J apan", "Ja pan", "Ġin duce", "Ġind uce", "Ġindu ce", "Ġ å¯", "Ġå ¯", "о зв", "оз в", "_END POINT", ". horizontal", ".h orizontal", "Ġacceler ated", "Ġaccelerate d", "r imon", "ri mon", "rim on", "I VES", "IV ES", "IVE S", "Trans actions", "Transaction s", "L ean", "Le an", "ĠS OUR", "ĠSO UR", "wh ether", "y g", "Ġ oid", "Ġo id", "Ġoi d", "Ġ EntityManager", "ĠEntity Manager", "OUN TRY", "OUNT RY", "Ġ fila", "Ġf ila", "Ġfil a", "Ġfi la", "OLUM NS", "OLUMN S", "IN UE", "INU E", "Ġ Anchor", "ĠAn chor", "ĠAnc hor", "ĠAnch or", "TR AN", "TRA N", "w oo", "wo o", "block quote", "ĠN urse", "ĠNurs e", "ĠNur se", "ĠC arp", "ĠCar p", "ĠCa rp", "Ġrede em", ". try", ".t ry", ".tr y", "Ġ JP", "ĠJ P", "Ġ timestamps", "Ġtimestamp s", "Ġ?> \"><", "Ġ?>\" ><", "Ġ?>\"> <", "Ġ REMOVE", "ĠRE MOVE", "ĠREM OVE", "ĠStar bucks", "Re ally", "Real ly", "Ġflo oded", "Ġflood ed", ". Callback", ".C allback", ".Call back", "Drop Down", "i pro", "ip ro", "Ġt ended", "Ġten ded", "Ġtend ed", "l te", "lt e", "Ġproportion s", "Ġproport ions", "- te", "-t e", "ĠR ena", "ĠRe na", "ĠRen a", "l icate", "lic ate", "li cate", "lica te", "for ces", "force s", "forc es", ". extra", ".ex tra", ".ext ra", ". authenticate", ".auth enticate", "в од", "во д", "¡ °", "Ġfor ControlEvents", "Ġ senha", "Ġs enha", "Ġsen ha", "Ġk ein", "Ġke in", "Ġmin ist", "Ġmi nist", "Ġmini st", "Ġ Preference", "ĠP reference", "ĠPre ference", "ĠPref erence", "ĠPrefer ence", "ĠTele graph", "Ñĥ п", "str pos", "Ġillness es", "Ġp igs", "Ġpi gs", "Ġpig s", "Ġget Intent", "ĠgetInt ent", "S ol", "So l", "Ġ ¡", "Ġ ¡", "( cpu", "(c pu", "(cp u", "[ prop", "[p rop", "s creens", "screen s", "') ;?>", "'); ?>", "Ġ Acts", "ĠA cts", "ĠAct s", "ĠAc ts", "Ġstr dup", "Ġa verages", "Ġaverage s", "Ġaver ages", "a nal", "an al", "ana l", "ĠCas ual", "Group Box", "ĠHand book", "/ comments", "/com ments", "/comment s", "Ġnumber ed", "Ġnumb ered", "Ġbroad casting", "Ġbroadcast ing", "çĽ ij", ".native Element", ". mu", ".m u", "Ġ updatedAt", "Ġupdated At", "ĠDoes n", "ĠDoe sn", ". AC", ".A C", ". coll", ".c oll", ".co ll", ".col l", "Ġrec order", "Ġrecord er", "_ sha", "_s ha", "_sh a", "B g", "b il", "bi l", "Ġbol ts", "Ġbolt s", "Ġ ç¬", "Ġç ¬", "Ġim posing", "Ġimp osing", "ĠInformation en", "_ flashdata", "_flash data", "e conomic", "ec onomic", "R emark", "Re mark", "Rem ark", "u cas", "uc as", "Ġ Officers", "ĠOff icers", "ĠOffice rs", "ĠOfficer s", "Ġ TER", "ĠT ER", "ĠTE R", "W alk", "Wal k", "Wa lk", "Ġmerc ado", "_ generate", "_g enerate", "_gen erate", "_gene rate", "_gener ate", "H Y", "C alling", "Call ing", "Cal ling", "s nap", "sn ap", "script Id", ". operation", ".op eration", ".o peration", ".oper ation", "ĠFl ame", "ĠFla me", "ĠFlam e", "l iness", "li ness", "line ss", "lin ess", "lines s", "Ġr ented", "Ġren ted", "Ġrent ed", "_ toggle", "_t oggle", "- changing", "-ch anging", "-chan ging", "Ġ TY", "ĠT Y", "' util", "'u til", "E EP", "EE P", "Ġ graphql", "Ġgraph ql", "Ġ Uni", "ĠU ni", "ĠUn i", "Ġim pulse", "Ġimp ulse", "Ġimpuls e", ". Basic", ".B asic", "Ġenerg ies", "Ġener gies", "Ġenergie s", "M ARY", "MA RY", "MAR Y", "ĠMar cel", "ĠMarc el", "Ġm ortal", "Ġmor tal", "Ġmort al", "Ġf res", "Ġfr es", "Ġfre s", "m ens", "me ns", "men s", "m otion", "mo tion", "mot ion", "Ġs ampled", "Ġsample d", "Ġsam pled", "Ġsamp led", "âĢľ That", "i day", "id ay", "ida y", "qu ipment", "quip ment", "get Int", "Ġ Absolute", "ĠA bsolute", "ĠAbs olute", ", '\"", ",' \"", "u ned", "un ed", "une d", ". share", ".s hare", ".sh are", ".sha re", "Ġ })(", "Ġ} )(", "Ġ}) (", "m mm", "mm m", "ĠR ising", "ĠRi sing", "ĠRis ing", "ä» »", "Ġun employed", "x fa", "xf a", ". follow", ".f ollow", "ĉ ĉĉĉĠĠĠĠĠĠ", "ĉĉ ĉĉĠĠĠĠĠĠ", "ĉĉĉĉ ĠĠĠĠĠĠ", "ĉĉĉ ĉĠĠĠĠĠĠ", "ĉĉĉĉĠ ĠĠĠĠĠ", "ĉĉĉĉĠĠĠ ĠĠĠ", "ĉĉĉĉĠĠ ĠĠĠĠ", "ĉĉĉĉĠĠĠĠ ĠĠ", "ĉĉĉĉĠĠĠĠĠ Ġ", "s lt", "sl t", ". Phone", ".P hone", ".Ph one", "Ġkn ives", "Ġ eve", "Ġe ve", "Ġev e", "on Click", "] ))čĊ", "]) )čĊ", "])) čĊ", "Ġ Witness", "ĠW itness", "ĠWit ness", "ĉ NS", "ĉN S", "Ġ EOS", "ĠE OS", "ĠEO S", "ĠSte fan", "ĠStef an", "ĠPr iest", "ĠPri est", "âĢĶ which", "Get String", ". By", ".B y", "Ġup stairs", "Ġdetr iment", "b roken", "br oken", "bro ken", "em bro", "emb ro", "embr o", "Ġnic otine", "i lion", "il ion", "ili on", "ilio n", "Ġaston ishing", "_ aff", "_a ff", "_af f", "Ġ Lesson", "ĠL esson", "ĠLe sson", "ĠLess on", "ĠLes son", "Ġacc idental", "Ġaccident al", "od or", "odo r", "Ġde cir", "Ġdec ir", "Ġnew Name", "+ .", "çĽ ¸", "igs list", "Ġ Github", "ĠG ithub", "ĠGit hub", "Ġsuccess ive", "Ġsuc cessive", "r acial", "ra cial", "rac ial", "raci al", "Ġen viron", "Ġenv iron", "Ġenvi ron", "éªĮ è¯ģ", "Ġred irected", "Ġredirect ed", "T OTAL", "TOT AL", "Ġgrab bing", "Ġgra bbing", "ĠL ance", "ĠLa nce", "ĠLan ce", "ĠLanc e", "Ġfor fe", "_ CB", "_C B", "å¾ ®", "El apsed", "_ way", "_w ay", "(Dialog Interface", "_ measure", "_me asure", "_meas ure", "x bb", "xb b", "D og", "Do g", "De part", "Dep art", "- src", "-s rc", "re solver", "res olver", "resolve r", "with standing", "_ shell", "_s hell", "_sh ell", "Ġ LastName", "ĠLast Name", "ĠAv iation", "Ġbeg inner", "Ġbegin ner", "(\" %.", "(\"% .", "( tool", "(t ool", "(to ol", "Ġ нов", "Ġн ов", "Ġно в", ": init", ":i nit", "( API", "(A PI", "(AP I", "ĠMorris on", "ĠMorr ison", "vt Color", "Ġsta ple", "Ġstap le", "/ INFO", "Ġsuper natural", "Ġsupern atural", "Ġste ak", "t imeline", "time line", "tim eline", "zz le", "\" `ĊĊ", "\"`Ċ Ċ", "\"` ĊĊ", "Second ary", "ĠNe pal", "ĠNep al", ". StringUtils", ".String Utils", "Ġ adam", "Ġa dam", "Ġad am", "Ġada m", "Ġ (...", "Ġ( ...", "Ġ(. ..", "Ġsub stitution", "Ġsubstit ution", "Ġsubst itution", "Ġ boarding", "Ġbo arding", "Ġboard ing", "Ġ Keyword", "ĠKey word", "ĠAss ault", "dbc Template", "Ġ orderId", "Ġorder Id", "( engine", "(e ngine", ".assert That", "ĠV enus", "ĠVen us", "Ġhom icide", "Ġhomic ide", "ĠA val", "ĠAv al", "ĠAva l", "Ġg utter", "Ġgut ter", "Ġ Supported", "ĠS upported", "ĠSup ported", "ĠSupport ed", "/ part", "/p art", "Ġac claimed", "Ġacclaim ed", "H istor", "Hi stor", "His tor", "Hist or", "Ġm eses", "Ġme ses", "Ġmes es", "ü ber", "üb er", "ĠRe new", "ĠRen ew", "ĠRene w", "Ġg ras", "Ġgr as", "Ġgra s", "Ġ Ek", "ĠE k", "Ġ infile", "Ġin file", "Ġinf ile", "in dy", "ind y", ". music", ".m usic", ".mu sic", ". Scroll", ".S croll", ".Sc roll", "ĠA ges", "ĠAg es", "ĠAge s", "ĠNar uto", "ĠG ather", "ĠGa ther", "ĠGat her", "Ġconfirm ing", "= (\"", "=( \"", "Ġp itched", "Ġpitch ed", "Ġpit ched", "o ley", "ol ey", "ole y", "F rance", "Fr ance", "Fran ce", "Fra nce", "Franc e", "+ '\"", "+' \"", "$ total", "$t otal", "Ġ onde", "Ġo nde", "Ġon de", "Ġd itch", "Ġdit ch", "_ sigma", "_s igma", "_sig ma", "Ġcontin uity", "Ġcontinu ity", "r eward", "re ward", "rew ard", "- load", "-l oad", "-lo ad", "Ġpro ceso", "Ġproc eso", "Ġproces o", "L ocked", "Loc ked", "Lock ed", "st aw", "sta w", "Ġsp inal", "Ġspin al", "Ġspi nal", "l azy", "la zy", "laz y", "! ==", "!= =", "j est", "je st", "jes t", "Ġd un", "Ġdu n", "ĠRod gers", "ĉ grid", "ĉg rid", "ĉgr id", "Ġlo gos", "Ġlog os", "Ġlogo s", "ĠBen gal", "ĠBeng al", ". super", ".s uper", ".sup er", "Pro vides", "Provid es", "Provide s", "Prov ides", "Ġnut rient", ". Timestamp", ".T imestamp", ".Time stamp", "IZ ATION", "åĨ Į", "Ġf ats", "Ġfa ts", "Ġfat s", "ĠX xx", "c tica", "ct ica", "ctic a", "Target s", "Tar gets", "Ġcont ours", "Ġcontour s", "Ġre ordered", "Ġreorder ed", ": Array", ":A rray", "Ġtoler ate", "Ġtol erate", "V ir", "Vi r", "Ġter ribly", "Ġterr ibly", "Ġb ricks", "Ġbr icks", "Ġbrick s", "Ġbri cks", "( &_", "(& _", "h b", "P ortal", "Port al", "Por tal", "ĠB read", "ĠBr ead", "ĠBre ad", ". which", ".wh ich", "ÂŃ t", "as InstanceOf", "Ġj object", "Ġjob ject", "Ġjo bject", "ĉ length", "ĉl ength", "ĉlen gth", "_ MT", "_M T", "; \">čĊ", ";\" >čĊ", ";\"> čĊ", "_ EXIST", "_EX IST", "Ġmat ernal", "Ġma ternal", "Ġmater nal", "R EL", "RE L", "Ġê²½ ìļ°", "h ee", "he e", "Ġ layouts", "Ġlayout s", "Ġlay outs", "ĠL ap", "ĠLa p", "a isy", "ai sy", "ais y", "Ġst umbled", "Ġstumble d", "ĠU IG", "ĠUI G", "ĠS co", "ĠSc o", "Ġim paired", "Ġimp aired", "Ġimpair ed", "RES SED", "RESS ED", "Ġab uses", "Ġabuse s", "V F", "A RB", "AR B", ". NAME", ".N AME", "r ch", "rc h", "pr imir", "prim ir", "pri mir", "_ completed", "_com pleted", "_comp leted", "_complete d", "Ġp enny", "Ġpen ny", "Ġpenn y", "Ch rome", "Chr ome", "( begin", "(b egin", "(be gin", "er nen", "ern en", "erne n", "- checkbox", "-check box", "Plain OldData", "ĠL PC", "ĠLP C", "r ade", "ra de", "rad e", "s pir", "sp ir", "spi r", "Ġcon ceived", "Ġconce ived", "Ġconceive d", "T ips", "Tip s", "Ti ps", "ĠIo T", "ĠG an", "ĠGa n", "èģ Ķ", "Ġbi ases", "Ġbias es", "Ġconsult ants", "Ġconsultant s", "Ġconsulta nts", "p led", "pl ed", "ple d", "_ ht", "_h t", "associ ated", "assoc iated", "associate d", "] ,ĊĊ", "], ĊĊ", "],Ċ Ċ", "Ġdelight ful", "ĠÑĤ ек", "ĠÑĤе к", "Hel vetica", "( load", "(l oad", "(lo ad", "- expand", "-exp and", "_W IDGET", "t oa", "to a", "Ġ Akt", "ĠA kt", "ĠAk t", "Ġo mn", "Ġom n", "Ġcl auses", "Ġclause s", "Ġcla uses", "In tel", "Int el", "*/ }Ċ", "_ registration", "_reg istration", "Ġold Value", "Ġrest oring", "Ġresto ring", "Ġun real", "Ġunre al", "O VER", "OVE R", "OV ER", "ĉĊ ĉĊĉĊ", "ĉĊĉĊ ĉĊ", "A TS", "AT S", "_ probe", "_p robe", "_pro be", "_pr obe", "_prob e", "Ġdi visor", "Ġdiv isor", "Ġdivis or", ".update Dynamic", "å¹ ³", "Produ ces", "Prod uces", "st amp", "sta mp", ".j boss", "ĉ task", "ĉt ask", "! (:", "!( :", "Ġpsych ic", "@ class", "@c lass", "M artin", "Mar tin", "Mart in", "Ġ Passed", "ĠP assed", "ĠPass ed", "ĠPas sed", "clar ations", "claration s", "h el", "he l", "а Ñĩ", "ĉ copy", "ĉc opy", "- bin", "-b in", "z an", "za n", "i gram", "ig ram", "igr am", "া à¦", "( sig", "(s ig", "(si g", "ĠC aval", "ĠCa val", "ĠCav al", "_ ##", "Ġ %=", "Ġ% =", "out lined", "outline d", "ĠA cid", "ĠAc id", "Ġunpredict able", "- dashboard", "-d ashboard", "Hex String", "+ c", ". Public", ".P ublic", "Ạ©", "Ġcon veyor", "Ġconvey or", "Ġ EB", "ĠE B", "Ġselect s", "Ġsel ects", "Ġsele cts", "Ġkn ocking", "Ġknock ing", "ĠC ec", "ĠCe c", "IB UTES", "IBUT ES", "IBUTE S", "ow aÄĩ", "owa Äĩ", "g atsby", "* v", "ent ropy", "entr opy", "Ġdispatch ed", "Ġdisp atched", "Ġ camel", "Ġc amel", "Ġca mel", "Ġcame l", "Ġcam el", "ĠSat urn", "ĠSa turn", "Ġover weight", "( phone", "(p hone", "(ph one", "par able", "para ble", "% B", "_v ectors", "_vector s", "_vec tors", "_vect ors", "_ve ctors", "Ġbr ewing", "Ġbre wing", "Ġbrew ing", "Ġ Tk", "ĠT k", "Ġ Downloads", "ĠDown loads", "ĠDownload s", "Ġ Saved", "ĠS aved", "ĠSave d", "ĠSa ved", "ĠSav ed", ". Price", ".P rice", ".Pr ice", "Ġc urved", "Ġcur ved", "Ġcurve d", "ĠParent hood", "ĠParen thood", "è ¶", ".p nl", "plete ly", "plet ely", ". Day", ".D ay", "Ġadvert isers", "Ġadvertis ers", "Ġadvertise rs", "Ġadvertiser s", "Ġe jec", "Ġej ec", "Ġpr zed", "Ġprz ed", "Ġprze d", "ë ¯", "! ';Ċ", "!' ;Ċ", "ĠK ush", "ĠKu sh", "Ġ TAB", "ĠT AB", "ĠTA B", "Ġ quests", "Ġqu ests", "Ġque sts", "Ġquest s", "Ġques ts", "Ġcoinc idence", "Ġcoincide nce", "umm ies", "ĠKash mir", "ĠEth ics", "ĠEthi cs", "_ growth", "_g rowth", "Ġ aktiv", "Ġak tiv", "Ġakt iv", "Ġgroup ing", "Ġgrou ping", "å¢ ŀ", "_ truth", "_tr uth", "åIJ ¬", "t odos", "to dos", "todo s", "tod os", "i set", "is et", "ise t", "Tex Coord", "ä tt", "ät t", "ĠZ ur", "ĠZu r", "ro ys", "roy s", "_M AGIC", "_MAG IC", "Ġbrew ery", "( State", "ĠSM ALL", "ĠSMA LL", "ĠPl ants", "ĠPlan ts", "ĠPlant s", "ĠPla nts", "it bart", "e acher", "each er", "ea cher", "ĠAd elaide", "L u", "Ġf ick", "Ġfi ck", "Ġfic k", "und les", "undle s", "_ loaded", "_lo aded", "_load ed", "и е", "P oll", "Pol l", "Po ll", "r itic", "ri tic", "rit ic", "E LY", "EL Y", "Ġ +'", "Ġ+ '", "ĠProf ession", "Ġst amps", "Ġstamp s", "Ġsta mps", "ĠS ew", "ĠSe w", "s crollView", "scroll View", "Ġcomm unist", "Ġcommun ist", "/ problems", "/pro blems", "/problem s", "} čĊčĊčĊčĊ", "}čĊ čĊčĊčĊ", "}čĊčĊ čĊčĊ", "}čĊčĊčĊ čĊ", ", o", "Ġ udp", "Ġu dp", "Ġud p", "Ġob ese", "Ġobe se", "ap prove", "app rove", "appro ve", "anc ellation", "ancel lation", "ancell ation", "_ Game", "_G ame", "Ġ Hashtable", "ĠHash table", "ĠHas htable", "adaptive Styles", "Ġposs esses", "Ġpossess es", ". matcher", ".m atcher", ".match er", ".mat cher", "function al", "M rs", "Mr s", "ĉ save", "ĉs ave", "Ġ DbType", "ĠDb Type", "Ġ ken", "Ġk en", "Ġke n", "get Context", "Ġ mans", "Ġm ans", "Ġman s", "Ġma ns", "( rel", "(r el", "(re l", "ĠBrother hood", ") `Ċ", ")` Ċ", "è§ £", ". Information", ".In formation", "OutOfRange Exception", "ĠS ek", "ĠSe k", "C as", "Ca s", "Ġblog gers", "Ġblogger s", "E ither", "( \"\"\"", "(\" \"\"", "(\"\" \"", "Ġp inch", "Ġpin ch", "Ġco arse", ") p", "ĠP ulse", "ĠPu lse", "ĠPul se", "Ġlearn t", "Ġlear nt", "Ġdent ist", "Ġon change", "Ġdirect ives", "Ġdirective s", "( actions", "(a ctions", "(action s", "(act ions", "ny der", "ĠS hir", "ĠSh ir", "ĠShi r", "T rait", "Tr ait", "Tra it", "_ dep", "_d ep", "_de p", "Ġ PET", "ĠP ET", "ĠPE T", "Ġ REP", "ĠR EP", "ĠRE P", ".App Settings", "cu ador", "ide nav", "iden av", "Ġen vi", "Ġenv i", "Ġsl ammed", "Ġslam med", "Ġ Shoot", "ĠS hoot", "ĠSh oot", "ĠSho ot", "Ġ dateFormat", "Ġdate Format", ".j oda", "ve ys", "vey s", "Ġ ).ĊĊ", "Ġ) .ĊĊ", "Ġ). ĊĊ", "Ġ).Ċ Ċ", "Ġcar eg", "Ġca reg", "Ġcare g", "Ġ Parallel", "ĠPar allel", "_ translation", "_trans lation", ". functions", ".function s", ".fun ctions", ". obs", ".o bs", ".ob s", "Runtime Exception", "[ ]=", "[] =", "ov erview", "over view", "ĠS chl", "ĠSc hl", "ĠSch l", "Ġno isy", "Ġnoi sy", "ĠOn PropertyChanged", "S ending", "Send ing", "Sen ding", "Ġunf amiliar", "U pon", "Up on", "ĠPr ints", "ĠPrint s", "ĠPri nts", ". typ", ".t yp", "Ġfle eing", "Ġflee ing", "ĉ move", "ĉm ove", "ĉmov e", "( Un", "(U n", "Ġ qr", "Ġq r", "× ľ", "_ beta", "_b eta", "_be ta", "_bet a", "Ġsk ies", "Ġski es", "ĉ me", "ĉm e", "W ND", "WN D", "Ġst ickers", "Ġstick ers", "Ġsticker s", "b las", "bl as", "bla s", "Ġins erts", "Ġinsert s", "Ġinser ts", "Ġinse rts", "Ġver ses", "Ġvers es", "Ġverse s", "ĠD ew", "ĠDe w", "Ġt angible", "Ġtang ible", "Ġh echo", "Ġhe cho", "P OL", "PO L", "Ġte ardown", "Ġtear down", "om nia", "I BE", "IB E", ". cover", ".c over", ".co ver", "_ strategy", "_str ategy", "^ -", "set Position", "u ale", "ual e", "ua le", "S igned", "Sign ed", "Sig ned", "Ġ iface", "Ġif ace", "Ġi face", "as eline", "ase line", "asel ine", ".set Time", "ĠMin eral", "ĠMine ral", "ĠMiner al", "ĠF ighting", "ĠFight ing", "s kins", "sk ins", "ski ns", "skin s", "Ġdiscrim in", "Ġd ansk", "Ġdans k", "Ġdan sk", "ĠPr inceton", "ĠPrince ton", "ac ist", "aci st", "Ġ ());Ċ", "Ġ( ));Ċ", "Ġ() );Ċ", "Ġ()) ;Ċ", "tr acks", "tra cks", "track s", "imon ial", "a decimal", "ad ecimal", "ade cimal", "EP ROM", "ug gle", "ugg le", ". Notification", ".Not ification", "$ mail", "$m ail", "c antidad", "cant idad", "ĠJ ung", "ĠJun g", "ĠJu ng", "Ġsee kers", "Ġseek ers", "Ġseeker s", "Ġpl ausible", "t ier", "ti er", "tie r", "е ж", "еР¶", "Ġ rapper", "Ġr apper", "Ġrap per", "Ġra pper", "Ġrapp er", "Ġ Mana", "ĠM ana", "ĠMan a", "ĠMa na", "ĠHttp StatusCode", "ĠHttpStatus Code", "Ġbur nt", "Ġburn t", "l oses", "lo ses", "lose s", "los es", "Ġ Foto", "ĠF oto", "ĠFo to", "ĠFot o", "Ġ JsonObject", "ĠJson Object", "In stagram", "Inst agram", "Ġ syscall", "Ġsys call", "Ġreal ities", "ĠMAT LAB", ":^ {Ċ", "T ERM", "TE RM", "TER M", "ĠC bd", "Ġ Paragraph", "ĠPar agraph", "ĠPara graph", "Ġtrav és", "Ġconstruct ing", "Ġs wal", "Ġsw al", "Ġp ige", "Ġpi ge", "Ġpig e", "L LLL", "LL LL", "LLL L", "- existing", "-ex isting", "G ets", "Get s", "Ge ts", "Ġmel ted", "Ġmelt ed", "Ġmit igate", "Ġmitig ate", "H en", "He n", "Ġ hm", "Ġh m", "i mas", "im as", "ima s", "Ġ Ao", "ĠA o", "ĠP erez", "ĠPer ez", "ĠPe rez", "ĠPere z", "Ġ DAL", "ĠD AL", "ĠDA L", "Ġ ëĭ¤", "Ġëĭ ¤", "Ġdi vis", "Ġdiv is", "Storyboard Segue", "Ġ Modify", "ĠMod ify", "ĠModi fy", "ĠÃľ ber", "_O VERRIDE", ".p em", ".pe m", "un tos", "unt os", "unto s", "Ġesp añ", "Ġespa ñ", "Ġ{ ?", "Ġ PAY", "ĠP AY", "ĠPA Y", "_ ipv", "_i pv", "_ip v", "ĠF ury", "ĠFu ry", "ĠFur y", "__ .__", "__. __", "e low", "el ow", "elo w", "-c entered", "-center ed", "-cent ered", "che cks", "check s", "_ Reg", "_R eg", "_Re g", "-J avadoc", "ĉ load", "ĉl oad", "ĠLike wise", "ĠLik ewise", "ا Ùħ", "ا٠ħ", "U NE", "UN E", ". sem", ".s em", ".se m", "x cb", "xc b", "ĠC ave", "ĠCa ve", "ĠCav e", "_ sleep", "_s leep", "Ġsil ently", "Ġsilent ly", "Ġ Extreme", "ĠExt reme", "ĠExtr eme", ". ToUpper", ".To Upper", "ĉ CHECK", "ĉC HECK", "Ġ cue", "Ġc ue", "Ġcu e", "ĠQ ByteArray", "Ġcor rupted", "Ġcorrupt ed", "Ġ Dé", "ĠD é", "Ġim ped", "Ġimp ed", "Ġimpe d", "Get Name", "Ġinaccur ate", "Ġs ober", "Ġso ber", "Ġsob er", "е е", "еРµ", "Ġ barcode", "Ġbar code", "Ġba rcode", "-- ){Ċ", "--) {Ċ", "in ki", "ink i", "Ġ ép", "Ġé p", "Ġd ri", "Ġdr i", "Ġ ALT", "ĠA LT", "ĠAL T", "> >>>>>>>", ">>>> >>>>", ">>>>>>> >", "on ta", "ont a", "[ L", "Ġint eres", "Ġinter es", "Ġinte res", "ver ting", "vert ing", "Ġd iagnostics", "Ġdi agnostics", "Ġdiagnostic s", "p dev", "pd ev", "è ©", "Ġ Integrated", "ĠInt egrated", "ĠIntegr ated", ") .'", "). '", "_ gc", "_g c", "$ text", "$t ext", ". games", ".g ames", ".game s", ".ga mes", "ĠT erra", "ĠTer ra", "ĠTerr a", "' Re", "'R e", ". transfer", ".trans fer", "_F IFO", "get Model", "Ġb land", "Ġbl and", "Ġbla nd", "ĠCol eman", "ĠCole man", "Ġpr imes", "Ġprim es", "Ġprime s", "Ġpri mes", "Ġ æĪ", "Ġæ Ī", "Ġcross es", "n k", "G ING", "GIN G", "GI NG", "Ġ '^", "Ġ' ^", "Ġ Blob", "ĠB lob", "ĠBl ob", "ĠBlo b", "Ġinter course", "ĠBl vd", "Ġweigh s", "_ regular", "_reg ular", "ĠPer th", "ĠPert h", "Ġsepar ating", "Ġseparat ing", "Ġb illed", "Ġbill ed", "Ġbil led", ".tab Control", "Ġp uppet", "Ġpup pet", "Ġutil ization", "Ġutiliz ation", "Ġutiliza tion", "Ġ âĸł", "Ġâĸ ł", "Ġsuc ces", "Ġsucc es", "Ġl amps", "Ġla mps", "Ġlamp s", "Ġlam ps", "_ proj", "_p roj", "_pro j", "_pr oj", "E ric", "Er ic", "Ġre novation", "Ġren ovation", "Ġrenov ation", "ĠF amilies", "ĠFam ilies", "ĠFamil ies", "ĠFamilie s", "Ġ Bits", "ĠB its", "ĠBit s", "ĠBi ts", "part ials", "partial s", "- Men", "-M en", "s olution", "sol ution", "Ġd warf", "Ġdw arf", "Ġdwar f", ". INTEGER", ".IN TEGER", "Ġ LOCK", "ĠL OCK", "ĠLO CK", "ĠLOC K", ". ct", ".c t", "Ġ excerpt", "Ġex cerpt", "Ġexcer pt", "Ġ Pix", "ĠP ix", "ĠPi x", "Ġ FirstName", "ĠFirst Name", "AN TED", "ANT ED", "ANTE D", "ĠAd mir", "- help", "-h elp", "-he lp", "P rior", "Pr ior", "Pri or", "Ġ Align", "ĠAl ign", "ĠAli gn", ". INSTANCE", ".IN STANCE", "Line Edit", "(' /:", "('/ :", "Ġ inet", "Ġin et", "Ġi net", "Ġine t", "od us", "odu s", ".p kl", ".pk l", "Ġ KY", "ĠK Y", "u pert", "up ert", "uper t", "upe rt", "Ġn erves", "Ġnerv es", "Ġnerve s", "Ġner ves", "_ gradient", "_g radient", "_grad ient", "} ','", "}' ,'", "}', '", "_un ref", "Ġs aturated", "Ġsatu rated", "Ġsatur ated", "Ġ Connected", "ĠConnect ed", "ĠConn ected", "Ġ FN", "ĠF N", "EX IT", "Ġtele port", "Ġav ait", "Ġava it", "Page Route", "Ġdivorce d", "Ġdivor ced", "( lang", "(l ang", "f st", "fs t", "ĠT yr", "ĠTy r", "Ġm essenger", "Ġmess enger", "i fstream", "if stream", "X S", "ĠB anking", "ĠBank ing", "ĠBan king", "Ġinfect ious", "ĠM ons", "ĠMon s", "ĠMo ns", "_ LOOP", "_LO OP", "Ġzur ück", "Ġob tener", "Ġobt ener", "Ġobten er", "/ repos", "/re pos", "V el", "Ve l", "a cro", "ac ro", "acr o", "Ġ userRepository", "Ġuser Repository", "style Type", "Ġ SRC", "ĠS RC", "ĠSR C", "VML INUX", "rec ursive", "/ bar", "/b ar", "_ chip", "_c hip", "_ch ip", "_chi p", "o minated", "om inated", "omin ated", "Ġ Nit", "ĠN it", "ĠNi t", "âĢĶ to", "ĠBudd h", "ĠBud dh", "о меÑĢ", "ом еÑĢ", "ĠM AG", "ĠMA G", "Ġ CHE", "ĠC HE", "ĠCH E", "_ den", "_d en", "_de n", ". raises", ".r aises", ".raise s", ".ra ises", "_ degree", "_d egree", "_de gree", "_deg ree", "Ġpump kin", "_ templates", "_t emplates", "_template s", "_temp lates", "_tem plates", "_ MEDIA", "_M EDIA", "_MED IA", "Ġ Timeline", "ĠT imeline", "ĠTime line", "ĠTim eline", "Ġ bots", "Ġb ots", "Ġbo ts", "Ġbot s", "Object Type", "Ġbu ys", "Ġbuy s", ". posts", ".post s", ".pos ts", ".po sts", "C AL", "CA L", "wa iting", "wait ing", "ĠDaniel s", "ĠDani els", "Ġd abei", "Ġda bei", "Ġdab ei", "Ġ Sigma", "ĠS igma", "ĠSig ma", "i lor", "il or", "ilo r", "i gel", "ig el", "ige l", ", W", "A DS", "AD S", "( panel", "(p anel", "(pa nel", "ì² ´", "it ating", "ita ting", "itat ing", ". palette", ".p alette", ".pa lette", "Ġmos quito", "Ġt ego", "Ġte go", "Ġteg o", "( parseInt", "(parse Int", "Ġdes pués", "p romise", "pro mise", "prom ise", "Ġ wij", "Ġw ij", "Ġwi j", "type script", "types cript", "Ġ Tv", "ĠT v", "_IDENT IFIER", ") .ĊĊĊ", "). ĊĊĊ", ").ĊĊ Ċ", ").Ċ ĊĊ", "_ flat", "_f lat", "_fl at", "it su", "its u", "U SR", "US R", "ex perience", "- fit", "-f it", "-fi t", "ph inx", "phi nx", "phin x", "_ thresh", "_th resh", "_thr esh", "Ġide ally", "Ġideal ly", "ĠFree man", "ĠFre eman", "ĠFreem an", ", DB", ",D B", "_ rw", "_r w", "çŃ ī", "U b", "_ statistics", "_stat istics", "=\" \"><", "=\"\" ><", "=\"\"> <", "Ġch ore", "Ġcho re", "Ġchor e", "Ġy ork", "Ġyo rk", "inst alled", "install ed", "Add itionally", "Additional ly", "Ġp stmt", "Ġpst mt", "yl ko", ": :Ċ", ":: Ċ", "F orest", "For est", "Fore st", "Fo rest", "Ġhead set", "Ġheads et", "Ġg allon", "Ġgal lon", "Ġgall on", "ÑĢ ем", "ÑĢе м", "Ġwithdraw n", "Ġwithd rawn", "Ġ Candidate", "ĠC andidate", "ĠCandid ate", "Ġmel ting", "Ġmelt ing", "Ġfree zer", "Ġfreeze r", "Ġ hl", "Ġh l", "_ HELP", "_HE LP", "_HEL P", "m ime", "mi me", "( /*", "(/ *", "Ġth irst", "Ġthi rst", "$ return", "$r eturn", "$ret urn", "member of", "е б", "еР±", "Ġ HttpServletRequest", "ĠHttp ServletRequest", "ĠHttpServlet Request", "( ob", "(o b", "_ Result", "_Res ult", "Ġassert ed", "Ġfulfill ing", "Ġfulfil ling", "Ġstretch es", "Ġstret ches", "par ated", "pa rated", "para ted", "parate d", "-f unded", "Ġ åĽ", "Ġå Ľ", "in gles", "ing les", "ingle s", "_ ca", "_c a", ". condition", ".con dition", ".cond ition", "Ġ Displays", "ĠDis plays", "ĠDisplay s", "ĠDisp lays", "Ġ orang", "Ġo rang", "Ġor ang", "Ġora ng", "Ġoran g", "Ġ CRE", "ĠC RE", "ĠCR E", "Ġgl Bind", "Ġ Selector", "ĠSe lector", "ĠSelect or", "ĠSel ector", "ĠSele ctor", "/ type", "/t ype", "ĠAlex a", "ĠAle xa", "ched ules", "chedule s", "ĠPen insula", "Ġ parity", "Ġp arity", "Ġpar ity", "Ġpari ty", "ĉ dest", "ĉd est", "ĉdes t", "ĉde st", "ĠDo ors", "ĠDoor s", "čĊ ĉčĊ", "_ dimension", "_d imension", "_dim ension", "Ġ aload", "Ġa load", "Ġal oad", "Ġalo ad", ".St oredProcedure", "( paren", "(p aren", "(par en", "(pa ren", "ĠBur ke", "ĠBurk e", "' )]Ċ", "') ]Ċ", "')] Ċ", "- engine", "-e ngine", "-eng ine", "Ġ quir", "Ġqu ir", "Ġq uir", "Ġqui r", "ĠH ybrid", "ĠHy brid", "ĠD oe", "ĠDo e", "Ġout lines", "Ġoutline s", "ĠT rends", "ĠTr ends", "ĠTre nds", "ĠTrend s", "_ NV", "_N V", "per iments", "periment s", "peri ments", "ĠH in", "ĠHi n", "? ',", "?' ,", "ĉ Text", "ĉT ext", "F UL", "FU L", "Ġsm ells", "Ġsmell s", "Ġ slick", "Ġs lick", "Ġsl ick", "Ġslic k", "Ġmis erable", "Ġmiser able", "ĠArray Adapter", "Ġparam String", "H om", "Ho m", "_l iterals", "_literal s", "_lit erals", "us uarios", "usuario s", "usu arios", "Ġprompt ing", "_ lazy", "_l azy", "_la zy", "Ġ Activation", "ĠAct ivation", "ĠActiv ation", "_ oc", "_o c", "We ak", "Ġan ecd", "ĠU CLA", "ĠUC LA", "= re", "=r e", "iss ement", "isse ment", "ĠEsc orts", "ĠEscort s", "Ex cellent", "Ġ Pause", "ĠP ause", "ĠPa use", "Ġ repositories", "Ġre positories", "Ġrepos itories", "T OR", "TO R", "ar iate", "ari ate", "aria te", "ariat e", "_ iso", "_i so", "_is o", "up dates", "update s", "upd ates", "ha lb", "hal b", "udi ante", "udiant e", "ë¡ Ŀ", "Ġna ive", "ĠP eg", "ĠPe g", "ĠL ounge", "ĠLo unge", "ĠLou nge", "AR GIN", "ARG IN", "( bin", "(b in", "On ClickListener", "OnClick Listener", "Ġ FAILED", "ĠFA ILED", "ĠFAIL ED", "Ġ lite", "Ġl ite", "Ġli te", "Ġlit e", "Ġd zie", "Ġdz ie", "Ġdzi e", "Ġ Literal", "ĠL iteral", "ĠLiter al", "ĠLit eral", "ĠLite ral", "i vor", "iv or", "ivo r", "f cntl", "fc ntl", "fcn tl", "Ġe ats", "Ġeat s", "Ġea ts", "Ġ qed", "Ġq ed", "Un lock", "r iding", "ri ding", "rid ing", "und ai", "unda i", "= M", "AT TER", "ATT ER", "Configure Await", "ic ias", "ici as", "icia s", "ust omed", "ustom ed", "usto med", "Ġsuccess ion", "Ġsuc cession", "Ġsucc ession", "end Time", "ĠJ upiter", "Ġj udging", "Ġjud ging", "d ration", "dr ation", "dra tion", "_ docs", "_d ocs", "_doc s", "_do cs", ". mo", ".m o", "Ġeduc ators", "Ġeducator s", "ĠV ine", "ĠVi ne", "ĠVin e", "C ond", "Con d", "Co nd", "[ out", "[o ut", "q b", "\\ Validator", "Ġmean ings", "Ġmeaning s", "Ġpres ently", "Ġpresent ly", "Ġdiv iding", "Ġdivid ing", "otten ham", "as cular", "asc ular", "Ġtrail ers", "Ġtra ilers", "Ġtrailer s", "Ġtrai lers", "Ġ CLOSE", "ĠC LOSE", "ĠCL OSE", "а ми", "ам и", "âĢĻ ai", "âĢĻa i", "Ġ Gain", "ĠG ain", "ĠGa in", "w or", "wo r", "Ġpl anner", "Ġplan ner", "Ġdistrib uting", "v at", "va t", "mon ths", "month s", "mont hs", "x label", "xl abel", "H F", "V iol", "Vi ol", ".BASE LINE", "еÑĤ ÑģÑı", "Ġ Rotate", "ĠR otate", "ĠRot ate", "Ġ txn", "Ġt xn", "Ġtx n", ": bold", ":b old", "Ġb loss", "Ġbl oss", "Ġblo ss", "Forg ery", "Forge ry", "( embed", "(em bed", "Ġj ako", "Ġja ko", "Ġjak o", "s printf", "the ir", "Ġexhib its", "Ġexhibit s", "- static", "-st atic", "-stat ic", "he cy", "hec y", "get ActiveSheet", ". clients", ".c lients", ".client s", ".cl ients", ".cli ents", "ãģ į", "_ hide", "_h ide", "_hi de", "_hid e", "[ word", "[w ord", "C b", "add Item", "a xe", "ax e", "_ radio", "_r adio", "_rad io", "_ra dio", "_radi o", "a lion", "al ion", "ali on", "mod ifier", "Ġsat uration", "Ġsatu ration", "Ġsatur ation", "Ġde nom", "Ġden om", "_ pixels", "_p ixels", "_pixel s", "_pix els", "m ess", "me ss", "mes s", "( fl", "(f l", "a tif", "at if", "ati f", "Ġ secs", "Ġs ecs", "Ġse cs", "Ġsec s", "Ġpro stitution", "Ġprostit ution", "Ġprost itution", "Ġprostitu tion", "Ġprostitut ion", "Ġgrand children", "Ġparad ise", "ĠF eld", "ĠFe ld", "ĠFel d", "_B INARY", "_BIN ARY", "it ous", "ito us", "itou s", "à ¹Ħ", "๠Ħ", "Ġfl ashing", "Ġflash ing", "-s ided", "-side d", "Ġcontrad iction", "Ġcontradict ion", "/ *ĊĊ", "/* ĊĊ", "/*Ċ Ċ", "y label", "yl abel", "yla bel", "ĠT et", "ĠTe t", "Ġadm ire", "Ġadmir e", "r eso", "re so", "res o", "Ġl etz", "Ġle tz", "Ġlet z", "Ġ SEARCH", "ĠSE ARCH", "s lots", "sl ots", "slot s", "ĠRe wards", "ĠRew ards", "ĠReward s", "ĠH og", "ĠHo g", "Ġ NSData", "ĠNS Data", "st ash", "sta sh", "F all", "Fa ll", "Fal l", "ĠA mer", "ĠAm er", "Line arLayout", "Linear Layout", "/ photos", "/photo s", "/ph otos", "Ġfe ather", "Ġfeat her", "Ġ |čĊ", "Ġ| čĊ", "Down loads", "Download s", ".Start sWith", "Ġ //#", "Ġ// #", "Ġ/ /#", "ine Transform", "Ġaff id", "Ġaf fid", "V tbl", "ĠR ogue", "ĠRo gue", "ĠRog ue", "s cribed", "scribe d", "scri bed", "Ġf auc", "Ġfa uc", "ĠMon roe", "Ġdecl ares", "Ġdeclar es", "Ġdeclare s", "mod ern", "mode rn", "r eon", "re on", "reo n", "ay be", "P ASS", "PA SS", "f ers", "fer s", "fe rs", "_MULT I", "_MUL TI", "ĠMath ematics", "ĠMathematic s", "Ġsud ah", "_ATT ACH", "Ġnumber With", "ĠSol omon", "ĠSolo mon", "j in", "ji n", "ograf ia", "ogr afia", "ö l", "_ design", "_d esign", "_de sign", "_des ign", "c ulated", "cul ated", "culate d", "cu lated", "ĠL una", "ĠLu na", "ĠLun a", "i esz", "ies z", "ie sz", "Ġ =>'", "Ġ= >'", "Ġ=> '", "Ġreve lations", "Ġrevel ations", "Ġrevelation s", "A long", "Al ong", "( ed", "(e d", "Ġ Filename", "ĠF ilename", "ĠFile name", "ĠFil ename", "ĠFi lename", "Ġ ylabel", "Ġy label", "S ecure", "Sec ure", "Ġbus ca", "Ġbusc a", "ag nosis", "agn osis", "_RE CE", "_REC E", "Ġover lapping", "Ġoverlap ping", "Ġoverl apping", "Ex tent", "Ext ent", "Ġant icipation", "Ġanticip ation", "Ġantic ipation", "Check s", "Che cks", "ĠAL SO", "ĠALS O", "o rc", "or c", "iling ual", "it ational", "itation al", "itat ional", "Ġadv ancement", "Ġadvance ment", "o uro", "ou ro", "our o", "Ġ Predicate", "ĠP redicate", "ĠPred icate", "å¾ Ĺ", "e ria", "er ia", "eri a", "ĠP ierce", "ĠPi erce", "ĠPier ce", "o rio", "or io", "ori o", "Ġmer its", "Ġmerit s", "Ġpe anut", "Ġpea nut", ". Package", ".P ackage", "ĠCon duct", "ĠCond uct", "_ SENSOR", "_S ENSOR", "_SENS OR", "Ġbo iling", "Ġboil ing", "Ġin tra", "Ġint ra", "Ġintr a", "Ġ IGN", "ĠI GN", "ĠIG N", "ĠF ur", "ĠFu r", ". Refresh", ".Re fresh", ".Ref resh", "Ġ Reach", "ĠR each", "ĠRe ach", "_ decoder", "_de coder", "_dec oder", "_decode r", ". Exp", ".Ex p", ".E xp", "Ġ ÑĤак", "ĠÑĤ ак", "ĠÑĤа к", "p ill", "pi ll", ", Q", "ĠGr ill", "ĠGri ll", "Ġp opping", "Ġpop ping", "Ġpo pping", ". Ag", ".A g", "Ġpro yecto", "Ġmile age", "Ġec ological", "Ġeco logical", "] ]);Ċ", "]] );Ċ", "]]) ;Ċ", "Ġ ÂŃ", "Ġ Ń", "sub plot", "a cad", "ac ad", "aca d", "Ġ Trying", "ĠT rying", "ĠTr ying", "ĠTry ing", "rec ipes", "recipe s", "$ criteria", "$c riteria", "ĠPer sian", "ĠPers ian", "- bound", "-b ound", "-bo und", "M ASK", "MA SK", "MAS K", "Ġ Gesture", "ĠG esture", "ĠGes ture", "ĠGest ure", "Ġ kk", "Ġk k", "ĠP VC", "ĠPV C", "Ġpro hibition", "Ġprohib ition", "Ġprohibit ion", "Ġcom ando", "Ġco mando", "Ġcoma ndo", "Ġ LOOK", "ĠL OOK", "ĠLO OK", "Sh opping", "Shop ping", "Ġdist ortion", "Ġdistort ion", "< Boolean", ".Get Length", "um pt", "ump t", "\\ Product", "ell ery", "elle ry", "eller y", "Ġfire wall", "form atted", "format ted", ". redis", ".re dis", ".r edis", ".red is", "Ġ esa", "Ġe sa", "Ġes a", "ĠRh ode", "S om", "So m", ". non", ".n on", ".no n", "Ġ ').", "Ġ' ).", "Ġ') .", "Ġ getView", "Ġget View", "ạ n", "p rus", "pr us", "Mat thew", "Ġs ia", "Ġsi a", "ĠF ors", "ĠFor s", "ĠFo rs", "G PU", "GP U", "ient ras", "ien tras", "_ INST", "_IN ST", "_I NST", "_INS T", "Ġo larak", "Ġol arak", "Ġola rak", "Ġimport ing", "Ġimp orting", "T CP", "TC P", "/ \");Ċ", "/\" );Ċ", "/\") ;Ċ", "e ither", "ei ther", "Ġfresh ly", "c ascade", "cas cade", "( character", "(char acter", "ĠJe ep", "o tics", "ot ics", "otic s", "oti cs", "_ UTIL", "_UT IL", ".Xtra Printing", ".first Child", "ĠEx cell", "ĠExcel l", "ĠExc ell", "Ġd vd", "Ġdv d", "Ġt aller", "Ġtal ler", "Ġta ller", "Ġtall er", "Ġ ras", "Ġr as", "Ġra s", "y pass", "yp ass", "Ġassign s", "Ġgr iev", "Ġgri ev", "- more", "-m ore", "J D", "ĠBur ns", "ĠBurn s", "' >čĊ", "'> čĊ", ". Dependency", ".D ependency", ".Dep endency", ". QueryString", ".Query String", ". Owner", ".O wner", "Ġ expiry", "Ġex piry", "Ġexp iry", "T hu", "Th u", "( Vec", "(V ec", "Ġhazard ous", "Ġ rpm", "Ġr pm", "Ġrp m", "AP ON", "APO N", "Ġadd Target", "s ville", "sv ille", "p Net", "Ġ Img", "ĠI mg", "ĠIm g", "Ġ TIMER", "ĠT IMER", "ĠTIM ER", "ĠTIME R", "ĠTI MER", ". Animation", ".An imation", "Ġ bek", "Ġb ek", "Ġbe k", "Ġas sort", "Ġass ort", "Ġle bih", "Ġbody Parser", "Ġvibr ating", "Ġvib rating", "I DL", "ID L", "Ġbutter knife", "in ters", "int ers", "inter s", "inte rs", "Ġpersu ade", "ĠLGBT Q", "è ĭ", ". soft", ".s oft", ".so ft", "Ġbe ams", "Ġbeam s", "_ sur", "_s ur", "_su r", ". Def", ".D ef", ".De f", "Ġ labs", "Ġl abs", "Ġla bs", "Ġlab s", "ĉ plt", "ĉp lt", "ĉpl t", "Ġ skins", "Ġs kins", "Ġsk ins", "Ġskin s", "Ġski ns", "Ġtransfer ring", "Ġtransf erring", "Ġimag inary", "Ġimagin ary", "_ End", "_E nd", "; background", "Ġ laps", "Ġl aps", "Ġla ps", "Ġlap s", "_ COMMENT", "_COM MENT", "_COMM ENT", "( SDL", "(S DL", "o nds", "on ds", "ond s", ". Record", ".Re cord", ".Rec ord", "ĠIm plements", "ĠImp lements", "ĠImplement s", "ĠImpl ements", "_ ticks", "_t icks", "_tick s", "_ti cks", "( )))ĊĊ", "() ))ĊĊ", "()) )ĊĊ", "())) ĊĊ", "()))Ċ Ċ", "Ġa rose", "Ġar ose", "] ?", "Ġ Mp", "ĠM p", "ĠI Command", "Ġsculpt ure", "Ġcon tracted", "Ġcontract ed", "Ġcontr acted", "< HTML", "Ġcal end", "a ty", "at y", "/ Sub", "/S ub", "Ġkv inn", "Ġkvin n", "_ IGNORE", "ĠSh ane", "ĠSha ne", "ĠShan e", "M LS", "ML S", "Ġstim ulate", "Part ition", "Ġ mun", "Ġm un", "Ġmu n", "ó m", "er ala", "era la", "eral a", "- account", "-a ccount", "-ac count", ". Binary", ".B inary", "c é", "Ġse ize", "Ġseiz e", "Ġsei ze", "conn ections", "connect ions", "connection s", "Ġ ĊĠĠĠĠĠĠĠĠĊ", "ĠĊ ĠĠĠĠĠĠĠĠĊ", "Ġ Diagnostic", "ĠDi agnostic", "V ISIBLE", "VIS IBLE", "Ġ Runs", "ĠR uns", "ĠRun s", "ĠRu ns", "Ġim pressions", "Ġimpress ions", "Ġimpression s", "Ġimpr essions", "s uite", "su ite", "suit e", "o ble", "ob le", "obl e", "~ -", "ak ukan", "aku kan", "< Person", "

\">\" >\"> \">< /", "_ indexes", "_index es", "Ġ valuation", "Ġval uation", "Ġvalu ation", "Ġlife long", "Ġlif elong", "Ġexp edition", "Ġexped ition", "( Yii", "(Y ii", "Ġp ains", "Ġpain s", "Ġpa ins", "Ġpai ns", "Ġ PRI", "ĠP RI", "ĠPR I", "Ġ Mixed", "ĠM ixed", "ĠMix ed", "ĠMi xed", "Ġ Changing", "ĠCh anging", "ĠChan ging", "ĠChang ing", "German y", "Ger many", "comm unication", "communic ation", ". organ", ".org an", ".o rgan", ".or gan", "ĠMar athon", "ĠMara thon", "get Path", "Ġ Accuracy", "ĠAc curacy", "ĠAcc uracy", "Ġ Globals", "ĠG lobals", "ĠGlobal s", "ĠGlob als", "') }}'", "Ġ'\" >'", "Ġ'\"> '", "k inson", "kin son", "kins on", "Ġ кол", "Ġк ол", "Ġко л", "ogn itive", "_ li", "_l i", "Ġim minent", "Ġimm inent", "Ġaff inity", "Ġaf finity", ". signal", ".s ignal", ".sign al", ".sig nal", "Ġn otch", "Ġnot ch", "ĠSteel ers", "ĠSteele rs", "max length", "K K", "ĠEu gene", "ĠEug ene", "_ PWM", "_P WM", "_PW M", "r oi", "ro i", "Ġ âĹı", "Ġâ Ĺı", "ĠâĹ ı", "ĠH amburg", "ĠHam burg", ". Must", ".M ust", "Ġ axe", "Ġa xe", "Ġax e", "en ef", "ene f", "Ġamb itions", "Ġambit ions", "Ġambition s", "Ġ Species", "ĠS pecies", "ĠSp ecies", "ĠSpec ies", "ĠSpe cies", "ĠSt ress", "ĠStr ess", "ĠStre ss", "Ġa while", "Ġ бÑĥд", "Ġб Ñĥд", "ĠбÑĥ д", "Ġwith stand", "Ġ Decoder", "ĠDe coder", "ĠDec oder", "ĠDecode r", "_ inventory", "_in ventory", "Ġ{ ččĊ", "Ġ tgt", "Ġt gt", "Ġtg t", "Ġrail road", "W ASHINGTON", "Ġnegot iated", "Ġnegotiate d", "N ST", "NS T", "- phone", "-p hone", "-ph one", ", U", "Ġexerc ising", "á» ¥", "_P IXEL", "_PIX EL", "av ors", "avor s", "avo rs", "ite rated", "iter ated", "iterate d", "Ġv ampire", "Ġvamp ire", "a dal", "ad al", "ada l", "In grese", "Ing rese", "Ġ ung", "Ġu ng", "Ġun g", "j ective", "ject ive", ". cells", ".c ells", ".cell s", "Ġ nano", "Ġn ano", "Ġna no", "Ġnan o", "Ġ markdown", "Ġmark down", "_ RULE", "_R ULE", "( events", "(e vents", "(event s", "(ev ents", "Ġl uggage", "Ġlug gage", "M ESSAGE", "MESS AGE", "ig keit", "$ count", "$c ount", "Attribute Name", "IG INAL", "IGIN AL", "_ Ent", "_E nt", "Ġ BF", "ĠB F", "Ġ COMMENT", "ĠCOM MENT", "ĠCOMM ENT", "_ ini", "_in i", "_i ni", "ĠEurope ans", "ĠEuropean s", "ĠB elle", "ĠBe lle", "ĠBel le", "ĠBell e", "åij ½", ") ['", ")[ '", "åº Ķ", "ĠUs eful", "ĠUse ful", ". reference", ".re ference", ".ref erence", "( )\",", "() \",", "()\" ,", "_ grade", "_g rade", "_gr ade", "_grad e", "ĠK aw", "ĠKa w", "Ġsent encing", "Ġsocial ism", "mon ster", "mons ter", "_L AYER", "Ġdeep est", "Ġdee pest", "w k", "Ġ Noise", "ĠN oise", "ĠNo ise", "# ##ĊĊ", "## #ĊĊ", "### ĊĊ", "###Ċ Ċ", "Ġpr éc", "Ġpré c", "o tle", "ot le", "ÑĤ е", "a uf", "au f", "i bal", "ib al", "iba l", "Ġcon quer", "Ġconqu er", "> Email", ">E mail", "Ġamb ulance", "O AD", "OA D", "Ġ (\"%", "Ġ( \"%", "Ġ(\" %", "Ġ FI", "ĠF I", ". fixture", ".f ixture", ".fix ture", "Ġt erse", "Ġter se", "Ġters e", "ĠĠ ĠĠĉĉĉĉ", "ĠĠĠĠ ĉĉĉĉ", "ĠĠĠ Ġĉĉĉĉ", "ĠĠĠĠĉ ĉĉĉ", "ĠĠĠĠĉĉ ĉĉ", "ĠĠĠĠĉĉĉ ĉ", "Ġsanct uary", "u gi", "ug i", "Ġ Comparator", "ĠCom parator", "ĠCompar ator", "Definition s", "Ġast hma", "Ġl act", "Ġla ct", "Ġlac t", "Ġhard wood", ". clock", ".c lock", ".cl ock", "Ġattr acting", "Ġattract ing", "ĠM our", "ĠMo ur", "ĠMou r", "( distance", "(d istance", "(dist ance", "(di stance", "ic its", "ici ts", "icit s", "Ġb onne", "Ġbon ne", "Ġ ACCESS", "ĠAC CESS", "ĠACC ESS", ".Deserialize Object", "Ġ Typed", "ĠT yped", "ĠType d", "ĠTy ped", "ĠTyp ed", "Ġj eu", "Ġje u", "Ġ appId", "Ġapp Id", "ĠC lara", "ĠCl ara", "ĠClar a", "ĠCla ra", "Ġ HF", "ĠH F", "ĠRe ich", "ĠRei ch", "ip ples", "ipp les", "ipple s", "// --------------------------------------------------------------------------------", "//---------------------------------------------------------------- ----------------", "//---------------------------------------------------------------------------- ----", "//------------------------------------------------ --------------------------------", "//-------------------------------- ------------------------------------------------", "//---------------- ----------------------------------------------------------------", "_ delivery", "_d elivery", "_del ivery", "erial ization", "Ġplaint iffs", "Ġplaintiff s", "S cient", "Sc ient", "Sci ent", "sh opping", "shop ping", "Ġ Dummy", "ĠD ummy", "ĠDum my", "ĠW ald", "ĠWal d", "ĠWa ld", "Group Name", "Ġ inscription", "Ġin scription", "Ġins cription", "e log", "el og", "elo g", ": :::::::", ":: ::::::", ":::: ::::", ":::::: ::", "::: :::::", "::::: :::", "::::::: :", "_ ld", "_l d", "Back Pressed", ". Raw", ".R aw", "ĠOn Trigger", "Ġmuseum s", "Ġmuse ums", "Ġ Been", "ĠB een", "ĠBe en", "ĠBee n", "ĠAdvent ures", "ĠAdventure s", "Ġs late", "Ġsl ate", "Ġsla te", "Ġ lett", "Ġl ett", "Ġle tt", "Ġlet t", "Ġs und", "Ġsu nd", "Ġsun d", "ĠG in", "ĠGi n", "ĠMechan ical", "ĠMech anical", ". ship", ".s hip", ".sh ip", "App Component", "Ġdest ined", "Ġdestin ed", "Ġdw elling", "Ġdwell ing", "Pro filer", "Profile r", "Prof iler", "Pre pare", "ze ich", "Ġsil icon", "( has", "(h as", "Ġ# %", "V IDEO", "VID EO", "Ġcollabor ate", "L in", "Li n", "Ġ scopes", "Ġsc opes", "Ġscope s", "Ġsco pes", "Ġscop es", "( className", "(class Name", "( sd", "(s d", "an din", "and in", "andi n", ". ham", ".h am", "Service Impl", "-de scribed", "-des cribed", "Ġir ony", "Ġiron y", "st ial", "sti al", "ĠHu awei", "( repo", "(re po", "(rep o", "Ġunexpected ly", "ĠK ai", "ĠKa i", ". install", ".inst all", "\\ xf", "\\x f", "Ġex hibited", "Ġexhib ited", "Ġexhibit ed", "_ TCP", "_T CP", "_TC P", "ĠO x", "_ CHO", "_C HO", "_CH O", "Ġprostitu erte", "Ġprostituer te", "Ġ vä", "Ġv ä", "Ġs ito", "Ġsit o", "Ġsi to", "Ġconstitu ents", "Ġconstituent s", "ĠContinue d", "ĠContin ued", "Ġ SAVE", "ĠS AVE", "ĠSA VE", "r ss", "rs s", "/ message", "/m essage", "u bes", "ub es", "ube s", "Ġmisd emean", "Ġtax ation", "Ġtaxa tion", "Ġstory line", "h air", "ha ir", "hai r", "ĠF inds", "ĠFin ds", "ĠFind s", "ĠFi nds", "S IG", "SI G", "ver ification", "~ =", ". hp", ".h p", "It erable", "Iter able", "Ñĭ е", "at ori", "ator i", "ato ri", "Ġ ctr", "Ġc tr", "Ġct r", "R x", "_ );ĊĊ", "_);Ċ Ċ", "_) ;ĊĊ", "d ag", "da g", ". pin", ".p in", ".pi n", "Ġp seud", "Ġin vo", "Ġinv o", "Ñģ ÑĤÑĢ", "ÑģÑĤ ÑĢ", "_ pix", "_p ix", "_pi x", "为 空", "Ġsw orn", "Ġswo rn", "âĢĶ or", "_ registry", "_reg istry", "Ġdis asters", "Ġdisaster s", "Ġ ROI", "ĠR OI", "ĠRO I", "Ġ âĢķ", "ĠâĢ ķ", "ak tu", "akt u", "f orest", "fo rest", "fore st", "for est", "be iten", "beit en", "bei ten", "âĢĶ I", "u eva", "ue va", "e gt", "eg t", "Ġsp ikes", "Ġspi kes", "Ġspike s", "U RES", "UR ES", "URE S", "Ġ Recommended", "ĠRe commended", "ĠRecomm ended", "ĠRecommend ed", "Ġexplo ited", "Ġexploit ed", "ĠFreder ick", "_ COMPLETE", "_COMP LETE", "ĠDr ugs", "ĠDrug s", "!!! !!!!!", "!!!! !!!!", "!!!!! !!!", "ĠR iv", "ĠRi v", "S TOP", "ST OP", "R OOM", "RO OM", "Ġ PASSWORD", "ĠP ASSWORD", "ĠPASS WORD", "C ookies", "Co okies", "Cookie s", "Cook ies", ". El", ".E l", "á» Ń", "ĠB ert", "ĠBe rt", "ĠBer t", "Ġ hashed", "Ġh ashed", "Ġhas hed", "Ġhash ed", "Ġha shed", "ic ester", "ice ster", "ices ter", "Ġdecor ator", "Ġ queryString", "Ġquery String", ": ;Ċ", "Ġ\" [\"", "Ġ\"[ \"", "ot ope", "oto pe", "- Americ", "-A meric", "-Am eric", "ĠMatthew s", "ĠMatth ews", "U RAL", "UR AL", "URA L", "âĢľ ,", "S ummer", "Sum mer", "f os", "fo s", "_CONT AINER", "_ ACK", "_A CK", "_AC K", "Ġ filtr", "Ġf iltr", "Ġfil tr", "Ġfi ltr", "Ġfilt r", "_ disp", "_d isp", "_dis p", "_di sp", "_ Re", "_R e", "Ġfac ile", "Ġfacil e", "а ÑĪ", "Ġìķ Ĭ", "Ġe ben", "Ġeb en", "Ġsp rink", "Ġspr ink", "ĠQ uint", "ĠQu int", "ĠQui nt", "> V", "Ġhistor ians", "Ġhistorian s", "Ġhistoria ns", "Ġhisto rians", "our met", "Ġ Monitoring", "ĠMonitor ing", "led ger", "ledge r", "c ott", "co tt", "cot t", "Ġ ware", "Ġw are", "Ġwar e", "Ġwa re", "G GLE", "GG LE", "c ars", "ca rs", "car s", "ĠM EDIATEK", "Ġvol upt", "_ View", "_V iew", "H EL", "HE L", "( copy", "(c opy", "(co py", "( stats", "(st ats", "(stat s", "Ġch romosome", "Ġchrom osome", "ĠCurt is", "- conf", "-con f", "-co nf", "( asset", "(as set", "Ġh vor", "Ġhv or", "File System", "< >();čĊ", "<>( );čĊ", "<> ();čĊ", "o coder", "oc oder", "oco der", "ocode r", "ĠC annon", "ĠCan non", "ĠCann on", ") x", "Ġ Smooth", "ĠSm ooth", "ĠS AS", "ĠSA S", "_ ce", "_c e", "ĉ prev", "ĉp rev", "ĉpr ev", "ĉpre v", "_ movie", "_m ovie", "_mov ie", "_mo vie", "E c", "_ wall", "_w all", "< Button", ".ĊĊ", "/> .ĊĊ", "/>.Ċ Ċ", "/>. ĊĊ", "o genesis", "ogen esis", "ogene sis", "Ġ OPTIONS", "ĠOPTION S", "ĠOPT IONS", "up tools", "upt ools", "Ġmilit ant", "Ġmil itant", "Ġmili tant", "Ġex ited", "Ġexit ed", "i gar", "ig ar", "iga r", "Ġ COMM", "ĠCO MM", "ĠCOM M", "Ġ Disposable", "ĠD isposable", "ĠDis posable", "ay cast", "Ġrow span", "Ġrows pan", "Ġsyn thes", "Ġsynth es", "Ġsynt hes", "Ġso ndern", "Ġsond ern", "Ġ Ċ", "]- ->Ċ", "ĠJ acket", "ĠJack et", "ĠJac ket", "ĠJa cket", "R ATION", "RA TION", ".get SelectedItem", ".getSelected Item", "- init", "-in it", "-i nit", "Ġ Registers", "ĠReg isters", "ĠRegister s", "_ sep", "_s ep", "_se p", "Ġ Toolkit", "ĠTool kit", ". dict", ".d ict", ".di ct", "Ġ xlabel", "Ġx label", "Ġxl abel", "\\ Table", "t oc", "to c", "_ combo", "_c ombo", "_com bo", "_comb o", "Ġ Compact", "ĠComp act", "Ġr ugged", "Ġrug ged", "à¥ĩ à¤", "- management", "-man agement", "') }}\">Ċ", "')}} \">Ċ", "')}}\" >Ċ", "')} }\">Ċ", "')}}\"> Ċ", "Ġ Stamp", "ĠSt amp", "ĠSta mp", "ĠStam p", "ı l", "r ox", "ro x", "Ġlandscape s", "Ġlandsc apes", "_ NOTE", "_N OTE", "_NO TE", "_NOT E", "mon ary", "c ab", "ca b", "Ġmo et", "x af", "xa f", "r code", "rc ode", "- cli", "-c li", "-cl i", "_ gate", "_g ate", "[ event", "[e vent", "S PORT", "SP ORT", "g ia", "gi a", "Ġ SUPER", "ĠS UPER", "ĠSU PER", "ĠSUP ER", "/ Login", "_ shutdown", "_sh utdown", "int errupt", "inter rupt", "Ġpret ending", "Ġpretend ing", "Ġf ringe", "Ġfr inge", "Ġfri nge", "ĠR eds", "ĠRe ds", "ĠRed s", "Ġ CUDA", "ĠC UDA", "ĠCU DA", "Ġ UNIX", "ĠUN IX", "v it", "vi t", "Ġ brig", "Ġb rig", "Ġbr ig", "Ġbri g", "d rv", "dr v", "Ġ Connector", "ĠConnect or", "ĠConn ector", "There fore", "Ġ lia", "Ġl ia", "Ġli a", "D etection", "De tection", "Det ection", "Detect ion", "_ actor", "_a ctor", "_ac tor", "_act or", "Ġtemp file", "Ġecc entric", "- role", "-r ole", "-ro le", "Ġp adx", "Ġpa dx", "Ġpad x", "d ent", "de nt", "den t", "West ern", "Ġ ê·¸", "Ġê ·¸", "Ġê· ¸", "ĠApplication Record", "Ġcampaign ing", "_ runner", "_r unner", "_run ner", "ĠC ivic", "ĠCi vic", "ĠCiv ic", "a leigh", "ale igh", "Ġdir ekt", "Ġdire kt", ".s ul", "Ġ Ġĉĉĉ", "ĠĠ ĉĉĉ", "ĠĠĉ ĉĉ", "ĠĠĉĉ ĉ", "a nten", "an ten", "ant en", "ante n", "Ġ issuer", "Ġiss uer", "Ġissue r", "Ġissu er", "Ġassert ions", "Ġassertion s", "( orig", "(o rig", "(or ig", "AT IO", "Ġle aned", "Ġlean ed", "ä s", ". DTO", ".D TO", "ex plode", "expl ode", "explo de", ". Observable", ".O bservable", "Ġstagger ing", "Ġkidn apped", "Ġprogram mers", "Ġprogramme rs", "Ġprogrammer s", "Ġprogramm ers", "ĠIn nov", "ĠInn ov", ". parameter", ".param eter", "Ġd omination", "Ġdo mination", "Ġdom ination", "Ġdomin ation", "Ġdomina tion", "Ġske ptic", "Ġskept ic", "Ġ æĺ¯", "Ġæĺ ¯", "Ġav oids", "Ġavoid s", ". Verify", ".Ver ify", "ub by", "ubb y", "Ġ ASN", "ĠA SN", "ĠAS N", "Ġform ato", "Ġformat o", "Ġforma to", "ĠBeat les", "_ brand", "_b rand", "_br and", "Ġin set", "Ġins et", "Ġinse t", "y outu", "you tu", "Ġ toc", "Ġt oc", "Ġto c", "- final", "-f inal", "-fi nal", "-fin al", "Sh owing", "Show ing", "ĠD oub", "ĠDo ub", "ĠDou b", "ĠM esa", "ĠMe sa", "ĠMes a", "A dj", "Ad j", "_ medium", "_m edium", "_med ium", "Create s", "Cre ates", "Creat es", "( endpoint", "(end point", "ĉ UP", "ĉU P", "b bie", "bb ie", "Ġ stalk", "Ġs talk", "Ġst alk", "Ġsta lk", "Ġstal k", ".data bind", ".datab ind", ". Scan", ".S can", ".Sc an", "ag ents", "age nts", "agent s", "agen ts", "$ ,", "ind ividual", "+ )/", "+) /", "ĉ vm", "ĉv m", "( notification", "(not ification", "Ġin ex", "Ġi nex", "Ġine x", "Ġ Classification", "ĠClass ification", "r eno", "re no", "ren o", "Ġo lig", "Ġol ig", "Ġoli g", "- rated", "-r ated", "-rate d", "-ra ted", "Ġform ulation", "Ġformula tion", "Ġformul ation", "' ,{", "', {", "Ġa cept", "Ġac ept", "Ġace pt", "_ unpack", "_un pack", "_ CA", "_C A", ". Pow", ".P ow", "ĉ im", "ĉi m", "Ġal uminium", "Ġalum inium", "A NO", "AN O", "Ġ xn", "Ġx n", "Ġc ómo", "Ġcó mo", "Ġ Ingredient", "ĠIng redient", "Ġseiz ures", "Ġseizure s", "åħ ±", "ific ador", "ificado r", "Ġs iguiente", "Ġsigu iente", "ĠIn fragistics", "Ġd uplicated", "Ġduplicate d", "Ġdup licated", "Ġduplic ated", "ĠD ee", "ĠDe e", "Ġn ø", "Ġ ACCEPT", "ĠAC CEPT", "( crate", "(c rate", "(cr ate", "иÑĤ елÑĮ", "иÑĤе лÑĮ", "- less", "-l ess", "-le ss", "Ġ infinity", "Ġin finity", "Ġinf inity", "Ġinfinit y", "An alyzer", "Analy zer", "- Day", "-D ay", "r itt", "ri tt", "rit t", "( cin", "(c in", "(ci n", "ĠG y", "Ġmulti plied", "Ġmultip lied", "u chi", "uch i", "uc hi", "ĠBald win", "/ ip", "/i p", "Ġshort cuts", "Ġshortcut s", ". ADD", ".A DD", ".AD D", "Ġv igor", "Ġvi gor", "Ġvig or", "_ instruction", "_in struction", "_instr uction", "( ;", "_ eta", "_e ta", "_et a", "è¿ ŀ", "utor ials", "utorial s", "Ġboost ing", "b v", "Ġacknowled ges", "Ġacknowledge s", "List ening", "Listen ing", "F AQ", "FA Q", "; b", "( (-", "(( -", "Ġarchitect s", "Ġarchit ects", "Ġz we", "Ġzw e", "Ġp uls", "Ġpul s", "Ġpu ls", "Ġget Count", "ĠgetC ount", "ver bs", "verb s", "ãĢ ľ", "( Collection", "(C ollection", "k re", "kr e", "Ġjurisdiction s", "Ġjuris dictions", "_ bridge", "_b ridge", "_br idge", "ĠC rack", "ĠCr ack", "ĠCra ck", "Ġ Difficulty", "ĠDiff iculty", "K O", "Res ervation", "_ requires", "_re quires", "_require s", "T our", "To ur", "ãģĹ ãģŁ", "ãģĹãģ Ł", ". setCurrent", ".set Current", "Ġ ky", "Ġk y", "ĠAlb any", "ĠAlban y", "Ġ è§", "Ġè §", "l ler", "ll er", "lle r", "ag na", "agn a", "work ers", "worker s", "wor kers", ". blank", ".bl ank", "ĠPr ayer", "ĠPra yer", "M IC", "MI C", "Ġresil ience", "Te X", "Ġ Languages", "ĠL anguages", "ĠLanguage s", "st udy", "stu dy", "stud y", "ĉ curr", "ĉc urr", "ĉcur r", "Ġenzym es", "Ġenzyme s", "S lug", "Sl ug", "Ġ íĮĮ", "ĠíĮ Į", "st ral", "str al", "stra l", "Ġtum ors", "Ġtumor s", "Ġseg unda", "Ġsegu nda", "= '{", "=' {", "in struction", "instr uction", "ĠL isp", "ĠLi sp", "ĠLis p", "/ info", "/in fo", "Ġ\" {$", "Ġ\"{ $", ",: ),", ",:) ,", "Ġ gv", "Ġg v", "( ErrorMessage", "(Error Message", "Ġ '=", "Ġ' =", "} -${", "}- ${", ". Documents", ".Document s", ".Doc uments", "\" Well", "\"We ll", "\"W ell", "Ġreminis cent", "Ġg az", "Ġga z", "ir opr", "iro pr", "e hr", "eh r", "Ġsup pressed", "Ġsuppress ed", "Ġsupp ressed", "er sh", "ers h", ".scroll To", "Ġ cadena", "Ġc adena", "Ġcad ena", "Ġcade na", "Ġgame State", "ÃŃ m", "( conv", "(con v", "(co nv", "Ġ Tomorrow", "ĠTom orrow", "ĠC CT", "ĠCC T", "M ongo", "Mon go", "Mo ngo", "u lg", "ul g", ". Camera", ".C amera", ". handlers", ".handle rs", ".handler s", ".hand lers", "m ph", "mp h", "Ġ stk", "Ġs tk", "Ġst k", "Ġgen etics", "Ġgene tics", "Ġgenetic s", "AC ING", "Tr ivia", "Tri via", "ĠB am", "ĠBa m", "( marker", "(m arker", "(mark er", ". Stretch", ".St retch", ".Str etch", "ĠSun ni", "ĠB etty", "ĠBe tty", "ĠBet ty", "ĠBett y", ". tolist", ".t olist", ".to list", "un likely", ". Rectangle", ".Rect angle", "ob solete", "IL ON", "inner Text", "em bourg", "emb ourg", "a N", "ĠV ehicles", "ĠVehicle s", "un lock", ": utf", "n ob", "no b", "Ġ Seeing", "ĠSe eing", "ĠSee ing", "ĠN EVER", "ĠNE VER", "Ġ tls", "Ġt ls", "Ġtl s", "Ġf illes", "Ġfil les", "Ġfill es", "Ġfille s", "Ġbenef ited", "Ġbenefit ed", "ĠC lint", "ĠCl int", "ĠClin t", "ĠCli nt", "*/ ),", "*/) ,", ". fold", ".f old", "Ġpos ible", "Ġposi ble", "A DED", "AD ED", "ADE D", "t house", "th ouse", ". DAL", ".D AL", "Ġ Odd", "ĠO dd", "ĠOd d", "r okes", "ro kes", "roke s", "rok es", "ĠS unny", "ĠSun ny", "ĠPartial Eq", "_ Buffer", "_B uffer", "ĠL evi", "ĠLe vi", "ĠLev i", "long rightarrow", "el don", "eld on", "eldo n", "g ages", "ga ges", "gage s", "_ warn", "_w arn", "_war n", ".Create Table", "ĠD ip", "ĠDi p", "_ questions", "_question s", "_quest ions", ". logic", ".log ic", "Ġ #\"", "Ġ# \"", "={ ()=>", "={() =>", "={( )=>", "Ġ tep", "Ġt ep", "Ġte p", "Ġju icy", "ì Ĥ¬", "ìĤ ¬", "en ko", "enk o", "ial ect", "ia lect", "iale ct", "Ù ī", "Ġon board", "Ġ æı", "Ġæ ı", "ĉ rt", "ĉr t", "_ UTF", "_U TF", "_UT F", "ĠQ Action", "ĠQA ction", "âĢ ŀ", "( Component", "( audio", "(a udio", ". hit", ".h it", "g te", "gt e", "Ġprogram med", "Ġprogramme d", "Ġprogramm ed", "state Params", "Ġpoly ester", "f ires", "fi res", "fire s", "fir es", "by ss", "] =(", "]= (", "_ quality", "_q uality", "_qu ality", "_qual ity", "Of Day", "ĠF airy", "ĠFair y", "ĠFa iry", "Ġy elled", "Ġyell ed", "o pl", "op l", "( userName", "(user Name", "Ġ Difference", "ĠD ifference", "ĠDiff erence", "Ġeval uations", "Ġevaluation s", "Ġevalu ations", "iff any", "Ġcycl ists", "Ġcyc lists", "Ġcyclist s", "Ġ cidade", "Ġc idade", "Ġcid ade", "Ġtext book", "Ġprof iling", "Ġprofil ing", "_ _),", "__ ),", "__) ,", "d ea", "de a", ". activate", ".act ivate", ".activ ate", "Ġind ications", "Ġindic ations", "Ġindication s", "Ð ķ", "Touch UpInside", "Ġinval uable", "Ġ MASK", "ĠM ASK", "ĠMA SK", "ĠMAS K", "Ġcont end", "Ġconten d", "Ġconte nd", "F req", "Fr eq", "Fre q", "Ġrec ruits", "Ġrecru its", "Ġrecruit s", "( interval", "(int erval", "(inter val", "Ġ UserProfile", "ĠUser Profile", "Ġ'./ ../", "Ġ'. /../", "e du", "ed u", "_ Callback", "_C allback", "_Call back", "Ġan alogy", "Ġanal ogy", "Ġanalog y", "Ġana logy", "ĠT rophy", "ĠTr ophy", "ĠTro phy", "app hire", "V ideos", "Video s", "ĠC her", "ĠCh er", "ĠChe r", "ĠH av", "ĠHa v", "âĢ¦ \"", ". validator", ".valid ator", "g fx", "gf x", "ĠU Object", "class names", "classname s", "t riangle", "tr iangle", "tri angle", "Ġ Encoder", "ĠE ncoder", "ĠEn coder", "ĠEnc oder", "ĠEncode r", ". spy", ".s py", ".sp y", "Ġpred ators", "Ġpredator s", "= status", "=s tatus", "- safe", "-s afe", ": \",Ċ", ":\" ,Ċ", ":\", Ċ", "Ġ Including", "ĠIn cluding", "Ġ{ };čĊ", "Ġ{} ;čĊ", "Ġ{}; čĊ", "* cos", "*c os", "Ġend ured", "Ġendure d", ".sul ake", "Ġnurs ery", "Ġnurse ry", "Ġfrag rance", "Ġre building", "Ġrebuild ing", "Ġ nth", "Ġn th", "Ġnt h", "ĠFr aser", "ĠFra ser", ".set Date", "ĠV ince", "ĠVi nce", "ĠVin ce", "_ REST", "_RE ST", "_R EST", "_RES T", "Ġvent ilation", "Ġventil ation", "æµ ·", "cri bes", "cribe s", ". asm", ".as m", ".a sm", "lp Vtbl", "ĠA be", "ĠAb e", "u isine", "uis ine", ", array", ",a rray", ",arr ay", "ĉ className", "ĉclass Name", "err als", "erra ls", "erral s", "Ġ 'ĊĊ", "Ġ' ĊĊ", "Ġ'Ċ Ċ", "Check out", "Ġs olicit", "Ġso licit", "Ġsol icit", "Ġsolic it", "A ux", "Au x", "_ capture", "_c apture", "_cap ture", "Ġr ibs", "Ġrib s", "Ġri bs", "r agon", "ra gon", "rag on", "v iol", "vi ol", "vio l", "to pics", "top ics", "topic s", "Function Flags", "ĠM arty", "ĠMar ty", "ĠMart y", "b ike", "bi ke", "ĠT ucker", "ĠTu cker", "( kernel", "(k ernel", "Ġ Ops", "ĠO ps", "ĠOp s", "Close Operation", "/ demo", "/d emo", "/de mo", "i lda", "il da", "ild a", "ĠlÃŃ nea", "AP PING", "APP ING", "Ġsu ites", "Ġsuit es", "Ġsuite s", "Ġsui tes", ".visit VarInsn", "u rus", "ur us", "uru s", "Ġ Minute", "ĠMin ute", "( manager", "(m anager", "(man ager", "Ġbutter fly", "Ġa pare", "Ġap are", "Ġapar e", "Ġapa re", "Ġw olves", "Ġwol ves", "J WT", "ĠS alon", "ĠSal on", "ĠSa lon", "ĉ delay", "ĉd elay", "ĉde lay", "ĉdel ay", "- eslint", "-es lint", "is ations", "isation s", ". rpc", ".r pc", ") |(", ")| (", "ĠSnap chat", "/ mm", "/m m", "M N", "c eries", "ce ries", "cer ies", ".t extAlignment", ".text Alignment", "ĠFrank furt", "Ġ ado", "Ġa do", "Ġad o", "( newValue", "(new Value", "( access", "(a ccess", "(ac cess", "(acc ess", "( Expression", "Ġ SignIn", "ĠSign In", "ĠHa iti", "ĠHait i", "ĠHai ti", "_ tp", "_t p", ". setParameter", ".set Parameter", "Min ute", "Ġmanual s", "ric anes", "ricane s", "rica nes", "Ġ PTR", "ĠP TR", "ĠPT R", "Ġ Outer", "ĠO uter", "ĠOut er", "ĠOu ter", "Ġ getline", "Ġget line", "oc ations", "ocation s", "_ CD", "_C D", "ĠL yon", "ĠLy on", "/ gui", "/g ui", "_ live", "_l ive", "_li ve", "i dan", "id an", "ida n", ". geom", ".ge om", ".geo m", "Ġborder Bottom", "im uth", "imu th", "_ checkpoint", "_check point", "Ġm eu", "Ġme u", "ĠIr ving", "Ġpeu vent", "( MAX", "(M AX", "Ġ ARCH", "ĠAR CH", "ĠARC H", "Ġp ov", "Ġpo v", ".source forge", "Ġjam ais", "Ġ ark", "Ġa rk", "Ġar k", "ĠBaghd ad", "Ġ CLEAR", "ĠC LEAR", "ĠCL EAR", "Menu Bar", "Ġtr ois", "Ġtro is", "CHED ULE", "Ġ #čĊ", "Ġ# čĊ", "( Call", "(C all", "$ order", "( Material", "(M aterial", "(Mat erial", "Ġencontr ado", "$ list", "$l ist", "ĠMETHOD S", ". beginTransaction", ".begin Transaction", "_M AG", "_MA G", "Style Sheet", "Ġmajor s", "Ġmaj ors", "Ġindef initely", "Ġindefinite ly", "c leanup", "clean up", "Ġhome land", "Ġhom eland", "( dto", "(d to", "(dt o", "D ates", "Date s", "Da tes", "Dat es", "P resentation", "Present ation", "Ġ DK", "ĠD K", "={` /", "ĉ Key", "ĉK ey", "( Block", "(B lock", "_ checkbox", "_check box", "ne eds", "need s", "nee ds", "Ġon Complete", "r ico", "ri co", "ric o", "Ġg leich", "Ġgle ich", "Ġ xm", "Ġx m", "O OD", "OO D", "B etter", "Bet ter", "ĠSQL ITE", ". Book", ".B ook", "x ad", "xa d", "ĠG one", "ĠGo ne", "ĠGon e", "ĉ dp", "ĉd p", "Ġdev otion", "Ġ stm", "Ġs tm", "Ġst m", "Ġob sess", "Ġobs ess", "Ġ Backend", "ĠBack end", "Qu eries", "Que ries", "I k", "/ /****************************************************************", "// ****************************************************************", "Ġdivide nds", "Ġdivid ends", "Ġdividend s", ".parent Element", "} \")ĊĊ", "}\" )ĊĊ", "}\")Ċ Ċ", "}\") ĊĊ", "ĠMaterial PageRoute", ": num", ":n um", "Ġexp lic", "Ġexpl ic", "Ġ OL", "ĠO L", "l east", "le ast", "lea st", "O ops", "iment os", "imento s", "imen tos", "Ġins urers", "Ġinsure rs", "Ġinsurer s", "Ġhero ic", "ĉ fields", "ĉf ields", "ĉfield s", ".img ur", ".btn Cancel", "ĠDet ective", "ĠDetect ive", "( sm", "(s m", "ĠMutable LiveData", ". lab", ".l ab", "( ([", "(( [", "Ġha irst", "Ġhair st", "Ġhai rst", "Ġhairs t", "Ġ Transactions", "ĠTrans actions", "ĠTransaction s", "å¼Ģ å§ĭ", "Ġ stdClass", "Ġstd Class", "u ento", "uen to", "uent o", "G IS", "GI S", "_ cod", "_c od", "_co d", "In structions", "Instruction s", "Instr uctions", "C alls", "Call s", "Cal ls", "Pointer Type", "ĠR w", "Ġassort ment", "Ġ DIG", "ĠD IG", "ĠDI G", "+ r", "_ CERT", "_C ERT", "_CE RT", "Ġinst ability", "Ġv ib", "Ġvi b", "o nas", "on as", "ona s", "Ġr oku", "Ġro ku", "Ġrok u", "ap ellido", "Ġ angl", "Ġan gl", "Ġang l", "prene ur", "Ġfl uids", "Ġfluid s", "Ġflu ids", "is ease", "ise ase", "Ġd eed", "Ġde ed", "Ġdee d", "qu ist", "quis t", "qui st", "_CONST ANT", "Ġequ ilibrium", "_ delegate", "_de legate", "ĠQuant um", "r ei", "re i", "Cap abilities", "rect angle", "? ><", "?> <", "a lien", "al ien", "ali en", "alie n", "ĠJ ug", "ĠJu g", "D NA", "DN A", "T ickets", "Tick ets", "Ticket s", "Occ urs", "ĠH awk", "ĠHaw k", "ĠHa wk", ".setHorizontal Group", "\\ Collection", "\\C ollection", "ff iti", "ffi ti", "Ġre arr", "Ġrear r", ".setVertical Group", "Ġc avity", "Ġcav ity", "Ġadult e", "Ġadul te", "Fac ade", "Fa cade", "- wh", "-w h", "ĠL OL", "ĠLO L", "Ø °", "Ġgrand parents", "Sw ift", "ĉ wx", "ĉw x", "æīĢ æľī", "i fen", "if en", "ife n", "ff set", "B eyond", "// }ĊĊ", "//}Ċ Ċ", "Ġw ager", "Ġwa ger", "Ġwage r", "Ġwag er", "Ġ bury", "Ġb ury", "Ġbu ry", "Ġbur y", "Ġcomm ence", "Ġcomme nce", "Ġcommenc e", "reg istro", "registr o", "regist ro", "s cient", "sc ient", "sci ent", "Ġ Percent", "ĠPer cent", "ĠPerc ent", "Ġд олж", "Ġдол ж", "( identifier", "(id entifier", "(ident ifier", ".set Model", "Ġs eldom", "Ġsel dom", "n ton", "nt on", "Ġap pliance", "Ġappl iance", "a mus", "am us", "amu s", "rys ler", "Ġpan ties", "Ġpant ies", "engu ins", "enguin s", "Ġmi mic", "Ġmim ic", "Ġon Changed", "ĠonChange d", "Ġal coholic", "Ġalcohol ic", ".reload Data", "Ch arge", "Char ge", "Ġ Fax", "ĠF ax", "ĠFa x", "Ġj ScrollPane", "Emp resa", "Ġsh attered", "x ba", "xb a", "Font s", "Fo nts", "? s", "Ġpost season", "re tain", "ret ain", "reta in", "_ rates", "_r ates", "_rate s", "_ra tes", "_rat es", "Ġ requestCode", "Ġrequest Code", ". todo", ".t odo", ".to do", "´ s", "C HK", "CH K", "Ġ Keeping", "ĠKe eping", "ĠKeep ing", "ĠKee ping", "enge ance", "Ġvs code", "IP PING", "IPP ING", "Default CloseOperation", "_ raise", "_r aise", "_ra ise", "ĠO culus", "ĠOc ulus", "o grams", "og rams", "ogram s", "ogr ams", "ogra ms", "r aj", "ra j", "p ci", "pc i", "Ġcorros ion", ". handleSubmit", ".handle Submit", "Access ible", "ĠP iano", "ĠPi ano", "l ittle", "lit tle", "A CL", "AC L", "Äĩ e", ". unwrap", ".un wrap", "ĠCon vers", "ĠConv ers", "ĠLe ben", "ion eer", "ione er", "Ġ Merchant", "ĠM erchant", "ĠMer chant", "ĠMerch ant", "ĠJ orge", "Ġembr acing", "Ġ venta", "Ġv enta", "Ġvent a", "Ġven ta", "á st", "ás t", "Ġv iene", "Ġvi ene", "Ġvie ne", "< QString", "Ċ", "-g rowing", "-gr owing", "-grow ing", "Ġdeep copy", "A ck", "Ac k", "eg gies", "egg ies", "Ġ __(\"", "Ġ_ _(\"", "Ġ__ (\"", "Ġ__( \"", "Ġn oir", "Ġno ir", "Ġnoi r", "terror ism", "Ġan them", "Ġant hem", "Ġanth em", "a gency", "ag ency", "age ncy", "agen cy", "_ PACKAGE", "_PACK AGE", "Ġ Closure", "ĠC losure", "ĠClo sure", ". registry", ".reg istry", "Ġmamm als", "Ġmamma ls", "< L", "U ICollectionView", "UI CollectionView", "ĠLE Ds", "ĠLED s", "Ġv olley", "Ġvol ley", "Ġvoll ey", "( Buffer", "(B uffer", "_N ATIVE", "li bc", "lib c", "im plode", "impl ode", "Scroll Bar", "ĠMar ion", "ĠMario n", "ĠMari on", ". Contracts", ".Con tracts", ".Contract s", "_ At", "_A t", "ĠWe instein", "ĠWein stein", "compare To", "ĠH ose", "ĠHo se", "ĠHos e", "en ity", "eni ty", ". createQuery", ".create Query", "_ router", "_r outer", "_ro uter", "_route r", "Ġstim uli", "Ġ ++)", "Ġ+ +)", "Ġ++ )", "ĠCh amp", "ĠCha mp", "ĠCham p", "ĠBay ern", "ĠBayer n", "a ssa", "as sa", "ass a", ". va", ".v a", "Ġdistrib utors", "Ġdistributor s", "Ġfile private", "Ġdepart ed", "c ccc", "cc cc", "ccc c", "@ click", "@c lick", "ĠL unch", "ĠLun ch", "> L", "Ġb luetooth", "Ġbl uetooth", ". Deep", ".De ep", "- standing", "-st anding", "á cil", "ác il", "áci l", "Ġro oft", "Ġroof t", "Ġ Paths", "ĠP aths", "ĠPat hs", "ĠPath s", "ĠPa ths", "_ iterations", "_iter ations", "_iteration s", "Invalid ArgumentException", ". spi", ".s pi", ".sp i", "Ġ UIAlertAction", "ĠUIAlert Action", "u ye", "uy e", "sign in", "sig nin", ". priority", ".p riority", "ĠEs says", "ĠEss ays", "ĠEssay s", "=' {$", "='{ $", "Ġ è¿ĶåĽŀ", "Ġè¿ ĶåĽŀ", "_ signed", "_s igned", "_sign ed", "_sig ned", ". persist", ".p ersist", "Ġre design", "Ġred esign", "Ġrede sign", "Ġredes ign", "To Lower", "ĠNew man", "= start", "ĠIsrael is", "ĠIsraeli s", "as iswa", "asis wa", "S peech", "Spe ech", "Ġnum eros", "Ġnumer os", "Ġnumero s", "handle rs", "hand lers", "handler s", "ĠW ong", "ĠWo ng", "ĠWon g", "Ġм еÑĤод", "ĠмеÑĤ од", "We ights", "Weight s", "ĠGu jar", "t eil", "te il", "ĠNone theless", "ĠNon etheless", "_E FFECT", "Ġ vect", "Ġv ect", "Ġve ct", "Ġvec t", "ĠO sc", "ĠOs c", "Ġco ats", "Ġcoat s", "ĠW heat", "ĠWh eat", "ĠWhe at", "Ġge ek", "Ġgee k", "Ġ PROPERTY", "ĠP ROPERTY", "ĠPRO PERTY", "w orm", "wo rm", "wor m", "_ constants", "_con stants", "_const ants", "_constant s", "ĠB oulder", "ĠBou lder", "Ġ Parm", "ĠP arm", "ĠPar m", "ĠPa rm", "c ole", "co le", "col e", "Ġdefault Center", "ĠRo uge", "ĠRou ge", ": A", "x cf", "xc f", "ĠVen ice", "ĠVe nice", "m edian", "med ian", "medi an", "media n", "Ġred emption", "F resh", "Fr esh", "Fre sh", "Ġco sm", "Ġcos m", "Ġ figur", "Ġfig ur", "Ġref urb", "CO PE", ". cd", ".c d", "Ġch ords", "Ġchord s", "Ġchor ds", "ĠS gt", "Å į", "V PN", "VP N", "Ġ SEND", "ĠS END", "ĠSE ND", "ĠSEN D", "a inen", "ain en", "ai nen", "aine n", "_ accounts", "_account s", "_ac counts", "Ġt enth", "Ġte nth", "Ġten th", "Ġtent h", "Ġdiss olved", "Ġdissolve d", "< App", "", "Ġ' >", "Ġlegitim acy", "Ġ oo", "Ġo o", "S linky", "Sl inky", "Ġnational s", "Ġnation als", ". words", ".w ords", ".word s", "; p", "t rap", "tr ap", "tra p", "oman ip", "oma nip", "Ġc ues", "Ġcu es", "Ġcue s", "Ġgrad uating", "Ġgradu ating", "Ġsem aphore", "\" ]);ĊĊ", "\"] );ĊĊ", "\"]) ;ĊĊ", "\"]);Ċ Ċ", "\"]); ĊĊ", "ace y", "ac ey", "RE ET", "REE T", "G rab", "Gr ab", "ĠF elix", "ĠFe lix", "ĠFel ix", "( Id", "(I d", "_ neighbors", "_ne ighbors", "_neighbor s", "Ġmeaning less", "( del", "(d el", "(de l", "Ġj eder", "Ġje der", "Ġjed er", "Ġjede r", "ĠContent Values", ". absolute", ".a bsolute", ".abs olute", "/ cl", "/c l", "Ġ xb", "Ġx b", "d atum", "dat um", "Ġtort ured", "Ġtorture d", "Ġrub bing", "Ġru bbing", "S cores", "Sc ores", "Score s", "ĠðŁĺ ī", "Ġav ons", "Ġam sterdam", "E OS", "EO S", "H al", "Ha l", "Ġtrust worthy", "# =", ".EX TRA", "Ġm ano", "Ġman o", "Ġma no", "is icing", "isi cing", "- support", "-s upport", "-sup port", "ĉ cursor", "ĉc ursor", "Ġ Spo", "ĠS po", "ĠSp o", "ai massage", "aim assage", "M ission", "Miss ion", "[] {\"", "[]{ \"", "Ġpr inters", "Ġprint ers", "Ġprinter s", "Ġprin ters", "G REEN", "GRE EN", "GREE N", "Ġ teg", "Ġt eg", "Ġte g", "Ġabdom inal", "! ĊĊĊĊĊĊ", "!ĊĊ ĊĊĊĊ", "!Ċ ĊĊĊĊĊ", "!ĊĊĊĊ ĊĊ", "!ĊĊĊ ĊĊĊ", ". Short", ".S hort", ".Sh ort", "а зв", "аз в", "ĠGi fts", "ĠGift s", "} \")", "}\" )", "( binding", "(b inding", "(bin ding", "(bind ing", "x ce", "xc e", "âĢ ij", "in fos", "info s", "inf os", "Form Data", "Ġ dart", "Ġd art", "Ġda rt", "Ġdar t", "Ġ elems", "Ġe lems", "Ġel ems", "Ġele ms", "Ġelem s", "( inv", "(i nv", "(in v", "Y L", "t in", "ti n", "G ENER", "GE NER", "GEN ER", "á» ¯", "Ġ Taken", "ĠT aken", "ĠTake n", "ĠTa ken", "ĠTak en", "uc kle", "uck le", ": e", "Ġs pectral", "Ġspect ral", "Ġspectra l", ".b aidu", "/ ');Ċ", "/' );Ċ", "/') ;Ċ", "Ġgre edy", "Ġgreed y", "es ion", "esi on", ",,,, ,,,,", "Ġ/ >,Ċ", "Ġ/> ,Ċ", "Ġ/>, Ċ", "Internal ServerError", "NS NotificationCenter", "NSNotification Center", "Ġ Ai", "ĠA i", "Ġs pit", "Ġsp it", "Ġspi t", "Ġaug mented", "Ġaugment ed", "Ġstandard UserDefaults", "FIN ITY", "R ace", "Ra ce", ": C", "ĠRE CORD", "ĠREC ORD", "Ġ Highlight", "ĠHigh light", "Ġ' `", "Ġdef icits", "Ġdeficit s", "Ġn ei", "Ġne i", "Ġresearch ed", "T a", "Ġc opp", "Ġco pp", "Ġcop p", ".Get HashCode", ") :čĊčĊ", "): čĊčĊ", "):čĊ čĊ", "On Click", "ĠWell ington", "ĠWel lington", "Ġrev ival", "æ¯ Ķ", "éĹ ®", "Ġ NSS", "ĠN SS", "ĠNS S", "Ġf orn", "Ġfor n", "Ġfo rn", "Ġin té", "Ġint é", "ĠKu wait", "_ flip", "_f lip", "_fl ip", "_ bo", "_b o", "_ \\", "Ġocc urrences", "Ġoccurrence s", "Ġ Scientists", "ĠScient ists", "ĠScientist s", "S RC", "SR C", "o gens", "og ens", "ogen s", "oge ns", "i grant", "ig rant", "igr ant", "RE MOTE", "REM OTE", "Ġ SID", "ĠS ID", "ĠSI D", ". opts", ".op ts", ".o pts", ".opt s", "u ve", "uv e", "( )])Ċ", "() ])Ċ", "()] )Ċ", "Ġlibert arian", "ĠG lide", "ĠGl ide", "l esen", "le sen", "les en", "Ġ forme", "Ġfor me", "Ġform e", "ow ania", "owa nia", "owan ia", "Ġannoy ed", "D efs", "De fs", "Def s", "Ġ Executor", "ĠExec utor", "Ġ casts", "Ġc asts", "Ġca sts", "Ġcas ts", "Ġcast s", ". setChecked", ".set Checked", "Ġ Sharing", "ĠSh aring", "ĠSha ring", "ĠShar ing", ".Serialize Object", "Ġ selectors", "Ġselect ors", "Ġselector s", "Ġsel ectors", "Ġsele ctors", "_ OTHER", "_OT HER", "ë ¯¸", "ë¯ ¸", "( super", "(s uper", "( OS", "(O S", "_ VERIFY", "_VER IFY", "id unt", "< header", "';Ċ", "Ġ/> ';Ċ", "Ġ/>' ;Ċ", "Ġvid éo", "Ġvidé o", "ĠNe gro", "ĠNeg ro", "ĠL ords", "ĠLord s", "ĠLor ds", "ĠT ours", "ĠTo urs", "ĠTour s", "ĠTou rs", "Ġsoft ly", ". receive", ".re ceive", "Ġ ERC", "ĠE RC", "ĠER C", "Ġdata Set", "B adge", "Bad ge", "Ba dge", "ĉ Event", "ĉE vent", "Ġ perl", "Ġper l", "Ġpe rl", "Ġ {}\\", "Ġ{ }\\", "Ġ{} \\", "( sentence", "(s entence", "(sent ence", "Or Update", "Ġdim inish", "Ġdimin ish", "P IN", "PI N", "( draw", "(d raw", "(dr aw", ".To DateTime", ". EqualTo", ".Equal To", "( pin", "(p in", "(pi n", "-p encil", "l uent", "lu ent", "lue nt", "Ġ Caller", "ĠC aller", "ĠCal ler", "ĠCall er", "ĠCa ller", "Ġplay ful", "- '+", "-' +", "x ca", "xc a", "s wick", "sw ick", ") {}Ċ", "){ }Ċ", "} :${", "}: ${", "ĠM eth", "ĠMe th", "ĠMet h", ". getCell", ".get Cell", ".getC ell", ". break", ".b reak", "Ġ ymax", "Ġy max", "=' Ċ", "}`} >Ċ", "ĠH iro", "ĠHi ro", "ĠHir o", "( TRUE", "(TR UE", "as urer", "asure r", "asu rer", "Ġc uer", "Ġcu er", "Ġcue r", "U ber", "Ub er", ". Operation", ".O peration", ".Op eration", "Ġ olan", "Ġo lan", "Ġol an", "Ġola n", "Ġthr illing", "Ġthrill ing", "< Response", "ĠF emin", "ĠFe min", "ĠFem in", "Ġtr aversal", "Ġtravers al", "Ġp oc", "Ġpo c", "Ġ setStatus", "Ġset Status", "de clar", "dec lar", "decl ar", "std afx", "Ġaddict ive", "Ġ Btn", "ĠB tn", "Ġexplos ives", "Ġexplosive s", "ĠCo oking", "ĠCook ing", "ĠPl aint", "ĠPlain t", "ĠPla int", "Ġ accumulator", "Ġaccum ulator", "Ġ Appointment", "ĠApp ointment", ", password", ",p assword", "ĠF AR", "ĠFA R", "l uet", "lu et", "lue t", "Further more", "decl spec", "_ Statics", "_Static s", "_St atics", ". Dictionary", ".D ictionary", "\" >'.", "\"> '.", "\">' .", "ĉ valid", "ĉval id", "ĉva lid", "\" \",", "\"\" ,", "In strument", "Instr ument", "> J", "Ġno str", "Ġnos tr", "Ġnost r", "ĠR ift", "ĠRi ft", "ĠRif t", "_ Port", "_P ort", "Ġve ces", "Ġvec es", "[ ['", "[[ '", "Ġrall ies", "- series", "-s eries", "-se ries", "-ser ies", "Ġ vv", "Ġv v", ". uc", ".u c", "Ġr tn", "Ġrt n", "State Changed", "( ins", "(i ns", "(in s", "Ġ Cla", "ĠC la", "ĠCl a", "- -----------Ċ", "-- ----------Ċ", "---- --------Ċ", "-------- ----Ċ", "--- ---------Ċ", "------------ Ċ", "----- -------Ċ", "---------- --Ċ", "------ ------Ċ", "----------- -Ċ", "------- -----Ċ", "--------- ---Ċ", "c us", "cu s", "Ġ Reload", "ĠR eload", "ĠRe load", "ĠRel oad", "// ------------------------------------------------------------------------------------------------", "//---------------------------------------------------------------- --------------------------------", "//---------------------------------------------------------------------------- --------------------", "//------------------------------------------------ ------------------------------------------------", "//-------------------------------- ----------------------------------------------------------------", "//-------------------------------------------------------------------------------- ----------------", "//---------------- --------------------------------------------------------------------------------", ". seconds", ".se conds", ".second s", ".sec onds", "_ destination", "_d estination", "_dest ination", "Ġscre wed", "Ġscr ewed", "Ġscrew ed", "> c", "Th ickness", "Des igner", "Design er", "Ġgr ids", "Ġgrid s", "Ġgri ds", "n Äħ", "( cookie", "(c ookie", "(co okie", "T rip", "Tr ip", "Tri p", "- Mobile", "-M obile", "Ġv oll", "Ġvo ll", "Ġvol l", "Ġgen ital", "Ġconf isc", "ĠConfeder ate", "Ġ webView", "Ġweb View", "Ġ mise", "Ġm ise", "Ġmis e", "Ġmi se", "Ġc ler", "Ġcl er", "Ġcle r", "( selection", "(s election", "(se lection", "(select ion", "(sel ection", "$ date", "$d ate", "Ġsharp en", "Ġshar pen", "r agen", "ra gen", "rag en", "rage n", "And Update", "Ġre mix", "Ġrem ix", "Ġh tons", "Ġht ons", "Ġhton s", "R W", "M PI", "MP I", "Ġretrie val", "Ġretr ieval", "Ġrich est", "Ġri chest", "Ġric hest", "Ġriches t", ". Decode", ".De code", ".Dec ode", ":init Components", "ĠT Value", "ĠTV alue", "S aint", "Sa int", "@ include", "Ġ PERSON", "ĠPER SON", ". sep", ".s ep", ".se p", "Ġ LDAP", "ĠLD AP", "g ba", "gb a", "Ġgro ÃŁe", "ĠgroÃŁ e", "Ġreli ably", "Ġ DFS", "ĠD FS", "ĠDF S", ".get ItemId", ".getItem Id", "Ġpré sent", "Ġprés ent", ". getToken", ".get Token", "Ġch inese", "Ġchin ese", "Ġ Meal", "ĠMe al", "Y OU", "YO U", "\" > >ĊĊ", "Ġ> >ĊĊ", "Ġ>> ĊĊ", "b ower", "bo wer", "bow er", "Ġsw apped", "Ġswap ped", "/ install", "Ġs inks", "Ġsin ks", "Ġsink s", "etr ize", "etri ze", "Ġdec lines", "Ġdecl ines", "Ġdecline s", "ĉ mysql", "ĉm ysql", "ĉmy sql", "Ġ CString", "ĠC String", "ĠCS tring", "ĠM otionEvent", "ĠMotion Event", ". Language", ".L anguage", "R oad", "Ro ad", "ÑĤ еÑĢ", "ÑĤе ÑĢ", "asc imento", "' ))->", "') )->", "')) ->", ". about", ".a bout", ".ab out", "( editor", "(e ditor", "(ed itor", "(edit or", "ĠR atings", "ĠRa tings", "ĠRating s", "ĠRat ings", "in come", "inc ome", "Å¡ e", ".de queueReusableCell", "ĠAust rian", "ĠAustria n", "ĠAustr ian", "Ġs ulla", "Ġsu lla", "Ġsul la", "ĠTrib unal", "Ġ Didn", "ĠDi dn", "ĠDid n", "о ваÑĢ", "ов аÑĢ", "ова ÑĢ", "Ġins pections", "Ġinspect ions", "Ġinspection s", "Ġinsp ections", "B oss", "Bo ss", "Ġcock tails", "Ġcocktail s", "Ġapolog ized", "Ġapologize d", "_ subplot", "_sub plot", "o pal", "op al", "opa l", "+ =(", "+= (", "Ġreson ance", "i bu", "ib u", "Ġ 리", "Ġë ¦¬", "Ġë¦ ¬", "r oma", "ro ma", "rom a", "re serve", "res erve", "rese rve", "p ls", "pl s", "ĠT ah", "ĠTa h", "a xies", "ax ies", "O PLE", "OP LE", "ĠDar ren", "ĠZ ombie", "_ Map", "_M ap", "Ġ ])ĊĊ", "Ġ] )ĊĊ", "Ġ])Ċ Ċ", "Ġ]) ĊĊ", "Ġ Qi", "ĠQ i", "ĠS ail", "ĠSa il", "ĠSai l", "Ġrestrict ive", "Ġeros ion", "- par", "-p ar", "W HITE", "WH ITE", "Ġold u", "Ġol du", "Ġap erture", "Ġbit coins", "Ġbitcoin s", "text o", "tex to", "ĠCom cast", "Ġtime less", "Ġtim eless", "en kins", "enk ins", "Ġfe eder", "Ġfeed er", "Ġfee der", "/ tmp", "/t mp", "res den", "+ '_", "+' _", ". Destroy", ".D estroy", ".De stroy", "Ġ çok", "Ġç ok", "Ġ DOCUMENT", "ĠD OCUMENT", "ĠDOC UMENT", ". lng", ".l ng", ". tagName", ".tag Name", "Ġk ullan", "Ġkul lan", "eg rate", "egr ate", "egra te", "Ġ( *.", "Ġ(* .", "ç¼ĸ è¾ij", "Ġhand shake", "s oc", "so c", "_ geometry", "_ge ometry", "_geo metry", "_geom etry", "ĠDam ascus", "Min or", "Mi nor", "ĠK afka", "ĠKaf ka", "ìĹ ¬", "Fl orida", "_ compute", "_com pute", "_comp ute", ". expr", ".ex pr", ".exp r", "Ġ paralle", "Ġpar alle", "Ġpara lle", "ĠD iaz", "ĠDi az", "ĠDia z", "c ir", "ci r", "[ target", "[t arget", "Ġj oking", "Ġjo king", "Ġg lor", "Ġgl or", "Ġglo r", "( setq", "(set q", "_ handlers", "_handler s", "_handle rs", "_hand lers", "H ang", "Ha ng", "Han g", "Ġf err", "Ġfe rr", "Ġfer r", "r iminal", "rim inal", "ĉ ĠĠĠĠĉĉ", "ĉĠĠĠ Ġĉĉ", "ĉĠ ĠĠĠĉĉ", "ĉĠĠ ĠĠĉĉ", "ĉĠĠĠĠ ĉĉ", "ĉĠĠĠĠĉ ĉ", "en ties", "ent ies", "enti es", "def ines", "define s", "- tax", "-t ax", "json p", "Ġ UPS", "ĠU PS", "ĠUP S", "m etro", "me tro", "met ro", "_ _;Ċ", "__ ;Ċ", "__; Ċ", "ĠUg anda", "] )):Ċ", "]) ):Ċ", "])) :Ċ", "_ td", "_t d", "x ae", "xa e", "l w", ". OS", ".O S", "Ġ Logged", "ĠLog ged", "a cid", "ac id", "aci d", "ĠM ayo", "ĠMay o", "ĠMa yo", "a spect", "as pect", "asp ect", "Ġvag inal", "Ġvagina l", "Ġinitial izing", "Ġste roids", "Ġster oids", "Ġsteroid s", "f iction", "fi ction", "fic tion", "G RE", "GR E", "g end", "ge nd", "gen d", "Ġli abilities", "Ġ Lets", "ĠL ets", "ĠLe ts", "ĠLet s", "M ech", "Me ch", "( nc", "(n c", "( change", "(ch ange", "(chan ge", "Ġconn ectors", "Ġconnect ors", "Ġconnector s", ": k", "Ġt ast", "Ġta st", "Ġtas t", "! \");ĊĊ", "!\" );ĊĊ", "!\");Ċ Ċ", "!\"); ĊĊ", "!\") ;ĊĊ", "th ings", "thing s", "thin gs", "r ophy", "ro phy", "rop hy", "roph y", "l uetooth", "lu etooth", "luet ooth", "Ġ SignUp", "ĠSign Up", ". ctrl", ".c trl", ".ct rl", "Ġthere in", "Ġther ein", "or da", "ord a", ". escape", ".e scape", ".es cape", "ig ator", "iga tor", "Ġpet rol", "Ġspec imen", "Ġspeci men", "Ġdeb uted", "Ġdebut ed", "- Pro", "-P ro", "Ġcr ises", "Ġcri ses", "Ġcris es", ".add View", "ëı Ļ", "- door", "-d oor", "-do or", "Ġm onet", "Ġmon et", "Ġmo net", "Ġm illis", "Ġmill is", "Ġmil lis", "Ġmilli s", "Ġ vier", "Ġv ier", "Ġvi er", "Ġvie r", "Internal Enumerator", "Ġ admins", "Ġad mins", "Ġadmin s", "Ġadm ins", "ĠL air", "ĠLa ir", "z in", "zi n", "get Query", "um bles", "umb les", "umble s", "L IMIT", "LI MIT", "ĠV ig", "ĠVi g", "_ song", "_s ong", "_so ng", "< Character", ": :.", ":: .", "_ hom", "_h om", "_ bp", "_b p", "ĠSup ervisor", "ĠSuper visor", "ĠSuperv isor", "sub mission", "ab ile", "abil e", "abi le", "Ġn oi", "Ġno i", "Or Create", "Ġp eel", "Ġpe el", "Ġpee l", "Ġon Start", "Ġsent iments", "Ġsentiment s", "v ehicles", "veh icles", "vehicle s", "Ġclass rooms", "Ġclassroom s", "Ġs zer", "Ġsz er", "Ġb ending", "Ġben ding", "Ġbend ing", "Ġlong evity", "Ġ acl", "Ġa cl", "Ġac l", "ĠAle ppo", "Ġ UM", "ĠU M", "ĠR icht", "ĠRich t", "ĠRic ht", "ĠRi cht", "Ġmulti processing", "Ġmultip rocessing", "DO MAIN", "DOM AIN", "\", \"+", "\",\" +", "_ YEAR", "_Y EAR", "Ġsc rape", "Ġscr ape", "Ġscrap e", "Ġsol itary", "Ġ\" ]\";Ċ", "Ġ\"] \";Ċ", "Ġ\"]\" ;Ċ", "/ errors", "/error s", "ìŀ ¬", "ľ ëł¥", "b etter", "bet ter", "bett er", "bette r", "ĉ number", "ĉn umber", "ĉnum ber", "Ġ LF", "ĠL F", "Ġ Across", "ĠA cross", "ĠAc ross", "Pub Med", "\\ \"\"", "\\\" \"", "ĠExcell ence", "Ġus ando", "Ġusa ndo", "ĠU IP", "ĠUI P", "Activity Indicator", "_ VOID", "_V OID", "_VO ID", "Ġbre eds", "Ġbreed s", "Ġbree ds", "ï½ ¥", "ues tas", "uest as", "uesta s", "ĠTre asure", "ustr alian", "ustral ian", "ustralia n", "( face", "(f ace", "ĠT ennis", "ĠTen nis", "ĠTenn is", "ĉ Int", "ĉI nt", "ĉIn t", "ĠH ansen", "ĠHan sen", "ĠHans en", "ç µ", ": I", "Ġ âľĶ", "Ġâľ Ķ", "G RAY", "GR AY", "GRA Y", "O USE", "OU SE", "OUS E", "Ġhe pat", "Ġhep at", "ł í", "A IR", "AI R", "ó ż", "Ġ queued", "Ġque ued", "Ġqueue d", "vin cia", "vi ncia", "vinc ia", "ĠCh romium", "ĠChrom ium", "Ġcompet ence", "Ġcompete nce", "un gal", "ung al", "unga l", "i lli", "il li", "ill i", "Ġget By", "Ġ Finder", "ĠF inder", "ĠFin der", "ĠFind er", "ĠFi nder", "Ġincap able", "Ġs add", "Ġsa dd", "Ġsad d", "Ġc ites", "Ġcit es", "Ġci tes", "Ġcite s", "ĠChurch ill", "S dk", "More over", "A spNet", "As pNet", "( Float", "(F loat", "$ password", "$p assword", "Ġ Connor", "ĠCon nor", "ĠConn or", "- session", "-s ession", "_ dm", "_d m", "* ))", "*) )", "Ġde utsch", "Ġdeut sch", "Ġ NX", "ĠN X", "Ġper ks", "Ġperk s", "_ SORT", "_S ORT", "_SO RT", "_TO OL", "_TOO L", "_ VISIBLE", "_V ISIBLE", "_VIS IBLE", ". asp", ".as p", ".a sp", "æĪ ĸ", "ĠBre ath", "D etect", "Det ect", "ĠD uel", "ĠDu el", "ĠDue l", ". cmb", ".c mb", ".cm b", "[ it", "[i t", ".Set Bool", "Ġnarc iss", "Ġab ide", "Ġabi de", "Ġej emplo", "ĠâĦ ķ", "Ġm ornings", "Ġmor nings", "Ġmorning s", "Ġcomp utes", "Ġcomput es", "Ġcompute s", ". ssl", ".s sl", ".ss l", "j t", "Ġm uchos", "Ġmuch os", "Ġmu chos", "Ġmucho s", "Ġmuc hos", "_ SS", "_S S", "[ end", "[e nd", "Ġb asin", "Ġbas in", "Ġba sin", "Ġalg unos", "Ġalgun os", "ĠCroat ia", "line width", "lin ewidth", "( tags", "(t ags", "(tag s", "( hidden", "(h idden", "ÃŃ cio", "ÃŃc io", "Ġa par", "Ġap ar", "Ġapa r", "Ġ ж", "ĠÐ ¶", "ä¸ İ", ". food", ".f ood", ".foo d", "ĠR ural", "ĠRu ral", "Ġbread th", "å½ ±", "( sess", "(s ess", "(se ss", "+ \")", "+\" )", "Ġ Paste", "ĠP aste", "ĠPa ste", "ĠPast e", "ĠPas te", "Ġserv idor", "ĠBit Set", "ĠT ran", "ĠTr an", "ĠTra n", "l aus", "la us", "v ette", "ve tte", "vet te", "e yes", "ey es", "eye s", "Ġ CLICK", "ĠCL ICK", "ĠCLI CK", "ĠV III", "ĠVI II", "ĠVII I", "ĠTur ns", "ĠTurn s", "ĠLe Bron", "ĠM uj", "ĠMu j", "Ġ Deg", "ĠD eg", "ĠDe g", "ĠAd ults", "ĠAdult s", "_ suite", "_s uite", "_su ite", "process able", "Ġ PHY", "ĠP HY", "ĠPH Y", "g hest", "gh est", ". Fail", ".F ail", "ĠS lack", "ĠSl ack", "c ej", "ce j", "\\ Carbon", "\\C arbon", "Ġsuper star", "Ġsupers tar", "Ġsuperst ar", "Ġhold ings", "Ġholding s", "Ġhol dings", "( forms", "(form s", "(for ms", "Ġ' #'", "Ġ'# '", "M ultip", "Multi p", "Mult ip", "Mul tip", "(\" [%", "(\"[ %", "- solid", "-s olid", "-so lid", "/ url", "/u rl", "- tier", "-t ier", "[ length", "[l ength", "[len gth", "Ġ StreamWriter", "ĠStream Writer", "ĠMarket place", "get text", "gett ext", "_T ICK", "_TI CK", "Ġ Forge", "ĠF orge", "ĠFor ge", "ĠForg e", "Ġblack jack", "ĠDO ES", "ĠDOE S", "ĠM atters", "ĠMat ters", "ĠMatt ers", "ĠMatter s", "ĠMatte rs", "w aves", "wa ves", "wave s", "wav es", "Ġwhisper ed", "Ġ lush", "Ġl ush", "Ġlu sh", "ìĺ ¤", "d igital", "digit al", "dig ital", "Ġw rink", "Ġwr ink", "ĠH ogan", "ĠHo gan", "ĠHog an", "Ġrust ic", "Ġrus tic", ".Apply Resources", "ĠH ardy", "ĠHar dy", "ĠHard y", "os omes", "oso mes", "osome s", "A UT", "AU T", ". STATE", ".ST ATE", "Ġnarr atives", "Ġnarrative s", "ĉ store", "ĉst ore", "b ib", "bi b", "ĉ Scanner", "ĠC ody", "ĠCo dy", "ĠCod y", "\\ Repositories", "Ġre union", "Ġreun ion", "an dum", "and um", "âĢĻ h", "Ġsn iff", "NS Bundle", "Ġcompreh end", "_ USAGE", "_US AGE", "_ occ", "_o cc", "_oc c", "URRE NCY", "J NI", "Ġspecial izing", "Ġ visions", "Ġv isions", "Ġvis ions", "Ġvision s", "Ġdo lore", "Ġdol ore", "Ġdolor e", "Ġ vá", "Ġv á", "ĠChe vy", "Ġ Styled", "ĠSt yled", "ĠStyle d", "ĠSty led", "imp act", "a llen", "al len", "all en", "alle n", "Ġ kart", "Ġk art", "Ġka rt", "Ġkar t", "ĠTable t", "ĠTab let", "st uff", "stu ff", "re esome", "ree some", "rees ome", "а ÑĤоÑĢ", "аÑĤ оÑĢ", "аÑĤо ÑĢ", "//---------------------------------------------------------------- -----------Ċ", "//- --------------------------------------------------------------------------Ċ", "_ Admin", "_Ad min", "Ġcell phone", "Ġ autoplay", "Ġaut oplay", "Ġauto play", "Ġautop lay", "Ġc ambio", "Ġcam bio", "Ġcamb io", "Ġcambi o", "Ġmar itime", "Ġmari time", "_ BOOT", "_B OOT", "_BO OT", "- quarter", "-qu arter", "Ġlat ina", "Ġlatin a", "ĠAJ AX", "e quiv", "equ iv", "ĠFront ier", "Ġ XY", "ĠX Y", "} ]Ċ", "}] Ċ", "ĠR ough", "ĠRo ugh", "ĠRou gh", ". proto", ".pro to", ".prot o", ".pr oto", "Ġcorrect ness", "Ġfa cil", "Ġfac il", "Ġ Reached", "ĠRe ached", "ĠReach ed", "ãģĿ ãģ®", "V IS", "VI S", ". ps", ".p s", "Ġstr ncpy", "Ġdif fusion", "Ġdiff usion", ".start Activity", "� ��", "�� �", "Ġacc omp", "Ġac comp", "Ġaccom p", "AME SPACE", "AMES PACE", "imon ials", "imonial s", "ĠB last", "ĠBl ast", "aby rin", "Ġd ome", "Ġdo me", "Ġdom e", "Ġext rav", "Ġextra v", "Ġextr av", "Ġ yen", "Ġy en", "Ġye n", "Ġcul inary", "P RI", "PR I", "ĠComm unities", "ĠCommun ities", "n id", "ni d", "_ operations", "_oper ations", "_operation s", ". hs", ".h s", "ĠM ilton", "ĠMil ton", "Ġno ises", "Ġnoise s", "Ġnoi ses", "Autoresizing Mask", "( cid", "(c id", "(ci d", "} ĊĊĊĊĊĊ", "}Ċ ĊĊĊĊĊ", "}ĊĊ ĊĊĊĊ", "}ĊĊĊ ĊĊĊ", "}ĊĊĊĊ ĊĊ", "}ĊĊĊĊĊ Ċ", "] },Ċ", "]} ,Ċ", "]}, Ċ", "Ġ Detection", "ĠD etection", "ĠDe tection", "ĠDet ection", "ĠDetect ion", "ta bla", "tab la", "tabl a", "Ġlib erties", "Ġlibert ies", "Ġliber ties", "_D YNAMIC", "w get", "wg et", "ĠT ür", "ĠP ascal", "ĠPa scal", "ĠPas cal", "Trans parent", "Del ayed", "Delay ed", "] ()", "]( )", "ĠHer bert", "ĠHerb ert", "< ActionResult", "", "}- >", "Ġpas ado", "Ġpasa do", "th ank", "tha nk", "than k", "_ Delete", "_De lete", "ĠBr ighton", "ĠBright on", "ĠBrig hton", ", unsigned", "ä½ľ èĢħ", "Ġaspir ations", "Ġaspiration s", "- how", "-h ow", "R ose", "Ro se", "Ros e", "= ((", "=( (", "_ needed", "_ne eded", "_need ed", "_ plural", "_pl ural", "< Application", " >ĊĊ", ">> ĊĊ", ">>Ċ Ċ", "Ġsurface d", "Ġsurf aced", "Ġìł Ģìŀ¥", "ĠìłĢ ìŀ¥", "pl atz", "plat z", "pla tz", "ĉ email", "ĉe mail", "ĉem ail", "cept ors", "ceptor s", "cep tors", "\" >(", "\"> (", "Ġe pile", "Ġep ile", "è¯ »", "ĠDe bt", "ĠDeb t", "åij Ĭ", "N OP", "NO P", "\" https", "\"http s", ": j", "Form Item", "_ LICENSE", "_L ICENSE", ".get Double", ".getD ouble", "ĠAg enda", "ĠAge nda", "ĉ finally", "ĉf inally", "ĉfinal ly", "( filters", "(f ilters", "(filter s", "(fil ters", "( av", "(a v", "ç¾ İ", "A PER", "AP ER", "APE R", "Ġ lava", "Ġl ava", "Ġla va", "Ġlav a", "еÑĢ ж", ") )))ĊĊ", ")) ))ĊĊ", "))) )ĊĊ", "))))Ċ Ċ", ")))) ĊĊ", "Ġfa ulty", "Ġfault y", "_ nm", "_n m", "Ġt rava", "Ġtr ava", "Ġtra va", "Ġtrav a", "( Bitmap", "(B itmap", "(Bit map", "Ġspe eding", "Ġspeed ing", "> ').", ">' ).", ">') .", "Ġscreen ed", "Ġscre ened", "_ roll", "_r oll", "_ro ll", "ĠMac Book", "Ġ AUD", "ĠA UD", "ĠAU D", "Ġdiagn ose", ". Generate", ".G enerate", ".Gen erate", "Ġ ^^", "Ġ^ ^", "Ġs trs", "Ġst rs", "Ġstr s", "[ Test", "[T est", "Ġr ansom", "Ġran som", "ĠDH CP", "el den", "eld en", "Ġinterpret ations", "Ġinterpretation s", "( )].", "() ].", "()] .", "flat Map", "Ġline Height", "_ mount", "_m ount", "_mo unt", "ĠW izards", "ĠWizard s", "Ġsl uts", "Ġslut s", "Ġslu ts", "eh ler", "o dal", "od al", "oda l", "Ġmilit ia", "Ġmil itia", "å ²", "ear ned", "earn ed", "Ġmis ery", "Ġmise ry", "Ġmiser y", "int val", "f und", "fun d", "fu nd", "Ġh ides", "Ġhide s", "Ġhi des", "Ġhid es", "Ġdi arr", "Ġdia rr", "ĠWes ley", "Ġ xmm", "Ġx mm", "Ġxm m", "Ġqu em", "Ġque m", "Ġq uem", "ĠAr abs", "ĠArab s", "ĠAra bs", "if th", "ift h", "ategor ized", "ategori zed", "D isposable", "Dis posable", "P ure", "Pu re", "_NOT IFY", "sn ippet", "ĠGar rett", "ĠGarr ett", ". running", ".r unning", ".run ning", ". weights", ".weight s", ".we ights", "Ġ (--", "Ġ( --", "Ġ(- -", "Ġin variant", "Ġinv ariant", "äºĭ 件", "Ġ Allowed", "ĠAll owed", "ĠAllow ed", "d irs", "dir s", "di rs", "Ġpass ions", "Ġpassion s", "Ġ lad", "Ġl ad", "Ġla d", "Ġ Flush", "ĠF lush", "ĠFl ush", "ĠFlu sh", "m enus", "men us", "menu s", ": block", ":b lock", "Ġcom pra", "Ġcomp ra", "Ġcompr a", ".ch omp", "al locator", "all ocator", "alloc ator", "alloca tor", "Ġcur ated", "Ġcu rated", "Ġ Knowing", "ĠKn owing", "ĠKnow ing", "ĠPatt erson", "Ġt elah", "Ġte lah", "Ġtel ah", "Ġtela h", "' ex", "'e x", "Ġdo omed", "Ġdoom ed", "Ġphil anth", "o tty", "ot ty", "ott y", ". styles", ".st yles", ".style s", "Own ed", "Ġallerg ies", "Ġaller gies", "= params", "oc ese", "oce se", "it elist", "ite list", "itel ist", "iteli st", "Ġ Sending", "ĠS ending", "ĠSen ding", "ĠSend ing", "b ef", "be f", "or rar", "orr ar", "orra r", "Ġ Não", "ĠN ão", "ĠF argo", "ĠFar go", "ĠL ub", "ĠLu b", "Ġ Combined", "ĠComb ined", "ĠCombine d", "_ given", "_g iven", "ĉ ĉĉĉĉĠĠĠĠ", "ĉĉ ĉĉĉĠĠĠĠ", "ĉĉĉĉ ĉĠĠĠĠ", "ĉĉĉ ĉĉĠĠĠĠ", "ĉĉĉĉĉ ĠĠĠĠ", "ĉĉĉĉĉĠ ĠĠĠ", "ĉĉĉĉĉĠĠĠ Ġ", "ĉĉĉĉĉĠĠ ĠĠ", "Ġre conciliation", "Ġreconc iliation", "Pattern s", "az ard", "aza rd", "azar d", "Ġbio mass", "Ġbiom ass", "ĠH ouses", "ĠHouse s", "ĠHo uses", "ĠHou ses", "resp uesta", "c co", "cc o", "/ topics", "/to pics", "/top ics", "/topic s", "ĠY uk", "ĠYu k", "Ġweak ened", "Ġweaken ed", "_ calendar", "_c alendar", "_cal endar", "Ġmulher es", "ĠM arl", "ĠMar l", "ĠMa rl", "Ġs ine", "Ġsi ne", "Ġsin e", "ĠT il", "ĠTi l", "ĠSo uls", "ĠSou ls", "ĠSoul s", "ĠDe utsche", "ĠDeutsch e", "ĠF OLLOW", "Ġp ipelines", "Ġpipe lines", "Ġpipeline s", "Ġpip elines", "ĠBever ly", "_DIP SETTING", "\" #", "Ġ Proto", "ĠPro to", "ĠPr oto", "ĠProt o", ". big", ".b ig", ".bi g", "ĠS avings", "ĠSav ings", "ĠSaving s", "ĠT anz", "ĠTa nz", "ĠTan z", "j un", "ju n", "Ġ Gamma", "ĠG amma", "ĠGa mma", "ĠGam ma", "ĠS add", "ĠSa dd", "ĠSad d", "Ġadv isors", "Ġadvis ors", "Ġadvisor s", "Ġro ast", "Ġun ters", "Ġunt ers", "Ġunter s", "ud ies", "udi es", "_ lon", "_l on", "_lo n", "- pointer", "-point er", "-po inter", "ĠElement Ref", "\\ Builder", "example Input", ". webdriver", ".web driver", "data Type", "Ġ Quite", "ĠQ uite", "ĠQu ite", "ĠQui te", "ĠQuit e", "ĠCelt ics", "ĠCel tics", "ĠCeltic s", "u il", "ui l", "- defense", "-def ense", "b ish", "bi sh", "bis h", "ĠUI Window", "Ġ Suddenly", "ĠS uddenly", ". hot", ".h ot", ". reason", ".re ason", "Ġg ör", "Ġgö r", "A MD", "AM D", ". Multi", ".M ulti", ".Mult i", "auth enticated", "authenticate d", "reg ions", "region s", "; (", "а ÑĢам", "аÑĢ ам", "аÑĢа м", "ĠKir by", "$ route", "$r oute", "PREC ATED", "ĠDur ham", "o wo", "ow o", "ĠPer forms", "ĠPerform s", "Ġdisreg ard", "n st", "ns t", "ĠP ols", "ĠPol s", "ĠPo ls", "Ġget P", "\" ]:", "\"] :", "-color ed", "-col ored", "( Keys", "(Key s", "ĠAl leg", "ĠAll eg", "ĠAlle g", "_ modify", "_mod ify", "_ loading", "_lo ading", "_load ing", "s trained", "str ained", "stra ined", "strain ed", "Ġat roc", "Ġatr oc", "_p hr", "_ph r", "< Sprite", "", "c eph", "ce ph", "cep h", ".DateTime Picker", ". \";ĊĊ", ".\" ;ĊĊ", ".\";Ċ Ċ", ".\"; ĊĊ", "ĠT ie", "ĠTi e", ", item", ",i tem", ",it em", "Ġm enn", "Ġme nn", "Ġmen n", "G as", "Ga s", "o cha", "oc ha", "och a", "_ virtual", "_v irtual", "Ġmaster piece", "_ sequences", "_se quences", "_sequence s", "L TE", "LT E", "Ġ Submission", "ĠSub mission", "C aller", "Call er", "Cal ler", "Ca ller", "$ \\", "S port", "Sp ort", "Spo rt", "ag us", "agu s", "Constraint Maker", "Ġco loc", "Ġcol oc", "Ġ wig", "Ġw ig", "Ġwi g", "Ġ У", "ĠÐ £", "ĉ Array", "ĉA rray", "L ooks", "Lo oks", "Look s", "ĠG TA", "ĠGT A", ". steps", ".st eps", ".step s", "atch ewan", "_ ranges", "_r anges", "_range s", "ext Alignment", "ĠBren nan", "Ġab straction", "Ġabs traction", "Ġabstract ion", "Ġabst raction", "uler Angles", ". misc", ".m isc", ".mi sc", "Ġantib odies", "Ġex ponential", "Ġexponent ial", "Ġ CHANNEL", "ĠCH ANNEL", "exp ense", "' y", "Ġdetect ives", "Ġdetective s", "Ġpur ported", "Y STEM", "YS TEM", "YST EM", "Ġradio active", "ĠLat ina", "ĠLatin a", ". Encoding", ".En coding", ".Enc oding", ". TAG", ".T AG", "x in", "xi n", "D egree", "De gree", "Deg ree", "ur acion", "ura cion", "p rices", "pr ices", "price s", "pri ces", "ĠRefer entialAction", "Ġr arity", "Ġrar ity", "Ġp iles", "Ġpi les", "Ġpil es", "Ġpile s", "g ende", "ge nde", "gen de", "gend e", "_ projects", "_project s", "_proj ects", "_ globals", "_g lobals", "_global s", "_glob als", ". startTime", ".start Time", "Ġ 구", "Ġê µ¬", "Ġêµ ¬", "SE CTION", "SEC TION", "_ publish", "_p ublish", "_pub lish", "F ault", "Fa ult", "D DL", "DD L", "_ prior", "_p rior", "_pr ior", "_pri or", "M om", "Mo m", "Ġth icker", "Ġthick er", "Ġthi cker", "Ġ sequelize", "Ġsequ elize", "Ġsequel ize", "Ġess entials", "Ġessential s", "s tras", "st ras", "str as", "stra s", "in tr", "int r", "> (()", ">( ()", ">(( )", ". management", ".man agement", ".manage ment", "e il", "ei l", "éĹ Ń", "A ware", "Aw are", ". City", ".C ity", "ĠAr bit", "ĠArb it", "_ DM", "_D M", "_ keyboard", "_key board", "L Object", "LO bject", "- webpack", "-web pack", "ĠNew port", "Ġprincipal Column", "leg ant", "Ġp allet", "Ġpal let", "Ġpall et", "Ġfract ure", "Ġfrac ture", "Ġ gmail", "Ġg mail", "Ġgm ail", ". Meta", ".M eta", ".Me ta", "A bove", "Ab ove", ". KeyEvent", ".Key Event", "j it", "ji t", "_ macro", "_m acro", "_mac ro", "_ma cro", "_P USH", "_PUS H", "á» ©", "/ controller", "/control ler", "åĬł è½½", "Ġsuperf icial", "exter ity", "Ġ mensagem", "Ġm ensagem", "Ġmens agem", "W ind", "Win d", "Wi nd", "i ston", "is ton", "ist on", "isto n", ".open api", "и ÑĢов", "иÑĢ ов", "Ġ Serializer", "ĠS erializer", "ĠSerial izer", "ĠSerialize r", "uct ive", "Ġ zar", "Ġz ar", "Ġza r", "P laces", "Pl aces", "Place s", ". Static", ".St atic", ".Stat ic", "B a", "Ġin advert", "ĠIndones ian", "ĠIndonesia n", "_I PV", "_IP V", "( horizontal", "(h orizontal", "Ġ getTitle", "Ġget Title", "ide press", "ĠConsole Color", "i pers", "ip ers", "ipe rs", "iper s", "$ out", "$o ut", "Ġfest ive", "Ġeven ings", "Ġevening s", "Ġeve nings", ". GetData", ".Get Data", "uit ka", "ĠManual s", "us sed", "uss ed", "_ Max", "_M ax", ". Chat", ".C hat", ".Ch at", "ĠA ircraft", "ĠAir craft", "= com", "=c om", "F OUND", "FO UND", "a pro", "ap ro", "apr o", "Ġtre asures", "Ġtreasure s", "_ alive", "_a live", "_al ive", "Ġg adget", "Ġgad get", "e king", "ek ing", "eki ng", "Button Down", "B rowsable", ".PER MISSION", "P ASSWORD", "PASS WORD", "Ġ HASH", "ĠH ASH", "ĠHAS H", "ĠHA SH", "f é", "\\ TestCase", "\\Test Case", "LO SS", "LOS S", "o thers", "other s", "oth ers", ", J", "Ġass hole", "Ġassh ole", "w erk", "we rk", "wer k", "Ġm ã", ". ie", ".i e", "e vil", "ev il", "evi l", "kont akte", "/ ///////////////////////////////////////////////////////////////////////////////Ċ", "/// /////////////////////////////////////////////////////////////////////////////Ċ", "//////////////////////////////////////////////////////////////////////////// ////Ċ", "//////////////////////////////////////////////////////////////////////////////// Ċ", "= sys", "=s ys", "ĉ lock", "ĉl ock", "ĉloc k", "-- ;ĊĊ", "--;Ċ Ċ", "--; ĊĊ", "_ FUN", "_F UN", "Fill Color", "ó a", "p rend", "pr end", "pre nd", "Ġcom pressor", "Ġcompr essor", "Ġcompress or", "M other", "Mo ther", "Mot her", "ĠAr cher", "ĠArch er", "ĠArc her", ". goto", ".g oto", ".go to", "Ġwür de", "Ġbam boo", "Ġbamb oo", "ï¼ İ", "Ġ Trees", "ĠT rees", "ĠTr ees", "ĠTree s", "ĠTre es", "Ġb umper", "Ġbump er", "Ġbum per", "Ġsa usage", "Ġsau sage", "ĠEl asticsearch", "ĠElastic search", "Ġhor izontally", "Ġhorizontal ly", "ĠG ul", "ĠGu l", "Im mutable", "Imm utable", "Ġ loser", "Ġl oser", "Ġlo ser", "Ġlos er", "Ġlose r", "Ġab orted", "Ġabort ed", "- demo", "-d emo", "-de mo", "-dem o", "ĠH atch", "ĠHat ch", "Ġ unde", "Ġu nde", "Ġun de", "Ġund e", "Ġpro cesso", "Ġprocess o", "Ġproc esso", "Ġproces so", "- call", "-c all", "-cal l", "-ca ll", "In come", "Inc ome", "å ĥ", "_ returns", "_return s", "'] .\"'", "']. \"'", "'].\" '", "( sw", "(s w", "C BS", "CB S", "am ilies", "ami lies", "amil ies", "ĠYour self", "ĠYours elf", "ĠH olt", "ĠHol t", "ĠHo lt", ". MON", ".M ON", "ৠĩ", "ÑĪ е", "a non", "an on", "ano n", "Ġ FontAwesome", "ĠFont Awesome", "pro ducer", "produ cer", "prod ucer", "produce r", "j r", "Ġm au", "Ġma u", "ĉ inter", "ĉint er", "ĉin ter", "Ġdish onest", "Ġm agna", "Ġmag na", "Ġmagn a", "ĠColl ective", "ĠCollect ive", "Ġvra iment", "Ġvrai ment", "Ġcho ix", "st ay", "sta y", "Ġwel ding", "Ġweld ing", "r ising", "ri sing", "ris ing", ", min", ",m in", "ĠF ate", "ĠFa te", "ĠFat e", "g lob", "gl ob", "RGB A", "RG BA", "Ġd ette", "Ġde tte", "Ġdet te", "V en", "Ve n", "Ġembarrass ment", ". DELETE", ".DE LETE", "g regar", "greg ar", "gre gar", "- render", "-r ender", "-re nder", "-ren der", "( bucket", "(b ucket", "\" >ĊĊĊ", "\"> ĊĊĊ", "\">Ċ ĊĊ", "\">ĊĊ Ċ", ".wait Key", "Bus y", "Bu sy", "Ġdifferent iation", "ĠC ST", "ĠCS T", ". Constant", ".Con stant", ".Cons tant", "Ġline Number", "( matches", "(m atches", "(match es", "(mat ches", "Ġ websocket", "Ġweb socket", "Ġwebs ocket", "Ġbar red", "Ġbarr ed", "Ġpued es", "Ġpu edes", "Ġpuede s", "M ono", "Mon o", "Mo no", "C ORE", "CO RE", "COR E", "I ID", "II D", "ĠĠ ĠĠčĊčĊ", "ĠĠĠĠ čĊčĊ", "ĠĠĠ ĠčĊčĊ", "ĠĠĠĠčĊ čĊ", "Ġpúb lico", "le aning", "lean ing", "lea ning", "Ġclean sing", "Ġcleans ing", "Ġc ris", "Ġcr is", "Ġcri s", "ĠDev ils", "ĠDevil s", "_ SETTING", "_SET TING", "unt ary", "unta ry", ". );Ċ", ".) ;Ċ", "Ċ ĠĠĠĊ", "[ curr", "[c urr", "[cur r", "t sy", "ts y", "ĠAlex is", "ĠAle xis", "r itel", "ri tel", "rit el", "rite l", "Ġpet roleum", "Ġpetrol eum", ".pre processing", "m atter", "mat ter", "For Result", "- license", "-l icense", "Ġtravel lers", "Ġtrav ellers", "Ġtraveller s", "Ġ Dispatcher", "ĠDispatch er", "ĠDisp atcher", "enn ifer", "Ġdigest ive", "P ED", "PE D", "hib ition", "hibit ion", "MAS ConstraintMaker", "ĠW att", "ĠWat t", "ĠWa tt", "B enef", "Ben ef", ".set View", "d to", "dt o", "T EE", "TE E", "ĠPel osi", "_EX TRA", "_EXT RA", "Ġmed als", "Ġmedal s", "x hr", "fore cast", "for ecast", "Ġn argin", "Ġnar gin", "o uns", "ou ns", "oun s", "- fill", "-f ill", "-fi ll", "_CUR SOR", "Ġsup ervised", "Ġsuper vised", "Ġsuperv ised", "Ġsupervise d", "Ġt urf", "Ġtu rf", "Ġtur f", "ĠEd gar", "POS ITION", "POSIT ION", "Ġ categoryId", "Ġcategory Id", "â ī", "_ ER", "_E R", "ủ a", "Sh own", "Show n", ". ll", ".l l", "_POL ICY", "( ),'", "() ,'", "(), '", "Ġ Prev", "ĠP rev", "ĠPr ev", "ĠPre v", "ĠString Field", "ĉ Global", "ĉG lobal", "as sed", "ass ed", "asse d", "Through out", "o stringstream", ".awt extra", "Ġsl opes", "Ġslo pes", "Ġslope s", "Ġ Sequential", "ĠSe quential", "ĠSequ ential", "Ġgi orn", "Ġgio rn", "Ġ zelf", "Ġz elf", "Ġze lf", "Ġzel f", "Ġvers atility", "Ġversa tility", "le neck", "len eck", "lene ck", ". cgi", ".c gi", ".cg i", "Ġdoub ling", "Ġdou bling", "ĠBang kok", "Ġbu urt", "Ġusu ário", "st udio", "stu dio", "stud io", "Ġje unes", "Ġjeune s", "Ġjeu nes", "Ġm uted", "Ġmut ed", "Ġmu ted", "Ġmute d", "Ġ ips", "Ġi ps", "Ġip s", "_ fraction", "_f raction", "_fr action", "_frac tion", "& &(", "&& (", "Ġst unt", "Ġstu nt", "Ġstun t", "') ;?> čĊ", "}> čĊ", "Ġev apor", "b able", "ba ble", "bab le", "Ġ PRICE", "ĠPR ICE", "ĠPRI CE", "Ġ æ³", "Ġæ ³", "lu cent", "Ġv amp", "Ġva mp", "ĠTechn ician", "Ġunique ness", "Ġuniqu eness", "M es", "Me s", "ur ban", "urb an", ".param etrize", "ĠRe play", "ĠRep lay", "S essions", "Session s", "em br", "emb r", "- Americans", "-American s", "-Americ ans", "_PRO XY", "Ġp ian", "Ġpi an", "Ġ trie", "Ġt rie", "Ġtr ie", "Ġtri e", "Ġ Destructor", "ĠD estructor", "ĠDe structor", "Game State", "ĠI MF", "ĠIM F", "c hin", "ch in", "chi n", "Ġ porte", "Ġp orte", "Ġport e", "Ġpor te", "ĠS wal", "ĠSw al", "åŁ İ", "Sub string", "i ming", "im ing", "imi ng", "imin g", "/ Library", "/L ibrary", "Ġfright ened", "w rites", "write s", "wr ites", "Ġrec ursos", "Ġrecurs os", "ar Result", "_INIT IALIZ", "_INITIAL IZ", "Ġ Badge", "ĠB adge", "ĠBad ge", "ĠBa dge", "_ crc", "_c rc", "_cr c", "E ight", "ĠDIST INCT", "Ġ thro", "Ġth ro", "Ġthr o", "@ Xml", "Ġ Legendary", "ĠLegend ary", "- twitter", "-t witter", "-tw itter", "_ easy", "_e asy", "Ġ +++", "Ġ+ ++", "Ġ++ +", "( DATA", "(D ATA", ". Locale", ".L ocale", ".Local e", ".Lo cale", "Ġk ä", "Ġn urt", "Ġnu rt", "Ġnur t", "Ġcr uis", "Ġcru is", "_ ios", "_i os", "_io s", "Ġs ensing", "Ġsens ing", "Ġsen sing", "_ Line", "_L ine", "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "p ong", "pon g", "po ng", "o leon", "ol eon", "ole on", "Ġwild card", "çĶ¨æĪ· åIJį", "Ġbeg ging", "R od", "Ro d", "Ġ Ãİ", "Ġà İ", "_ CELL", "_C ELL", "_CE LL", "Research ers", ". selector", ".se lector", ".select or", ".sel ector", "_ ing", "_in g", "_i ng", "Ġas piring", "Ġaspir ing", "Ġasp iring", "Ġimm ortal", "Ġy min", "_ robot", "_r obot", "_ro bot", "Ġpl ur", "Ġplu r", "B TC", "BT C", "ĠD ID", "ĠDI D", "Ġpier cing", "* u", "_ DEFINED", "_DEF INED", "_DEFIN ED", "_DEFINE D", "ĠT hi", "ĠTh i", "i taire", "it aire", "ita ire", "( media", "(m edia", "(me dia", "- ons", "-on s", "-o ns", "Ġch efs", "Ġche fs", "Ġchef s", "Ġ\" *.", "Ġ\"* .", "/ AP", "/A P", "Ġr azor", "Ġraz or", "Ġsearch Data", "Ġ =&", "Ġ= &", "Ġ ãĢĤ", "ĠãĢ Ĥ", "Ġm ourn", "Ġmo urn", "Ġmou rn", "Ġmour n", "t ingham", "ting ham", "Ġ oli", "Ġo li", "Ġol i", "ĠVer non", "ĠVern on", "_ RS", "_R S", "ŀ æĢ§", "Ġf ácil", "a ngen", "an gen", "ang en", "ange n", "ce lain", "cel ain", "cela in", "Ġ ail", "Ġa il", "Ġai l", "l est", "le st", "les t", "ĠQ COMPARE", "g ain", "ga in", "Ġ ε", "ĠÎ µ", "ĠK ob", "ĠKo b", "Ġ Fault", "ĠF ault", "ĠFa ult", "ĠFaul t", "_ configs", "_config s", "_conf igs", "ç»ĵ æŀľ", ". +", "c alar", "ca lar", "cal ar", "cala r", "( colors", "(color s", "(col ors", "M ul", "Mu l", "_ ART", "_A RT", "_AR T", "Ġexperiment ing", "er men", "erm en", "ĠAng lo", ".Fixed Single", "S ea", "Se a", "Ġ ctxt", "Ġc txt", "Ġctx t", "Ġct xt", ". slider", ".s lider", ".sl ider", ".slide r", "C ollapse", "Coll apse", "G rey", "Gr ey", "Gre y", "Ġ fld", "Ġf ld", "Ġfl d", "- proof", "-p roof", "-pro of", ". capacity", ".cap acity", "get Parent", "ĠCom pliance", "Ġbur gl", "Ġburg l", "- rec", "-r ec", "-re c", "Ġover written", "M U", "Ġr outers", "Ġro uters", "Ġroute rs", "Ġrout ers", "Ġrouter s", "Ġrou ters", "ĉ Model", "ĉM odel", "Ġfantas ies", "av ian", "avi an", "avia n", "_ prec", "_p rec", "_pr ec", "_pre c", "ĠSc andin", "ĠScan din", "Ġ// <", "Ġ/ /<", "/ oct", "/o ct", "Ġceremon ies", "Mon ths", "Month s", "Mont hs", "un dy", "und y", "Ġqu ed", "Ġque d", "Ġq ued", "ĠN ou", "ĠNo u", "ĠV ibr", "ĠVi br", "ĠVib r", ". rgb", ".r gb", "Ġcit rus", "Ġbr aces", "Ġbra ces", "Ġbrace s", "- uppercase", "-upper case", "get Table", "Ġd opo", "Ġdo po", "Ġdop o", "ĠK err", "ĠKe rr", "ĠKer r", "_ CHILD", "_CH ILD", "- cloud", "-c loud", "-cl oud", "ĉ Matrix", "ĉM atrix", "ĉMat rix", "Ġgarden ing", "Ġgard ening", "S ing", "Si ng", "Sin g", "al most", "alm ost", "Require ments", "Requirement s", "ugu ay", "( Property", "(P roperty", "sub scriber", "subscribe r", "F AST", "FA ST", "re action", "react ion", "rea ction", "( lp", "(l p", ") })Ċ", ")} )Ċ", ")}) Ċ", "` ).", "`) .", ". wallet", ".w allet", ".wall et", "_ exchange", "_ex change", ". Maximum", ".Max imum", "Ġ Verb", "ĠV erb", "ĠVer b", "ĠVe rb", "âĶ ģ", "( )<", "() <", "ï¼Ľ Ċ", "R OT", "RO T", "C ARD", "CA RD", "CAR D", "u bit", "ub it", "ubi t", "{ @", "_ kel", "_k el", "_ke l", "Ġ Tooltip", "ĠTo oltip", "ĠTool tip", "My SQL", "Main Activity", "a rf", "ar f", "Ġm align", "Ġmal ign", "Ġse inen", "Ġsein en", "Ġseine n", "Ġsei nen", "ap ist", "api st", "apis t", "Ġ< %", "Method Impl", "M il", "Mi l", "ĠM ick", "ĠMi ck", "ĠMic k", ". depend", ".d epend", ".de pend", ".dep end", "< ID", " >&", ">> &", "ĉ ok", "ĉo k", "- low", "-l ow", "-lo w", ". usuario", ".us uario", "n ested", "ne sted", "nes ted", "nest ed", "X B", "OUR S", "OU RS", ". BorderColor", ".Border Color", "Ġb row", "Ġbr ow", "Ġbro w", "Ġ Ðķ", "ĠÐ ķ", "c orr", "co rr", "cor r", "ĠRed skins", "ĠReds kins", ".get Tag", ".get Transaction", "Ġst igma", "har dt", "hard t", "ĠPlayer Prefs", "al sy", "als y", "uc son", "ucs on", "L anguages", "Language s", "ĠOl ivia", "ĠOliv ia", "Ġt ac", "Ġta c", "Ġb li", "Ġbl i", "Ġc aval", "Ġca val", "Ġcav al", "Ġconsolid ated", "Ġconsolidate d", "Ġper il", "Ġpe ril", "Ġperi l", "Ġd ele", "Ġde le", "Ġdel e", "Ġform ulated", "Ġformula ted", "Ġformul ated", "Ġformulate d", "Ġhigh ways", "Ġhighway s", ". spawn", ".s pawn", ".sp awn", "= =$", "== $", "ĠN iet", "ĠNi et", "ĠNie t", "Ġv eggies", "Ġveg gies", "y po", "yp o", "- rule", "-r ule", "ĠV ie", "ĠVi e", "/e pl", "Ġenf ants", "string Literal", "Ġtough est", "Ġtou ghest", "bu yer", "buy er", "Ġcov ariance", "Ġ ili", "Ġi li", "Ġil i", "ĠSoph ie", "Ġ BAB", "ĠB AB", "ĠBA B", "Ġ \"),", "Ġ\" ),", "Ġ\") ,", "ĠU k", "current Index", "_ userdata", "_user data", ". codec", ".co dec", ".code c", ".cod ec", "ĠPun jab", "ĠS NP", "ĠSN P", "l ol", "lo l", "adv ance", "Ġcom fy", "Json Ignore", "Ġfashion able", "Ġ ICON", "ĠI CON", "ĠIC ON", "ĠICO N", "Ġ ora", "Ġo ra", "Ġor a", "ĠP ricing", "ĠPr icing", "ĠPri cing", "< num", " E", "t ering", "ter ing", "te ring", "teri ng", "/ screens", "/s creens", "/screen s", "Ġheight ened", "аÑĢ ÑĤ", "Author ities", "_ bbox", "_b box", "_bb ox", "ü nst", "ün st", "üns t", ". fontSize", ".font Size", "Ġ BOOLEAN", "ĠBO OLEAN", "div ide", "di vide", "divid e", "ĠS loven", "ĠSl oven", "ĠSlo ven", "ĠSlov en", "u cer", "uc er", "uce r", "Ù Ĵ", "st ub", "stu b", "Ġnavig ating", ": animated", "_ NOW", "_N OW", "_NO W", "_ vect", "_v ect", "_vec t", "_ve ct", "} {Ċ", "}{ Ċ", "@ (", "Ġtele com", "Ġtel ecom", "Ġcontract ing", "Ġcontr acting", "ĠAss ange", "Ġextract ing", "Ġextr acting", "Ġgr ö", "c obra", "co bra", "cob ra", ". DIS", ".D IS", "Ġc rab", "Ġcr ab", "Ġcra b", "Ġt witch", "Ġtw itch", "Ġ verts", "Ġv erts", "Ġver ts", "Ġvert s", "Ġve rts", "Ġreject s", "Ġrej ects", "ĉ format", "ĉfor mat", "ĉform at", "Ġre generation", "Ġreg eneration", ". Sys", ".S ys", "s olve", "sol ve", "ĉ dialog", "ĉd ialog", "s hi", "sh i", "m eter", "me ter", "met er", "( best", "(b est", "(be st", "valid ators", "validator s", "Ġon wards", "Ġonward s", "Ġg uru", "Ġgu ru", "Ġmod erator", "Ġmoder ator", "ow ied", "owie d", "owi ed", "ex periment", "r ub", "ru b", "Ġ mqtt", "Ġm qtt", "Ġmq tt", "ĠCa ucas", "Ġnational ism", "Ġm ange", "Ġman ge", "Ġma nge", "Ġmang e", "ĉ ImGui", "/ Edit", "/E dit", "Ġ inh", "Ġin h", "Ġi nh", "Ġint ellig", "Ġintel lig", "ero kee", "ĉ export", "ĉex port", "ĉexp ort", "Ġdiscrim inate", "Ġdiscrimin ate", "sub tract", "ĠM oodle", "ĠMoo dle", "ĠMood le", "en ser", "ens er", "ense r", "ĠGu ides", "ĠGuid es", "ĠGuide s", "ĠGui des", "R AP", "RA P", "- hot", "-h ot", "_ grp", "_g rp", "_gr p", ". picture", ".p icture", ".pic ture", "X A", "Ġinit View", "_ Comm", "_C omm", "_Com m", "Ġoverd ose", "Ġ +ĊĊ", "Ġ+ ĊĊ", "Ġ+Ċ Ċ", "ĠS ilent", "ĠSil ent", "sh ows", "show s", "Ġinter polate", "Ġinterpol ate", "Ġinterp olate", "Form ation", "Format ion", "Ġb isc", "Ġbi sc", "Ġbis c", "mark ets", "market s", "( SC", "(S C", "Z e", "Ġ Networking", "ĠNetwork ing", "ĠNet working", "Ġad renal", "Ġadr enal", "ĠG uns", "ĠGu ns", "ĠGun s", "et eor", "ete or", "De clared", "Decl ared", "Declare d", "orge town", "orget own", "Ġk arena", "Ġka rena", "Ġkar ena", "/ password", "/p assword", "/pass word", "_ addresses", "_add resses", "_address es", "_addr esses", "IT ERAL", "ITE RAL", "ITER AL", "B uzz", "Bu zz", "ĠCon way", "( case", "(c ase", "(ca se", "P WD", "PW D", "he iro", "hei ro", "( act", "(a ct", "(ac t", "* *čĊ", "** čĊ", "( ));ĊĊĊ", "() );ĊĊĊ", "());Ċ ĊĊ", "()) ;ĊĊĊ", "());ĊĊ Ċ", "()); ĊĊĊ", "Ġa nv", "Ġan v", "Ġ ..ĊĊ", "Ġ. .ĊĊ", "Ġ.. ĊĊ", "Ġ..Ċ Ċ", "( MenuItem", "(Menu Item", "( mail", "(m ail", "_ sections", "_s ections", "_se ctions", "_section s", "ĉ net", "ĉn et", "Ġp lut", "Ġpl ut", "Ġplu t", "Ġw rench", "Ġwr ench", "/ object", "/o bject", "ĠI st", "ĠIs t", "Ġ VIS", "ĠV IS", "ĠVI S", "/ pub", "/p ub", "al ten", "alt en", "alte n", "Ġguitar s", "Ġguit ars", "Ġantib iotic", "Ġantibiot ic", "ï¼ ĸ", " ¹", "Ġ \"+\"", "Ġ\" +\"", "Ġ\"+ \"", "form ula", "Ġba bes", "Ġbab es", "Ġbabe s", "Ġ Prompt", "ĠP rompt", "ĠProm pt", "Ġe nim", "Ġen im", "/ player", "/p layer", "/pl ayer", "/play er", "ĉ ref", "ĉr ef", "ĉre f", "Ġby Äĩ", "Ġcons umes", "Ġconsum es", "Ġconsume s", "ĠH ast", "ĠHas t", "ĠHa st", "ĠT ao", "ĠTa o", "Ġ '))Ċ", "Ġ' ))Ċ", "Ġ') )Ċ", "Ġc lam", "Ġcl am", "Ġcla m", "Ġthigh s", "Ġmot if", "Ġmo tif", "Api Operation", "Ġ WL", "ĠW L", "get C", "ĉ flags", "ĉf lags", "ĉflag s", "oint ments", "ointment s", "Ġeconomic al", "Ġeconom ical", "need le", "nee dle", "x ls", "xl s", "pr actice", "ut zer", "utz er", "time ofday", "- output", "-out put", "Ġ findById", "Ġfind ById", "ĠfindBy Id", "ĠB uddy", "ĠBudd y", "ĠBu ddy", "ĠBud dy", "Ðŀ ÑĤ", "S even", "Se ven", "ĠB ark", "ĠBar k", "ĠBa rk", "Ġen voy", "Ġenv oy", "_ algorithm", "_al gorithm", "åĪ ©", "Ġball istic", "ç§ »", "r ades", "ra des", "rad es", "rade s", "ĉ doc", "ĉd oc", "ĉdo c", "rodu cing", "rod ucing", "ĠE ating", "ĠEat ing", "ĠEa ting", "Un mount", "/data Tables", "_ bonus", "_b onus", "Ġl itt", "Ġli tt", "Ġlit t", "p ps", "pp s", ") localObject", "pe rf", "per f", "Ġ Helvetica", "ĠHel vetica", "sh utdown", "/ ml", "/m l", ". tokens", ".t okens", ".token s", "ĠHard core", ", row", ",r ow", "/ bg", "/b g", "S caler", "Sc aler", "Scale r", "âĢĶ as", "âĢĶa s", "_log its", "âĢĻ int", "âĢĻin t", "âĢĻi nt", "ĉ App", "ĉA pp", "Impl icit", "Imp licit", ".F printf", "E TO", "ET O", "Ġ terra", "Ġt erra", "Ġter ra", "Ġterr a", "Ġpossess ing", ". rstrip", ".r strip", ".rs trip", ", ),", ",) ,", "= yes", "=y es", "Ġ Stripe", "ĠSt ripe", "ĠStr ipe", "ĠStrip e", "? =", "ne utral", ". good", ".g ood", ".go od", "Ġk ennen", "Ġke nnen", "Ġken nen", "Ġkenn en", "ĠS ung", "ĠSun g", "ĠSu ng", "f ault", "fa ult", "ystate change", "Can adian", "',' \".$", "ĠM its", "ĠMi ts", "ĠMit s", "æ nd", "Ġ STRUCT", "ĠSTR UCT", "ĠURL WithString", "ĠCom pass", "ĠComp ass", "Ġ --ĊĊ", "Ġ- -ĊĊ", "Ġ-- ĊĊ", "Ġ--Ċ Ċ", "ĠNS LayoutConstraint", "| min", "|m in", "- adjust", "-ad just", "Ġre built", "Ġreb uilt", "L IGHT", "/ se", "/s e", "- mount", "-m ount", "v pn", "vp n", "valid ated", "validate d", "( QObject", "(Q Object", "Ġign ition", "ĠChar gers", "ĠCharg ers", "ĠCharge rs", "ĠCharger s", "RYPT O", "]initWith Frame", "Ġ Fluid", "ĠFl uid", "ĠFlu id", "Ġca dre", "Ġcad re", "Ġnom inations", "Ġnomin ations", "Ġnomination s", "Ne ill", "Neil l", "ĠH ou", "ĠHo u", "Ġcurrent s", "Ġcurr ents", "_ gene", "_g ene", "_gen e", "_ge ne", "( inp", "(i np", "(in p", "P aris", "Par is", "Pa ris", "z ÄĻ", "ag gregate", "Ġ assoc", "Ġas soc", "Ġass oc", "we eted", "weet ed", "er rat", "err at", "erra t", "âĢĵ ĊĊ", "Ġ' /',Ċ", "Ġ'/ ',Ċ", "Ġ'/' ,Ċ", "Ġ'/', Ċ", "f ixture", "fix ture", "Ġ Highest", "ĠH ighest", "ĠHigh est", "ĠHi ghest", "amb ient", "ambi ent", "Ġ chmod", "Ġch mod", "Ġ conte", "Ġc onte", "Ġcon te", "Ġcont e", "Ġco nte", "Ġs ensual", "Ġsens ual", "Ġgar ment", "z ers", "ze rs", "zer s", "Ġ Powered", "ĠP owered", "ĠPower ed", "ĠPow ered", "dom ains", "domain s", "R eward", "Re ward", "Rew ard", "i omanip", "Ġcock pit", "out file", "Ġ builtin", "Ġb uiltin", "Ġbuilt in", "Ġins isting", "Ġinsist ing", ". vars", ".v ars", ".var s", ".va rs", "zip code", "Ġ ����", "Ġ� ���", "f ails", "fa ils", "fail s", "Ġconsolid ation", "_ oid", "_o id", "Plan et", "Plane t", "Ġ =\",", "Ġ= \",", "Ġ=\" ,", "ĉ el", "ĉe l", "U ILT", "UI LT", "UIL T", "ä tz", "ät z", "af ari", "afa ri", "ĠMc Cl", "ĠMcC l", "T imeline", "Time line", "Tim eline", "E sta", "Est a", "Es ta", "Ġ fram", "Ġf ram", "Ġfr am", "Ġfra m", "Y E", "Ġcere bral", "Of Month", "ĠP regn", "ĠPre gn", "Ġкл аÑģÑģ", "ĠклаÑģ Ñģ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠF res", "ĠFr es", "ĠFre s", "Ap proved", "Appro ved", ". Special", ".S pecial", ".Spec ial", ".Sp ecial", "ĠProte stant", "ĠProtest ant", "Ġall ergy", "Ġallerg y", "Ġaller gy", "_ pcm", "_p cm", "_pc m", "ĉ Copyright", "ĉC opyright", "Ġsuper Class", "\" strconv", "ĠMoh amed", "Ġ' //", "Ġ'/ /", "Fore Color", "Ar thur", "Art hur", "ĠJ ungle", "ĠJun gle", "ĠJu ngle", "ĠJung le", "Ġve ins", "Ġvein s", "S ad", "Sa d", "Ġback ups", "Ġbackup s", "ĠOp inion", "û t", "Ġinter mitt", "o dyn", "od yn", "ody n", "ĠChrist ina", "Ġ andre", "Ġand re", "Ġan dre", "Ġevac uation", "p alette", "pa lette", "pal ette", "h orse", "hor se", "ĠRes ident", "ĠHas san", "ĠHass an", ". Nil", ".N il", "Ġa isle", "Ġais le", "Ġ Growing", "ĠG rowing", "ĠGr owing", "ĠGro wing", "ĠGrow ing", "Ġblog info", "/ sql", "/s ql", "_ ioctl", "_io ctl", "S caling", "Sc aling", "Ġ Monad", "ĠMon ad", "ĠMo nad", "ĠMona d", "_ cpp", "_c pp", "_cp p", "ĠH utch", "ĠHut ch", "ĠApple WebKit", "Exp ense", "_ JOB", "_J OB", "_JO B", "Ġpoint less", "From Body", "an tal", "ant al", "anta l", "Ġdepict ing", "Ġ CELL", "ĠC ELL", "ĠCE LL", "ĠCEL L", "Ġre fin", "Ġref in", "ĠC NC", "ĠCN C", "ì¹ ĺ", "_ dimensions", "_dim ensions", "_dimension s", "Ġ SAN", "ĠS AN", "ĠSA N", "Ġ aft", "Ġa ft", "Ġaf t", "Ġfoot steps", "c coli", "cc oli", "cco li", "_ PHONE", "_P HONE", "_PH ONE", "/ math", "/m ath", "/mat h", "- kind", "-k ind", "Ġ Means", "ĠMe ans", "ĠMean s", "ich ael", "icha el", ". guna", ".g una", "Ġinaug uration", "Ġinaugur ation", "-dr iving", "( delete", "(de lete", "(del ete", "Ġ totalCount", "Ġtotal Count", "_ MC", "_M C", ". Extension", ".Ext ension", "Com mercial", "Comm ercial", "Ġz Index", "< Customer", "$", "\"> $", "Ġe bay", "Ġeb ay", "Ġc aptive", "Ġca ptive", "Ġcapt ive", "pl iant", "ĠCalculate s", "ĠCalcul ates", "ĠCalc ulates", "ol ta", "olt a", "es ting", "est ing", "esti ng", "_ revision", "_re vision", "_rev ision", "Ġm ús", "Ġmú s", "+ m", "\",\" \",\"", "\",\"\", \"", "WH AT", "Ġcompass ionate", "Ġcompassion ate", "h arga", "har ga", "[ random", "[r andom", "[rand om", "Ġ modulo", "Ġmod ulo", "( sn", "(s n", "Ġoccup ations", "Ġoccupation s", "/ ///Ċ", "// //Ċ", "//// Ċ", "/// /Ċ", "ĉ board", "ĉb oard", "ĠB alk", "ĠBa lk", "ĠBal k", "w iÄħ", "wi Äħ", "Ġ Wifi", "ĠW ifi", "ĠWi fi", ". Profile", ".Pro file", ".Pr ofile", ": maj", ":m aj", "ĉ mat", "ĉm at", "LOCK S", "LOC KS", "(j Button", "Ġ ('$", "Ġ( '$", "Ġ(' $", "M ur", "Mu r", "æĮ ī", "b ble", "bb le", "Ġ frog", "Ġf rog", "Ġfr og", "Ġfro g", "- hide", "-h ide", "Ġbroad caster", "Ġbroadcast er", "ภŀ", "h aled", "ha led", "hal ed", "Ġam using", "_ predictions", "_pre dictions", "_pred ictions", "_predict ions", "_prediction s", "_ intr", "_in tr", "_int r", "Ġe agle", "Ġea gle", "Ġeag le", "аÑĤ елÑĮ", "аÑĤе лÑĮ", "Ġ getList", "Ġget List", "ps ilon", "psi lon", "Ġcharacter ization", "AR DS", "ARD S", "Ġre location", "Ġrel ocation", "Ġreloc ation", "Ġr ulers", "Ġrule rs", "Ġru lers", "Ġruler s", "P AY", "PA Y", "ĠDef initely", "_ Action", "_A ction", "_Act ion", "Ġc losures", "Ġclos ures", "Ġclosure s", "Ġf actual", "Ġfact ual", "Ġfac tual", "o dynamic", "od ynamic", "odyn amic", "odynam ic", "Ġpreca utions", "Ġprecaution s", "n iej", "ni ej", "nie j", "ĠPart ies", "ĠPar ties", "ĠParti es", "ĠSub aru", "ĠSu baru", "Ġcous ins", "Ġcousin s", "ar beit", ". money", ".m oney", ".mo ney", ".mon ey", "g unta", "gun ta", "( and", "(a nd", "(an d", "get item", ".Style Priority", "Ġs lid", "Ġsl id", "s ingleton", "single ton", "sing leton", "Ġg arn", "Ġgar n", "Ġga rn", "ĠP AS", "ĠPA S", "Ġd azz", "Ġda zz", "a ż", "Ġbog us", "ĠM og", "ĠMo g", "Ġrival ry", "i sol", "is ol", "iso l", "Ġland marks", "Ġlandmark s", "ñ as", "ña s", "B ern", "Be rn", "Ber n", "ĠS achs", "ĠSa chs", "ĠSac hs", "ĠSach s", "Ġ \")ĊĊ", "Ġ\" )ĊĊ", "Ġ\") ĊĊ", "Ġ\")Ċ Ċ", "Ġhost ility", "Ġhos tility", "_m ex", "_me x", "m ere", "mer e", "me re", "M ot", "Mo t", "p ictureBox", "picture Box", "Def ense", "Ġaffid avit", "other wise", ". directory", ".d irectory", ".direct ory", "_ UnityEngine", "_Un ityEngine", "- blog", "-b log", "-bl og", ". skin", ".s kin", ".sk in", "p hem", "ph em", "phe m", "Ap ellido", "er chant", "[ class", "[c lass", "Ġ wart", "Ġw art", "Ġwar t", "Ġwa rt", ". \"[", ".\" [", "a leur", "al eur", "ale ur", "/ back", "/b ack", "ĠĠĠĠ ĉĠĠĠ", "ĠĠĠ ĠĉĠĠĠ", "ĠĠĠĠĉ ĠĠĠ", "ĠĠĠĠĉĠ ĠĠ", "Ġprecip itation", "Ġob struction", "Ġobstruct ion", "Ġp Obj", "Ġ rupt", "Ġr upt", "Ġru pt", "U CKET", "UCK ET", "a ye", "ay e", "æİ Ĵ", "g x", "Ġe cl", "Ġec l", "Ġsecre cy", "/ Header", "ĠLe sb", "ĠLes b", "Ġ lei", "Ġl ei", "Ġle i", "Ġ Bulletin", "ĠBull etin", "ĠBullet in", "Ġgive away", ". Home", ".H ome", "_ ROOM", "_R OOM", "_RO OM", "\" W", "Ġco work", "Ġcow ork", "_ ra", "_r a", "ĠC ycling", "ĠCy cling", "ĠCycl ing", "ĠCyc ling", "ĠP aw", "ĠPa w", "Ġp upil", "Ġpup il", "/ arch", "/a rch", "/ar ch", "ĠFile Utils", "é¦ ĸ", "r sp", "rs p", "Ġfreedom s", "Ġfreed oms", "ĠL ear", "ĠLe ar", "} `).", "}` ).", "Ġbow ls", "Ġbowl s", "/ block", "/b lock", "/bl ock", "_ logging", "_log ging", "Ġme thane", "Ġmeth ane", "Ġh orns", "Ġhor ns", "Ġhorn s", "Ġwonder fully", "Ġwonderful ly", "Ġalter ations", "Ġalteration s", "Ġex ile", "l sen", "ls en", "lse n", "_ pause", "_p ause", "_pa use", "_ LANGUAGE", "_L ANGUAGE", "_LANG UAGE", "ĠUS DA", "ĠUSD A", "_ mysql", "_m ysql", "_my sql", "_AM OUNT", "ĠL IFE", "ĠLI FE", "Ġyoung sters", "Ġyoungster s", "Ġri ots", "Ġriot s", "Ġrio ts", "[ E", "Ġun forgettable", ", },Ċ", "Dis posed", "Dispose d", "Disp osed", "ĠAss assin", "ĠAssass in", "U NG", "UN G", "ĠNew sp", "ĠNews p", "User Service", ": aload", ":a load", "+ ',", "+' ,", "Ġsett lers", "Ġsettle rs", "Ġscre ams", "Ġscream s", "Ġincon venience", ". Rotate", ".R otate", "Ġj ars", "Ġja rs", "Ġjar s", "ĠP uzzle", "ĠPu zzle", "Ġm est", "Ġme st", "Ġmes t", "ar si", "ars i", "ĠS harma", "ĠSh arma", "ĠShar ma", "| (", ". ds", ".d s", "ĠSa cred", "ĠSac red", "ĠSacr ed", "_ evt", "_e vt", "_ev t", "Ġexp resses", "Ġexpress es", "Ġexpr esses", "Ġexpres ses", "Ġh och", "Ġho ch", "Ġhoc h", "ĠD uch", "ĠDu ch", "ĠDuc h", ". calls", ".c alls", ".call s", ".cal ls", "t hr", "th r", "ĠShe ffield", ". AlertDialog", ".Alert Dialog", "Ġrad ically", "Ġradical ly", "Ġt rous", "Ġtr ous", "Ġtro us", "Ġtrou s", "Ġprev ailing", "Ġprevail ing", "ĠWW II", "âĢĻ n", "ens ely", "ense ly", "Ġ Yesterday", "ĠY esterday", "ĠSir ius", "ĠSiri us", "Ġkill ers", "Ġkil lers", "Ġkiller s", "Ġ FFT", "ĠF FT", "ĠFF T", "Ġ oval", "Ġo val", "Ġov al", "' ):čĊ", "') :čĊ", "'): čĊ", "Ġ ìłķë³´", "Ġìłķ ë³´", "ou rage", "our age", "Ġ Checkbox", "ĠCheck box", "Work book", ". defer", ".de fer", ".def er", "_ floor", "_f loor", "_fl oor", "Ġc ouncill", "Ġcouncil l", "Ġnors ke", "Ġnorsk e", "m oil", "mo il", "o rea", "or ea", "ore a", "Ġmark eted", "Ġmarket ed", "_ SUR", "_S UR", "_SU R", "x AA", "xA A", "Ġst ained", "Ġsta ined", "Ġstain ed", "e ut", "eu t", "ĠM eng", "ĠMe ng", "ĠMen g", "Ġ ieee", "Ġi eee", "Ġie ee", ". extern", ".ex tern", ".ext ern", "e gie", "eg ie", "Ġr app", "Ġrap p", "Ġra pp", "ĠPy ongyang", "' class", "M ob", "Mo b", "Ġinitial Value", "_ wave", "_w ave", "Ġ jab", "Ġj ab", "Ġja b", "Ġmascul ine", "Ġampl ifier", "Ġ tty", "Ġt ty", "Ġtt y", "Path Component", "_ xt", "_x t", "ĠG FP", "ĠGF P", "/ sec", "/s ec", "/se c", "ĉ dispatch", "ĉdis patch", "mark down", "ĠS chn", "ĠSc hn", "ĠSch n", "b ole", "bo le", "bol e", "· ·", "mouse move", "Ġ errMsg", "Ġerr Msg", "Ġa sign", "Ġas ign", "Ġasi gn", "_ mono", "_m ono", "_mon o", "_mo no", "To Selector", "ĠZ u", "( Rect", "(R ect", "Ġ ErrorCode", "ĠError Code", "l atin", "la tin", "lat in", "ang ible", "angi ble", "v tk", "vt k", "CG Size", "P okemon", "Pok emon", "Ġclass mates", "Ġat tracts", "Ġattr acts", "Ġattract s", "ĠT atto", "ĠTat to", "ul tan", "ult an", "ulta n", "ol óg", "Ġh alted", "Ġhal ted", "Ġhalt ed", "ठ¨", "ĠK art", "ĠKar t", "ĠKa rt", "Ġ ue", "Ġu e", "_Init Structure", "_InitStruct ure", "Test Class", "ĠAir bnb", "_ \",", "_\" ,", "Ġchar coal", "Ġ ipc", "Ġi pc", "Ġip c", "Ġ Stretch", "ĠSt retch", "ĠStr etch", ".g lide", ".gl ide", "lates AutoresizingMaskIntoConstraints", "Ġp otion", "Ġpo tion", "Ġpot ion", "ITT LE", "Ġcount ert", "Ġcounter t", "_ hd", "_h d", "pre pared", "prepare d", "prep ared", "A ds", "Ad s", "ĠV ampire", "ro bots", "robot s", "rob ots", ".Create Index", "Status Label", "Ġt ucked", "af ür", "U t", "Ġswe ater", "Ġsweat er", "_ FN", "_F N", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġĉ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĉ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĉ", "at aka", "ata ka", "atak a", "Ġeyeb rows", "Ġeyebrow s", "a coes", "ac oes", "aco es", "u den", "ud en", "ude n", ".Linear LayoutManager", ".LinearLayout Manager", "Ġs way", "Ġsw ay", "Ġmult in", "Ġmulti n", "Ġmul tin", "( ))))Ċ", "() )))Ċ", "()) ))Ċ", "())) )Ċ", "()))) Ċ", "Ġ NSUInteger", "ĠNS UInteger", "ĠMy Base", "P artner", "Part ner", "uts chen", "utsch en", "utsche n", "ĠC ater", "ĠCa ter", "ĠCat er", ".set BackgroundColor", ".setBackground Color", "Ġaccompl ishment", "Ġaccomplish ment", "_ problem", "_pro blem", "_prob lem", ".d td", ".dt d", "Ġ pageNumber", "Ġpage Number", "ĠpageNum ber", "Ġj ackets", "Ġjack ets", "Ġjacket s", "Ġc ropped", "Ġcr opped", "Ġcro pped", "Ġcrop ped", "u els", "ue ls", "uel s", "ĠH ep", "ĠHe p", "Ġc apped", "Ġcap ped", "Ġca pped", "* Math", "*M ath", "_ callbacks", "_callback s", "_call backs", "Ġp ubb", "Ġpub b", "Ġpu bb", "ĠBrun swick", ". respond", ".res pond", ".resp ond", "[ \"_", "[\" _", "Ġbed ding", "hyth m", "O X", "( speed", "(s peed", "(sp eed", "Ġpest icides", "Ġpestic ides", "Ġpesticide s", "Ġ -------", "Ġ- ------", "Ġ-- -----", "Ġ---- ---", "Ġ--- ----", "Ġ----- --", "Ġ------ -", ". Blue", ".B lue", ".Bl ue", "Ġn oodles", "Ġnood les", "ĠG oes", "ĠGo es", "Ġs aver", "Ġsa ver", "Ġsave r", "Ġsav er", "o xy", "ox y", "_ completion", "_com pletion", "_comp letion", "ĠSw inger", "ĠSwing er", "Ġ getDate", "Ġget Date", "Ġm inded", "Ġmin ded", "Ġmind ed", "int egration", "integr ation", "ĠLot us", "( stop", "(s top", "(st op", "(', ');Ċ", "(',' );Ċ", "(',') ;Ċ", "Ġfl oods", "Ġflo ods", "Ġflood s", "Ġ Workflow", "ĠWork flow", "Ġe rupted", "Ġerupt ed", "M acro", "Mac ro", "Ma cro", "ĠS auce", "ĠSa uce", "ĠSau ce", "Ġ eventName", "Ġevent Name", "\\ Input", "Break ing", "Bre aking", "ĉ when", "ĉw hen", "_ pw", "_p w", "I NDER", "IN DER", "IND ER", "ĠWell ness", "Ġv oxel", "Ġvox el", "ĠM ell", "ĠMe ll", "ĠMel l", "Ġ MEDIA", "ĠM EDIA", "ĠMED IA", "S ENS", "SE NS", "SEN S", "ĠF unds", "ĠFund s", "ĠFun ds", "ĠFu nds", "ĠM ild", "ĠMil d", "ĠMi ld", "< Array", "Ċ", "') ;?>Ċ", "'); ?>Ċ", "');?> Ċ", "Ġtemp ting", "Ġtempt ing", "Ġtest ament", "Ġb ible", "Ġbi ble", "Ġbib le", "Ġconsult ed", "Ġconsul ted", "ĠIndex Error", "è¨ ĺ", "Ġkey pad", "Ġke ypad", "i zzo", "iz zo", "izz o", "( ok", "(o k", "Ġwh atsapp", "Ġwhats app", "ĠRemote Exception", "Ġte amed", "Ġteam ed", "Ġtea med", "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", "» ,", "Ġ getTime", "Ġget Time", "d iag", "di ag", "dia g", "is sy", "iss y", "Ġ hed", "Ġh ed", "Ġhe d", "Ġkn ots", "Ġknot s", "j om", "jo m", "Ġf unnel", "Ġfun nel", "-m ails", "-mail s", "Ġexp orting", "Ġexport ing", "Ġ VL", "ĠV L", "ĠK arn", "ĠKar n", "ĠKa rn", "ĠBuddh ism", "ĠAl lan", "ĠAll an", "ĠAlla n", "_R ADIUS", "Ġw ording", "Ġword ing", "Ġwor ding", "Ġ Forget", "ĠF orget", "ĠFor get", "ĠForge t", "ĠForg et", "ĠCor ona", "ĠCoron a", "i phy", "ip hy", "iph y", "Ġlim burg", "Ġlimb urg", "ug gy", "ugg y", "ĠUser Repository", "i min", "im in", "imi n", "( ele", "(e le", "(el e", "Ġlabel led", "Ġlab elled", "ç¤ ¾", "ĠH erman", "ĠHer man", "ĠHerm an", ". qq", ".q q", "Ġ \"));Ċ", "Ġ\" ));Ċ", "Ġ\") );Ċ", "Ġ\")) ;Ċ", "ie ber", ". Translate", ".Trans late", "r yn", "ry n", "Ġdes env", "Ġdese nv", "u md", "um d", "Sim ply", "ĉ mode", "ĉm ode", "ĉmod e", "R pc", "Rp c", "ĠVal encia", "ĠVale ncia", "Ġstaff ers", "Ġstaffer s", "Ġse lv", "Ġsel v", "ĠS pike", "ĠSp ike", "ĠSpi ke", "Ġd elic", "Ġde lic", "Ġdel ic", "Ġ eru", "Ġe ru", "Ġer u", "_ DT", "_D T", "J udge", "Jud ge", "Ju dge", "á» ķ", "ĠB asin", "ĠBa sin", "ĠBas in", ". mutable", ".m utable", ".mu table", "\" url", "Ġtar iff", "Ġtarif f", "ĠSlee ve", "ĠSle eve", "Ġ flare", "Ġfl are", "Ġfla re", ". dropout", ".drop out", "Ġb rides", "Ġbr ides", "Ġbri des", "Ġbride s", "Ġbrid es", ") ),čĊ", ")) ,čĊ", ")), čĊ", "_ constraints", "_con straints", "_constraint s", "d estruct", "de struct", "Out line", "Ġdisappe ars", "Ġdisappear s", "_ locked", "_l ocked", "_lock ed", "_loc ked", "ĠNS LocalizedString", "c ke", "ck e", "ĉ null", "ĉn ull", "ad resse", "adr esse", "adress e", "Ġt opping", "Ġto pping", "Ġtop ping", "Ġtopp ing", "ĠJ oker", "ĠJo ker", "b ishop", "bi shop", "bis hop", "bish op", "н оÑģÑĤÑĮ", "но ÑģÑĤÑĮ", "ноÑģÑĤ ÑĮ", "ноÑģ ÑĤÑĮ", "and ering", "ander ing", "ande ring", "_ amp", "_a mp", "_am p", "= time", "=t ime", "_ Space", "_S pace", "_P ULL", "' =", "Ġant iqu", "Ġanti qu", "Ġc ach", "Ġca ch", "Ġcac h", "_ __ĊĊ", "__ _ĊĊ", "___ ĊĊ", "O NES", "ON ES", "ONE S", "о Ñı", "Ġ unread", "Ġun read", "Ġunre ad", "Ġunr ead", ". policy", ".p olicy", ".pol icy", "oooo oooo", "ëŁ ¬", "Ġ usted", "Ġu sted", "Ġus ted", "Ġust ed", "ĠR ece", "ĠRe ce", "ĠRec e", "Ġal lem", "Ġall em", "Ġalle m", "ãĥ¼ ãĤ¹", "ãĥ¼ãĤ ¹", "ĠThough ts", "ĠThought s", "ve illance", "i strate", "ist rate", "istr ate", "istra te", "istrat e", "_ lane", "_l ane", "_la ne", "Ġf amed", "Ġfam ed", "Ġfa med", "Ġfame d", ". GetName", ".Get Name", "Ġsmooth er", "Ġsmo other", "Ġ Qualified", "ĠQual ified", "a zers", "az ers", "aze rs", "azer s", "_ geo", "_g eo", "_ge o", "F ax", "Fa x", "ĠM inds", "ĠMin ds", "ĠMi nds", "ĠMind s", "Ġ Raises", "ĠR aises", "ĠRa ises", "ĠRaise s", "ĠRai ses", "Ġtrans cripts", "Ġtran scripts", "Ġtranscript s", "Con versation", "Ġre marked", "Ġrem arked", "Ġremark ed", "Ġremar ked", "ëĤ ĺ", "d ling", "dl ing", "Ġdeploy ing", "Ġshared Application", "Ġ kp", "Ġk p", "FontAwesome Icon", "_ dummy", "_d ummy", "re iben", "reib en", "rei ben", "ĠJane iro", "Dir ections", "Direction s", "Direct ions", "Di rections", "Dire ctions", ".get Bean", ".getB ean", "s ass", "sa ss", "Ġcomm anders", "Ġcommand ers", "Ġcommander s", "Ġcommande rs", "v ation", "va tion", "vat ion", "error Code", "ĠAl loy", "ĠAll oy", ". localized", ".local ized", "Ð ij", "Ġdish washer", "Ġ Soup", "ĠS oup", "ĠSo up", "ĠSou p", "N u", "_ Default", "_D efault", "_De fault", "_Def ault", "Ġun even", "Ġune ven", "Ġ />\";Ċ", "Ġ/ >\";Ċ", "Ġ/> \";Ċ", "- Based", "-B ased", "Ġseam lessly", "Ġseamless ly", "- null", "-n ull", "Ġ XC", "ĠX C", "Ġst ew", "Ġste w", "( delay", "(d elay", "(de lay", "(del ay", "AT ORS", "ATOR S", "ATO RS", "ĠWhe eler", "ĠWheel er", "\" H", "e ast", "ea st", ". air", ".a ir", ".ai r", "âĢľ But", "Object Context", "success fully", "successful ly", "_ land", "_l and", "_la nd", "Ġf olds", "Ġfol ds", "Ġfold s", "Ġfo lds", "_ COORD", "_CO ORD", "Ġsub po", ".get Address", ".g etAddress", "in str", "ins tr", "inst r", "Material s", "Ñĥ ÑģÑĤ", "ÑĥÑģ ÑĤ", "de posit", "dep osit", "- last", "-l ast", "-la st", "_ GRAY", "_G RAY", "_GR AY", "_GRA Y", "= find", "=f ind", "Ġmut ant", "Ġmu tant", "Ġlesb ienne", "Ġlesbi enne", "let cher", "R OUGH", "RO UGH", "ur eka", "ure ka", ". capture", ".c apture", ".cap ture", "Ġ enn", "Ġe nn", "Ġen n", "Ġ ([[", "Ġ( [[", "Ġ([ [", "ĠF lu", "ĠFl u", "Ġ taskId", "Ġtask Id", "ĠHus sein", "ĠHuss ein", ". folder", ".f older", ".fold er", "Ġa usterity", "IST RATION", "ISTR ATION", "_ Impl", "_I mpl", "注 æĦı", "Ġdec ree", "Ġdecre e", "- chat", "-c hat", "-ch at", "Ġim plication", "Ġimp lication", "Ġimpl ication", "Ġimplic ation", "Ġgu esses", "Ġguess es", "ul kan", "ulk an", "An alytics", "Analy tics", ". plus", ".p lus", ".pl us", "COM MAND", "COMM AND", "е ли", "ел и", "» ĊĊ", "»Ċ Ċ", "_ SITE", "_S ITE", "_SI TE", "Ġ equalTo", "Ġequal To", "Support FragmentManager", "Ġ Recording", "ĠRec ording", "ĠRecord ing", "å®Į æĪIJ", "Ġbag gage", "Ġpitch ers", "Ġpit chers", "Ġpitcher s", "ĠE h", "o que", "oq ue", "ĉ cnt", "ĉc nt", "Ġ =>$", "Ġ= >$", "Ġ=> $", "/ foo", "/f oo", "I RA", "IR A", "ĠSat ellite", "bo rah", "bor ah", "Ġ }}\"Ċ", "Ġ} }\"Ċ", "Ġ}} \"Ċ", "Ġ}}\" Ċ", "ĠE nds", "ĠEn ds", "ĠEnd s", "ĠS pray", "ĠSp ray", "ĠSpr ay", ", param", ",p aram", ". Chrome", ".Ch rome", "* q", "th ought", "though t", "ib rated", "ibr ated", "ibrate d", "Ġth ieves", "Ġbenefici aries", "En tered", "Ent ered", "Enter ed", "ottes ville", "otte sville", "Ġveter in", "Ġvet erin", "By ID", "qu ipe", "quip e", "qui pe", "um ption", "ump tion", "umpt ion", "- unit", "-un it", "-u nit", "Execution Context", "@ s", "ĠG iov", "ĠGi ov", "ĠGio v", ". ToolTip", ".Tool Tip", "_ friend", "_f riend", "( attributes", "(at tributes", "(attribute s", "Ġd umping", "Ġdump ing", "Ġdum ping", "Ġ JC", "ĠJ C", "_ DOCUMENT", "_D OCUMENT", "_DOC UMENT", "ĠAr mour", "ĠArm our", "( insert", "(in sert", "(ins ert", ". HorizontalAlignment", ".Horizontal Alignment", "Ġ Qed", "ĠQ ed", "ãģĦ ãģ¾ãģĻ", "/ git", "/g it", "Ġ YYYY", "ĠY YYY", "ĠYY YY", "ĠCar diff", "ĠCard iff", "Ġ apa", "Ġa pa", "Ġap a", "org anic", "organ ic", "ĠWhere as", "ĠWhe reas", "Ġ æĿ", "Ġæ Ŀ", "ĠM ia", "ĠMi a", "Ġdemol ition", "Ġs cars", "Ġsc ars", "Ġsca rs", "Ġscar s", "Ġ pai", "Ġp ai", "Ġpa i", "Ġre tries", "Ġret ries", "Ġretrie s", "Ġretr ies", "Ġ rq", "Ġr q", "ĠD enis", "ĠDe nis", "ĠDen is", "( Utils", "(Util s", "Ġallev iate", "Ġ PIC", "ĠP IC", "ĠPI C", "i due", "id ue", "Ġacknowled ging", "Ġ// ////////////////////////////////", "Ġ////////////////// ////////////////", "ç¡® å®ļ", "Ä «", "\\ Json", ". binary", ".b inary", ".bin ary", "Ġx type", "Ġxt ype", "sign als", "signal s", "Ġ Appearance", "ĠAp pearance", "& r", "} s", "C i", "ĠI llum", "ĠIl lum", "ĠIll um", "p orate", "por ate", "po rate", "pora te", "h og", "ho g", "Ġ indexOf", "Ġindex Of", "\\ Command", "_ parallel", "_par allel", "ĠSher lock", "í ĥ", "Ġ\" \")čĊ", "Ġ\"\" )čĊ", "Ġ\"\") čĊ", "//////////////// ////////////////////////////////////////////////////////////////////////////////", "//////////////////////////////// ////////////////////////////////////////////////////////////////", "//////////////////////////////////////////////////////////////// ////////////////////////////////", "//////////////////////////////////////////////// ////////////////////////////////////////////////", "//////////////////////////////////////////////////////////////////////////////// ////////////////", "Ġcritic ize", "Ġ Soap", "ĠSo ap", "Ġ Matcher", "ĠM atcher", "ĠMat cher", "ĠMatch er", "Ġgr illed", "Ġgrill ed", "Ġgrille d", "* T", "Ġad ore", "Ġado re", "ul ling", "ull ing", "Ġje doch", "Ġjed och", "_ refs", "_re fs", "_r efs", "_ref s", "lean up", "ĠJ AXB", "ĠJA XB", "Ġr oses", "Ġro ses", "Ġrose s", "Ġros es", "ĠL iam", "ĠLi am", "ĠLia m", "size i", "siz ei", "Ġget char", "Ġgetch ar", "Ġt arde", "Ġtar de", "Ġtard e", "- tooltip", "-to oltip", "-tool tip", "Ġqual ifier", "Ġ Intermediate", "ĠInter mediate", "_ Window", "_W indow", "ĠM alta", "ĠMal ta", "Dis connect", "e where", "ew here", "C ampo", "Cam po", "Camp o", "Ġirr ational", "l edo", "le do", "led o", "Ġ DN", "ĠD N", "AR GV", "ARG V", "Ġout ro", "Ġou tro", "Ġoutr o", "Ġth irteen", "Jose ph", "Jos eph", "M AR", "MA R", "/ gl", "/g l", "J ess", "Je ss", "ĠPsych iat", "Ġpadding Bottom", "- loop", "-l oop", "-lo op", "/ fonts", "/font s", "_ seen", "_s een", "_se en", "Te ams", "Team s", "React DOM", "( man", "(m an", "( xpath", "(x path", ". getSimpleName", ".get SimpleName", "> (*", ">( *", "ĠP vt", "ĠPv t", "Ġel ders", "Ġelder s", "Ġelde rs", "Ġ pies", "Ġp ies", "Ġpie s", "Ġpi es", ".user Agent", "- region", "-reg ion", "ĠGre eks", "ĠGreek s", "ĠGree ks", "( fragment", "(f ragment", "(fr agment", "s tu", "st u", "Ġcouncil s", "Ġst amina", "Ġsta mina", "ĠGod dess", "è ¥¿", "è¥ ¿", "Ġphilosoph ers", "Ġphilosopher s", "Ġperson e", "Ġpers one", "Ġperso ne", "ĠL ose", "ĠLo se", "ĠLos e", "Ġ CLR", "ĠC LR", "ĠCL R", "Ġ Docs", "ĠD ocs", "ĠDo cs", "ĠDoc s", "Ġso ak", "Ġ HOLDER", "ĠH OLDER", "ĠHOLD ER", "ĠHOL DER", "Ġb ells", "Ġbel ls", "Ġbell s", "hash Code", "R ATE", "RA TE", "_WE IGHT", "in ous", "ino us", "inou s", "en dra", "end ra", "oph obic", "Ġp rose", "Ġpro se", "Ġpr ose", "Ġpros e", "Ġf inely", "Ġfin ely", "Ġfine ly", "/ oauth", "/o auth", "( space", "(s pace", "(sp ace", "a dge", "ad ge", "ĠM ama", "ĠMa ma", "ĠMam a", "Ġstring Buffer", "Ġst int", "Ġm isma", "Ġmis ma", "Ġmism a", "Ġvill ains", "Ġvillain s", "Ġvilla ins", "ĠCrime a", "Ġdipl oma", "Ġdiplom a", "Ġпо Ñģл", "ĠпоÑģ л", "ĠB ea", "ĠBe a", "( join", "(j oin", "Ġ íķ´", "Ġíķ ´", "CH AT", "CHA T", "p ering", "pe ring", "per ing", "peri ng", "ĠC ros", "ĠCr os", "ĠCro s", "Ġmon keys", "Ġmonkey s", "Ġp reds", "Ġpr eds", "Ġpre ds", "Ġpred s", "y la", "yl a", ", ,,", ",, ,", "Ġv ibrator", "Ġvibr ator", "Ġ NU", "ĠN U", "åħ Ī", "f ant", "fa nt", "fan t", "z et", "ze t", "Ġb ietet", "un ft", "s worth", "sw orth", ". Flow", ".F low", ".Fl ow", "Ġpsych ed", "Ġpsy ched", "Ġpsyche d", "ĠContin ental", "ĠContinent al", "> t", "Ġqu ilt", "Ġq uilt", "Ġqui lt", "Ġquil t", ". UP", ".U P", "Ġexpans ive", "Dis pose", "Disp ose", "( language", "(l anguage", "C aps", "Cap s", "Ca ps", "_ ZONE", "_Z ONE", "Ġre cycle", "Ġr ecycle", "Ġrec ycle", "Ġrecycl e", "Ġ Managed", "ĠMan aged", "ĠManage d", "ĠMana ged", "current Color", ". broadcast", ".b roadcast", "sign In", ". prom", ".p rom", ".pro m", ".pr om", "l lu", "ll u", "ue blo", "Ġpun ches", "Ġpunch es", "Ġaut omat", "Ġauto mat", "Ġautom at", "Ġassign ing", "Ġcreate User", "ĠAl lied", "ĠAll ied", "Ġcon ductor", "Ġconduct or", "Ġcond uctor", "Ġconduc tor", "Ġcondu ctor", "Ĥ ¨", "Ġs addle", "Ġsad dle", "Ġsadd le", "Ġ dni", "Ġd ni", "Ġdn i", "o medical", "omed ical", "- West", "-W est", "Positive Button", "Ġ italic", "Ġit alic", "? [", "( trigger", "(tr igger", "Ġele phants", "Ġelephant s", "\":\" \",\"", "\":\"\" ,\"", "Ġcal iber", "raft ed", "raf ted", "d igits", "digit s", "dig its", "Ġ marshal", "Ġm arshal", "Ġmar shal", "Ġmarsh al", "Ġmars hal", "m illiseconds", "mill iseconds", "m arkers", "mark ers", "mar kers", "marker s", "m om", "mo m", "/ place", "/p lace", "/pl ace", "Ġhol istic", ": t", "# ,", "Ġb oto", "Ġbo to", "Ġbot o", "Ġnause a", "Ġnau sea", "ĠSh ooting", "ĠShoot ing", "ĠSho oting", "i tech", "it ech", "ite ch", "Ġtext Status", "< Class", " ())Ċ", ">( ))Ċ", ">() )Ċ", ">()) Ċ", "ADD RESS", "ADDR ESS", "B ST", "BS T", "et zt", "etz t", "ĠQ gs", "S ense", "Sen se", "Exception Handler", "ĠC hu", "ĠCh u", ".get OwnProperty", "Ġexerc ised", "Ġexercise d", "i otic", "io tic", "iot ic", "ĠRe leases", "ĠRelease s", "Ġp interest", "o lie", "ol ie", "oli e", "i soft", "is oft", "iso ft", "Ġsequ encing", "Ġpa dre", "Ġpad re", "Ġpadr e", "] ));čĊ", "]) );čĊ", "])) ;čĊ", "])); čĊ", "( radius", "(r adius", "(rad ius", ". med", ".m ed", ".me d", "ain ties", "aint ies", ".Object Model", "Ġ emple", "Ġem ple", "Ġemp le", "Ġseg uro", "Ġsegu ro", "St ars", "Star s", "Ġqual itative", "le mn", "lem n", "á» ±", "> \").", ">\" ).", ">\") .", "Ġ gx", "Ġg x", "- cert", "-c ert", "-ce rt", "ĠA STM", "ĠAS TM", "ĠAST M", "Ġ fullname", "Ġfull name", "Ġful lname", "Ġte lemetry", "Ġtele metry", "ĠCamb odia", "_ ul", "_u l", "ĠCl are", "ĠClar e", "ĠCla re", "C USTOM", "Q C", "ĠU ns", "ĠUn s", "Ġ HTTPS", "ĠHTTP S", "ĠPar kinson", "ĠPark inson", "ancy box", "', '.", "',' .", "T ue", "Tu e", ". getLast", ".get Last", "Ġ abi", "Ġa bi", "Ġab i", "Äħ d", "A st", "As t", "Ġ Editing", "ĠEd iting", "ĠEdit ing", ". Unity", ".Un ity", ".Unit y", "j mp", "jm p", "Ġm ats", "Ġmat s", "Ġma ts", "Ġshared Preferences", "Cap tain", "Capt ain", ". pageSize", ".page Size", "Ġ rtl", "Ġr tl", "Ġrt l", "Ġan meld", "Runtime Object", "Ġdem ande", "Ġdemand e", "( \";", "(\" ;", "se ite", "sei te", "- headed", "-head ed", "-he aded", "ĠK ra", "ĠKr a", "Ġ FONT", "ĠF ONT", "ĠFO NT", "` \\", "Class NotFoundException", ". avg", ".a vg", ".av g", "a tical", "at ical", "atic al", "ati cal", "atica l", "A j", "Ġpermit ting", "Ġperm itting", "P roj", "Pro j", "Pr oj", "ERR Q", "Ġcre ampie", "Ġcream pie", "ĠBuy er", "ĠBu yer", "- modules", "-mod ules", "-module s", "ĠSunday s", "ĠSun days", "ĠSund ays", "| `Ċ", "Ġday time", "Ġ +(", "Ġ+ (", "Ġgl itch", "Ġ Operand", "ĠOper and", "ĠOpera nd", "Ġtox ins", "Ġtoxin s", "i nya", "in ya", "iny a", "D NS", "DN S", "ĠS as", "ĠSa s", "C ake", "Ca ke", "ĠNational s", "ĠNation als", ". addTo", ".add To", "Ġs inking", "Ġsin king", "Ġsink ing", "Ġcompreh ension", "Ġs cor", "Ġsc or", "Ġsco r", "a gements", "ag ements", "age ments", "agement s", "agem ents", "Ġt ard", "Ġta rd", "Ġtar d", "Ġm arching", "Ġmar ching", "Ġmarch ing", "ĠM TV", "ĠMT V", "Ġs ane", "Ġsa ne", "Ġsan e", "Create Info", "Ạ¯", "Ġend Index", "ĉ layout", "ĉl ayout", "Ġ åIJį", "ĠåIJ į", "S ITE", "SI TE", "ĠT HERE", "ĠTHE RE", "ĠTH ERE", "Ġ[ {'", "Ġ[{ '", "opath ic", "opa thic", "Ġtrans mitter", "Ġtransmit ter", "/ body", "/b ody", "Ġp und", "Ġpun d", "Ġpu nd", "Ġ Closing", "ĠC losing", "ĠCl osing", "ĠClo sing", "Ġ setattr", "Ġset attr", "Ġ bounded", "Ġb ounded", "Ġbo unded", "Ġbound ed", "At las", "Atl as", "s uming", "sum ing", "su ming", "( times", "(t imes", "(time s", "(ti mes", "p arer", "par er", "pare r", "pa rer", "y nom", "yn om", "fe it", "Ġf rem", "Ġfr em", "Ġfre m", "- leg", "-l eg", "-le g", "ĠB ras", "ĠBr as", "ĠBra s", "> #", "Ġì¶ ľëł¥", "Ġì¶ľ ëł¥", "Ġ INSTANCE", "ĠIN STANCE", "ĠINST ANCE", "ĠC ouch", "ĠCo uch", "ĠCou ch", "_ hosts", "_host s", "lik elihood", ". Marker", ".M arker", ".Mark er", ".Mar ker", "ĠM asks", "ĠMas ks", "ĠMask s", "Ġc ereal", "Ġce real", "Ġcere al", "ut ilities", "util ities", "Ġelement al", "Ġele mental", "Ġelem ental", "Ġdist orted", "Ġdistort ed", "in active", "c ry", "cr y", "W L", "UPPORT ED", ". Throws", ".Th rows", ".Throw s", "/ schema", "/s chema", "s erie", "se rie", "ser ie", ". \"',", ".\" ',", ".\"' ,", "ĠBened ict", "ĠBene dict", "- picker", "-p icker", "-pic ker", "ig gs", "igg s", "ĠP irate", "ĠPi rate", "ĠPir ate", "åij¨ æľŁ", "ĠThe ma", "ĠTh ema", "ĠThem a", "ĠSouth ampton", "Ġarray With", "ĠPaul a", "ĠPa ula", "Ġpred ictor", "Ġpredict or", "Ġpredic tor", "- Ass", "-A ss", ". userid", ".user id", ".use rid", "Ġ peri", "Ġp eri", "Ġper i", "Ġpe ri", "Ġexagger ated", "u rate", "ur ate", "ura te", "urat e", "arse ille", "ĠCon cent", "ĠConc ent", "ĠConce nt", "ĠP ik", "ĠPi k", "Ġ@ _;ĊĊ", "Ġ@_;Ċ Ċ", "Ġ@_ ;ĊĊ", "Ġform ations", "Ġformat ions", "Ġformation s", "Ġden omin", "Ġdenom in", "\" />.Ċ", "\"/ >.Ċ", "\"/> .Ċ", "end edor", "ended or", "Ġpan cre", "Ġpanc re", "Ġ amt", "Ġa mt", "Ġam t", "Ġon Resume", "on Delete", "ĠB CH", "ĠBC H", ") (\"", ")( \"", "m ovement", "move ment", "mo vement", "mov ement", "Ġpot assium", "", "Ġ-- ->", "Ġ--- >", "ĠP PC", "ĠPP C", "i sz", "is z", "ake FromNib", "Ġ Disp", "ĠD isp", "ĠDis p", "ĠDi sp", "ĠAth letics", "ĠAthletic s", "Ġnight club", "G OOD", "GO OD", ".set Geometry", "+ [", "/ send", "/s end", "/se nd", "Ġbin aries", "Ġr áp", "Ġrá p", ": req", ":r eq", "-con suming", "-cons uming", "er time", "ert ime", "erti me", "UP DATED", "UPDATE D", "_ nullable", "_null able", "V IN", "VI N", "u lia", "ul ia", "uli a", "c yan", "cy an", "Ġmisunder standing", "Ġmisunderstand ing", "o rical", "or ical", "ori cal", "oric al", "deg rees", "degree s", "Le ading", "Lead ing", ". AR", ".A R", "ic kest", "ick est", "N uevo", "uf oria", "Ġgo odies", "Ġgood ies", "Ġf ores", "Ġfor es", "Ġfore s", "Ġfo res", "() <<\"", "()<< \"", "()< <\"", "ad emic", "ade mic", "adem ic", "Action Creators", "server name", "( nt", "(n t", "db Context", "Ġair borne", "Ġexhib itions", "Ġexhibition s", "Ġexhibit ions", "c ele", "ce le", "cel e", "Ġt ela", "Ġte la", "Ġtel a", "< Movie", "", "() \">", "()\" >", ".set PreferredSize", "ĠM ID", "ĠMI D", "ĠA less", "ĠAl ess", "ĠAle ss", "Ġhorse power", "Ġa tm", "Ġat m", "ĠPack aging", "Ġc iphertext", "Ġcipher text", "Request Method", "Ġbe iden", "Ġbei den", "Ġbeide n", "è £", "ĠP OW", "ĠPO W", ".Write Header", "d irector", "dir ector", "direct or", "dire ctor", "- but", "-b ut", "ãģł ãģķãģĦ", "in cer", "ince r", "inc er", "_ dn", "_d n", "! !!!!", "!! !!!", "!!! !!", "!!!! !", "Ġmanufact ures", "Ġmanufacture s", ". TextUtils", ".Text Utils", "Ġcon sciously", "Ġconsc iously", "Ġconscious ly", "Ġb ounced", "Ġbounce d", "c ulture", "cul ture", "cult ure", "ĠS par", "ĠSp ar", "ĠSpa r", "ĠP iper", "ĠPi per", "ĠPipe r", "ĠPip er", ". press", ".p ress", ".pre ss", ".pr ess", ".pres s", "- owner", "-o wner", "Ġe valuator", "Ġeval uator", "Ġevalu ator", "Ġ STREAM", "ĠST REAM", ".PictureBox SizeMode", "Ġsu gars", "Ġsugar s", "Ġsug ars", "Screen Width", "Ġnext State", "Ġiv ory", "Ġbr unch", "Ġbrun ch", "d ensity", "dens ity", "_ OW", "_O W", "ĠCoron avirus", "ĠC FR", "ĠCF R", "b ak", "ba k", "\\ Category", "\\C ategory", "æķ° ç»Ħ", "Ġinvoke virtual", "} ()Ċ", "}( )Ċ", "Ġs ujet", "Ġsu jet", "- marker", "-m arker", "-mark er", "-mar ker", "is digit", "isd igit", "ĠM obil", "ĠMo bil", "ĠMob il", "ĠJsonRequest Behavior", "_ REMOTE", "_RE MOTE", ".exists Sync", "Ġrich es", "Ġri ches", "Ġric hes", ".p resenter", ".present er", ".pres enter", "Ġgl Color", "Ġh anya", "Ġha nya", "Ġhan ya", "Ġfort ress", "Ġfl ashed", "Ġflash ed", "Ġfla shed", "v iz", "vi z", "requ ently", "requent ly", "b uat", "bu at", "$ con", "$c on", "> |", ". Func", ".F unc", "Ġhum orous", "Ġhumor ous", "u em", "ue m", ". ZERO", ".Z ERO", "ĠS TL", "ĠST L", "ĠB uk", "ĠBu k", "/ sample", "/s ample", "ĠG ros", "ĠGr os", "ĠGro s", "Rec ipes", "Recipe s", "Ġinf lated", "Ġinfl ated", "Ġinflate d", "Ġsw ung", ": F", "F acing", "Fac ing", "Fa cing", ". Theme", ".Th eme", ".The me", "н ик", "ни к", "Ġspl endid", "Ġrequest Id", ".Center Screen", "/ autoload", "/auto load", "embed ded", "_ depart", "_de part", "_dep art", "Ġ Ports", "ĠP orts", "ĠPort s", "ĠPo rts", "ĠPor ts", "๠ĥ", "ай д", "disc ussion", "_ consum", "_con sum", "_cons um", "Ġsc outs", "Ġsco uts", "Ġscout s", "Ġcol abor", "Ġcola bor", ". Stage", ".St age", ". nano", ".n ano", ".nan o", "el dorf", "eld orf", "eldo rf", "Ġgem acht", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "Ġpolicy makers", "Ġpolicym akers", "_P KT", "_PK T", ", Th", ",T h", "o ky", "ok y", "_ UID", "_U ID", "_UI D", "P ing", "Pin g", "Pi ng", "Ġor chest", "Ġorch est", "Ġorc hest", "Ġop tics", "Ġopt ics", "Ġoptic s", "u han", "uh an", "ĠX OR", "ĠXO R", "Ġespañ ol", "ĠAd idas", "r ng", "rn g", "m ans", "man s", "ma ns", ".v stack", "Ġget away", "Ġh ierarchical", "Ġhier archical", "ano ia", "anoi a", "ĠBitmap Factory", "re alm", "rea lm", "real m", "ĉ ap", "ĉa p", "_ apps", "_a pps", "_app s", "_ap ps", "- divider", "-div ider", "-di vider", ". drawer", ".d rawer", ".draw er", "ĠH ARD", "ĠHA RD", "ĠHAR D", "'] ;?>Ċ", "']; ?>Ċ", "'];?> Ċ", "- packed", "-p acked", "-pack ed", "æ² »", "_STRUCT URE", "[ Y", "i Param", "( eq", "(e q", "Ġencompass es", "Ġ\\ ĊĊ", "Ġ\\Ċ Ċ", "- >[", "-> [", "& utm", "g roupon", "gr oupon", "group on", "gro upon", "st rate", "str ate", "stra te", "D Y", "om orphic", "' :[", "': [", "Ġgrav itational", "ĠM icha", "ĠMich a", "ĠMi cha", "ĠMic ha", "ĠT encent", "ĠTen cent", "Ġco ached", "Ġcoach ed", "ì¶ ľ", "Ñĥ менÑĤ", "Ñĥм енÑĤ", "/ mobile", "/m obile", "Mouse Down", "b ud", "bu d", "ĠY as", "ĠYa s", "Ġ Providers", "ĠPro viders", "ĠProvid ers", "ĠProvider s", "ĠProvide rs", "ĠProv iders", "N Z", "ĉ report", "ĉre port", "ĉrep ort", "err msg", "Ġimage Path", "act erial", "acter ial", "acteria l", "ĠM anga", "ĠMan ga", "ĠMa nga", "ĠMang a", "wick lung", "( usuario", "(us uario", "\" ));čĊčĊ", "\") );čĊčĊ", "\")) ;čĊčĊ", "\"));čĊ čĊ", "\")); čĊčĊ", "/ ***", "/* **", "/** *", "Ġorgan ise", "Ġorganis e", "Index ed", "_ QUAL", "_Q UAL", "_QU AL", "( PyObject", "(Py Object", "Ġsurrender ed", "PO CH", "ĠN OTES", "ĠNOT ES", "ĠNO TES", "ĠNOTE S", "\\ \\\"", "\\\\ \"", "- job", "-j ob", "Ġseven ty", "Ġsevent y", "Ġsev enty", "# ###Ċ", "## ##Ċ", "#### Ċ", "### #Ċ", "ĠMan or", "ĠMa nor", "Ġdown right", "Ġtime frame", "ins urance", "che cker", "check er", "Ġ SECRET", "ĠSE CRET", "ĠSEC RET", "Ġecho es", "Ġech oes", "ĠCar men", "ĠCarm en", ".set HorizontalAlignment", ".setHorizontal Alignment", "Ġ isChecked", "Ġis Checked", "Ġ TOR", "ĠT OR", "ĠTO R", "_ nn", "_n n", "( '(", "(' (", "Fetch Request", "ĠPrint ed", "Fl uid", "Ġ STACK", "ĠST ACK", "ĠSTA CK", "G ES", "GE S", "a igned", "aign ed", "i gor", "ig or", "igo r", ". Unknown", ".Un known", "C BC", "CB C", "ĠCarl son", ". URI", ".U RI", ".UR I", "Ġp light", "Ġpl ight", "/ start", "/st art", "/star t", "ĠPerson nel", "Ġ PREFIX", "ĠP REFIX", "ĠPRE FIX", "ĠPREF IX", ", **", ",* *", "Ġli mite", "Ġlimit e", "Ġlim ite", "_ heat", "_h eat", "_he at", "% ï¼Į", "ĠD onne", "ĠDon ne", "get Node", "ĠScient ology", "Ġc omet", "Ġcom et", "Ġco met", "Ġcome t", "Ġwen ig", "A side", "As ide", "ĠM PEG", "ĠMP EG", "' ?", "vari ably", ". endDate", ".end Date", "Ġun cont", "Ġunc ont", "Ġuncon t", "Ġ Scores", "ĠS cores", "ĠSc ores", "ĠScore s", "ĠSco res", "ĠScor es", "Ġ LoginForm", "ĠLogin Form", ". generated", ".g enerated", ".generate d", ".gener ated", ", ch", ",c h", "- mar", "-m ar", "ĠN ed", "ĠNe d", "Ġ eventId", "Ġevent Id", "+ p", "ĠS IN", "ĠSI N", "/ reset", "/re set", "/res et", ".RE ACT", "ĠMe ssi", "ĠMess i", "ĠMes si", "_R ANK", "_RA NK", ".write File", "Ġc ripp", "Ġcr ipp", "Ġcri pp", "es thetic", "est hetic", "ERS IST", "Ġreim bursement", "Ġreimburse ment", "Current Value", "Ġu nin", "Ġun in", "Ġuni n", "Down Latch", "Ġpadding Right", "Ġst ocked", "Ġstock ed", "/ '.", "/' .", "Ġre payment", "Ġrep ayment", "Ġrepay ment", "t rak", "tr ak", "tra k", "/ backend", "/back end", "Ġиз мен", "C SR", "CS R", "Ġprevent ive", "Ġpant alla", "_ trim", "_t rim", "_tr im", "_tri m", "P edido", "Ped ido", "h ospital", "Ġmanage able", "route Params", "text ures", "texture s", "tex tures", ". .....ĊĊ", ".. ....ĊĊ", "... ...ĊĊ", ".... ..ĊĊ", "..... .ĊĊ", "...... ĊĊ", "Ġsé lection", "Name ValuePair", "Ġpol lut", "Ġpoll ut", "M odes", "Mode s", "Mod es", "Mo des", "ĠL aud", "ĠLa ud", "ĠLau d", "j ay", "ja y", "ĠU rs", "ĠUr s", "Ġs igner", "Ġsign er", "Ġsig ner", "Ġ JJ", "ĠJ J", "ĠCh erokee", "_EX ISTS", "_EXIST S", "Ġd war", "Ġdw ar", "Ġ ($('#", "Ġ( $('#", "Ġ($ ('#", "Ġ($( '#", "Ġre ef", "> {$", ">{ $", "ĠB aylor", "ĠBay lor", "Ġ ModelState", "ĠModel State", "- _", "ĠStruct ures", "ĠStructure s", "Ġsou vent", "Spec ify", "( pipe", "(p ipe", "(pi pe", "Ġfr acking", "Ġfrac king", "ĠG PA", "ĠGP A", "Ġb ele", "Ġbe le", "Ġbel e", "ĉ ĉĉĉĉĉĉĠĠĠ", "ĉĉ ĉĉĉĉĉĠĠĠ", "ĉĉĉĉ ĉĉĉĠĠĠ", "ĉĉĉ ĉĉĉĉĠĠĠ", "ĉĉĉĉĉ ĉĉĠĠĠ", "ĉĉĉĉĉĉ ĉĠĠĠ", "ĉĉĉĉĉĉĉ ĠĠĠ", "ĉĉĉĉĉĉĉĠ ĠĠ", "ĉĉĉĉĉĉĉĠĠ Ġ", "ĠMinor ity", "Ġt ud", "Ġtu d", "Ġopen ness", "ĠIllustr ated", "Ġoxid ation", "Ġ NK", "ĠN K", "ĉ Update", "Ġ EMS", "ĠE MS", "ĠEM S", "ĠTe ddy", "ĠTed dy", "Ġgener als", "Ġgen erals", "Ġgeneral s", "Ġgenera ls", "ĉ Mat", "ĉM at", "Ġrad ios", "Ġradio s", "Ġradi os", "ĠAnt ique", "ĠAnti que", "c onomy", "con omy", "conom y", "cono my", "ĠSquad ron", ") ','", ")', '", ")' ,'", "å£ °", "Ġy oure", "Ġyou re", "Ġyour e", "Ġyo ure", "ĠMain Page", "Ġbeh aviours", "Ġbehaviour s", "en ght", "eng ht", "(@\" %@\",", "Ġtest case", "Ġ Compilation", "ĠComp ilation", "Ġflav ours", "Ġflavour s", "Ġ Extend", "ĠExt end", "il lator", "ill ator", "illa tor", "Ġ coh", "Ġc oh", "Ġco h", "Ġs pline", "Ġsp line", "Ġspl ine", "Ġ KG", "ĠK G", "- pay", "-p ay", "Ġcommun ism", "ĠBusiness es", "oc king", "ock ing", ". MaxLength", ".Max Length", "ass andra", "qu iring", "quir ing", "qui ring", "a dden", "ad den", "add en", "ĠJ eb", "ĠJe b", "_ fault", "_f ault", "_fa ult", "[ file", "[f ile", "Ġpromin ence", "disc iplinary", "âĢĶ they", "âĢĶthe y", "_ extent", "_ex tent", "_ext ent", "Ġ VIC", "ĠV IC", "ĠVI C", "Ġen tails", "Ġent ails", "Ġentail s", ". partner", ".p artner", ".part ner", "Ġhipp oc", "Le ague", "çĶ ·", "w ipe", "wi pe", "- spinner", "-sp inner", "-spin ner", "Ġsal ute", "ĠS urgical", "ĠSurg ical", "( outputs", "(out puts", "(output s", "work ed", "wor ked", "[ strlen", "[str len", "app ointed", "appoint ed", "ĠH eg", "ĠHe g", "ĠAC PI", "( [^", "([ ^", "u ala", "ual a", "ua la", "_ tol", "_t ol", "_to l", "ĠR it", "ĠRi t", ". Payment", ".P ayment", ".Pay ment", "k owski", "Ġw almart", "Ġwal mart", "require ments", "ĠFIN SEQ", "_ BACKGROUND", "_BACK GROUND", "ĠOs borne", "( errorMessage", "(error Message", "Report ing", "Rep orting", "Ġa uctions", "Ġau ctions", "Ġauction s", "Ġcom bos", "Ġcomb os", "Ġcombo s", "ĠNot iced", "ĠNotice d", "_ oct", "_o ct", "_oc t", "Ġprim ero", "Ġprime ro", "Ġprimer o", "t aire", "ta ire", "_ hr", "_h r", "Ġм од", "Ġмо д", "Ġcontrad ictory", "Ġcontradict ory", "= \"@", "=\" @", "ach ines", "achine s", "achi nes", "(opt arg", "ĠP enguin", "ĠPeng uin", "ĠAb bas", "ĠAbb as", "Ġsub lime", "Ġpage able", "ĠDef ensive", "Ġdistinct ly", "ĠAutom atically", "ĠAutomatic ally", "Under standing", "Equality Comparer", "g ota", "go ta", "got a", "Ġ\" ::", "Ġ\": :", "Ġpul ver", "ĠB attles", "ĠBattle s", "ĠBatt les", "Ġun paralleled", "T CHA", "TC HA", "Ġconstr ued", "- aff", "-a ff", "Ġpre cursor", "Ġprec ursor", "-l fs", "Ġmad uras", "ĠD aisy", "ĠDa isy", "ĠDai sy", "ĠAr beits", "ĠArbeit s", ". Management", ".Man agement", "ĉ In", "ĉI n", "Ġro bes", "Ġrob es", "Ġrobe s", "Ġsp éc", "âĢľ (", "Ġm aternity", "Ġmat ernity", "ex tent", "ext ent", "Ġ Spacer", "ĠSp acer", "ĠSpace r", "ĠSpa cer", "Did Appear", "ĉ us", "ĉu s", ".getRequest Dispatcher", "( cols", "(c ols", "(co ls", "(col s", "Ġplum met", "ì ħ", "Ġ{ ĊĊĊĊ", "Ġ{Ċ ĊĊĊ", "Ġ{ĊĊ ĊĊ", "Ġ{ĊĊĊ Ċ", "é rica", "ér ica", "éri ca", "éric a", "Ġ Sizes", "ĠS izes", "ĠSize s", "ĠSi zes", "ĠSiz es", ". enum", ".e num", ".en um", ". Highlight", ".High light", "Ġ!! }ĊĊĊ", "Ġ? >ĊĊĊ", "Ġ?> ĊĊĊ", "Ġ?>Ċ ĊĊ", "Ġ?>ĊĊ Ċ", "W enn", "We nn", "Ġcl imax", "Ġclim ax", "Ġcli max", "Ġc rem", "Ġcr em", "Ġcre m", "_ that", "_t hat", "_th at", "[ âĢ¦", "_ domains", "_domain s", "_dom ains", "_RE PLY", "Ġcomp leta", "Ġcomple ta", "Ġcomplet a", "Ġcompl eta", "V EST", "VE ST", "VES T", "_ particle", "_p article", "_part icle", "Ġs op", "Ġso p", "Ġfatal ities", "impl ify", "imp lify", "ĠS KF", "ĠSK F", "Ġin fusion", "Ġinf usion", "ĠJ avier", "ĠJa vier", "Ġb allet", "Ġball et", "Ġbal let", "Ġam igo", "Ġami go", ". want", ".w ant", "Ġcol lagen", "Ġcoll agen", "Ġcollage n", "ĠLaw yer", ". Statement", ".St atement", ".State ment", ".Stat ement", ". rt", ".r t", "b aar", "ba ar", "End Point", "ĠB ek", "ĠBe k", "S HIP", "SH IP", "Ġpatri arch", "ĠA unt", "ĠAu nt", "_ TM", "_T M", "Ġ mÃŃn", "Ġm ÃŃn", "ĠmÃŃ n", "Ġm astered", "Ġmaster ed", "Ġma stered", "Ġmas tered", "Ġmast ered", "W XYZ", "WX YZ", "Ġes pos", "Ġesp os", "= logging", "=log ging", "Ġrighteous ness", "t orrent", "tor rent", "Ġ bst", "Ġb st", "Ġbs t", "_ CHAIN", "_CH AIN", "Ġout skirts", "( rotation", "(r otation", "(rot ation", "Ġ' .')", "Ġ'. ')", "Ġ'.' )", "igr ants", "igrant s", "+ lsi", "+l si", "ĠCC TV", "ĠCCT V", "_PH ASE", ". azure", ".a zure", "_ Process", "_P rocess", "_Pro cess", "v ae", "va e", "ĠT ropical", "ĠAn kara", "ĠAnk ara", "image View", "_RUN NING", "Ġ* )__", "Ġ*) __", "ế n", "( cli", "(c li", "(cl i", "sc atter", "Ġs che", "Ġsc he", "Ġsch e", "Reg istrar", "Registr ar", "Ġa iring", "Ġair ing", "Ġai ring", "Ġpy plot", "is ión", "isi ón", "/ customer", "/c ustomer", "/custom er", "Ġs implement", "Ġsim plement", "Ġsimple ment", "Ġsimp lement", "Ġsimpl ement", "Ġclass y", "Ġcl assy", "Ġclas sy", "ĠD WC", "ĠDW C", "ĠBas har", "ĠBash ar", "ĠDE VELO", "ĠV ick", "ĠVi ck", "ĠVic k", "a vail", "av ail", "ava il", "ĠH ö", "_ extend", "_ext end", "dr Fc", ".is NotBlank", "Ġpl ais", "Ġpla is", "| }Ċ", "Ġporn ofil", "Ġporno fil", "l abs", "la bs", "lab s", "Ġ haus", "Ġh aus", "Ġha us", "Ġorig inating", "Ġorigin ating", "Ġsurround s", "Ġ QUAL", "ĠQ UAL", "ĠQU AL", "m eg", "me g", "/ logger", "/log ger", "/lo gger", "[ obj", "[o bj", "Ġirres ponsible", "Ġ PublicKey", "ĠPublic Key", "H ONE", "HO NE", ": '/", ":' /", "i box", "ib ox", "ibo x", "ĠF Vector", "| {Ċ", "ata loader", "atal oader", "h awks", "hawk s", "H DR", "HD R", "Ġescal ation", "ĠPods Dummy", "e lite", "el ite", "eli te", "Ġpre sup", "Ġpres up", "C ached", "Cache d", "Ca ched", "> G", ". optimizer", ".opt imizer", ".optim izer", ".optimize r", "Ġ Visible", "ĠV isible", "ĠVis ible", "´ Ģ", "Ġ nen", "Ġn en", "Ġne n", "Ġ pcs", "Ġp cs", "Ġpc s", "Ġ Idle", "ĠI dle", "ĠId le", "[ Any", "[A ny", "Ġkey boards", "Ġkeyboard s", "ĠCOMP ONENT", "Ġtit anium", "Ġtitan ium", "( mut", "(m ut", "(mu t", "ĠLed ger", "Ġprosper ous", "etro fit", "_ LL", "_L L", "_ patient", "_p atient", "_pat ient", "Ġ pdata", "Ġp data", "Ġpd ata", "Ġ kontakte", "Ġkont akte", "Ġkontakt e", "S wipe", "Sw ipe", "Ġcheer ful", "ĠHond uras", "\" ][$", "\"] [$", "\"][ $", "Ġhem orrh", "\" :\"+", "\": \"+", "\":\" +", "Ġ leasing", "Ġle asing", "Ġinst alls", "Ġinstall s", "Ġinstal ls", "ĠP ax", "ĠPa x", "ĠLog istics", "ĠLogistic s", "Ġkin etic", "ĠP hon", "ĠPh on", "_ movement", "_m ovement", "_move ment", "_mov ement", "_mo vement", "ĉ bytes", "ĉbyte s", "Ġcin co", "ĠMad ness", "\" )+", "\") +", "Ġ JE", "ĠJ E", "_ ij", "_i j", "Scene Manager", "ĠB ust", "ĠBus t", "ĠBu st", "p test", "pt est", "pte st", "a ea", "ae a", "Ġb esser", "Ġbes ser", "ÃŃ g", "д ин", "ди н", "( tasks", "(t asks", "(task s", "(\" (\"", "(\"( \"", "set Type", "( outfile", "(out file", "ĉ reset", "ĉres et", "ĉre set", "Ġ ARC", "ĠA RC", "ĠAR C", "Ġmús ica", "ĠSh elf", "ĠShe lf", "ĠShel f", "Ġmin Y", "p ch", "pc h", "Ġwe iber", "Ġwei ber", "iss or", "Ġtr ouve", "Ġtro uve", "Ġtrou ve", "ĉ Button", "ĉB utton", "Ġre generated", "Ġreg enerated", "Ġregenerate d", "Å £i", "Å£ i", "im achinery", "b locking", "bl ocking", "block ing", ".data Tables", "_ frac", "_f rac", "_fr ac", "ĠAdv antage", ".visit Method", "éĩį æĸ°", "Ġextra pol", "Ġextr apol", "Ġte asing", "Ġtea sing", "Ġteas ing", "ĠH itch", "ĠHit ch", "ĠGe ek", "ĠGee k", "E SCO", "ES CO", "ESC O", "Ġ wich", "Ġw ich", "Ġwi ch", "ĉ ax", "ĉa x", "_ decor", "_de cor", "_dec or", "Ġscreen Width", "ĠSo phia", "ĠSoph ia", "F orgot", "For got", "Forg ot", ". uni", ".un i", ".u ni", "ĠVen ture", "ĠVent ure", "_ collision", "_c ollision", "_coll ision", "Ġlaw maker", "( Edit", "(E dit", "b lers", "ble rs", "bl ers", "bler s", "Ġ getNext", "Ġget Next", "âĢĶ you", "Media Player", "ĠH orde", "ĠHor de", "ĠCongress man", "obs ervations", "observ ations", "observation s", "ĉ property", "ĉp roperty", "ĉprop erty", "Ġ< --", "Ġ<- -", "Created At", "u byte", "ub yte", "uby te", "Ġquar antine", "Ġdist ressed", "Ġdistr essed", "Ġdistress ed", "_A PB", "_AP B", "ĠGood man", "ãĤ «", "Ġrecom end", "_ PRINTF", "_PRINT F", "D ONE", "DO NE", "DON E", "Bind able", "r strip", "rs trip", "rst rip", "cent aje", "Ġ Unexpected", "ĠUn expected", "ĠS CHOOL", "ĠProfessional s", "ĠProfession als", "ĠGPU s", "ĠGP Us", "L esson", "Le sson", "Les son", "Less on", "Ex clusive", "Ġat rav", "Ġatr av", "ĠD ank", "ĠDan k", "ĠDa nk", "ĠLaw yers", "ĠLawyer s", "ĠWal ton", "ĠWalt on", "> []", ">[ ]", "Ġa loud", "Ġal oud", "Ġalo ud", "=\" ../../../", "=\"../ ../../", "=\"../../ ../", "Ġdeb ating", "ĠA VG", "ĠAV G", "_V OL", "_VO L", "/ cgi", "/c gi", ". deg", ".d eg", ".de g", ": g", ".Info f", ".Inf of", "Measure Spec", ". song", ".s ong", ".so ng", ".son g", "m tree", "mt ree", "ul ls", "ull s", "J ordan", "ĠC overs", "ĠCo vers", "ĠCover s", "ĠCov ers", "ĠCove rs", "Ġattrib utable", "Ġj edis", "Ġje dis", "Ġjed is", "iat rics", "iatric s", "Ġrot terdam", "Ġ meld", "Ġm eld", "Ġme ld", "Ġmel d", "Ġ ContentType", "ĠContent Type", "Ġman tle", "Ġmant le", "Ġ alice", "Ġa lice", "Ġal ice", "Ġali ce", "_ duplicate", "_d uplicate", "_dup licate", "/ Internal", "Ġ filesize", "Ġfile size", "Ġfiles ize", "ĉ fire", "ĉf ire", "ĉfi re", "r ese", "re se", "res e", "on dere", "ond ere", "onder e", "onde re", "Ġfamiliar ity", "ĠC rest", "ĠCr est", "ĠCre st", "ĠCres t", "Ġk arma", "Ġkar ma", "Ġtor ino", "Ġm esa", "Ġme sa", "Ġmes a", "/ temp", "/t emp", "Ġc hir", "Ġch ir", "Ġchi r", "Ġ Overflow", "ĠOver flow", "Ġten emos", "u nik", "un ik", "uni k", "N EXT", "NE XT", "A lle", "Al le", "All e", "Ġn xt", "Ġnx t", "M art", "Mar t", "Ma rt", "Ġ atl", "Ġa tl", "Ġat l", "Ġperiod o", "Ġperi odo", "_ you", "_y ou", "Ġ} )).", "Ġ}) ).", "Ġ})) .", "int estinal", ".Adapter View", "Ġhes itant", "Ġcompar atively", "Ġcomparative ly", ". UInt", ".U Int", ".UI nt", "( viewModel", "(view Model", "Ġsang at", "Ġ Responsive", "ĠRes ponsive", "ĠRespons ive", "ĠZ ack", "ĠZa ck", "ĠZac k", "â ħ", "J AVA", "JA VA", "ĠFull er", "ĠFu ller", "ĠFul ler", "Ġ âĿ¤", "ĠâĿ ¤", ". Consumer", ".Con sumer", ".Cons umer", "Ġ ank", "Ġa nk", "Ġan k", "Ġre actors", "Ġreact ors", "Ġreactor s", "f uck", "fu ck", "_ rat", "_r at", "_ra t", "Ġsession Factory", "_ backward", "_back ward", "Ġscram bled", "Ġscramble d", "ĉ th", "ĉt h", "Ġins ensitive", "Ġch amps", "Ġcha mps", "Ġcham ps", "Ġchamp s", "Ġ nginx", "Ġng inx", "Ġcon hec", "Ġconhe c", "ĠJ asper", "ĠJas per", ". fm", ".f m", "Strict Equal", "ach sen", "achs en", "- Nov", "-N ov", "-No v", "l assen", "lass en", "las sen", ". integration", ".int egration", "( lbl", "(l bl", "Com pose", "Comp ose", "ĠF on", "ĠFo n", "à ļ", "Gr atis", "ĠL ime", "ĠLim e", "ĠLi me", "ĠAdapter View", "Ġpoison ed", "Ġpois oned", "anch ors", "anchor s", "设 计", "'] ?>\"", "']?> \"", "Ġpro cur", "Ġproc ur", "It aly", ". MONTH", ".MON TH", "ĠL UA", "ĠLU A", "ĠLith uania", "ĠHe ads", "ĠHead s", "_CH UNK", "ĠP USH", "ĠPU SH", "ĠPUS H", "Aspect Ratio", "Ġ weg", "Ġw eg", "Ġwe g", "Ġv ids", "Ġvi ds", "Ġvid s", "ĠW ein", "ĠWe in", "ĠWei n", "ĉ INT", "ĉI NT", "ĉIN T", "session Id", "Ind ustry", "Ġden ounced", "JK LM", "ĠVan essa", ". Identifier", ".Id entifier", "p ropri", "pro pri", "prop ri", "Ġ иг", "Ġи г", "Ġté cn", "Ġtéc n", "Ġm osaic", "Ġmos aic", "Stream Reader", "- Th", "-T h", "f orth", "for th", "fort h", "Ġad herence", "Ġadher ence", "Ġadhere nce", "b ate", "ba te", "bat e", "Ġkn ights", "Ġknight s", "s ounds", "so unds", "sound s", "sou nds", "Ġs alle", "Ġsa lle", "Ġsal le", "O MET", "OM ET", "OME T", "ãĤ¹ ãĥĪ", "- tm", "-t m", "ĠR he", "ĠRh e", ".File OutputStream", "åĪĨ ç±»", "Ġ ENG", "ĠE NG", "ĠEN G", "h oliday", "hol iday", "Ġ Congratulations", "ĠCong ratulations", ") (Ċ", ")( Ċ", "Ġaggregate s", "Ġaggreg ates", "H OOK", "HO OK", "e wire", "ew ire", "Sen ator", "Ġembed dings", "Ġembedding s", "e py", "ep y", "( COM", "(C OM", "Ġrob ber", "ä ter", "ät er", "w ang", "wa ng", "wan g", "_ teacher", "_t eacher", "_te acher", "Ġresent ment", "Ġlett uce", "er reur", "err eur", "erre ur", "( ic", "(i c", "ĠT actical", "ĠTac tical", "Ġ Contracts", "ĠCon tracts", "ĠContract s", "ĠContr acts", "Ġm ænd", "Ġsit ios", "Ġsiti os", "Ġsitio s", "Ġbast ante", "Ġnue vos", "Ġnuevo s", "ĉN drFc", "Ġprivate Key", "uc ch", "ucc h", "MM dd", "Ġ è¾ĵåĩº", "Ġè¾ĵ åĩº", "um ba", "umb a", "@ foreach", ": \");ĊĊ", ":\" );ĊĊ", ":\");Ċ Ċ", ":\") ;ĊĊ", "Ġslip pery", "ĠKey stone", "ĠKe ystone", "ĠKeys tone", "Ġpione ering", "Ġpioneer ing", "_ triangle", "_t riangle", "_tr iangle", "_tri angle", "( \"Ċ", "(\" Ċ", "ĉ ĉĉĉĉĉĉĉĠĠ", "ĉĉ ĉĉĉĉĉĉĠĠ", "ĉĉĉĉ ĉĉĉĉĠĠ", "ĉĉĉ ĉĉĉĉĉĠĠ", "ĉĉĉĉĉ ĉĉĉĠĠ", "ĉĉĉĉĉĉ ĉĉĠĠ", "ĉĉĉĉĉĉĉĉ ĠĠ", "ĉĉĉĉĉĉĉ ĉĠĠ", "ĉĉĉĉĉĉĉĉĠ Ġ", "ĠInt ervention", "ĠInter vention", "S CI", "SC I", "Ġc JSON", "Ġter minating", "Ġterm inating", "Ġtermin ating", "ë ¹Ħ", "ë¹ Ħ", "Ġbaby s", "Ġbab ys", "Sub set", "Ġ ë¡", "Ġë ¡", "Ġseu lement", "Ġseul ement", "Ġseule ment", "Ġm uestra", "Ġmue stra", "En tre", "Ent re", "Entr e", "以 ä¸Ĭ", "n go", "ng o", "\" bytes", "QR ST", "QRS T", "Ġy pos", "Ġyp os", "person a", "pers ona", "Ġ Deploy", "ĠDe ploy", "ĠDep loy", "c ee", "ce e", "Ġ à®", "Ġà ®", ". goal", ".go al", "Ġhabit ats", "Ġhabitat s", "Ġ isAdmin", "Ġis Admin", "Ġexplo iting", "Ġexploit ing", "Ġvent il", "Ġven til", "ĠB alls", "ĠBall s", "ĠBal ls", "ا ب", "Ø§Ø ¨", "Ġmind fulness", "Ġmindful ness", "( kwargs", "(k wargs", "Ġre sembling", "Ġresembl ing", "Ġch oir", "Ġcho ir", "Ġon BackPressed", "ĠSEC URITY", "/ gtest", "/g test", "Ġjust ices", "Ġjustice s", "Ġinteger Value", "b lah", "bl ah", "bla h", "ĠA im", "ĠAi m", "_ finalize", "_final ize", "k eh", "ke h", "ĠComplex ity", "Ġaug ust", "get ElementsByTagName", "Ġp reach", "Ġpr each", "Ġpre ach", "Ġpron unciation", "Ġ Trash", "ĠTr ash", "ĠTra sh", "- percent", "-per cent", "_PR IV", "_PRI V", "ĠH unts", "ĠHun ts", "ĠHu nts", "ĠHunt s", "ĠC urse", "ĠCur se", "u ellen", "ue llen", "uel len", "uelle n", "uell en", "Ġheavy weight", "X i", "ĉ selected", "ĉselect ed", "ĉse lected", "ĠMcC oy", "å¼Ĥ 常", "| =Ċ", "|= Ċ", "ĠBattle field", "Item Image", "Ġded uctions", "Ġdeduct ions", "Ġdeduction s", "ĠElement al", "ĠEle mental", "ĠElem ental", "( ));//", "() );//", "()) ;//", "()); //", "ĠBur k", "ĠBu rk", "} )čĊčĊ", "}) čĊčĊ", "})čĊ čĊ", "sw ift", "/ function", "/f unction", "Us ually", "Usu ally", "_ St", "_S t", "_fe ats", "_feat s", "Ġ IsValid", "ĠIs Valid", "Ġz ad", "Ġza d", "Image Context", "Ġ classname", "Ġclass name", "Ġdon ner", "Ġdonne r", "Ġdonn er", "Ġ-- >ĊĊĊ", "Ġ-->Ċ ĊĊ", "Ġ--> ĊĊĊ", "Ġ-->ĊĊ Ċ", "Ġmotor cycles", "Ġmotorcycle s", "+' /'+", "+'/ '+", "Ġ setBackground", "Ġset Background", "\\ CMS", "\\C MS", ". AllArgsConstructor", ".All ArgsConstructor", "ĠLex ington", ". examples", ".ex amples", ".example s", ".exam ples", "ĠP urs", "ĠPur s", "ĠPu rs", "Push Matrix", "Ġ================================================= =============", ".add Target", "p ora", "por a", "po ra", "Full screen", "Ġgo of", "Ġgoo f", "h len", "hl en", "hle n", "ä ge", "ĠC URL", "ĠCUR L", "ĠCU RL", "Ġ Interesting", "ĠInter esting", "ĠInterest ing", "Ġretrie ves", "Ġretrieve s", "Ġretr ieves", "_ Obj", "_O bj", "in ness", "inn ess", "inne ss", "- ----ĊĊ", "-- ---ĊĊ", "---- -ĊĊ", "--- --ĊĊ", "----- ĊĊ", "-----Ċ Ċ", ".t sv", ".ts v", "( IM", "(I M", "ĠBr aves", "ĠBra ves", "ĠBrave s", "_ ISR", "_I SR", "_IS R", "o sti", "os ti", "ost i", "á» ĵ", "ĠEx terior", "ĠExt erior", "ĠCourt ney", "Ġresid ues", "Ġresidue s", "T ier", "Ti er", ".* ;čĊčĊ", ".*;čĊ čĊ", ": black", ":b lack", "web View", "\" path", "Ġm asa", "Ġma sa", "Ġmas a", "] !='", "]!= '", "Ġ Matching", "ĠM atching", "ĠMat ching", "ĠMatch ing", "d ur", "du r", "J vm", "= context", "_ RING", "_R ING", "Ġpro ponents", "Ġprop onents", "ĠQString Literal", "Ġ inflate", "Ġin flate", "Ġinf late", "Ġinfl ate", "< Float", " \">čĊ", "Ġ?>\" >čĊ", "Ġ?>\"> čĊ", "_C OST", "_CO ST", "i linear", "il inear", "ili near", "iline ar", "ilin ear", "Ġ Workspace", "ĠWork space", "ĠWorks pace", "Ġs pel", "Ġsp el", "Ġspe l", "ag ogue", "ago gue", "agog ue", "ĠMillenn ium", "ĠPop ulate", "Ġ nid", "Ġn id", "Ġni d", ".parse Color", "S olar", "So lar", "Sol ar", "ĠG ad", "ĠGa d", "Ġ ì¤ij", "Ġì ¤ij", "Ġì¤ ij", "ĠK amp", "ĠKa mp", "ĠKam p", "ĉ rm", "ĉr m", "Ġb enz", "Ġbe nz", "Ġben z", "Ġ Honestly", "ĠH onestly", "ĠHonest ly", "Ġelectro de", "Ġelectr ode", "ĠPr airie", "ĠPra irie", "Ġ PROFILE", "ĠPRO FILE", "ĠPROF ILE", "ĠOri ental", "ĠOrient al", "ĠO LED", "ĠOL ED", "/cop yleft", "awa ii", "awai i", "( products", "(product s", ") \\<", ")\\ <", "- created", "-c reated", "-create d", "-cr eated", ".Many ToMany", "\" How", "\"H ow", "Ġв Ñĭп", "ĠвÑĭ п", "Ġmitochond rial", "_ testing", "_t esting", "_test ing", "( created", "(c reated", "(create d", "(cr eated", "Ġ getField", "Ġget Field", "_E VAL", "_EV AL", "] .\"", "]. \"", "ĠF SM", "ĠFS M", "ĠR ita", "ĠRi ta", "ĠRit a", "Ġ åıĤæķ°", "Ġåı Ĥæķ°", "ĠåıĤ æķ°", "Ġc ôt", "Ġcô t", "ĠIns ight", "ĉ mysqli", "ĉm ysqli", "ĉmysql i", "_ timing", "_t iming", "_tim ing", "_ti ming", "I DO", "ID O", ") ))))Ċ", ")) )))Ċ", "))) ))Ċ", ")))) )Ċ", "CO VERY", "COVER Y", ". imag", ".i mag", ".im ag", "C DF", "CD F", "l ust", "lu st", "lus t", "i ckt", "ic kt", "ick t", "_ FP", "_F P", ". ','", ".' ,'", ".', '", "g cc", "gc c", "Ġkur z", "Ġku rz", "_p wm", "_pw m", "Ġodp owied", "Ġ Barrier", "ĠBar rier", "ĠBarr ier", "/************************************************************************ ***Ċ", "p ak", "pa k", "- Israel", "ĠRut gers", "Ġselected Item", "ĠRam irez", "F arm", "Far m", "Fa rm", "Ġcal endars", "Ġcalendar s", "Ġcalend ars", "g zip", "gz ip", "Ġblock buster", "ĠPly mouth", "çľ Į", "res ponses", "response s", "respons es", ".Dialog Interface", "- grand", "-g rand", "-gr and", "Ġ getSource", "Ġget Source", "ĠgetS ource", "Ġdej tings", "Ġdejting s", "Ġt ieten", "Ġti eten", "Ġtie ten", "Ġcondem nation", "Ġcondemn ation", "Ġcontin uar", "Ġcontinu ar", "Ġcontinua r", ".Mock Mvc", "/ english", "Ġ MediaPlayer", "ĠMedia Player", "com puted", "comp uted", "compute d", "comput ed", "ĠCl ippers", "ĠClip pers", "ĠCli ppers", "( delegate", "(de legate", ". Slf", ".S lf", "Ġ ë¡ľ", "Ġë¡ ľ", "ĠT ide", "ĠTi de", "Ġih rem", "Ġihr em", "Ġihre m", "ĠW an", "ĠWa n", "Ñĥ ÑİÑī", "ÑĥÑİ Ñī", "} ><", "}> <", "Disc ussion", "Discuss ion", "Ġw atts", "Ġwat ts", "Ġwatt s", "- minus", "-m inus", "-min us", "ĠJul iet", "ĠJulie t", "ĠJuli et", "éĽ ħ", "Ġcon cluding", "Ġconcl uding", "and scape", "ands cape", "Ġúlt ima", "ĠD ERP", "ĠDE RP", "ĠDER P", "Ġsign Up", "ĠSecond ly", "W AIT", "WA IT", "l ds", "ld s", ". callbacks", ".call backs", ".callback s", "( hour", "(h our", "im ators", "ima tors", "imator s", "imat ors", "vol ent", "A AF", "AA F", "e driver", "ed river", "ĠMath ematic", "< Tuple", "'", "Ġ/> '", "{ j", "_AB ORT", "E ther", "Et her", "Eth er", "Ġeduc ator", "Ġpreca ution", "Ġfinger tips", "Ġfingert ips", "get Var", "cam atan", "- debug", "-de bug", "ĠR AF", "ĠRA F", "[ arg", "[a rg", "Ġr aced", "Ġrace d", "Ġrac ed", "Ġra ced", "Ġts unami", ".f link", ".fl ink", "Ġgl yc", "Ġgly c", "u ko", "uk o", "Ġ Multiply", "ĠM ultiply", "ĠMulti ply", "ĠMultip ly", "Ġre distribution", "Ġred istribution", "Ġredistrib ution", "Ġredis tribution", "A GO", "AG O", "Ġ Routine", "ĠR outine", "ĠRout ine", "Ġ opr", "Ġo pr", "Ġop r", "( lower", "(l ower", "(low er", "(lo wer", "ĠFun ktion", "ĠFunk tion", ". dk", ".d k", "Ġ egt", "Ġe gt", "Ġeg t", "_B ASIC", "sys call", "ĠL SD", "ĠLS D", "Ġ Duplicate", "ĠD uplicate", "ĠDup licate", "_ sell", "_s ell", "_se ll", "_sel l", "Ġerror Handler", "_ ips", "_i ps", "_ip s", "Ġ erv", "Ġe rv", "Ġer v", "an nie", "ann ie", "anni e", "(resource Name", "Ġbott led", "Ġbottle d", "Ġcraw ling", "Ġcrawl ing", "e gment", "eg ment", ".set Tag", "Ġ rss", "Ġr ss", "Ġrs s", "ĠQu arry", "ĠQuar ry", "_ exact", "_ex act", ". jwt", ".j wt", "ĠBo ards", "ĠBoard s", "o pi", "op i", "Ġn asal", "Ġna sal", "Ġnas al", "Ġ XYZ", "ĠX YZ", "ĠXY Z", ". ud", ".u d", "North ern", "Nor thern", "Ġact ivating", "Ġactiv ating", "e dx", "ed x", "ov ah", "ova h", "Ġ indx", "Ġin dx", "Ġi ndx", "Ġind x", "Alert Dialog", "Ġt ienes", "Ġti enes", "Ġtie nes", "Ġtiene s", "an nya", "ann ya", "anny a", "_ pan", "_p an", "_pa n", "( decimal", "(d ecimal", "(de cimal", "(dec imal", ". Dict", ".D ict", ".Di ct", "Ġsubsidi aries", "Product Name", "F ew", "Fe w", "d ato", "da to", "dat o", "od ied", "odi ed", "odie d", "- under", "-un der", "-u nder", "Ġ ê²ĥ", "Ġê² ĥ", "çīĪ æľ¬", "at ism", "atis m", "ati sm", "[ Math", "[M ath", ". '<", ".' <", "( infile", "(in file", "Ġde notes", "Ġden otes", "Ġdenote s", "$ class", "$c lass", "_SEC URITY", "Ġsew age", "m elon", "me lon", "mel on", "( Character", "(Char acter", "/ github", "/g ithub", "/git hub", "Ġgl aring", ". Guid", ".G uid", "_ sparse", "_s parse", "_sp arse", "Ġ Margin", "ĠM argin", "ĠMar gin", "ĠMarg in", "_ dns", "_d ns", "_dn s", "Ġme iner", "Ġmein er", "Ġmeine r", "Ġleft ist", "ĉ loc", "ĉl oc", "a bytes", "aby tes", "abyte s", "Ġequipment s", "Ġequip ments", "ex po", "exp o", "ĠSom erset", "E K", "æį ¢", "Ġlect urer", "Ġlecture r", "Ġmem iliki", "æł ¸", "ç´ ł", "p ron", "pr on", "pro n", ": pointer", "b orrow", "bor row", "ĠProt ective", "ĠProtect ive", "_ cf", "_c f", "Ġ ÐķÑģли", "ĠÐķ Ñģли", "b pp", "bp p", "' ;ĊĊĊĊ", "';Ċ ĊĊĊ", "';ĊĊ ĊĊ", "'; ĊĊĊĊ", "';ĊĊĊ Ċ", "at urally", "atur ally", "atural ly", "_ NAV", "_N AV", "Ġpe ptide", "> d", "Ġ ifstream", "Ġif stream", "Ġi fstream", "_FACT ORY", "_FACTOR Y", "' );//", "') ;//", "'); //", "j oined", "join ed", "jo ined", "m ong", "mon g", "mo ng", "Ġtime spec", "Ġtimes pec", "Ġdest abil", "Ġdesta bil", "Ġ autop", "Ġaut op", "Ġauto p", "Ġau top", "- limit", "-l imit", "-li mit", "public ation", "pub lication", "ĠD enn", "ĠDe nn", "ĠDen n", ". Memory", ".M emory", "( skb", "(s kb", "(sk b", "ĠAna heim", "_RETURN TRANSFER", "o ueur", "ou eur", "( _('", "(_ ('", "l egt", "le gt", "leg t", "ist ingu", "isting u", "ĉ priv", "ĉp riv", "ĉpr iv", "Ġredirect s", "M t", "Ġal leen", "Ġall een", "Ġalle en", "Ġ PointF", "ĠPoint F", "Ġ omin", "Ġo min", "Ġom in", "Ġc itt", "Ġcit t", "Ġci tt", "ĠT age", "ĠTag e", "ĠTa ge", "ĠW alls", "ĠWall s", "ĠWal ls", "á» ī", "Ġoccup ying", "Ġoccupy ing", "x BF", "xB F", "r angle", "ra ngle", "ran gle", "rang le", "Ġrel ational", "Ġrelation al", "Ġrelat ional", "- org", "-o rg", "-or g", "Ġ jpg", "Ġj pg", "Ġjp g", "- derived", "Ġmal function", "ĠB enson", "ĠBen son", "( scroll", "(s croll", "(sc roll", "Ġ XD", "ĠX D", "H oly", "Ho ly", "Hol y", "( commands", "(command s", "(comm ands", "Ġt ipping", "Ġti pping", "Ġtip ping", "Ġpr imitives", "Ġprim itives", "Ġprimitive s", "Ġsex le", "Call Check", "Ġ MASTER", "ĠM ASTER", "ĠMA STER", "ĠMAS TER", "_ TEAM", "_TE AM", ".setRequest Header", "_ specs", "_sp ecs", "_spec s", "Ġs erge", "Ġser ge", "Ġserg e", ". Master", ".M aster", ".Ma ster", "Ġ ims", "Ġi ms", "Ġim s", ".Spring BootTest", "pay pal", "ĠW ANT", "ĠWA NT", "ĠWAN T", ". Inst", ".I nst", ".In st", "ĠCar pet", "ĠCarp et", "Ġwrong ly", "( $('.", "($ ('.", "($( '.", "($(' .", "Ġ bild", "Ġb ild", "Ġbi ld", "Ġbil d", ". Roll", ".R oll", "ĠU rb", "ĠUr b", "- can", "-c an", "-ca n", "ãģı ãģłãģķãģĦ", "ãģıãģł ãģķãģĦ", "olib eral", " čĊčĊ", "Ġ-->čĊ čĊ", "ĠMa hm", "ĠMah m", "} \";ĊĊ", "}\" ;ĊĊ", "}\";Ċ Ċ", "Ġ dq", "Ġd q", "ĠPublish ers", "ĠPublisher s", "ĠA mpl", "ĠAm pl", "ĠAmp l", "ĠDaniel le", "ĠDani elle", "Ġ tern", "Ġt ern", "Ġte rn", "Ġter n", "èµ ·", "no ÅĽÄĩ", "e in", "ei n", "ĠAsync Storage", "u nger", "un ger", "ung er", "unge r", "ro uw", "rou w", "Ġsc issors", "/ assert", "/as sert", ". bucket", ".b ucket", "/ archive", "/a rchive", "/arch ive", "_ Man", "_M an", "Ġint oler", "Ġinto ler", "Ġ ()=>", "Ġ( )=>", "Ġ() =>", "Ġ ÐĴÑĭ", "ĠÐĴ Ñĭ", "Ġs ai", "Ġsa i", ". xy", ".x y", ". \"čĊ", ".\" čĊ", "Ġur inary", "e sub", "es ub", "IST ICS", "ISTIC S", "Ġ κ", "ĠÎ º", "Ġcompl iments", "Ġcompliment s", "Ġtypings Japgolly", "i har", "ih ar", "Exp ansion", "ĠS erving", "ĠSer ving", "ĠServ ing", "_ students", "_st udents", "_student s", "ĠX BOOLE", "( il", "(i l", "Ġ ì²ĺ", "Ġì² ĺ", "Ġj ó", "( tol", "(t ol", "(to l", "( JS", "(J S", "ĉ CG", "ĉC G", "Ġ DRAW", "ĠD RAW", "ĠDR AW", "t wig", "tw ig", "Ġo at", "Ġoa t", "_ smooth", "_sm ooth", "ĠC SL", "ĠCS L", "Ġo sob", "Ġos ob", "Ġens uing", "Ġb anker", "Ġbank er", "Ġban ker", "ĠBack pack", "_ ping", "_p ing", "_pin g", "_pi ng", "Ġ wishlist", "Ġw ishlist", "Ġwish list", "= ax", "=a x", "ĉ ĠĠĠĊ", "ĉĠĠĠ Ċ", "ĉĠ ĠĠĊ", "ĉĠĠ ĠĊ", "Dis ney", "ste ady", "stead y", "\" >%", "\"> %", "Ġproph ets", "Ġprophet s", "Ġ ZX", "ĠZ X", "Ġminimal ist", "Ġminim alist", ". PLAIN", ".PL AIN", "Se attle", "Seat tle", ". ordinal", "Ġ PIPE", "ĠPI PE", "Ġret orna", "Ġretorn a", "Ġj ugador", "Ġjug ador", "ĠB ret", "ĠBr et", "ĠBre t", "ĠâĶ ľ", "Ġp lush", "Ġpl ush", "Ġplus h", "Ġplu sh", "UL ATOR", "ULA TOR", "S orting", "Sort ing", ".grid y", ".gr idy", "ect omy", "_ activ", "_ac tiv", "_act iv", "r ack", "ra ck", "rac k", "Inter active", "ĠAntar ctica", "ĠAntarctic a", "Ġv engeance", "en so", "ens o", "_ known", "_k nown", "up plier", "upp lier", ". Modules", ".Mod ules", ".Module s", "ĠConnection State", "éļ IJèĹı", "éļIJ èĹı", "@ FindBy", "Ġ placer", "Ġpl acer", "Ġplace r", "Ġplac er", "Ġpla cer", "\\ model", "< ()>", "<( )>", ".is Successful", ".isSuccess ful", "- good", "-g ood", "-go od", "b z", "ĠDr aco", "ĠDra co", "Ass istant", "- extra", "-ex tra", "-ext ra", "аб лиÑĨ", "Ġhyp ocrisy", "Ġt st", "Ġts t", "ĠA gr", "ĠAg r", "$ txt", "$t xt", "Ġlog istic", "l icensed", "lic ensed", "license d", "ĠH of", "ĠHo f", "Ġ tat", "Ġt at", "Ġta t", "( iv", "(i v", "Ġint oxic", "Ġinto xic", "Ġintox ic", "post Id", "_ strike", "_st rike", "_str ike", "Ġhum iliation", "Ġhumili ation", "p codes", "pc odes", "\" sync", "\"s ync", "( recipe", "(rec ipe", "+ N", "r ente", "re nte", "ren te", "rent e", "ĉ Client", "ĉC lient", "ycop g", "ĠZur ich", "ĠZu rich", "Ġ Profiles", "ĠPro files", "ĠProf iles", "ĠProfile s", "C ountries", "Count ries", "Ġp ict", "Ġpi ct", "Ġpic t", "Ġroll out", "requ encies", "Ġp atched", "Ġpat ched", "Ġpatch ed", "Ġcar tridges", "Ġcartridge s", "Ġsh ading", "Ġsha ding", "J ar", "Ja r", "Ġsalv age", "ĠT axes", "ĠTax es", "ĠTa xes", "Ġstand by", "ap oran", "apor an", "apo ran", "E igen", ". angular", "Ġ Nested", "ĠN ested", "ĠNe sted", "ĠNest ed", "ĠNes ted", "ä º«", "äº «", "Ġ isVisible", "Ġis Visible", "ĠDw ight", "_BR ANCH", ". Delay", ".D elay", ".De lay", "Ġk end", "Ġke nd", "Ġken d", "Ġfacilit ated", "Ġfacilitate d", "Ġfacil itated", ". flatMap", ".flat Map", "Ġs anta", "Ġsan ta", "Ġsant a", "ĉ Send", "ĉS end", "/ messages", "/m essages", "/message s", "Ġof Type", "ĉ swap", "ĉs wap", "ĉsw ap", "# plt", "ĠTur ks", "ĠTurk s", "N ES", "NE S", "Ġprogress ively", "Ġprogressive ly", "ĠRes idence", "Ġ TREE", "ĠT REE", "ĠTR EE", "ĠTRE E", "Ġn oen", "Ġno en", "Ġnoe n", "d io", "di o", "Ġ nelle", "Ġn elle", "Ġne lle", "Ġnel le", "Ġnell e", "Ġso gar", "Ġsog ar", "i tti", "it ti", "itt i", "week ly", "Ġambigu ity", "_ Settings", "_S ettings", "_Set tings", "W are", "War e", "Wa re", ". neo", ".n eo", ".ne o", "_ DST", "_D ST", "_DS T", "Ġ æĸ¹", "Ġæĸ ¹", "p rep", "pr ep", "pre p", "l obby", "lob by", "@ email", "/ movie", "/m ovie", "Ġfun kc", "Ġfunk c", "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", "ÂŃ s", "Ġguard ians", "Ġguardian s", "- pos", "-p os", "-po s", "Ġconfig uring", "Ġconfigur ing", "ĠC PS", "ĠCP S", "ĠD eus", "ĠDe us", "Ġvidé os", "Ġvidéo s", "_ empresa", "_emp resa", "Ġsl apped", "Ġsla pped", "Ġslap ped", "< Model", "',Ċ", "\"> ',Ċ", "\">' ,Ċ", "\">', Ċ", "_X DECREF", "ĠBuzz Feed", "_M ARGIN", "P LOY", ". small", ".s mall", ".sm all", "Ġm imeType", "Ġmime Type", "Ġh olog", "Ġho log", "Ġhol og", "ĉ camera", "ĉc amera", "l ias", "li as", "lia s", "Ġsusp ense", "od ynam", "ody nam", "odyn am", "b au", "ba u", "Ġgrave yard", "_ named", "_n amed", "_name d", "_na med", "\": \"'", "\":\" '", "Ġ ************************************************", "Ġ******************************** ****************", "Ġ******** ****************************************", "Ġ**************** ********************************", "Ġ************************ ************************", "Ġ**************************************** ********", "Ġgame Over", "Ġ LENGTH", "ĠLE NGTH", "ĠLENG TH", "ĉ screen", "ĉs creen", "ĉsc reen", "Ġdo InBackground", "_ dependencies", "_depend encies", "_dep endencies", "Ġ rtc", "Ġr tc", "Ġrt c", "/ up", "/u p", "_ ROM", "_R OM", "_RO M", "H all", "Ha ll", "Hal l", "Ġdef iciencies", "( te", "(t e", "' #", "_ equiv", "_e quiv", "_equ iv", "Ġpre order", "ĠA xe", "ĠAx e", "о мÑĥ", "ом Ñĥ", ".send File", "Ġ filt", "Ġf ilt", "Ġfil t", "Ġfi lt", "Ġ Limits", "ĠL imits", "ĠLim its", "ĠLi mits", "ĠLimit s", "ĠCaval iers", ". discount", ".dis count", ".disc ount", "âĨ IJ", "ĠW it", "ĠWi t", "QRST UV", "Ġ ij", "Ġi j", "Ġt egen", "Ġte gen", "Ġteg en", "Ġ :\",", "Ġ: \",", "Ġ:\" ,", "diff iculty", "p unkt", "pun kt", "punk t", "ĠEm ails", "ĠEmail s", "ch lor", "chl or", "( fun", "(f un", ". Uint", ".U int", ".Ui nt", "ĠSt all", "ĠSta ll", "_ verified", "_ver ified", "u D", "File Type", "Ġple asures", "Ġpleasure s", "Ġpleas ures", "Ġjud iciary", "Ġs ham", "Ġsh am", "Ġsha m", "i pur", "ip ur", "_ PLUS", "_PL US", "of fers", "off ers", "offer s", "( foo", "(f oo", "_ GT", "_G T", "ĉ core", "ĉc ore", "EN TION", "ENT ION", "ĠLib eration", "ĠLiber ation", "Command Line", "_ department", "_de partment", "_dep artment", "_depart ment", ". Ar", ".A r", "_ neighbor", "_ne ighbor", "Ġ Submitted", "ĠSub mitted", "ĠSubmit ted", "Ġ Ċ", "\"> -->Ċ", "Ġdro its", "Ġdroit s", "Ġhomosexual s", "Ġab duction", "ĉ widget", "ĉw idget", "$ headers", "$header s", "ĠD AR", "ĠDA R", "Ġf la", "Ġfl a", "th reat", "thr eat", "Ġl ouis", "Ġlo uis", "Ġlou is", ". GetProperty", ".Get Property", "\" Just", "( frames", "(f rames", "(frame s", "(fr ames", "r yo", "ry o", "prof ession", "| i", "íķ´ ìĦľ", "( sv", "(s v", "Ġun recognized", "I onic", "Io nic", "Ion ic", "F ashion", "Screen State", "Ġ Incoming", "ĠIn coming", "ĠInc oming", "Not Nil", "Ġsyn cing", "Ġsync ing", "e mie", "em ie", "emi e", "Ġth ermo", "Ġther mo", "Ġtherm o", "_ procs", "_pro cs", "_pr ocs", "_proc s", "Ġincons istency", "Ġinconsist ency", "rel igious", ". mj", ".m j", "Ġperson n", "Ġpers onn", "Ġperso nn", "Ġmoment os", "Ġmomento s", "or arily", "Ġ æĬ", "Ġæ Ĭ", "_ne urons", "Ill ustr", "im oto", "imo to", "i lik", "il ik", "ili k", "ĠW oj", "ĠWo j", "Tr ading", "Trad ing", "Tra ding", "Ġapp are", "Ġap pare", "Ġappar e", "Ġentre prises", "Ġentreprise s", "a chat", "ac hat", "ach at", "acha t", "Ġ ¬", "Ġ ¬", "Ġne igh", "Ġnei gh", "BUTTON DOWN", "ĠMa her", "ĠMah er", "a ghan", "ag han", "agh an", "- hash", "-h ash", "-has h", "\" f", "Ġclient ele", "Ġcliente le", ".add Button", "ĉ SP", "ĉS P", "Q i", "Ġg rated", "Ġgr ated", "Ġgrat ed", "Ġgra ted", "Ġgrate d", "PO SITE", "POS ITE", "POSIT E", ": >", "ĠH owell", "ĠHow ell", "ĠHo well", "ĠHowe ll", "ĠCompar ative", "Ġ ISC", "ĠI SC", "ĠIS C", "ÂŃ i", "O cean", "D avis", "Da vis", "ĠFil me", "ĠFilm e", "W ins", "Win s", "Wi ns", "ĠJ IT", "oc cer", "occ er", "ĠC orm", "ĠCo rm", "ĠCor m", "ENCH MARK", "rc hive", "rch ive", "i cação", "ic ação", "ica ção", "Ġm ata", "Ġmat a", "Ġma ta", "Ġchild birth", "ĠOption ally", "ĠOptional ly", "E ns", "En s", "Ġx http", "Ġel ucid", "_Osc InitStruct", ") )):Ċ", ")) ):Ċ", "))) :Ċ", "Ġint uit", "Ġ Donate", "ĠDon ate", "Ġcorre lates", "Ġcorrel ates", "Ġcorrelate s", "> Delete", "Ġe quipe", "Ġequ ipe", "Ġequip e", "Ġb oca", "Ġbo ca", "Ġinf latable", "Ġinfl atable", "e rah", "er ah", "era h", "ĠDateTime Kind", "Ġcal ves", "\\ Lib", "\\L ib", "Ġem lrt", "ĠTr ilogy", "ĠTri logy", "ĠP anc", "ĠPan c", "ĠPa nc", "ĠD uis", "ĠDu is", "ĠpelÃŃcul a", "W ARDS", "WARD S", "WAR DS", "_DE TECT", "_DET ECT", "-section al", "d hcp", "dh cp", "For Row", "- destruct", "-d estruct", "-de struct", "Ġ Presenter", "ĠP resenter", "ĠPres enter", "ĠPresent er", "/ slick", "/s lick", "/sl ick", ", on", ",o n", "ĠCit adel", "logged in", "logg edin", "_ subtype", "_sub type", "Ġs igue", "Ġsi gue", "Ġsig ue", "Ġsigu e", "Ġc uring", "Ġcur ing", "Ġcu ring", "ĠFire wall", "Ġfluores cence", "ĠItalian s", "ĠItalia ns", "ĠItal ians", "иÑĤ ÑģÑı", ". getStyle", ".get Style", "In Seconds", "j ie", "ji e", "- Smith", "-S mith", "Ġx link", "Ġxl ink", "Ġsub missive", "о нÑĤ", "он ÑĤ", "arbon ate", "ĠF aul", "ĠFa ul", "_ goals", "_go als", "_goal s", "ĠCommission ers", "ĠCommissioner s", "chart Instance", "_POST FIELDS", "Ġmed ial", "Ġmedia l", "Ġmedi al", "Ġm anos", "Ġman os", "Ġma nos", "Ġmano s", "Ġd elt", "Ġde lt", "Ġdel t", "s vm", "sv m", ". Apis", ".A pis", ".Api s", ".Ap is", "e phy", "ep hy", "eph y", "Ġasym pt", "Ġapp Delegate", "Ġimpro bable", "c ka", "ck a", "s imd", "si md", "sim d", "/ Error", "/E rror", ". âĢĵ", "Ġ PTS", "ĠP TS", "ĠPT S", "d eer", "de er", "dee r", "Ġs ina", "Ġsi na", "Ġsin a", "m agnitude", "ID ADE", "IDA DE", "IDAD E", "'] }'", "']} '", "Ġmay ores", "Ġmayor es", "Ġmayo res", "ĉ comment", "ĉcom ment", "/ console", "/con sole", "\" @", "v olt", "vo lt", "vol t", ". sell", ".s ell", ".se ll", ".sel l", "ĠM acy", "ĠMac y", "ĠMa cy", "Ġme lod", "Ġmel od", "Ġim ágenes", "_ chg", "_c hg", "_ch g", "Ġin out", "Ġi nout", "id ente", "ide nte", "ident e", "iden te", ") '),Ċ", ")' ),Ċ", ")'), Ċ", "d ni", "dn i", ". blob", ".b lob", ".bl ob", "Ġtyp ography", "Ġe erie", "Ġee rie", "Ġeer ie", "_ OID", "_O ID", "p esan", "pe san", "pes an", "a jan", "aj an", "aja n", "Ġch opping", "Ġcho pping", "Ġchop ping", "Ġbl uff", "a df", "ad f", "_ bases", "_b ases", "_base s", ". Formatter", ".Form atter", ".Format ter", ".For matter", "Ġ\\ %", "ĠPage Info", "Car rier", "ĠCal ibration", "c omo", "com o", "co mo", "-b odied", "Ġfinanc ier", "Ġ INA", "ĠI NA", "ĠIN A", ". ERR", ".E RR", "Ġho odie", "Ġhood ie", "ĠS anity", "ĠSan ity", "gu arded", "guard ed", ".opend aylight", "IS MATCH", "ISM ATCH", "High lights", "Highlight s", "ü nk", "ün k", "an iem", "ani em", "anie m", "ang ered", "ange red", "anger ed", "assign ments", "assignment s", "Ġregistr ado", "Ġregist rado", "ĠU PPER", "ĠUP PER", "ampil kan", "a shire", "as hire", "ash ire", "ashi re", "ĠNik ola", "ĠNi kola", "ĠNikol a", "ĠC FL", "ĠCF L", "ĠH DC", "ĠHD C", "Ġp oids", "Ġpo ids", "Ġpoi ds", "ĠI Ps", "ĠIP s", "Ġprevent ative", "ips oid", "i fix", "if ix", "ifi x", ". camel", ".c amel", ".ca mel", ".cam el", ". ga", ".g a", "V olumes", "Volume s", "Vol umes", "- ste", "-s te", "-st e", "Y ahoo", "Ya hoo", "_s ibling", "_si bling", "H ighest", "High est", "Hi ghest", "opt group", "Ġkvin na", "Ġkvinn a", "âĢĿ ãĢĤĊĊ", "âĢĿãĢĤ ĊĊ", "ĠAppl iances", "Ġ \"><", "Ġ\" ><", "Ġ\"> <", "' )\")Ċ", "') \")Ċ", "')\" )Ċ", "h tt", "ht t", "ĠIdent ified", "Ġpencil s", "Ġpenc ils", "Ġmember Id", "Ġappend String", ".load Data", "Ġmock Mvc", "Ġj ub", "Ġju b", "ĠS lut", "ĠSl ut", "ĠTai pei", "st att", "stat t", "sta tt", "P olit", "Pol it", "Po lit", "Ġpart ager", "Did Change", "Incre ases", "Increase s", ") }.", ")} .", "ĠB aba", "ĠBa ba", "ĠBab a", "_CL IP", "_CLI P", "[ unit", "[u nit", "Ġ клÑİÑĩ", "Ġк лÑİÑĩ", "Ġalc uni", "ĠL ola", "ĠLo la", "ĠLol a", "Ġcl inging", "Ġclin ging", "Ġcling ing", "@ PostMapping", "( concat", "(con cat", "Ġ ssid", "Ġs sid", "Ġss id", "ĠF auc", "ĠFa uc", "o kit", "ok it", "oki t", "ĠRecord ed", "á lez", "ál ez", "ále z", "($ ('<", "($( '<", "($(' <", ".assertIs Not", "Ġk ali", "Ġka li", "Ġkal i", "V olt", "Vo lt", "Vol t", "Ġwarm ly", "Ġsc ares", "Ġsca res", "Ġscar es", "Ġscare s", "g etti", "get ti", "ge tti", "gett i", "füh rt", "führ t", "_ does", "_d oes", "_do es", ". EMAIL", ".E MAIL", "im ations", "imation s", "imat ions", "Ġspring fox", "ĠD ecom", "ĠDe com", "ĠDec om", "ar cy", "arc y", "Ġgl itches", "Ġglitch es", "ĠM off", "ĠMo ff", "ĠV oll", "ĠVol l", "ĠVo ll", ". between", ".b etween", "Ġco orden", "Ġcoord en", "ĠPart icularly", "G BP", "GB P", "Ġ semble", "Ġs emble", "Ġsem ble", "Ġsembl e", "East ern", "_M SB", "_MS B", "] ){čĊ", "]) {čĊ", "]){ čĊ", "m organ", "mo rgan", "mor gan", "ĠE VAL", "ĠEV AL", "d ere", "de re", "der e", "H OUSE", "HO USE", "m oire", "mo ire", "ist ique", "isti que", "_l stm", "_lst m", "_ls tm", "- commit", "-com mit", "-comm it", "yster ious", "Ġtw ink", "Ġtwin k", "- thumbnails", "-th umbnails", "-thumbnail s", "e nÃŃ", "en ÃŃ", ": '',", ":' ',", ":'' ,", "Ġblack out", "ĠFloor s", "ĠFlo ors", "Ġso fas", "Ġsofa s", "Ġsof as", "Ġ oui", "Ġo ui", "Ġou i", "le shoot", "les hoot", "lesh oot", "ĠR aq", "ĠRa q", "- abs", "-a bs", "-ab s", "Ġk ra", "Ġkr a", "M ining", "Min ing", "Mi ning", "Mini ng", "s haft", "sh aft", "sha ft", ".set Columns", ".setColumn s", "Cl azz", "Cla zz", "PRE TTY", ". playlist", ".play list", "éĸ ¢", "-Sah aran", "M ING", "MI NG", "MIN G", "ĉ bl", "ĉb l", "è® ®", "j f", "DO CKER", "DOC KER", "hop efully", "hope fully", "( ignore", "(i gnore", "ĠUsers Controller", "ĠMitar beiter", "Ġ LES", "ĠL ES", "ĠLE S", "Ham ilton", "- metadata", "-m etadata", "-meta data", "Ġ KK", "ĠK K", "ikt ig", "Ġwoll te", "Ġwol lte", "egr ator", "egra tor", "] bool", ", current", ",c urrent", "Ġvalue Type", "Ġexcav ation", "o land", "ol and", "ola nd", "olan d", "Ġv erv", "Ġver v", "Ġve rv", "/ filepath", "/file path", "Auth Provider", "Ġpro crast", "ĉ ULONG", "ĉU LONG", "_MEM BERS", "_MEMBER S", "Ġup lift", "ĠAut onomous", "Ġart works", "Ġartwork s", "ĠOut reach", "Ġp ore", "Ġpo re", "Ġpor e", "Home page", "Dialog Title", "Ġ Generating", "ĠG enerating", "ĠGener ating", "ĠGene rating", "P ARSE", "PAR SE", "Ġsem anas", "Ġsemana s", "Ġhum ano", "Ġhuman o", "JSGlobal Scope", "Ġvo lte", "Ġvol te", "Ġvolt e", "Ġb ella", "Ġbe lla", "Ġbel la", "Ġbell a", "(is instance", "Ġp lc", "Ġpl c", "\\ Catalog", "\\C atalog", "Ġeste emed", "Ġesteem ed", "éĽ ·", "( suffix", "(s uffix", "Ġswe eps", "Ġsweep s", "ĉ ORDER", "Ġdo ivent", "Ġdoi vent", "ĠS warm", "ĠSw arm", "Ġ Compiled", "ĠComp iled", "ĠCompile d", "get Page", "A DR", "AD R", ".R ichTextBox", "Ġ Naming", "ĠN aming", "ĠNa ming", "ĠNam ing", "ag ged", "agg ed", "ĠG ANG", "ĠGA NG", "r asing", "ra sing", "ras ing", "od eled", "ode led", "odel ed", "Ġg ala", "Ġga la", "Ġgal a", "ĠJS Name", "d df", "dd f", "Ġil lust", "Ġill ust", "ĠLan sing", "ĠLans ing", "[ port", "[p ort", "- death", "-de ath", "Ġdin heiro", "ĠE ighth", "ĠEight h", "Ġ bian", "Ġb ian", "Ġbi an", "st Ã¥", "Ġvers ión", "ĠLinear Gradient", "ĠH arding", "ĠHar ding", "ĠHard ing", ". *)", ".* )", "e czy", "ec zy", "ecz y", "$ header", "Ġv Ã¥r", "ĠvÃ¥ r", "Un checked", "Ġk oje", "Ġko je", "ĠPal adin", "( ))),", "() )),", "()) ),", "())) ,", "G iving", "Gi ving", "( )})Ċ", "() })Ċ", "()} )Ċ", "Ġd ips", "Ġdi ps", "Ġdip s", "F riendly", "Friend ly", "Ġport rays", "Ġportray s", "Ġhel ium", "Ġinsurg ency", "_ expiry", "_ex piry", "_exp iry", "ĠstringByAppending String", "Ġa antal", "Ġaan tal", "s lope", "sl ope", "m ast", "ma st", "mas t", ".get Integer", ".getInt eger", "Ġ ########################", "Ġ######## ################", "Ġ################ ########", "Ġ############ ############", "_PIPE LINE", "Ġd ensely", "Ġdense ly", "Ġdens ely", "Ġmut ating", "m idi", "mi di", "mid i", "ĠSe it", "a yne", "ay ne", "NOW LED", "ĠDes mond", "ĠF Name", "ĠFN ame", "ĠN airobi", "\\ Context", "Ġcal cular", "Ġcalcul ar", "Ġcalc ular", "- den", "-d en", "-de n", "Ġ cott", "Ġc ott", "Ġco tt", "Ġcot t", "] ):čĊ", "]) :čĊ", "]): čĊ", "ĠRecommend ation", "ĠRo lex", "ĠRole x", "ĠRol ex", "Ġvalidation Result", ". pat", ".p at", ".pa t", "Ġn Ãły", "ĠRest Client", "ĠG PI", "ĠGP I", "ĠAshe ville", "Ġ OSP", "ĠO SP", "ĠOS P", "ĠPER MISSION", "ÐĶ аÑĤа", "/ notification", "/not ification", "K night", "Kn ight", "_ Word", "_W ord", "ĠB ender", "ĠBe nder", "ĠBen der", "ĠBend er", "r anking", "ran king", "rank ing", "Ġpart ida", "Ġparti da", "_ reservation", "_res ervation", "Ì Ģ", "Ġm Name", "Ġg etch", "Ġget ch", "Ġb orr", "Ġbo rr", "Ġbor r", "Ġdilig ent", "Disc uss", "æŃ£ åľ¨", "ape ake", "i oned", "ion ed", "io ned", "ione d", "-N azi", ". cum", ".c um", "ĠK ron", "ĠKr on", "ĠKro n", "= $('#", "=$ ('#", "=$( '#", "/ single", "/s ingle", "Ġerot isch", "ĠV ib", "ĠVi b", "Ġrat ified", "Ġconcert ed", "ĠREG ARD", "Ġdo br", "Ġdob r", ".Driver Manager", "' r", "Port able", "Por table", "ĉ suite", "ĉs uite", "Ġrel aciones", "Ġrelacion es", "ĠD op", "ĠDo p", "emp loi", "empl oi", "emplo i", "D OB", "DO B", "Ġcr umbs", "Ġ xls", "Ġx ls", "Ġxl s", "_ Application", "_App lication", "(' :',", "(': ',", "Ġ-- ----------------------------------------------------------------------Ċ", "Ġ---------------------------------------------------------------- --------Ċ", "Ġ------------------------------------------------------------ ------------Ċ", "m se", "ms e", "Ġb erk", "Ġbe rk", "Ġber k", "Ġ ReturnValue", "ĠReturn Value", "ĠB elly", "ĠBel ly", "ĠBell y", "Ġc amar", "Ġca mar", "Ġcam ar", "ĠPe ek", "ĠPee k", "el sing", "els ing", "Ġnot ifies", "ĠTr istan", "ĠTri stan", "ĠG AR", "ĠGA R", "em me", "emm e", "ĠElev ated", "_ CSV", "_C SV", "_CS V", "( chalk", "(ch alk", "Ġtw enties", "ĠSearch Result", "= search", "=s earch", "ĠMix ing", "ĠMi xing", "ý t", "Ġrecru iter", "Ġrecruit er", "ĠIDE OGRAPH", "ĠA go", "ĠAg o", "( Operation", "(O peration", "(Op eration", "$ values", "$value s", "$val ues", "Ġworld ly", "ĠRos enberg", "ĠRosen berg", "ĠConfigure Services", "> ** Ċ", "...\" >Ċ", "Ġsn ork", "Ġsno rk", "_ opacity", "_op acity", "ĠinitWith NibName", "i ado", "ia do", "iad o", "A AC", "AA C", "Ġ ]).", "Ġ] ).", "Ġ]) .", "; z", "_ paragraph", "_par agraph", "_para graph", "Ġn oses", "Ġno ses", "Ġnos es", "Ġnose s", "st ands", "stand s", "sta nds", "stan ds", "i fr", "if r", "_m E", "I raq", "Ir aq", ". Predicate", ".P redicate", "e naire", "en aire", "ena ire", "] ]];Ċ", "]] ];Ċ", "Ġ unidad", "Ġun idad", "Ġuni dad", "Ġretire es", "Ġretir ees", "_ hello", "_h ello", "Ġ modele", "Ġmod ele", "Ġmodel e", "Ġmode le", "ĠUITableView Controller", "ĠUIT ableViewController", "f write", "fw rite", "_ numero", "_num ero", "_numer o", "_ visited", "_vis ited", "_visit ed", "Ġrec ebe", "Ġrece be", "( Notification", "Fant astic", "_ submenu", "_sub menu", "ĠP EM", "ĠPE M", "ĠC upertino", "ĠCup ertino", "approx imately", "cl assed", "class ed", "clas sed", ".Read String", "Ġdomic ile", "_ PW", "_P W", "Ġball park", "ĠK ale", "ĠKa le", "ĠKal e", "con tra", "cont ra", "contr a", "_ favorite", "_f avorite", "/ of", "/o f", "Q uite", "Qu ite", "Quit e", "Qui te", "Ġ OTA", "ĠO TA", "ĠOT A", "Ġacceler ometer", "di dn", "did n", "| ^", "ĠRohing ya", "ivi crm", "ivic rm", "ann abin", "anna bin", "обÑĭ ÑĤи", "o rado", "or ado", "ora do", "' )+", "') +", "Ha unted", ", ID", ",I D", "( UIAlertAction", "u rv", "ur v", "_ bel", "_b el", "_be l", "ĠMex icans", "ĠMexican s", "/ terms", "Ġ Painter", "ĠP ainter", "ĠPa inter", "ĠPaint er", "ĠPain ter", "Input Label", "ĠV inci", "ĠVin ci", "ĠRo sie", "ĠRos ie", "\\ uc", "\\u c", "< Menu", "", "Ġ'\" >", "_ gs", "_g s", "Ġcomp il", "n ard", "na rd", "nar d", "- exc", "-e xc", "-ex c", "Ġrh yme", "Ġb utto", "Ġbut to", "Ġbutt o", "s ays", "sa ys", "say s", "ant asy", "anta sy", "antas y", "ë ¸", "Ġcitt Ãł", "Ġch eg", "Ġche g", "Time String", "Ġpos itivity", "Ġposit ivity", "ĠD abei", "ĠDa bei", "Ġ wang", "Ġw ang", "Ġwa ng", "Ġwan g", "Ġes cre", "Ġesc re", "\" c", "ĉ video", "ĉv ideo", "Ġ Ranked", "ĠR anked", "ĠRank ed", "ĠRan ked", ". strings", ".string s", ".str ings", "> >>(", ">> >(", ">>> (", "Ġин ÑĤеÑĢ", "ĠинÑĤ еÑĢ", "Ġre sta", "Ġr esta", "Ġres ta", "Ġrest a", "[: ,:", "[:, :", "Ġren dre", "Ġrend re", "Ġde ser", "Ġdes er", "Ġdese r", "J os", "Jo s", "Ġdis ruptions", "Ġdisrupt ions", "Ġdisruption s", "Ġо пеÑĢ", "Ġоп еÑĢ", "s ampling", "sam pling", "samp ling", "sup press", "Ġcontainer View", "ĠSeam less", "Ġ airy", "Ġa iry", "Ġair y", "Ġai ry", "Ġon load", ".Window Manager", "ĠP LA", "ĠPL A", "br aco", "bra co", ".set PositiveButton", "Ġp du", "Ġpd u", "Ġg si", "Ġgs i", "Ġ Cli", "ĠC li", "ĠCl i", "_gr adients", "_grad ients", "_gradient s", "Ñı д", "ĠWh isper", "c stdint", "Ġl äng", "Ġlä ng", "Ġform ulations", "Ġformulation s", "Ġformul ations", "é nom", "én om", "ourn emouth", "[ $_", "[$ _", "Ġordin arily", ".set Username", ".setUser name", "Ġfacult ies", "MIT TED", "/ values", "/value s", "Ġwe ir", "Ġwei r", "ĠA pt", "ĠAp t", "M Z", "ĉ cf", "ĉc f", "u cken", "uc ken", "uck en", "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", "def ense", "[ iVar", "[i Var", "ĠBusiness Exception", "Select ors", "Selector s", "Sel ectors", "Sele ctors", "( coordinates", "(co ordinates", "ĠRe sets", "ĠRes ets", "ĠReset s", "ĠDr inks", "ĠDrink s", "o leans", "ole ans", "olean s", "(st ypy", "_ IOC", "_I OC", "_IO C", ". xxx", ".x xx", ".xx x", "ĠS later", "ĠSl ater", "ĠSlate r", "ĠB elize", "ĠBel ize", "Ġ /************************************************************************", "Ġ/ ************************************************************************", "Ġ/**************************************************************** ********", "ad din", "add in", "_ep isodes", "_episode s", "Ġis chem", "Ġisc hem", "legal ArgumentException", "D anny", "Dan ny", "Ġ pared", "Ġp ared", "Ġpar ed", "Ġpa red", "Ġpare d", ".code haus", "ĠAs sy", "ĠAss y", "ĉ Rect", "ĉR ect", "â ŀ", ". lista", ".l ista", ".list a", ".li sta", "Ġв аÑĪ", "Ġва ÑĪ", "Ġv ets", "Ġve ts", "Ġvet s", "H WND", "HW ND", "is oner", "ison er", "iso ner", "Ġ xo", "Ġx o", "Ġor ally", "Ġoral ly", "Ġ Stmt", "ĠSt mt", ".r nn", "ĠD PI", "ĠDP I", "ĠSt rikes", "ĠStr ikes", "ĠStrike s", ".setViewport View", "Ġèĩª åĬ¨çĶŁæĪIJ", "Y ELLOW", "GL enum", "part ners", "partner s", "Ġ Implicit", "ĠImp licit", "ĠImpl icit", "Ġt ako", "Ġta ko", "Ġtak o", "âĢĻ elle", "âĢĻe lle", "Ġerm ög", "total Count", "G il", "Gi l", "ĉ work", "ĉw ork", "Ġpr atic", "Ġpra tic", "Ġprat ic", "in ati", "ina ti", "a bies", "ab ies", "abi es", "ĠSk inner", "ĠSkin ner", "Ġspirit ed", "Ġspir ited", "Ġpancre atic", "Ġh df", "Ġhd f", "' em", "'e m", "Ġpsych osis", "Ġpsycho sis", "o licit", "ol icit", "olic it", "oli cit", "Ġ\" {\"", "Ġ\"{ \"", "_at ual", "_a tual", "Ġé lect", "Ġél ect", "TE AM", "Ġd ak", "Ġda k", "ĠSW AT", ". FragmentManager", ".Fragment Manager", "Ġprovision ing", "l ifetime", "life time", "lif etime", "_EXT ENSIONS", "_EXTENSION S", "Ġ CASCADE", "ĠC ASCADE", "ĠCAS CADE", "Ġ ![", "Ġ! [", "( KP", "(K P", "Ġ vem", "Ġv em", "Ġve m", "ĠInter racial", "ĠInterr acial", "' ]},Ċ", "'] },Ċ", "']} ,Ċ", "sp acer", "space r", "spa cer", "_ kv", "_k v", "W arehouse", "Ware house", "R DD", "RD D", "_f sm", "_fs m", ".Stretch Image", ", Yes", ",Y es", "ĠRefuge e", "ĠBr inging", "ĠBring ing", "Ġv álido", "Ġvál ido", ". intersection", ".inter section", "Ġsp ooky", "Ġspo oky", "_ portal", "_p ortal", "_port al", "_por tal", "Ġ moth", "Ġm oth", "Ġmot h", "Ġmo th", "ĠZ odiac", "ĠSOC IAL", "M imeType", "'] }}", "Ġ----- ->", "Ġ------ >", "_ Blue", "_B lue", "_Bl ue", "Ġbot anical", "Ġfr ags", "Ġfra gs", "Ġfrag s", "Ġfamil ial", "Ġfamilia l", "- du", "-d u", "Ġse izing", "Ġseiz ing", "Ġsei zing", "( blocks", "(b locks", "(block s", "(bl ocks", ". rd", ".r d", ".check NotNull", "Ġm iser", "Ġmis er", "Ġmi ser", "Ġmise r", "Ġmax x", "Ġma xx", "ĠK nee", "ĠKn ee", "View Item", "Inner HTML", "D anger", "Da nger", "Dan ger", "( (__", "(( __", "((_ _", "Ġprz ypad", "Ġprzy pad", "create Url", "* *,", "** ,", "ĠDecor ating", "ATE GY", "ATEG Y", "? >/", "?> /", ". Designer", ".Des igner", ".Design er", "hex digest", "ĠEvery where", "all eries", "alle ries", "aller ies", ".TEXT URE", ". Blocks", ".B locks", ".Bl ocks", ".Block s", "z ell", "ze ll", "zel l", "Ġpre ço", "S uddenly", "input Email", "( sync", "(s ync", ". bd", ".b d", "gold en", "> ');", ">' );", ">') ;", "ĠDick inson", "> >(Ċ", ">> (Ċ", ">>( Ċ", "Ġ QUEUE", "ĠQUE UE", "Ġ getColumn", "Ġget Column", "ĠgetC olumn", "ĠS AND", "ĠSA ND", "ĠSAN D", ". piece", ".p iece", ".pi ece", "l icer", "lic er", "li cer", "lice r", "Fl utter", "Ġget Version", "Ġresource Id", "o gl", "og l", "ÅĤ aw", "ÅĤa w", ". Branch", ".Br anch", "ĉ web", "ĉw eb", "Ġfr amerate", "Ġframe rate", "Ġfram erate", "P PP", "PP P", "Ġf ray", "Ġfr ay", "Ġfra y", "C NT", "CN T", "Ġinform atie", "Ġinformat ie", "' ]čĊčĊ", "'] čĊčĊ", "']čĊ čĊ", "ne as", "nea s", "Header Code", "Ġ æ¸", "Ġæ ¸", "Ġt rg", "Ġtr g", "raw types", "H onda", "Ho nda", "Hon da", "Ġmark eter", "Ġmarket er", "Ġ requestData", "Ġrequest Data", "Ġ Pg", "ĠP g", "ĉ not", "ĉn ot", "ĉno t", "Ġpage Info", "Ġakt uellen", "Ġaktu ellen", "ãģ ķãĤĵ", "ãģķ ãĤĵ", "Ġ AMS", "ĠA MS", "ĠAM S", "push ViewController", "ĉ AL", "ĉA L", "Ġv ests", "Ġve sts", "Ġvest s", "Ġves ts", "p roduce", "pro duce", "produ ce", "prod uce", "-m ême", "ĠRah man", "F unny", "Fun ny", "E Z", "_ Valid", "_Val id", "Ġsquad ron", "Ġ lash", "Ġl ash", "Ġla sh", "Ġlas h", "Ġ irm", "Ġi rm", "Ġir m", "i asco", "ias co", "ĠP aran", "ĠPar an", "ĠPa ran", "ĠPara n", "Ġpet ites", "Ġpetite s", "Ġpetit es", "ĠDec ay", "Ġun initialized", "priv ileged", "Ġm bedtls", "å¤ĩ 注", "Ġ ^.", "Ġ^ .", "Ġec static", "D etroit", "Det roit", "Ġp arten", "Ġpart en", "Ġpar ten", "Ġparte n", "Ġsou venir", ".get Login", ".getLog in", "моÑĤ ÑĢ", "мо ÑĤÑĢ", "en ção", "ĠmÃŃn imo", "ĠAccess ed", "ĠAcc essed", "r ió", "ri ó", "M ic", "Mi c", "ĠV ocal", "ĠVo cal", "ĠVoc al", ".Set String", "Ġmens ajes", "Ġmensaje s", "åĢ į", "Ġattr avers", "ĠA ph", "ĠAp h", "Ġ ');čĊ", "Ġ' );čĊ", "Ġ') ;čĊ", "Ġ'); čĊ", "ü nde", "ün de", "ünd e", "Ġenchant ed", "Ġench anted", "ĠRoot State", "ĠCLOSE D", "ĉ ĉĉĉĉĉĉĉčĊ", "ĉĉ ĉĉĉĉĉĉčĊ", "ĉĉĉĉ ĉĉĉĉčĊ", "ĉĉĉ ĉĉĉĉĉčĊ", "ĉĉĉĉĉ ĉĉĉčĊ", "ĉĉĉĉĉĉ ĉĉčĊ", "ĉĉĉĉĉĉĉĉ čĊ", "ĉĉĉĉĉĉĉ ĉčĊ", "Ġcal iente", "or ris", "orr is", "Ġphysic ists", "Ġphysicist s", "h wnd", "hw nd", "_ vi", "_v i", "Ġráp ido", "Ġcapital ized", "Ġcapitalize d", "ed By", "Ġmach ining", "Ġh ubby", "Ġhub by", "ĠSt acy", "ĠSta cy", ". Bus", ".B us", "d rink", "dr ink", "H ur", "Hu r", "Ġpr opia", "Ġprop ia", "Unit Test", "Ġmiscon ception", "_ _));Ċ", "__ ));Ċ", "__) );Ċ", "__)) ;Ċ", "/ dc", "/d c", "ĠMay weather", "_m C", ". createFrom", ".create From", "ĠQ Painter", "ro psych", "rops ych", "inn itus", "a yas", "ay as", "aya s", "Ġg eg", "Ġge g", "( dw", "(d w", "Ġus ado", "Ġusa do", "Ġtr ickle", "Ġtrick le", "Ġann ihil", "Ġanni hil", "ĠP asta", "ĠPa sta", "ĠPast a", "ĠPas ta", "Ġ ++Ċ", "Ġ+ +Ċ", "Ġ++ Ċ", "(Expected Conditions", ".post Value", "i cap", "ic ap", "ica p", "ĠDon etsk", "_ soup", "_s oup", "_so up", "- publish", "-p ublish", "ĠP b", "m entions", "ment ions", "mention s", "AC CEPT", ". Pull", ".P ull", ", âĢĻâĢĻ", ",âĢĻ âĢĻ", "Ġret arded", "Ġretard ed", "_ ATOM", "_AT OM", "ĠTerm inator", "ĠTermin ator", "- court", "-c ourt", "-co urt", "ĠCLLocation Coordinate", "Ġrev erence", "Ġreve rence", "Ġrever ence", "ĠS SC", "ĠSS C", "ut ely", "ute ly", "ĠW ON", "ĠG SL", "ĠGS L", "f rei", "fr ei", "fre i", ".get Longitude", ".getLong itude", "Ġopen FileDialog", ".B utter", ".But ter", "- important", "-import ant", "_M ANY", "_MAN Y", "_MA NY", "ĠG ong", "ĠGo ng", "ĠGon g", "âĢľ How", "Ġg orge", "Ġgor ge", "= msg", "=m sg", "ĠE zek", "ĠEz ek", "create Command", ": checked", "Ġinfo graphic", "Ġinf ographic", ". WEST", ".W EST", "D irs", "Dir s", "Di rs", "Ġgu arda", "Ġguard a", "Ġguar da", "Ġbee tle", "Ġbeet le", "< small", " Loading", "_ mA", "_m A", ".get Random", "b lings", "bl ings", "bling s", "Ġche eses", "Ġcheese s", "Ġchees es", "t ti", "tt i", ". âĢ¢", "ĠBurg ess", "ender it", "ende rit", ". ',čĊ", ".' ,čĊ", ".', čĊ", "(\" \"+", "(\"\" +", "a cb", "ac b", "% p", "index ed", "inde xed", "_ predicate", "_p redicate", "_pred icate", "nes ia", "Ġb ied", "Ġbi ed", "ĠC IT", "ĠCI T", "( Pos", "(P os", "_ radi", "_r adi", "_rad i", "_ra di", "ä»· æł¼", "B iz", "Bi z", "ĠAdoles cent", "Ġv iên", "Ġvi ên", "c ycl", "cy cl", "_ Cancel", "_C ancel", "Ġcon clusive", "Ġconclus ive", "Ġconcl usive", "Ġappell ate", "Ġappel late", "inform atics", "S J", "Ġel ective", "Ġelect ive", "role Id", "Fetch er", "ĉ Command", "(\" (%", "(\"( %", "Ġf art", "Ġfa rt", "Ġfar t", "I LA", "IL A", "get Block", "A USE", "AU SE", "Ġ дан", "Ġд ан", "Ġда н", "ĠAr te", "ĠArt e", "Ġnot ifying", "Ġnotify ing", "Ġg ele", "Ġge le", "Ġgel e", ". same", ".s ame", ".sa me", ".sam e", "ĠRe gel", "ĠReg el", "Ġ BaÅŁ", "ĠB aÅŁ", "ĠBa ÅŁ", ". creation", ".c reation", "Ġ VN", "ĠV N", "_ community", "_comm unity", "Ġuns ustainable", "S EX", "SE X", "Ġgrid Size", "res cia", "avers able", "(', ')[", "(',' )[", "(',') [", "ĠPh elps", "á»ķ i", "ANCE LED", "ANCEL ED", "- IS", "-I S", ".run ners", ".runner s", "ĠSt okes", "ĠSto kes", "ĠStoke s", ". Produ", ".P rodu", ".Pro du", ".Pr odu", "Ġwh ipping", "Ġwhip ping", "_ac quire", "Ġinvestig ación", "f ried", "fr ied", ".copy With", "ĠHard cover", "- Se", "-S e", "áŀ¶ áŀ", "inv itation", "les ai", "ĠD orm", "ĠDo rm", "ĠDor m", "ĠÑģпиÑģ ка", "Ġconcaten ated", "Ġconcatenate d", "o phil", "op hil", "oph il", "Ġth inker", "Ġthink er", "Ġthin ker", "/font awesome", "ĠLe opard", "ĠLeo pard", "Ġ\" /\");Ċ", "Ġ\"/ \");Ċ", "Ġ\"/\" );Ċ", "Ġresidual s", "ĠMicro wave", "ĠMic rowave", "Ġcon forme", "Ġconform e", "th rop", "thr op", "thro p", "Ġdis emb", "Ġdi semb", "Ġdise mb", "ĠO MG", "ĠOM G", "ĠDisc ipline", "ĠAc robat", "/ repository", "/re pository", "/repos itory", "d fa", "df a", "_ MED", "_M ED", "_ME D", "buf io", "Ġméth ode", "_H OLD", "i asi", "ia si", "ias i", "_ legacy", "_leg acy", ") ččĊ", "æ£ Ģ", "Get ProcAddress", "Ġy ay", "Ġya y", "ot ence", "ote nce", "oten ce", "order id", "orde rid", "- tw", "-t w", "Ġd early", "Ġdear ly", "In coming", "Inc oming", "/ il", "/i l", "Ġneuro p", "Ġneu rop", "Ġneur op", "u cz", "uc z", ") ;čččĊ", "); čččĊ", "ĠInnov ative", "Ġpro fund", "Ġprof und", "ig mat", "igma t", "igm at", "Selection Mode", "re levant", ". GO", ".G O", "Ġbru ises", "Ġs ach", "Ġsa ch", "Ġsac h", "o def", "od ef", "ode f", "Ġre imb", "Ġreim b", "/ desktop", "/d esktop", "- spot", "-s pot", "-sp ot", "un dance", "und ance", "unda nce", "undan ce", "Ent ropy", "Entr opy", "\\ core", "Ġs uger", "Ġsu ger", "Ġsug er", "Ġ Mvc", "ĠM vc", "ĠGN OME", "_ indx", "_in dx", "_i ndx", "_ind x", "ĠYY STYPE", "ĠYYS TYPE", "ĠMat lab", "ĠC IF", "ĠCI F", "Ġ *))", "Ġ* ))", "Ġ*) )", "Ġproduct List", "Ġ Alright", "ĠAl right", "ace mark", "ac emark", "ÑĤ ив", "ÑĤи в", "mod ification", "int ernational", "inter national", "intern ational", "Ġh omers", "Ġhome rs", "Ġhom ers", "Ġho mers", "Ġhomer s", "Ġd icts", "Ġdi cts", "Ġdict s", "Ġdic ts", "ĠQ Font", ". SQLite", ".SQL ite", "Ġtransplant ation", "ĠMessageBox Button", "ĠEl ves", "' ]])Ċ", "'] ])Ċ", "']] )Ċ", "(Q Icon", "Ġcin emas", "Ġcinema s", "Ġcine mas", "CO ORD", "- China", "-Ch ina", "Ġkh ẩu", "æĪij çļĦ", "Ġsk ulls", "Ġskull s", "Ġpain staking", "Ġpains taking", "f ce", "fc e", ".XR Label", "Ġ specifier", "Ġspec ifier", "Ġprefer ring", "Ġpref erring", "/ activity", "( Photo", "á lt", "ál t", ". lot", ".l ot", ".lo t", "' '.", "'' .", "an nonce", "ann once", "annon ce", "anno nce", ".google code", "- pdf", "-p df", "ĠP oke", "ĠPo ke", "ĠPok e", "_ ACL", "_A CL", "_AC L", "Ġend owed", "dis cover", "disc over", ".o mg", ".om g", "Ġwood land", ". Magic", ".M agic", "Ġvol ont", "Not Allowed", "Ġc have", "Ġch ave", "Ġcha ve", "B MW", "BM W", "',' =',", "','= ',", "ĠS IX", "ĠSI X", "æĪij 们", "Ġko sher", "Ġkos her", "Ġas piration", "Ġaspir ation", "Ġasp iration", "i ntl", "in tl", "int l", "_ref ptr", "' +Ċ", "'+ Ċ", "ment or", "men tor", ". club", ".c lub", ".cl ub", "Window State", ". ARR", ".A RR", ".AR R", "Ġ zza", "Ġz za", "Ġzz a", "Ġmessage Type", ". equ", ".e qu", ".eq u", "T hor", "Th or", "Tho r", "Ġin just", "Ġinj ust", "Ġg ums", "Ġgu ms", "Ġgum s", "Ġborder Side", "/ ////", "// ///", "//// /", "/// //", "ĠTrans mit", "Ġbuf size", "Ġh ak", "Ġha k", "Ġ ellas", "Ġel las", "Ġell as", "Ġella s", "R ANDOM", "RAND OM", "ĉ mc", "ĉm c", "Ġp ea", "Ġpe a", "e ko", "ek o", "document o", "Ġhyster ia", "Ġare nas", "Ġar enas", "Ġaren as", "Ġarena s", "Ġgun men", "Ġm ike", "Ġmi ke", "Ġmik e", "Ġimp unity", "at isation", "atis ation", "_ Zero", "_Z ero", "_COMP ANY", "ĠG ors", "ĠGo rs", "ĠGor s", "Ġuse Class", "( redis", "(r edis", "(re dis", "(red is", "ĠRUN NING", "ĠB air", "ĠBa ir", "ĠBai r", "ve lte", "vel te", "velt e", "Ġ ','.", "Ġ', '.", "Ġ',' .", "а ÑĤÑĮÑģÑı", "аÑĤÑĮ ÑģÑı", "ö st", "ös t", "encode URIComponent", "_ restrict", "_re strict", "Ġdec als", "Ġ Pedido", "ĠP edido", "ĠPed ido", "Ġalter cation", "Dis plays", "Display s", "Disp lays", "ĠApp licants", "ĠApplicant s", "C US", "CU S", "Text area", "ĠAng ola", ". future", ".f uture", "ĠU SHORT", "ĠUS HORT", "Ġsuppress ing", "Ġsupp ressing", "Ġset zen", "AP olynomial", "Ġt och", "Ġto ch", "Ġtoc h", "Ġhall mark", "Ġ $$$", "Ġ$ $$", "Ġ$$ $", "ĠCHAR SET", ". rpm", ".r pm", "ĠD ich", "ĠDi ch", "---- ----------------", "-------- ------------", "---------------- ----", "------------ --------", "----- ---------------", "---------- ----------", "------ --------------", "----------- ---------", "------------- -------", "------- -------------", "--------- -----------", "--------------- -----", "-------------- ------", "_ parm", "_p arm", "_par m", "_pa rm", "è¿ ĺ", "a cciones", "acc iones", "acci ones", "accion es", "h ait", "ha it", "hai t", "WARD ED", "WAR DED", "_ routing", "_r outing", "_ro uting", "ĠN OM", "ĠNO M", "Ġen clave", "ĠL otto", "ĠLot to", "ĉ fr", "ĉf r", "complex Content", "ĠBall ard", "k ube", "ku be", "/ win", "/w in", ".getColumn Model", "_RE PLACE", "Header Value", "Ġest udiantes", "Ġ apis", "Ġa pis", "Ġap is", "Ġapi s", "Ġb pm", "Ġbp m", "Ġ TypeName", "ĠType Name", "And Get", "r ita", "ri ta", "rit a", "Pl ans", "Plan s", "> Note", ">N ote", ">No te", "Ġfet isch", "Ġt oned", "Ġto ned", "Ġton ed", "Ġtone d", "_ goto", "_g oto", "_go to", "on sense", "ons ense", "Ġm olds", "Ġmo lds", "Ġmol ds", "Ġmold s", "Ġinfiltr ation", "ĠGuerr ero", "ub bo", "ubb o", "c ki", "ck i", "( $(\".", "($ (\".", "($( \".", "_ activities", "_act ivities", "_activ ities", "( changes", "(ch anges", "(chan ges", "(change s", "Ġof App", "ĠKe pler", "ĠD emp", "ĠDe mp", "ĠDem p", "ĠCont inent", "ĠContin ent", ". Ticks", ".T icks", ".Tick s", "Ġ Unsigned", "ĠUn signed", "ĠUns igned", "ĠJah res", "ĠJahr es", "ĠJahre s", "Ġfresh men", "ĠArch ived", "ĠArchive d", "ĠкоÑĤоÑĢ Ñĭй", "Ġ' ::", "Ġ': :", "T utorial", "C c", "Ġtable LayoutPanel", "from Json", ". levels", ".level s", "_trans ient", "Ġendors ing", "Ġ DIC", "ĠD IC", "ĠDI C", "l auf", "la uf", "Ġsh red", "Ġshr ed", "_E MIT", "_EM IT", "ific antly", "ificant ly", "A LA", "AL A", "/ proto", "/pro to", "/pr oto", "Ġnarr owing", "Ġnarrow ing", "Ġnar rowing", "U tc", "Ut c", "F actors", "Fact ors", "Factor s", "Fac tors", "Fa ctors", "Ġsent ient", "æŀ IJ", "lix ir", "ĠC ROSS", "m eteor", "met eor", "Ġgr oin", "Ġgro in", "Ġ mdb", "Ġm db", "Ġmd b", "ĠRot terdam", "Ġcom ida", "ĠOp Code", "Ġ DefaultValue", "ĠDefault Value", "Permissions Result", "Ġheter ogeneous", "Ġm oot", "Ġmo ot", "Ġmoo t", "Ġde ceived", "Ġdece ived", "Ġdeceive d", "-in dependent", "ĠObject OutputStream", "Ġover power", ". dup", ".d up", "Ġ ldb", "Ġl db", "Ġld b", "Ġdomestic ally", "Ġdomest ically", "Ġbe stellen", "Ġbest ellen", "Ġbeste llen", "Ġ lov", "Ġl ov", "Ġlo v", "ĠContract ors", "ĠContr actors", "ĠContractor s", "Tri angles", "Triangle s", "Ġfod der", "Ġfil mes", "Ġfilm es", "Ġfilme s", "ä¼ ģ", "Ġrev olver", "Startup Script", "/ validation", "ĠResource Type", "i ÅŁ", "ĠL az", "ĠLa z", "f ef", "fe f", "Ġl stm", "Ġlst m", "Ġls tm", "{ *", ". attachment", ".attach ment", ".att achment", ". hits", ".h its", ".hit s", "e with", "ew ith", "D OG", "DO G", "Al abama", "Ġmedium s", "Ġmedi ums", ".m Context", "- cols", "-c ols", "-col s", "-co ls", "åı ĭ", ". notice", ".not ice", "Ġat tn", "Ġatt n", "ĠP acking", "ĠPac king", "ĠPack ing", "Ġ Ln", "ĠL n", "_COM PLEX", "_COMP LEX", "/ Users", "/User s", ".save txt", ".sav etxt", "ĠR ounds", "ĠRo unds", "ĠRound s", "ĠRou nds", "?,?, ?,?,", "Ġin gl", "Ġing l", "Ġ ROC", "ĠR OC", "ĠRO C", "_ female", "_f emale", "_fe male", "ĠSt ard", "ĠStar d", "ĠSta rd", "] ];", "]] ;", "Ġwrest lers", "Ġwrestler s", "Ġtorrent s", "Ġs inh", "Ġsi nh", "Ġsin h", " ĊĊ", "Ċ Ċ", "ë³ µ", "s ense", "sen se", "how ever", ". Physics", ".Ph ysics", "Inf rastructure", "ĠS acr", "ĠSa cr", "ĠSac r", "F el", "Fe l", "ĠD ISTRIBUT", "é ments", "ém ents", "ément s", "ĠValid ates", "ĠValidate s", "#### ########################################################", "################################ ############################", "############ ################################################", "################################################ ############", "############################ ################################", "######################################################## ####", "Ġ |/", "Ġ| /", "Ġe sl", "Ġes l", "Ġré seau", "ĠB ip", "ĠBi p", "BY TES", "BYTE S", "_W ATER", "T urning", "Turn ing", "Tur ning", "E LS", "EL S", "Ġj uxtap", "Ġlesb ische", "ý ch", "( Unknown", "(Un known", "N eo", "Ne o", "@ JsonProperty", "@Json Property", "Ġal umnos", "Ġalum nos", "Ġalumno s", "ĠRaq qa", "im ei", "ime i", ".get Bounds", ".getB ounds", ".Mouse EventHandler", ".MouseEvent Handler", "# ######", "## #####", "#### ###", "### ####", "##### ##", "###### #", "Generic Type", "/ cms", "/c ms", "/cm s", "Ġturn o", "Ġtur no", "Ġ мин", "Ġм ин", "Ġми н", "Ġfolk lore", "ĠE vo", "ĠEv o", "Ġconduct ivity", "Ġle ben", "Ġgear box", "- vs", "-v s", "Ġ ÏĨ", "ĠÏ Ĩ", "Ġdrink ers", "Ġ conexao", "Ġcon exao", "Ġconex ao", "ĠTe eth", "ĠTee th", "Ġget Arguments", "ĠR AT", "ĠRA T", "ent ious", "enti ous", "E duc", "Ed uc", "+ W", "ĠInstitution al", "ĠInstitut ional", "ĠB ord", "ĠBo rd", "ĠBor d", "is Equal", "( pwd", "(p wd", "Ġign ited", "Ġignite d", "ĠR ousse", "ĠRou sse", "Ġimpact ful", "ĠM alk", "ĠMal k", "ĠMa lk", "Ġg eral", "Ġge ral", "Ġger al", "ĠP ivot", "Ġa zt", "Ġaz t", "Ġcsv file", "ĠR ope", "ĠRo pe", "ĠS OLUTION", "ĠSOL UTION", "ĠAr bitrary", "ĠArbit rary", "Ġl etto", "Ġlet to", "Ġlett o", ".Mouse Adapter", "Ġ }}}", "Ġ} }}", "Ġ}} }", "ĠSa ilor", "ĠSail or", "ĠSai lor", "d era", "de ra", "der a", "P utting", "Put ting", "Ġconcent rates", "Ġconcentr ates", "Ġconcentrate s", "Ġauth Domain", "âĢĿ çļĦ", "-f inals", "-final s", "-fin als", ", strlen", ",str len", ",st rlen", "Mu on", "ĠOrd inary", "fire fox", "ĠLa TeX", "ĠH und", "ĠHun d", "ĠHu nd", "engine ering", "/ blue", "/b lue", "/bl ue", "ed TextBox", "(\" \");", "(\"\" );", "(\"\") ;", "ĠC DDL", "ĠCD DL", "k ept", "ke pt", "Ġ GetString", "ĠGet String", "K ir", "Ki r", "() ='", "ĠO CD", "ĠOC D", "ant ium", "anti um", "$ menu", "$m enu", "ĠAppalach ian", "Secret ary", "ë¥ ĺ", "ี ย", "Sem antic", "Ġ *[", "Ġ* [", "e stone", "es tone", "est one", "esto ne", "ung kin", "Max Y", "- tone", "-t one", "-to ne", "-ton e", "\" };čĊ", "\"} ;čĊ", "_ Part", "_P art", "< Member", "ĊĊ", "'> ĊĊ", "'>Ċ Ċ", "L ic", "Li c", "ĠMir age", "ĠMi rage", "ĠAssembly FileVersion", "Te V", "ĠValue EventListener", "-s olving", "T ho", "Th o", "rou lette", "_ WP", "_W P", "Ġunint errupted", "Ġfield Type", ". Typed", ".T yped", ".Type d", "Ġa mour", "Ġam our", "Ġamo ur", "Ġmock ery", "Ġmocker y", "( vol", "(v ol", "(vo l", "ĠSub committee", "ĠR uf", "ĠRu f", "e rox", "er ox", "ero x", ":UIButtonType Custom", "Ġ Blur", "ĠBl ur", "ĠBlu r", "Ġwy kon", "n ces", "nc es", "nce s", "ASH BOARD", "! !\");Ċ", "!! \");Ċ", "Ġmurder ers", "Ġmurderer s", ". daily", ".d aily", ".da ily", "ĠDI AG", "j ing", "ji ng", "jin g", "Ġdol phin", "Ġl òng", "Ġb ö", "ĠV ocabulary", ".St Object", "' )\">", "') \">", "')\" >", "Ġz un", "Ġzu n", "Ġscrim mage", "tr éal", "ĠL ig", "ĠLi g", "[ vi", "[v i", "C ole", "Col e", "Co le", "Ġfrost ing", ". Players", ".P layers", ".Pl ayers", ".Player s", ".Play ers", "- translate", "-trans late", "Fe els", "Feel s", "Fee ls", "=\\\" /", "=\\ \"/", ".Butter Knife", "Ġ? >;Ċ", "Ġ?> ;Ċ", "Ġ avi", "Ġa vi", "Ġav i", "in nie", "inn ie", ". Failure", ".F ailure", ".Fail ure", "Ġsp indle", "Ġspin dle", "Configuration Exception", "_ hop", "_h op", "Ġpos ição", "Ġposi ção", "Ġ Await", "ĠA wait", "ĠAw ait", "UIImage PickerController", "ĉ day", "ĉd ay", "Ġge nom", "Ġgen om", "C ab", "Ca b", "ĠÑĢ езÑĥлÑĮÑĤаÑĤ", "ĠÑĢезÑĥлÑĮÑĤ аÑĤ", "OR IGINAL", "Ġejac ulation", "( tcp", "(t cp", "(tc p", "SE COND", "SEC OND", "Ġt onic", "Ġto nic", "Ġton ic", "Ġ ListBox", "ĠList Box", "Ġ ĉĉĊ", "Ġĉ ĉĊ", "Ġĉĉ Ċ", "( )>Ċ", "() >Ċ", "()> Ċ", "Ġqu atre", "Ġquat re", "Ġqua tre", "ượ ng", "with Errors", ". Maybe", ".M aybe", ", âĢ¦", "token Id", "_UN DEF", "Ġfresh ness", "ĠAmendment s", "ĠAmend ments", ".map box", ". CV", ".C V", "( blog", "(b log", "(bl og", "_get time", ". quest", ".q uest", ".qu est", "s parse", "sp arse", "spar se", "Ġre sale", "Ġres ale", "Ġenthusi astically", "Ġenthusiastic ally", "Ġenthusiast ically", "ĠProstit utas", "W a", "C argo", "Car go", ". Parcelable", ".Parcel able", "S ENSOR", "SEN SOR", "SENS OR", "ĠR yu", "ĠRy u", "La ughs", "Laugh s", "_ Native", "_N ative", "/ pg", "/p g", "y sts", "yst s", "ys ts", "Ġphot oc", "Ġphoto c", "ç® Ģ", "ad opt", "ado pt", ". species", ".s pecies", ".sp ecies", ".spec ies", "conc iliation", "Adjust ed", "Adj usted", ".Firebase Auth", "ut tle", "utt le", "ord ination", "ordin ation", "Ġm unch", "Ġmun ch", "ĠS take", "ĠSt ake", "ĠSta ke", ". ping", ".p ing", ".pi ng", ".pin g", "an ker", "ank er", "anke r", "(QString Literal", "Ġsub script", "Ġsubs cript", "Ġsubscri pt", "Ġ ĠĉĊ", "ĠĠ ĉĊ", "ĠĠĉ Ċ", "ĠM CC", "ĠMC C", "_ Cmd", "_C md", "se xy", "sex y", "i ou", "io u", "ĠM ANY", "ĠMA NY", "ĠMAN Y", "Ġn anny", "Ġnan ny", "T RAIN", "TR AIN", "TRA IN", "Ġflour ishing", "Ġflourish ing", "ĠW atches", "ĠWatch es", "ĠWat ches", "ĠQ Map", "ĠF erm", "ĠFe rm", "ĠFer m", "Ġw asm", "Ġwas m", "Ġwa sm", "ĠA bed", "ĠAb ed", "ĠAbe d", "_ UD", "_U D", "ĠG lasses", "ĠGl asses", "ĠGlass es", "ĠGlas ses", "+ v", "Att end", ". Chain", ".Ch ain", "Ġdec ency", "Ġdece ncy", "ĠSup plementary", "ĠSupplement ary", "h unter", "hunt er", "- txt", "-t xt", "Ġ\" }\";Ċ", "Ġ\"} \";Ċ", ".set WindowTitle", "( \"", "Ġmasc ara", "( Profile", "åĬ Łèĥ½", "åĬŁ èĥ½", "im ité", "imit é", "imi té", "Ġwild fires", "Ġwildfire s", "- ROM", "-R OM", ".is On", "( groupId", "(group Id", "Re pair", "Rep air", "accum ulate", "Ġ< \",", "Ġhand written", "Ġach eter", "Ġache ter", "ĠM GM", "ĠMG M", "ĠIr ma", "-> {_", "->{ _", "g ee", "ge e", "c riminal", "cr iminal", "Ġèĭ¥ è¦ģ", "Ġmoment arily", "\" )!=", "\") !=", "_ lit", "_l it", "_li t", "Ġexpires In", ". \").", ".\" ).", ".\") .", "éķ¿ 度", "Ġfr ække", "v lc", "vl c", "Ġor bs", "Ġorb s", ") ,$", "), $", "Ġvent ured", "Ġventure d", "/ >\\", "/> \\", "ch arm", "char m", "cha rm", "N uitka", "el dig", "eld ig", "ato nin", "aton in", "W itness", "- lat", "-l at", "-la t", "Ġset Hidden", "Ġrel ics", "Ġreli cs", "Ġrelic s", "Ġcons ulate", "Ġconsul ate", ". IGNORE", "\" After", "\"A fter", "Ġs etAddress", "Ġset Address", "Ġbeste ht", "Ġ' ')ĊĊ", "Ġ'' )ĊĊ", "Ġ'') ĊĊ", "Ġ'')Ċ Ċ", ".x axis", "Ġser ão", "Ġmis led", "Ġmi sled", "_UN IFORM", "ĠV IA", "ĠVI A", "in cr", "inc r", "Ġzen ith", "Ġvis cosity", "Ġvisc osity", "Ġthin ly", ".get SharedPreferences", ". ErrorCode", ".Error Code", "\" ),\"", "\") ,\"", "\"), \"", "ĠMillion en", "ĠMilli onen", "Ġ/ >)Ċ", "Ġ/> )Ċ", "Scroll Indicator", "-se eking", "ĠPOLIT ICO", "as ca", "asc a", "_ rl", "_r l", "N avig", "Nav ig", "Na vig", "(full file", "Ġsol itude", "Ġ juven", "Ġju ven", "Ġha uling", "Ġhaul ing", "ĠMac ros", "ĠMacro s", "ĠG ry", "ĠGr y", "Ġexerc itation", "ĠATT ACK", "Tick Count", "Ġ rites", "Ġr ites", "Ġrit es", "Ġri tes", "Ġd oe", "Ġdo e", "Particle System", "Ġ slu", "Ġs lu", "Ġsl u", "Window Text", "Ġ ClassName", "ĠClass Name", "Ġs lander", "Ġsl ander", "Ġsla nder", "ĉ Port", "ĉP ort", "j ong", "jo ng", "jon g", "? a", ".D ial", ".Di al", "âĢĶ at", "âĢĶa t", "$ objPHPExcel", "$obj PHPExcel", "Ġso ar", "E NN", "EN N", "appe ared", "appear ed", "Ġquot id", "Ġquo tid", "e machine", "em achine", "ema chine", "Ġ nip", "Ġn ip", "Ġni p", "Ġmicro time", "ĠAl ma", "; !", "---------------- --------------------------------------------------------------------------------", "-------------------------------- ----------------------------------------------------------------", "---------------------------------------------------------------- --------------------------------", "------------------------------------------------ ------------------------------------------------", "---------------------------------------------------------------------------- --------------------", "-------------------------------------------------------------------------------- ----------------", "-------------------- ----------------------------------------------------------------------------", "ĠP assage", "ĠPass age", "ĠPas sage", "Ġdump sters", "Ġdumpster s", "Ġdumps ters", "Ġ Exclude", "ĠEx clude", "ĠExc lude", "Ġsuggest ive", "ĠCircularProgress Indicator", "_ clr", "_c lr", "_cl r", "Array Type", "IL LA", "ILL A", "Elapsed Time", "Dr iven", "Drive n", "Ġresource Name", "ĠG arrison", "ĠGarr ison", "se rir", "ser ir", "- ahead", "-a head", "Ġp innacle", "ĠEs presso", "S parse", "Sp arse", "Ġas says", "Ġass ays", "Ġassay s", "ĠGirl friend", "i mid", "im id", "imi d", "] ='\\", "]= '\\", "]=' \\", "ONG LONG", "ONGL ONG", "Ġportray ing", "L ane", "La ne", "Ġb úsqueda", "Ġrein forcements", "Ġreinforce ments", "Ġreinforcement s", "ĠSpread sheet", "ĠArray Collection", ", arr", ",a rr", "light box", "ic ana", "ica na", "ican a", "< \"", "build ers", "builder s", "K id", "Ki d", "ĠMat SnackBar", "EX PR", "EXP R", "od cast", "ĠFoundation s", "ĠFound ations", "Ġ inds", "Ġin ds", "Ġi nds", "Ġind s", "=' ${", "='$ {", "F izz", "Fi zz", "- functional", "-function al", "( workspace", "(work space", "Ġstem med", "_ patches", "_p atches", "_patch es", "_pat ches", "ĠJar vis", "RE ADING", "READ ING", "Ġdisrespect ful", "ĠQ Dom", "Ġ$ {Ċ", "Ġ${ Ċ", "e status", "es tatus", "est atus", "Re ached", "Reach ed", "! .ĊĊ", "!. ĊĊ", "I LT", "IL T", "ĠN DEBUG", "ĠC ourage", "ĠCour age", "ĠCou rage", "birth date", "ĠT ing", "ĠTi ng", "ĠTin g", "Ġutil izado", "Ġutiliz ado", "Ġutiliza do", "án chez", "Out door", "Ġhand guns", "Ġhandgun s", "Ref Count", "É Ļ", "r omo", "ro mo", "rom o", "Ġt ts", "Ġtt s", ". She", ".S he", ".Sh e", "Ġ Pane", "ĠP ane", "ĠPan e", "ĠPa ne", "ãĢij, ãĢIJ", "ĠIO CTL", "ĠIOC TL", "/ black", "/b lack", "/bl ack", "in scription", "ins cription", "Ġbi opsy", "Ġbio psy", "Ġ TimeInterval", "ĠT imeInterval", "ĠTime Interval", ".Test Check", "ĠGUI Style", "Ġ Capability", "ĠCap ability", "ĠBei trag", "ĠBeit rag", "don nees", "T reatment", ". backup", ".back up", "Ġsign ings", "Ġsig nings", "Ġsigning s", "Ġsignin gs", "ĠB oca", "ĠBo ca", "d rm", "dr m", ". MAIN", ".M AIN", "Ġgo ede", "Ġgoed e", "Ġ Markup", "ĠMar kup", "ĠMark up", "G REE", "GR EE", "GRE E", "ĠBase Service", ". Creator", ".C reator", "Ġj ails", "Ġja ils", "Ġjail s", "ĠK ahn", "ĠKa hn", "ĠKah n", "Ip Address", "AC HI", "ACH I", "Ġin hibited", "Ġinhib ited", "Ġinhibit ed", "Ġ@ $_", "Ġ@$ _", "ĠAs sass", "ĠAss ass", "Ġenv iado", "Ġenvi ado", "Her oes", "Hero es", "ÐŁ еÑĢ", "ĠM aven", "ĠMa ven", ". ls", ".l s", "Ġ ive", "Ġi ve", "Ġiv e", "| RF", "|R F", "Ġresize Mode", "Ġrum pe", "_ attachments", "_attach ments", "_attachment s", "T U", "Ġtact ile", "Ġtac tile", "Attempt ing", "Ġro bin", "Ġrob in", "y aw", "ya w", "Ġmerc enaries", "ĠHab itat", "ĠHabit at", "end date", "Ġ oxy", "Ġo xy", "Ġox y", "ĉ Random", "ĉR andom", "o hon", "oh on", "oho n", "Is Null", "ĠValidation Result", "ãĥ ļ", "um bed", "umb ed", "p pv", "pp v", "Ġ arp", "Ġa rp", "Ġar p", "ich ick", "ichi ck", "_r nn", "ĠT FT", "ĠTF T", "Tex Image", "\" On", "Ġ Sampler", "ĠS ampler", "ĠSam pler", "ĠSample r", "ĠSamp ler", "t opl", "to pl", "top l", "Ġj ane", "Ġja ne", "Ġjan e", "y ling", "yl ing", "ĠUN ICODE", "Tab Index", "< {Ċ", "<{ Ċ", "s uspend", "sus pend", "uv ian", ", application", "ол иÑĩеÑģÑĤво", "y at", "ya t", "e zier", "ez ier", "ezi er", "ĠCH UNK", "ĠAd ler", "/ Add", "/A dd", "Ġ KeyValue", "ĠKey Value", "Ġspos ób", "S ampling", "Sam pling", "ch ers", "che rs", "cher s", "_ AMD", "_A MD", "_AM D", "R u", ".Must Compile", "N ation", "Na tion", "Nat ion", "As soc", "Ass oc", "Man aging", "Mana ging", "ĠEn gl", "ĠEng l", "_ GB", "_G B", "Ġsucc inct", "Ġdis liked", "Ġdislike d", "ĠI ke", "ĠIk e", "Bullet in", "_ARCH IVE", "Pro posal", "Prop osal", "Ġjog ging", ".C REATED", ".CREATE D", "Ġc hol", "Ġch ol", "Ġcho l", "è£ ħ", "Į ¨", "- push", "-p ush", "Ġres erva", "Ġreserv a", "co rev", "core v", "cor ev", "è tre", "T HR", "TH R", "Ġincompet ence", "Ġchar isma", "æĦ Ł", "Ġ\" ==", "Ġ\"= =", "B TN", "BT N", "Ġ Locator", "ĠL ocator", "ĠLoc ator", "i vet", "iv et", "ive t", "(' .')Ċ", "('. ')Ċ", "('.') Ċ", "('.' )Ċ", "Ġfor IndexPath", "ô me", "ôm e", "Ġcapac it", "w aters", "wa ters", "water s", "wat ers", "ĠWR ONG", "h oa", "ho a", "ĠM IPS", "ĠMI PS", "Ġe miss", "Ġem iss", "ĠJacqu eline", "( cmp", "(c mp", "(cm p", "Ġe ens", "Ġeen s", "Ġee ns", "L eo", "Le o", ". timing", ".t iming", ".tim ing", "CLU SION", "CLUS ION", "Ġ (\"-", "Ġ( \"-", "Ġ(\" -", "åĵ Ī", ". kode", ".k ode", "ĠUnder t", "ĠUnd ert", "Ġbe wild", "Ġbew ild", "ĠEs sen", "ĠEss en", ". hd", ".h d", "Ġren egot", "Ġm ower", "Ġmo wer", "Ġl sp", "Ġls p", "Ġpen chant", "Ġman oe", "Ġmano e", "Ġ agli", "Ġa gli", "Ġag li", "Ġre cal", "Ġr ecal", "Ġrec al", "ĠOPER ATION", "(^ )(", "Ġ ν", "ĠÎ ½", "Ġ Scoped", "ĠSc oped", "ĠScope d", "ĠSco ped", "Ġ @\"Ċ", "Ġ@ \"Ċ", "Ġ@\" Ċ", "= label", "=l abel", "[ loc", "[l oc", "I ntl", "In tl", "Int l", "ĠN z", "table t", "tab let", "tabl et", ". ColumnName", ".Column Name", "Ġscreen Size", "D Bus", "DB us", "co oked", "cook ed", "- registration", "-reg istration", "âĢľ One", "- non", "-n on", "-no n", "ĠwiÄĻ c", "Ġc osta", "Ġco sta", "Ġcost a", "Ġcos ta", ".add Tab", ". conditions", ".condition s", ".cond itions", "ĠH ess", "ĠHe ss", "MEM ORY", "ĠAval anche", "() }}Ċ", "()} }Ċ", "Ġtri plet", "Ġtrip let", "Ġtriple t", "Ġl abyrinth", "ĠNode List", "ĠN YT", "ĠNY T", "Ġy eni", "Ġye ni", "Ġyen i", "d ff", "df f", ".Html Controls", "A VIS", "AV IS", "/ Math", "/M ath", "Ġ memcmp", "Ġmem cmp", "ا Ø¡", "Ø§Ø ¡", "о ÑģÑĮ", "оÑģ ÑĮ", "c rap", "cr ap", "( pages", "(p ages", "(page s", "(pa ges", "Ġl xml", "Ġlx ml", "ĠQ DateTime", "_t cb", "_tc b", "Ġ openid", "Ġopen id", "Ġsyn aptic", "ĠM DMA", "ĠMD MA", "( slug", "(s lug", "(sl ug", "ig matic", "igma tic", "igm atic", "igmat ic", "e nor", "en or", "eno r", "Ġcr amped", "Ġcram ped", "G OP", "GO P", "Ń IJ", ".is File", "ĠD ifferential", "ĠDifferent ial", "Ġ =\"\";Ċ", "Ġ=\" \";Ċ", "ĉ ĉĉĠĠĠĠĉ", "ĉĉ ĉĠĠĠĠĉ", "ĉĉĉ ĠĠĠĠĉ", "ĉĉĉĠĠĠ Ġĉ", "ĉĉĉĠ ĠĠĠĉ", "ĉĉĉĠĠ ĠĠĉ", "ĉĉĉĠĠĠĠ ĉ", "ĠC ooke", "ĠCo oke", "ĠCook e", "ĉU FUNCTION", "Ġpersever ance", "Relative Layout", "IMPORT ANT", "Ġe xon", "Ġex on", "Ġ он", "Ġо н", "i base", "ib ase", "iba se", "( CONT", "(C ONT", "(CON T", "n ovation", "no vation", "nov ation", "nova tion", "ä½ ķ", "[ sub", "[s ub", "Admin Controller", "HTTP Header", "c rear", "cre ar", "cr ear", "ĠN IR", "ĠNI R", "ĠDrop DownList", "Ġval ide", "Ġvalid e", "Ġva lide", "Ġde hydration", ". ']", ".' ]", "( WIN", "(W IN", "Ġ ...\\", "Ġ. ..\\", "Ġ... \\", "Ġ.. .\\", "Ġphoto shop", "Ġphotos hop", "ĉ Init", "ĉI nit", "ĉIn it", "_ cou", "_c ou", "_co u", "Ġtime Zone", "dar win", "r omatic", "ro matic", "rom atic", "roma tic", "Navigation ItemSelectedListener", "b rates", "br ates", "bra tes", "brate s", "] --;Ċ", "Ġtraged ies", "ĠPed iatrics", "ĠPediatric s", "SM ART", "- API", "-A PI", "ĠMessage Lookup", "ĉ vo", "ĉv o", "Ġprejud ices", "Ġprejudice s", "Ġ mA", "Ġm A", "U ps", "Up s", "ĠMISS ING", "ĉ ad", "ĉa d", "C ream", "Cre am", "Cr eam", "ĠT b", "ĠM ona", "ĠMon a", "ĠMo na", "_ ghost", "_g host", "ĉ types", "ĉt ypes", "ĉtype s", "ĉtyp es", "E mb", "Em b", "ĠDocument ary", "' );ĊĊĊĊ", "') ;ĊĊĊĊ", "');Ċ ĊĊĊ", "');ĊĊ ĊĊ", "'); ĊĊĊĊ", "');ĊĊĊ Ċ", "Ġl up", "Ġlu p", "_ Reference", "_Re ference", "_Ref erence", "ĠB ATCH", "ĠBAT CH", "Ġintertw ined", "< Cell", "", "Ġf oyer", "Ġfo yer", "'util isation", "ĠMü ller", "ĠFet ish", "Ġdefault Manager", "Ġback track", "B ah", "Ba h", "Exp licit", "Expl icit", "_ ASCII", "_A SCII", "_ASC II", "Ġm Activity", "( Msg", "(M sg", "Ġ ê²Į", "Ġê² Į", "ĠTER MS", "ĠTERM S", "ĠAn gie", "ĠAng ie", "H SV", "HS V", "ĠMos que", ". Names", ".N ames", ".Name s", "íĬ ¼", "r este", "re ste", "res te", "rest e", "_ parms", "_p arms", "_par ms", "_pa rms", "_parm s", "Ġg aping", "Ġgap ing", "Ġga ping", "Ġc ropping", "Ġcr opping", "Ġcro pping", "Ġcrop ping", "Data Frame", "Ġrespons iveness", "Ġresponsive ness", "_ undo", "_un do", "_u ndo", "_ tran", "_t ran", "_tr an", "_tra n", ". terminate", ".term inate", "Ġitalian e", "Ġitalia ne", "Ġwalk through", "Ġattract iveness", "Ġattractive ness", "д е", "_ STS", "_S TS", "_ST S", "_ learn", "_l earn", "_le arn", "Ġchocolate s", "Ġchocol ates", "ier archical", "- thinking", "-th inking", "Ġ )))", "Ġ) ))", "Ġ)) )", "ish ments", "ishment s", ".Log f", ".Lo gf", "ĠT MZ", "ĠTM Z", "ĠCan ary", "f oil", "fo il", "ĠV accine", "ĠVacc ine", ". vx", ".v x", "ĠSur round", "Inter mediate", "Ġ iov", "Ġi ov", "Ġio v", "v ais", "va is", "' ;\";Ċ", "'; \";Ċ", "ï½ŀ ĊĊ", "éĢģ æĸĻ", "âĢ¦ it", "Se ats", "Sea ts", "Seat s", "C lar", "Cl ar", "Cla r", "W ars", "War s", "Wa rs", "ĠHutch inson", "ĠH asan", "ĠHas an", "ĠHa san", "! ')ĊĊ", "!' )ĊĊ", "!')Ċ Ċ", "ĠRich ie", "ĠRi chie", "che iden", "cheid en", "( $('", "($ ('", "($( '", "Y ork", "Yo rk", "Ġl ids", "Ġli ds", "Ġlid s", "Ġal phanumeric", "Ġalpha numeric", "ĠG lock", "ĠGl ock", "ĠGlo ck", ". shapes", ".sh apes", ".shape s", ".sha pes", "Ġsp arking", "Ġspark ing", "Ġspar king", "_ epsilon", "_e psilon", "_eps ilon", "up licated", "uplic ated", "uplicate d", ". dirty", ".d irty", ".dir ty", "] )==", "]) ==", "ĠìľĦ ì¹ĺ", "Ġs cn", "Ġsc n", "Ġ /****************************************************************", "Ġ/ ****************************************************************", "_PRE VIEW", "_ HC", "_H C", "ield ing", "iel ding", "f gets", "fg ets", "ĠAdd ison", "Ġproduct Service", "- figure", "-f igure", "( retval", "(ret val", "z ano", "za no", "zan o", "Ġaut ob", "Ġauto b", "ĉ sd", "ĉs d", "_ numer", "_n umer", "_num er", "ĠSet LastError", "ĠF ior", "ĠFi or", "ific ance", "ifica nce", "Unt itled", "Ġin field", "Ġinf ield", "Ġ{ }));Ċ", "Ġ{} ));Ċ", "Ġ{}) );Ċ", "Ġs pac", "Ġsp ac", "Ġspa c", "Ġr ookies", "Ġro okies", "Ġrookie s", "(des cribing", "n gen", "ng en", "nge n", "ி à®", ". rdf", ".r df", ".rd f", ". Mutex", ".M utex", "Ġkne eling", "Ġknee ling", "Ġ QE", "ĠQ E", "set Max", "Read Stream", "Ġ ventas", "Ġvent as", "Ġven tas", "Ġventa s", "s ut", "su t", "cm peq", "cmp eq", ".WriteAll Text", "ĠEx perienced", "ĠExperience d", "$ __", "$_ _", "Ġka um", "ĠL IS", "ĠLI S", "Ġdocument os", "Ġdocumento s", "_HE ALTH", "i contains", "icon tains", "icont ains", "Ġart isans", "Ġartisan s", "OW NER", "OWN ER", "Ġb linked", "Ġblink ed", "get Display", "Ġt oen", "Ġto en", "Ġtoe n", "Ġrow Num", "Ġav ril", "Ġin vis", "Ġinv is", "ĠK ear", "ĠKe ar", "toBe InTheDocument", "a pur", "ap ur", "Ġr acked", "Ġrac ked", "Ġrack ed", "ĠMc Master", "ĠMcM aster", "_ATTR IB", "H az", "Ha z", "Ġfact ura", "Ġfac tura", "/ ts", "/t s", "ĠÑĢаз меÑĢ", "ĠÑĢазм еÑĢ", "Ġ zf", "Ġz f", "Ġshort fall", ". fasta", ".f asta", ".fast a", ".fa sta", "ĠCONST ANT", ". managed", ".man aged", ".manage d", "g ems", "ge ms", "gem s", "Shared Pointer", "Ġbl urry", "Ġblur ry", "b rightness", "bright ness", "( components", "(com ponents", "(component s", "(comp onents", "Ġ ...\"ĊĊ", "Ġ... \"ĊĊ", "Ġ.. .\"ĊĊ", "Ġ...\" ĊĊ", "Ġ...\"Ċ Ċ", "S ELL", "SE LL", "SEL L", "ĠIllustr ator", ".get Channel", "Ġtrou vé", "y sters", "yst ers", "ys ters", "yster s", "Ġv ois", "Ġvo is", "Ġvoi s", "ĠL inden", "ĠLin den", "ĠLind en", "Ġem ojis", "Ġemoji s", "Ġemo jis", "Ġb rawl", "Ġbr awl", "Ġbra wl", "ĠM SR", "ĠMS R", "ĠE lo", "ĠEl o", "ĠCroat ian", "ĠCroatia n", "Popup Menu", "L ewis", "Le wis", ". JWT", ".J WT", "Ġaston ished", "B ush", "Bus h", "Bu sh", "( itemId", "(item Id", "Ġdet achment", "Ġdetach ment", "ĠEn core", "ĠEnc ore", "å° Ķ", "Ġre kl", "Ġr ekl", "Ġrek l", "Ġc ram", "Ġcr am", "Ġcra m", ") $/", ")$ /", ".get Host", "_ recommend", "_re commend", "- HT", "-H T", "_cal ibration", "Auth enticate", ".firebase app", "UN IX", "ĉ Camera", "ĉC amera", "ĠHE AP", "I deal", "Ide al", ". office", ".off ice", "Ġgoof y", "Ġgoo fy", "( Symbol", "(S ymbol", "Ġjo uer", "Ġjou er", "_part itions", "_partition s", "Ġrapid ement", "Ġrapide ment", "ĠGNU NET", "ĠGN UNET", "id User", "Ġsuper vise", "Ġsuperv ise", "( Contact", "A WN", "AW N", "ãģ ĺ", "Ġna am", "Ġa ust", "Ġau st", "Ġaus t", "åľ¨ 线", "_ softmax", "_soft max", "Allow Anonymous", "amm able", "amma ble", "RO UTE", "ROUT E", "* D", "Ġ aden", "Ġa den", "Ġad en", "Ġade n", "ĠCrist ina", "ĠCrist iano", "Ġblood stream", "sub class", "_ persona", "_person a", "CH ILD", "- know", "-k now", "Ġnavigation Options", "ĠZuk unft", "ĠPix ar", "Ty ler", "Ġunder world", "Ġsincer ity", "Ġdisp enser", "Ġdispens er", "Ġk ter", "Ġkt er", "id ders", "idd ers", ".add Node", "- checked", "-check ed", "Ġkey st", "Ġke yst", "Ġkeys t", "ĠW TO", "ĠWT O", ". signals", ".sign als", ".signal s", "Ġadvent urer", "Ġadventure r", "ĠP ang", "ĠPan g", "ĠPa ng", "\\ R", "= pos", "=p os", "Ġdispens aries", "ĠClose t", "ĠClo set", "(\" {\\\"", "(\"{ \\\"", "id eon", "ide on", "ideo n", "Ġnécess aire", "( )\"Ċ", "() \"Ċ", "()\" Ċ", "_RECE IVED", "Ġrésult ats", "Ġm oden", "Ġmod en", "Ġmode n", "Ġmo den", "ĠIceland ic", "; d", ". allowed", ".all owed", ".allow ed", "(new User", "Ġmerc iless", ".Wait For", "Ġday care", "ĠCon veyor", "Ġ Ù", "ا Ù", "า à¸", "Ñ Ł", "ÑŁ ÑŁ", "Ġ à¸", "Ġà ¸", "à¹Ģ à¸", "i á»", "ãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢ", "Ġ اØ", "Ġا Ø", "ॠĪ", "Ġ ãĢĢ", "ĠãĢ Ģ", "Ñ Ĺ", "i á»ĩ", "iá» ĩ", "ÑŁ ÑŁÑŁÑŁ", "ÑŁÑŁ ÑŁÑŁ", "ÑŁÑŁÑŁ ÑŁ", "à¥ĩ à¤Ĥ", "à¥ĩठĤ", "Ñĸ д", "ा र", "ाठ°", "ÙĨ د", "Ñĸ в", "Ġ ब", "Ġठ¬", "Ġ à¤ľ", "Ġठľ", "à ¥¤", "ॠ¤", "н Ñĸ", "ठĹ", "Ġ Ø¢", "ĠØ ¢", "Ġ न", "Ġठ¨", "Ñ Ķ", "Ġ ÑĢа", "ĠÑĢ а", "Ġ à¤ħ", "Ġठħ", "Ñģ ÑĮ", "Ġ व", "Ġठµ", "ÑĨ Ñĸ", "Ġv á»", "³ ت", "Ġ द", "Ġठ¦", "n ÄĽ", "Ġ ल", "Ġठ²", "Ġ ãĢĢĠãĢĢ", "ĠãĢĢ ĠãĢĢ", "ĠãĢĢĠ ãĢĢ", "ॠĤ", "ठ¦", "à¸Ń à¸ĩ", "ÙĪ ÙĨ", "ठµ", "a ÅŁ", "๠Ĥ", "ι κ", "Ġ र", "Ġठ°", "Ġ ви", "Ġв и", "à¥į य", "à¥įठ¯", "ा न", "ाठ¨", "Ġ از", "Ġا ز", "ĠØ§Ø ²", "ا Ùĩ", "ا٠ĩ", "Ľ i", "Ġh á»", "à¥ĭ à¤Ĥ", "i ế", "ĠÄij á»", "ठ¯", "Ï į", "Ġc ủ", "Ġ بر", "Ġب ر", "Ġ ÙħÛĮ", "ĠÙħ ÛĮ", "Ġ اÛĮ", "Ġا ÛĮ", "Ġ à¤Ĩ", "ĠठĨ", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ि य", "िठ¯", "ÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁ", "в и", "ر د", "н Ñĥ", "ÙĬ ÙĨ", "ι α", "Ġ त", "Ġठ¤", "Ñĩ и", "Ġ à¤ķर", "Ġà¤ķ र", "ا ز", "Ø§Ø ²", "a ÄŁ", "Ġ à¤ī", "Ġठī", "ठ¬", "ÏĦ α", "ت ر", "Ùĩ ا", "ร ะ", "j ÃŃ", "Î ij", "а ÑĤи", "аÑĤ и", "Ġ à¤Ĺ", "ĠठĹ", "Ġ ÑĤа", "ĠÑĤ а", "Ú Ĩ", "ठľ", "า à¸Ļ", "าภĻ", "Ġ à¤Ń", "ĠठŃ", "ि à¤ķ", "िठķ", "á v", "Ġ Ú¯", "Ï İ", "า ย", "าภ¢", "Ġ à¤Ķ", "ĠठĶ", "ÅĻ ÃŃ", "ا ÙĪ", "ا٠Ī", "Ġ Ñī", "ĠÑ ī", "Ġ à¤Ķर", "Ġà¤Ķ र", "ен нÑı", "Ġ Ú©Ùĩ", "ĠÚ© Ùĩ", "ठ¡", "ÏĦ ο", "ε ι", "Ġ à¤ĩ", "Ġठĩ", "à¥į त", "à¥įठ¤", "ठŁ", "Û ±", "Ġ ØĮ", "ĠØ Į", "Ïģ ο", "η ÏĤ", "ë ¬", "Ñĸ н", "i á»ģ", "iá» ģ", "i ên", "iê n", "Ġ вÑĸд", "Ġв Ñĸд", "ĠвÑĸ д", "d ı", "ÙĦ ÛĮ", "Ġ ز", "ĠØ ²", "Ïģ α", "Ġ ÛĮ", "า à¸ĩ", "าภĩ", "Ġth á»", "Ġ à¹Ģà¸", "Ġà¹Ģ à¸", "i á»ĩn", "iá»ĩ n", "ا ÙĬ", "ا٠Ĭ", "ан нÑı", "ÑĢ е", "Î Ł", "å Ĵ", "ا Ø´", "Ø§Ø ´", "ा ल", "ाठ²", "ëħ Ħ", "Ġ य", "Ġठ¯", "Ġ را", "Ġر ا", "ठ¼", "Ñĥ в", "ÙĪ Ùħ", "Ġ عÙĦ", "Ġع ÙĦ", "ί α", "à¥Ī à¤Ĥ", "à¥ģ à¤", "า ม", "าภ¡", "Ġm á»Ļt", "Ġ à¤ı", "Ġठı", "ãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢ", "Ġ पर", "Ġप र", "Ġ اÙĨ", "Ġا ÙĨ", "Ġ اÛĮÙĨ", "Ġا ÛĮÙĨ", "ĠاÛĮ ÙĨ", "Ġv Ỽi", "Ġvá» Ľi", "Î £", "ठļ", "Û °", "i á»ĥ", "iá» ĥ", "า à¸ģ", "าภģ", "Î Ļ", "ا ع", "Ø§Ø ¹", "Ñĸ й", "à¹ģ ล", "Ùĩ اÛĮ", "Ùĩا ÛĮ", "Ñĩ а", ". :.:", ".: .:", ".:. :", "ÏĦ η", "Ġ Îij", "ĠÎ ij", "ر ÛĮ", "Ġn gh", "Ġng h", "ν α", "à¹ĥ à¸Ļ", "ि त", "िठ¤", "Ġ και", "Ġκ αι", "Ġκα ι", "ÏĦ ε", "à¥į à¤Ł", "à¥įठŁ", "μ α", "л Ñĥ", "ý m", "ÏĢ ο", "à¥Ī ।", "ï¼ ¼", "ر ÙĬ", "н иÑħ", "ни Ñħ", "Ïģ ι", "Ù Ģ", "ÑĢ о", "Ġ à¤ļ", "Ġठļ", "ा त", "ाठ¤", "ا ÙĤ", "ا٠Ĥ", "Ġ श", "Ġठ¶", "ĠÄij á»Ļ", "ĠÄijá» Ļ", "é ho", "iá»ģ u", "ภ¨", "Ñĸ лÑĮ", "Ñĸл ÑĮ", "uy á»", "Û ²", "Ġn Äĥ", "Ïī ν", "Ġ ÏĦοÏħ", "ĠÏĦ οÏħ", "ĠÏĦο Ïħ", "к ий", "ки й", "í ĸ", "Ġ Ñīо", "ĠÑī о", "à¥į व", "à¥įठµ", "Ġ اÙĦØ£", "ĠاÙĦ Ø£", "ا ئ", "Ø§Ø ¦", "t ı", "Ġ ÏĦο", "ĠÏĦ ο", "¬ ¬", "Ġ Ø·", "ĠØ ·", "Ùħ اÙĨ", "Ùħا ÙĨ", "Ġ Îł", "ĠÎ ł", "д и", "ภ¶", "ि à¤ı", "िठı", "ãģ£ ãģŁ", "ãģ£ãģ Ł", "ÛĮ Ùħ", "ÃŃ nh", "ÃŃn h", "r av", "ra v", "ÄĽ t", "Î ķ", "Ġ Ñıк", "ĠÑı к", "ç Ĥ", "à¸Ń à¸Ļ", "ãģ¦ ãģĦ", "ि ल", "िठ²", "Ñĸ ÑĤ", "з а", "á p", "ठ§", "Ġ êµ", "Ġê µ", "à¹ģ ละ", "à¹ģล ะ", "ÃŃ ch", "ÃŃc h", "Ġ Ø¢ÙĨ", "ĠØ¢ ÙĨ", "ت Ùĩ", "Ġ Ùħع", "ĠÙħ ع", "н ий", "ни й", "Æ°á»Ľ c", "Ġ اÙĦع", "Ġا ÙĦع", "ĠاÙĦ ع", "ر ب", "ा म", "ाठ®", "Ġ رÙĪ", "Ġر ÙĪ", "é «", "ı y", "Ġh á»į", "Ġhá» į", "ÑĤÑĮ ÑģÑı", "Ġ Îļ", "ĠÎ ļ", "Ġ à¤ĩस", "Ġà¤ĩ स", "ï¼ ¿", "Ġ ÚĨ", "Ġ ÙĪاÙĦ", "ĠÙĪ اÙĦ", "ĠÙĪا ÙĦ", "íķ Ļ", "ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ", "Ġ vý", "Ġv ý", "ि स", "िठ¸", "ữ ng", "س ÛĮ", "Ġ ìĥ", "Ġì ĥ", "ा à¤Ĥ", "ाठĤ", "ï½ ¤", "à¹ĩ à¸Ļ", "Ġ थ", "Ġठ¥", "l arak", "la rak", "lar ak", "lara k", "â y", "t ÄĽ", "ν ο", "Ġ ÙħÙĪ", "ĠÙħ ÙĪ", "Ġng Æ°á»Ŀi", "å ¦", "ÙĬ د", "il ir", "ili r", "ا ØŃ", "Ø§Ø Ń", "Ġ ãĢ", "Ù ĭ", "Ġ ÑĢоз", "ĠÑĢ оз", "Ġ й", "ĠÐ ¹", "Ġd ụ", "à¹Ģ à¸Ľ", "à¹ĢภĽ", "ั à¸ĩ", "л е", "ा य", "ाठ¯", "ï¿ £", "ÙĪ اÙĨ", "ÙĪا ÙĨ", "Ġth á»ĥ", "Ġthá» ĥ", "ã ĥ½", "ãĥ ½", "ü ÅŁ", "ç Ł", "Ġ ο", "ĠÎ ¿", "Ġ Σ", "ĠÎ £", "ÛĮ ت", "ั à¸ģ", "Î ¤", "Ġ à¤ıà¤ķ", "Ġà¤ı à¤ķ", "Ġ ÙĩÙħ", "ĠÙĩ Ùħ", "ìĽ Ķ", "Ġ Îľ", "ĠÎ ľ", "Ġ à¸Ħ", "ĠภĦ", "¯ ¸", "ا رÛĮ", "ار ÛĮ", "ि न", "िठ¨", "Ġnh ững", "Ġnh Æ°", "и ÑĤи", "иÑĤ и", "ãĥ³ ãĥ", "à¹Ģ ร", "à¹Ģภ£", "Ġ ÐĽ", "ĠÐ Ľ", "ÑĢ Ñĸ", "á d", "ü y", "i ye", "iy e", "Ġ Îķ", "ĠÎ ķ", "Ġ ส", "Ġภª", "Ïĥ η", "Ġ ë¬", "Ġë ¬", "ï »", "ठ£", "Î Ĺ", "ठ¶", "Ġ ÙħØŃ", "ĠÙħ ØŃ", "ÙĦ ÙĬ", "Ġ με", "Ġμ ε", "Ġp ÅĻÃŃ", "ĠpÅĻ ÃŃ", "Î Ŀ", "à¥į ष", "à¥įठ·", "t ir", "ti r", "ر اÙĨ", "را ÙĨ", "ĠÄij á»ĭ", "ĠÄijá» ĭ", "Ġ коÑĤ", "Ġк оÑĤ", "Ġко ÑĤ", "к ÑĢа", "λ ο", "Ġ ÏĦη", "ĠÏĦ η", "Ñī е", "ÏĦ ικ", "ÏĦι κ", "ั à¹ī", "i ết", "iế t", "α ν", "í Ķ", "к иÑħ", "ки Ñħ", "Ġ поÑģ", "Ġп оÑģ", "Ġпо Ñģ", "t ır", "tı r", "à¥į म", "à¥įठ®", "ر Ùģ", "ÄĽ l", "ठŃ", "o vé", "ov é", "Ġl á»", "à¹Ħ à¸Ķ", "ãģª ãģĦ", "ภ©", "i á»ĩu", "iá»ĩ u", "Î ¾", "Ġ عÙĦÙī", "Ġع ÙĦÙī", "ĠعÙĦ Ùī", "д Ñĥ", "Ġdụ ng", "а ÑĢа", "аÑĢ а", "ा द", "ाठ¦", "o ž", "ÙĦ Ùĩ", "ÙĦ Ùħ", "н оÑĹ", "но ÑĹ", "Û± Û", "à¸Ĥ à¸Ńà¸ĩ", "Î ¡", "à¥Ģ à¤Ĥ", "Ġ пÑĸд", "Ġп Ñĸд", "Ġ फ", "Ġठ«", "ภĺ", "ε ÏĤ", "ा स", "ाठ¸", "à¹ĥ ห", "о ва", "ов а", "ت ÛĮ", "à¸Ń ย", "ภį", "Ġn Äĥm", "ĠnÄĥ m", "ÏĦ ι", "ÙĪ ÛĮ", "Ġ мÑĸ", "Ġм Ñĸ", "Ġ اÙħ", "Ġا Ùħ", "ÏĢ ÏĮ", "Ġ zá", "Ġz á", "ठĪ", "Ġ à¤ĸ", "Ġठĸ", "Ġ nÄĽ", "Ġn ÄĽ", "c ÃŃ", "ÙĨ Ú¯", "Ñģ и", "Î ¶", "n á", "Ŀ i", "Å ©", "Ø ¦", "Ġ اÙĦس", "Ġا ÙĦس", "ĠاÙĦ س", "á»ij c", "Ạ½", "ا ج", "Ø§Ø ¬", "Ùħ ا", "êµ Ń", "о Ñİ", "د ر", "à¹Ģ à¸ģ", "à¹Ģภģ", "ภł", "à ¡ng", "á ng", "án g", "íķ ©", "Ġ ÏĦηÏĤ", "ĠÏĦ ηÏĤ", "ĠÏĦη ÏĤ", "Ġ Ñĸн", "ĠÑĸ н", "о ÑĹ", "à¥ĩ श", "à¥ĩठ¶", "ภĭ", "à¥ĭ à¤Ĺ", "л Ñĸ", "Ġp ÅĻed", "ĠpÅĻ ed", "ĠpÅĻe d", "Äį nÃŃ", "Ġ ка", "Ġк а", "Ġ Τ", "ĠÎ ¤", "á»Ļ i", "v ÃŃ", "ÑĢ Ñı", "ा à¤ľ", "ाठľ", "а Ñħ", "ि र", "िठ°", "า ส", "าภª", "d ır", "dı r", "Ø ¢", "Î ļ", "Ġ ÎŃ", "ĠÎ Ń", "Ġt ại", "iá»ĩ c", "i ến", "iế n", "Ġ غ", "ĠØ º", "ا Ø®", "Ø§Ø ®", "Ġ اÙĦØŃ", "Ġا ÙĦØŃ", "ĠاÙĦ ØŃ", "Ġ бÑĥ", "Ġб Ñĥ", "Ġv á»ģ", "Ġvá» ģ", "м Ñĸ", "Ùħ ÙĦ", "m Ä±ÅŁ", "à¸Ľ ระ", "à¸Ľà¸£ ะ", "ο Ïį", "ε ί", "Ġर ह", "н им", "ни м", "ع د", "Ġ باÙĦ", "Ġب اÙĦ", "Ġبا ÙĦ", "¤ ij", "ç ł", "Ġo lm", "Ġol m", "Ïİ Î½", "Ġh á»įc", "Ġhá»į c", "ا ست", "Ø§Ø ³Øª", "اس ت", "า ว", "าภ§", "ÙĪ ب", "Ñĸ Ñı", "Ġ ÙĩاÛĮ", "ĠÙĩ اÛĮ", "ĠÙĩا ÛĮ", "ë§ Ī", "ॠĮ", "Ġ ÄĮ", "ĠÄ Į", "ठı", "ا دÙĩ", "اد Ùĩ", "Ġ اÙĪ", "Ġا ÙĪ", "н Ñĭм", "нÑĭ м", "Ạ±", "Ùħ ÙĨ", "iá»ĩ t", "l aÅŁ", "la ÅŁ", "Ñĸ з", "ÙĪ س", "Ġl Ãłm", "ĠlÃł m", "ĠÄij ến", "ĠÄijế n", "प न", "Ġ ÛĮÚ©", "ĠÛĮ Ú©", "Ġ ÙĦÙĦ", "ĠÙĦ ÙĦ", "Ġ mÄĽ", "Ġm ÄĽ", "Ġ براÛĮ", "Ġبر اÛĮ", "ा ह", "ाठ¹", "Ġ Ùħر", "ĠÙħ ر", "e ç", "à¸Ń ร", "ε Ïģ", "ั à¸Ķ", "к он", "ко н", "n ou", "no u", "Ġ год", "Ġг од", "ู à¹ī", "à¹Ģ ล", "à¹Ģภ¥", "Ú ĺ", "ĠÄij á»ĭnh", "ĠÄijá»ĭ nh", "ĠÄij ó", "а нов", "ан ов", "ано в", "Ġ Ùģر", "ĠÙģ ر", "ا رد", "ار د", "Ñĸ ÑĹ", "à¸Ħ ร", "à¥į थ", "à¥įठ¥", "c ak", "ca k", "ÑĨ ÑĸÑĹ", "ÑĨÑĸ ÑĹ", "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", "Ùĩ ر", "ॠī", "Ġg iá»", "Ġgi á»", "í Ĩ", "âĢĮ ÙĩاÛĮ", "âĢĮÙĩا ÛĮ", "à¥ģ र", "à¥ģठ°", "Ġ à¸ģ", "Ġภģ", "Å Ī", "æ ¨", "ÎŁ Î", "า à¸Ħ", "าภĦ", "кÑĢа ÑĹ", "ả o", "o ÄŁ", "Ġ सम", "Ġस म", "Ġv iá»ĩc", "Ġs ẽ", "Ġ ná", "Ġn á", "ÙĬ Ùħ", "£ p", "ö y", "ÙĪ ز", "Ġ κα", "Ġκ α", "Ùħ د", "n ÃŃm", "nÃŃ m", "o vá", "ov á", "ा व", "ाठµ", "ा ।", "à¥į स", "à¥įठ¸", "ç ·", "ặ c", "Ġ à¸ŀ", "Ġภŀ", "ï½ Ģ", "ô i", "Ġ ợ", "Ġá» Ł", "ο ÏĤ", "Ġtr ên", "м Ñĥ", "ÑģÑĮ к", "ภŁ", "o vat", "ov at", "ova t", "Ġm á»", "í ı", "Ġ во", "Ġв о", "ε ν", "à¥Ĥ र", "Ú¯ اÙĩ", "ĠÄij á»Ļng", "ĠÄijá»Ļ ng", "Ú© ÙĨ", "Ñī и", "Ġ пÑĢа", "Ġп ÑĢа", "ĠпÑĢ а", "ü rk", "ür k", "ÙĪ ع", "ấ p", "n ý", "Ġ quan", "Ġqu an", "Ġq uan", "Ġqua n", "Ñĸ Ñĩ", "Ġ να", "Ġν α", "Ġन ह", "Ġ Ú©ÙĨ", "ĠÚ© ÙĨ", "c ı", "çĿ Ģ", "б о", "Ġ اس", "Ġا س", "ĠØ§Ø ³", "è »", "ا ÙĨÛĮ", "اÙĨ ÛĮ", "à¸ķ ร", "ÏĦ ά", "Ġ Ø£ÙĨ", "ĠØ£ ÙĨ", "éĤ £", "Ġ ม", "Ġภ¡", "к ÑĤ", "i ê", "Ġhá» £p", "ت Ùħ", "Ġ بÙĨ", "Ġب ÙĨ", "h od", "ho d", "ι Ïĥ", "ห à¸Ļ", "Ġ ÑĹ", "ĠÑ Ĺ", "л ив", "ли в", "Ġ کرد", "ĠÚ© رد", "Ġکر د", "Ġ ÙħØ´", "ĠÙħ Ø´", "ا Ø·", "Ø§Ø ·", "ب ÙĬ", "Ġ ร", "Ġภ£", "د Ùħ", "ÙĦ اÙħ", "ÙĦا Ùħ", "à¹Ī ว", "Ġ ÙĨÙħ", "ĠÙĨ Ùħ", "Ġ æĹ", "Ġæ Ĺ", "é ħ", "н оÑģÑĤ", "но ÑģÑĤ", "ноÑģ ÑĤ", "i á»ĥm", "iá»ĥ m", "êµ IJ", "a yı", "ay ı", "Ġ بÙĪد", "Ġب ÙĪد", "ĠبÙĪ د", "Ú¯ ر", "Ġh iá»ĩn", "Ġhi á»ĩn", "ç ³", "ÑģÑĤ вен", "ÑģÑĤв ен", "ÑģÑĤве н", "Ġà¤ķर न", "Ġ ÏĦην", "ĠÏĦ ην", "ĠÏĦη ν", "Ġ à¸Ń", "ĠภŃ", "Ġ Ùħت", "ĠÙħ ت", "ģ n", "ج Ùħ", "λ λ", "Ġ ÑĢе", "ĠÑĢ е", "ิ à¸Ķ", "Ġ اÙĦÙĤ", "Ġا ÙĦÙĤ", "ĠاÙĦ ÙĤ", "α Ïģ", "Ġ यह", "Ġय ह", "n ÃŃch", "nÃŃ ch", "ÑĶ ÑĤÑĮÑģÑı", "Ġ à¸Ĺ", "ĠภĹ", "ÛĮ Ø´", "ÅĻ e", "Ġn ebo", "Ġne bo", "Ġneb o", "Ġ Ñĩа", "ĠÑĩ а", "l ou", "lo u", "ÑģÑĤ во", "ÑģÑĤв о", "Ġ Ч", "ĠÐ §", "à¸Ħ ว", "Ùĩ Ùħ", "à¹Ģ à¸Ķ", "à¹ĢภĶ", "Ġ à¹ģ", "Ġ à¹Ĥ", "Û ³", "Å© ng", "Ġ nej", "Ġn ej", "Ġne j", "ÛĮ Ú©", "Ġs á»Ń", "Ùģ ر", "Î ł", "Ġп ок", "Ġпо к", "ĠاÙĦ ÙĨ", "Ġv Å¡", "á º«", "Ạ«", "Ġnh Ãł", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ή ÏĤ", "ο Ïģ", "Ġ Ïĩ", "ĠÏ ĩ", "à¹Ģ à¸Ĺ", "à¹ĢภĹ", "Ñĥ лÑĮ", "Ñĥл ÑĮ", "ãħ ĩ", "Ġ yıl", "Ġy ıl", "Ġyı l", "ÑĢ од", "ÑĢо д", "ί ν", "ìĹ Īëĭ¤", "ìĹĪ ëĭ¤", "ا ص", "Ø§Ø µ", "ĠÄij ầu", "à¥ĩ à¤ķ", "à¥ĩठķ", "ÑĢ ом", "ÑĢо м", "ãģĵ ãģ¨", "Ġ ار", "Ġا ر", "ĠØ§Ø ±", "å¥ ¹", "Ġ تØŃ", "Ġت ØŃ", "Å¡ tÄĽ", "Å¡t ÄĽ", "à¥į ल", "à¥įठ²", "à¥į à¤ķ", "à¥įठķ", "Ġ کار", "ĠÚ© ار", "u jÃŃ", "uj ÃŃ", "Ġ à¤īन", "Ġà¤ī न", "Ġ αÏĢÏĮ", "Ġα ÏĢÏĮ", "ĠαÏĢ ÏĮ", "Ġm Ãł", "ž ÃŃ", "Ġ à¸Ī", "ĠภĪ", "a lı", "al ı", "ठ«", "Ñĩ еÑģ", "Ñĩе Ñģ", "Ġ عÙĨ", "Ġع ÙĨ", "æķ Ļ", "ï¾ Ĩ", "ि à¤Ĥ", "िठĤ", "Ġs á»±", "в оÑĢ", "во ÑĢ", "Ġth á»±c", "ë į°", "ëį °", "ãģ¦ ãģĦãĤĭ", "ãģ¦ãģĦ ãĤĭ", "à¹Ī à¸ĩ", "ت ب", "Ġnh iá»ģu", "ĥ n", "ĠÄij á»ĵ", "ĠÄijá» ĵ", "Ġ ห", "Ġภ«", "Û µ", "m ÄĽ", "ạ t", "Ġch ÃŃnh", "ĠchÃŃ nh", "ĠchÃŃn h", "μ ÎŃ", "an ı", "Ġb á»ĭ", "ằ ng", "ÅĻ ed", "ÅĻe d", "é Ł", "á nh", "án h", "ÙĢ ÙĢ", "Ġ Ùħس", "ĠÙħ س", "á»ĭ ch", "Ä ĥn", "Äĥ n", "o vánÃŃ", "ov ánÃŃ", "ová nÃŃ", "ován ÃŃ", "à¹Ī าà¸ĩ", "à¹Īา à¸ĩ", "Ġ à¸Ľ", "ĠภĽ", "Ġn Æ°á»Ľc", "Ð ±Ð¾ÑĤ", "б оÑĤ", "бо ÑĤ", "ı yor", "ıy or", "ĠØ® ÙĪد", "ĠØ®ÙĪ د", "Û ¹", "Ġ Ùħد", "ĠÙħ د", "Ġ üz", "Ġü z", "ì ½", "ÙĪ ÙĤ", "ë¥ ´", "л ек", "ле к", "Ġc ả", "ол ог", "оло г", "à¹ī à¸Ńà¸ĩ", "à¹īà¸Ń à¸ĩ", "m iÅŁ", "mi ÅŁ", "à¹ī ว", "Ä ©", "Î ľ", "à¸Ń à¸ģ", "＿ ＿", "ठĸ", "Ġ Я", "ĠÐ ¯", "ë ¬´", "ë¬ ´", "اÛĮ ÛĮ", "s ké", "sk é", "uy ên", "e ÅŁ", "á i", "ú ng", "ún g", "Ãł o", "Ñĸ Ñģ", "ç ¶", "Ġ à¤Ĩप", "Ġà¤Ĩ प", "ï º", "Î Ľ", "Ġ ê³µ", "Ġê³ µ", "Ġ ÐĨ", "ĠÐ Ĩ", "Ġà¤ħ पन", "Ġà¤ħप न", "ứ ng", "ÏĮ ÏĤ", "Ġngh iá»ĩ", "Ġnghi á»ĩ", "Ġ اÙĦب", "Ġا ÙĦب", "ĠاÙĦ ب", "à¥ĭ न", "Ġ à¤Ł", "ĠठŁ", "Ġ ìľł", "Ġìľ ł", "Ġc Å©ng", "ĠcÅ© ng", "Ġà¤ī स", "Ġ ड", "Ġठ¡", "ĠØ´ دÙĩ", "Ġشد Ùĩ", "ี à¹ī", "Û ´", "ặ t", "æĸ ¯", "Ġ ëį", "Ġë į", "Ġп л", "б и", "ê³ Ħ", "ο ν", "Ġç ık", "Ġçı k", "Ġbu lun", "Ġbul un", "س Ùħ", "a ç", "ا ÙĨÙĩ", "اÙĨ Ùĩ", "ÛĮ ز", "l eÅŁ", "le ÅŁ", "ắ c", "ا Ú©", "Ġस à¤ķ", "Ġ оÑĢг", "Ġо ÑĢг", "ĠоÑĢ г", "Ġ à¸Ļ", "ĠภĻ", "ा थ", "ाठ¥", "Ġ ÙħÙĤ", "ĠÙħ ÙĤ", "ĠÎĶ E", "Ñİ ÑĤÑĮ", "ÑİÑĤ ÑĮ", "á»Ļ c", "Ġ η", "ĠÎ ·", "s ob", "so b", "Ġth eo", "Ġthe o", "å ŀ", "Ġ اÙĦØ´", "ĠاÙĦ Ø´", "à¹Ģ à¸ŀ", "à¹Ģภŀ", "ÎŃ ÏĤ", "à¹Ģ à¸Ĥ", "à¹ĢภĤ", "å Ļ", "ि श", "िठ¶", "Ġ باز", "Ġب از", "Ġبا ز", "ÑĢ об", "ÑĢо б", "Ġγ ια", "μ ε", "Ġ باش", "Ġب اش", "Ġبا Ø´", "ा à¤ĩ", "ाठĩ", "Ġqu y", "Ġq uy", "λ ε", "ا Ùĥ", "ا٠ĥ", "Ġ ÑĢок", "ĠÑĢ ок", "Ġ Türk", "ĠT ürk", "ĠTür k", "Ġ Ð¥", "ĠÐ ¥", "ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ", "æ ©", "Ġph ải", "à¸Ħ วาม", "à¸Ħว าม", ": ::", ":: :", "l ÃŃ", "Ġj sou", "Ġjs ou", "ÛĮ ÙĦ", "ál nÃŃ", "áln ÃŃ", "Ķ Ķ", "í ĸī", "íĸ ī", "æĥ ³", "l á", "Ġ ÏĥÏħ", "ĠÏĥ Ïħ", "Ñĭ ва", "Ñĭв а", "Ġnh ất", "à¸Ń ม", "Û ¸", "e cek", "ec ek", "ece k", "Ñĸ ÑĢ", "ÙĪ Ø´", "λ α", "Ġ ÎĴ", "ĠÎ Ĵ", "о ÑĢа", "оÑĢ а", "Ùģ ت", "e dir", "ed ir", "edi r", "Ñĥ Ñħ", "ä¸ ĸ", "ĠУ кÑĢаÑĹ", "ĠУкÑĢа ÑĹ", "Ġ íĶ", "Ġí Ķ", "ά ν", "Ġ شر", "ĠØ´ ر", "ĠاÙĦ ج", "е ÑĢед", "еÑĢ ед", "еÑĢе д", "ìĺ ģ", "Ġh Ãłnh", "ï¿£ ï¿£", "м е", "ÑİÑĤ ÑģÑı", "ĠØ¥ ÙĦÙī", "ĠØ¥ÙĦ Ùī", "ìĹ ħ", "Ġ تر", "Ġت ر", "к ом", "ко м", "Ġ شد", "ĠØ´ د", "Ġ اÙĦÙĥ", "Ġا ÙĦÙĥ", "ĠاÙĦ Ùĥ", "Ġ ÏĥÏĦο", "ĠÏĥ ÏĦο", "à¥į द", "à¥įठ¦", "ëł ¤", "Ñĥ ваннÑı", "Ñĥв аннÑı", "Ġth ì", "ê ´Ģ", "ê´ Ģ", "κ ε", "س ب", "íĥ Ģ", "Ġ ï¼ı", "Ġï¼ ı", "Ġ à¹ģละ", "Ġà¹ģ ละ", "Ġà¹ģล ะ", "Ġ ÏĮ", "ĠÏ Į", "н иÑĨ", "ни ÑĨ", "Ġ ÐĿа", "ĠÐĿ а", "Ñı в", "l ü", "ι ο", "ÙĨ دÙĩ", "ÙĨد Ùĩ", "ÙĦ Ùĥ", "Ġng Ãły", "Ġnh ân", "Ġ ^{", "Ġ^ {", "ॠĥ", "Ġg erek", "Ġge rek", "Ġger ek", "Ġgere k", "ا رÙĩ", "ار Ùĩ", "Ġc Æ¡", "Ġ à¸ķ", "Ġภķ", "æ Ĥ", "çĶ °", "à¥Īà¤Ĥ ।", "ั ว", "v ÄĽ", "ö z", "и ли", "ил и", "Ġph áp", "Ġphá p", "ê¸ Ī", "Ġ ÎŁ", "ĠÎ Ł", "Ġp ÅĻi", "ĠpÅĻ i", "Ġ ìĸ´", "Ġìĸ ´", "Ġд ол", "Ġдо л", "ÙĪ رد", "ÙĪر د", "à¹Ģ ม", "à¹Ģภ¡", "Ïĥ ε", "า à¸Ĺ", "าภĹ", "o Ãłi", "ร ม", "Û ¶", "Ġ à¸ļ", "Ġภļ", "i yet", "iy et", "iye t", "ÏĦ αι", "ÏĦα ι", "ìĦ ł", "Ġ εÏĢ", "Ġε ÏĢ", "ि व", "िठµ", "ê¹ Į", "г а", "ĠÑģ лÑĥ", "ĠÑģл Ñĥ", "Ġh ình", "Ġ داÙĨ", "Ġد اÙĨ", "Ġà¤Ĺ य", "ÙĬ ا", "è ij", "à¤Ĥ त", "Ġ ساÙĦ", "Ġس اÙĦ", "ëł Ī", "l erin", "le rin", "ler in", "leri n", "à¥ĩ त", "à¥ĩठ¤", ".: .:.:.:", ".:.: .:.:", ".:. :.:.:", ".:.:.: .:", ".:.:. :.:", ".:.:.:. :", "Ġ ëħ", "Ġë ħ", "Ġ اÙĦØ¥", "ĠاÙĦ Ø¥", "ả ng", "ản g", "è Ħ", "ο λ", "п ов", "по в", "Ġ θ", "ĠÎ ¸", "Û ·", "Ġn ó", "Ġd Ã¼ÅŁ", "Ġdü ÅŁ", "Ġt iế", "Ġti ế", "ÙĪ ج", "Ġj sem", "Ġjs em", "Ạ¡ng", "ạ ng", "ạn g", "ãģĤ ãĤĭ", "à¸Ń à¸ļ", "ÙĪ ÙĬ", "à¤ķ र", "Ġ де", "Ġд е", "¯ ¼", "Ġ но", "Ġн о", "ÑĨ Ñĸй", "ÑĨÑĸ й", "Ïĥ ÏĦ", "к ие", "ки е", "Ïĥ ει", "Ïĥε ι", "ìķ Ī", "Ġh Æ¡n", "Ġà¤ķ ह", "ا ض", "Ø§Ø ¶", "ì ¸", "ãĥ Ł", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ãĤĪ ãģĨ", "ा ,", "е ÑĢи", "еÑĢ и", "ë© °", "í ĶĦ", "íĶ Ħ", "Ġп оÑģÑĤ", "Ġпо ÑģÑĤ", "ĠпоÑģ ÑĤ", "Ø® ر", "à¥ĭ त", "â u", "к ой", "ко й", "d aki", "da ki", "í ħ", ": :::::::::::::::", ":: ::::::::::::::", ":::: ::::::::::::", ":::::: ::::::::::", ":::::::: ::::::::", "::: :::::::::::::", "::::: :::::::::::", "::::::: :::::::::", "::::::::: :::::::", ":::::::::: ::::::", "::::::::::: :::::", ":::::::::::: ::::", "::::::::::::: :::", ":::::::::::::: ::", "::::::::::::::: :", "Ġ öz", "Ġö z", "ÑĢ аж", "ÑĢаР¶", "ÑĢа ж", "nÃŃ ho", "ห ล", "Ġ ÏĥÏĦη", "ĠÏĥ ÏĦη", "ĠÄij á»ģ", "ĠÄijá» ģ", "Ġk á»", "i á»ĥn", "iá» ĥn", "iá»ĥ n", "ÅĻ i", "Ġkter é", "¢ ħ", "ü ç", "ÙĬ Ùģ", "Ġ lý", "Ġl ý", "Ġth á»Ŀi", "Ġthá» Ŀi", "Ġthá»Ŀ i", "Ġ ìĨĮ", "ĠìĨ Į", "н ÑĮ", "Ð Ĩ", "ÑĤ ÑĢ", "à¸ĩ าà¸Ļ", "к оÑĹ", "ко ÑĹ", "μ ο", "Ġs ür", "Ġsü r", "uy á»ģn", "uyá» ģn", "Ġ Ùħا", "ĠÙħ ا", "à¤Ĥ à¤Ĺ", "ĠÄij á»ĵng", "ĠÄijá»ĵ ng", "ò n", "à¥ģ ल", "à¥ģठ²", "à¥į प", "à¥įठª", "λ η", "Ùħ ر", "п ÑĢи", "пÑĢ и", "i yle", "iy le", "ा प", "ाठª", "Ġà¤ħ न", "Ġ ÑĶ", "ĠÑ Ķ", "Ġy ön", "Ġyö n", "ÙĦ Ùģ", "a dır", "ad ır", "adı r", "á ½", "Ġ ê³ł", "Ġê³ ł", "Ø® ص", "im iz", "imi z", "åľ ĭ", "Ġ над", "Ġн ад", "Ġна д", "Ġ ÅĻ", "ĠÅ Ļ", "н оÑģÑĤÑĸ", "но ÑģÑĤÑĸ", "ноÑģÑĤ Ñĸ", "ноÑģ ÑĤÑĸ", "Ġ اÙģ", "Ġا Ùģ", "а нÑĸ", "ан Ñĸ", "à¥ĩ à¤Ł", "à¥ĩठŁ", "Ġ ë§IJ", "Ġë§ IJ", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "Ġ ìĬ¤", "ĠìĬ ¤", "ิ à¸ķ", "å Ħ", "ÛĮ Ùĩ", "о ÑĪ", "ž it", "ži t", "ìĭ ¤", "à¥Ģ न", "Ġ î", "æ¥ Ń", "à¥ĩ न", "à¥ĩठ¨", "Ġ Ø°", "ĠØ °", "Ġl oại", "Ġlo ại", "à¹Ħ à¸Ľ", "Ñĸ к", "Ġ кÑĢа", "Ġк ÑĢа", "à¥ĭ र", "ุ à¸Ķ", "ĠاÙĦ ر", "ĠÑģ об", "ĠÑģо б", "า à¸Ĭ", "าภĬ", "Ġसà¤ķ त", "Ġ ÎĿ", "ĠÎ Ŀ", "ا ÙħÙĩ", "اÙħ Ùĩ", "à¹ī าà¸Ļ", "à¹īา à¸Ļ", "Ġtr ình", "Ġtrì nh", "Ġ اÙĦÙģ", "Ġا ÙĦÙģ", "ĠاÙĦ Ùģ", "Ġ اÙĦد", "ĠاÙĦ د", "u nun", "un un", "unu n", "о ÑĤов", "оÑĤ ов", "оÑĤо в", "Æ° ợ", "Æ°á» Ł", "ĠÑģ во", "ĠÑģв о", "ί αÏĤ", "ία ÏĤ", "ấ n", "ог да", "à¸Ĺ ย", "Ġb yl", "Ġby l", "ØŃ Ø¯", "à¸ģ ล", "ب Ùĩ", "Ġ vÄĽ", "Ġv ÄĽ", "è¢ «", "Ġ Ø¢Ùħ", "ĠØ¢ Ùħ", "ĠÄij iá»ģu", "å ¨", "Ġk dy", "Ġkd y", "Ġ بÙĪ", "Ġب ÙĪ", "ẫ n", "ìľ ł", "ा à¤ķ", "ाठķ", "k ů", "Ġtr Æ°á»Ŀng", "ic ké", "ick é", "н иÑı", "ни Ñı", "Ġ ÏĢοÏħ", "ĠÏĢ οÏħ", "ĠÏĢο Ïħ", "Æ°á»Ł ng", "н омÑĥ", "но мÑĥ", "ном Ñĥ", "à¹Ī à¸Ļ", "ู à¹Ī", "Ġk ết", "Ġkế t", "Ġ ï¼¼", "Ġï¼ ¼", "Ġ ìĭł", "Ġìĭ ł", "i ç", "Ġn Äĥng", "ĠnÄĥ ng", "Äį ÃŃ", "ÑĤ Ñı", "ÑĢ еб", "ÑĢе б", "Ùĭ ا", "Ú¯ ÛĮ", "ãĥ İ", "Ġkar ÅŁ", "в Ñĸ", "Ġph ần", "à¸Ī ะ", "ắ t", "ر Ø©", "ิ à¸ĩ", "ิ à¹Ī", "ा à¤Ī", "ाठĪ", "า à¸ŀ", "าภŀ", "ÙĨ ÛĮ", "ìĹ °", "b ÄĽ", "Ġ اÙĦص", "ĠاÙĦ ص", "í Ĺ", "Ġ سر", "Ġس ر", "l ara", "la ra", "lar a", "ëĭ ¨", "Ġ ÙĤر", "ĠÙĤ ر", "è İ", "ب د", "Ġй ого", "à¥į ह", "à¥įठ¹", "Ġc ách", "Ġcá ch", "Ġcác h", "íķĺ ê³ł", "Ġ ÏĢÏģο", "ĠÏĢ Ïģο", "Ġ تع", "Ġت ع", "Ĵ Ī", "Ġ вод", "Ġв од", "Ġво д", "ç¥ ŀ", "к им", "ки м", "Ġd á»±", "à¹Ģ ห", "à¹Ģภ«", "а на", "ан а", "Ġ ï½", "Ġï ½", "Ġb aÄŁ", "Ġba ÄŁ", "Ġप ह", "Ġ cao", "Ġc ao", "Ġca o", "Ïģ ÏĮ", "ÙĨ ج", "ा à¤ı", "ाठı", "Ġ å¹´", "Ġå¹ ´", "Ġngh iá»ĩp", "Ġnghiá»ĩ p", "Û² Û°", "к аÑı", "ка Ñı", "Ïģ ί", "Ġ бол", "Ġб ол", "Ġбо л", "Ġgi á", "Ġ зд", "Ġз д", "à¥ĩ ल", "à¥ĩठ²", "Ġc ấp", "à¹Ģ ส", "à¹Ģภª", "Ïģ γ", "Ġ ìĤ", "Ġì Ĥ", "d ÄĽ", "à¥ģ न", "à¥ģठ¨", "ì Ī", "ı lan", "ıl an", "л аÑģ", "ла Ñģ", "Ġ ว", "Ġภ§", "Ġ Ïĥε", "ĠÏĥ ε", "Ġ Ø«", "ĠØ «", "Ġ Ц", "ĠÐ ¦", "çĤ º", "Ġb üy", "Ġbü y", "е ÑĨ", "å¤ ª", "Ġब न", "о гÑĢа", "ог ÑĢа", "Ġп ÑĢоÑĤ", "ĠпÑĢ оÑĤ", "ĠпÑĢо ÑĤ", "Ġl ượng", "Ġd ön", "Ġdö n", "ร à¸ĩ", "а ло", "ал о", "Ġ جÙħ", "Ġج Ùħ", "à¥Ī ,", "Ġ 미", "Ġë ¯¸", "Ġ ê¹", "Ġê ¹", "ÙĪ ت", "à¥Ģ य", "à¸Ī าà¸ģ", "Ġch ất", "Î ©", "Ġkh ác", "Ġkhá c", "Ġth áng", "j Å¡ÃŃ", "ĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂł", "á»ij t", "ห ร", "Ñĸ л", "åħ ī", "å Ĥ", "ÙĦ Ø©", "Ġ ê±°", "Ġê± °", "о воÑĢ", "ов оÑĢ", "ово ÑĢ", "iá»ĥ u", "Ġ меÑĤ", "Ġм еÑĤ", "а ÑĶ", "Ġ ÑĩаÑģ", "ĠÑĩ аÑģ", "ĠÑĩа Ñģ", "Ïģ ε", "ì¹ ´", "âĢĮ Ø´", "ë¬ ¼", "ú c", "âĢĮ Ùĩا", "i á»ģn", "iá» ģn", "iá»ģ n", "st av", "sta v", "í ŀ", "ĠÙĨ ظ", "Ĩ Ĵ", "Ġ ÏĦα", "ĠÏĦ α", "Ġ заб", "Ġз аб", "Ġза б", "Ùĥ Ø©", "Ġг ÑĢÑĥ", "ĠгÑĢ Ñĥ", "в о", "Ġ Ùħج", "ĠÙħ ج", "Ġ sah", "Ġs ah", "Ġsa h", "ب ÙĦ", "ع Ø©", "Ñĥ ÑĪ", "ĠÑĤ ем", "ĠÑĤе м", "í ĭ", "e ck", "ec k", "Ïī ÏĤ", "ÙĬ ت", "ìĹ Ī", "ç ĭ", "Ø° ا", "ì łĢ", "ìł Ģ", "Ġн аÑģ", "Ġна Ñģ", "Ġ поÑĩ", "Ġп оÑĩ", "Ġпо Ñĩ", "æł ¡", "Ï Ī", "Ñģ кой", "Ñģк ой", "Ñģко й", "ü c", "ÙĤ ÙĦ", "Ġп оз", "Ġпо з", "Ġ оÑģоб", "ĠоÑģ об", "า ล", "าภ¥", "н Ñĭми", "нÑĭ ми", "нÑĭм и", "о лод", "ол од", "оло д", "è ¼", "Ġ دÛĮ", "Ġد ÛĮ", "Ġ ÑĥÑģÑĤ", "ĠÑĥ ÑģÑĤ", "ĠÑĥÑģ ÑĤ", "Ġ 무", "Ġë ¬´", "Ġë¬ ´", "ÙĬ س", "ë° ©", "à¥į à¤ļ", "à¥įठļ", "и ла", "ил а", "Ġn ên", "н ие", "ни е", "ι ν", "lar ını", "ların ı", "à¹Ģ à¸Ļ", "à¹ĢภĻ", "ÙĨ ت", "a ģı", "aÄŁ ı", "ım ız", "ımı z", "ĠاÙĦ Ø®", "à¹Ģ ว", "à¹Ģภ§", "à¥į न", "à¥įठ¨", "Ġ Ïħ", "ĠÏ ħ", "Ġ íĨ", "Ġí Ĩ", "Ạ»", "ิ à¹Ĥ", "α ÏĤ", "м еÑĤ", "ме ÑĤ", "Ġ zp", "Ġz p", "Ġje ho", "ี ยà¸Ļ", "ีย à¸Ļ", "ÑĦ оÑĢ", "ın ız", "ını z", "k lad", "kl ad", "kla d", "íĮ Į", "uy á»ĩ", "uyá» ĩ", "ι ά", "Ġ ãĢģ", "ĠãĢ ģ", "Ø´ ر", "æ© Ł", "Ġ تا", "Ġت ا", "Ġ зна", "Ġз на", "Ġзн а", "س تاÙĨ", "ست اÙĨ", "à¥ĩ र", "à¥ĩठ°", "ë§ ¤", "ç ĥ", "Ġ же", "Ġж е", "า à¸Ķ", "าภĶ", "Ġ ض", "ĠØ ¶", "é Ń", "Ġн аз", "Ġна з", "Ġ ÛĮا", "ĠÛĮ ا", "e né", "en é", "ั ย", "íĸ Īëĭ¤", "íĸĪ ëĭ¤", "Ġ بد", "Ġب د", "à¥ģ à¤ķ", "à¥ģठķ", "ÑĤ ов", "ÑĤо в", "ì° ¨", "Ùĩ د", "à¸Ķ ย", "Ġho ặc", "Ġ ÐŁÑĢи", "ĠÐŁ ÑĢи", "ĠÐŁÑĢ и", "ÙĨ ا", "çİ ĭ", "Ñĥ ваÑĤи", "Ñĥв аÑĤи", "Ñĥва ÑĤи", "à¸ļ ร", "Ġà¤ķ रत", "Ġà¤ķर त", "Ïĥ ηÏĤ", "Ïĥη ÏĤ", "Ø ¤", "éķ ·", "åħ ĭ", "Ġ دار", "Ġد ار", "ั à¹Ī", "Æ¡ i", "า à¸Ī", "าภĪ", "ý mi", "ým i", "ấ u", "Ġد ست", "Ġدس ت", "k em", "ke m", "Ġ оÑģнов", "ĠоÑģ нов", "ëª ¨", "Ïģ ά", "æ ħ", "Ġ اب", "Ġا ب", "ĠØ§Ø ¨", "å£ «", "Ħ ĸ", "Î Ķ", "ÙĬ Ùĥ", "í İ", "Ġy üz", "a dı", "ad ı", "า à¸ķ", "าภķ", "ä» Ģ", "ìĿ´ ëĭ¤", "Ġ zv", "Ġz v", "Ġ tÄĽ", "Ġt ÄĽ", "Ġ íĸ", "Ġí ĸ", "ठ¥", "Ġ लà¤Ĺ", "Ġल à¤Ĺ", "ìĺ Ģ", "Ġ ан", "Ġа н", "ç Ĺ", "ìĹ Ń", "н ÑĸÑģÑĤÑĮ", "нÑĸ ÑģÑĤÑĮ", "нÑĸÑģÑĤ ÑĮ", "Å ŀ", "Ġph át", "Ġphá t", "ÙĤ Ø©", "Ġth ế", "Ġ ï¾", "Ġï ¾", "ì² ľ", "Ġ ìĦł", "ĠìĦ ł", "à¹ĥ à¸Ĭ", "i êu", "iê u", "ÄŁ ini", "ÄŁi ni", "ÄŁin i", "ÙĤ د", "Ġkter ý", "Ñģ кий", "Ñģк ий", "Ñģки й", "à¥į ड", "à¥įठ¡", "t adır", "ta dır", "Ġ Ñģм", "ĠÑģ м", "ÙĪ Ùģ", "ا رÙĬ", "ار ÙĬ", "å¾ ·", "ิ ม", "Ø® ت", "å¾ Ī", "Ġ гоÑĢ", "Ġг оÑĢ", "ï¼Į æĪij", "Ġ ìĺģ", "Ġìĺ ģ", "Ġ ëıĻ", "Ġëı Ļ", "Ñģ а", "à¹Ģ à¸Ħ", "à¹ĢภĦ", "ë ¯¼", "ë¯ ¼", "ึ à¹Ī", "Ġl iên", "Ġli ên", "Ġ Ùĩا", "ĠÙĩ ا", "ler ini", "leri ni", "lerin i", "Ġ ÑĨе", "ĠÑĨ е", "ا ÙĦÛĮ", "اÙĦ ÛĮ", "Ġ मह", "Ġम ह", "Ġv ụ", "Ġvá» ¥", "Ġxu ất", "ิ à¸ģ", "ĠпÑĢо ÑĨ", "Ġ αν", "Ġα ν", "ÑĢ им", "ÑĢи м", "Ġc ần", "Ġ иÑħ", "Ġи Ñħ", "н оÑİ", "но Ñİ", "Ġt ÃŃnh", "ĠtÃŃ nh", "ĠtÃŃn h", "Ġb á»Ļ", "Ñĸ м", "Ġnh áºŃn", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ÙĬ Ùĩ", "äº ļ", "Ġоб ла", "Ġобл а", "Ġ à¤ĺ", "Ġठĺ", "n ých", "ný ch", "æĿ ij", "ÙĦ س", "Ġне об", "ا بة", "اب Ø©", "v á", "ο Ïħν", "οÏħ ν", "ÑĢ еÑĤ", "ÑĢе ÑĤ", "a sında", "as ında", "ası nda", "Ġ yar", "Ġy ar", "Ġya r", "ĠÄij iá»ĥm", "ĠÄiji á»ĥm", "н Ñİ", "ा à¤Ĺ", "ाठĹ", "Ġ Ú©Ø´", "ĠÚ© Ø´", "Ñĥ з", "Ġ à¸Ķ", "ĠภĶ", "ả m", "к ами", "ка ми", "кам и", "Ġ ÎĻ", "ĠÎ Ļ", "à¹Ģ à¸ķ", "à¹Ģภķ", "Ġl Ỽ", "Ġlá» Ľ", "ÙĤ ÛĮ", "k ou", "ko u", "ÙĦ ب", "и ва", "ив а", "æ ĵ", "Ạ¹", "κ α", "ë² ķ", "èĤ ²", "á»ij n", "Ġbel ir", "íĨ ł", "ÏĦ ή", "Ñĭ ÑĪ", "ãĤ ĥ", "Ġ або", "Ġа бо", "Ġаб о", "s ký", "sk ý", "à¥Ī स", "Ġп ÑĢоÑģÑĤ", "ĠпÑĢ оÑģÑĤ", "ĠпÑĢо ÑģÑĤ", "ĠпÑĢоÑģ ÑĤ", "ekt edir", "ekte dir", "a ž", "à¹Ī à¸Ń", "Ġ оÑģÑĤ", "Ġо ÑģÑĤ", "ĠоÑģ ÑĤ", "Ġb ảo", "Ġ 大", "Ġå¤ §", "Ñĭ м", "Ġm ů", "Æ°á»Ľ ng", "åı Ĺ", "ÙĪ Ùĩ", "Ġ Ñĥп", "ĠÑĥ п", "Ùĥ ÙĨ", "Ġ ÏĦÏīν", "ĠÏĦ Ïīν", "ëħ ¸", "Ġ à¸Ĭ", "ĠภĬ", "Ġ ÑĤого", "ĠÑĤ ого", "ĠÑĤо го", "Ġ Ш", "ĠÐ ¨", "ìĿ´ íĬ¸", "à¹Ģ à¸Ń", "à¹ĢภŃ", "и нÑĥ", "ин Ñĥ", "ĺ ħ", "uy á»ĥn", "uyá» ĥn", "í ĴĪ", "íĴ Ī", "ạ nh", "ạn h", "Ġ ãĥ½", "Ġãĥ ½", "ÑĤ обÑĭ", "ÑĤо бÑĭ", "Ġt ạo", "å· Ŀ", "ĠÄij á»iji", "Ġ ëıĦ", "Ġëı Ħ", "ä¹ ħ", "Ġ تÙħ", "Ġت Ùħ", "а ÑĢи", "аÑĢ и", "st vÃŃ", "Ġc ùng", "íŀ Ī", "Ġt arih", "Ġtar ih", "ì ¤ij", "ì¤ ij", "í Ĥ", "Ġ دÙĪ", "Ġد ÙĪ", "ì ¡", "а лÑĸ", "ал Ñĸ", "ภIJ", "Ġc òn", "и ÑĤÑĮÑģÑı", "иÑĤÑĮ ÑģÑı", "Ġव ह", "ÅĻ eb", "ÅĻe b", "éĽ »", "Ġ ми", "Ġм и", "o vÄĽ", "ov ÄĽ", "Ġd ân", "ÑĨ ÑĸÑı", "ÑĨÑĸ Ñı", "ÛĮ ست", "ÛĮس ت", "åŃ ¸", "Ġ ür", "Ġü r", "ص ÙĦ", "ÑĢ иÑĤ", "ÑĢи ÑĤ", "า ห", "าภ«", "ãģ¦ ãģĦãģŁ", "ãģ¦ãģĦ ãģŁ", "θ η", "ç ĸ", "Ø Ł", "i ÅŁtir", "iÅŁ tir", "iÅŁti r", "ĠУкÑĢаÑĹ Ð½Ð¸", "ĠУкÑĢаÑĹн и", "ë° ĺ", "à¥ĩ à¤ĸ", "à¥ĩठĸ", "Ġv á»ĭ", "Ġvá» ĭ", "Î ¥", "Ġ ãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢ ĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢ ĠãĢĢ", "ĠãĢĢĠ ãĢĢĠãĢĢ", "Ġb ằng", "Ġt á»ķ", "Ġtá» ķ", "о ли", "ол и", "๠Ĩ", "e zi", "ez i", "Ġ ни", "Ġн и", "Ġ ÎĽ", "ĠÎ Ľ", "Ġr ất", "μ ÏĢ", "ж д", "ा रत", "ार त", "Ġu ž", "à¥ĩ स", "à¥ĩठ¸", "ا ÙĨد", "اÙĨ د", "Ġb ý", "à¥ĭ ल", "d ÄĽl", "dÄĽ l", "ìķ ĺ", "Ġ جد", "Ġج د", "å ³", "ื à¹ī", "Ġb ản", "ạ ch", "ạc h", "ĠÅŁ ey", "Ġ Ùĩر", "ĠÙĩ ر", "Ġ jen", "Ġj en", "Ġje n", "Ġв Ñĸн", "ĠвÑĸ н", "es inde", "esi nde", "esin de", "Ġ हम", "Ġह म", "çł Ķ", "à¸ļ à¸ļ", "Ġch ức", "Ġchứ c", "ึ à¸ĩ", "m alar", "ma lar", "mal ar", "ĠdeÄŁ il", "æĿ ±", "Ġt ác", "Ġtá c", "Ġk iÅŁ", "Ġki ÅŁ", "Ġt á»±", "Ġtá» ±", "à¥į ध", "à¥įठ§", "à¸Ļ à¸Ĺ", "ÎŁ Î¥", "ÎŁÎ ¥", "ÑģÑĮ кого", "ÑģÑĮк ого", "ÑģÑĮко го", "Ġ ध", "Ġठ§", "Ġ ìĿĺ", "ĠìĿ ĺ", "ÙĨ Ø©", "ü s", "è «", "Ġtaraf ından", "ħ n", "Ġk inh", "Ġki nh", "Ġkin h", "Ïĥ ι", "à¥Ģ à¤ķ", "íı ¬", "ا ÙħÙĦ", "اÙħ ÙĦ", "ĠV iá»ĩt", "Ġ ÏĦον", "ĠÏĦ ον", "ĠÏĦο ν", "Ġ تÙĨ", "Ġت ÙĨ", "Ġà¤ħ ध", "à¹Ī าà¸Ļ", "à¹Īา à¸Ļ", "r ı", "à¤Ĥ द", "é ª", "Ġch úng", "Ġchú ng", "г и", "ÏĦ αν", "ÏĦα ν", "Ġд оп", "Ġдо п", "н Ñĸй", "нÑĸ й", "он алÑĮ", "она лÑĮ", "Î ĵ", "Ġb üyük", "Ġbü yük", "Ġbüy ük", "á ¼", "à¥Ģ र", "Ø° Ùĩ", "Ġ ìķĦìĿ´", "ĠìķĦ ìĿ´", "Ġdo anh", "Ġ ÅĻÃŃ", "ĠÅĻ ÃŃ", "ÑĨ Ñı", "Ġt Æ°", "Ġ सर", "Ġस र", "Ġm ÃŃst", "ĠmÃŃ st", "Ġë° ı", "Ø´ ÙĨ", "Ñĸ б", "Ġ ãĢĢãĢĢ", "ĠãĢĢ ãĢĢ", "çĻ ½", "о Ñģп", "оÑģ п", "к Ñĸв", "кÑĸ в", "Ġt ế", "ãģ Ń", "Ġt Ỽi", "Ġtá» Ľi", "Ġ ìļ°", "Ġìļ °", "æľ ĥ", "ا ÛĮد", "اÛĮ د", "æ §", "ìł IJ", "Ġd urum", "Ġdu rum", "Ġdur um", "à¹Ģ à¸Ĭ", "à¹ĢภĬ", "à¥Ģ त", "ĠÙĩ ÙĪ", "à¥Ĥ प", "Ġgö re", "Ġgör e", "Ġ ÑĢоб", "ĠÑĢ об", "Ġth iết", "Ġthi ết", "a jÃŃ", "aj ÃŃ", "ĠاÛĮ راÙĨ", "âĢ ı", "ÑģÑĮ коÑĹ", "ÑģÑĮк оÑĹ", "ÑģÑĮко ÑĹ", "ç ħ", "Ġ ìĦ¸", "ĠìĦ ¸", "á» «", "Ġ à¸Ĥ", "ĠภĤ", "ů m", "ëŀ Į", "ι κή", "ικ ή", "Ġм ог", "Ġмо г", "ÙĨ ÙĬ", "ãģ ļ", "ा ब", "ाठ¬", "æ ¢", "ع Ùĩ", "ÑĶ м", "Ġ ά", "ĠÎ ¬", "οÏħ ÏĤ", "ز ار", "زا ر", "ê± ´", "s ká", "sk á", "Ġ اÙĬ", "Ġا ÙĬ", "Ġi lg", "Ġil g", "Ġ sı", "Ġs ı", "e leri", "el eri", "eler i", "ele ri", "Ġ ÎĹ", "ĠÎ Ĺ", "u yor", "uy or", "uyo r", "ठ·", "ि म", "िठ®", "е ва", "ев а", "ä»Ģ ä¹Ī", "ุ à¹Ī", "à¹ī าà¸ĩ", "à¹īา à¸ĩ", "Ġh iá»ĩu", "Ġhi á»ĩu", "Ġ اع", "Ġا ع", "ĠØ§Ø ¹", "Ġö zel", "Ġöz el", "ν η", "ëĦ ¤", "Ġto Ãłn", "Ġm oh", "Ġmo h", "ĠÑı кÑĸ", "ĠÑıк Ñĸ", "ç Ĭ", "mak tadır", "makta dır", "makt adır", "ت اب", "تا ب", "Ġ ÑģÑĥ", "ĠÑģ Ñĥ", "Ġ yük", "Ġy ük", "Ġ Χ", "ĠÎ §", "з на", "зн а", "о Ñħ", "Æ° u", "à¸Ĺ ร", "ãħ ĭ", "Ġkar ÅŁÄ±", "ĠkarÅŁ ı", "Ùħ ÛĮ", "Ġ ÑĨÑĸ", "ĠÑĨ Ñĸ", "ا دÛĮ", "اد ÛĮ", "à¥Ģ ।", "Ïģ η", "л ов", "ло в", "å¤ «", "Ġph ân", "Ġп оп", "Ġпо п", "ç· ļ", "Ñı н", "ุ à¸ĵ", "ÑģÑĤ Ñĥп", "ίν αι", "ίνα ι", "ĠÑĢ окÑĥ", "ĠÑĢок Ñĥ", "l arda", "lar da", "è» Ĭ", "Ïģ Ïī", "ÙĪ اÙĩ", "ÙĪا Ùĩ", "è ħ", "à¥į रत", "à¥įर त", "å· ±", "Ġ ÑĢÑĥ", "ĠÑĢ Ñĥ", "Ġth á»ĭ", "Ġthá» ĭ", "ĠÄij iá»ĩn", "ĠÄiji á»ĩn", "ìĸ ij", "n ého", "né ho", "ส ม", "ê° ģ", "a cÃŃ", "ac ÃŃ", "Ġг ода", "Ġгод а", "k az", "ka z", "Ġb öl", "Ġbö l", "Ġg ian", "Ġgi an", "Ġgia n", "à¸Ľ ร", "ï¾ ŀ", "ั à¸ķ", "Ġg erç", "Ġger ç", "Ġ اج", "Ġا ج", "ĠØ§Ø ¬", "Ġ ή", "ĠÎ ®", "Ùij Ùİ", "Ñģ кого", "Ñģк ого", "Ñģко го", "ÑĢ аÑħ", "ÑĢа Ñħ", "Ġ Åł", "ĠÅ ł", "Ġ à¤Ľ", "ĠठĽ", "о ÑģÑĤÑĸ", "оÑģ ÑĤÑĸ", "оÑģÑĤ Ñĸ", "ë³ ¸", "ÑģÑĮ кий", "ÑģÑĮк ий", "Û± Û¹", "Û±Û ¹", "Ñĥ ва", "Ñĥв а", "ا ÙĦÙħ", "اÙĦ Ùħ", "ĠÙħ ص", "ëį ĺ", "b ÃŃ", "Ġ ÙĪج", "ĠÙĪ ج", "ÏĦ ÏĮ", "e bilir", "eb ilir", "Ġt iếp", "Ġti ếp", "Ġtiế p", "é ¤", "Ġ ä¸Ģ", "Ġä¸ Ģ", "ĠÑģ ÑĢед", "ë Ĥ¨", "ëĤ ¨", "ε Ïģι", "εÏģ ι", "ا Ø«", "Ø§Ø «", "Ñģ ов", "Ñģо в", "Ïĩ ε", "Ġ ë¶Ħ", "Ġë¶ Ħ", "Ġta ké", "Ġtak é", "Ġd üz", "Ġdü z", "Ġ íıī", "Ġíı ī", "Ġ اص", "Ġا ص", "ĠØ§Ø µ", "ĠÏĥ ÏĦην", "ĠÏĥÏĦη ν", "ë° Ķ", "Ġh á»Ļi", "Ġhá»Ļ i", "ر Ùĩ", "ب ÛĮ", "в е", "Ġا ÙĦØ·", "ĠاÙĦ Ø·", "Ġ ÑĢез", "ĠÑĢ ез", "ĠÑĢе з", "ب ار", "با ر", "Ġgi ải", "Ġgiả i", "ãģ« ãģª", "ol eÄį", "ole Äį", "ठł", "; :", "ä½ ı", "Ú© Ùĩ", "Ġ Φ", "ĠÎ ¦", "Ġ ÑĥÑĩ", "ĠÑĥ Ñĩ", "âĹı âĹı", "ู à¸ģ", "à¥ĩ व", "à¥ĩठµ", "Ïĥ α", "Ġ اÙĨت", "Ġا ÙĨت", "ĠاÙĨ ت", "Ġв п", "Ġqu ả", "e nin", "en in", "eni n", "Ġ êµIJ", "Ġêµ IJ", "μ ά", "Ú© ت", "ÙĤ Ùĩ", "Ġ Türkiye", "ĠTür kiye", "ĠTürk iye", "Ġth ức", "Ġthứ c", "íĹ ĺ", "iá»ĩ m", "Ġत à¤ķ", "Ġ éĩ", "Ġé ĩ", "़ ा", "ĠØ£ ÙĪ", "á le", "ál e", "ç© ¶", "ĠÅŁ ekil", "ĠÅŁek il", "к ого", "ко го", "ког о", "ÑĪ иÑħ", "ÑĪи Ñħ", "ا ÛĮØ´", "اÛĮ Ø´", "ت ÙĨ", "н ей", "не й", "à¸Ĺ ำ", "Ġ Ñıв", "ĠÑı в", "ر Ùħ", "Ġm áy", "Ġmá y", "ห ม", "ı yla", "ıy la", "Ġc ầu", "Ġд об", "Ġдо б", "Ġ ìŀ¥", "Ġìŀ ¥", "o vý", "ov ý", "ι κÏĮ", "ικ ÏĮ", "Ġ ãħĩ", "Ġãħ ĩ", "Ġ ÑĤеÑĢ", "ĠÑĤ еÑĢ", "ĠÑĤе ÑĢ", "Į Ĵ", "س ÙĬ", "Ġol uÅŁ", "Ġb yla", "Ġby la", "Ġbyl a", "ع ÙĦ", "Ġ ÙĥاÙĨ", "ĠÙĥ اÙĨ", "б оÑĢ", "бо ÑĢ", "ì² Ń", "ãĥ ı", "u bl", "ub l", "Ġ اخ", "Ġا Ø®", "ĠØ§Ø ®", "ÙĦ ÙĪد", "ÙĦÙĪ د", "ت ÙĬ", "l adı", "la dı", "lad ı", "Ġ Ã¶ÄŁ", "Ġö ÄŁ", "r uh", "ru h", "ç ¿", "Ġ بعد", "Ġب عد", "Ġبع د", "ÎĻ Îij", "i dir", "id ir", "idi r", "ãģ« ãģ¯", "Ġs öy", "Ġsö y", "Ġkh ách", "Ġkhác h", "Ġkhá ch", "ÑĨ е", "Ġ Ø´ÙĪد", "ĠØ´ ÙĪد", "ĠØ´ÙĪ د", "ç ¸", "Ġ ëħ¸", "Ġëħ ¸", "ú p", "Ġn eden", "Ġne den", "Ġned en", "Ġh óa", "Ġà¤ī प", "Ïĥ ειÏĤ", "Ïĥει ÏĤ", "æĪ ¿", "Ġ ³³", "ĠÂł Âł", "Ġ ìķĮ", "Ġì ķĮ", "Ġìķ Į", "à¥Ģ ,", "´ ij", "ê u", "ÑĢ ок", "ÑĢо к", "à¹Ģ à¸Ī", "à¹ĢภĪ", "Ġε ίναι", "Ġ بÙĦ", "Ġب ÙĦ", "Ġ Ñģов", "ĠÑģ ов", "ĠÑģо в", "Ġö nem", "Ġön em", "Ġöne m", "Ġ à¸ĭ", "Ġภĭ", "ì§Ģ ë§Į", "å® ĺ", "ê² ©", "ìĦ Ŀ", "Ġ až", "Ġa ž", "Ġd uy", "Ġdu y", "ãģ¨ ãģĦ", "Ø Ľ", "δ ο", "θ ε", "Ùĥ اÙĨ", "ठ¢", "ा à¤ĵ", "ाठĵ", "Ġd á»ĭch", "Ġdá»ĭ ch", "á»Ļ ng", "á»Ļn g", "ส ำ", "Ä ı", "Ġ ÑĹÑħ", "ĠÑĹ Ñħ", "α λ", "e Äį", "ç² ¾", "Ġ зв", "Ġз в", "èĩª å·±", "Ġ اÙĦÙĦÙĩ", "ĠاÙĦ ÙĦÙĩ", "ĠاÙĦÙĦ Ùĩ", "Ġ СÑĤ", "ĠС ÑĤ", "Ġ سÙĨÚ¯", "Ġس ÙĨÚ¯", "ĠسÙĨ Ú¯", "Ġ дом", "Ġд ом", "Ġдо м", "г оÑĤов", "го ÑĤов", "гоÑĤ ов", "п овÑĸд", "пов Ñĸд", "по вÑĸд", "Ġ Bá»Ļ", "ĠB á»Ļ", "à¥įय à¤ķ", "Ø· Ø©", "м ов", "мо в", "à¸Ĺ าà¸ĩ", "ึ à¸ģ", "Ġ Ñĸз", "ĠÑĸ з", "à¥ĭ à¤ľ", "Ġgö ster", "Ġ باشد", "Ġبا شد", "Ġباش د", "i leri", "il eri", "ile ri", "iler i", "ĠÑģ еб", "Ñī о", "Ġãħĩ ãħĩ", "ب ت", "Ñģ е", "à¥ĩ à¤ľ", "à¥ĩठľ", "Ġl ên", "Ġ تÙĪ", "Ġت ÙĪ", "Ñĸ ÑģÑĤÑĮ", "ÑĸÑģ ÑĤÑĮ", "ÑĸÑģÑĤ ÑĮ", "ï¾Ĩ ï¾Ĩ", "Ġth Æ°á»Ŀng", "Ġol duÄŁu", "Ġoldu ÄŁu", "ĠolduÄŁ u", "v ÄĽt", "vÄĽ t", "ìĨ į", "ãģĿ ãģĨ", "Ġ ìĦ±", "ĠìĦ ±", "ë° ľ", "Ġ à¸ģาร", "Ġà¸ģ าร", "Ġ Ø´Ùĩر", "ĠØ´ Ùĩر", "ĠØ´Ùĩ ر", "s led", "sl ed", "ả nh", "ản h", "æŀ Ĺ", "l acak", "la cak", "lac ak", "Ġm ình", "Ú© ÛĮ", "Ġ à¹ĥà¸Ļ", "Ġd ùng", "Ġdù ng", "Ġм аÑģ", "Ġма Ñģ", "ÑĦ ек", "æ° Ķ", "é §", "Ġ اØŃ", "Ġا ØŃ", "ĠØ§Ø Ń", "èµ °", "ÎĻ Îļ", "à¥ĩ ।", "ÑģÑĮ ка", "ÑģÑĮк а", "Ġ ÑĩаÑģÑĤ", "ĠÑĩ аÑģÑĤ", "ĠÑĩа ÑģÑĤ", "ĠÑĩаÑģ ÑĤ", "lar ının", "ların ın", "larını n", "Ġ ê¹Ģ", "Ġê¹ Ģ", "ì¸ µ", "н ими", "ни ми", "ним и", "èª ŀ", "åĢ ĭ", "Ġ êµŃ", "Ġêµ Ń", "к оÑĢ", "ко ÑĢ", "m aya", "ma ya", "may a", "ิ à¹Ĥà¸Ļ", "ิà¹Ĥ à¸Ļ", ". ศ", "Ġh á»ĩ", "Ġhá» ĩ", "Ġ تÙĤ", "Ġت ÙĤ", "γ κ", "Ġà¤Ĩप à¤ķ", "Ñģ ÑĤоÑĢ", "ÑģÑĤ оÑĢ", "ĠÄij o", "Ġch ủ", "ا ÛĮت", "اÛĮ ت", "ĠQu á»ijc", "г лÑı", "гл Ñı", "ãĢĤ ãĢįĊĊ", "ãĢĤãĢį ĊĊ", "Ġn Ãło", "à¸Ń ล", "æĬ Ĭ", "ÙĪ رت", "ÙĪر ت", "Ġb ude", "Ġbu de", "Ġbud e", "æĽ ¸", "e lik", "el ik", "eli k", "Ġ جÙĩ", "Ġج Ùĩ", "ĠبÙĪ ابة", "èĬ ±", "د ار", "دا ر", "Ġb ýt", "Ġbý t", "Ñĩ е", "ãĤĵ ãģł", "ĠÙħ Ø·", "l ere", "le re", "ler e", "ÎĹ Î£", "íĺ ķ", "âĸ į", "ÄŁ u", "Ġв з", "ÙĬ ز", "ĠÐł оÑģ", "íĭ °", "Ġد اش", "ì§ ij", "a tı", "at ı", "m esi", "me si", "mes i", "ãĤī ãĤĮ", "ů v", "r át", "rá t", "оÑģ об", "åIJ Ħ", "uy á»ĩn", "uyá»ĩ n", "åģ ļ", "ü st", "üs t", "éĩ İ", "α Ïĥ", "Ġm ặt", "е лов", "ел ов", "ело в", "åį ļ", "д ж", "Ġد ارد", "Ġدار د", "Ġf ark", "Ġfa rk", "Ġfar k", "à¹ī วย", "à¹īว ย", "о ни", "он и", "Ġب Ø®", "à¥ģ त", "à¥ģठ¤", "ĠÄij ây", "α Ïģα", "αÏģ α", "Ġ δια", "Ġδ ια", "Ġδι α", "Ġ è¯", "Ġè ¯", "к аÑħ", "ка Ñħ", "ch áz", "z enÃŃ", "ze nÃŃ", "zen ÃŃ", "ÑĢ оп", "ÑĢо п", "à¥Ģ म", "í Ĩµ", "íĨ µ", "d ü", "à¸ł าà¸ŀ", "Ġ íĬ", "Ġí Ĭ", "ÙĪ ا", "Ġt á»ijt", "Ġtá»ij t", "ï¼Ł ãĢįĊĊ", "ï¼ŁãĢį ĊĊ", "Ġ æľĪ", "Ġnh Æ°ng", "ĠnhÆ° ng", "Ġne ž", "à¥ĭ ड", "ìĹIJ ê²Į", "à¤Ĥ ड", "¶ Į", "Ġ меÑģÑĤ", "Ġм еÑģÑĤ", "ा à¤ģ", "ाठģ", "ì¦ Ŀ", "ĠÄij ang", "ĠÄija ng", "à¸Ń à¸Ķ", "í ĽĦ", "á»į i", "sk ého", "ské ho", "Ġд ок", "Ġдо к", "Ġ تص", "Ġت ص", "Ġph òng", "Ġ ê°ķ", "Ġê° ķ", "Ġtr Æ°á»Ľc", "í ijľ", "Ù Ķ", "Ġph ÃŃ", "Ġch á»įn", "ä¹ IJ", "ĠÅŁek ilde", "ĠÅŁekil de", "Ġ íİ", "Ġí İ", "é º", "ë £¨", "ë£ ¨", "à¥Ī ।Ċ", "à¥Ī। Ċ", "ÙĪ رÛĮ", "ÙĪر ÛĮ", "Ñģ ÑĤÑĢа", "ÑģÑĤ ÑĢа", "ÑģÑĤÑĢ а", "il di", "ild i", "Ġα Ïħ", "в аннÑı", "ван нÑı", "ìļ ¸", ". âĢľĊĊ", ".âĢľ ĊĊ", "ĠÑĤак же", "ëĵ ±", "е ка", "ек а", "æī į", "Ùħ Ø©", "Ġph Æ°Æ¡ng", "é© ¬", "ãĢĢ ĠãĢĢ", "ãĢĢĠ ãĢĢ", "ov ých", "ový ch", "ี ยà¸ĩ", "ีย à¸ĩ", "ĠT ru", "ĠTr u", "е Ñģп", "еÑģ п", "st up", "stu p", "Ä Į", "Ġdal Å¡ÃŃ", "ز ÛĮ", "Ġ 매", "Ġë§ ¤", "Ġ обÑĢаз", "Ġоб ÑĢаз", "ĠобÑĢа з", "Ġaç ık", "Ġaçı k", "ê° ķ", "Ùģ ادÙĩ", "Ú¯ اÙĨ", "à¹ī à¸Ļ", "ẩ n", "å·¥ ä½ľ", "Ġ तर", "Ġत र", "ÙĬ ع", "Ġ ãĢĬ", "ĠãĢ Ĭ", ", âĢľ", "Ġ nev", "Ġn ev", "Ġne v", "ั à¸į", "ÄŁ ını", "ģın ı", "Ġ jin", "Ġj in", "Ġji n", "ا خت", "اخ ت", "س ر", "Ġt Ãłi", "Ġkter á", "Ġا ÙĦÙĦ", "ĠاÙĦ ÙĦ", "ठħ", "iz met", "izm et", "à¥ģ म", "à¥ģठ®", "า ะ", "าภ°", "Ġ ê·", "Ġê ·", "l ıģı", "lı ģı", "lıģ ı", "çı ¾", "li ÄŁi", "liÄŁ i", "êµ °", "a lık", "al ık", "alı k", "Ġد ÙĪر", "ĠدÙĪ ر", "Ġ ìĭ¤", "Ġìĭ ¤", "Ġз аÑģ", "Ġза Ñģ", "ÙĤ ÙĬ", "Ġ ứng", "Ġ ÙĥÙĩ", "ĠÙĥ Ùĩ", "ÎŁ Σ", "ÎŁÎ £", "è¨ Ń", "ç Į", "ãģĦ ãģŁ", "íĺ Ħ", "Ġ ÑĤе", "ĠÑĤ е", "е ÑĢÑĸ", "еÑĢ Ñĸ", "s ız", "sı z", "Ġ ý", "Ġà ½", "д ов", "до в", "Ġ à¤ĩसà¤ķ", "Ġà¤ĩस à¤ķ", "г од", "го д", "Ġby lo", "Ġbyl o", "าà¸Ħ ม", "е нием", "ен ием", "ени ем", "ение м", "Ð ¨", "æľ ¯", "Ġप हल", "Ġपह ल", "Ġ aÅŁ", "Ġa ÅŁ", "ि à¤ľ", "िठľ", "åĵ ¡", "в аÑĢ", "ва ÑĢ", "à¹ī ำ", "â ĮĴ", "ov án", "ová n", "Ġgi úp", "Ð ¥", "ĠÑģ Ñĥд", "ĠÑģÑĥ д", "Ġà¤ķ म", "ạ m", "ر س", "Ġ 人", "Ġ بÛĮ", "Ġب ÛĮ", "Ġà¤īन à¤ķ", "ë¦ ½", "áºŃ y", "Ġv áºŃt", "л ÑıеÑĤÑģÑı", "лÑı еÑĤÑģÑı", "лÑıеÑĤ ÑģÑı", "Ġs eç", "Ġse ç", "Ġ ì½", "Ġì ½", "ÑĢ Ñĥж", "ÑĢÑĥ ж", "ت ص", "| :", "Ġ ëł", "Ġë ł", "и ми", "им и", "Ġ лÑİб", "ĠлÑİ Ð±", "Ġ à¸ľ", "Ġภľ", "ï¼Į ä½Ĩ", "Ġ нав", "Ġн ав", "Ġна в", "âĢ ¬", "à¹Ī าย", "à¹Īา ย", "Ġ رس", "Ġر س", "s iniz", "sin iz", "ë ¨", "е ниÑİ", "ен иÑİ", "ени Ñİ", "Ġ ล", "Ġภ¥", "ا سÛĮ", "اس ÛĮ", "ॠľ", "ĠÙ¾ ÛĮØ´", "ĠÙ¾ÛĮ Ø´", "ί δ", "Ġ Ù¾ÛĮ", "ĠÙ¾ ÛĮ", "еÑĢж ав", "ठĨ", "ĠdÃ¼ÅŁ ün", "å¿ «", "ÑĢ еÑģ", "ÑĢе Ñģ", "åħ «", "ÑĤ Ñĸ", "ि à¤Ł", "िठŁ", "Ġ ÑĤеÑħ", "ĠÑĤ еÑħ", "ĠÑĤе Ñħ", "ú t", "ÙĨ Ùĩ", "Ġ ÙĨØ´", "ĠÙĨ Ø´", "çĻ º", "Ġ ê°¤", "Ġê° ¤", "л ед", "ле д", "Ġ ëĵ¤", "Ġëĵ ¤", "Ġbi lg", "Ġbil g", "Ġsp oleÄį", "Ġspol eÄį", "Ġspole Äį", "ĠÄij Æ¡n", "Ġ à¤īत", "Ġà¤ī त", "Ġtr á»ĭ", "Ġ عÙħ", "Ġع Ùħ", "Ġ ।", "Ġà ¥¤", "Ġॠ¤", "Ġú Äį", "ãģ ¸", "ว à¸ģ", "ĠÑģл ÑĥÑĩа", "ĠÑģлÑĥÑĩ а", "ĠÑģлÑĥ Ñĩа", "á» įng", "á»į ng", "á»įn g", "åı Ī", "и ÑĤÑĥ", "иÑĤ Ñĥ", "æľī éĻIJ", "ë¦ °", "ëĭ ĺ", "Ġho ạt", "ĠìĿ´ ëıĻ", "з наÑĩ", "зна Ñĩ", "зн аÑĩ", "Ġاست ÙģادÙĩ", "ĠпÑĢо ÑĨеÑģ", "ĠпÑĢоÑĨ еÑģ", "an ın", "anı n", "г Ñĥ", "Ġ اÙĦØ«", "ĠاÙĦ Ø«", "æĹ¥ æľ¬", "ι κά", "ικ ά", "ĠÑĹ ÑĹ", "ì§ ģ", "i nu", "in u", "Ġس از", "ãĤ ¡", "ï¾ ī", "Ġ اÙĤ", "Ġا ÙĤ", "Ġk ế", "ů sob", "à¹ĩ à¸ģ", "åIJ §", "æ¼ Ķ", "Ñī ие", "Ñīи е", "ç Ĩ", "ÑĮ ого", "à¥ĭ à¤Ł", "ا Ù¾", "ا٠¾", "å ®¤", "å® ¤", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "Ġtr iá»ĥn", "Ġtri á»ĥn", "Ġt áºŃp", "é£ Ł", "ë ¹", "ĠÑĩеÑĢ ез", "Ġ ÑĨи", "ĠÑĨ и", "Ñģ Ñĥ", "Ġ нем", "Ġн ем", "Ġне м", "Ġ аÑĢ", "Ġа ÑĢ", "Ġ ÙĦا", "ĠÙĦ ا", "Ġ ì§Ħ", "Ġì§ Ħ", "ç Ł³", "çŁ ³", "Ġп ÑĢоб", "ĠпÑĢ об", "ĠпÑĢо б", "Ġ ìĽIJ", "ĠìĽ IJ", "ÛĮ ÙĨÛĮ", "ÛĮÙĨ ÛĮ", "Ñİ Ñĩи", "âĢ į", "Û± Û³", "Û±Û ³", "ã Ĥ¬", "ãĤ ¬", "çłĶ 究", "í Ĥ¤", "íĤ ¤", "Ġger çek", "Ġgerç ek", "ĠØŃ Ø³", "íĶ ¼", "èĤ ¡", "Ġ ÏĦι", "ĠÏĦ ι", "ĠvÅ¡ ech", "ĠvÅ¡e ch", "Ġv ì", "ا ÙĨÙĬ", "اÙĨ ÙĬ", "ĠÙĩ ست", "Ġ ëĤ¨", "Ġë Ĥ¨", "ĠëĤ ¨", "ÅĻ ej", "ÅĻe j", "е ÑĢг", "еÑĢ г", "Ġs öz", "Ġsö z", "ĠاÙĦ Ùħت", "ĠاÙĦÙħ ت", "Ġch ế", "á»ĵ i", "åı ¤", "âĸį âĸį", "á»ĵ ng", "á»ĵn g", "ãĥ ¢", "Ġ ди", "Ġд и", "ε λ", "Ġ она", "Ġо на", "Ġон а", "Ġ най", "Ġн ай", "Ġна й", "Ġ _{", "Ġ_ {", "п ол", "по л", "al iz", "ali z", "Ġt Äĥng", "Ġ dÃŃ", "Ġd ÃŃ", "é p", "Ġ ÙĦÙħ", "ĠÙĦ Ùħ", "Ġm ož", "Ġmo ž", "Ġng oÃłi", "è Ĺ", "Ġ Ñĩем", "ĠÑĩ ем", "ĠÄij á»ķ", "ĠÄijá» ķ", "е ÑĤа", "еÑĤ а", "åı ²", "ĠÑģ каз", "ĠÑģк аз", "ĠÑģка з", "ãĤ¿ ãĥ¼", "а нÑĮ", "ан ÑĮ", "Ġg öz", "Ġgö z", "ë³ Ħ", "ãģĭ ãģ£ãģŁ", "Ġ ëįĶ", "Ġëį Ķ", "ĠÙĨ ÙĤ", "Ġ ÑĥÑĩа", "ĠÑĥ Ñĩа", "ĠÑĥÑĩ а", "Ġsa hip", "Ġsah ip", "ĠÑģ пе", "ĠÑģп е", "ί οÏħ", "ίο Ïħ", "ì ½Ķ", "ì½ Ķ", "Ġ ëĪ", "Ġë Ī", "m am", "ma m", "Ġr oce", "Ġro ce", "Ġroc e", "Ġ ÙĨاÙħ", "ĠÙĨ اÙħ", "еÑĢ аÑĤÑĥ", "еÑĢа ÑĤÑĥ", "ı p", "ãģĦ ãģ¦", "Ġ íķĻ", "Ġíķ Ļ", "Ġ à¤ĩन", "Ġà¤ĩ न", "å ©", "Ġnh iên", "a tır", "at ır", "atı r", "ÅĻ enÃŃ", "ÅĻe nÃŃ", "ÅĻen ÃŃ", "د Ø©", "ãĥª ãĥ¼", "ล à¸ĩ", "Ġ éĢ", "Ġé Ģ", "Ġ à¹Ģà¸Ľ", "Ġà¹Ģ à¸Ľ", "Ġà¹ĢภĽ", "д Ñĸ", "ÎŃ Ïģ", "ìĦ ¤", "г ÑĢа", "es ine", "esi ne", "esin e", "Ġ ее", "Ġе е", "Ġ iki", "Ġi ki", "Ġik i", "Ġت ج", "lar ına", "ları na", "ların a", "d ür", "dü r", "ĠاÙĦ Ø°", "Ùħ ت", "ĠठIJ", "ि द", "िठ¦", "Ġ ë¹", "Ġë ¹", "ÑĦоÑĢм а", "ÑĦоÑĢ ма", "Ġ они", "Ġо ни", "Ġон и", "г оÑĢ", "го ÑĢ", "н еÑģ", "не Ñģ", "ìĺĢ ëĭ¤", "ıl dı", "Ġ çek", "Ġç ek", "Ġ дов", "Ġд ов", "Ġдо в", "د ÛĮ", "Ġ ÄĮesk", "ĠÄĮ esk", "ĠÄĮes k", "ÑĪ а", "Ġ ات", "Ġا ت", "ĠØ§Ø ª", "å± ĭ", "æĸ ¼", "Ġ práv", "Ġp ráv", "Ġpr áv", "Ġprá v", "é mu", "ém u", "å¸ Ī", "ãħĭ ãħĭ", "Ġil gili", "Ġilg ili", "Ġilgi li", "ห ว", "ठĩ", "ा ष", "ाठ·", "ëŀ ij", "as yon", "asy on", "ÑĨ ÑĮ", "à¹ģ à¸ķ", "ợ i", "Ġв ÑĭÑģ", "ĠвÑĭ Ñģ", "ÑĸлÑĮ ки", "ĠкоÑĤоÑĢ Ñĭе", "н ики", "ни ки", "ник и", "Ġ اد", "Ġا د", "ĠØ§Ø ¯", "Ġ živ", "Ġž iv", "Ġži v", "Ġα ÏĢο", "ĠαÏĢ ο", "ر ض", "ا Ø©", "Ø§Ø ©", "Ġk dyž", "Ġkdy ž", "ữ a", "Ġ ëĮĢíķľ", "ĠëĮĢ íķľ", "Ġt ôi", "Ġtô i", "Ñĥ ÑĶ", "ز ر", "Ġ å¥", "Ġå ¥", "ãĥĭ ãĥĭ", "ب Ø©", "ÏĦ οÏĤ", "ÏĦο ÏĤ", "ÑĨи он", "Ġ ÙħÙĬ", "ĠÙħ ÙĬ", "Ġ Äĥn", "ĠÄ ĥn", "à¥ĩ à¤Ĺ", "à¥ĩठĹ", "Ġ ÑĢег", "ĠÑĢ ег", "ĠÑĢе г", "ĠlỼ n", "ì¤ Ģ", "ìĭ ¬", "Ġb iết", "Ġbi ết", "a ları", "al arı", "ala rı", "alar ı", "Ùģ ÙĬ", "ä¸ĸ çķĮ", "Ġне обÑħодим", "Ġнеоб Ñħодим", "à¸Ļ ว", "ν ÏĦ", "Ġ ảnh", "íĸ Ī", "Ġ वर", "Ġव र", "h led", "hl ed", "hle d", "ิ à¸Ī", "æŃ »", "ĠاÙĦ تÙĬ", "ĠاÙĦت ÙĬ", "н оÑģ", "но Ñģ", "p rav", "pr av", "pra v", "Ñı ÑĤи", "ÑıÑĤ и", "Ñī а", "ÙĪ ÙĨÙĩ", "ÙĪÙĨ Ùĩ", "Ġ aÄŁ", "Ġa ÄŁ", "à¸ŀ ระ", "à¸ŀร ะ", "Ġth á»ijng", "Ġthá» ijng", "ÑĦ и", "Ġг олов", "Ġгол ов", "Ġk hoa", "Ġkh oa", "Ġkho a", "Ġ ëłĪ", "Ġëł Ī", "ãģ Ĵ", "Ġget ir", "Ġge tir", "Ø´ ت", "ж еннÑı", "жен нÑı", "е нÑĸ", "ен Ñĸ", "Ġgi ữ", "Ġgiá» ¯", "ler inin", "leri nin", "lerin in", "lerini n", "à¥Ģ व", "éģ ¸", "स र", "ĠÑĩ елов", "à¥į à¤ľ", "à¥įठľ", "ĠбÑĥ ло", "Ġ اÙĨد", "Ġا ÙĨد", "ĠاÙĨ د", "ั à¸Ļà¸Ĺ", "ัà¸Ļ à¸Ĺ", "è® ©", "Ġq uyá»ģn", "Ġquy á»ģn", "ĠØŃ Ø§ÙĦ", "ì² ĺ", "Ġ лÑİд", "ĠлÑİ Ð´", "Ïģ Ïĩ", "алÑĮ но", "ãĢĢ ãĥ½", "ê¸ ī", "ãĤ ±", "ĠÙħ رد", "ĠÙħر د", "Ġ ông", "Ġô ng", "Ġ اش", "Ġا Ø´", "ĠØ§Ø ´", "大 åѦ", "ì¦ Ī", "æĪ ¦", "e yi", "ey i", "Ġ ÐIJн", "ĠÐIJ н", "ि प", "िठª", "Ġt iêu", "Ġti êu", "Ø´ ÛĮ", "ắ n", "é ŃĶ", "éŃ Ķ", "ãģ¨ ãģĦãģĨ", "ãģ¨ãģĦ ãģĨ", "Ġ ìłĢ", "Ġì łĢ", "Ġìł Ģ", "к ÑĤи", "кÑĤ и", "Ġ ÙħØŃÙħد", "ĠÙħØŃ Ùħد", "Ġ íĨµ", "Ġí Ĩµ", "ĠíĨ µ", "ุ ม", "åį ¡", "о ÑĢов", "оÑĢ ов", "оÑĢо в", "к оÑİ", "ко Ñİ", "Ġl á»±c", "å³ ¶", "Ġ رÙĪز", "Ġر ÙĪز", "ĠرÙĪ ز", "Ñħ Ñĸд", "Ġh á»ĵ", "Ġhá» ĵ", "Ġ ül", "Ġü l", "Ġ Ø´Ùħ", "ĠØ´ Ùħ", "ÙĨ س", "ب ÙĪ", "Ġth êm", "ạ c", "åº ľ", "e chn", "ec hn", "ech n", "Ġ Îļα", "ĠÎļ α", "èij Ĺ", "çľ ¼", "á h", "Ġ ι", "ĠÎ ¹", "ê¹Į ì§Ģ", "m az", "ma z", "λο γ", "Ġjs me", "Ġ á¼", "Ġá ¼", "Ġп ÑĢави", "ĠпÑĢ ави", "ĠпÑĢав и", "ĠпÑĢа ви", "к лад", "Ġth ủ", "Ġthá» §", "s ah", "sa h", "ÄŁ it", "ÄŁi t", "Ġ ÙģÛĮ", "ĠÙģ ÛĮ", "ен но", "à¥ģ à¤Ľ", "à¥ģठĽ", "ãģ »", "çĻ ¾", "и ÑĤа", "иÑĤ а", "ĠбÑĭ ло", "ĠбÑĭл о", "Ġv ys", "Ġvy s", "Ġ ì¶ľ", "Ġì¶ ľ", "ắ ng", "ắn g", "ĠÄij ại", "ĠÙħ ÙĪرد", "ĠÙħÙĪ رد", "ĠÙħÙĪر د", "е ла", "ел а", "Ñĸ ÑĪ", "л еннÑı", "лен нÑı", "æ IJ", "Ġ нед", "Ġн ед", "Ġне д", "i yat", "iy at", "iya t", "ì ¼", "Ġol duÄŁunu", "ĠolduÄŁ unu", "ĠolduÄŁu nu", "د اÙĨ", "دا ÙĨ", "í Ŀ", "Ġ سÛĮ", "Ġس ÛĮ", "ี à¸ģ", "ÄĽ st", "ım ı", "ä¸ī ä¸ī", "ãĤ ½", "ĠÑĤ еп", "ĠÑĤе п", "Ġ ÑĢай", "ĠÑĢ ай", "ĠÑĢаР¹", "ĠÑĢа й", "ा ध", "ाठ§", "Ġ ìĤ¬ëŀĮ", "ĠìĤ¬ ëŀĮ", "ĠT rung", "ĠTr ung", "ĠTru ng", "ï¼ı ï¼ı", "Ġt âm", "Å¡ enÃŃ", "Å¡e nÃŃ", "Å¡en ÃŃ", "ãĥ į", "Ġ ÏĦοÏħÏĤ", "ĠÏĦ οÏħÏĤ", "ĠÏĦοÏħ ÏĤ", "Ġ нÑĸ", "Ġн Ñĸ", "в ид", "ви д", "æ ¿", "Ġ ظ", "ĠØ ¸", "ãĥ ¯", "ì ¢ħ", "ì¢ ħ", "в аÑĤи", "ва ÑĤи", "ваÑĤ и", "Ġqu á", "ภ¤", "ĠÄij Æ°á»Ŀng", "à¥ģ द", "à¥ģठ¦", "r oj", "ro j", "Ġ ÑĥÑģ", "ĠÑĥ Ñģ", "é¦ Ļ", "ì½ ĺ", "Ġ ÙĪت", "ĠÙĪ ت", "ม าà¸ģ", "มา à¸ģ", "åĪ ĩ", "Ġ án", "Ġá n", "Ġм ед", "ìĹIJ ëĬĶ", "Ġh lav", "Ġhl av", "ر ت", "à¹ĥ à¸Ī", "æ´ ²", "Ġ лÑĸ", "Ġл Ñĸ", "æ Īĺ", "æĪ ĺ", "ÙĪ ÙĨد", "ÙĪÙĨ د", "è¶ ³", "åĭ Ļ", "çĶ ³", "Ġ ì±", "Ġì ±", "ĠìĿ´ëıĻ íķ©ëĭĪëĭ¤", "Ñī еÑģÑĤв", "Ñīе ÑģÑĤв", "Ġ ë¶Ī", "Ġë ¶Ī", "Ġë¶ Ī", "ÙĦ ÙĪ", "ü ven", "èĪ ĩ", "Ġgi Ỽi", "Ġgiá» Ľi", "Ġ ÙĪÙĤ", "ĠÙĪ ÙĤ", "Ġê°¤ ë¡ľê·¸", "Ġ عاÙħ", "Ġع اÙħ", "ĺ IJ", ": ::::", ":: :::", ":::: :", "::: ::", "Ġ Ñĥд", "ĠÑĥ д", "- ÑĤо", "-ÑĤ о", "Ġ ÑĦоÑĢ", "ĠÑĦ оÑĢ", "и ни", "ин и", "ãģĹ ãģĦ", "ãģĹãģ Ħ", "Ġê°¤ë¡ľê·¸ ë¡ľ", "ãģ ³", "ãĥ© ãĤ¤", "e ná", "en á", "Ġ nez", "Ġn ez", "Ġne z", "Ġönem li", "Ġ ниÑħ", "Ġн иÑħ", "Ġни Ñħ", "à¤Ĥ स", "Ġà¤īस à¤ķ", "à¥įर द", "Ġn ói", "Ġnó i", "Ùĥ ÙĦ", "ิ ว", "κ ο", "à¥ģ à¤ĸ", "à¥ģठĸ", "ö yle", "öy le", "ά λ", "ó ng", "ón g", "ĠداÙĨ Ø´", "Ġз б", "ì »", "à¸ľ ล", "ëĵ¤ ìĿ´", "Ġe tk", "Ġet k", "ر ات", "را ت", "Ġ εκ", "Ġε κ", "ÑĤ ÑĢа", "ÑĤÑĢ а", "à¥į तर", "à¥įत र", "à¤Ĥ ब", "Ġм ÑĸÑģ", "ĠмÑĸ Ñģ", "æł ¹", "ãĥ Ļ", "Ġt á»ī", "Ġtá» ī", "à¹Ģ à¸ĭ", "à¹Ģภĭ", "ìĪ ł", "ï¼Į ä¸į", "ìĺ ¨", "Ġm ÄĽst", "ĠmÄĽ st", "ģ µ", "a zı", "az ı", "r ada", "ra da", "rad a", "ÏĢ α", "m é", "ÙĨ اÙħÙĩ", "ÙĨا ÙħÙĩ", "ÙĨاÙħ Ùĩ", "ا ÛĮÙĦ", "اÛĮ ÙĦ", "μ η", "l uk", "lu k", "Ùĥ ÙĬ", "Ġ ï¼ī", "Ġï¼ ī", "Ġ деÑĤ", "Ġд еÑĤ", "Ġде ÑĤ", "Ġiç inde", "Ġiçin de", "Ġiçi nde", "Ñı м", "Ġd Æ°á»", "ĠdÆ° á»", "ĠпÑĢед ÑģÑĤав", "ü re", "ür e", "åķ Ĭ", "ĠÑĤ ÑĢÑĥ", "ĠÑĤÑĢ Ñĥ", "es ini", "esi ni", "esin i", "Ġ але", "Ġа ле", "Ġал е", "ãĥ³ ãĥī", "ãĥ³ãĥ ī", "à¥ĥ त", "ε Ïħ", "à¥ģ à¤Ĩ", "à¥ģठĨ", "Ġh iç", "Ġhi ç", "çĶ º", "Ġ Ðĸ", "ĠÐ ĸ", "ç ħ§", "çħ §", "k á", "Ġtr á»įng", "Ġ تش", "Ġت Ø´", "ा श", "ाठ¶", "ĠÙħ Ø«", "e tim", "et im", "eti m", "Ġth ấy", "Ġब ह", "ع ت", "ึ à¹ī", "Ġs ev", "Ġse v", "Ñģ ÑĤа", "ÑģÑĤ а", "Ġc ứ", "Ġt iá»ģn", "Ġti á»ģn", "à¥Ģ à¤ľ", "Ñı г", "ĠоÑĢг ани", "ĠоÑĢган и", "Ġб Ñĭл", "ĠбÑĭ л", "t ür", "tü r", "Ġب ازÛĮ", "Ġبا زÛĮ", "Ġباز ÛĮ", "Ġ ìŀ¬", "Ġìŀ ¬", "व र", "æľīéĻIJ åħ¬åı¸", "k up", "ku p", "Ġ iyi", "Ġi yi", "Ġiy i", "íķĺ ê²Į", "ãĢĢ l", "ãĤ· ãĥ§", "ا رة", "ار Ø©", "ส ร", "Ġt ÃŃch", "ĠtÃŃ ch", "Ġ каÑĢ", "Ġк аÑĢ", "Ġка ÑĢ", "и б", "ĠвÑĸд повÑĸд", "ĠвÑĸдпов Ñĸд", "Ġpo dle", "Ġpod le", "à¥įर à¤ķ", "i yon", "iy on", "к оном", "ко ном", "кон ом", "Ġ μÎŃ", "Ġμ ÎŃ", "Ġп ÑĢоиз", "ĠпÑĢо из", "Ġ âĢı", "ĠâĢ ı", "m ektedir", "mekte dir", "Ω ÎĿ", "Ġb áo", "à¸Ī ำ", "ëį Ķ", "ë¸ Į", "Ġs ợ", "ÛĮ رÛĮ", "ÛĮر ÛĮ", "о нÑĥ", "он Ñĥ", "ın daki", "ında ki", "ınd aki", "алÑĮ ного", "алÑĮно го", "μ β", "л из", "ли з", "Ġjej ich", "æĸ ½", "ä¾ ¿", "l eÅŁtir", "le ÅŁtir", "leÅŁ tir", "ĠÙĪ Ø£", "Ġस ब", "l erde", "ler de", "Ġ ÚĨÙĩ", "ĠÚĨ Ùĩ", "ÏĦ ÎŃ", "Ġg ì", "Ġ Ãļ", "Ġà ļ", "ĠÑĢаÑģ п", "ĠÑĢа Ñģп", "Ġt üm", "à¹Ģ à¸ĩ", "à¹Ģภĩ", "èIJ ½", "ìĨ ¡", "à¹Ħ à¸Ĺย", "m Ä±ÅŁtır", "mÄ±ÅŁ tır", "mÄ±ÅŁtı r", "Ġ ÙĤرار", "ĠÙĤر ار", "Ġ à¸Ħาส", "Ġà¸Ħ าส", "Ġk ıs", "Ġkı s", "о ваниÑı", "ов аниÑı", "ова ниÑı", "овани Ñı", "ован иÑı", "ãĤĤ ãģ®", "د اÙħ", "دا Ùħ", "ìľ ¡", "ol oj", "olo j", "ĠпоÑģл е", "ĠпоÑģ ле", "Ġ Так", "ĠТ ак", "ĠТа к", "Ġб олее", "Ġбол ее", "ĠÄij á»ķi", "ĠÄijá»ķ i", "l ak", "la k", "í ħĮ", "íħ Į", "Ġa yn", "Ġay n", "Ñı Ñģ", "Ġп ог", "Ġпо г", "Ġar asında", "Ġaras ında", "Ġara sında", "Ġarası nda", "Ī ¬", "à¥Ĥ ल", "Ġ ανα", "Ġα να", "Ġαν α", "Ġq uyết", "Ġquy ết", "Ġthu á»Ļc", "Ġd ün", "Ġdü n", "Ġp ÅĻes", "ĠpÅĻ es", "ĠpÅĻe s", "ÑĦ Ñĸ", "Ġ å¸", "Ġå ¸", "ا ÙĦÙĬ", "اÙĦ ÙĬ", "Ġп овеÑĢ", "Ġпо веÑĢ", "Ġпов еÑĢ", "Ñĩ ина", "Ñĩи на", "Ñĩин а", "s ko", "sk o", "çµ IJ", "Ø ¡", "Ġ гÑĢа", "Ġг ÑĢа", "ĠгÑĢ а", "о ÑĤи", "оÑĤ и", "Ġqu á»ijc", "ÑĨ Ñĸв", "ÑĨÑĸ в", "l endir", "len dir", "lendi r", "в Ñĸд", "вÑĸ д", "Ġж иÑĤ", "ü yor", "üy or", "ï¼Į ä»ĸ", "lar ında", "ları nda", "ların da", "Ġu yg", "Ġuy g", "Ġtr ÃŃ", "Ġ Ø´ÙĨ", "ĠØ´ ÙĨ", "ا بÙĦ", "اب ÙĦ", "æ· ±", "Âł p", "Ñģ каÑı", "Ñģк аÑı", "Ñģка Ñı", "о ÑĤа", "оÑĤ а", "ÙĪ Ø·", "Ġ اط", "Ġا Ø·", "ĠØ§Ø ·", "ä¾ Ĩ", "Ġз аÑĤ", "Ġза ÑĤ", "Ġ име", "Ġи ме", "Ġим е", "à¹Ģà¸Ĺ ศ", "ëĭ ´", "n ÄĽnÃŃ", "nÄĽ nÃŃ", "nÄĽn ÃŃ", "Ñĥ лÑı", "Ñĥл Ñı", "- п", "å ĺ", "Ġв ип", "Ġви п", "аÑĢа кÑĤ", "à¹Ģ à¸ļ", "à¹Ģภļ", "ç¦ ı", "Ïģ Ïİ", "س Ùĩ", "à¥Į र", "Ġdi ÄŁer", "à¹Ĥ à¸Ķย", "à¹Ĥà¸Ķ ย", "ĠÑģп оÑģоб", "ĠÑģпоÑģ об", "åį ·", "è ĸ", "а нÑĤ", "ан ÑĤ", "Ñİ ÑĤÑĮÑģÑı", "ÑİÑĤÑĮ ÑģÑı", "ĠÑį ÑĤом", "ĠÑįÑĤ ом", "ĠÑįÑĤо м", "Ġ ï½Ģ", "Ġï½ Ģ", "ส าม", "ì m", "ĠÑĪ к", "Ġ à¸Ľà¸£à¸°", "Ġà¸Ľ ระ", "Ġà¸Ľà¸£ ะ", "़ à¥Ģ", "e kl", "ek l", "m uÅŁ", "mu ÅŁ", "ĠÑĤак ож", "ÙĪ سط", "ÙĪس Ø·", "Ġ Äįi", "ĠÄį i", "ี à¸Ļ", "ÛĮ ÙĨÙĩ", "ÛĮÙĨ Ùĩ", "ÄĽ k", "å½ ¼", "le rine", "ler ine", "leri ne", "lerin e", "ĠÄij ất", "à¥ģ à¤ı", "à¥ģठı", "ол оÑģ", "оло Ñģ", "Ġ å°ı", "Ġå° ı", "ز ÙĬØ©", "زÙĬ Ø©", "Ġв ла", "à¥Ģ ल", "Ġ etti", "Ġe tti", "Ġet ti", "Ġett i", "ĠÑģ оÑģÑĤав", "ĠÑģо ÑģÑĤав", "ĠÑģоÑģÑĤ ав", "ÙĦ اÙĦ", "ÙĦا ÙĦ", "Ġ çİ", "Ġç İ", "ĠpÅĻÃŃ pad", "ëŁ °", "ุ à¸ģ", "Ġ Ñĩи", "ĠÑĩ и", "å ħį", "åħ į", "n ÄĽjÅ¡ÃŃ", "nÄĽ jÅ¡ÃŃ", "ิ ล", "åį Ģ", "s kých", "sk ých", "ský ch", "า ศ", "าภ¨", "åIJ Ĺ", "Ġ íĺĦ", "Ġíĺ Ħ", "Ġal ın", "å§ Ķ", "à¸ŀ ร", "až d", "Ġб ÑĸлÑĮ", "ĠбÑĸ лÑĮ", "ĠбÑĸл ÑĮ", "à¹Ī วà¸Ļ", "à¹Īว à¸Ļ", "o og", "oo g", "a cı", "ac ı", "l ıģ", "lı ÄŁ", "Ġk hu", "Ġkh u", "Ġh izmet", "Ġ éĽ", "Ġé Ľ", "Ġ Îĺ", "ĠÎ ĺ", "Ġde ÄŁer", "ĠdeÄŁ er", "åħ Ń", "Ġ دÙĩ", "Ġد Ùĩ", "Ġn ÄĽk", "ĠnÄĽ k", "à¸Ħ à¸Ļ", "е ÑĤÑĮ", "еÑĤ ÑĮ", "ب اÙĨ", "با ÙĨ", "ÏĦ ική", "ÏĦικ ή", "ÏĦι κή", "ĠÄij á»ĭa", "ĠÄijá»ĭ a", "Ġ Công", "ĠC ông", "íĮ IJ", "Ġк огда", "ĠÚ© ÙĨد", "ĠÚ©ÙĨ د", "ãģ§ ãģį", "ĠÏĢ εÏģι", "ĠÏĢεÏģ ι", "ĠÏĢε Ïģι", "lar dan", "larda n", "Ġ зем", "Ġз ем", "ت ÙĪاÙĨ", "تÙĪ اÙĨ", "è³ ĩ", "li kle", "lik le", "Ġt ụ", "Ġtá» ¥", "Ġd ẫn", "Ġn ay", "Ġna y", "Ġ ÑģÑĤоÑĢ", "ĠÑģ ÑĤоÑĢ", "ĠÑģÑĤ оÑĢ", "ĠÑģÑĤо ÑĢ", "ĠØ´ Ùħا", "ĠØ´Ùħ ا", "Ø« ر", "Ġd edi", "Ġde di", "Ġded i", "к ое", "ко е", "ë ijIJ", "ëij IJ", "ÑĨ ев", "ÑĨе в", "ج Ùĩ", "Ġm ůže", "Ġmů že", "Ġmůž e", "à¥ģ प", "à¥ģठª", "à¥įर म", "Ġ taÅŁ", "Ġt aÅŁ", "Ġta ÅŁ", "оÑĢ ÑĤ", "γ Ïģα", "çĻ ¼", "า à¸ļ", "าภļ", "iá» ħn", "iá»ħ n", "ĠÙħ ست", "ĠÙħس ت", "л екÑģ", "ле кÑģ", "лек Ñģ", "Ġ prav", "Ġp rav", "Ġpr av", "Ġpra v", "Ġд оÑģ", "Ġдо Ñģ", "Ġd Ä±ÅŁ", "Ġ zem", "Ġz em", "Ġze m", "Ġg iao", "Ġgi ao", "Ġgia o", "Ġv last", "Ġvl ast", "Ġvlas t", "ĠÑį ÑĤого", "ĠÑįÑĤ ого", "ĠÑįÑĤо го", "ï½ °", "ว à¸ĩ", "ÑĢ ой", "ÑĢо й", "Ġbir lik", "e ný", "en ý", "Ġ ëĭ¨", "Ġëĭ ¨", "ов ани", "ова ни", "ован и", "é£ İ", "íı ī", "Ġz ah", "Ġza h", "б а", "åĬ ©", "éĢ ²", "ê ¶Į", "Ġd iye", "Ġdi ye", "Ġdiy e", "à¤Ĥ à¤ķ", "Ġch uyá»ĥn", "Ġ ìĹŃ", "ĠìĹ Ń", "Ġ ÑĤÑĢи", "ĠÑĤ ÑĢи", "ĠÑĤÑĢ и", "Ġö nce", "Ġön ce", "Ġönc e", "ï¼Į è¿Ļ", "o ại", "л еÑĤ", "ле ÑĤ", "ĠÏĥ Ïħν", "ĠÏĥÏħ ν", "l ád", "lá d", "ç e", "t ü", "Ġ Äįást", "ĠÄį ást", "ĠÄįá st", "Ġ εν", "Ġε ν", "Ġb iá»ĩt", "Ġ é«", "Ġé «", "à¥ĭ à¤ķ", "ÙĦ ات", "ÙĦا ت", "ب اÙĦ", "با ÙĦ", "e cies", "ec ies", "eci es", "Ġ ëĭ¹", "Ġëĭ ¹", "à¸Ĭ à¸Ļ", "ÏĦ αÏĤ", "ÏĦα ÏĤ", "à¥į ण", "à¥įठ£", "u jÃŃcÃŃ", "uj ÃŃcÃŃ", "ujÃŃ cÃŃ", "Äį et", "Äįe t", "Ġп об", "Ġпо б", "ÙĪ ار", "ÙĪا ر", "i yas", "iy as", "iya s", "Ġd ruh", "Ġdr uh", "Ġdru h", "د د", "ÏĮ ν", "ÑĢ ен", "ÑĢе н", "า รà¸ĸ", "าร à¸ĸ", "ä½ İ", "ìķ ½", "ÑĢ оз", "ÑĢо з", "ëĬĶ ëį°", "ãĤĵ ãģª", "Äį enÃŃ", "Äįe nÃŃ", "Äįen ÃŃ", "**** ********", "******** ****", "****** ******", "***** *******", "******* *****", "Ġ Ρ", "ĠÎ ¡", "ĠÑĤ омÑĥ", "ĠÑĤо мÑĥ", "ĠÑĤом Ñĥ", "ร à¸ģ", "à¥ģ स", "à¥ģठ¸", "ä¹ Ŀ", "å°± æĺ¯", "£ i", "éĺ ²", "Ùĥ ر", "ĠÑį ÑĤи", "ĠÑįÑĤ и", "ĠÚ© Ø´ÙĪر", "ĠÚ©Ø´ ÙĪر", "Ġ ê°IJ", "Ġê° IJ", "Ġ ад", "Ġа д", "Ġ داد", "Ġد اد", "éģ İ", "Ù «", "Ġl áºŃp", "Ġ اÙĦÙĩ", "Ġا ÙĦÙĩ", "ĠاÙĦ Ùĩ", "æľ Ľ", "Ġ تÙĩ", "Ġت Ùĩ", "ì§ Ī", "ãģ§ ãģĤãĤĭ", "ãģ§ãģĤ ãĤĭ", "Ġ меж", "Ġм еж", "ĠÑĢ езÑĥлÑĮÑĤ", "ĠÑĢез ÑĥлÑĮÑĤ", "ç į", "е мÑĥ", "ем Ñĥ", "Ġ تÙĪاÙĨ", "Ġت ÙĪاÙĨ", "ĠتÙĪ اÙĨ", "Ġ راÙĩ", "Ġر اÙĩ", "Ġرا Ùĩ", "ãĥ¼ ãĥł", "ãĥ¼ãĥ ł", "åĦ ¿", "å± ŀ", "б Ñĭ", "á ¿", "à¸Ħ ล", "à¥ĭ à¤Ī", "üt ün", "à¤Ĺ र", "ìķĺ ëĭ¤", "âĪ §", "Ġ ì°¨", "Ġì° ¨", "çµ Ħ", "μα ÏĦα", "ุ à¸Ļ", "Ġ ÑĤом", "ĠÑĤ ом", "ĠÑĤо м", "еÑĢ в", "Îij Σ", "ĠiÅŁ lem", "ع Ùħ", "ë ĥ", "ãĥ Ħ", "ا Ùģت", "اÙģ ت", "åĬ ŀ", "Ġ nes", "Ġn es", "Ġne s", "av aÅŁ", "ava ÅŁ", "ĠÙĨ ÛĮز", "ĠÙĨÛĮ ز", "å¼ º", "Ġ éĻ", "Ġé Ļ", "Ñĸн нÑı", "æ² ³", "á ÅĻ", "æĿ IJ", "ĠØ£ ÙĬ", "Ġ ì¹´", "Ġì¹ ´", "Ġn enÃŃ", "Ġne nÃŃ", "Ġnen ÃŃ", "Ġ ÙĪÙħ", "ĠÙĪ Ùħ", "Ġ Ú©Ùħ", "ĠÚ© Ùħ", "i ếu", "iế u", "Ġ æ°", "Ġæ °", "åĮ »", "Ġz or", "Ġzo r", "ί Ïĥ", "ि ध", "िठ§", "Ġп оказ", "Ġпо каз", "Ġпок аз", "Ġпока з", "ह र", "Ġiç er", "ØŃ Ø©", "ि à¤ĸ", "िठĸ", "а да", "ад а", "تر ÛĮÙĨ", "ترÛĮ ÙĨ", "Ġ bao", "Ġb ao", "Ġba o", "Ġx ã", "à¹Ģ à¸Ħร", "à¹Ģà¸Ħ ร", "Ġngh Ä©", "à¹ģ à¸ļà¸ļ", "à¹ģà¸ļ à¸ļ", "ĠdoÄŁ ru", "ĠdoÄŁr u", "Ñĸ ÑĤи", "ÑĸÑĤ и", "Ġ بÙĬÙĨ", "Ġب ÙĬÙĨ", "ĠبÙĬ ÙĨ", "Ġ леÑĤ", "Ġл еÑĤ", "ا غ", "Ø§Ø º", "ÛĮ Ú©ÛĮ", "ÛĮÚ© ÛĮ", "r áv", "rá v", "à¥į âĢį", "âĢĻ nin", "âĢĻn in", "Ġ ย", "Ġภ¢", "åį Ĭ", "Ġк оли", "Ġкол и", "Ġко ли", "Ġtr ợ", "éĿ Ĵ", "ëŀ Ģ", "Ġ ë¨", "Ġë ¨", "Ġ ÙĪر", "ĠÙĪ ر", "ï¾ Ĭ", "è§ Ĥ", "Ġ пи", "Ġп и", "н Ñĥв", "нÑĥ в", "il mesi", "ilm esi", "س تÙĩ", "ست Ùĩ", "Ġд еÑĢжав", "ĠдеÑĢж ав", "å® ĥ", "åĪ ¥", "ëħ Ģ", "л ÑģÑı", "à¤Ĥ ध", "Ġ ÑĤи", "ĠÑĤ и", "ĠpÅĻ ip", "ĠpÅĻi p", "п и", "á» ĵn", "á»ĵ n", "о ваÑĤÑĮ", "ов аÑĤÑĮ", "ова ÑĤÑĮ", "ìĿ´ ëĿ¼", "æľ Ŀ", "Ġ ëĺIJ", "Ġë ĺIJ", "Ġëĺ IJ", "ĠÎŃ Î½Î±", "ĠÎŃν α", "ãģ¾ ãģ§", "ج اÙħ", "جا Ùħ", "Ġ ëĬ", "Ġë Ĭ", "н Ñĸв", "нÑĸ в", "ÏĢ οÏħ", "ÏĢο Ïħ", "Ġ زÙħاÙĨ", "Ġز ÙħاÙĨ", "ĠزÙħ اÙĨ", "æĽ ²", "Ġ ÙħÙĩ", "ĠÙħ Ùĩ", "ëł ¨", "ä¸ ĥ", "ãģ¨ ãģĹãģ¦", "l abilir", "la bilir", "lab ilir", "о же", "ож е", "å¤ ľ", "ĠнÑĥж но", "å½ ©", "çĪ ±", "Ġho Ãłn", "ün ü", "Ġ ëĦ¤", "ĠëĦ ¤", "Ġ جÙĨ", "Ġج ÙĨ", "Ġn ÄĽj", "ĠnÄĽ j", "к ими", "ки ми", "ким и", "Ġa ynı", "Ġayn ı", "Ġ ÙĥÙĦ", "ĠÙĥ ÙĦ", "Ġnh au", "Ạ³", "ÙĬ ات", "ÙĬا ت", "Ġm ezi", "Ġme zi", "Ġmez i", "Ġ ÑĢек", "ĠÑĢ ек", "ĠÑĢе к", "Ġ tür", "Ġt ür", "Ġ говоÑĢ", "Ġг овоÑĢ", "Ġfaz la", "åĩ Ĩ", "ÑĪ ий", "ÑĪи й", "ÐŁ ÑĢи", "ÐŁÑĢ и", "ÑĢ оÑģÑĤ", "ÑĢоÑģ ÑĤ", "ÑĢо ÑģÑĤ", "ĠоÑĢг ан", "ĠоÑĢ ган", "n ým", "ný m", "Ġ ÑĢод", "ĠÑĢ од", "Ġ ÙĪÛĮ", "ĠÙĪ ÛĮ", "ic ký", "ick ý", "ë¦ ¼", "ï½ ²", "æĢ İ", "ĠÙĩ ذا", "ĠÑĩ аÑģÑĤи", "ĠÑĩа ÑģÑĤи", "ĠÑĩаÑģ ÑĤи", "ĠÑĩаÑģÑĤ и", "ÃŃ r", "á»ĩ nh", "á»ĩn h", "Ġ íĹ", "Ġí Ĺ", "ê »", "lu ž", "ÃŃ l", "c ÃŃch", "cÃŃ ch", "å® Ł", "ãģł ãģ£ãģŁ", "ÙĬ رة", "ÙĬر Ø©", "Ġv Äĥn", "æ¸ ¯", "Ġ ÏĦιÏĤ", "ĠÏĦι ÏĤ", "ا رت", "ار ت", "Ġv ấn", "âĶģâĶģ âĶģâĶģ", "å¯ ¾", "Ïģ ÎŃ", "Ġг одÑĥ", "Ġгод Ñĥ", "Ġ سب", "Ġس ب", "ا رات", "ار ات", "ارا ت", "е лей", "ел ей", "еле й", "Ġз аÑħ", "Ġза Ñħ", "Ġ важ", "Ġв аж", "Ġва ж", "Ġt á»īnh", "Ġtá» īnh", "Ġtá»ī nh", "ا بع", "اب ع", "Ġ à¤ľà¤¬", "Ġà¤ľ ब", "Ġà¤IJ स", "Ġ дÑĥ", "Ġд Ñĥ", "Ġ é«ĺ", "Ġé «ĺ", "Ġé« ĺ", "ê² ł", "н ее", "не е", "ï½ Į", "Ġм ал", "Ġма л", "è¾ ¹", "ãģł ãģij", "à¹ī ร", "ÙĤ Ø·", "Ġb ên", "Ġs eb", "Ġse b", "ĠØ® ÙĪاÙĩ", "ĠØ®ÙĪ اÙĩ", "s iz", "si z", "Ġol ur", "Ġ ëĶ°", "ĠëĶ °", "Ġ ì¢ĭ", "Ġì¢ ĭ", "Ġs vÄĽt", "Ġsv ÄĽt", "ĠsvÄĽ t", "ic ká", "ick á", "á» ¹", "Ġqu ản", "Ġquả n", "Ġ иÑģ", "Ġи Ñģ", "Ġz aÄį", "Ġza Äį", "ื à¸Ńà¸Ļ", "ืà¸Ń à¸Ļ", "ÑĶ Ñİ", "ि ष", "िठ·", "ç Ĭ¶", "çĬ ¶", "Ïĥ μ", "ั ส", "ó c", "Ġ беÑĢ", "Ġб еÑĢ", "Ġ íĿ", "Ġí Ŀ", ";: ;:", "Ġ پس", "ĠÙ¾ س", "Ġ ëijIJ", "Ġë ijIJ", "Ġëij IJ", "н иÑĩ", "ни Ñĩ", "Ġо ÑĩенÑĮ", "ĠоÑĩ енÑĮ", "ĠìķĦìĿ´ ì½ĺ", "Ġ θα", "Ġθ α", "Ġв ÑģÑĤ", "ĠвÑģ ÑĤ", "ا دة", "اد Ø©", "Ġdev am", "ื à¸Ńà¸ĩ", "ืà¸Ń à¸ĩ", "ĠлÑİ Ð´Ð¸", "ĠлÑİд и", "ìĺ Ī", "á»± a", "Ñı Ñħ", "âĢĮ اÛĮ", "Ġ سÙĪ", "Ġس ÙĪ", "å° ¼", "Ġth ứ", "Ġthá» ©", "m eye", "me ye", "mey e", "Ġ èµ", "Ġè µ", "èī ¯", "ĠdeÄŁ iÅŁ", "ÑĪ Ñĸ", "Ġtr ợ", "ĠâĢİ #", "çĹ ħ", "ìĽ Į", "Ġk de", "Ġkd e", "Î §", "æ ¤", "ĠÑħ аÑĢакÑĤ", "æ ĩ", "Ġb iến", "Ġbi ến", "ÙĤ ع", "åŁ Ł", "Ġн еп", "Ġне п", "Ġd ů", "Ġп иÑĤ", "Ġпи ÑĤ", "ĠÑĤ ÑĢеб", "ĠÑĤÑĢ еб", "ا زÛĮ", "از ÛĮ", "Ġ طر", "ĠØ· ر", "Ġ ÙħÙĦ", "ĠÙħ ÙĦ", "Ġt ham", "Ġth am", "Ġtha m", "Ġ ÙĪجÙĪد", "ĠÙĪج ÙĪد", "Ġs vé", "Ġsv é", "é§ ħ", "ا ÛĮÙĨ", "اÛĮ ÙĨ", "Ġt iên", "Ġti ên", "s tru", "st ru", "str u", "Ġv áºŃy", "ü ne", "ün e", "Ġ à¹Ģม", "Ġà¹Ģ ม", "Ġà¹Ģภ¡", "Ġr ằng", "а ÑĤÑĥ", "аÑĤ Ñĥ", "äº ij", "н иÑĤ", "ни ÑĤ", "ä¼ Ĭ", "ÙĪ ص", "Ġ éĿ", "Ġé Ŀ", "ĠпÑĢоб лем", "d eki", "de ki", "dek i", "** ************", "******** ******", "****** ********", "******* *******", "************ **", "ò a", "ĠÄijá»ģ u", "ãĤĮ ãģŁ", "ا رس", "ار س", "ãģª ãģı", "ا ÙĤع", "اÙĤ ع", "è» į", "Ùĥ Ùħ", "Äį as", "Ġk ỳ", "Ġká» ³", "Ø´ Ùħ", "à¥ĩ ड", "à¥ĩठ¡", "éĺ ¿", "Ġje jÃŃ", "Ġjej ÃŃ", "Ġ æĻ", "Ġæ Ļ", "Ġ Ä°ÅŁ", "ĠÄ° ÅŁ", "ar dım", "ard ım", "Ġसम य", "Ġ ÐĿо", "ĠÐĿ о", "i lerin", "il erin", "ile rin", "iler in", "ileri n", "Ġع بد", "Ġعب د", "n ÃŃk", "nÃŃ k", "ĠØ´ Ú©ÙĨ", "ĠØ´Ú© ÙĨ", "ิ à¸Ĺย", "ิà¸Ĺ ย", "á» ħ", "ÑĢ ез", "ÑĢе з", "Ġch ứng", "Ġchứ ng", "Ġ :.", "Ġ: .", "Ġ पत", "Ġप त", "Ġž ivot", "Ġživ ot", "å¢ ĥ", "« a", "Ġt rung", "Ġtr ung", "ни кÑĸв", "ник Ñĸв", "ĠاÙĦ ÙħÙĨ", "ĠاÙĦÙħ ÙĨ", "ĠÑĢ аÑģÑģ", "ĠÑĢаÑģ Ñģ", "Ġ жив", "Ġж ив", "Ġз акон", "Ġза кон", "Ġзак он", "Ġзако н", "Ġ 목", "Ġëª ©", "Ġz áv", "Ġzá v", "Ġh akk", "Ġha kk", "Ġhak k", "ä» ¤", "ĠÑı кий", "ĠÑıк ий", "Ġ بÙĬ", "Ġب ÙĬ", "λ ÎŃ", "oc uk", "ocu k", "Ġ Ñİ", "ĠÑ İ", "à¸ģ ว", "Ġ اÙĨÚ¯", "Ġا ÙĨÚ¯", "ĠاÙĨ Ú¯", "à¥ģ à¤Ĥ", "à¥ģठĤ", "Ġ nám", "Ġn ám", "Ġná m", "á»ķ ng", "Ġж ел", "Ġже л", "ĠÄij ặc", "Äį it", "Äįi t", "Ġ ê±´", "Ġê± ´", "Ġب ÛĮØ´", "ĠبÛĮ Ø´", "кÑĢаÑĹ Ð½", "Ġ ÙĪÙĩ", "ĠÙĪ Ùĩ", "н еннÑı", "нен нÑı", "Ġ à¹Ģà¸ŀ", "Ġà¹Ģ à¸ŀ", "Ġà¹Ģภŀ", "о мен", "ом ен", "Ġl ần", "Ġ عÙħÙĦ", "Ġع ÙħÙĦ", "ĠعÙħ ÙĦ", "Ġî ģµ", "Ä ŀ", "ÑĸÑģ лÑı", "Æ° ng", "ा फ", "ाठ«", "à¸Ĺ à¸ĺ", "д ен", "де н", "ĠÑī об", "ĠÑīо б", "Ñĩ ив", "Ñĩи в", "ılı r", "ıl ır", "ا عات", "اع ات", "j ÃŃcÃŃ", "jÃŃ cÃŃ", "ë² ¨", "ÚĨ Ùĩ", "ا رج", "ار ج", "ĠÙ¾ رÙĪ", "Ġپر ÙĪ", "Ġо дин", "Ġод ин", "Ġоди н", "л ин", "ли н", "б Ñĥ", "Ġसर à¤ķ", "åĢ Ļ", "ë¶Ģ íĦ°", "à¥Īà¤Ĥ ,", "å ´", "à¹Ĥ ล", "Ġv Å¡ak", "ĠvÅ¡ ak", "Ġоп ÑĢед", "ì ±", "æ ½", "Ġdá»± ng", "p ráv", "pr áv", "ิ ส", "Ġnh iá»ĩm", "Ġil iÅŁ", "Ġili ÅŁ", "Ġе Ñīе", "Ġje Å¡tÄĽ", "Ġ ÑĢаÑģÑĤ", "ĠÑĢ аÑģÑĤ", "ĠÑĢаÑģ ÑĤ", "ĠÑĢа ÑģÑĤ", "ภ®", "à¤Ĥ à¤Ł", "âĢĮ Ú©", "Ġ بÛĮÙĨ", "Ġب ÛĮÙĨ", "ĠبÛĮ ÙĨ", "o vou", "ov ou", "ovo u", "æĻ ®", "ί εÏĤ", "о ÑĢоÑĪ", "оÑĢ оÑĪ", "оÑĢо ÑĪ", "Ġol mak", "Ġolm ak", "Ġolma k", "Ġst át", "di ÄŁi", "Ġt ình", "Ġ dÄĽ", "Ġd ÄĽ", "ĠÚ¯ رÙģ", "Ġگر Ùģ", "Ïĥ ο", "Ġ ÑĥÑĤ", "ĠÑĥ ÑĤ", "íķĻ êµIJ", "ั à¸IJ", "า à¸Ń", "าภŃ", "ĠÄij ặt", "Ġмог ÑĥÑĤ", "ĠмогÑĥ ÑĤ", "ë° °", "t ik", "ti k", "ª ½", "li ÄŁ", "ÏĢ ε", "Ġ èĢ", "Ġè Ģ", "k ü", "ad ece", "ade ce", "κ ÏĮ", "Ġ дÑĸ", "Ġд Ñĸ", "ầ m", "çĦ¡ ãģĹ", "Û²Û° Û±", "èµ Ľ", "оÑģ Ñĥд", "Ġ ìķĪëĤ´", "ĠìķĪ ëĤ´", "Ġ ÐĶж", "ĠÐĶ ж", "åº §", "ic kých", "ick ých", "ický ch", "Ġ ìłģ", "Ġì łģ", "Ġìł ģ", "à¥ĩ ,", "ov ého", "ové ho", "Ġv ẫn", "Ġbirlik te", "Ġर à¤ĸ", "Ġ ÙĨÙĩ", "ĠÙĨ Ùĩ", "ÙĤ ر", "प र", "e tÃŃ", "et ÃŃ", "Ġ ÑĤÑĭ", "ĠÑĤ Ñĭ", "Ģ ìĿ´", "Ġà¤ħ ल", "Ġм оже", "Ġмож е", "Ġмо же", "ãĤ ´", "Ġs tran", "Ġst ran", "Ġstr an", "Ġstra n", "Ø· ر", "è¿Ļ 个", "Ġ بع", "Ġب ع", "åĨ Ľ", "ek tir", "ekt ir", "Ġh Æ°á»Ľng", "ÙĨ اÙĨ", "ÙĨا ÙĨ", "Ġठij", "Ġà ¤ij", "ÏĮ ÏĦη", "о Ñģк", "оÑģ к", "åį ĥ", "as ına", "ası na", "Ġ Ø´Ùĩ", "ĠØ´ Ùĩ", "Ġ деÑĢ", "Ġд еÑĢ", "Ġде ÑĢ", "ĠÙħ خت", "ĠÙħØ® ت", "Ġ ØŃÙĤ", "ĠØŃ ÙĤ", "ãĥ ¾", "س اÙĨ", "Ġc ung", "Ġcu ng", "ко ÑĢиÑģÑĤ", "коÑĢ иÑģÑĤ", "ÏĦ ικά", "ÏĦικ ά", "ÏĦι κά", "Ġв она", "Ġво на", "ب ا", "ãģķ ãĤĮãģŁ", "ãģķãĤĮ ãģŁ", "n out", "no ut", "nou t", "Ġ ı", "ĠÄ ±", "è§ ī", "ĠÃ¶ÄŁ ren", "Ġ ì½Ķ", "Ġì ½Ķ", "Ġì½ Ķ", "å¸ ¦", "Ñģ лов", "Ñģл ов", "Ġε ÏĢι", "ĠεÏĢ ι", "ê° IJ", "ĠÙħ رب", "ĠÙħر ب", "ĠÙģÛĮ ÙĦÙħ", "Ġк ÑĢов", "Ġ ëį°", "Ġë į°", "Ġëį °", "ा ण", "ाठ£", "Ġel ekt", "Ġele kt", "Ġelek t", "Ġ наÑĢод", "Ġн аÑĢод", "Ġна ÑĢод", "ĠнаÑĢ од", "ÛĮ دÙĩ", "ÛĮد Ùĩ", "ç´ Ħ", "Ġп ÑĢоÑĦ", "ĠпÑĢ оÑĦ", "ĠпÑĢо ÑĦ", "Ïģ οÏĤ", "Ïģο ÏĤ", "Ġ ãħ", "ä¸į æĺ¯", "Ġ à¤ľà¤¨", "Ġà¤ľ न", "ั ล", "Ġص ÙĪرت", "ĠصÙĪر ت", "ãĥ ľ", "Ġà¤Ĺ à¤Ī", "ÄŁi tim", "ÄŁit im", "ÑģÑĮ киÑħ", "ÑģÑĮк иÑħ", "Ġ лег", "Ġл ег", "Ġت ÙĪÙĦ", "ĠتÙĪ ÙĦ", "Ġ ìļ´", "Ġìļ ´", "ع ر", "Ġm Ãłu", "ĠmÃł u", "г ов", "го в", "æ³ ¢", "in deki", "ind eki", "inde ki", "ìłģ ìĿ¸", "ấ m", "Ġ íĻķ", "ĠíĻ ķ", "Ġب اÛĮد", "Ġبا ÛĮد", "ĠباÛĮ د", "à¹Į à¸Ĺ", "Ġk endi", "Ġken di", "Ġkend i", "ี ว", "ิ à¸ģาร", "ิà¸ģ าร", "ิà¸ģา ร", "ĠÚ© ردÙĩ", "Ġکرد Ùĩ", "Ġکر دÙĩ", "å· ´", "ठģ", "ร าà¸Ĭ", "à¥į श", "à¥įठ¶", "Ġ ÐĶлÑı", "ĠÐĶ лÑı", "å¥ ĩ", "ĠÑĥ ÑģÑĤанов", "ĠÑĥÑģÑĤ анов", "ĠÑĥÑģÑĤан ов", "й ÑĤе", "ãĤ ĩ", "ά Ïģ", "Ġ Ю", "ĠÐ ®", "Ġlu áºŃt", "ãĢ ī", "è´ ¨", "د ا", "Ġdü zen", "Ġdüz en", "ส à¸Ļ", "ÑĢ он", "ÑĢо н", "d ıģı", "dı ģı", "dıģ ı", "âĢĻ da", "âĢĻd a", "Ġfark lı", "Ñħ ов", "Ñħо в", "l án", "lá n", "Ñĩ аÑģ", "Ñĩа Ñģ", "Ñĩ ин", "Ñĩи н", "Ġ ì°¸", "Ġì° ¸", "ì ´Ī", "ì´ Ī", "ÑĨ ип", "ÑĨи п", "ç ¹", "éĸ Ģ", "ж а", "ÑĢ ован", "ÑĢов ан", "ÑĢо ван", "ÑĢова н", "à¸ĵ ะ", "ÙĦÙĬ زÙĬØ©", "Ïĩ ει", "Ïĩε ι", "à¥Ī .", "к Ñģп", "кÑģ п", "ا ÙĪر", "اÙĪ ر", "Ġng uyên", "Ġnguy ên", "ãģ« ãĤĪ", "à¥ĩ म", "à¥ĩठ®", "Ïĥ ÏĦε", "ÏĥÏĦ ε", "ت ÙĪ", "Äį ek", "Äįe k", "ÑĨ Ñĭ", "Ġ 물", "Ġë¬ ¼", "Ñį ÑĤ", "Ġka zan", "Ġkaz an", "Ùģ س", "e hir", "eh ir", "в ÑĸÑĤ", "вÑĸ ÑĤ", "Ġد ÙĪÙĦ", "ĠدÙĪ ÙĦ", "Ġ ëĵľ", "Ġëĵ ľ", "Ġà¤ļ ल", "е ÑģÑĤва", "еÑģÑĤв а", "еÑģÑĤ ва", "δ α", "Ġб Ñĥв", "ĠбÑĥ в", "Ġ ÐĿе", "ĠÐĿ е", "ØŃ Ø±", "огÑĢа ÑĦ", "Ġroz hod", "Ġrozh od", "Ġви коÑĢиÑģÑĤ", "Ġвико ÑĢиÑģÑĤ", "Ġy êu", "λ οÏĤ", "λο ÏĤ", "Ú© س", "Ġ شب", "ĠØ´ ب", "ิ ษ", "æ¯ į", "Ġд оÑĢ", "Ġдо ÑĢ", "Ġngh á»ĩ", "Ġt rang", "Ġtr ang", "Ġtra ng", "Ġtran g", "à¥ĩ द", "à¥ĩठ¦", "Ġt ìm", "Ñĩ но", "Ġ اÙħا", "Ġا Ùħا", "ĠاÙħ ا", "éģ ĭ", "Ú© ر", "k é", "Ġ vÄĽt", "Ġv ÄĽt", "ĠvÄĽ t", "Ġн аÑģÑĤ", "Ġна ÑģÑĤ", "ĠнаÑģ ÑĤ", "Ġ æ±", "Ġæ ±", "Ġ åĽ½", "ĠåĽ ½", "Ġgi ảm", "Ġgiả m", "ا دÙĬ", "اد ÙĬ", "ëĤ ľ", "ë¡ ł", "Ġ ､", "Ġï½ ¤", "Ġд енÑĮ", "Ġде нÑĮ", "Ġден ÑĮ", "ÑĨ ÑĸÑİ", "ÑĨÑĸ Ñİ", "Ġh ạn", "Ġhạ n", "ẳ ng", "ẳn g", "λ ή", "e yen", "ey en", "eye n", "ä¸ Ķ", "æŃ ¦", "ĠÑĦ ак", "à¹Ī à¸Ńà¸Ļ", "à¹Īà¸Ń à¸Ļ", "Ġ οι", "Ġο ι", "ز Ùħ", "ãģĹ ãģ¦ãģĦãĤĭ", "ãģĹãģ¦ ãģĦãĤĭ", "ãģĹãģ¦ãģĦ ãĤĭ", "л ива", "ли ва", "лив а", "âĢķ âĢķ", "Ġ öl", "Ġö l", "Ġ à¤ĵ", "Ġठĵ", "Ñģ ÑĤÑĸ", "ÑģÑĤ Ñĸ", "à¸ģ รรม", "à¸ģร รม", "Ġt ục", "Ġtụ c", "Ġgö rün", "Ġgör ün", "ãģĹ ãģ¾", "ãģĹãģ ¾", "Ġ ì¦", "Ġì ¦", "é ¦¬", "é¦ ¬", "Ġмож на", "Ġ Ú©ÙĦ", "ĠÚ© ÙĦ", "Ġ ÑĨенÑĤ", "ĠÑĨ енÑĤ", "ĠÑĨе нÑĤ", "ĠÑĨен ÑĤ", "Ġ ìĻ¸", "ĠìĻ ¸", "Î ĺ", "ç ĩ", "Ġg elen", "Ġge len", "Ġgel en", "Ġgele n", "Ġ اÙĬÙĨ", "Ġا ÙĬÙĨ", "ĠاÙĬ ÙĨ", "ĠØ¢ ب", "Ġà¤Ĩ य", "ัà¸ģ ษ", "Ñģ им", "Ñģи м", "Ġб олÑĮÑĪ", "ĠболÑĮ ÑĪ", "Ġм н", "о ди", "од и", "ĠÄ° l", "Ġ à¤Ĩर", "Ġà¤Ĩ र", "е ÑĤе", "еÑĤ е", "ÑĨ иÑİ", "ÑĨи Ñİ", "áºŃ u", "Ġt iếng", "Ġtiế ng", "Ġtiến g", "ë ¶ģ", "ë¶ ģ", "æ§ ĺ", "Ġн аÑĪ", "Ġна ÑĪ", "ม า", "âĢĻ ın", "âĢĻı n", "ãĥĥ ãĥĹ", "ÙĪ جÙĩ", "ÙĪج Ùĩ", "Ġ ØŃد", "ĠØŃ Ø¯", "á vá", "áv á", "ر ÙĪØ´", "رÙĪ Ø´", "Ġ дейÑģÑĤв", "Ġд ейÑģÑĤв", "ãģ£ ãģ¦ãģĦãĤĭ", "ãģ£ãģ¦ ãģĦãĤĭ", "ãģ£ãģ¦ãģĦ ãĤĭ", "Ïģ ή", "Ġ üst", "Ġü st", "Ġt iết", "Ġti ết", "Ġtiế t", "ac aÄŁ", "aca ÄŁ", "Ġ ÐŁÐ¾", "ĠÐŁ о", "é Ĭ", "ë¨ ¸", "c hod", "ch od", "cho d", "ĠØ¢Ùħ ÙĪز", "ãģŁ ãĤģ", "Ġch uyên", "Ġuy gu", "Ġuyg u", "н ÑĸÑģÑĤ", "нÑĸ ÑģÑĤ", "ë ´", "æİ §", "Ñĥ ÑİÑĤÑĮ", "ÑĥÑİ ÑĤÑĮ", "ÑĥÑİÑĤ ÑĮ", "Äį i", "ãģ ¹", "à¥Ĥ न", "æĹ ©", "ãĥĩ ãĤ£", "è Ĵ", "ĠØ´ خص", "ĠÑħ оÑĤ", "ĠÚ©ÙĨ ÛĮد", "г л", "à¸Ń à¸Ńà¸ģ", "à¸Ńà¸Ń à¸ģ", "éĢ Ļ", "Ġز ÛĮر", "ĠزÛĮ ر", "íķ Ń", "ĠÃĸ z", "åij ³", "ØŃ Ø¯Ø©", "ØŃد Ø©", "Ġk ažd", "Ġ ÑĨвеÑĤ", "ĠÑĨ веÑĤ", "Ġ ç¾", "Ġç ¾", "Ġк ож", "Ġко ж", "Ġ ÐŃÑĤо", "ĠÐŃ ÑĤо", "ÑıÑĤ елÑĮ", "ла ÑģÑĮ", "лаÑģ ÑĮ", "âĢĮ Ø´ÙĪد", "âĢĮØ´ ÙĪد", "μ ι", "Ġ æ²", "Ġæ ²", "Ġs üre", "Ġsü re", "Ġsür e", "ล ะ", "éħ Ĵ", "ึà¸ģ ษ", "λ λά", "λλ ά", "ç ij", "Ġ ìĥĪ", "Ġìĥ Ī", "Ġस ह", "ĠH Ãł", "리 ê³ł", "ص ر", "Ġ æĬķ", "ĠæĬ ķ", "éł Ń", "Ġb á»ĩnh", "ĠìĥĿ ê°ģ", "Ġà¤ħ à¤Ń", "ê³µ ì§Ģ", "ì Ķ", "á» Ŀi", "á»Ŀ i", "ç ŃĶ", "çŃ Ķ", "Ġb Ãłi", "ĠbÃł i", "о дÑĸ", "од Ñĸ", "า à¸Ĥ", "าภĤ", "ни ков", "ник ов", "Ġdön em", "Ġdö nem", "ว ม", "ãĥĨ ãĤ£", "ा रण", "ार ण", "о ги", "ог и", "Ġk iá»ĥm", "Ġki á»ĥm", "о ÑĦ", "äº Ī", "åĨ ³", "ا ÙĦات", "اÙĦ ات", "اÙĦا ت", "Ġn ếu", "Ġ cest", "Ġc est", "Ġce st", "Ġces t", "ز Ø´", "Ùİ ÙĦ", "Ġت Ø£", "ĠÄij ạo", "Ïį ν", "Ġв нÑĥ", "Ġ جاÙħ", "Ġج اÙħ", "Ġجا Ùħ", "i vnÃŃ", "iv nÃŃ", "Ġìŀ ĪìĬµëĭĪëĭ¤", "ĠìŀĪ ìĬµëĭĪëĭ¤", "Ï Ĭ", "æĦ Ľ", "ãĥ Ľ", "м Ñĸн", "мÑĸ н", "Ġt ÃŃm", "ĠtÃŃ m", "ằ m", "ê· ł", "äº ķ", "Ġx ây", "Ġ ìĽĶ", "ĠìĽ Ķ", "е лен", "ел ен", "еле н", "Ġ à¹Ĥà¸Ķย", "Ġà¹Ĥ à¸Ķย", "ا ÙĦÙĩ", "اÙĦ Ùĩ", "Ġb ất", "á»ĵ m", "âĢĮ Ú¯", "ÙĪ رة", "ÙĪر Ø©", "ب ات", "با ت", "Ġb án", "ẫ u", "اÙĨ ÙĪÙĨ", "اÙĨÙĪ ÙĨ", "Ġzá kon", "á ž", "ì¶ Ķ", "à¹ģ à¸ģ", "ãĤį ãģĨ", "ÑĢ оÑĤ", "ÑĢо ÑĤ", "ç ĵ", "Ġв они", "Ġво ни", "Ġx ác", "Ġ دÛĮگر", "ĠدÛĮ گر", "ÏĢ οι", "ÏĢο ι", "Ġне Ñģк", "ĠнеÑģ к", "ر سÛĮ", "رس ÛĮ", "Ġ ëĿ¼", "Ġë Ŀ¼", "ت ÙĦ", "λ ά", "ĠÑıв лÑıеÑĤÑģÑı", "ä¾ Ŀ", "Ġ åħ¬", "Ġåħ ¬", "Ĺ i", "Ġ íĬ¹", "ĠíĬ ¹", "Ùĥ ÙĪÙĨ", "ÙĥÙĪ ÙĨ", "ắ p", "جÙħ ÙĪع", "ÏĨ οÏģ", "ÏĨο Ïģ", "е ло", "ел о", "Ġg üven", "Ġgü ven", "Ġм ай", "Ġма й", "ĠÑģ оз", "ĠÑģо з", "à¸ģ ระ", "à¸ģร ะ", "Ġا سÙĦاÙħ", "Ġاس ÙĦاÙħ", "Ġ Ñīе", "ĠÑī е", "Ġs á»ijng", "Ġsá»ij ng", "à¥į ब", "à¥įठ¬", "Ú© ار", "کا ر", "Ġthu áºŃt", "Ġ nÃŃ", "Ġn ÃŃ", "第 ä¸Ģ", "è¦ ĸ", "à¹Ģà¸ģ ม", "ا ÙĬØ©", "اÙĬ Ø©", "Ġ ÎĪ", "ĠÎ Ī", "ãĤ ¶", "ĠÙħ ÙĪÙĤع", "ĠÙħÙĪ ÙĤع", "Ġ åĴ", "Ġå Ĵ", "è¡ ĵ", "Ġ Ðŀд", "ĠÐŀ д", "Ġ ä¸ī", "Ġä¸ ī", "ler inde", "leri nde", "lerin de", "ĠÑģв оÑĹ", "ĠÑģво ÑĹ", "à¥Ģ à¤ı", "Ġth Æ°Æ¡ng", "Ïĥ ÏĦο", "ÏĥÏĦ ο", "Ġ غÙĬر", "Ġغ ÙĬر", "Ġ پر", "ĠÙ¾ ر", "ĠÑģеб е", "Ġв к", "Ġk hai", "Ġkh ai", "ãĤ Ģ", "ĠÙĨ ظر", "ĠÙĨظ ر", "Ġдок Ñĥм", "à¹ĩ à¸ļ", "Ġ íķľêµŃ", "Ġíķľ êµŃ", "ï½ ī", "å·¥ ç¨ĭ", "Ġ ÙĪÙĦ", "ĠÙĪ ÙĦ", "ØŃ ÙĬ", "Ġп ла", "Ġпл а", "Ġ Ä°stanbul", "ĠÄ° stanbul", "âĢĻ de", "âĢĻd e", "а лÑģÑı", "ал ÑģÑı", "ĠØ¢ÙĨ Ùĩا", "Ġ اÙĩ", "Ġا Ùĩ", "Ġ ê´Ģ리", "Ġê´Ģ 리", "Ġ anh", "Ġa nh", "Ġan h", "Å¡ ÃŃm", "Å¡ÃŃ m", "lar la", "ï¼ Ŀ", "n ostÃŃ", "no stÃŃ", "nost ÃŃ", "nos tÃŃ", "ÑģÑĤ ве", "ÑģÑĤв е", "ÛĮ Ùģ", "Ġ گرد", "ĠÚ¯ رد", "Ġگر د", "ãĤĮ ãĤĭ", "Ġv á»±", "Ġvá» ±", "ÄĽ nÃŃ", "ÄĽn ÃŃ", "Ġgö rev", "Ġgör ev", "Ġgöre v", "Ġyıl ında", "Ġyılı nda", "Ġyı lında", "Ġl ợi", "Ġlá» £i", "Ġan lam", "Ġп ÑĢовод", "ĠпÑĢо вод", "ĠпÑĢов од", "ÑĨ Ñİ", "Ġ åī", "Ġå ī", "Ġë§ İ", "ÑĢ аÑģ", "ÑĢа Ñģ", "Ġ Ž", "ĠÅ ½", "Ú© اÙĨ", "کا ÙĨ", "Ð Ļ", "ãģ£ ãģ¨", "ãģ£ãģ ¨", "Ú© ÙĦ", "า ยà¸Ļ", "าย à¸Ļ", "ع اÙĦ", "عا ÙĦ", "Ġ ký", "Ġk ý", "ĠмаÑĤ еÑĢи", "Ġма ÑĤеÑĢи", "ê» ĺ", "ıl ması", "μ ÎŃν", "μÎŃ Î½", "ĠÙĨ ÙħÛĮ", "ĠÙĨÙħ ÛĮ", "Ġcu á»Ļc", "Ġδ εν", "Ġδε ν", "å¹ ²", "＿ ＿＿＿", "＿＿ ＿＿", "＿＿＿ ＿", "à¥Ģ à¤Ł", "Ġçık ar", "Ġçı kar", "Ġkon uÅŁ", "Ġkonu ÅŁ", "иÑĤ елÑĮно", "иÑĤелÑĮ но", "lan tı", "lant ı", "à¹Ħ ล", "å¾ ĭ", "Ġ íĶ¼", "ĠíĶ ¼", "ìĻ ¸", "Ġs áng", "éģ Ķ", "о жд", "ож д", "Ġ آخر", "ĠØ¢ خر", "il ece", "ile ce", "à¥Ī न", "Ġ jedn", "Ġj edn", "Ġje dn", "Ġjed n", "ĠÑģпе ÑĨи", "´ Ŀ", "Ġ Úĺ", "Ġ ãĢĤĊ", "ĠãĢĤ Ċ", "èģ Į", "Ġ ÙĨÛĮ", "ĠÙĨ ÛĮ", "ÑĤ оÑĢа", "ÑĤо ÑĢа", "ÑĤоÑĢ а", "λ ι", "Ġ ÙĪب", "ĠÙĪ ب", "iÅŁ im", "iÅŁi m", "ç» ´", "ãĢĢ i", "Ġm ua", "Ġmu a", "Ġj iž", "Ġji ž", "è¶ Ĭ", "ãĤĴ è¦ĭ", "Ġn á»Ļi", "à¥į à¤Ĺ", "à¥įठĹ", "ç¨ ®", "Ġ ãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢ", "à¹ĥ หม", "à¹ĥห ม", "Ġ ÎĨ", "ĠÎ Ĩ", "ÙĨ دÛĮ", "ÙĨد ÛĮ", "ĠÑģ Ñĩ", "Ġl á»ĩ", "Ġlá» ĩ", "l ub", "lu b", "еÑĢ ÑĤ", "Ġ اطÙĦ", "Ġا Ø·ÙĦ", "Ġاط ÙĦ", "ĠÑģ еÑĢед", "ĠÑģеÑĢ ед", "Ġ éģ", "Ġé ģ", "Ġз ал", "Ġза л", "ÙĨ ÛĮÙĨ", "ÙĨÛĮ ÙĨ", "çŁ¥ éģĵ", "Ø¢ ÙĨ", "Ġ кап", "Ġк ап", "Ġка п", "Ġ à¹Ħม", "Ġà¹Ħ ม", "ů vod", "ův od", "ĠÙ¾ اÛĮ", "Ġپا ÛĮ", "ÑĤ ÑĢи", "ÑĤÑĢ и", "Ġi ht", "Ġih t", "๠Ĭ", "Ġв ÑģÑĸ", "ĠвÑģ Ñĸ", "Ġt hay", "Ġth ay", "Ġtha y", "å Ĩµ", "åĨ µ", "Ġ عÙĨÙĪاÙĨ", "ĠعÙĨ ÙĪاÙĨ", "Ġ Î¥", "ĠÎ ¥", "ภĿ", "ε ÏĦαι", "εÏĦ αι", "iyor du", "ï¼Į èĢĮ", "çļĦ 人", "Ġ सà¤Ń", "Ġस à¤Ń", "à¹ī à¸Ńย", "à¹īà¸Ń ย", "ι κο", "ικ ο", "ãĤĵ ãģ§", "ì¡ ±", "ÙĨج ÙĦÙĬزÙĬØ©", "Ġž ád", "ÑĢ ави", "ÑĢа ви", "ÑĢав и", "γ γ", "æµ ĭ", "о ÑĨÑĸ", "ãĢĢ ãĢĢĠãĢĢ", "ãĢĢãĢĢ ĠãĢĢ", "ãĢĢãĢĢĠ ãĢĢ", "Ġतर ह", "Ġ ëĨ", "Ġë Ĩ", "à¥Ģ à¤ļ", "à¹Ī ม", "Ġg á»ĵm", "Ġk iá»ĩn", "Ġki á»ĩn", "è· Ł", "Î ¦", "es inin", "esi nin", "esini n", "esin in", "é ¥", "é« Ķ", "о Ñĩно", "оÑĩ но", "र ण", "æĺ ¥", "ç¶ ĵ", "Ġ بار", "Ġب ار", "Ġبا ر", "ê· ¼", "éĻ ħ", "Ġ سÙĬ", "Ġس ÙĬ", "Ñģ ÑĥÑĤ", "ÑģÑĥ ÑĤ", "ì µľ", "å± ħ", "ĠÄį esk", "ĠÄįe sk", "Îij ÎĿ", "Ġd iá»ĩn", "Ġdi á»ĩn", "Ġ εί", "Ġε ί", "à¸ĩ à¸Ĺ", "ãĤ ©", "Ġv á»±c", "Ġvá»± c", "в ав", "ва в", "t ıģı", "tı ģı", "tıģ ı", "Ġ ëªħ", "Ġëª ħ", "η ν", "в иÑĤ", "ви ÑĤ", "Ġ Ø£Ùĥ", "ĠØ£ Ùĥ", "Ġп ÑĢоп", "ĠпÑĢ оп", "ĠпÑĢо п", "r ak", "ra k", "ÑĢ аÑĤи", "ÑĢа ÑĤи", "ÑĢаÑĤ и", "ĠÄij ánh", "ĠÄijá nh", "ÑĢ еп", "ÑĢе п", "ê ´ij", "ê´ ij", "е ÑĨÑĮ", "еÑĨ ÑĮ", "Ġब त", "Ġ åĮĹ", "ĠåĮ Ĺ", "Ġs át", "l edi", "le di", "led i", "ìłģ ìľ¼ë¡ľ", "ů j", "Û° Û°", "Ġnas ıl", "Ġ ÙĪس", "ĠÙĪ س", "Ġ εξ", "Ġε ξ", "в Ñĭ", "ç½ Ĺ", "ارÛĮ Ø®", "à¸Ľ ล", "ί κ", "Ġ ê¸Ī", "Ġê¸ Ī", "åĩ ł", "å¼ ·", "è¿ Ķ", "Ġnh á»ı", "å¾ Ģ", "Ġда же", "Ġç ev", "к Ñĸ", "Ġ Ø£Ùħ", "ĠØ£ Ùħ", "ี ส", "ส ามารà¸ĸ", "สาม ารà¸ĸ", "Ġ ÐĦ", "ĠÐ Ħ", "Ñħод иÑĤ", "ë ĸ", "Ġtr uyá»ģn", "Ġtruy á»ģn", "Ġ ÑģÑĤан", "ĠÑģÑĤ ан", "ĠÑģÑĤа н", "ëĵ¤ ìĿĢ", "ا ÙĦت", "اÙĦ ت", "़ à¥ĩ", "Ġ à¤ħब", "Ġà¤ħ ब", "æķ ¸", "Ġд ÑĸÑı", "ĠдÑĸ Ñı", "ĠÙħ تر", "ĠÙħت ر", "Ġ ë¸", "Ġë ¸", "ï¾ į", "Ġ ê³¼", "Ġê³ ¼", "Ġ زÛĮ", "Ġز ÛĮ", "ëŁ ¼", "Ġ ÐŁÐµÑĢ", "ĠÐŁ еÑĢ", "Ġs ık", "Ġsı k", "н оÑģÑĤÑĮÑİ", "ноÑģÑĤÑĮ Ñİ", "ноÑģÑĤ ÑĮÑİ", "Ġ eden", "Ġe den", "Ġed en", "ا در", "اد ر", "ã Ħ", "Ġ леÑĩ", "Ġл еÑĩ", "ĠÙĩ Ø°Ùĩ", "ض ÙĪع", "ضÙĪ ع", "ĠìķĦ ëĭĪ", "ĠìķĦëĭ Ī", "ir ket", "irk et", "Ġ اگر", "Ġا گر", "ĠÑħ оÑĩ", "Ġб ан", "Ġба н", "íĶ Į", "æĢİ ä¹Ī", "è Ľ", "Ġब à¤ļ", "ĠÚ© تاب", "çī Į", "Ġд ва", "Ġдв а", "ج ر", "Ġп ÑĢоÑģÑĤо", "ĠпÑĢоÑģÑĤ о", "ĠпÑĢоÑģ ÑĤо", "Ġà¤Ĩ व", "Ġm ức", "į ¼", "Ġ jÃŃ", "Ġj ÃŃ", "íİ ĺ", "Ġt amam", "Ġta mam", "Ġtam am", "åĪ Ľ", "ภĴ", "п еÑĩ", "пе Ñĩ", "à¥ĭ स", "Ġ Ñģем", "ĠÑģ ем", "Ġt Æ°Æ¡ng", "ä¸ ģ", "ī ´", "Ġ ÑĢоÑģ", "ĠÑĢ оÑģ", "Ġ маÑĶ", "Ġм аÑĶ", "Ġма ÑĶ", "æŃ Į", "Ġ داÙĨÙĦÙĪد", "ĠداÙĨ ÙĦÙĪد", "ĠL oÃłi", "ĠLo Ãłi", "Ġed ilm", "Ġedi lm", "Ġedil m", "Ġk onu", "Ġko nu", "Ġkon u", "ĠاÙĦ Ùħر", "ĠاÙĦÙħ ر", "Ġu laÅŁ", "Ġul aÅŁ", "Ġyük sek", "ο ι", "Ùİ ÙĨ", "Ġ bÄĽ", "Ġb ÄĽ", "ãĤ·ãĥ§ ãĥ³", "ï¿£ ￣￣￣", "￣￣ ￣￣", "￣￣￣ ï¿£", "Ġg üç", "Ġgü ç", "Ġ اÙĪÙĦ", "Ġا ÙĪÙĦ", "ĠاÙĪ ÙĦ", "Ġ ма", "Ġм а", "Ġب خش", "Ġبخ Ø´", "ा à¤ĸ", "ाठĸ", "Ġв иÑģ", "Ġви Ñģ", "ž enÃŃ", "že nÃŃ", "žen ÃŃ", "Ġz působ", "Ġzp ůsob", "z nam", "zn am", "Ġ رÙĪÛĮ", "Ġر ÙĪÛĮ", "ĠرÙĪ ÛĮ", "åĭ Ŀ", "। Ċ", "ÙĦ ÙĤ", "Ġж из", "ÑĢ Ñĸв", "ÑĢÑĸ в", "ĠÑĥ пÑĢав", "ĠÑĥп ÑĢав", "Ġph á»ij", "ic ros", "icro s", "Ġ à¹ģà¸ķ", "Ġà¹ģ à¸ķ", "Ġ ë°ķ", "Ġë° ķ", "ÙĪ ات", "ÙĪا ت", "ï¼Į ä¸Ģ", "ан Ñģ", "ç´ ļ", "ย à¸Ļ", "à¹ģ à¸Ĥ", "Ġgi áo", "Ġgiá o", "äºĮ äºĮ", "Ġ Ä°s", "ĠÄ° s", "ìĬ ¹", "Ġo lacak", "Ġol acak", "Ġola cak", "Ġ Các", "ĠC ác", "Ġ ÑĢÑĥб", "ĠÑĢ Ñĥб", "ĠÑĢÑĥ б", "ẹ p", "ÄŁ iniz", "ÄŁini z", "ÄŁin iz", "ãģª ãģ©", "Ġ моÑĢ", "Ġм оÑĢ", "Ġмо ÑĢ", "ĠÑģ дел", "ÙĦ ÙħاÙĨ", "ÙĦÙħ اÙĨ", "n ém", "né m", "å° į", "Ġd ne", "Ġdn e", "ì¶ľ ìŀ¥", "ع ب", ": ::::::", ":: :::::", ":::: :::", ":::::: :", "::: ::::", "::::: ::", "Î Ĵ", "e ket", "ek et", "Ġ ÑĢеÑĪ", "ĠÑĢ еÑĪ", "ĠÑĢе ÑĪ", "è Ī¬", "èĪ ¬", "Ġ íĻĶ", "ĠíĻ Ķ", "ص د", "Ġ маÑĢ", "Ġм аÑĢ", "Ġма ÑĢ", "Ñı ж", "Ø´ ار", "ãģ ²", "Ġ اÙĦÙĬ", "Ġا ÙĦÙĬ", "ĠاÙĦ ÙĬ", "Ù į", "à¤Ĥ à¤ľ", "м Ñĭ", "Ġka rar", "Ġkar ar", "Ġkara r", "ÙĦÛĮ سÛĮ", "ÙĦÛĮس ÛĮ", "า à¸ĵ", "าภĵ", "ç¾ ¤", "Ġol ması", "Ġolm ası", "Ġolma sı", "Ġhaz ır", "γÏģα ÏĨ", "¯ u", "в ол", "во л", "ĠÑģ ÑĤаÑĢ", "ĠÑģÑĤ аÑĢ", "ĠÑģÑĤа ÑĢ", "o vala", "ov ala", "ova la", "oval a", "Ġв озмож", "Ġвоз мож", "Ġ дав", "Ġд ав", "Ġда в", "é¢ ¨", "ر ا", "Ġдоп ом", "ê² ĥ", "Ġ ìĺ¬", "Ġìĺ ¬", "Ġ åİ", "Ġå İ", "Ġ 못", "Ġëª »", "u ç", "í ļ", "l ük", "lü k", "ä¸Ń å¿ĥ", "Ġ दर", "Ġद र", "Ġ âĹĨ", "ĠâĹ Ĩ", "Ġt ay", "Ġta y", "Ġب سÛĮ", "Ġبس ÛĮ", "Ġ ÏĥÏĦα", "ĠÏĥ ÏĦα", "ĠÙħ Ø®", "Ñı Ñī", "å· ®", "ภī", "ëł ¹", "à¹ĥà¸Ļ à¸ģาร", "Ġ ÙĩÙĨ", "ĠÙĩ ÙĨ", "ãģ ¶", "л Ñĸд", "лÑĸ д", "å į°", "åį °", "Ġs ao", "Ġsa o", "ÅĻ ad", "리 ëĬĶ", "Ñģ лед", "Ñģл ед", "åĶ ®", "Ġ |:", "Ġ| :", "æķĻ èĤ²", "Ġм ол", "Ġмо л", "ĠÙĩ ÙĬ", "ë ģ", "Ġ кÑĥлÑĮ", "Ġк ÑĥлÑĮ", "ĠкÑĥ лÑĮ", "' nin", "'n in", "Ġ خر", "ĠØ® ر", "Ġge nel", "Ġgen el", "Ġgene l", "Ġt á»Ń", "Ġtá» Ń", "Ġkur ul", "Ġkuru l", "ен ÑĤи", "енÑĤ и", "à¥ĭ à¤ľà¤¨", "à¥ĭà¤ľ न", "è¿Ļ æł·", "Ġм Ñĸж", "ĠмÑĸ ж", "Ġngh iá»ĩm", "Ġnghiá»ĩ m", "ĠÏĢ ολ", "ĠÏĢο λ", "æĭ Ľ", "Ġà¤Ĺ à¤ı", "ầ y", "Ġc ảm", "Ġcả m", "ç´ °", "rı ca", "Ġ عÙĦÛĮ", "Ġع ÙĦÛĮ", "ĠعÙĦ ÛĮ", "ิ à¹ī", "h ur", "hu r", "Ġch Æ°a", "Ñĥ ÑĶÑĤÑĮÑģÑı", "ÑĥÑĶ ÑĤÑĮÑģÑı", "ãģ© ãģĨ", "Ñĥ л", "ิ ร", "Ġ æľī", "ä¼ ¼", "ÑĦ еÑĢ", "ÑįÑĤ омÑĥ", "æĹ ħ", "ĠÙħ ÙĪج", "ĠÙħÙĪ ج", "Ġ 본", "Ġë³ ¸", "Ġgi á»Ŀ", "Ġgiá» Ŀ", "Ġk iến", "Ġki ến", "à¹Ī วย", "à¹Īว ย", "Ġd üny", "Ġdü ny", "Ġdün y", "Ġ زÙħ", "Ġز Ùħ", "о вÑĸ", "ов Ñĸ", "ĠÑĨ ÑĮого", "ิ à¸ļ", "Ġ ìĨIJ", "ĠìĨ IJ", "èIJ ¥", "Ġ ÑĢÑĸз", "ĠÑĢ Ñĸз", "Ġh á»Ĺ", "Ġhá» Ĺ", "ÑĢ Ñĸб", "ÑĢÑĸ б", "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ìľ¼ ë©°", "äºĨ ä¸Ģ", "ĠÙĤ بÙĦ", "ĠÙĤب ÙĦ", "é¾ Ļ", "Ġ گذ", "ĠÚ¯ Ø°", "Ġ ÙĤد", "ĠÙĤ د", "ãģª ãģĭãģ£ãģŁ", "Ġ à¹Ģร", "Ġà¹Ģ ร", "Ġà¹Ģภ£", "å¸ Į", "ĠÑģ Ñħ", "Ġг ÑĢом", "ĠгÑĢ ом", "ĠгÑĢо м", "åĽ ¢", "Ġ ì§ij", "Ġì§ ij", "Ġ лÑĥÑĩ", "Ġл ÑĥÑĩ", "åħ µ", "Ġ ÐŀÑĤ", "ĠÐŀ ÑĤ", "Ġmu á»ijn", "ãģĺ ãĤĥ", "o vnÃŃ", "ov nÃŃ", "ë©´ ìĦľ", "ë³ Ģ", "Ġн еб", "Ġне б", "Ñģ ии", "Ñģи и", "ÙĨ Ùħ", "ÄŁ in", "ÄŁi n", "Ġt oho", "Ġto ho", "Ġtoh o", "en iz", "eni z", "ب اش", "با Ø´", "ĠÑģ лÑĥж", "ĠÑģл Ñĥж", "ĠÑģлÑĥ ж", "Ġb ợi", "Ġüzer e", "Ġüz ere", "Ġs adece", "Ġsad ece", "ĠÏĢ αÏģ", "ĠÏĢα Ïģ", "³³³³³³³³ ³³³³³³³³", "³³³³³³³ ³³³³³³³³³", "³³³³³ ³³³³³³³³³³³", "³³³³³³³³³ ³³³³³³³", "³³³³³³³³³³³ ³³³³³", "èĮ ĥ", "ÏĦ ικÏĮ", "ÏĦικ ÏĮ", "ÏĦι κÏĮ", "Ġ äºĮ", "ãĤĪãģĨ ãģ«", "è ŀ", "ãģ® ãģ¯", "Ġ ÑĥкÑĢаÑĹн", "ĠÑĥ кÑĢаÑĹн", "Ġb ắt", "в ан", "ва н", "Ġ ÑģÑĤÑĢа", "ĠÑģ ÑĤÑĢа", "ĠÑģÑĤ ÑĢа", "ĠÑģÑĤÑĢ а", "è¡ Ģ", "nu tÃŃ", "nut ÃŃ", "o kt", "ok t", "รà¸ĩ à¹Ģร", "Ġ صÙģ", "Ġص Ùģ", "åħ ļ", "ÏĦ ί", "ï¼ģ ãĢįĊĊ", "ï¼ģãĢį ĊĊ", "ĠÑĤем п", "ĠÑĤе мп", "é¡ Į", "Ġs luž", "Ġslu ž", "Ñĥ ков", "Ñĥк ов", "Ġngh Ä©a", "ĠnghÄ© a", "çĶ ²", "Ġd á»ħ", "о ви", "ов и", "ÏĦ Ïħ", "ر ÛĮÚ©", "رÛĮ Ú©", "ĠA nh", "ĠAn h", "ĠвÑģ его", "ĠвÑģе го", "âĢĮ Ú©ÙĨ", "âĢĮÚ© ÙĨ", "te ÅĻÃŃ", "Ġm ục", "Ùĩ ÙĨÚ¯", "ÙĩÙĨ Ú¯", "Ġ ÐŁÐ¾Ñģ", "ĠÐŁ оÑģ", "ĠÐŁÐ¾ Ñģ", "Úĺ Ùĩ", "ĠاÙĦ غ", "æĿ ¾", "y sl", "ys l", "Ġyap ılan", "Ġyapı lan", "Ġyapıl an", "çĪ ¶", "Ġm ạnh", "ر اÙĩ", "را Ùĩ", "à¸Ķ à¸ĩ", "o Äį", "ë§ IJ", "åł Ĥ", "м аÑĤ", "ма ÑĤ", "Ġ eÅŁ", "Ġe ÅŁ", "ÙĪÙħ ات", "Ġس اخت", "åĽł 为", "Ġп ÑĢий", "ĠпÑĢ ий", "ĠпÑĢи й", "ıl mÄ±ÅŁ", "é¤ ¨", "ุ à¸ĩ", "Ġ ëģ", "Ġë ģ", "à¸ķ าม", "à¸ķา ม", "åIJ ī", "μ ή", "Ġ æľ¬", "Ġzá klad", "ت ØŃ", "è¾ ¼", "Ġв Ñĸй", "ĠвÑĸ й", "ĠÙħÙĨ Ø·", "Ġto án", "к аÑĢ", "ка ÑĢ", "Ġ ÐĹа", "ĠÐĹ Ð°", "ĠпÑĢи мен", "ĠпÑĢим ен", "ãĤĭ ãģ¨", "ั à¸Ĺ", "ÛĮ س", "ĠاÙĨ جاÙħ", "ĠاÙĨج اÙħ", "Ġع ÙĦÙĬ", "ĠعÙĦ ÙĬ", "़ ि", "e ných", "en ých", "ený ch", "ĠL iên", "ĠLi ên", "Ġ па", "Ġп а", "éļ Ĭ", "Ġmo hou", "Ġmoh ou", "Ġк ÑĸлÑĮ", "Ġ Το", "ĠΤ ο", "ا ÙĦب", "اÙĦ ب", "ÎŃ Î½", "Ġna bÃŃ", "Ġnab ÃŃ", "ç i", "ler den", "lerde n", "Ġth anh", "Ġthan h", "Ġtha nh", "Ġb ütün", "Ġ åŁ", "Ġå Ł", "ì¸ ł", "Ġz at", "Ġza t", "ÙĬ ÙĪ", "Ġμ ια", "uy ết", "Ñij н", "åĪ Ĵ", "ли во", "лив о", "à¹Ī à¸Ńà¸ĩ", "à¹Īà¸Ń à¸ĩ", "ä»ĸ 们", "Ġб аг", "Ġба г", "ि à¤Ń", "िठŃ", "ĠÑĤ ам", "ĠÑĤа м", "Ġп ÑĢеп", "ĠпÑĢ еп", "ĠпÑĢе п", "ิ à¸Ĭ", "âĢĻ Ñıз", "âĢĻÑı з", "ĠPh ân", "ж ен", "же н", "à¥Ī à¤ķ", "ĠÑģлÑĥÑĩа е", "Ġ .:", "Ġ. :", "åѦ æł¡", "Ä° N", "ç¾ ©", "ĠÑģ ÑĤо", "ĠÑģÑĤ о", "Ġ हर", "Ġह र", "Ïħ ν", "Ġx em", "Ġxe m", "Ġб ÑĥÑĤи", "ĠбÑĥ ÑĤи", "Ñģ иÑĤ", "Ñģи ÑĤ", "çª ģ", "à¥į à¤Ľ", "à¥įठĽ", "åij ¢", "ï¼Į ä¹Ł", "e nÄĽ", "en ÄĽ", "Ġ κά", "Ġκ ά", "iy orum", "iyor um", "ĠÚ¯ Ùģت", "âĹıâĹı âĹıâĹı", "ั ม", "Ġ Ðļон", "ĠÐļ он", "ĠÐļо н", "н оÑĪ", "но ÑĪ", "ниÑĨ ÑĤ", "ü zel", "üz el", "s ÃŃ", "å¸ «", "ص ÙĪÙĦ", "çĥ Ń", "ĠÄij ủ", "ĠÄijá» §", "ãĤ ®", "æķ ħ", "ĠÅ¡ kol", "ĠÅ¡k ol", "Ñĩ ен", "Ñĩе н", "à¹Ģ ย", "à¹Ģภ¢", "à¸Ļ à¸Ļ", "ÙĢ ÙĢÙĢÙĢ", "ÙĢÙĢ ÙĢÙĢ", "ÙĢÙĢÙĢ ÙĢ", "Ġ üç", "Ġü ç", "å¿ µ", "ãĥª ãĤ¢", "Ġ íĻĺ", "ĠíĻ ĺ", "Ġ éĩij", "Ġéĩ ij", "çı Ń", "Ġ Ñģклад", "ĠÑģ клад", "ĠÑģк лад", "Ñı ми", "Ñıм и", "ü f", "Ġh ã", "ĠÄIJ ại", " Ĥ", "åĦ ª", "Ġbul unan", "Ġbulun an", "ĠاÙĦ ÙħØŃ", "ĠاÙĦÙħ ØŃ", "æĪ ı", "Ġ è©", "Ġè ©", "Ġн оÑĢм", "ĠноÑĢ м", "Ġchu ẩn", "Ġз аÑģÑĤ", "Ġза ÑģÑĤ", "ĠзаÑģ ÑĤ", "Ġ vÃŃce", "ĠvÃŃ ce", "ĠvÃŃc e", "Ð ĸ", "Ġà¤Ĩ ध", "Ġ Äįas", "ĠÄį as", "Ġ боÑĢ", "Ġб оÑĢ", "Ġбо ÑĢ", "Ïģ ια", "Ïģι α", "ĠÙħ اÙĩ", "ĠÙħا Ùĩ", "Ġ íħ", "Ġí ħ", "ÅĻ el", "ÅĻe l", "Ñı ви", "Ñıв и", "ÏĦ εÏĤ", "ÏĦε ÏĤ", "i nÄĽ", "in ÄĽ", "Ġп еÑĢе", "ĠпеÑĢ е", "éķ ĩ", "à¥įठŀ", "Ġ éĺ", "Ġé ĺ", "à¹Ī าว", "à¹Īา ว", "ร ร", "Ġ سÙĩ", "Ġس Ùĩ", "в али", "ва ли", "вал и", "çķ Ļ", "ĠÑĦ Ñĥнк", "ĠÑĦÑĥн к", "Ġ íĸī", "Ġí ĸī", "Ġíĸ ī", "Ùģ Ùĩ", "çĶŁ æ´»", "èģ ŀ", "o kud", "ok ud", "oku d", "Ġ ìĤ´", "ĠìĤ ´", "ı zı", "ız ı", "Ġпо лÑĥ", "Ġпол Ñĥ", "ï¼Į ä½ł", "Ø´ اÙĨ", "æ± º", "б ÑĢÑı", "оÑģÑĥд аÑĢ", "Ġo yun", "Ġoy un", "а нии", "ан ии", "ани и", "Ġp rů", "Ġpr ů", "Ġn áv", "Ġná v", "Ġм енÑı", "Ġмен Ñı", "Ġìŀ ĺ", "Ġ Ä°n", "ĠÄ° n", "Ġth ÃŃch", "ĠthÃŃ ch", "ĠÄij ảm", "åľ Ĵ", "Ġв же", "Ġl oÃłi", "Ġlo Ãłi", "Ġ Ðŀн", "ĠÐŀ н", "м еÑģÑĤ", "ме ÑģÑĤ", "Ġ ξ", "ĠÎ ¾", "ãĢ ħ", "Ġch iế", "Ġchi ế", "Ñĩ Ñĸ", "Ġ íijľ", "Ġí ijľ", "ëĭ ¬", "Ġ ëĭ¬", "Ġëĭ ¬", "à¥Ģ ड", "ÑĢ алÑĮ", "ÑĢа лÑĮ", "ÑĢал ÑĮ", "d ik", "di k", "Ġ íĨł", "ĠíĨ ł", "ëŁ ī", "Ġ صÙĨ", "Ġص ÙĨ", "Ġs tej", "Ġst ej", "Ġste j", "Ġа кÑĤив", "Ġак ÑĤив", "ĠакÑĤ ив", "ĠакÑĤи в", "Ġ é¦", "Ġé ¦", "Ġ à¹Ħà¸Ķ", "Ġà¹Ħ à¸Ķ", "æĬĢ æľ¯", "Ġp rostÅĻed", "Ġpro stÅĻed", "Ġprost ÅĻed", "å® ³", "ãģ IJ", "Ġol uÅŁtur", "ĠoluÅŁ tur", "e lop", "el op", "elo p", "ãģ¡ ãĤĥ", "éĥ İ", "ض ا", "Ġ خط", "ĠØ® Ø·", "ë° ķ", "е ÑģÑı", "еÑģ Ñı", "ĠÙĩ ÛĮ", "н ад", "на д", "Ġng Ãłnh", "ÑĢ ÑĥÑĪ", "ÑĢÑĥ ÑĪ", "ãģĦ ãģĦ", "Ġü rün", "Ġür ün", "à¸Ń à¸ķ", "à¥ĭ प", "Ġs ayı", "Ġsa yı", "Ġsay ı", "à¥Ģ स", "е ниÑħ", "ен иÑħ", "ени Ñħ", "Ġ Ñģим", "ĠÑģ им", "ĠÑģи м", "à¥Ģ द", "å¤ ī", "à¹Ī วม", "à¹Īว ม", "Ġ à¹Ģà¸Ĥ", "Ġà¹Ģ à¸Ĥ", "Ġà¹ĢภĤ", "å·² ç»ı", "а ÑĤо", "аÑĤ о", "ĠÑĢай он", "í ĥĿ", "íĥ Ŀ", "Ġ ÑĤÑĢа", "ĠÑĤ ÑĢа", "ĠÑĤÑĢ а", "l ayan", "la yan", "lay an", "ế p", "ा à¤Ł", "ाठŁ", "Ø® اب", "人 æ°ij", "å® Ŀ", "è Ĩ", "èª į", "n aÄį", "na Äį", "Ġî ł", "ĠÐļ и", "ĠbaÅŁ ka", "ĠbaÅŁk a", "c ů", "ض ع", "èĪ ª", "ี ม", "Ñĭ ми", "Ñĭм и", "ÎĻ Σ", "Ġشر کت", "ย ว", "Ġmus ÃŃ", "Ġmu sÃŃ", "Ġн ал", "Ġна л", "ี à¸Ĺ", "Ġ áp", "Ġá p", "ร าย", "æ² ¹", "l eme", "le me", "lem e", "Ġ मन", "Ġम न", "à¹Ħ à¸Ł", "а ÑĤив", "аÑĤ ив", "аÑĤи в", "¸ ı", "èŃ °", "Ïĥ ÏĦα", "ÏĥÏĦ α", "íĸ ¥", "е ÑĤÑĥ", "еÑĤ Ñĥ", "ĠÑģв Ñıз", "ĠÑģвÑı з", "ед еÑĢа", "ĠØ® ارج", "า ษ", "าภ©", "âĢĮ Ù¾", "Ñĸ г", "é¡ ŀ", "Ġkh ả", "ĠÑģ пÑĢав", "ĠÑģп ÑĢав", "è¡ Ĺ", "ãĥķ ãĤ¡", "ãĥķãĤ ¡", "Ġм еждÑĥ", "Ġмеж дÑĥ", "Ñĥ ли", "Ñĥл и", "Ġب زر", "ÑĨ ен", "ÑĨе н", "Ġek onom", "د ÙĨ", "ا ÙħÛĮ", "اÙħ ÛĮ", "าส à¸ķร", "ĠnÄĽ kol", "ĠnÄĽk ol", "g ün", "з и", "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", "ç¦ »", "Ġtr Æ°á»Łng", "ı i", "íİ ¸", "Ġ ÑĢеб", "ĠÑĢ еб", "ĠÑĢе б", "åº ķ", "Ġت ارÛĮØ®", "н има", "ни ма", "ним а", "Ġth ân", "аÑĤ елÑĮно", "аÑĤелÑĮ но", "ĠاÙĦ Ø°ÙĬ", "ĠاÙĦØ° ÙĬ", "ÙĪ ÙĨÛĮ", "ÙĪÙĨ ÛĮ", "Ġ éĥ", "Ġé ĥ", "Ġb ình", "Ġbì nh", "ικ ήÏĤ", "ική ÏĤ", "à¸ŀ ล", "تÙħ اع", "ĠPr aha", "ĠPra ha", "Ġ ÑģÑĤав", "ĠÑģÑĤ ав", "ĠÑģÑĤа в", "د ÙĬد", "دÙĬ د", "Ġgi ữa", "Ġgiữ a", "ĠпÑĢо вед", "ĠпÑĢов ед", "Âł k", "ÙĨد Ú¯ÛĮ", "ÑĨ ий", "ÑĨи й", "ç Ĵ", "ĠاÙĦ Ø£Ùħ", "ĠاÙĦØ£ Ùħ", "Ġ è´", "Ġè ´", "Ø¥ ÙĨجÙĦÙĬزÙĬØ©", "ĠìŀĪ ìĹĪëĭ¤", "ĠìŀĪìĹĪ ëĭ¤", "ç ·¨", "ç· ¨", "ัà¸Ļ à¸ĺ", "ĠÑĢок Ñĸв", "Ġc áo", "Ġcá o", "Ġkh ó", "Ġ ÙĨÙĪع", "ĠÙĨ ÙĪع", "ĠÙĨÙĪ ع", "س ÙĦ", "Ġ ÑĥÑģлов", "ĠÑĥ Ñģлов", "ĠÑĥÑģл ов", "ĠÑĥÑģ лов", "Ġcứ u", "ов ого", "ово го", "ि à¤Ĺ", "िठĹ", "Ķ ëĭ¤", "æĿ İ", "Ġbö lg", "Ġböl g", "Ġn gu", "Ġng u", "Ġh ữu", "Ġhá» ¯u", "н ии", "ни и", "ìł Ī", "Ġп ÑĢом", "ĠпÑĢ ом", "ĠпÑĢо м", "åı Į", "Ġd Æ°á»Ľi", "ĠdÆ°á» Ľi", "ĠdÆ° Ỽi", "Ð ®", "ÙĬ Ø´", "æ¸ ©", "ëı ħ", "Ġз мÑĸ", "Ġзм Ñĸ", "θη κε", "ĠbaÄŁ lı", "Ġüzer inde", "Ġ تغ", "Ġت غ", "Ġп ÑĢогÑĢа", "ĠпÑĢ огÑĢа", "ĠпÑĢо гÑĢа", "ĠпÑĢог ÑĢа", "i ž", "Ġ ç¥", "Ġç ¥", "Ġy ardım", "Ġyard ım", "Ġyar dım", "ÂĢ ÂĢ", "ÂĢ Ģ", "Ġ Ñĥв", "ĠÑĥ в", "Ġ rů", "Ġr ů", "Ġch iến", "Ġchi ến", "Ġchiế n", "ν οÏĤ", "νο ÏĤ", "ãģ¨ ãģª", "ا ÙĨت", "اÙĨ ت", "è° ·", "ÃŃ sk", "ÃŃs k", "is inde", "isi nde", "isin de", "Ġд ог", "Ġдо г", "è¿ ½", "Ġп ÑĢоÑĤив", "ĠпÑĢо ÑĤив", "ĠпÑĢоÑĤ ив", "ĠпÑĢоÑĤи в", "Ïģ οÏħ", "Ïģο Ïħ", "ãģ® ãģĭ", "Ġb azı", "Ġba zı", "Ġbaz ı", "ı rak", "ır ak", "à¥ĩ ष", "à¥ĩठ·", "ĠÙħ شار", "ĠÙħØ´ ار", "Ġ ìĸij", "Ġìĸ ij", "Ġ нез", "Ġн ез", "Ġне з", "Ġ Ø°ÙĦÙĥ", "ĠØ° ÙĦÙĥ", "èª ¿", "åĤ Ļ", "ĠÑĤ ÑĢан", "ĠÑĤÑĢ ан", "ĠÑĤÑĢа н", "ĠÏĢ αÏģα", "ĠÏĢαÏģ α", "ĠÏĢα Ïģα", "ÛĮ Ùħت", "ÛĮÙħ ت", "Ġt iến", "Ġti ến", "Ġtiế n", "ĠÙĩ ÙħÙĩ", "ĠÙĩÙħ Ùĩ", "e fon", "ef on", "» .ĊĊ", "». ĊĊ", "».Ċ Ċ", "Ġ ÙĨد", "ĠÙĨ د", "ج ÙĦ", "Ġد ادÙĩ", "Ġداد Ùĩ", "Ġ вед", "Ġв ед", "Ġве д", "Ġ sın", "Ġs ın", "Ġsı n", "ĠÑģ вÑĸÑĤ", "ĠÑģв ÑĸÑĤ", "e lerin", "el erin", "eler in", "ele rin", "eleri n", "âĪ ¨", "Ġy ür", "д ан", "да н", "Ġ ÐŀÑģ", "ĠÐŀ Ñģ", "Ġh ạng", "Ġhạn g", "Ġhạ ng", "è® ¸", "Ïĥ ÏĦη", "ÏĥÏĦ η", "uy ến", "Ġн аб", "Ġна б", "Ġ оÑħ", "Ġо Ñħ", "Ïĥ Ïī", "Ġby ly", "Ġbyl y", "Ñģ киÑħ", "Ñģк иÑħ", "Ñģки Ñħ", "l amak", "la mak", "lam ak", "lama k", "и ÑĤоÑĢ", "иÑĤ оÑĢ", "Ġy atır", "Ġya tır", "Ġyat ır", "ĠпÑĢоиз вод", "Ġ جÙħع", "Ġج Ùħع", "ĠجÙħ ع", "Å ł", "æıIJ ä¾Ľ", "Ġpr vnÃŃ", "Ġprv nÃŃ", "Ġα ÏĢ", "íĻ ©", "ĠпÑĢа кÑĤи", "ler inden", "lerin den", "lerinde n", "ĠнеобÑħодим о", "åº ·", "Ùİ Ø§", "Ġ سÙĨ", "Ġس ÙĨ", "Ä° L", "Ġ ê´ij", "Ġê ´ij", "Ġê´ ij", "Ġ PÅĻ", "ĠP ÅĻ", "ç ŀ", "ĠÑĤемп еÑĢаÑĤÑĥ", "Ġka bul", "Ġkab ul", "Ġbu dou", "Ġbud ou", "ÑĨÑĸ оналÑĮ", "ÑĨÑĸон алÑĮ", "ï½ ľ", "Ġç ocuk", "Ġçocu k", "ĠÑĤ ÑĸлÑĮки", "b yt", "by t", "ãĥ ¤", "ĠÑģÑĤ аÑĤ", "ĠÑģÑĤа ÑĤ", "Ġ æĿ±", "ĠæĿ ±", "le žit", "اس طة", "ุ ร", "i êm", "iê m", "ĠкÑĥлÑĮ ÑĤÑĥ", "Ġ пон", "Ġп он", "Ġпо н", "Ä© nh", "åĸ ľ", "н ев", "не в", "ÑĶ н", "ĠÑģо оÑĤ", "ë Ŀ", "çĪ ¾", "Ġtu á»ķi", "k anı", "kan ı", "สำ หร", "ا عت", "اع ت", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "д еÑĢж", "деÑĢ ж", "Ġоб лаÑģÑĤи", "Ġобла ÑģÑĤи", "ĠоблаÑģ ÑĤи", "ĠоблаÑģÑĤ и", "Ġобл аÑģÑĤи", "Ġv ừa", "Ġvá» «a", "Ġ ÙħÙħ", "ĠÙħ Ùħ", "à¸ģ ำ", "à¹ģ ม", "iver sit", "ivers it", "à¹ģ ส", "æ¬ §", "l anan", "la nan", "lan an", "ÙĬ ÙĨØ©", "ÙĬÙĨ Ø©", "س Ø©", "ĠлÑİд ей", "ร รม", "รร ม", "Ġ ì±Ħ", "Ġì± Ħ", "Ġ 天", "Ġå¤ ©", "ен нÑĭÑħ", "à¹ģ ห", "Ġs práv", "Ġsp ráv", "Ġspr áv", "èŃ ¦", "ï¼ ľ", "ั à¸Ĵ", "il ecek", "ile cek", "ilece k", "Ġ æŁ", "Ġæ Ł", "Ġ èĭ±", "Ġèĭ ±", "ĠÑħ оÑĢоÑĪ", "ëł ĩ", "Û² Û°Û°", "Û²Û° Û°", "æĬ ¤", "Ġl ã", "ÅĻÃŃ zenÃŃ", "ÅĻÃŃz enÃŃ", "ĠتÙĪÙĦ ÛĮد", "éļ Ľ", "ãĤĮ ãģ°", "á Å¡", "ارÙĬ Ø®", "æĶ »", "Ġkho ảng", "Ġkhoản g", "éĻ į", "о ван", "ов ан", "ова н", "Ġg ây", "âĢĻn ın", "Ø£ ÙĨ", "m iÅŁtir", "mi ÅŁtir", "miÅŁ tir", "miÅŁti r", "Ġs ức", "Ġsứ c", "к ÑĥÑģ", "кÑĥ Ñģ", "Ġüzer ine", "ÄŁ ü", "ا بر", "اب ر", "ï¼Į å°±", "Ì £", "Ġ ëıĮ", "Ġëı Į", "Ġtr á»±c", "æĶ¶ å½ķ", "æī ¿", "ĠN á»Ļi", "Ġ çĻ¾", "ĠçĻ ¾", "ÑĪ ÑĮ", "ج Ø©", "ë² ł", "ठī", "à ¸ı", "ภı", "Ġü lk", "Ġül k", "ĠÙĩست ÙĨد", "ัà¸ļ à¸ģาร", "ĠÑı ка", "ĠÑıк а", "ã İ", "Ġ Як", "ĠЯ к", "Ġг де", "t iv", "ti v", "ãĢ Ī", "лÑİ Ñĩа", "лÑİÑĩ а", "ा ।Ċ", "ा। Ċ", "Ġ ÙħاÙĨ", "ĠÙħ اÙĨ", "ĠÙħا ÙĨ", "Ġd lou", "Ġdl ou", "Ġ ãĥķ", "Ġãĥ ķ", "ठĽ", "Ġph ục", "Ġphụ c", "a kat", "ak at", "aka t", "Ð ¬", "as ını", "ĠæĬķ 稿", "ÑĢ ев", "ÑĢе в", "Ġv yt", "Ġvy t", "Ġz mÄĽ", "Ġzm ÄĽ", "ÏĦ Ïī", "è ¬", "Ġ Ñĥм", "ĠÑĥ м", "Ġuz un", "Ġp roti", "Ġpro ti", "Ġpr oti", "Ġprot i", "ĠÑģо ÑģÑĤоÑı", "ĠÑģоÑģÑĤ оÑı", "ัà¸Ĵ à¸Ļ", "a tik", "at ik", "ati k", "Ġ à¸ł", "Ġภł", "Ġà¤Ĩ द", "lar ından", "ların dan", "larında n", "æĢ ¥", "ãĥ¼ ãĤ¯", "ãĥ¼ãĤ ¯", "ĠÙĦ ÙĦÙħ", "ĠÙĦÙĦ Ùħ", "Ùģ تÙĩ", "Ùģت Ùĩ", ". :.", ".: .", "üç ük", "ол ева", "à¹Į Ċ", "ĠпеÑĢ ев", "ĠпеÑĢе в", "ĠÙĨ سب", "ĠÙĨس ب", "е леннÑı", "ел еннÑı", "елен нÑı", "' ın", "'ı n", "ν Ïī", "è¡ £", "Ġ دÙĬ", "Ġد ÙĬ", "åį ĩ", "Ġbel irt", "Ġbelir t", "Ġ /:", "Ġ/ :", "èij ī", "Ġv yh", "Ġvy h", "çļĦ ä¸Ģ", "èĥ Į", "Ġ ìĹ´", "ĠìĹ ´", "о ла", "ол а", "Ġ تب", "Ġت ب", "á ci", "ác i", "ा à¤ī", "ाठī", "ภİ", "çĶ ¢", "à¥Ī ल", "Ġ ÙĤØ·", "ĠÙĤ Ø·", "ëĦ Ī", "ắ m", "ÑĢ Ñıд", "ÑĢÑı д", "Ġph ụ", "ĠÙĪ اÙĤع", "ĠÙĪا ÙĤع", "Ġm erk", "Ġme rk", "Ġmer k", "Ġch á»ijng", "å¯ Ł", "ا بط", "اب Ø·", "us unda", "usu nda", "Ġод на", "ž el", "že l", "ĠÑģ Ñĥм", "ĠÑģÑĥ м", "Ġph ù", "Ġ ζ", "ĠÎ ¶", "Ġz av", "Ġza v", "e dn", "ed n", "Ġp otÅĻeb", "Ġpot ÅĻeb", "ĠÚ©ÙĨ ÙĨد", "ĠÑĢ азв", "ĠÑĢаз в", "ĠÑĢа зв", "¿ ł", "ĠاÙĦ ز", "Ġm ÄĽl", "ĠmÄĽ l", "Ġ ÑģÑĤанов", "ĠÑģÑĤ анов", "ĠÑģÑĤан ов", "ĠÑģÑĤа нов", "Ġ درÛĮ", "Ġد رÛĮ", "Ġدر ÛĮ", "Ġt ượng", "ã ģµ", "ãģ µ", "Ġд ви", "Ġдв и", "ÑĮ Ñı", "è £½", "è£ ½", "Ġ تÙĦ", "Ġت ÙĦ", "Å¡ Å¥", "ãģª ãĤī", "Ġà¤ķ à¤Ī", "Å¡ i", "âĢĮ است", "Ġk ỹ", "Ġká» ¹", "ë§ Ŀ", "Ġà¤Ĩ à¤ľ", "ãĥ ´", "Ġb á»ı", "du ÄŁu", "duÄŁ u", "Ġ æ¯", "Ġæ ¯", "п еÑĢ", "пе ÑĢ", "ا ÙĦÙĬØ©", "اÙĦ ÙĬØ©", "اÙĦÙĬ Ø©", "æīĢ 以", "åħ °", "Ġ oran", "Ġo ran", "Ġor an", "Ġora n", "Ġ íŀ", "Ġí ŀ", "Ïĥ ία", "Ïĥί α", "Ġph ủ", "ĠбÑĭ ла", "ĠбÑĭл а", "Ñĩ ива", "Ñĩи ва", "Ñĩив а", "Ġ ê°Ħ", "Ġê° Ħ", "о лÑĸ", "ол Ñĸ", "Ùĥ ت", "å ħ§", "åħ §", "à¥Ĥ à¤Ł", "Ġ ëĸ", "Ġë ĸ", "Ġ ÙĦÙĩ", "ĠÙĦ Ùĩ", "ëłĪ ìĿ´", "Ġh ız", "å¤ ı", "ĠæĬķ稿 æĹ¥", "éļ ¾", "ĵ °", "г лÑıд", "глÑı д", "гл Ñıд", "ì n", "Ġ меÑĢ", "Ġм еÑĢ", "Ġ ãĢij", "ĠãĢ ij", "Ġ обÑī", "Ġоб Ñī", "um hur", "çł ´", "л иÑģÑĮ", "ли ÑģÑĮ", "sp ÄĽ", "ر ÙĬÙĤ", "رÙĬ ÙĤ", "Ġ تÙģ", "Ġت Ùģ", "Ġا ÙĦÙĪ", "ĠاÙĦ ÙĪ", "çµ ±", "а лоÑģÑĮ", "ал оÑģÑĮ", "ало ÑģÑĮ", "Ġm ô", "Ġv á»ĩ", "Ġvá» ĩ", "Ġ δι", "Ġδ ι", "Ġ зн", "Ġз н", "Ġ بØŃ", "Ġب ØŃ", "ت Ùī", "Ġ ì§ģ", "Ġì§ ģ", "Ġvel mi", "uyá» ħn", "Ġph ạm", "ÑģÑĤв ом", "ÑģÑĤво м", "ĠÙĪ اÙĦÙħ", "ĠÙĪاÙĦ Ùħ", "ĠÙĪا ÙĦÙħ", "ĠбÑĭ ли", "ĠбÑĭл и", "ا Ø°", "Ø§Ø °", "ÄĽ ÅĻ", "â Ħĸ", "âĦ ĸ", "Ġп олож", "Ġпо лож", "Ġпол ож", "า à¸ģาร", "าà¸ģ าร", "ĠÄį lán", "Îķ Ρ", "Ġ ìĤ°", "ĠìĤ °", "β α", "Ġ æĹ¥æľ¬", "ĠæĹ¥ æľ¬", "ز د", "ĠÙĨ ÛĮست", "ĠÙĨÛĮ ست", "Ġha yat", "Ġhay at", "Ġhaya t", "ç¢ º", "à¹Ģ วล", "à¹Ģว ล", "ĠCh ÃŃnh", "ĠChÃŃ nh", "ï¼Į æĺ¯", "ĠÙĪ اØŃ", "ĠÙĪا ØŃ", "èı ¯", "Ġή ÏĦαν", "Ġx á»Ń", "ĠÄį erv", "ĠÄįer v", "ĠÄįe rv", "ĠÙħد ÛĮر", "é Ĩ", "ĠëĪ Ī", "ç» Ń", "Ġt ên", "ìĸ ¸", "Ġort aya", "Ġorta ya", "Ġ жен", "Ġж ен", "Ġже н", "Ġn Æ¡i", "ен нÑĭе", "ÑĦ екÑĤив", "ÑĦек ÑĤив", "íĿ ¬", "Ġkh á»ı", "ĠÄij a", "os yal", "osy al", "à¸Ľà¸£à¸° à¹Ģà¸Ĺศ", "Ġo dst", "Ġod st", "Ġ à¸ĸ", "Ġภĸ", "Ġο ÏĢο", "æĶ¿ åºľ", "Ġb Ãłn", "ĠbÃł n", "ĠG iá»", "ĠGi á»", "Ġold uk", "Ġol duk", "Ġoldu k", "о вание", "ов ание", "ова ние", "овани е", "ован ие", "à¸Ń ส", "Ġ нев", "Ġн ев", "Ġне в", "ÏĦ Ïģο", "ÏĦÏģ ο", "Ġ ìĨį", "ĠìĨ į", "k ı", "Ġब ड", "Ġ ÏħÏĢ", "ĠÏħ ÏĢ", "Ġ Vý", "ĠV ý", "ï¾ Ħ", "çŃ ĸ", "ε ÏĨ", "Ġ åħ¨", "Ġåħ ¨", "ĠÙģ رÙĪØ´", "ĠÙģر ÙĪØ´", "ĠÙģرÙĪ Ø´", "ÙĤÛĮ ÙĤ", "ä¼ģ ä¸ļ", "ε Ïį", "èĻ Ł", "Ġa yr", "Ġay r", "ض ÙĪ", "Å¡ el", "Å¡e l", "Ġп ÑĸÑģлÑı", "ĠпÑĸÑģ лÑı", "Ñĸй Ñģ", "é¢ Ĩ", "Ú© تر", "کت ر", "л Ñĥб", "лÑĥ б", "è« ĸ", "æ° ¸", "ез пеÑĩ", "Ġ кам", "Ġк ам", "Ġка м", "ع داد", "عد اد", "ê±° ëŀĺ", "ู à¸ĩ", "ĠتÙĩ راÙĨ", "Ġ ëĦĪ", "ĠëĦ Ī", "ÑĢ ив", "ÑĢи в", "Ġ ÑĤоÑĢ", "ĠÑĤ оÑĢ", "ĠÑĤо ÑĢ", "ا Ùī", "ا٠ī", "' Ñıз", "'Ñı з", "ÙIJ ÙĬ", "Ġkh ÃŃ", "Ġ ÑĪÑĤ", "ĠÑĪ ÑĤ", "Ġ ξε", "ĠÎľ ε", "Ġb iri", "Ġbi ri", "Ġbir i", "è ĩ´", "èĩ ´", "Ñĥ вав", "Ñĥв ав", "Ñĥва в", "ãģĪ ãĤĭ", "Ġд иÑģ", "Ġди Ñģ", "а ÑİÑĤ", "аÑİ ÑĤ", "ص ب", "åĿ ĩ", "о лÑİ", "ол Ñİ", "èĭ ¥", "Ġ اث", "Ġا Ø«", "ĠØ§Ø «", "s ou", "so u", "åIJ ĥ", "ãģ® ãģł", "ub lik", "ubl ik", "л ей", "ле й", "Âł m", "Ġíıī ê·ł", "ạ y", "ε ÏĢ", "t ık", "tı k", "Ġv yu", "Ġvy u", "ع ÙĪد", "Ġд оз", "Ġдо з", "Ġl á»ĭch", "è³ ª", "à¥ģ à¤Ī", "à¥ģठĪ", "ั à¸ŀ", "Ġt ém", "Ġté m", "Ġ kaç", "Ġk aç", "Ġka ç", "Ġc ái", "Ġcá i", "Ġ μα", "Ġμ α", "âĢ¦âĢ¦ ãĢįĊĊ", "í Ī¬", "ر ÙĪÙĩ", "رÙĪ Ùĩ", "Ġ rych", "Ġr ych", "Ġry ch", "Îij Τ", "Ġ ÑĢÑĸв", "ĠÑĢ Ñĸв", "ë³ ij", "åģ ¥", "Ġzd rav", "Ġ عدد", "Ġع دد", "Ġعد د", "èį ī", "δ ια", "δι α", "Ġv áºŃn", "Ñĭ ÑĤ", "Ġкол иÑĩ", "Ġко лиÑĩ", "Ġколи Ñĩ", "ÏĮ ÏĦε", "Ġb ırak", "Ġ ØŃÙħ", "ĠØŃ Ùħ", "Ġch á»ĭ", "é» Ħ", "ĠاÙĦÙħت ØŃدة", "ื à¸Ńà¸ģ", "ืà¸Ń à¸ģ", "Ġз али", "Ġза ли", "Ġзал и", "Ġnh anh", "âĢĮ تÙĪاÙĨ", "ëĿ ½", "Ġت ÙĪسط", "ĠتÙĪ سط", "ĠتÙĪس Ø·", "è¦ģ æ±Ĥ", "а лÑĥ", "ал Ñĥ", "ün kü", "ünk ü", "ãģª ãĤĵ", "Ġ Trong", "ĠT rong", "ĠTr ong", "ĠTro ng", "à¸Ļ ะ", "åij ¼", "Ġ ÙĬÙħ", "ĠÙĬ Ùħ", "и ки", "ик и", "ĠÑĤ ÑĥÑĤ", "ĠÑĤÑĥ ÑĤ", "Ġya ÅŁam", "ĠyaÅŁ am", "Ġm á»įi", "é ĽĦ", "éĽ Ħ", "ĠØŃ Ø¶", "Ġав ÑĤом", "ĠавÑĤ ом", "Ġसब स", "Ġy ếu", "ãĤ¹ ãĤ¿", "Ïĩ ή", "Ñĸ Ñİ", "è ĺ", "ิ ย", "Ġm ev", "Ġme v", "ick ého", "ické ho", "ि ह", "िठ¹", "åŃ £", "θ ή", "Ġब ढ", "ĠاÙĦ Ùħس", "ĠاÙĦÙħ س", "ÏĦ οÏħ", "ÏĦο Ïħ", "ek li", "ekl i", "Ġде ÑĢев", "ĠдеÑĢ ев", "å¸ Ń", "æ² Ļ", "ãģ« ãĤĤ", "Ġo blast", "Ġob last", "Ġobl ast", "Ġh á»Ļ", "Ġhá» Ļ", "Ġ å¹³", "Ġå¹ ³", ".:.:.:.: .:.:.:.:", ".:.:.:. :.:.:.:.:", "Ġ éĸ", "Ġé ĸ", "Ġ جز", "Ġج ز", "ĠÙĩÙħ ÚĨ", "ä¸ ¦", "ÑĨ еп", "ÑĨе п", "ा Ċ", "ä¸Ń çļĦ", "'n ın", "Ġ íķĺëĬĶ", "Ġíķĺ ëĬĶ", "ÑĶ ÑĹ", "Ġ بش", "Ġب Ø´", "åį ´", "ä¹ ł", "ĠاطÙĦ اعات", "ĠاطÙĦاع ات", "Ġ ë²ł", "Ġë² ł", "Ġکرد ÙĨ", "Ġکر دÙĨ", "ा ड", "ाठ¡", "Ġà¤ħ र", "ĠH á»į", "ĠHá» į", "ĠгÑĢом ад", "Ġ ست", "ĠØ ³Øª", "Ġس ت", "ÏĦι ÏĤ", "Ġan cak", "Ġanc ak", "Ġ ог", "Ġо г", "Ġk teÅĻÃŃ", "Ġ æ¬", "Ġæ ¬", "Ġ Ngh", "ĠN gh", "ĠNg h", "Ġt edy", "Ġte dy", "Ġted y", "Ġ ÏĢο", "ĠÏĢ ο", "Ġqu ân", "Ġб Ñĥли", "ĠбÑĥ ли", "è¯ Ĩ", "Ġt ừng", "Ġtá» «ng", "Ġtừ ng", "人 çļĦ", "ี à¸ģาร", "ีà¸ģ าร", "Ġκα ÏĦα", "Ġpo uze", "Ġpou ze", "¡ ng", "ĠØ¢ ر", "Ġ ÑĤÑĥ", "ĠÑĤ Ñĥ", "Ġt á»·", "Ġtá» ·", "ĠD anh", "ĠDan h", "ĠDa nh", "о ном", "он ом", "Ñģ ий", "Ñģи й", "Ġ à¹Ģà¸Ķ", "Ġà¹Ģ à¸Ķ", "Ġà¹ĢภĶ", "£ ¨", "Å¡ k", "ãĥĥ ãĥī", "ar dır", "ard ır", "Ġyö net", "Ġyön et", "Ñĥ вали", "Ñĥв али", "Ñĥва ли", "åħĪ çĶŁ", "Ġ ÐIJÑĢ", "ĠÐIJ ÑĢ", "Ġprot ože", "Ġproto že", "Ġ íģ¬", "Ġíģ ¬", "Ġjed not", "Ġjedn ot", "Ġjedno t", "Ġt ý", "éĩ ĩ", "Ġ หร", "Ġห ร", "Ġ åľ°", "Ġåľ °", "çº ¢", "Ġм олод", "Ġмол од", "Ġмо лод", "iên g", "iê ng", "ĠÏĮ ÏĦι", "Ġد اشتÙĩ", "Ġداش تÙĩ", "Ġداشت Ùĩ", "Ġuy gun", "Ġuyg un", "Ġuygu n", "Ġоп еÑĢа", "ĠопеÑĢ а", "åı «", "Ġ ап", "Ġа п", "Ġ кÑĥÑĢ", "Ġк ÑĥÑĢ", "ĠкÑĥ ÑĢ", "ا عة", "اع Ø©", "un uz", "unu z", "Ġ ìĤ¬ì§Ħ", "ĠìĤ¬ ì§Ħ", "Ġv ô", "ç ok", "ço k", "Ġ èģ", "Ġè ģ", "ÑĤе ÑĢеÑģ", "ÑĤеÑĢ еÑģ", "Ġ استاÙĨ", "Ġا ستاÙĨ", "Ġاست اÙĨ", "Ġاس تاÙĨ", "а лаÑģÑĮ", "ала ÑģÑĮ", "à¥ģ व", "à¥ģठµ", "á» ³", "Ġl Æ°u", "Ġ Та", "ĠТ а", "Ġl á»±a", "' ÑĶ", "Ġ üy", "Ġü y", "Ġ ÛĮÚ©ÛĮ", "ĠÛĮ Ú©ÛĮ", "ĠÛĮÚ© ÛĮ", "æ ¾", "н ем", "не м", "Ġ خاÙĨ", "ĠØ® اÙĨ", "ĠÑį лек", "ÙĤ اÙĦ", "л ок", "ло к", "ĠÄij ẹp", "à¥ī ल", "Ġm ůž", "Ġmů ž", "ëĭ¤ ëĬĶ", "Ġ íķĺëĤĺ", "Ġíķĺ ëĤĺ", "ÙĦ ت", "çİ° åľ¨", "м о", "Ïħ Ïĥ", "ãģŁ ãģ¡", "ĠìłĦ ìĦ¸", "à¥į à¤Łà¤°", "à¥įà¤Ł र", "ع ات", "عا ت", "د ÙĪ", "ä¿ º", "æ¥ ½", "æ£ ®", "Ġл иÑģÑĤ", "Ġли ÑģÑĤ", "δ ι", "å¯ Į", "ĠÄij Æ°a", "в еÑģÑĤи", "ве ÑģÑĤи", "веÑģÑĤ и", "д о", "ан нÑĸ", "Ġü ret", "Ġür et", "Ġg á»įi", "ĠÑģ воÑİ", "ĠÑģв оÑİ", "ĠÑģво Ñİ", "á» «ng", "ừ ng", "Ġt ất", "äºļ æ´²", "á ce", "ác e", "N Ãį", "Ġ ÑĢÑĭ", "ĠÑĢ Ñĭ", "æ» ¡", "Ïģ εÏĤ", "Ïģε ÏĤ", "åħį è´¹", "л оÑĤ", "ло ÑĤ", "æĻ º", "Ġα γ", "Ġà¤ħ म", "Ġ ç´", "Ġç ´", "о до", "од о", "Ñħ и", "Ġngu á»ĵn", "éĥ¨ åĪĨ", "в аÑĤ", "ва ÑĤ", "ĠÑĤ еб", "ĠÑĤе б", "з аÑĨÑĸÑĹ", "за ÑĨÑĸÑĹ", "Ġ ÐŁÑĢо", "ĠÐŁ ÑĢо", "ĠÐŁÑĢ о", "ع ÛĮ", "Ġ ÙĪÙĬ", "ĠÙĪ ÙĬ", "ëŀ ľ", "Ġne by", "Ġneb y", "Ġج دÛĮد", "Ġجد ÛĮد", "ÄŁ imiz", "ÄŁim iz", "£ ½", "Ġà¤Ĩ त", "Ġà¤Ń र", "æī ĺ", "å®ī åħ¨", "Ġëĵ¤ ìĸ´", "ب رد", "بر د", "Ġê²ĥ ìĿ´", "äº ²", "æ° ı", "ал Ñĸз", "алÑĸ з", "l ack", "la ck", "lac k", "ĠÙħخت ÙĦÙģ", "ا ÙĨÙĬØ©", "اÙĨ ÙĬØ©", "اÙĨÙĬ Ø©", "Ġ ì²Ń", "Ġì² Ń", "Ġ виÑĤ", "Ġв иÑĤ", "Ġви ÑĤ", "Ġhar eket", "Ġhare ket", "Ġharek et", "é ¨", "à¸Ļ ำ", "Ġب رخ", "Ġبر Ø®", "å£ ²", "Ñĩ ай", "Ñĩа й", "Ġan lat", "Ġà¤ħ व", "ĠاÙģ ز", "Ġh ết", "ĠÚĨ ÙĨد", "éĹ ľ", "пÑĢи ÑĶм", "g ı", "Ġk omp", "Ġkom p", "Ġko mp", "Ġl Ỽp", "ĠlỼ p", "Ġm á»Ĺi", "Ġmá» Ĺi", "à¸Ľà¸£à¸° à¸ģ", "Ġ haf", "Ġh af", "Ġha f", "Ġ eder", "Ġe der", "Ġed er", "Ġзд оÑĢов", "à¥Ĥ म", "ëł ¸", "Ġo nun", "Ġon un", "Ġonu n", "ĠÙħر دÙħ", "ĠÙħرد Ùħ", "ĠÐľ аÑĢ", "ĠÐľÐ° ÑĢ", "Ġìĸ´ ëĸ", "м ан", "ма н", "Ġ ÑģилÑĮ", "ĠÑģ илÑĮ", "ĠÑģи лÑĮ", "ĠÑģил ÑĮ", "ç¶ ²", "ë¸ Ķ", "л ÑıеÑĤ", "лÑı еÑĤ", "ĠнеÑģк олÑĮко", "ĠнеÑģколÑĮ ко", "l andır", "land ır", "lan dır", "landı r", "Ġв д", "ĠÙĨ ÙĪ", "ãģ İ", "ÑĤ ин", "ÑĤи н", "ت Ø´", "а ний", "ан ий", "ани й", "Ġt ÅĻ", "Ñģ иÑħ", "Ñģи Ñħ", "л ом", "ло м", "æŃ ©", "ãİ ¡", "Ġ ØŃر", "ĠØŃ Ø±", "æĭ į", "e nou", "en ou", "eno u", "Ġв ели", "Ġвел и", "Ġве ли", "Ġ δη", "Ġδ η", "s ka", "sk a", "主 è¦ģ", "ا ÙģØ©", "اÙģ Ø©", "ĠболÑĮ ÑĪе", "ĠболÑĮÑĪ е", "ิ ศ", "çĽ Ĭ", "ĠÙģ ÙĤØ·", "ĠÙģÙĤ Ø·", "å¨ ģ", "Ġh Æ°á»Łng", "ĠD oÄŁ", "ĠDo ÄŁ", "Ġd Ãłi", "Ġ гоÑĤов", "Ġг оÑĤов", "ĠгоÑĤ ов", "Ġв ам", "Ġва м", "âĢ ī", "ा à¤ļ", "ाठļ", "åħ ¸", "à¹ĥ หà¸į", "à¹ĥห à¸į", "Ġ ç«", "Ġç «", "ekt ör", "Ġв ел", "Ġве л", "Ġ ÙĦÙĪ", "ĠÙĦ ÙĪ", "Ø´ تÙĩ", "شت Ùĩ", "æĺ ¾", "ả y", "à¹Ĥ ม", "Ġt á»ķng", "Ġtá»ķ ng", "Ġtá»ķn g", "ĠповеÑĢ Ñħ", "ÑĹ Ð²", "Ġph ép", "çļ ĩ", "Ġп оÑĢÑıд", "Ġпо ÑĢÑıд", "ĠпоÑĢ Ñıд", "ĠÑģооÑĤ веÑĤ", "ठĿ", "ĠÑģеб Ñı", "Ġ ëĤł", "ĠëĤ ł", "Ġб Ñĥла", "ĠбÑĥ ла", "à¹ī าย", "à¹īา ย", "Ġ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢ", "ĠÙħ جÙħÙĪع", "ï¼Į 以", "Ġب ÙĪدÙĩ", "ĠبÙĪد Ùĩ", "ĠبÙĪ دÙĩ", "μ ÏĮ", "Ġ íݸ", "Ġíİ ¸", "eÅŁ it", "Ñİ Ñīие", "ÑİÑī ие", "Ñİ ÑīиÑħ", "ÑİÑī иÑħ", "åŁº éĩij", "Ġت ØŃت", "ĠتØŃ Øª", "Ġв лаÑģ", "Ġвла Ñģ", "ler le", "ãĤ ²", "ëĬ ĺ", "è ĵ", "m anın", "man ın", "manı n", "ìŀ Ī", "Ġz ast", "Ġza st", "Ġzas t", "ĠÑĩелов ек", "à¥ĩ ब", "à¥ĩठ¬", "p eÄį", "pe Äį", "Ġبر ÙĨاÙħÙĩ", "Ġs lov", "Ġsl ov", "Ġslo v", "ĠnÄĽ jak", "ĠnÄĽj ak", "ê· ľ", "à¥ĩ ह", "à¥ĩठ¹", "èĹ ¤", "ĠبÛĮ شتر", "ĠبÛĮØ´ تر", "il iz", "ili z", "Ġ ëĶĶ", "Ġë ĶĶ", "ĠëĶ Ķ", "ا زÙĩ", "از Ùĩ", "ت د", "Ġe tm", "Ġet m", "Ġëĭ¤ 른", "Ġ vů", "Ġv ů", "å° Ħ", "Ġк лаÑģ", "Ġкл аÑģ", "Ġкла Ñģ", "в ÑĢоп", "æ´ ¾", "ĠÄij ình", "Ñĥ ÑİÑĤ", "ÑĥÑİ ÑĤ", "Ñĥ еÑĤÑģÑı", "ÑĥеÑĤ ÑģÑı", "éľ ²", "Ġ ÑģкоÑĢ", "ĠÑģ коÑĢ", "ĠÑģк оÑĢ", "Ġв аÑģ", "Ġва Ñģ", "íķĺ ìĺĢëĭ¤", "íķĺìĺĢ ëĭ¤", "Ġ داشت", "Ġد اشت", "Ġداش ت", "Ġ çĦ", "Ġç Ħ", "Ġ 西", "Ġè ¥¿", "Ġκα ÏĦά", "ॠ¦", "ìĹ Ĩ", "Ġ خدÙħ", "ĠØ® دÙħ", "Ġخد Ùħ", "ا سÙħ", "اس Ùħ", "Îij Ρ", "ĠA ma", "ĠAm a", "å¥ ¥", "Ġبزر Ú¯", "Ġ ÐĴÑĸн", "ĠÐĴ Ñĸн", "Ġ Åĺ", "ĠÅ ĺ", "Ġ à¸Īาà¸ģ", "Ġà¸Ī าà¸ģ", "ĠÑħаÑĢакÑĤ еÑĢ", "ĠÄij á»Ļi", "ĠÄijá»Ļ i", "ĠÑĢоз виÑĤ", "ĠÑĢозви ÑĤ", "ĠпÑĢоÑĦ еÑģ", "Ġкон ÑĤÑĢ", "ĠконÑĤ ÑĢ", "ÎŁ ÎĽ", "ÎŁÎ Ľ", "Ġm inh", "Ġmin h", "Ġmi nh", "ä¼ ij", "ì ª½", "Ġch Æ¡i", "з аÑĨии", "за ÑĨии", "ĠдÑĸÑı лÑĮ", "ë Ĩ", "Ġn gay", "Ġng ay", "Ġnga y", "à¥Ĥ à¤Ĥ", "Ġiht iy", "éĽ ª", "Ġ ê´Ģ리ìŀIJ", "Ġê´Ģ리 ìŀIJ", "Ġc ụ", "Ġ ì§Ī", "Ġì§ Ī", "ÙĬ Ø«", "ặ p", "ÙĪ اع", "ÙĪا ع", "ãģĤ ãģ£ãģŁ", "Ġ çľ", "Ġç ľ", "Ġ ìļ°ë¦¬", "Ġìļ° 리", "à¹Ī à¸ĩà¸Ĥ", "à¹Īà¸ĩ à¸Ĥ", "Ġ çŃ", "Ġç Ń", ") ØĮ", "Ãł m", "ÙĦ ÛĮÙĦ", "ÙĦÛĮ ÙĦ", "Ġ 걸", "Ġê± ¸", "алÑĮ ниÑħ", "æŶ åĢĻ", "un dan", "und an", "unda n", "Ġ Gün", "ĠG ün", "ĠGü n", "Ġ topl", "Ġt opl", "Ġto pl", "Ġtop l", "ĠÑĢек омен", "ĠاÙĨت خاب", "Ãł u", "Äį ka", "ë° Ģ", "Ġк ÑĢаÑģ", "ĠкÑĢа Ñģ", "л оп", "ло п", "å¼ µ", "ĠاÙĦ Ùħع", "ĠاÙĦÙħ ع", "m ÃŃn", "mÃŃ n", "Ġv iết", "Ġvi ết", "Ġ ê°ĻìĿĢ", "Ġê°Ļ ìĿĢ", "ut eÄį", "ute Äį", "Ġ nech", "Ġn ech", "Ġne ch", "Ġnec h", "çµ Ĥ", "ãģª ãģĮ", "ay ın", "ayı n", "Ġ Äįin", "ĠÄį in", "ĠÄįi n", "ch ázÃŃ", "cház ÃŃ", "اÙģ ظ", "ÑĢ оваÑĤÑĮ", "ÑĢов аÑĤÑĮ", "ÑĢо ваÑĤÑĮ", "ÑĢова ÑĤÑĮ", "à¹Ħ ร", "Ġ ãĤ¤", "ĠãĤ ¤", "Ġзаб олева", "Ġ å±±", "Ġå± ±", "Ġka dın", "Ġkad ın", "ÏĦ ηÏĤ", "ÏĦη ÏĤ", "а лиÑģÑĮ", "ал иÑģÑĮ", "али ÑģÑĮ", "Ġh ük", "åĵ ¥", "Ġп еÑĢи", "ĠпеÑĢ и", "ÅĻ ád", "Ġà¤ħ स", "Ġ ÑģÑĤвоÑĢ", "ĠÑģÑĤ воÑĢ", "ĠÙĪ ÛĮÚ©ÛĮ", "ĠÙĪÛĮ Ú©ÛĮ", "Ġ ì¡", "Ġì ¡", "Ġc á»Ńa", "Ġcá»Ń a", "Ġh iá»ĥu", "游 æĪı", "ÑĮ омÑĥ", "Ġg ó", "Ġt oh", "Ġto h", "Ġб ла", "Ġбл а", "Ġ åij", "Ġå ij", "Ġп ло", "Ġпл о", "и ÑĪ", "ĠÄij ấu", "s kou", "sk ou", "sko u", "ãĤĪ ãĤĬ", "ู à¸Ľ", "Ġr á»ĵi", "оп ÑĢоÑģ", "н олог", "ĠÑĤ ÑĢав", "ĠÑĤÑĢ ав", "ĠÑĤÑĢа в", "ĠWay back", "Ġ à¹Ĩ", "ĠÑĥ ÑĩаÑģÑĤ", "ĠÑĥÑĩ аÑģÑĤ", "ĠÑĥÑĩа ÑģÑĤ", "ĠÑĥÑĩаÑģ ÑĤ", "ĠпÑĢеп аÑĢа", "Ġd ạng", "ĠÃľ n", "à¹Ħล à¸Ļ", "Ġد اخ", "Ġs Æ¡", "Ġk oy", "Ġko y", "ëĿ¼ ê³ł", "ĠÄij úng", "à¥ĩà¤Ĥ ,", "Ġgeç ir", "ĠÑıк Ñīо", "Ñģ ÑĤÑĢо", "ÑģÑĤ ÑĢо", "ÑģÑĤÑĢ о", "ен ÑĤов", "енÑĤ ов", "Ñĸ ж", "к ÑĥÑİ", "кÑĥ Ñİ", "Ġe ÄŁitim", "ĠeÄŁit im", "à¥įर स", "Ġ Сп", "ĠС п", "ا تÛĮ", "ات ÛĮ", "ãģij ãĤĭ", "ÏĦ Ïīν", "ÏĦÏī ν", "Ġ км", "Ġк м", "âĸįâĸį âĸįâĸį", "j ist", "ji st", "jis t", "ÑĤ ак", "ÑĤа к", "Ġ åIJįåīį", "ĠåIJį åīį", "é¡ Ķ", "л Ñĭ", "Ġkh ảo", "Ġkhả o", "âĢĻ Ñı", "ĠÙħ ÙĦÛĮ", "ĠÙħÙĦ ÛĮ", "l ož", "lo ž", "Ġ ìĸ¸", "Ġìĸ ¸", "Ġg ần", "Ġ à¤ľà¤°", "Ġà¤ľ र", "ब र", "Îķ Σ", "า à¸Ľ", "าภĽ", "Ġn ás", "Ġná s", "form ace", "forma ce", "Ġet mek", "Ġetm ek", "Ġetme k", "в еÑģÑĤ", "ве ÑģÑĤ", "ìĸ´ ìļĶ", "Ġत थ", "ĠÑģ ек", "ξ η", "æ¯ Ľ", "B ir", "Bi r", "Ġ ìŀĦ", "Ġìŀ Ħ", "Ġv ardır", "Ġvar dır", "Ġvardı r", "ÙĪ اÙĦ", "ÙĪا ÙĦ", "Ä° R", "ov ané", "ova né", "ovan é", "н аÑĢод", "на ÑĢод", "наÑĢ од", "à¸Ħ ำ", "e mek", "em ek", "eme k", "ĠÎķ ÏĢ", "Ġ ÅĻe", "ĠÅĻ e", "ãģ¾ ãģĽ", "uyá»ĩ t", "Ġ ìĸ¼", "Ġìĸ ¼", "r ů", "Ġ onu", "Ġo nu", "Ġon u", "à¹Ģà¸ķ à¸Ńร", "од аÑĢ", "ода ÑĢ", "ز Ùĩ", "Ġk av", "Ġka v", "о нÑĭ", "он Ñĭ", "Ġв еÑģ", "Ġве Ñģ", "ìĤ¬ ì§Ģ", "Ġг ла", "à Ŀ", "ĠÙĤ ÛĮÙħت", "çķ ¥", "à¸ĸ าà¸Ļ", "Äį il", "Äįi l", "Ġ ä¸ĩ", "Ġä¸ ĩ", "è¾ ĥ", "åħ ħ", "Ġ ÑĢед", "ĠÑĢ ед", "ĠÑĢе д", "ม ห", "am ilia", "ami lia", "amil ia", "à¥ĩ à¤ķर", "à¥ĩà¤ķ र", "Ġt á»iji", "Ġtá»ij i", "Ùģ ÛĮ", "ÑĢ ÑĸÑĪ", "ÑĢÑĸ ÑĪ", "ìķ ł", "à¸Ļ ส", "à¸Ī ร", "à¥ĩ शन", "à¥ĩश न", "ĠÙħÙĪ ضÙĪع", "æī ¹", "Ġob sah", "Ġobs ah", "Ġнав Ñĩ", "Ġdes tek", "Ġdest ek", "Ġdeste k", "Ġ zas", "Ġz as", "Ġza s", "å ĵį", "åĵ į", "üm üz", "ümü z", "Ġ çŁ", "Ġç Ł", "Ġ è¨", "Ġè ¨", "Ù ¬", "ç» Ī", "Ġz de", "Ġzd e", "Ġz áp", "Ġzá p", "à¥Ĥ सर", "à¥Ĥस र", "ìĿ´ ì§Ģ", "çļ ®", "l om", "lo m", "ॠ§", "ÙĦ اÙĤ", "ÙĦا ÙĤ", "à¸Ļ à¸ķ", "íĮ ħ", "л ада", "ла да", "лад а", "m asına", "mas ına", "ması na", "ãģ® ãģ§", "ëĵ¤ ìĿĦ", "Ġн аг", "Ġна г", "m asını", "mas ını", "ãĤ Ŀ", "ın ıf", "ını f", "åĽ ´", "Ġböl üm", "å¥ ĸ", "æ¨ Ļ", "ÙĦ اØŃ", "ÙĦا ØŃ", "Ġг оÑģÑĥдаÑĢ", "داÙĨ ÙĦÙĪد", "Ġп оÑĤÑĢеб", "ĠпоÑĤ ÑĢеб", "ĠÑĢ оÑĨÑĸ", "о га", "ог а", "ĠÑģлед ÑĥеÑĤ", "Ġп аÑĢа", "ĠпаÑĢ а", "Ġпа ÑĢа", "é ¼", "ãģį ãģŁ", "ί ζ", "Ġb á»ij", "ÑĤ Ñĸв", "ÑĤÑĸ в", "ï¼Į 她", "f amilia", "éł ħ", "Ġد ÙĦ", "Ġs kup", "Ġsk up", "Ġsku p", "еÑĩ ение", "ãģĵãģ¨ ãģĮ", "à¥Ģ ब", "ุ ล", "¨ ë¶Ģ", "ĠاÙĦع رب", "Ġ ç¾İ", "Ġç¾ İ", "ĠاÙĦ ÙħÙĪ", "ĠاÙĦÙħ ÙĪ", "Ġ Ø¥ÙĨ", "ĠØ¥ ÙĨ", "Ġná sled", "Ġnás led", "Ġt omu", "Ġto mu", "Ġtom u", "Î Ħ", "Ġз ави", "Ġза ви", "Ġзав и", "Ġn hu", "Ġnh u", "ĠpÅĻed stav", "ìłķ ë³´", "o kol", "ok ol", "oko l", "Ġк ÑĢи", "a du", "ad u", "Ġ каÑĤ", "Ġк аÑĤ", "Ġка ÑĤ", "Ġ ÑįÑĦ", "ĠÑį ÑĦ", "в ал", "ва л", "m ayı", "ma yı", "may ı", "ĠÑĩаÑģ ÑĤо", "ĠÑĩаÑģÑĤ о", "Ġtr anh", "Ġtra nh", "Ġtran h", "ائ ÙĦ", "ãĤĪãģĨ ãģª", "Ġp oh", "Ġpo h", "ìĥģ ìľĦ", "Ġs ắc", "Ùĥ س", "Ġ мÑĥ", "Ġм Ñĥ", ". ::", ".: :", "ë Ī", "» Ċ", "Ġ ÙĨÚ¯", "ĠÙĨ Ú¯", "ÙIJ ÙĨ", "н иком", "ни ком", "ник ом", "Ñħ а", "Ġ μοÏħ", "Ġμ οÏħ", "Ġμο Ïħ", "ĠNg uyá»ħn", "ĠвÑĭ Ñģок", "ĠвÑĭÑģ ок", "Ġ ÐŁÐ¾Ð´", "ĠÐŁ од", "ĠÐŁÐ¾ д", "ĠпÑĢи ÑĢод", "à¥ĭ ध", "िà¤ķ ल", "и ÑĢа", "иÑĢ а", "ëĭ¤ ê³ł", "Ġm ajÃŃ", "Ġma jÃŃ", "Ġmaj ÃŃ", "Ġv ùng", "Ġtarih inde", "Ġtarihi nde", "Ġ ваÑĢ", "Ġв аÑĢ", "Ġва ÑĢ", "н иÑĤÑĮ", "ни ÑĤÑĮ", "ниÑĤ ÑĮ", "ει ÏĤ", "Ġ åĩº", "Ġåĩ º", "dy ž", "ÏĦ Ïİν", "ÏĦÏİ Î½", "ä½ĵ èĤ²", "Ġ à¹Ģว", "Ġà¹Ģ ว", "Ġà¹Ģภ§", "Ġà¤ħ à¤ļ", "Ġ اÙĨÚ¯ÙĦÛĮسÛĮ", "ĠاÙĨÚ¯ ÙĦÛĮسÛĮ", "à¥įय म", "Ġgel iÅŁ", "æ¹ ĸ", "Ġ اک", "Ġا Ú©", "Ġп лан", "Ġпл ан", "Ġпла н", "k yt", "ky t", "ا بÛĮ", "اب ÛĮ", "κ ι", "Ġc hung", "Ġch ung", "Ġchu ng", "ान à¤ķ", "s ı", "Ġt inh", "Ġti nh", "Ġtin h", "ĠÑģÑĤ ол", "ĠÑģÑĤо л", "ÑģÑĤ ÑĢÑĥ", "ÑģÑĤÑĢ Ñĥ", "Ġли ÑĪе", "ĠлиÑĪ е", "Ġви ÑĢоб", "il miÅŁ", "ilm iÅŁ", "Ġ зÑĸ", "Ġз Ñĸ", "ç» Ĩ", "åĢ Ĵ", "ãĤ· ãĥ£", "åŃ ©", "Ġ à¹Ĥรà¸ĩà¹Ģร", "Ġà¹Ĥ รà¸ĩà¹Ģร", "Ġà¹Ĥรà¸ĩ à¹Ģร", "íĻ ľ", "ĠбÑĥд е", "ĠбÑĥ де", "Ġyak laÅŁ", "èĩª åĪĨ", "Ġ ÙģÙĪ", "ĠÙģ ÙĪ", "С Т", "Ġso run", "Ġsor un", "Ġsoru n", "à¹Ģ à¸ł", "à¹Ģภł", "Ġc ô", "в иÑĩ", "ви Ñĩ", "ëĵ¤ ìĿĺ", "Ġtr iá»ĩu", "Ġtri á»ĩu", "Ġr õ", "Ġ ãģ«", "ÄŁ im", "ÄŁi m", "iyor uz", "è ľ", "à¥įर व", "Ġس Ù¾", "Ġ ìĦľìļ¸", "ĠìĦľ ìļ¸", "δ ε", "еÑĢ ÑĪ", "Ġ أس", "ĠØ£ س", "äº ŀ", "è¯ į", "п ÑĤом", "ฤ ษ", "Ġساز ÙħاÙĨ", "Ġlu ôn", "Ùĩ ÙĪر", "c ü", "аÑĤ кÑĥ", "Ġo labilir", "Ġol abilir", "Ġolab ilir", "Ġola bilir", "Ġ ìĹ°êµ¬", "ĠìĹ° 구", "ен ной", "енно й", "Ġ æĪij", "ĠæĪ ij", "Ġ него", "Ġн его", "Ġне го", "Ġнег о", "Ġ. **************", "ิ à¸ĺ", "Ġ ãĤ·", "ĠãĤ ·", "ت Ùģ", "ÐŁ ÑĢо", "ÐŁÑĢ о", "Ġhakk ında", "Ġhakkı nda", "Äį nÄĽ", "ĠM ỹ", "é ½", "ĠÏĥ ÏĦον", "ĠÏĥÏĦο ν", "Ġ âm", "Ġâ m", "§ ظ", "ĠÅŁ irket", "æĥħ åĨµ", "ĠØ¢ÙħÙĪز Ø´", "λ εÏħ", "λε Ïħ", "Ùħ Ùĩ", "è¦ ı", "ãģ¨ æĢĿ", "Ġ ÙĪع", "ĠÙĪ ع", "ÏĪ η", "Ïģ οÏį", "Ïģο Ïį", "Ġ ÂłĊ", "ĠÂł Ċ", "δ η", "ÑĪ ов", "åĪ ¤", "Ġm ắt", "æĭ ¿", "à¸Ļ à¸Ķ", "éĻ Ħ", "à¹ī ม", "ĠÄij ạt", "Ġg üzel", "Ġgü zel", "m Ã¼ÅŁ", "Ðŀ ÐĴ", "çĭ ¬", "리 를", "Ġп лаÑĤ", "Ġпл аÑĤ", "Ġпла ÑĤ", "Ġngh á»ĭ", "ĠÑĤак иÑħ", "ĠÑĤа киÑħ", "б иÑĢа", "би ÑĢа", "Ġн ек", "Ġне к", "ÑģÑĮ кÑĸ", "ÑģÑĮк Ñĸ", "رÙĬ اض", "o nu", "on u", "à¥ĭ म", "ĠGi Ỽi", "ĠGiá» Ľi", "èŀ į", "é ²", "ĠGe nel", "ĠGen el", "ĠGene l", "åĬ ¿", "Ġ вÑĸ", "Ġв Ñĸ", "å§ IJ", "è© ¦", "ĠжиÑĤ ÑĤÑı", "Ġ ìĺ¨", "Ġìĺ ¨", "åĩº æĿ¥", "Ġt á»ij", "Ġtá» ij", "Ġl ao", "Ġla o", "ί ο", "ĠÎł α", "н иÑĤелÑĮ", "ниÑĤ елÑĮ", "ниÑĤе лÑĮ", "éļ İ", "Ġви кон", "Ġвик он", "Ġвико н", "ĠÙģ عاÙĦ", "ĠÙģع اÙĦ", "à¹Ģ ศ", "à¹Ģภ¨", "ÏĮ γ", "ĠоÑĢгани з", "ĠоÑĢган из", "Ġ емÑĥ", "Ġе мÑĥ", "Ġем Ñĥ", "Ġ ÙĬع", "ĠÙĬ ع", "ĠÙħ ب", "ाल य", "ĠÎľ ÏĢ", "é ¸", "ù a", "ê¸ ¸", "Ġ ÄIJiá»ģu", "ĠÄIJ iá»ģu", "ε ίο", "εί ο", "äº ī", "ượ t", "ÑĢа зÑĥ", "ÑĢаз Ñĥ", "ĠоÑĤ ÑĢим", "ĠоÑĤÑĢи м", "Ġ طب", "ĠØ· ب", "Ġ 以", "æĸ Ĺ", "ë° ±", "à¤ĩ स", "ë§Į ìĽIJ", "ãĢģ ãģĿãģ®", "ĠëķĮ 문", "ĠØ¢ ÛĮ", "С Ðł", "ض ÙĦ", "æ ĵį", "æĵ į", "k azy", "ka zy", "kaz y", "ส ว", "â ng", "ân g", "à¤Ĥ à¤Ń", "н ÑĸÑĩ", "нÑĸ Ñĩ", "ั à¸ĩà¸ģ", "ัà¸ĩ à¸ģ", "Ġبر رسÛĮ", "ر دÙĩ", "رد Ùĩ", "Ġm ẫu", "à¹Ī วà¸ĩ", "à¹Īว à¸ĩ", "ĠداÙĨØ´ گاÙĩ", "d ıģ", "dı ÄŁ", "ĠT á»ķng", "ĠTá»ķ ng", "第 äºĮ", "c ÃŃm", "cÃŃ m", "Ġb öyle", "Ġbö yle", "ë ¶Ī", "ë¶ Ī", "ĠÙħÙĨ ابع", "à¥ĥ ष", "е ÑĤÑĭ", "еÑĤ Ñĭ", "åĨ ·", "åĽ Ń", "Ġت ÙĪجÙĩ", "ĠتÙĪ جÙĩ", "åĪ »", "æŀ ģ", "à¤Ł न", "л ан", "ла н", "Ġ íĥĢ", "Ġíĥ Ģ", "ä½ IJ", "Ġ обÑĭ", "Ġо бÑĭ", "Ġоб Ñĭ", "å¸ Ŀ", "ì» ¤", "å® Ī", "èµ· æĿ¥", "Ġ ãĥ¬", "Ġãĥ ¬", "çİ ī", "à¹Ģ หล", "à¹Ģห ล", "и не", "ин е", "ห าร", "หา ร", "éļ ı", "Ġг аз", "ĠاÙĦ عÙħÙĦ", "ĠاÙĦع ÙħÙĦ", "ĠاÙĦعÙħ ÙĦ", "à¥ģ à¤Ŀ", "à¥ģठĿ", "Ïģ ιο", "Ïģι ο", "Ġv ám", "Ġvá m", "Ġع ÙĨد", "ĠعÙĨ د", "ÙĨد گاÙĨ", "ï¼Į éĤ£", "Ġна Ñħод", "á no", "án o", "ÛĮ اÙĨ", "ÛĮا ÙĨ", "ĠØ£ ع", "Ġ ÑĢади", "ĠÑĢ ади", "ĠÑĢа ди", "ĠÑĢад и", "Ġм ене", "Ġмен е", "Ġú da", "Ïĩ ν", "ÑĥлÑı ÑĢ", "à¥Ģ प", "Ġpou žÃŃ", "Ġ ä¸", "ĠÙĤ اÙĨÙĪÙĨ", "ι κοÏį", "ικ οÏį", "ικο Ïį", "á y", "Ġç öz", "ÏĦ Ïģ", "ÙĨ اÙħ", "ÙĨا Ùħ", "ุ à¸ķ", "åĵ ª", "ÙĬ ب", "ä¹ °", "ÐĶ лÑı", "Ġ ëłĪ벨", "ĠëłĪ 벨", "ุ à¸ļ", "н ÑĥÑĤи", "нÑĥ ÑĤи", "нÑĥÑĤ и", "è½ »", "ĠÎľ α", "Ġ è¦", "Ġè ¦", "аÑĤ ков", "Ġ ëĪĦ", "ĠëĪ Ħ", "Ġt uyá»ĥn", "Ġtuy á»ĥn", "Ùİ Ùħ", "ĠвÑĭ пол", "ĠвÑĭп ол", "Ġst udi", "Ġstud i", "Ġstu di", "ĠpÅĻ ek", "ĠpÅĻe k", "Ġз ам", "Ġза м", "Ġmat eri", "Ġma teri", "Ġmate ri", "Ġmater i", "åİ ĭ", "Ġ ал", "Ġа л", "Ġ à¸ļร", "Ġà¸ļ ร", "Ø· ØŃ", "ĠÙħر Ú©", "Ġ ìĭ¬", "Ġìĭ ¬", "ĠÙĤ ابÙĦ", "ĠÙĤاب ÙĦ", "ĠÐIJ ле", "ĠÐIJл е", "ın tı", "Ġ å»", "Ġå »", "Ä° K", "ëħĦ ëıĦ", "Ñĭ ваÑĤÑĮ", "Ñĭв аÑĤÑĮ", "Ñĭва ÑĤÑĮ", "Ġdev let", "社 ä¼ļ", "ëĤ ł", "Ġko lay", "Ġkol ay", "Ġkola y", "ĠÑĢазв иÑĤи", "ĠÑĢазви ÑĤи", "а ди", "ад и", "ئ ÙĬس", "a dıģı", "ad ıģı", "adı ģı", "adıģ ı", "Îij ÎĽ", "Ġ hoa", "Ġh oa", "Ġho a", "Ġ ศ", "Ġภ¨", "ı ÅŁtır", "Ä±ÅŁ tır", "ÑĢ Ñİ", "Ġк аÑĩе", "Ġка Ñĩе", "¼ åIJĪ", "åħ ´", "Ġ ê·¸ëŁ¬", "Ġê·¸ 룬", "Ġм ÑĸÑģÑĤ", "ĠмÑĸ ÑģÑĤ", "ĠмÑĸÑģ ÑĤ", "Ġм не", "Ġмн е", "ãĥ¼ ãĤº", "ãĥ¼ãĤ º", "ç§ Ģ", "Ġع ÙĦÙĬÙĩ", "ĠعÙĦ ÙĬÙĩ", "ĠعÙĦÙĬ Ùĩ", "Ġ ìĭľê°Ħ", "Ġìĭľ ê°Ħ", "Ġà¤ĺ र", "Ġ Ñĥг", "ĠÑĥ г", "åıij å±ķ", "ı ÅŁÄ±", "Ä±ÅŁ ı", "Ġ ìĪľ", "ĠìĪ ľ", "Ġ íĻľ", "ĠíĻ ľ", "æ¡ £", "Ġn okt", "Ġno kt", "Ġnok t", "l ém", "lé m", "ен нÑĭй", "Ġب Ùħ", "à¥ĩ य", "à¥ĩठ¯", "о дав", "од ав", "ода в", "à¹Ĥ ร", "ï¼Į æľī", "ا ÙĬات", "اÙĬ ات", "اÙĬا ت", "ا ÛĮÙĩ", "اÛĮ Ùĩ", "Ġà¤īप य", "Ġs mÄĽ", "Ġsm ÄĽ", "Ø´ د", "Ш ÐIJ", "Ġا ÙħاÙħ", "ĠاÙħ اÙħ", "ĠاÙħا Ùħ", "æ¿ Ģ", "Ġho ạch", "об ÑĢаз", "обÑĢаР·", "à¥ĭ ह", "ĠÑĢеб ен", "иÑĤ елÑı", "иÑĤе лÑı", "ãģªãģĮ ãĤī", "س اÙĦ", "Ġ à¸Īำ", "Ġà¸Ī ำ", "Ġ خاص", "ĠØ® اص", "Ġg eri", "Ġge ri", "Ġger i", "ठĺ", "Ġ ìº", "Ġì º", "à¹ģ à¸Ĺ", "âĢĮ ÛĮ", "Ú¯ رÛĮ", "گر ÛĮ", "ا Ùħبر", "اÙħ بر", "ÑĪ Ñĥ", "Ġp hong", "Ġph ong", "Ġphon g", "и мо", "им о", "п а", "Ġ ìµľê³ł", "Ġìµľ ê³ł", "Ġ нам", "Ġн ам", "Ġна м", "o stÃŃ", "os tÃŃ", "ost ÃŃ", "is ini", "isi ni", "isin i", "Ġд Ñĥже", "ĠдÑĥ же", "Ñģ ком", "Ñģк ом", "Ñģко м", "ĠпÑĢод Ñĥк", "ÏĮ ÏĦηÏĦα", "ÏĮÏĦη ÏĦα", "a ln", "al n", "is ine", "isi ne", "isin e", "è¿ ľ", "алÑĮ ной", "алÑĮно й", "त र", "t ıģ", "tı ÄŁ", "Ġë Ĵ", "è¿ĺ æĺ¯", "ĠÙħ Ø«ÙĦ", "ĠÙħØ« ÙĦ", "ìľ ¨", "ï¾ ĺ", "åĪ ¸", "ç ¶ļ", "ç¶ ļ", "ج اد", "جا د", "Ġ кÑĥ", "Ġк Ñĥ", "åĢ ij", "o vu", "ov u", "Ġs Ä©", "Ġ ìłIJ", "Ġìł IJ", "ĠÑĥ ÑĢов", "ि à¤ļ", "िठļ", "ov ali", "ova li", "oval i", "Ġ ÙĪÙĨ", "ĠÙĪ ÙĨ", "Ġ ìĿĮ", "ĠìĿ Į", "Ġк г", "า à¸ĺ", "าภĺ", "ÏĦ Ïģα", "ÏĦÏģ α", "ž dy", "à¹Į à¸ķ", "Ġ nÄĽm", "ĠnÄĽ m", "Ġ Це", "ĠЦ е", "n oho", "no ho", "Ġëĭ¤ ìĭľ", "Ġté to", "Ġb iá»ĥu", "ĠY ön", "Ġpr áce", "Ġprá ce", "à¥ī र", "Ġch ÃŃ", "ов ой", "ово й", "Ġm ợ", "Ġmá» Ł", "èª ª", "Ïİ ÏĤ", "в олÑı", "во лÑı", "вол Ñı", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "å¯ ¦", "é» ŀ", "Ġà¤ı व", "Ïħ να", "Ïħν α", "å² ¡", "kh ông", "ĠpÅĻÃŃpad ÄĽ", "å Ĺ", "Ġبد ÙĪÙĨ", "Ïĥ κε", "Ïĥκ ε", "Ġdik kat", "ĠAn cak", "ĠAnc ak", "Ġt iá»ĩn", "Ġti á»ĩn", "éĿ Ļ", "Ġ ìĿ¼ë°ĺ", "ĠìĿ¼ ë°ĺ", "ĠÄį len", "ìķ ħ", "ा à¤ĩन", "ाà¤ĩ न", "ãģ£ ãģ¦ãģĦãģŁ", "ãģ£ãģ¦ ãģĦãģŁ", "ãģ£ãģ¦ãģĦ ãģŁ", "ĠìĿ´ ìļ©", "ÙĪ ÙħÛĮ", "ÙĪÙħ ÛĮ", "i ná", "in á", "ặ ng", "ặn g", "Ïİ Ïģα", "ÙĨ ÙĬØ©", "ÙĨÙĬ Ø©", "в аж", "ва ж", "è İ·", "èİ ·", "å© ļ", "ĠÅŁ u", "Ġ ãģĬ", "Ġد رب", "Ġدر ب", "Ġd iá»ħn", "ÅĻ eba", "ÅĻe ba", "ÅĻeb a", "as ının", "asını n", "ç½ij ç«Ļ", "н ÑĮого", "нÑĮ ого", "нÑĮо го", "ĠاÙĦØ£ ÙĪÙĦ", "ικ ÎŃÏĤ", "Ġz ÃŃsk", "о ло", "ол о", "ĠÑį ÑĤоÑĤ", "ĠÑįÑĤ оÑĤ", "ĠÑįÑĤо ÑĤ", "Ġp okud", "Ġpo kud", "Ġpok ud", "è² »", "е ÑĢÑĸв", "еÑĢ Ñĸв", "еÑĢÑĸ в", "ãĥķ ãĤ£", "ãĥķãĤ £", "иÑĤÑĥ а", "Ġv yd", "Ġvy d", "о лож", "ол ож", "оло ж", "л ÑıÑĤÑĮ", "лÑı ÑĤÑĮ", "ÙĤ Ùħ", "æ´ ĭ", "æ§ ĭ", "Ġغ ÛĮر", "Ġ stÅĻed", "Ġst ÅĻed", "ĠstÅĻ ed", "ظ ر", "Ġhiç bir", "θ εί", "θε ί", "z nik", "zn ik", "д Ñĭ", "l uv", "lu v", "ĠÙħ ؤ", "ĠÚ¯ رÙĪÙĩ", "Ġگر ÙĪÙĩ", "Ġ ï¼īĊ", "Ġï¼ī Ċ", "t eri", "ter i", "te ri", "ĠÏħ ÏĢο", "ĠÏħÏĢ ο", "v oj", "vo j", "Ġبع ض", "Ġb ilin", "Ġbi lin", "Ġbil in", "Ġ رÙĪØ´", "Ġر ÙĪØ´", "ĠرÙĪ Ø´", "Ġоб Ñıз", "Ġ ï»", "Ġï »", "س ÙĨ", "Ġ ÏĢα", "ĠÏĢ α", "í į¼", "Ġt ÃŃn", "ĠtÃŃ n", "Ġ ´", "Ġ ´", "ìĤ¬ ìĿ´íĬ¸", "ìĤ¬ìĿ´ íĬ¸", "Ġ podob", "Ġpo dob", "Ġpod ob", "Ñī ее", "Ñīе е", "Ġ åįĹ", "Ġåį Ĺ", "Ġb ych", "Ġby ch", "о зи", "оз и", "ĠV Äĥn", "ØŃ Ùĩ", "åѦ éĻ¢", "ĠÅĻ ekl", "ĠÅĻe kl", "ĠÅĻek l", "립 ëĭĪëĭ¤", "Ġп ÑĢоÑģ", "ĠпÑĢ оÑģ", "ĠпÑĢо Ñģ", "κ ά", "ĠbaÅŁ ladı", "ủ y", "Ñİ Ð´Ð¶", "ाà¤ı à¤Ĺ", "à¤Ĥ à¤ļ", "Ġ ê´Ģ볨", "Ġê´Ģ 볨", "Ġв опÑĢоÑģ", "ĠÑģÑĤ аÑĤÑĮ", "ĠÑģÑĤаÑĤ ÑĮ", "ĠÑģÑĤа ÑĤÑĮ", "Ġy atırım", "Ġyatır ım", "н Ñĥла", "нÑĥ ла", "нÑĥл а", "ر اÙģ", "را Ùģ", "Ġç eÅŁit", "Ġà¤ī द", "å¤ ®", "Ġпо Ñıв", "åĽ½ 家", "ĠÑģооÑĤвеÑĤ ÑģÑĤв", "ìķ ¡", "ĠØ® ÙĪاÙĩد", "ĠØ®ÙĪاÙĩ د", "ĠØ®ÙĪ اÙĩد", "Å¡ Å¡ÃŃ", "Âł п", "ĠNh Ãł", "' '''", "'' ''", "''' '", "ï½ ¨", "à ħ", "Ġ ïº", "Ġï º", "ĠØ¢Ùħ رÛĮÚ©", "lar ımız", "larım ız", "ج ا", "Ùģ ÙĤ", "Ġ á»", "Ġá »", "Ġ ìķł", "Ġìķ ł", "Ġز باÙĨ", "ĠÑĤ воÑĢ", "ĠÑĤв оÑĢ", "н иÑĩеÑģ", "ни ÑĩеÑģ", "ниÑĩ еÑģ", "Ġк ни", "Ø® داÙħ", "à¸Ł ร", "Ġ ì¹ĺ", "Ġì¹ ĺ", "ว าม", "ĠÙħ ÙĩÙħ", "ĠÙħÙĩ Ùħ", "Ġs tol", "Ġst ol", "Ġsto l", "Ġed ilen", "Ġedi len", "Ġedil en", "Ġ pek", "Ġp ek", "Ġpe k", "اÙĨ ات", "اÙĨا ت", "алÑĮ нÑĸ", "Ġнеоб ÑħÑĸд", "à¹Ħ ว", "Ġश र", "Ġ íĮIJ", "ĠíĮ IJ", "Ò ij", "Ġ ним", "Ġн им", "Ġни м", "Ġ à¸ĺ", "Ġภĺ", "æĺ ł", "äº Ĵ", "ĠbaÅŁ ar", "ž i", "Ġм ног", "Ġмн ог", "l endi", "len di", "á vajÃŃ", "áv ajÃŃ", "n ict", "ni ct", "nic t", "Ġд Ñĥм", "ĠдÑĥ м", "éĻ ©", "Ïĥ Ïĥ", "i ky", "ik y", "алÑĮ нÑĭй", "Ġ ÙħÙĨت", "ĠÙħ ÙĨت", "ĠÙħÙĨ ت", "å® ®", "- за", "-з а", "еÑĢ к", "å¡ Ķ", "Ġμε ÏĦα", "oÄŁ un", "oÄŁu n", "ÎĹ Îľ", "à¥Īà¤Ĥ ।Ċ", "à¥Īà¤Ĥ। Ċ", "Äį ky", "å¹³ åı°", "à¥ĭ श", "Ġ ona", "Ġo na", "Ġon a", "Ġ bec", "Ġb ec", "Ġbe c", "ì ¢", "Ġc ây", "Ġcâ y", "k ün", "kü n", "Ġ à¤Ī", "ĠठĪ", "Ġr á»Ļng", "еÑĢ б", "å¹ ¸", "ï¾ IJ", "ĠпÑĸд пÑĢиÑĶм", "çĶ £", "Ġ ÏĦε", "ĠÏĦ ε", "ĠÙĨÙĤ Ø´", "о виÑħ", "ов иÑħ", "ови Ñħ", "ĠÙģ Ùī", "Ðļ ак", "Ùİ Ø±", "Ġ Щ", "ĠÐ ©", "алÑĮ нÑĭÑħ", "Ġk üçük", "èŃ ·", "æĭ ħ", "i caret", "ic aret", "ica ret", "icare t", "Ġ رÙģت", "Ġر Ùģت", "ĠرÙģ ت", "Ġод ного", "Ġодно го", "ÑĪ им", "ÑĪи м", "Ġ бÑĸ", "Ġб Ñĸ", "Ġuygu lam", "Ġ æĭ", "Ġæ ĭ", "ä½ Ľ", "u cu", "uc u", "d ÃŃ", "Å ĺ", "ئ Ø©", "ê± ¸", "Ù Į", "Ġ ÎłÏģο", "ĠÎł Ïģο", "Ġye rine", "Ġyer ine", "Ġyeri ne", "ĠÑĸн ÑĦоÑĢма", "Ġ å¤ĸ", "Ġå¤ ĸ", "ä» ķ", "н ав", "на в", "a rası", "ar ası", "ara sı", "aras ı", "à¸Ńà¸Ļ à¹Ħลà¸Ļ", "ا شت", "اش ت", "ز ÙĬ", "æ© ĭ", "Ġ ãĤ«", "ĠãĤ «", "èĥ½ åĬĽ", "å¥ Ĺ", "Ġpro h", "Ġpr oh", "Ġп ÑĢава", "ĠпÑĢ ава", "ĠпÑĢав а", "ĠпÑĢа ва", "Ỽ p", "Ġ à¸Ĥà¸Ńà¸ĩ", "Ġà¸Ĥ à¸Ńà¸ĩ", "Ġ ë´", "Ġë ´", "Ġl úc", "Ġ éķ", "Ġé ķ", "ب ÙĪد", "بÙĪ د", "r upa", "ru pa", "rup a", "ا زÙħ", "از Ùħ", "Ġ кан", "Ġк ан", "Ġка н", "ılı m", "ıl ım", "Ġ Ùĩد", "ĠÙĩ د", "ãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢ ĠãĢĢ", "ãĢĢĠ ãĢĢĠãĢĢ", "Ñĭ ваеÑĤ", "Ñĭв аеÑĤ", "Ñĭва еÑĤ", "Ø® اÙĨÙĩ", "خاÙĨ Ùĩ", "Ñĥ кÑĤ", "Ñĥк ÑĤ", "ĠçĻ¾ 度", "ĠnÄĽ co", "е мон", "ем он", "емо н", "Ġà¤ħ प", "Ġ ÎĮ", "ĠÎ Į", "ün ün", "ünü n", "æĸĩ åĮĸ", "ä¹ İ", "ä¸Ĭ çļĦ", "ÙĦ ÙĬÙħ", "ÙĦÙĬ Ùħ", "ĠtÄĽ ch", "ا سب", "اس ب", "âĢĻ ÑĶ", "Ġ Ú¯ÛĮ", "ĠÚ¯ ÛĮ", "Ġ ê·¼", "Ġê· ¼", "Ġtr ẻ", "μÎŃ Î½Î¿", "μÎŃν ο", "ãģĵãģ¨ ãĤĴ", "ìĿ´ ëĤĺ", "åĸ Ħ", "Ġtr ả", "åĪĨ æŀIJ", "Ġ dÄĽl", "Ġd ÄĽl", "ĠdÄĽ l", "Ñĥ Ñģка", "ÑĥÑģ ка", "ÑĥÑģк а", "Ġм ного", "Ġмн ого", "Ġмног о", "à¥Ī र", "μα ÏĦοÏĤ", "μαÏĦο ÏĤ", "Ġm ÃŃsto", "ĠmÃŃ sto", "ĠmÃŃst o", "Ġ ê°ģ", "Ġê° ģ", "Ġп ÑĢог", "ĠпÑĢ ог", "ĠпÑĢо г", "b aÅŁ", "ba ÅŁ", "а йÑĤе", "ай ÑĤе", "айÑĤ е", "Ġc á»ķ", "å¿ ľ", "ï¼ģ Ċ", "ç ı", "Ġbir çok", "Ġ íĺķ", "Ġíĺ ķ", "çµ Į", "ĠEv rop", "ĠÑģ оÑĨÑĸ", "ĠÑģо ÑĨÑĸ", "ä»ĸ çļĦ", "Ġ μÏĢο", "Ġμ ÏĢο", "ĠμÏĢ ο", "å¥ Ī", "Ġ Ú¯ÙĦ", "ĠÚ¯ ÙĦ", "ÙĪ ÙĦØ©", "ÙĪÙĦ Ø©", "æµ İ", "Ġ Ú©ÙĪ", "ĠÚ© ÙĪ", "± ä¹IJ", "ãģĹ ãģı", "ãģĹãģ ı", "çº ³", "ÑģÑĤв енно", "ÑģÑĤвен но", "éĽ ¢", "ा .", "Ġgerçek leÅŁtir", "ĠgerçekleÅŁ tir", "Ġ kır", "Ġk ır", "Ġkı r", "ì ³", "Ġг оÑģп", "å¹ ķ", "ìĦ ¼", "» .Ċ", "». Ċ", "к ÑĥÑĢ", "кÑĥ ÑĢ", "Ġ رÛĮ", "Ġر ÛĮ", "æĽ ¾", "ÙĪ رÙĬ", "ÙĪر ÙĬ", "лекÑģ анд", "ص Ùģ", "Ġc ảnh", "Ġcả nh", "å± Ĥ", "ãĤ Ĩ", "Ġ تس", "Ġت س", "ì° ½", "기 를", "Ġ à¹Ģà¸Ħ", "Ġà¹Ģ à¸Ħ", "Ġà¹ĢภĦ", "çŁ Ń", "Ġ ÑģÑĤÑĢо", "ĠÑģ ÑĤÑĢо", "ĠÑģÑĤ ÑĢо", "ĠÑģÑĤÑĢ о", "ĠÏĥ ÏĦιÏĤ", "ĠÏĥÏĦι ÏĤ", "à¥įय व", "Ġع ÙĦÙħ", "ĠعÙĦ Ùħ", "ĠÑģ иÑĤÑĥа", "ĠÑī одо", "ĠÑīо до", "åIJ Ľ", "Ùħ س", "ĠоÑĤ кÑĢÑĭ", "ĠоÑĤк ÑĢÑĭ", "Ġsp oj", "Ġspo j", "ĠÄij Äĥng", "Ġs avaÅŁ", "Ġsav aÅŁ", "ี ร", "sk ém", "ské m", "Ġ è¡Į", "Ġè¡ Į", "é ¹", "Ġ ÙĬÙħÙĥÙĨ", "ĠÙĬÙħ ÙĥÙĨ", "о вано", "ов ано", "ова но", "ован о", "Ġп ÑĢавилÑĮ", "ĠпÑĢав илÑĮ", "ĠпÑĢави лÑĮ", "ĠпÑĢавил ÑĮ", "Ġchiế c", "èĪ ¹", "éĵ ¶", "ĠоÑĤ д", "Ġ ìĿĢ", "ĠìĿ Ģ", "íħ Ķ", "Ġ Nej", "ĠN ej", "ĠNe j", "о не", "он е", "Ġk ız", "Ġkı z", "олог иÑĩеÑģ", "Ġ кÑĢаÑĹ", "ĠкÑĢа ÑĹ", "à¸ļ à¸Ńล", "æ¥ ¼", "Ġت ÙħاÙħ", "ĠتÙħ اÙħ", "Ġب ÛĮÙħ", "ĠبÛĮ Ùħ", "ĠÑģ Ñĥб", "ĠÑģÑĥ б", "v ý", "Ñģ кие", "Ñģк ие", "Ñģки е", "ëĮĢ ë¡ľ", "ëĮ Ģë¡ľ", "???? ????", "abilir siniz", "ан Ñģов", "анÑģ ов", "代 表", "Ġ매 매", "олог ÑĸÑĩ", "μ αν", "μα ν", "ак Ñģим", "акÑģ им", "ãĤ¤ ãĥ«", "Ġt ải", "Ġtả i", "Ùħ ÙĪ", "å® Ĺ", "n em", "ne m", "Ġkho ản", "Ġ паÑĤ", "Ġп аÑĤ", "Ġпа ÑĤ", "ан ÑĤа", "анÑĤ а", "Ġпом оÑī", "Ġ vod", "Ġv od", "Ġvo d", "Ġkay nak", "Ġkayn ak", "Ïĥ ÏĨ", "à¥Ĥ त", "du ÄŁ", "а ÑĤиÑģÑı", "аÑĤи ÑģÑı", "Ġ ç¥ŀ", "Ġç¥ ŀ", "ĠÑģ лова", "ĠÑģл ова", "ĠÑģлов а", "ĠÑģло ва", "ÑĢÑĥ кÑĤÑĥ", "ÑĢÑĥк ÑĤÑĥ", "ÑĢÑĥкÑĤ Ñĥ", "ĠmÄĽ sÃŃ", "Ùı Ùħ", "зна Ñĩа", "знаÑĩ а", "Ġ èī", "Ġè ī", "åѦ çĶŁ", "æ´ ¥", "Ùİ ÙĬ", "è§ Ī", "Ġ å®ī", "Ġå® ī", "Ġgör Ã¼ÅŁ", "ál nÄĽ", "áln ÄĽ", "ĠëĶ° ëĿ¼", "ĠÙħ ÙĪجÙĪد", "ĠÙħÙĪج ÙĪد", "ĠÄij ứ", "ĠÄijá» ©", "ĠçalÄ±ÅŁ malar", "ĠçalÄ±ÅŁma lar", "ĠÑı киÑħ", "ĠÑıк иÑħ", "Ġاج تÙħاع", "μ εν", "με ν", "èİ ī", "ç§ ¯", "ì¶ ķ", "à¥į शन", "à¥įश न", "Ġx ét", "Ġв ÑĤоÑĢ", "ĠвÑĤ оÑĢ", "çİ ©", "Âł ÐĿ", "ÑĪ ие", "ÑĪи е", "о ÑĢи", "оÑĢ и", "Ø£ س", "Ġthu á»ijc", "ëĭĪ ê¹Į", "ë ķĮ", "ÑĢ Ñĥп", "ÑĢÑĥ п", "Ñģ ÑıÑĤ", "ÑģÑı ÑĤ", "з Ñĭ", "ĠÑģ меÑĢ", "ĠÑģм еÑĢ", "Ġv yb", "Ġvy b", "ĠìĿ´ ìĥģ", "à¤ļ न", "Ġgel di", "Ġgeld i", "Û± Û°", "Û±Û °", "ικ Ïİν", "ĠÄIJ ức", "Ġд оÑģÑĤаÑĤ", "ĠдоÑģÑĤ аÑĤ", "Ġö nc", "Ġön c", "è¦ ª", "Ġ adı", "Ġa dı", "Ġad ı", "un ca", "unc a", "ĠاÙĦ تر", "ĠاÙĦت ر", "çķ ¶", "ĠФ едеÑĢа", "ĠФед еÑĢа", "лÑı ÑİÑĤÑģÑı", "лÑıÑİÑĤ ÑģÑı", "ĠÙĥ اÙĨت", "ĠÙĥاÙĨ ت", "æİ ¢", "Ġ Ñĥб", "ĠÑĥ б", "Ġ κο", "Ġκ ο", "ाà¤ĩ à¤Ł", "з н", "Ġm ôi", "Ġmô i", "Ġ ãĤµ", "ĠãĤ µ", "Ġна вÑĸ", "Ġнав Ñĸ", "ç» ¼åIJĪ", "Ġмин ÑĥÑĤ", "Ġми нÑĥÑĤ", "ĠминÑĥ ÑĤ", "d ık", "dı k", "ÑĢ Ñĥд", "ÑĢÑĥ д", "åľ ĸ", "ê° ¤", "ĠÄijo Ãłn", "è ¤", "à¥į वर", "à¥įव र", "ĠÃľn iversit", "а но", "ан о", "éĽ ¨", "ĠvÅ¡ech ny", "Ġëĭ¤ ìĿĮ", "ĠC umhur", "ĠCum hur", "Ġм Ñĥз", "ĠмÑĥ з", "a ÅŁtır", "aÅŁ tır", "Ġ ê±°ëŀĺ", "Ġê±° ëŀĺ", "Ġ é¡", "Ġé ¡", "žit ÃŃ", "ži tÃŃ", "Ġ à¸Ł", "ĠภŁ", "Ġthu ế", "Ġм Ñĥж", "ĠмÑĥ ж", "ĠÎij ν", "Ġد ÙĪÙħ", "ĠدÙĪ Ùħ", "ĠÑģ ин", "ĠÑģи н", "Ġ ÏīÏĤ", "ĠÏī ÏĤ", "m eler", "me ler", "mel er", "Ġ poÄį", "Ġp oÄį", "Ġpo Äį", "Ġколи Ñĩе", "ĠколиÑĩ е", "ĠK Äį", "è³ ½", "ĠоÑģ Ñĸб", "åı ¥", "ĠB öl", "à¸ĺ รรม", "Ġc ạnh", "å° ĩ", "Ġ ноÑģ", "Ġн оÑģ", "Ġно Ñģ", "èĦ ¸", "Ġgel ir", "о ÑĢон", "оÑĢ он", "оÑĢо н", "à¥įर à¤Ń", "ç» ĩ", "ุ à¹ī", "ाम ल", "Ġc âu", "Ġcâ u", "Ñij ÑĤ", "Ġ :|", "Ġ: |", "ãĤĮ ãģ¦", "Ġpos led", "Ġpo sled", "ãĤ¹ ãĥĨ", "ÑĸлÑĮ ÑĪ", "ен ÑĤÑĭ", "енÑĤ Ñĭ", "Ø® دÙħ", "Ġباش گاÙĩ", "Ġth Æ°", "á vánÃŃ", "áv ánÃŃ", "ává nÃŃ", "ëĬ IJ", "ĠØ£ ØŃ", "ر اد", "را د", "ĠبسÛĮ ار", "åĪ° äºĨ", "\" ;\"", "\"; \"", "å° İ", "Ġ ör", "Ġö r", "à¸Ĭ าà¸ķ", "g enus", "gen us", "Ġya kın", "Ġyak ın", "Ġ ÃŃt", "ĠÃŃ t", "reg num", "regn um", "Ġf iyat", "Ġfi yat", "н ÑĸÑħ", "нÑĸ Ñħ", "åľ° æĸ¹", "Ġbil gi", "Ġbilg i", "к ам", "ка м", "Ġs pol", "Ġsp ol", "Ġspo l", "ائ ÙĬ", "Ġ ÙĬÙĨ", "ĠÙĬ ÙĨ", "า หาร", "าห าร", "Ġب Ú¯", "é ĺħ", "éĺ ħ", "ĠاÙĦ شر", "ĠاÙĦØ´ ر", " ģ", "ĠÑĸн ÑĪиÑħ", "ĠÑĸнÑĪ иÑħ", "Ġtr ạng", "çģ £", "Ġc á»±c", "к ан", "ка н", "èĭ ı", "à Ķ", "Ġl á»Ŀi", "Ġlá» Ŀi", "Ñı Ñĩ", "Ġ ÙĪØŃ", "ĠÙĪ ØŃ", "ìĪ ľ", "Å ¸", "Ġв оÑģп", "Ġво Ñģп", "ĠвоÑģ п", "ì¡ Į", "Äį nÃŃch", "ÄįnÃŃ ch", "Ø® رÙī", "خر Ùī", "ائ ÙĬØ©", "ائÙĬ Ø©", "Ġsu ất", "æĩ ī", "ا ØŃÛĮ", "اØŃ ÛĮ", "Ġn áz", "Ġná z", "è¿Ļ ç§į", "Ġзаб езпеÑĩ", "Ġ ЧеÑĢ", "ĠЧ еÑĢ", "Ġзд ÑĸйÑģ", "åı ¦", "æĭ ¬", "à¥ģ ष", "à¥ģठ·", "μ ÏĨ", "ëĥ IJ", "Ðķ Ñģли", "é ¬", "Ġ íĥľ", "Ġíĥ ľ", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "Ġм л", "å´ İ", "Ùģ ع", "Ġ ÙĤدر", "ĠÙĤ در", "ĠÙĤد ر", "Ġv á»ijn", "å¦ ¹", "Ġ ÐĿаÑģ", "ĠÐĿ аÑģ", "ĠÐĿа Ñģ", "à¥į फ", "à¥įठ«", "ãĤ¸ ãĥ£", "Ġm ı", "ен Ñģ", "б Ñĥд", "бÑĥ д", "ĠØŃ ØªÙī", "Ġ ì²´", "Ġì² ´", "ĠÑĸ ÑģÑĤоÑĢ", "ĠÑĸÑģ ÑĤоÑĢ", "Ġgi ấy", "γ οÏģ", "γο Ïģ", "ëIJĺ ìĸ´", "Ġ íĤ", "Ġí Ĥ", "ĠÐŀд на", "ĠÙĨ ÙħÙĪد", "ĠÙĨÙħ ÙĪد", "Ġвип ад", "ĠìŀIJ ìĭł", "Ġj ste", "Ġjs te", "Ġ ëĵ±ë¡Ŀ", "Ġëĵ± ë¡Ŀ", "ek ten", "ekt en", "ekte n", "ĠÑĢ еÑĩ", "ĠÑĢе Ñĩ", "r odnÃŃ", "rod nÃŃ", "س تر", "ست ر", "ı t", "ä¹ħ ä¹ħ", "ĠØ® ÙĦاÙĦ", "ĠØ®ÙĦ اÙĦ", "Ġ ç¦", "Ġç ¦", "u luk", "ul uk", "ulu k", "l enen", "le nen", "len en", "lene n", "i lip", "il ip", "ili p", "è´ ¢", "Ġà¤ħ à¤ķ", "ĠY ıl", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "Ġ à¤Ŀ", "ĠठĿ", "ĠB ình", "Ġol muÅŁ", "Ġolm uÅŁ", "اÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©", "اÙĦØ¥ ÙĨجÙĦÙĬزÙĬØ©", "м енно", "мен но", "aln ız", "Ġ شرÙĥØ©", "Ġشر ÙĥØ©", "Ġس ÙĨØ©", "ĠسÙĨ Ø©", "è´ Ł", "ä½ľ åĵģ", "Ġ ìķ½", "Ġìķ ½", "ĠдÑĢÑĥг иÑħ", "ĠbaÄŁ lantı", "о дÑĥ", "од Ñĥ", "çļĦ æĺ¯", "ั à¸Ļà¸Ķ", "ัà¸Ļ à¸Ķ", "ĠкоÑĤоÑĢ ÑĭÑħ", "ĠاÙĦ ÙĪÙĦ", "ĠاÙĦÙĪ ÙĦ", "ê¸Ģ ìĥģìľĦ", "ĠÏĢ εÏģ", "ĠÏĢε Ïģ", "리 ìķĦ", "i bar", "ib ar", "iba r", "Ġ èĥ", "Ġè ĥ", "ãģŁ ãģĦ", "á j", "ĠìľĦ íķ´", "? âĢľĊĊ", "?âĢľ ĊĊ", "Ġ íİĺ", "Ġíİ ĺ", "Ġ ней", "Ġн ей", "Ġне й", "ĠÐĹ Ð°Ðº", "ĠÐĹа к", "Ġ ÐĴÑĸд", "ĠÐĴ Ñĸд", "е лÑĸ", "ел Ñĸ", "è¯ ¾", "åī ¯", "m adan", "ma dan", "mad an", "mada n", "æľ «", "ĠÏĢ ÏģÏĮ", "Ġп ÑģиÑħ", "Ġ ÑĤÑĸ", "ĠÑĤ Ñĸ", "Ùĥ ات", "Ġvys ok", "ê´Ģ 리", "ül tür", "ült ür", "Ġ à¹Ģà¸Ń", "Ġà¹Ģ à¸Ń", "Ġà¹ĢภŃ", "Ġ íķ©", "Ġíķ ©", "çĿ £", "Ġ ÑĢиÑģ", "ĠÑĢ иÑģ", "е ÑĢÑĮ", "еÑĢ ÑĮ", "ĠÚ© ÙĦÛĮ", "ĠÚ©ÙĦ ÛĮ", "Ġ ãĥŀ", "Ġãĥ ŀ", "Ġph ÃŃa", "ĠphÃŃ a", "å «", "ا Ú¯", "Ġ é¢", "Ġé ¢", "ĠÙĨ Ùģر", "ĠÙĨÙģ ر", "Ġ جاÙĨ", "Ġج اÙĨ", "Ġجا ÙĨ", "Ġ yas", "Ġy as", "Ġya s", "ж ениÑı", "же ниÑı", "жен иÑı", "ĠлÑĥÑĩ ÑĪе", "Ġ çº", "Ġç º", "Ġ мон", "Ġм он", "Ġмо н", "Ġت Ø®", "Ġ Ø´ÛĮ", "ĠØ´ ÛĮ", "Ġн екоÑĤоÑĢ", "Ġнек оÑĤоÑĢ", "алÑĮ нÑĭе", "Ġob chod", "Ġíķ¨ ê»ĺ", "Ġr iêng", "ãģķ ãĤĮãĤĭ", "ãģķãĤĮ ãĤĭ", "о кÑĥ", "ок Ñĥ", "ĠС ШÐIJ", "ë§ ģ", "Ġ Nếu", "ĠN ếu", "ĠA ÄŁ", "Ġд веÑĢ", "Ġдв еÑĢ", "Ġдве ÑĢ", "à¥ĭ ष", "Ġkh iến", "Ġkhi ến", "н его", "не го", "ì± ħ", "ั à¸ķร", "ัà¸ķ ร", "m alı", "ma lı", "mal ı", "Ġ ÙĬا", "ĠÙĬ ا", "ç§ij æĬĢ", "ื à¸Ļ", "ห มาย", "หม าย", "Ġ خص", "ĠØ® ص", "åĨ ľ", "ÃŃ me", "ÃŃm e", "ĠÑįÑĤ ой", "ĠÑįÑĤо й", "Ġ ìĹħ", "ĠìĹ ħ", "Ġ ä¹", "ä¼ ¯", "' ´", "Ùħ ÙĬÙĦ", "ÙħÙĬ ÙĦ", "à¸Ń à¸ĩà¸Ħ", "à¸Ńà¸ĩ à¸Ħ", "k ová", "ko vá", "kov á", "è¿Ļ ä¹Ī", "ãĢĤ æĪij", "ìĹIJ ìĦľëĬĶ", "ìĹIJìĦľ ëĬĶ", "Ġ ìļ©", "Ġìļ ©", "ë¹Ħ ìĬ¤", "Ġ ì¦Ŀ", "Ġì¦ Ŀ", "IT TE", "ITT E", "Ġ모 ëĵł", "ĠspoleÄį nosti", "ĠspoleÄįnost i", "Ġв ик", "Ġви к", "Ġt ÅĻÃŃ", "ĠtÅĻ ÃŃ", "é ³", "Ġ Ø®ÛĮ", "ĠØ® ÛĮ", "Ġp ož", "Ġpo ž", "Ġиме еÑĤ", "Ġd ÄĽt", "ĠdÄĽ t", "ĠÙħد ÙĦ", "Ġ мо", "Ġм о", "åį ı", "e nÃŃm", "en ÃŃm", "enÃŃ m", "é ī", "ا ظ", "Ø§Ø ¸", "Ġ teÅŁ", "Ġt eÅŁ", "Ġte ÅŁ", "Ġ veÅĻej", "Ġve ÅĻej", "L IC", "LI C", "ì§Ģ ëĬĶ", "Ñĭ ваÑİÑĤ", "Ñĭв аÑİÑĤ", "Ñĭва ÑİÑĤ", "ĠоÑĢг анÑĸ", "ĠоÑĢган Ñĸ", "nÃŃ mi", "nÃŃm i", "θ ÎŃ", "ãĤ¯ ãĥ©", "ãĥ¼ ãĥ³", "ãĥ¼ãĥ ³", "ли ÑģÑı", "i mdi", "im di", "imd i", "æ Ĩ", "ïº İ", "Ġìļ´ ìĺģ", "κ αν", "κα ν", "Ġ ë³µ", "Ġë³ µ", "Ġ ÐĨн", "ĠÐĨ н", "p lication", "pl ication", "t ah", "ta h", "Ġ ÐIJв", "ĠÐIJ в", "Ġc á»Ļng", "алÑĮ ноÑĹ", "алÑĮно ÑĹ", "Ġد ÙĪرÙĩ", "ĠدÙĪ رÙĩ", "ĠدÙĪر Ùĩ", "à¥įर य", "Ġ Ø®ÙĪ", "ĠØ® ÙĪ", "Ġв ÑĢа", "Ø¥ ÙĨ", "èĤ ī", "Ġo yn", "Ġoy n", "ĠT Æ°", "ĠÙĩ ÙħاÙĨ", "ĠÙĩÙħ اÙĨ", "ĠбÑĸлÑĮ ÑĪе", "ĠбÑĸлÑĮÑĪ е", "æĮ ¯", "ا ÙħØ©", "اÙħ Ø©", "å º«", "åº «", "Ġ ÑĢеж", "ĠÑĢ еж", "ĠÑĢе ж", "Ġدار ÙĨد", "ÑĢ ий", "ÑĢи й", "Ġ æĮ", "Ġæ Į", "Ġson uç", "Ġsonu ç", "Ġt ả", "ั à¸ĩà¸Ħ", "ัà¸ĩ à¸Ħ", "ë° Ľ", "Ġ мом", "Ġм ом", "Ġмо м", "ви Ñĩай", "виÑĩ ай", ". à¸Ħ", "Ġ à¤Ĩà¤Ī", "Ġà¤Ĩ à¤Ī", "åģ ĩ", "Ġpos kyt", "Ġpo skyt", "ĠÑģ Ñĥп", "ĠÑģÑĥ п", "ıyor du", "а ле", "ал е", "и ÑĨ", "Ġ θÎŃ", "Ġθ ÎŃ", "ãĤĩ ãģĨ", "ĠÑģв ой", "ĠÑģво й", "ม à¸Ļ", "Ġn ữa", "Ġnữ a", "v oÅĻ", "vo ÅĻ", "ا سÙĬ", "اس ÙĬ", "éĴ ±", "ãģĹ ãģ¦ãģĦãģŁ", "ãģĹãģ¦ ãģĦãģŁ", "ãģĹãģ¦ãģĦ ãģŁ", "ĠÄij ầy", "ا ÙĬر", "اÙĬ ر", "Ġar aÅŁtır", "Ġara ÅŁtır", "ì £", "ãģ¨ ãģ¯", "ĠÑģ поÑĢ", "ĠÑģп оÑĢ", "Ġê° Ģìŀ¥", "Ġê°Ģ ìŀ¥", "è¼ ī", "âĸ ¡", "Ġ ìĻĦ", "ĠìĻ Ħ", "оÑĢ аÑı", "оÑĢа Ñı", "Ïģ εί", "Ïģε ί", "ĠÑį ÑĤа", "ĠÑįÑĤ а", "ë©´ ìłģ", "ìĿ´ ìĬ¤", "ä½ ³", "æĻ ļ", "Ġk val", "Ġkv al", "Ġn á»ķi", "ÑĤ ами", "ÑĤа ми", "Ġпол ÑĸÑĤи", "ĠполÑĸ ÑĤи", "Ġ Ä°ng", "ĠÄ° ng", "ĠÄ°n g", "нÑĸ ÑģÑĤÑİ", "нÑĸÑģÑĤ Ñİ", "Ġ à¹Ģà¸ģ", "Ġà¹Ģ à¸ģ", "Ġà¹Ģภģ", "Ġ 민", "Ġë ¯¼", "è Ķ", "Ïģ ία", "Ïģί α", "æİ Ī", "Ġ çĤ", "Ġç Ĥ", "ĠÙĨÙħ اÛĮ", "Ġ ìŀ¡", "Ġìŀ ¡", "æŀ ¶", "اب ÙĤ", "Ñģ он", "Ñģо н", "ен ного", "енно го", "ĠÙħ ÛĮÙĦÛĮ", "ĠÙħÛĮ ÙĦÛĮ", "ĠÙħÛĮÙĦ ÛĮ", "Ġk urum", "Ġkur um", "Ġku rum", "Ġkuru m", "à¹Į ส", "Ġ ì´Ŀ", "Ġì ´Ŀ", "Ġì´ Ŀ", "ĠnÄĽk olik", "ĠnÄĽkol ik", "Ġ ÙĢ", "ĠÙ Ģ", "ĠзаÑģÑĤ оÑģ", "à¸Ķ à¸Ļ", "ÙĨ داÙĨ", "ÙĨد اÙĨ", "ÙĨدا ÙĨ", "ĠJ ap", "ĠJa p", "éĥ ¡", "à¥į à¤Ń", "à¥įठŃ", "Ġ à¹Ģà¸Ĭ", "Ġà¹Ģ à¸Ĭ", "Ġà¹ĢภĬ", "Ġ âĢ«", "ĠâĢ «", "é£ ŀ", "o vatel", "ov atel", "ova tel", "ovat el", "ĠÑĩа ÑģÑĤÑĮ", "ĠÑĩаÑģ ÑĤÑĮ", "ĠÑĩаÑģÑĤ ÑĮ", "Ġb á»ķ", "ãĤ¯ ãĥª", "ิ à¹Į", "Ġвид е", "Ġви де", "v ail", "va il", "Ì ī", "ÄŁ inde", "ÄŁi nde", "ÄŁin de", "ãģ¨ ãĤĤ", "âĢĮÚ© ÙĨد", "âĢĮÚ©ÙĨ د", "Ġ ëħĦ", "Ġëħ Ħ", "Ġ اÙĤتص", "ĠاÙĤ تص", "ï½ Ĺ", "Ïģ ιÏĥ", "Ïģι Ïĥ", "з д", "èĻ ½", "Ġth oại", "Ġtho ại", "Ġ ÙĪز", "ĠÙĪ ز", "Ġ mÃŃt", "Ġm ÃŃt", "ĠmÃŃ t", "ĠÑħ олод", "ĠÑħол од", "Ġ кÑĥп", "Ġк Ñĥп", "ĠкÑĥ п", "а ниÑħ", "ан иÑħ", "ани Ñħ", "Ġnh ìn", "ãģĭ ãģª", "Ġ Ðļом", "ĠÐļ ом", "ĠÐļо м", "ÏĦ εÏģ", "ÏĦε Ïģ", "ï¼Į åıª", "Ġol up", "Ġhá» ıi", "ë ij", "ĠnÄĽk ter", "i sÃŃ", "is ÃŃ", "ĠвикоÑĢиÑģÑĤ ов", "ìŀ ¡", "Ġà¤ķ ल", "Ġìľł ìłĢ", "ĠпÑĢ иб", "ĠпÑĢи б", "èĭ ¦", "Ġ мов", "Ġм ов", "Ġмо в", "Ġ หà¸Ļ", "Ġห à¸Ļ", "ëIJĺ ëĬĶ", "о ко", "ок о", "Ġоб еÑģп", "Ġk ez", "Ġke z", "л ÑıÑħ", "лÑı Ñħ", "ĠпÑĢо иÑģ", "Ġпо вин", "Ġпов ин", "ĠÐļ оÑĢ", "ĠÐļо ÑĢ", "ì¼ Ģ", "Ġ Ñģи", "ĠÑģ и", "Ġ ä¹ĭ", "Ġä¹ ĭ", "ĠâĢĶ Ċ", "ÑģÑĥÑĤ ÑģÑĤв", "ç °", "Ġ à¤ł", "Ġठł", "н аÑĤ", "на ÑĤ", "Ġs uy", "Ġsu y", "Ġ ÑģÑĭ", "ĠÑģ Ñĭ", "ĠÙĨ شاÙĨ", "ĠÙĨØ´ اÙĨ", "Ġна пÑĢав", "Ġнап ÑĢав", "ĠÑĨ ÑĮомÑĥ", "æĺ¯ ä¸Ģ", "Ġm üm", "Ġmü m", "ÑĶ мо", "ÑĶм о", "ĠاسÙĦاÙħ ÛĮ", "Ġza manda", "Ġzam anda", "Ġzaman da", "ÙĪ ÙħاÙĨ", "ÙĪÙħ اÙĨ", "ا ÙĦØŃ", "اÙĦ ØŃ", "Å¡t ÄĽnÃŃ", "Å¡tÄĽ nÃŃ", "Ġ Ðļак", "ĠÐļ ак", "ĠÐļа к", "¤ íĶĦ", "¤í ĶĦ", "ĠÙ¾ رد", "Ġپر د", "C ác", "ε ια", "ει α", "Ġ جÙĪ", "Ġج ÙĪ", "ĠÄijo ạn", "Ġà¤ĩ त", "Ġз ан", "Ġза н", "ĠÙħÙĨØ· ÙĤÙĩ", "ĠÙħ عÙĦ", "ĠÙħع ÙĦ", "Ġdo kon", "Ġdok on", "åIJ ¸", "ic kou", "ick ou", "å° ģ", "Ġк иÑģ", "Ġки Ñģ", "ัà¸ĩ หว", "i species", "is pecies", "isp ecies", "Ġнап ÑĢÑı", "æº ĸ", "Ġà¤ľ ल", "à¹Ģ à¸ī", "à¹Ģภī", "L AR", "LA R", "ĠÑĥÑģлов иÑı", "ĠWiki species", "ĠWik ispecies", "ระ à¸Ķ", "Ġ mey", "Ġm ey", "Ġme y", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "à¹ĩ à¸Ī", "å¾ Ĵ", "t ach", "ta ch", "u muz", "um uz", "umu z", "κ η", "à Ĭ", "Ġ ün", "Ġü n", "ĠB ITTE", "ĠBIT TE", "ĠÙħ ربع", "ĠÙħر بع", "ĠÙħرب ع", "ãĤ· ãĥ¥", "िस à¤ķ", "Ø· ÙĪر", "Ġв оÑģ", "Ġво Ñģ", "ï¾ Ł", "Ġy ayın", "Ġyay ın", "ãģĭ ãĤĬ", "л иÑı", "ли Ñı", "Ġп ÑĢин", "ĠпÑĢ ин", "ĠпÑĢи н", "ij ng", "ĠÙĨ Ø®", "Ġl ze", "Ġlz e", "à¥įष ण", "Ġ бо", "Ġб о", "Ġ ê¸Ģ", "Ġê¸ Ģ", "Ġgel iÅŁtir", "ĠgeliÅŁ tir", "à¸Ľà¸£à¸° à¸Ĭ", "å½ ¡", "Ġ ãĤª", "ĠãĤ ª", "ãģĪ ãģ¦", "н ÑĥÑĤÑĮ", "нÑĥ ÑĤÑĮ", "нÑĥÑĤ ÑĮ", "Ġ ç½", "Ġç ½", "Ġ маг", "Ġм аг", "Ġма г", "ãģ« ãģ¤", "н оÑģÑĤей", "ноÑģÑĤ ей", "Ġ ÙĦÙĬ", "ĠÙĦ ÙĬ", "æĢ ª", "ÑıÑĤ ÑģÑı", "ภij", "िय म", "Ġ ãĢİ", "ĠãĢ İ", "ÑĢ ÑĮ", "Ġm ạng", "t ım", "tı m", "ĠпеÑĢи од", "о гÑĥ", "ог Ñĥ", "ĠкоÑĤоÑĢ аÑı", "ĠкоÑĤ оÑĢаÑı", "리 ê°Ģ", "Ġãħ ¡", "Ġج اÛĮ", "Ġجا ÛĮ", "ĠпоÑĤ ÑĢÑĸб", "Å¡ en", "Å¡e n", "à¸Ń ะ", "ب ع", "ØŁ Ċ", "Ġ ë°©ë²ķ", "Ġë°© ë²ķ", "Ġ гоÑĢод", "Ġг оÑĢод", "ĠгоÑĢ од", "Ġ Ðĺн", "ĠÐĺ н", "Ġ оказ", "Ġо каз", "Ġок аз", "ر ÙĪز", "رÙĪ ز", "Ġili ÅŁk", "ĠiliÅŁ k", "å® £", "f orman", "form an", "for man", "forma n", "a daÅŁ", "ad aÅŁ", "ada ÅŁ", "ÙĬ ÙĦØ©", "ÙĬÙĦ Ø©", "ĠÐļ аÑĢ", "ĠÐļа ÑĢ", "Ġm ất", "æħ ĭ", "м п", "à¹Ĥ à¸Ļ", "ĠØŃÙĤ ÙĪÙĤ", "Ġд нÑı", "ĠëĴ ¤", "ा à¤ķर", "ाà¤ķ र", "ì²ĺ ëŁ¼", "âĢĮ Ø¢", "h angi", "han gi", "hang i", "è¡Į æĶ¿", "al iyet", "ali yet", "Ġ ì²ľ", "Ġì² ľ", "ĠY ap", "ĠYa p", "à¹Ĥ รà¸ĩ", "à¹Ĥร à¸ĩ", "ì§Ģ ëħ¸", "Ùİ Ùij", "Îij ÎĻ", "á na", "án a", "an dır", "and ır", "ระ à¸ļà¸ļ", "ระà¸ļ à¸ļ", "oÄŁ lu", "า à¸Īะ", "าà¸Ī ะ", "ẩ y", "ا ÙĪÙĦ", "اÙĪ ÙĦ", "ĠмаÑĤ еÑĢÑĸ", "ÎŁ ÎĿ", "ÎŁÎ Ŀ", "Ġin formace", "Ġinform ace", "ت ع", "à¸ļ à¸Ļ", "ĠÄĮesk é", "ĠÄĮes ké", "Ġte mel", "Ġtem el", ":::::::::::::::: ::::::::::::::::", "Ġ chia", "Ġch ia", "Ġchi a", "- Ñģ", "н еÑĢг", "не ÑĢг", "неÑĢ г", "Ġì° ¾", "ÑĢ ид", "ÑĢи д", "л оÑģÑĮ", "ло ÑģÑĮ", "ز ÙĦ", "ê°Ģ ëĬĶ", "a né", "an é", "Ġнав ÑĸÑĤÑĮ", "ĠнавÑĸ ÑĤÑĮ", "ä¸ĵ ä¸ļ", "Ġ 경기", "Ġê²½ 기", "Ġp ÅĻev", "ĠpÅĻ ev", "ĠpÅĻe v", "е ÑĤи", "еÑĤ и", "Ġ íĶĮ", "ĠíĶ Į", "н ÑıÑĤ", "нÑı ÑĤ", "à¥ģ श", "à¥ģठ¶", "лÑİ Ð´", "в иÑī", "ви Ñī", "å° ¾", "çļĦ äºĭ", "Ġ ëIJľ", "Ġë IJľ", "ĠëIJ ľ", "ر ÙĪÙģ", "رÙĪ Ùģ", "Ġ 女", "Ġå¥ ³", "κ ή", "Ġ Tuy", "ĠT uy", "ĠTu y", "Ġê²ĥ ìĿĦ", "Ġb unu", "Ġbu nu", "Ġbun u", "ĠÑĢаз лиÑĩ", "ĠD ün", "ãĤŃ ãĥ£", "ÑĢ ÑĥÑģ", "ÑĢÑĥ Ñģ", "Ġ мм", "Ġм м", "l oven", "lo ven", "love n", "lov en", "Ġot ev", "n oloj", "ES Ä°", "ü p", "Ġ èĤ", "Ġè Ĥ", "ικ ÏĮÏĤ", "ικÏĮ ÏĤ", "ض اء", "ضا Ø¡", "Ġ пеÑĩ", "Ġп еÑĩ", "ÅĻÃŃ klad", "ãģĵ ãĤį", "Å¡ tÃŃ", "Å¡t ÃŃ", "Ġبر Ú¯", "ãģĮ ãģĤãĤĭ", "Ñĸ ÑģÑĤ", "ÑĸÑģ ÑĤ", "à¥ī à¤ķ", "ÏĢ η", "ĠاÙĦÙħ ست", "ĠاÙĦÙħس ت", "Ġз ай", "Ġза й", "Ġch Æ°Æ¡ng", "о ÑĤÑĥ", "оÑĤ Ñĥ", "Ġ Сам", "ĠС ам", "Å¡ et", "Å¡e t", "ĠìŀĪ ìĹĪ", "ĠÙģ ار", "Ñĸ он", "ãĥĹ ãĥŃ", "Ġnh iá»ĩt", "in izi", "ini zi", "iniz i", "Ġc ož", "Ġco ž", "Ġà¤Ĩ न", "Ġsyst ém", "ر ÙĪع", "رÙĪ ع", "a yet", "ay et", "aye t", "ĠÙģر ÙĩÙĨÚ¯", "Ġ è¶", "Ġè ¶", "èģ ·", "è§Ĥ çľĭ", "н ок", "но к", "à¸IJ าà¸Ļ", "êµIJ ìľ¡", "k la", "kl a", "ãĤģ ãģ¦", "Îķ ÎĻ", "åĿ Ĺ", "Ġsk uteÄį", "à¥Ĥ à¤ľ", "ãģij ãģ¦", "N GC", "NG C", "Ġ åĢ", "Ġå Ģ", "ĠÑĢоз п", "nÃŃ ků", "nÃŃk ů", "ãĥ³ ãĤ¹", "ĠÐĴ еÑĢ", "Ġyüz de", "Ġ미 êµŃ", "ĠÙħ Ùī", "д еÑĢ", "де ÑĢ", "а ва", "ав а", "Ġmerk ez", "į ng", "Ġ ìĤ¼", "ĠìĤ ¼", "ĠÑĢоб оÑĤи", "ĠÑĢобоÑĤ и", "Ġ нÑĮого", "Ġн ÑĮого", "Ġ економ", "Ġе коном", "Ġек оном", "ĠÑĩелов ека", "ĠÑĩеловек а", "Ġ à¸ŀระ", "Ġà¸ŀ ระ", "Ġà¸ŀร ะ", "ãĥ Ĵ", "ãģ£ ãģ¦ãģĦ", "ãģ£ãģ¦ ãģĦ", "ä¼ Ĺ", "ĠпÑĢод ÑĥкÑĤ", "ĠпÑĢодÑĥк ÑĤ", "Ġy anı", "Ġyan ı", "à¥Ģव न", "Ġc áºŃp", "ĠAv rupa", "ा à¤Ń", "ाठŃ", "ĠìłĦ ìļ©", "æķ £", "ĠìľĦ íķľ", "Ñħод иÑĤÑĮ", "ÑħодиÑĤ ÑĮ", "Ġsın ır", "ü cret", "üc ret", "s uz", "su z", "æ¨ Ĥ", "Ġ ì°½", "Ġì° ½", "Ïģ ίοÏħ", "Ïģί οÏħ", "åĪ ļ", "Ø® ÙĦ", "ëłĩ ê²Į", "ج د", "Ġμ αÏĤ", "Ġμα ÏĤ", "áºŃ m", "k ara", "ka ra", "kar a", "ãĤ« ãĥ¼", "Ġkter ou", "ìĽ ¨", "ÑĦи ÑĨи", "oÄŁ raf", "Ġна пÑĢи", "Ġнап ÑĢи", "ãģij ãģ©", "Ġ éļ", "Ġé ļ", "ت باÙĦ", "تب اÙĦ", "ëŁ ½", "ìĶ ¨", "íĮĮ ìĿ¼", "Ïĩ α", "Ġuz ak", "Ġd òng", "Ġг олоÑģ", "Ġгол оÑģ", "Ïĥ ÏĦή", "ÏĥÏĦ ή", "ι λ", "Ø· Ùģ", "Ġê·¸ ëħĢ", "ãĤ¿ ãĤ¤", "ا ÙĨÚ¯", "اÙĨ Ú¯", "i nou", "in ou", "ino u", "л он", "ло н", "à¹ĩ ม", "Ġब द", "Ġkon usunda", "Ġkonusu nda", "Ġkonus unda", "Ġn âng", "ãģ¾ãģĽ ãĤĵ", "Ñĥ ÑİÑĤÑĮÑģÑı", "ÑĥÑİ ÑĤÑĮÑģÑı", "ÑĥÑİÑĤÑĮ ÑģÑı", "åŁ ¹", "ен ко", "ìł ij", "Ġ ÑĤов", "ĠÑĤ ов", "ĠÑĤо в", "Ġt ÅĻeba", "ĠtÅĻ eba", "ز اÙĨ", "زا ÙĨ", "is yon", "isy on", "Ġ ген", "Ġг ен", "Ġге н", "Ġ Pokud", "ĠP okud", "ĠPo kud", "ĠPok ud", "âĢĮ اÙĨد", "âĢĮاÙĨ د", "Ġг ÑĢÑĥд", "ĠгÑĢÑĥ д", "ĠгÑĢ Ñĥд", "Ġ خرÛĮد", "Ġخر ÛĮد", "λ λα", "λλ α", "Ġp ÅĻÃŃm", "ĠpÅĻ ÃŃm", "ĠpÅĻÃŃ m", "Ġ æ³ķ", "Ġæ³ ķ", "Ġز ÙĨدگÛĮ", "ĠزÙĨد Ú¯ÛĮ", "ạ p", "Ġ íĬ¸", "ĠíĬ ¸", "ĠÄij á»Ļc", "ĠÄijá»Ļ c", "Ġê·¸ ë¦¬ê³ł", "Ġ그리 ê³ł", "н из", "ни з", "Ġ ÙĬÙĤ", "ĠÙĬ ÙĤ", "l aÅŁtır", "la ÅŁtır", "laÅŁ tır", "ĠпÑĢав о", "ĠпÑĢа во", "Ñĥ Ñģк", "ÑĥÑģ к", "å° ½", "Ġप ड", "éĵ ģ", "Ġ ì·¨", "Ġì ·¨", "ĠاÙĦ بÙĬ", "ĠاÙĦب ÙĬ", " ¸", "ิม à¸ŀ", "Ġs vÄĽ", "Ġsv ÄĽ", "Ġб ал", "Ġба л", "Ġm ôn", "Ġmô n", "ĠD ữ", "ĠØ´ دÙĨ", "Ġشد ÙĨ", "Ġ ÙģÙĦ", "ĠÙģ ÙĦ", "Ġv znik", "Ġvz nik", "Ġch ứ", "ĠÑģÑĤ ÑĢÑĥкÑĤÑĥ", "ç¸ £", "ĠH oa", "ĠHo a", "í ĮĢ", "íĮ Ģ", "Ġ ÑĢÑĸÑĪ", "ĠÑĢ ÑĸÑĪ", "Ġвоз дÑĥ", "олÑĮ ÑĪ", "οÏħ με", "ู à¸Ļ", "Ġп ÑĢид", "ĠпÑĢ ид", "ĠпÑĢи д", "il mek", "ilm ek", "ĠاÙĦ ÙĤر", "ĠاÙĦÙĤ ر", "Į ĵ", "Ġ uç", "Ġu ç", "å¨ ĺ", "ec ektir", "ecek tir", "Ġ íħĮ", "Ġí ħĮ", "Ġíħ Į", "Ġ εÏħ", "Ġε Ïħ", "Ġh òa", "Ïģ Ïħ", "ึà¸ģษ า", "ĠÑĤеÑħ нолог", "ú i", "Ġbilg iler", "Ġbilgi ler", "Ġ ÙĤاÙĦ", "ĠÙĤ اÙĦ", "e dl", "ed l", "z nám", "zn ám", "á ly", "ál y", "åºĶ 该", "алÑĮ ний", "аÑĤ елÑı", "аÑĤе лÑı", "à¸Ļ วà¸Ļ", "à¸Ļว à¸Ļ", "Ġ ÐŁÐ¾Ð»", "ĠÐŁ ол", "ĠÐŁÐ¾ л", "à¸ŀ à¸Ļ", "ç¤ ¼", "Ġt asar", "Ġta sar", "Ġtas ar", "ĠÑĤ ой", "ĠÑĤо й", "Ġм еÑģÑı", "Ġ иÑģк", "Ġи Ñģк", "ĠиÑģ к", "Ġप द", "γ ή", "ا ختÙĩ", "اخ تÙĩ", "اخت Ùĩ", "è¿Ļ éĩĮ", "Ġch á»īnh", "Ġchá»ī nh", "ĠÙĤ سÙħ", "Ùİ Ùĩ", "er li", "åĽ½ éĻħ", "il iyor", "ili yor", "ĠØ´Ùĩر ستاÙĨ", "Ġve lk", "Ġvel k", "åĽ º", "Ġб ÑĸлÑĮÑĪ", "ĠбÑĸлÑĮ ÑĪ", "ãĥ¼ ãĥĹ", "ãĥ¼ãĥ Ĺ", "æŁ IJ", "ì§ ľ", "ĠÄĮ R", "Ġд ек", "Ġде к", "ر بÛĮ", "رب ÛĮ", "о виÑĩ", "ов иÑĩ", "ови Ñĩ", "Ġkap sam", "Ġkaps am", "ĠÙĦ Ø£", "Ġ анÑĤи", "Ġан ÑĤи", "Ġ ücret", "Ġü cret", "ê² ¬", "о ÑĢож", "оÑĢ ож", "оÑĢо ж", "ÛĮ ÙħÛĮ", "ÛĮÙħ ÛĮ", "è© ķ", "Ġ ë§ŀ", "Ġë§ ŀ", "Ġ ÑĢÑıд", "ĠÑĢ Ñıд", "ĠÑĢÑı д", "ĠÙĩÙħ راÙĩ", "â r", "ا بت", "اب ت", "ĠиÑģполÑĮзов аÑĤÑĮ", "ĠиÑģполÑĮз оваÑĤÑĮ", "к Ñģ", "âī ¡", "Ġo lay", "Ġol ay", "Ġola y", "èį ¯", "Ġo prav", "Ġop rav", "Ġopr av", "Ġدرب ارÙĩ", "Ġ ä¸ŃåĽ½", "Ġä¸Ń åĽ½", "и лÑģÑı", "ил ÑģÑı", "åį «", "ĠاÙĦ است", "ĠاÙĦاس ت", "ÙĪÛĮ ÛĮ", "ÑĢ еÑĪ", "ÑĢе ÑĪ", "Ġ ÙĨس", "ĠÙĨ س", "ãĢĤ åľ¨", "Ġ ÙĦØŃ", "ĠÙĦ ØŃ", "Ġko run", "Ġkor un", "ĠÙģ رد", "ĠÙģر د", "Ġо боÑĢ", "Ġоб оÑĢ", "Ġобо ÑĢ", "е ÑĪÑĮ", "еÑĪ ÑĮ", "Ġpod mÃŃn", "Ġ ë¬¸ìłľ", "Ġ문 ìłľ", "ĠdeÄŁer lendir", "ä¸į åIJĮ", "æ¶ ²", "ा हर", "ाह र", "íļ į", "à¥į à¤ł", "à¥įठł", "и ÑĤиÑģÑı", "иÑĤи ÑģÑı", "ا ÙĦع", "اÙĦ ع", "Ġd vÄĽ", "Ġdv ÄĽ", "ĠпеÑĢ ек", "ĠпеÑĢе к", "Ġ åħĥ", "Ġåħ ĥ", "Ġ aras", "Ġa ras", "Ġar as", "Ġara s", "Ġalt ında", "Ġaltın da", "Ġaltı nda", "Ġв за", "Ġвз а", "æĴ ĥ", "Ġmil yon", "Ġ åѦ", "ĠåŃ ¦", "Ġв аÑĢи", "ĠваÑĢ и", "Ġва ÑĢи", "ĠاÙĦع اÙĦÙħ", "' Ñı", "ÙĪ ÛĮس", "ÙĪÛĮ س", "Ġмож ÑĥÑĤÑĮ", "ãģij ãģŁ", "ìĿ´ ìĹĪëĭ¤", "ìĿ´ìĹĪ ëĭ¤", "ο Ïįν", "οÏį ν", "Ġ éŁ", "Ġé Ł", "Ġpost up", "Ġpo stup", "ü yük", "üy ük", "åĪ Ĭ", "Ġ ÙĤب", "ĠÙĤ ب", "Ġاص ÙĦÛĮ", "ĠاصÙĦ ÛĮ", "ÙĪ Ùī", "Ġrep ublik", "Ġ ÐĻ", "ĠÐ Ļ", "ģ m", "Ġб ел", "ा -", "Ñģ кое", "Ñģк ое", "Ñģко е", "Ġcu á»iji", "è² ·", "ี ยว", "ีย ว", "éĩį è¦ģ", "ู ม", "ĠÑĢозвиÑĤ кÑĥ", "Ġ ë°±", "Ġë° ±", "åĥ ¹", "Ġ åīį", "Ġåī į", "à¹Ħ à¸ĭ", "ãĢĮ âĢ¦âĢ¦", "à¥Į त", "Ú© رد", "کر د", "Ġza ÅĻÃŃzenÃŃ", "ส าร", "Ġle tech", "Ġlet ech", "l emek", "le mek", "lem ek", "leme k", "人 ãģ®", "Ġd ưỡng", "ĠdÆ°á» ¡ng", "ت ÙĤ", "Ġ åĵ", "Ġå ĵ", "åħ »", "Ġ ëıħ", "Ġëı ħ", "Ġ 루", "Ġë £¨", "Ġë£ ¨", "Ø° ÙĦÙĥ", "Ġ ìĿ¼ë³¸", "ĠìĿ¼ 본", "ĠAy rıca", "ĠÙ¾ Úĺ", "is inin", "isi nin", "isin in", "isini n", "Ġìĭ ¶", "Ú¯ ÛĮرÛĮ", "Ú¯ÛĮ رÛĮ", "Ú¯ÛĮر ÛĮ", "خص ص", "³ ç´°", "ĠмаÑĤеÑĢи ал", "k ové", "ko vé", "kov é", "ë§ ī", "ãģķ ãģĽ", "ĠÑĤак ой", "ĠÑĤа кой", "Ġtr áºŃn", "Ġ лиÑĨ", "Ġл иÑĨ", "Ġли ÑĨ", "Ġ åĽĽ", "ĠåĽ Ľ", "Ñĩ Ñĥ", "Ġ æ°´", "Ġæ° ´", "Ġdo lay", "Ġdol ay", "å½ ¹", "ÑĢ ива", "ÑĢи ва", "ÑĢив а", "Ġг ÑĢÑĥпп", "ĠгÑĢÑĥ пп", "ĠгÑĢÑĥп п", "Ġmüm kün", "л ена", "лен а", "ле на", "ëĿ¼ ëĬĶ", "åĪ© çĶ¨", "Ġr ahat", "Ġra hat", "ï¼ıï¼ı ï¼ıï¼ı", "æģ ©", "Ġ íķŃ", "Ġíķ Ń", "Ġ íĴ", "Ġí Ĵ", "Ġ ìĬ¹", "ĠìĬ ¹", "Ġch ân", "Ġ ãĤ¨", "ĠãĤ ¨", "Ġжиз ни", "çĸ ij", "ãĢĤ ä»ĸ", "리 ìĬ¤", "Ñĩ иÑħ", "Ñĩи Ñħ", "Ġ é¦ĸ", "Ġé¦ ĸ", "ÄĽ r", "Ġй омÑĥ", "Ġth áºŃt", "Ġìķ ŀ", "c ih", "ci h", "س ÙĦاÙħ", "سÙĦ اÙħ", "Ġs iyas", "Ġsi yas", "Ġ íĸĪ", "Ġíĸ Ī", "Ġк оÑĪ", "Ġко ÑĪ", "Ïĥ αν", "Ïĥα ν", "ÙĬ اÙĨ", "ÙĬا ÙĨ", "Ġd ö", "ाह त", "о ÑĢод", "оÑĢ од", "оÑĢо д", "о ваÑı", "ов аÑı", "ова Ñı", "ÑĨи оналÑĮ", "ÑĨион алÑĮ", "ائ Ùĩ", "Ġà¤ĸ र", "ĠÄij á»Ŀi", "ĠÄijá» Ŀi", "ä¸į ä¼ļ", "Ùĥ ز", "ี à¸Ħวาม", "ีà¸Ħ วาม", "l ıyor", "lı yor", "à¥ĭ द", "Ġ 충", "Ġì¶ ©", "Ġc á»ij", "à¹Ĥ à¸ķ", "Ġε ÏĢί", "ĠεÏĢ ί", "ĠпÑĢ Ñıм", "æ³ °", "ا ÙĦØ©", "اÙĦ Ø©", "j ÃŃm", "jÃŃ m", "Ġ би", "Ġб и", "Å¡ em", "Å¡e m", "ĠH á»Ļi", "à¸Ħ รà¸ĩ", "à¸Ħร à¸ĩ", "Ġh uyá»ĩn", "Ġhuy á»ĩn", "ç¯ Ģ", "l iÅ¡", "li Å¡", "ĠجÙĩ ت", "ç§ ĭ", "ĠÑĨ ел", "ĠÑĨе л", "Ġ лÑĸÑĤ", "Ġл ÑĸÑĤ", "ĠлÑĸ ÑĤ", "Ġ æ·", "Ġæ ·", "ж Ñĥ", "ãģĪ ãģŁ", "ë´ ī", "Ġ 머", "Ġë¨ ¸", "åł´ åIJĪ", "éĿ ©", "ãĥª ãĥ³", "ег да", "Ġbe nim", "Ġben im", "Ġbeni m", "çĽ Ł", "ãģ® ä¸Ń", "åĿ IJ", "ĠÃľniversit esi", "Ġko ÅŁ", "Ġп ож", "Ġпо ж", "iá»ĩ p", "ĠpÅĻ ij", "ĠpÅĻi j", "ëŀ ¨", "ĠاÙĦ أس", "ĠاÙĦØ£ س", "ár nÃŃ", "iế m", "Ġ èĬ", "Ġè Ĭ", "Ġ δε", "Ġδ ε", "å¨ ±ä¹IJ", "Ġ Æ°u", "Ġ çĦ¡", "ĠçĦ ¡", "Ġг ÑĢи", "ĠгÑĢ и", "Ġпо ÑįÑĤомÑĥ", "ĠÄij óng", "ĠÄijó ng", "ĠÄijón g", "ج اÙĨ", "جا ÙĨ", "Ġngh iên", "Ġnghi ên", "Ġا ÙĦاÙĨ", "ĠاÙĦ اÙĨ", "ÑĪ ей", "ÑĪе й", "à¹ģ รà¸ģ", "ĠÚĨ Ùĩار", "ĠÚĨÙĩ ار", "Ñİ Ñīий", "ÑİÑī ий", "ÏĮ Ïģ", "Ġ رÙħ", "Ġر Ùħ", "ì² ł", "Ġدست گاÙĩ", "Ġ دÛĮد", "Ġد ÛĮد", "ĠدÛĮ د", "ãĥĥãĤ¯ ãĤ¹", "ा मन", "ाम न", "ĠTh Ãłnh", "Ġth ẩm", "Ġc Ãłng", "ĠcÃł ng", "Ġdön Ã¼ÅŁ", "ĠпÑĢи гоÑĤов", "ĠпÑĢиг оÑĤов", "Ġk iÅŁi", "Ġki ÅŁi", "ĠkiÅŁ i", "ØŃ Øª", "Ġ ë²ķ", "Ġë² ķ", "é£ Ľ", "Ġit ibar", "Ġг лав", "Ġгла в", "Ġor tam", "Ġort am", "Ġorta m", "Ġm add", "Ġma dd", "Ġmad d", "Ġ оÑģÑĤав", "Ġо ÑģÑĤав", "ĠоÑģÑĤ ав", "ĠÙģÙĪ تباÙĦ", "ĠÙģÙĪت باÙĦ", "Ġan laÅŁ", "l eyen", "le yen", "ley en", "ç ´Ģ", "ç´ Ģ", "Ġ é£", "Ġé £", "/ lo", "/l o", "Ùħ ÙĪÙĦ", "ÙħÙĪ ÙĦ", "Ġд ÑĥÑħ", "ĠдÑĥ Ñħ", "Ġ ÙĦب", "ĠÙĦ ب", "л ег", "ле г", "Ġgö nder", "Ġgön der", "ÙĬ Ø·", "Ġ สำ", "Ġส ำ", "Ġv ás", "Ġvá s", "ĠÐŁ еÑĤ", "а лоÑģÑı", "ало ÑģÑı", "ì ¿ł", "ì¿ ł", "éĻ ½", "åĸ ®", "èĪ ŀ", "н Ñĥл", "нÑĥ л", "ÄŁ ine", "ÄŁi ne", "ÄŁin e", "Ġ ghi", "Ġg hi", "Ġgh i", "Ġ çµ", "Ġç µ", "ÙĬ ÙĨÙĬ", "ÙĬÙĨ ÙĬ", "Å ½", "Ġhük üm", "ĠD Ä±ÅŁ", "ĠÎŃ Ïĩει", "ĠÎŃÏĩ ει", "Ġ Ñģка", "ĠÑģ ка", "ĠÑģк а", "Ġ ÑĤим", "ĠÑĤ им", "ĠÑĤи м", "Ġп оÑģÑĤав", "Ġпо ÑģÑĤав", "ĠпоÑģÑĤ ав", "à¸Ļ าà¸Ķ", "à¸Ļา à¸Ķ", "d ül", "dü l", "Ġd va", "Ġdv a", "Ġ à¸Ħà¸Ļ", "Ġà¸Ħ à¸Ļ", "Ġchá»ĭ u", "Ġ èı", "Ġè ı", "à¹ģส à¸Ķà¸ĩ", "æ° £", "Ġ íĪ¬", "Ġí Ī¬", "Ġ Ñĩин", "ĠÑĩ ин", "ĠÑĩи н", "ãģ« ãģĬ", "ен ноÑģÑĤи", "енно ÑģÑĤи", "ÐIJ ÐĿ", "Ġh emen", "Ġhe men", "Ġhem en", "Ġ ait", "Ġa it", "Ġai t", "Ġ à¤Ĭ", "ĠठĬ", "æī §", "ĠA BD", "ĠAB D", "Ġκα θ", "æ´ Ľ", "ãĤ¢ ãĥ«", "à¹ī าà¸Ĺ", "à¹īา à¸Ĺ", "ÅĻ ez", "ÅĻe z", "d ÄĽji", "dÄĽ ji", "Ġt á»ĭch", "еннÑı м", "Ġв ÑģÑĤанов", "ĠвÑģÑĤ анов", "ĠاÙĦ بر", "ĠاÙĦب ر", "ÙĪÙħ تر", "k ách", "ká ch", "åº Ĭ", "л Ñĥж", "лÑĥ ж", "Ġ تد", "Ġت د", "ä¸ ½", "ر Ø®", "à¤Ĥ à¤ĸ", "èĩªå·± çļĦ", "å®ĺ ç½ij", "- Ñı", "à¹ĩ à¸Ķ", "èĦ ļ", "Ġ çķ", "Ġç ķ", "Ġiçer isinde", "Ġb iá»ĥn", "Ġbi á»ĥn", "Ġ à¸ģล", "Ġà¸ģ ล", "Ġy aÄŁ", "Ġya ÄŁ", "Ġ æ´", "Ġæ ´", "Ġ бÑĢа", "Ġб ÑĢа", "ع ار", "عا ر", "æĪ °", "à¥Ģ Ċ", "Ġlé Äį", "a ların", "alar ın", "aları n", "Ġ Îĸ", "ĠÎ ĸ", "а ÑĢÑı", "аÑĢ Ñı", "ãģĿ ãĤĵãģª", "ÅĪ uje", "ãĢĢ Ġ", "ĠsaÄŁ lık", "Ġдо ÑģлÑĸд", "ĠдоÑģ лÑĸд", "ÃŃ Å¡", "à¥įर श", "à¥ī न", "Ġgi ả", "بÙĪ اسطة", "å® ģ", "Ġs oud", "Ġso ud", "Ġsou d", "Ġк ÑĤо", "e sel", "es el", "ese l", "Ġп ам", "Ġпа м", "Ġ ÂłĠ", "ĠÂł Ġ", "ĠÄį lov", "æ· ·", "ห à¸į", "ĠOs man", "æ ¦Ĥ", "æ¦ Ĥ", "Ġ åĭ", "Ġå ĭ", "ï¼Į åħ¶", "Ġ à¸Ħร", "Ġà¸Ħ ร", "Ġmá» ģm", "Ġ ÑģоÑĢ", "ĠÑģ оÑĢ", "ĠÑģо ÑĢ", "çĨ ±", "Ġthu ê", "ر ج", "à¹Ĥล à¸ģ", "Ġ íķĺê³ł", "Ġíķĺ ê³ł", "ÙĬ دة", "ÙĬد Ø©", "ĠaÅŁ aģı", "Ġk á»ĥ", "Ġká» ĥ", "à¸ķ ำ", "λ ει", "λε ι", "çļĦ è¯Ŀ", "æ± ł", "ĠÑģ ÑĤен", "ĠÑģÑĤ ен", "Ġin cel", "Ġinc el", "Ġince l", "åº Ń", "ÑĤ оÑĩ", "ÑĤо Ñĩ", "Ġprob lém", "Ġprobl ém", "ÏĦ Ïĥ", "à¹ī à¸Ńà¸Ļ", "à¹īà¸Ń à¸Ļ", "ë³´ ëĭ¤", "Ġà¤Ĩ à¤Ĺ", "ν αÏĤ", "να ÏĤ", "ãģĦ ãĤĭ", "Ġd ục", "Ġdụ c", "Ġtoho to", "Ġtoh oto", "ëIJĺ ìĹĪëĭ¤", "ëIJĺìĹĪ ëĭ¤", "T J", "Ġви знаÑĩ", "ĠB unun", "ĠBu nun", "ĠBun un", "ĠBunu n", "à¤Ĥ बर", "à¤Ĥब र", "ĠÙĩÙħÚĨ ÙĨÛĮÙĨ", "Ġб Ñİдж", "Ñĥ ÑĢг", "ÑĥÑĢ г", "äº ®", "Ġμε γ", "Ġtop lum", "Ġtopl um", "ãģ£ ãģ", "о ÑĤо", "оÑĤ о", ": |", "éĿŀ 常", "ิ à¸Ĺà¸ĺ", "ิà¸Ĺ à¸ĺ", "éģ ķ", "âĢĮÙ¾ دÛĮ", "Ġз ÑĢоб", "à¹Į à¸Ķ", "Ġдолж ен", "Ġдол жен", "ĠmÄĽ sta", "ĠmÄĽst a", "ÛĮ Ø´Ùĩ", "ÛĮØ´ Ùĩ", "v atel", "va tel", "vat el", "Ġprov oz", "Ġ inan", "Ġin an", "Ġi nan", "à¤Ĥ प", "Ġpar ç", "ÑĢ аÑģÑĤ", "ÑĢа ÑģÑĤ", "ÑĢаÑģ ÑĤ", "üm ü", "Ġgi á»ijng", "Ġgiá» ijng", "æ¬ ¢", "Ø« ÙĬر", "ĠB akan", "ĠBa kan", "ĠBak an", "Ġ âĪ¨", "ĠâĪ ¨", "Ġ باÙĨ", "Ġب اÙĨ", "Ġبا ÙĨ", "Û± Û¸", "Û±Û ¸", "ãĤĤ ãģĨ", "land ı", "lan dı", "Ġyen iden", "Ġyeni den", "ÑĨ енÑĤ", "ÑĨе нÑĤ", "ÑĨен ÑĤ", "Ġде ÑıÑĤелÑĮ", "Ð ©", "Ġ rov", "Ġr ov", "Ġro v", "å®Į åħ¨", "ĠK ỳ", "s lu", "sl u", "Ġl ấy", "é¤ IJ", "ĠÑĩ олов", "ä¼ Ŀ", "ĠbaÅŁ v", "å° Ī", "ê³ ¡", "ãĢģ ãģĿãĤĮ", "Ġ PÅĻÃŃ", "ĠP ÅĻÃŃ", "ĠPÅĻ ÃŃ", "д ем", "де м", "ĠпÑĢо ек", "ร à¸ĸ", "建 设", "Ġмож лив", "æ® º", "ãģ¡ãĤĥ ãĤĵ", "æķ ij", "ĠÄį ty", "ĠÄįt y", "é¦ Ĩ", "о ÑĢÑĥ", "оÑĢ Ñĥ", "Ġ æĦ", "Ġæ Ħ", "Ġk ÃŃch", "λ οÏħ", "λο Ïħ", "ãģĦ ãģ¤", "Ġc Äĥn", "ĠcÄĥ n", "Ạµ", "Ġel de", "éº »", "ÄŁ e", "Ġdo bÄĽ", "Ġdob ÄĽ", "ा यर", "ाय र", "Ġ ãĥı", "Ġãĥ ı", "н ен", "не н", "Ġmůže te", "Ġmůž ete", "Ġна ÑģÑĤÑĥп", "ĠнаÑģÑĤ Ñĥп", "ìĭľ ê°Ħ", "ĠÑģим пÑĤом", "Ġ ÏĥÏį", "ĠÏĥ Ïį", "Ġ سÙĦ", "Ġس ÙĦ", "ε κ", "ร à¸ĵ", "á te", "át e", "ek ler", "ekl er", "ĠвÑĢем ени", "ĠвÑĢемен и", "âĢĮ ÙĩاÛĮÛĮ", "âĢĮÙĩاÛĮ ÛĮ", "ãģĬ ãĤĬ", "ж и", "Ñĭ ваеÑĤÑģÑı", "Ñĭв аеÑĤÑģÑı", "Ñĭва еÑĤÑģÑı", "ÑĭваеÑĤ ÑģÑı", "Ùħ اÙĨÛĮ", "ÙħاÙĨ ÛĮ", "Ùħا ÙĨÛĮ", "à¸ķ ล", "Ġ صد", "Ġص د", "Ġ вол", "Ġв ол", "Ġво л", "ìĬ Ī", "ĠÙĥ Ùħا", "ĠÙĥÙħ ا", "Ġnh ằm", "èģ ¯", "ov acÃŃ", "ova cÃŃ", "Ġë§Į ëĵ¤", "ÙĪ Ù¾", "Ġ ë¸Į", "Ġë¸ Į", "ب ÙĬØ©", "بÙĬ Ø©", "u yla", "uy la", "л ено", "лен о", "ле но", "èĮ ¶", "ÑĢ ей", "ÑĢе й", "Ġk li", "Ġkl i", "Ġüzer inden", "Ġüzerinde n", "н еÑĤ", "не ÑĤ", "r aÄį", "ra Äį", "ĠпÑĢа ÑĨÑİ", "Ġed iyor", "Ġedi yor", "ãģı ãģł", "Ġ Äįast", "ĠÄį ast", "ĠÄįas t", "i yi", "iy i", "éĬ Ģ", "Ġd ù", "Ùİ Ø¨", "ÙĪ ÙĬØ©", "ÙĪÙĬ Ø©", "å ª", "Ġs ınıf", "Ġsın ıf", "Ġس اعت", "Ġ ราย", "Ġร าย", "Ġза Ñıв", "Ġg ặp", "à¸Ń ว", "ĠØ« Ùħ", "ĠZ á", "ĠвÑĸд к", "i zik", "iz ik", "izi k", "Ġm ón", "Ġmó n", "Ġпов ÑĭÑĪ", "Ġ à¸ļาà¸Ĺ", "Ġà¸ļ าà¸Ĺ", "ĠÑģ ил", "ĠÑģи л", "æĥħ åł±", "Âł t", "ĠÐľ оÑģк", "Ġê²ĥ ìĿ´ëĭ¤", "Ġê²ĥìĿ´ ëĭ¤", "Ġ çIJ", "Ġç IJ", "ĠÙħدÛĮر ÛĮت", "ов оÑĹ", "ово ÑĹ", "Τ ο", "çº ª", "нÑĸ ÑĪе", "нÑĸÑĪ е", "Ġ ÐĽÑİ", "ĠÐĽ Ñİ", "η Ïĥη", "ĠÙĨسب ت", "ĠÙĨس بت", "m uz", "mu z", "ร ว", "ãĢģ ãģĤ", "Ġбол ез", "Ġtr ách", "ãĥ ¦", "à¹Ģà¸Ĥ า", "Ġê·¸ ëĬĶ", "ب رÛĮ", "بر ÛĮ", "æł ª", "ëĿ¼ ìĿ´", "Ġ íĮ¨", "Ġí Į¨", "ĠíĮ ¨", "íĬ ¹", "ľ ´", "ि ड", "िठ¡", "ÑĢо ме", "ÑĢом е", "è® ²", "Ġ ÑĤон", "ĠÑĤ он", "ĠÑĤо н", "Ñģ Ñĸ", "Ġ ç®", "Ġç ®", "åıĸ ãĤĬ", "ì° °", "Ġ ÙĪÙĦÛĮ", "ĠÙĪ ÙĦÛĮ", "ĠÙĪÙĦ ÛĮ", "Ġس Ø·ØŃ", "èı ľ", "н ами", "на ми", "нам и", "T ürk", "åİ Ĥ", "Ġf inan", "Ġfin an", "Ġfi nan", "ãģ« ãģªãĤĭ", "ãģ«ãģª ãĤĭ", "Ġ oby", "Ġo by", "Ġob y", "T rong", "Tr ong", "Tro ng", "Ġv yp", "Ġvy p", "à¥ģ ड", "à¥ģठ¡", "ìŀIJ ê°Ģ", "Ġ æīĢ", "Ġæī Ģ", "ÐĹ Ð°", "um lu", "uml u", "ëĵ Ŀ", "Ġм енÑĸ", "Ġмен Ñĸ", "ол ниÑĤелÑĮ", "олн иÑĤелÑĮ", "Ġú Äįin", "ĠúÄį in", "Ġb unun", "Ġbu nun", "Ġbun un", "Ġbunu n", "ĠÐłÐ¾Ñģ Ñģии", "в ÑģÑı", "Ġн Ñĸж", "ĠнÑĸ ж", "ิà¸Ķ à¸ķ", "غ Ø©", "Ä ļ", "Ġ سÙħ", "Ġس Ùħ", "Ġ Ðĺз", "ĠÐĺ з", "à¥ĩ प", "à¥ĩठª", "大 çļĦ", "ì¹ ľ", "Ġ иÑģÑĤ", "Ġи ÑģÑĤ", "ĠиÑģ ÑĤ", "Ġкон ÑģÑĤÑĢÑĥк", "Û± Û²", "Û±Û ²", "â l", "Ġ ÑĪиÑĢ", "ĠÑĪ иÑĢ", "ĠÑĪи ÑĢ", "ï¼ ł", "Ġar tık", "Ġart ık", "æŁ ĵ", "ä¹ ¡", "ÃŃ te", "ÃŃt e", "ĠNh áºŃt", "ĠÎĶ η", "Ġöl ç", "êµ ´", "о Ñıн", "оÑı н", "ëĵ± ë¡Ŀ", "Ġng ân", "Ġ бÑĥдÑĮ", "ĠбÑĥд ÑĮ", "ÎŁ Ρ", "ÎŁÎ ¡", "ì ´", "Ùħ ÙĪد", "ÙħÙĪ د", "ν ον", "νο ν", "Îķ ÎĿ", "çij ŀ", "ĠÅĻ ek", "ĠÅĻe k", "- âĢIJ", "ĠM erk", "ĠMe rk", "ĠMer k", "Ġоп ÑĢедел", "ĠопÑĢед ел", "Ïģ ιν", "Ïģι ν", "л аб", "ла б", "ëĦ¤ ìļĶ", "Ġб лиз", "Ġбл из", "Ġбли з", "Ġph á»iji", "Ġphá»ij i", "Ġдолж нÑĭ", "ĠÑį кÑģп", "ĠÑįк Ñģп", "à¸ļ à¸Ĺ", "à¸Ľà¸£à¸° ส", "ĠÙ¾Úĺ ÙĪÙĩ", "Ġ íķľëĭ¤", "Ġíķľ ëĭ¤", "ÏĦ οÏį", "ÏĦο Ïį", "Ùĩ ÙĨ", "Ġд од", "Ġдо д", "Ġk ayı", "Ġka yı", "Ġkay ı", "Ł ģ", "Ñģ иÑı", "Ñģи Ñı", "à¤Ĥ तर", "à¤Ĥत र", "Ġpod nik", "e vi", "ev i", "ÛĮ ÛĮر", "Т ак", "Та к", "к оп", "ко п", "н аÑħ", "на Ñħ", "ا سÙĩ", "اس Ùĩ", "à¸ĵ à¸ij", "Ġk há", "Ġkh á", "Ġy arat", "Ġya rat", "Ġyar at", "ĠاÛĮÙĨ Ú©Ùĩ", "Ø· بÙĬ", "طب ÙĬ", "Ġs ır", "Ġsı r", "ĠØ¢ÙħرÛĮÚ© ا", "Ġ बल", "Ġब ल", "k aç", "ka ç", "Ġ åı¯", "Ġåı ¯", "Ġ åħ¶", "Ġåħ ¶", ". ***", ".* **", "л ÑĸннÑı", "лÑĸн нÑı", "ä¹ ±", "o q", "æ ¦", "ãĤ ¼", "Ġf ır", "Ġk ê", "Ġìłľ ê³µ", "Ġ Ïĥη", "ĠÏĥ η", "а нÑĭ", "ан Ñĭ", "н ова", "но ва", "нов а", "à¸Ĭ าย", "ĠØ· ÙĪÙĦ", "à¥Ī य", "Ġ ì¹ľ", "Ġì¹ ľ", "ìĤ ´", "Ġп Ñĸв", "Ġlu áºŃn", "Ġà¤ī म", "åº ĥ", "à¹ĩ à¸Ńà¸ķ", "Ġس اÛĮت", "л Ñıн", "лÑı н", "ĠíķĦ ìļĶ", "Ġgör ül", "ĠÑĤеÑĢ иÑĤоÑĢ", "ĠÙĨ ØŃ", "е ма", "ем а", "Ġmn oh", "Ġ ãģ¯", "غ ÙĬر", "ĠÑģдел аÑĤÑĮ", "ç ģµ", "çģ µ", "Ġ ÐłÐ°Ð·", "ĠÐł аз", "ĠÐłÐ° з", "Ġг еÑĢ", "Ġге ÑĢ", "γ μα", "íķĺ ë©´", "ĠdeÄŁ iÅŁtir", "ĠdeÄŁiÅŁ tir", "ãĥ³ ãĥĨ", "ãĥ³ãĥ Ĩ", "å¸Ĥ åľº", "个 人", "ìĥ Ī", "ì¹ ¨", "èī º", "ÙĤ ت", "ĠÚ¯ رÙģتÙĩ", "ĠگرÙģ تÙĩ", "Ġگر ÙģتÙĩ", "ĠگرÙģت Ùĩ", "Ġ çİĭ", "Ġçİ ĭ", "ĠاÙĦ Ø°Ùĩ", "ĠاÙĦØ° Ùĩ", "λ Ïħ", "à¤ľ र", "Ġв ним", "ë¦ Ń", "ิ à¸Ĺ", "Ġ شاÙĩ", "ĠØ´ اÙĩ", "æĬķ èµĦ", "æĿIJ æĸĻ", "ĠÙĨ Ùģ", "èª ¬", "æĬ Ĺ", "Ġ аб", "Ġа б", "iy eti", "iye ti", "iyet i", "ç¾ ħ", "ÑĢ Ñĸз", "ÑĢÑĸ з", "Ġ สม", "Ġส ม", "i cÃŃ", "ic ÃŃ", "к ÑĥваннÑı", "кÑĥ ваннÑı", "Ġ ìķ¼", "Ġìķ ¼", "Ġ è½", "Ġè ½", "âĢ «", "Ġ διά", "Ġδ ιά", "Ġδι ά", "Ġд еп", "Ġде п", "ãĥ¼ ãĤ¿", "ãĥ¼ãĤ ¿", "Ġob jev", "Ġobj ev", "mé na", "Ġbe lg", "Ġbel g", "Ġ æ¥", "Ġæ ¥", "Ġn á»ģn", "Ġг ол", "Ġpost av", "Ġpo stav", "Ġت Ú©", "Ð «", "ĠпÑĸд ÑĤ", "ĠоÑĤ ноÑĪ", "Ġп ÑĢив", "ĠпÑĢ ив", "ĠпÑĢи в", "Ġ åŁº", "ĠåŁ º", "Ġн али", "Ġна ли", "Ġнал и", "ů ž", "Ġ yat", "Ġy at", "Ġya t", "ÅŁ a", "ÏĦ ήÏĤ", "ÏĦή ÏĤ", "ÑĨ ем", "ÑĨе м", "次 æķ°", "Ġb Ãł", "ÙĪ Ùĥ", "Ġ íĶĦë¡ľ", "ĠíĶĦ ë¡ľ", "ĠPh áp", "Ġ êµ°", "Ġêµ °", "è³ ŀ", "Ġoch ran", "Ġgere kir", "Ġgerek ir", "Ġ íļ", "Ġí ļ", "à¸ļ ล", "á me", "ám e", "Ġ بÛĮر", "Ġب ÛĮر", "ĠبÛĮ ر", "à¸Ĥ าย", "ов аний", "ова ний", "овани й", "ован ий", "Ġmož né", "âĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģ", "á lu", "ál u", "н ÑĤ", "¦ æĥħ", "à¹ģ รม", "ĠÑĦ Ñĸн", "ĠÄ° ç", "à¹Ī à¸Ńย", "à¹Īà¸Ń ย", "ê² ¨", "Ġh edef", "Ġhe def", "Ġhed ef", "ĠاÙĦ ÙħØ´", "ĠاÙĦÙħ Ø´", "à¹ī าม", "à¹īา ม", "å¯ Ħ", "Ġ ëĭµ", "Ġëĭ µ", "Ġ ô", "Ġà ´", "ла ÑģÑı", "лаÑģ Ñı", "Ä° T", "à¸Ķ ำ", "Ġher hangi", "Ġger eken", "Ġgere ken", "Ġgerek en", "е ÑĢеж", "еÑĢ еж", "еÑĢе ж", "ÙĪ Ø©", "ĠpÅĻ est", "ĠpÅĻes t", "ĠpÅĻe st", "ç§ij åѦ", "оÑģÑĤ аÑĤ", "ün den", "ünd en", "ünde n", "åĮħ æĭ¬", "Ġد Ùĩد", "ĠدÙĩ د", "ÑĪ иÑģÑĮ", "ÑĪи ÑģÑĮ", "н еÑĢ", "не ÑĢ", "Ñĸ дом", "Ñĸд ом", "Ġb iç", "Ġbi ç", "ìĭ Ń", "Ġhod not", "Ġze mÄĽ", "Ġzem ÄĽ", "ĠاÛĮ جاد", "Ġy ine", "Ġyi ne", "ि ण", "िठ£", "ĠاÙĦ بÙĦ", "ĠاÙĦب ÙĦ", "ĠN ÄĽ", "Ġpol ož", "Ġpo lož", "Ġpolo ž", "éĺħ 读", "å¸ ģ", "å¼ Ł", "ξ ε", "Ġ Má»Ļt", "ĠM á»Ļt", "ç £", "Û±Û³ Û¹", "ĠØ¢ ز", "ãģ ŀ", "Ġм еÑħ", "ย ม", "Ġ æ¨", "Ġæ ¨", "Ġo tur", "Ġot ur", "Ġd ầu", "Ġ ëĭ¤ìļ´", "Ġëĭ¤ ìļ´", "çĮ «", "Ġ Có", "ĠC ó", "Ġli dÃŃ", "Ġlid ÃŃ", "Ġark adaÅŁ", "Ġα λλά", "é¡ »", "ĠÙĩ ÙħÛĮÙĨ", "ĠÙĩÙħ ÛĮÙĨ", "è» ¢", "Ġ âĹĭ", "ĠâĹ ĭ", "ëıĦ ë¡Ŀ", " ĥ", "âĢĮØ´ دÙĩ", "âĢĮشد Ùĩ", "ĠØŃ ÙĬØ«", "ĠØŃÙĬ Ø«", "Ġnh óm", "Ïĥ Ïĩ", "ĠÑĤÑĢан Ñģп", "ĠÑĤÑĢанÑģ п", "Ġtan ım", "Ġtanı m", "ç´ į", "Ġba his", "Ġbah is", "ä¸ ¾", "Ġин ÑĦоÑĢма", "ĠинÑĦоÑĢм а", "ĠÑģ лож", "ĠÑģл ож", "ĠÑģло ж", "Ġk raj", "Ġkr aj", "Ġkra j", "Ġ ØŃÙĦ", "ĠØŃ ÙĦ", "Ġ ãĥĸ", "Ġãĥ ĸ", "ĠÙĨ ÙĤÙĦ", "ĠÙĨÙĤ ÙĦ", "Ġ ÐłÐ¾Ð·", "ĠÐł оз", "ĠÎij Ïħ", "lar dı", "ĠÙ¾ اس", "Ġپا س", "Ġ ìĭĿ", "Ġìĭ Ŀ", "ĠìłĦìļ© ë©´ìłģ", "ĠاÙĦ سÙĬ", "ĠاÙĦس ÙĬ", "با شد", "باش د", "ศ าสà¸ķร", "Ġk öy", "Ġkö y", "Ġ rok", "Ġr ok", "Ġro k", "Ġ 죽", "Ġì £½", "Ġì£ ½", "ĠÑģ ог", "ĠÑģо г", "Ġch ú", "éĺ ª", "ĠÄįást i", "ĠÄįá sti", "Ġз веÑĢ", "Ġзв еÑĢ", "Ġ низ", "Ġн из", "Ġни з", "ĠÃ¶ÄŁ ret", "Ġ ãĥİ", "Ġãĥ İ", "п е", "çĴ °", "Ġ èª", "Ġè ª", "ÙĪ ÙĦÙĩ", "ÙĪÙĦ Ùĩ", "Ä° M", "/ REC", "/R EC", "å¡ ŀ", "ĠÐĴ и", "/l oose", "/lo ose", "Ġп оÑħ", "Ġпо Ñħ", "Ġgen iÅŁ", "Ġth iá»ĩn", "Ġthi á»ĩn", "ti ÄŁi", "Ñĩ ие", "Ñĩи е", "о нд", "он д", "Ġп ÑĢиÑģ", "ĠпÑĢ иÑģ", "ĠпÑĢи Ñģ", "áz ky", "ĠDev let", "ç¦ ģ", "Ġ аг", "Ġа г", "i lere", "il ere", "ile re", "iler e", "ин кÑĥ", "Ġvar dı", "ãĢĢ ãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢ ãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢ ĠãĢĢ", "Ġë ĨĴ", "ĠëĨ Ĵ", "à¤Ĥ पन", "à¤Ĥप न", "Ġözel lik", "éļ ľ", "ìĸ´ ìĦľ", "ر ÙĬÙĥ", "رÙĬ Ùĥ", "ÙĪ بÛĮ", "ÙĪب ÛĮ", "ãĥ³ ãĥĢ", "ãĥ³ãĥ Ģ", "í Į¨", "íĮ ¨", "Ġसम à¤Ŀ", "ï¾Ĩï¾Ĩ ï¾Ĩï¾Ĩ", "Ġ ÙģÙĨ", "ĠÙģ ÙĨ", "ॠĿ", "Ġuv eden", "ÑĪ ими", "ÑĪи ми", "ÑĪим и", "Ġ à¹Ģล", "Ġà¹Ģ ล", "Ġà¹Ģภ¥", "Ġ 문ìĿĺ", "Ġ문 ìĿĺ", "ĠØŃ Ø±Ùģ", "ĠØŃر Ùģ", "Ġ عب", "Ġع ب", "ãĥ¬ ãĥĵ", "Ġ æŃ£", "ĠæŃ £", "ĠëĺIJ ëĬĶ", "ĠÚ©ÙĨ ÙĨدÙĩ", "ĠÚ©ÙĨÙĨد Ùĩ", "Ġα ÏħÏĦÏĮ", "ĠαÏħ ÏĦÏĮ", "Ġ 길", "Ġê¸ ¸", "Ġif ade", "Ġi fade", "Ġifad e", "Ġyap mak", "ãĥķ ãĤ©", "ãĥķãĤ ©", "Ġm ẹ", "Ġst rán", "Ġstr án", "Ġs vou", "Ġsv ou", "Ġsvo u", "Ġv ždy", "Ġtek rar", "ิ à¸į", "Ġ ìĵ°", "Ġì ĵ°", "Ġìĵ °", "o ÄŁu", "oÄŁ u", "Ġ Ú©ÛĮÙĦ", "ĠÚ© ÛĮÙĦ", "ĠÚ©ÛĮ ÙĦ", "и вÑģÑı", "ив ÑģÑı", "Ġë§IJ íĸĪëĭ¤", "ä¸ Ŀ", "à¤ı स", "ĠÑģÑĤ ÑĢаÑħ", "ĠÑģÑĤÑĢ аÑħ", "ĠÑģÑĤÑĢа Ñħ", "Ġsou Äįas", "Ġê·¸ 룰", "Ġ mÃ¼ÅŁ", "Ġm Ã¼ÅŁ", "Ġmü ÅŁ", "λ οÏį", "λο Ïį", "γ Ïī", "Ġt Æ°á»Łng", "Ġ å·¥", "Ġå· ¥", "Ġ اسÙħ", "Ġا سÙħ", "Ġاس Ùħ", "ÑĢ Ñĸм", "ÑĢÑĸ м", "à¹Ģ à¸Ľà¸¥", "à¹Ģà¸Ľ ล", "Ġ³³ Ġ³³", "Ùĩ اÛĮÛĮ", "ÙĩاÛĮ ÛĮ", "å¯ º", "Ġس رÛĮ", "Ġسر ÛĮ", "Ġк ваÑĢ", "Ġкв аÑĢ", "ĠØ´Ùħ ارÙĩ", "ĠØ´Ùħا رÙĩ", "Ġ صØŃ", "Ġص ØŃ", "о ÑģÑĤав", "оÑģÑĤ ав", "ॠ¨", "Ġ à¸Ħวาม", "Ġà¸Ħ วาม", "í ĥģ", "íĥ ģ", "éĢ Ĥ", "ب ØŃ", "ĠdeÄŁiÅŁ ik", "éĮ ²", "е ди", "ед и", "Ġ okol", "Ġo kol", "Ġok ol", "ĠÑģ оп", "ĠÑģо п", "Ġol mayan", "Ġolm ayan", "Ġolma yan", "çŃ ij", "Û± Û´", "Û±Û ´", "Ġ inclu", "Ġinc lu", "Ġincl u", "Ġ ê²ĮìŀĦ", "Ġê²Į ìŀĦ", "ÛĮ ستÙħ", "ÛĮست Ùħ", "ÛĮس تÙħ", "Ġ ç©", "Ġç ©", "ĠاÙĦÙĪÙĦ اÙĬات", "il mektedir", "ilm ektedir", "à Į", "Ùİ Ø¹", "ĠaÄŁ ır", "è¡ Ľ", "Ġe ski", "Ġes ki", "Ġesk i", "ê° Ŀ", "본 ëĭ¤", "人 åijĺ", "Úĺ ÛĮ", "Ġ ç¨", "Ġç ¨", "Ġм еÑģÑĤо", "ĠмеÑģÑĤ о", "v ů", "à¥įर ह", "ĠØ· رØŃ", "Ġطر ØŃ", "Ġا بÙĨ", "Ġاب ÙĨ", "Ġh iss", "Ġhis s", "Ġhi ss", "о ÑĢÑıд", "оÑĢ Ñıд", "Ġد Ùģ", "ÑĢ иÑģÑĤ", "ÑĢи ÑģÑĤ", "ÑĢиÑģ ÑĤ", "à¸Ĭ ม", "д еÑĤ", "де ÑĤ", "à¹Ģ หม", "à¹Ģห ม", "ë§Ī ìĤ¬ì§Ģ", ": .:.:", ":. :.:", ":.: .:", "éħ ¸", "Ġα ÏģÏĩ", "ĠαÏģ Ïĩ", "Ġn ữ", "ĠпоÑģ ад", "l um", "lu m", "ì º", "ãģ§ãģį ãĤĭ", "ìĸ µ", "ĠاÙĦ Ùħد", "ĠاÙĦÙħ د", "н Ñĸм", "нÑĸ м", "ر اÙĤ", "را ÙĤ", "Ġ ãĥĪ", "Ġãĥ Ī", "Ġod povÄĽ", "Ġodp ovÄĽ", "Ġbir bir", "Ġh ãy", "Ġhã y", "о вий", "ов ий", "ови й", "æ® ĭ", "éĥ½ æĺ¯", "è¿ ª", "Ġa raç", "Ġar aç", "Ġara ç", "ен ÑĤÑĸв", "енÑĤ Ñĸв", "æĬ ±", "d ál", "ĠÄIJ ông", "Ġhe sap", "Ġhes ap", "Ġا ÙĨساÙĨ", "ĠاÙĨ ساÙĨ", "ĠÙĬ ÙĪÙħ", "ĠÙĬÙĪ Ùħ", "ĠÙĨ ÙĪر", "ĠÙĨÙĪ ر", "åī ĩ", "çĹ Ľ", "Ġ ÙĨÙĬ", "ĠÙĨ ÙĬ", "алÑĮ на", "تب اط", "ल ब", "Ġkom un", "Ġko mun", "Ġs nad", "Ġsn ad", "Ġsna d", "åĽ £", "ر ÙĬد", "رÙĬ د", "elop ment", "Ġ иÑİ", "Ġи Ñİ", "à¥Ģ .", "Ġkıs a", "Ġkı sa", "ĠdeÄŁil dir", "ĠdeÄŁildi r", "à¹ī าร", "à¹īา ร", "Ġsv ého", "Ġsvé ho", "Ġobl asti", "Ġoblast i", "ÑĪ ли", "à¹Ģà¸Ĺ à¸ŀ", "ÑĢ еÑĤÑĮ", "ÑĢе ÑĤÑĮ", "ÑĢеÑĤ ÑĮ", "о во", "ов о", "Ġ íĤ¤", "Ġí Ĥ¤", "ĠíĤ ¤", "át ky", "ĠاÙĦ Ùģر", "ĠاÙĦÙģ ر", "èĺ Ń", "ÏĦ ον", "ÏĦο ν", "ĠÑģÑĤ оиÑĤ", "ĠÑģÑĤо иÑĤ", "Ùħ ØŃ", "Ġ à¹Ħ", "Ġà ¹Ħ", "ĠÑĤе бе", "ĠÑĤеб е", "íģ ´", "Ġm ÄĽla", "ĠmÄĽ la", "ĠmÄĽl a", "æݧ åĪ¶", "ĠCh ủ", "ìĬ ¨", "ÐIJ Т", "ا جع", "اج ع", "ìĻ ķ", "ç© ¿", "ол ее", "ห ลาย", "หล าย", "Ġd vou", "Ġdv ou", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ุ à¸Ĥ", "Ġb oz", "Ġbo z", "ิ à¸Ļà¸Ħ", "ิà¸Ļ à¸Ħ", "å¤ Ł", "Ġfa aliyet", "ĠÄį ÃŃs", "ãģ» ãģ©", "Ġ :/", "Ġ: /", "к ÑĸÑģÑĤÑĮ", "кÑĸ ÑģÑĤÑĮ", "Ġ ì¤Ģ", "Ġì¤ Ģ", "Ïģ αÏĤ", "Ïģα ÏĤ", "Ġод но", "æ ¢ħ", "æ¢ ħ", "Ñĥб ли", "н оз", "но з", "à¹Į ม", "Ġvý rob", "Ġvýro b", "Ġ κÏħ", "Ġκ Ïħ", "ÅĻ ev", "ÅĻe v", "Âł B", "ů že", "ůž e", "ä¼ļ 社", "ι β", "ÑĢ ованиÑı", "ÑĢов аниÑı", "ÑĢо ваниÑı", "ÑĢован иÑı", "ÑĢова ниÑı", "Ġc ev", "Ġce v", "ìĽ Ģ", "ál nÃŃch", "áln ÃŃch", "álnÃŃ ch", "Ġ ÑĢав", "ĠÑĢ ав", "ĠÑĢаР²", "ĠÑĢа в", "ç´ §", "åĢ Ł", "Ġ ÑŁ", "ĠÑ Ł", "ÙĪ ÙĨÙĬ", "ÙĪÙĨ ÙĬ", "о зÑı", "оз Ñı", "Ġз ов", "Ġk olem", "Ġko lem", "Ġkol em", "Ġkole m", "민 êµŃ", "ç¿ Ĵ", "Ġzam ÄĽst", "Ġ ìłij", "Ġìł ij", "Ġ زÙĨ", "Ġز ÙĨ", "ĠØ£ Ùģ", "Ġ 먹", "Ġë¨ ¹", "Ġtom to", "Ġ 첨ë¶Ģ", "Ġì² ¨ë¶Ģ", "s age", "sa ge", "ä¸į è¿ĩ", "е год", "ег од", "его д", "ÑĢ ож", "ÑĢо ж", "ĠпÑĢоÑĨ ед", "à¹Į à¸Ļ", "san ız", "âĢŀ Ø·", "æ´» åĬ¨", "о Ñĩки", "оÑĩ ки", "ë³´ 기", "åŁº æľ¬", "- Ñħ", "ло ÑģÑı", "ĠÙĩÛĮ ÚĨ", "ìĹ Ķ", "Ñĩ ного", "Ñĩно го", "Ġ à¤Ĺर", "Ġà¤Ĺ र", "Ġà¤ħ à¤Ĺ", "ãħĭãħĭ ãħĭãħĭ", "Ġ ãĤ¸", "ĠãĤ ¸", "ا سة", "اس Ø©", "åĬ ĩ", "à¹ī à¸ĩ", "Ġ 커", "Ġì» ¤", "n ými", "ný mi", "ným i", "ãĥ¬ ãĤ¹", "åĭ Ĵ", "Ġобла ÑģÑĤÑĸ", "ĠоблаÑģ ÑĤÑĸ", "ĠоблаÑģÑĤ Ñĸ", "ĠдÑĸÑıлÑĮ ноÑģÑĤÑĸ", "ãĥ¬ ãĤ¤", "Ïĩ αν", "Ïĩα ν", "à¹Ī าส", "à¹Īา ส", "ĠФ ÑĢан", "Ùĩ ÙĦ", "l ardır", "lar dır", "lardı r", "ØŃ Ø§Øª", "ů st", "Ġв одÑĭ", "Ġво дÑĭ", "Ġвод Ñĭ", "ĠدÙĪ ÙĦت", "ĠدÙĪÙĦ ت", "ĠÑģпе ÑĨÑĸ", "Ġth ất", "à¸Ń าหาร", "éł ĺ", "Ġter cih", "ĠÏĢÏģο Ïĥ", "Ġ ÅĻÃŃzenÃŃ", "ĠÅĻÃŃ zenÃŃ", "è§ī å¾Ĺ", "Ġd nes", "Ġdn es", "Ġdne s", "е Ñĩно", "еÑĩ но", "ãĥ ĺ", "Ġدار اÛĮ", "ĠÅŁ art", "ĠÅŁar t", "ë² ¤", "Ġ ë¶ģ", "Ġë ¶ģ", "Ġë¶ ģ", "е Ñı", "н ÑıÑĤÑĮ", "нÑı ÑĤÑĮ", "нÑıÑĤ ÑĮ", "Ġk vÄĽt", "Ġkv ÄĽt", "Ġتغ ÛĮÛĮر", "é¾ į", "Ġر ÙĨÚ¯", "ï¼Į åı¯", "Ġp iyas", "Ġpi yas", "Ġuyg ulan", "Ġuygu lan", "Ùİ Ø©", "ب ÙĬر", "بÙĬ ر", "и ваÑĤÑĮ", "ив аÑĤÑĮ", "ива ÑĤÑĮ", "Ġ íĹĪ", "ĠíĹ Ī", "ä¸ ¶", "è¿Ļ äºĽ", "Ġ گر", "ĠÚ¯ ر", "ç½ ª", "ä¸Ģ æł·", "Ġ ãĥª", "Ġãĥ ª", "Ġв ой", "Ġво й", "Ġs osyal", "Ġsos yal", "ุ à¸Ĺà¸ĺ", "ุà¸Ĺ à¸ĺ", "หม à¸Ķ", "ç» Ŀ", "ĠاÙĦ جÙħ", "ĠاÙĦج Ùħ", "ĠØ« بت", "Ġج ÙĨÚ¯", "ĠجÙĨ Ú¯", "л ении", "лен ии", "ле нии", "в аÑı", "ва Ñı", "Ġв оÑĤ", "Ġво ÑĤ", "ä¼ ¤", "Ġ หล", "Ġห ล", "ĠÙħÙĤ اÙĦÙĩ", "мÑĸ нÑĸ", "мÑĸн Ñĸ", "ìĺ ¬", "Ñĩ ий", "Ñĩи й", "ĠÙħ Ú©", "à¹Ĥ à¸Ľà¸£", "à¹Ĥà¸Ľ ร", "k rv", "kr v", "Ġ ÃŃch", "ĠÃŃ ch", "Ïī Ïĥη", "ек ÑĤоÑĢ", "екÑĤ оÑĢ", "Я к", "Ġp ÃŃs", "ĠÃĸ zel", "ĠÃĸz el", "Ġt Æ°á»Ľng", "Ġ ÐĶо", "ĠÐĶ о", "δ ιο", "δι ο", "ู à¸Ķ", "Ġt ük", "رÛĮ ÙĤ", ". ÐĴ", "Ġ åIJĪ", "ĠåIJ Ī", "ä¿ Ĥ", "Ġob dob", "Ġist edi", "ÑĪ ла", "æľī ä¸Ģ", "Ġвк лÑİÑĩа", "ĠвклÑİÑĩ а", "ĠتØŃ ÙĤÛĮÙĤ", "Ġ ÙĪÙĥ", "ĠÙĪ Ùĥ", "Ġ èĪ", "Ġè Ī", "Æ Ĵ", "μ εÏģ", "με Ïģ", "Ġ åģ", "Ġå ģ", "Ġ ìĹĨëĬĶ", "ĠìĹĨ ëĬĶ", "Âł d", "ĠB ắc", "à¸ģล าà¸ĩ", "ĠÑĩ Ñĥв", "Ġc ấu", "ĠH á»ĵ", "ĠHá» ĵ", "ĠÙģ اÛĮÙĦ", "ÏĦη γοÏģ", "ç± į", "Ġ بت", "Ġب ت", "ĠобÑĢаз ом", "æ± ī", "èĦ ij", "Ġgi ản", "Ġgiả n", "ε Ïģγ", "εÏģ γ", "ĠÐľ Ñĸ", "èĻ½ çĦ¶", "Ġ Khi", "ĠK hi", "ĠKh i", "Ñĩ ини", "Ñĩи ни", "Ñĩин и", "Ġà¤ħ à¤Ĺर", "Ġà¤ħà¤Ĺ र", "íķĺ ë©°", "ë² Ķ", "ãģ ģ", "в иÑħ", "ви Ñħ", "ĠвÑģ егда", "Ġ ç¶", "Ġç ¶", "ÑģÑĤв енной", "ÑģÑĤвен ной", "ÑģÑĤвенно й", "Ġyük sel", "æ¸ ¬", "Ġsı ras", "Ġsır as", "Ġsıra s", "ĠÏĢ ÏģÏİ", "èĢ ³", "ا ÛĮر", "اÛĮ ر", "د ÙĪد", "دÙĪ د", "ĠAl man", "ĠAlma n", "Ġver di", "Ġverd i", "ĠاÙĦ Ùħج", "ĠاÙĦÙħ ج", "ĠاÙĦ تع", "ĠاÙĦت ع", "ص Ø©", "Ġsı ra", "Ġsır a", "Äį in", "Äįi n", "Ġп еÑĢÑĪ", "ĠпеÑĢ ÑĪ", "æĬ ĺ", "ç© į", "ĠÑĤ об", "ĠÑĤо б", "Ġ ï¾ī", "Ġï¾ ī", "ภ¬", "æĿ Ģ", "iy di", "ี à¸ŀ", "çĵ ¦", "ĠавÑĤом об", "ä¸Ń æĸĩ", "à¥Ĥ द", "ĠbÄĽ hem", "Ġ PÅĻed", "ĠP ÅĻed", "ĠPÅĻ ed", "ãģĵ ãģĨ", "ั à¸Ī", "Ġ ï½Į", "Ġï½ Į", "Ġ ÙĩاÙĬ", "ĠÙĩ اÙĬ", "ĠÙĩا ÙĬ", "Ġs ạch", "æĸ¹ éĿ¢", "çķ °", "ÑĥÑĢ н", "Ġvý sled", "Ġth ần", "ï¼Į æīĢ以", "Ñĥ ка", "Ñĥк а", "íķĺ ëĭ¤", "Ġ बर", "Ġब र", "Ġж Ñĸн", "Äį nÃŃho", "ÄįnÃŃ ho", "Ġ ãģĮ", "ab ı", "v ánÃŃ", "vá nÃŃ", "æ´ Ĺ", "Ġи ÑģÑĤоÑĢ", "ĠиÑģ ÑĤоÑĢ", "ĠиÑģÑĤ оÑĢ", "ìĿ´ íĦ°", "Ġе лек", "а лаÑģÑı", "ала ÑģÑı", "Ġ znám", "Ġz nám", "Ġzn ám", "ĠØ· رÙģ", "Ġطر Ùģ", "Ġs ektör", "ê¹ Ģ", "ÙĪ ÙĤع", "ÙĪÙĤ ع", "ĠÙħ Ùĥ", "ÑĢе жд", "ÑĢеж д", "Ġk nih", "Ġkn ih", "Ġت عداد", "Ġتع داد", "Ġتعد اد", "åį ł", "ÑģÑĮ ке", "ÑģÑĮк е", "Ġ çĶµ", "京 éĥ½", "Ġر اÛĮ", "Ġرا ÛĮ", "g ın", "gı n", "ĠÙĨ ظاÙħ", "ĠÙĨظ اÙħ", "ĠÎł ολ", "ĠÎłÎ¿ λ", "ä¸Ģ èĪ¬", "Ġst ále", "Ġstál e", "ĠиÑģ Ñģлед", "Ġz práv", "Ġzp ráv", "Ġ ÑĩиÑģÑĤ", "ĠÑĩ иÑģÑĤ", "ĠÑĩиÑģ ÑĤ", "ĠÑĩи ÑģÑĤ", "ãĥ¼ ãĥŀ", "ãĥ¼ãĥ ŀ", "Ðŀ Ñģ", "ÑģÑĮ комÑĥ", "ÑģÑĮк омÑĥ", "ÑģÑĮко мÑĥ", "ĠpÅĻi prav", "ĠpÅĻip rav", "ëĮĢ íĸī", "Ġh alk", "Ġha lk", "Ġhal k", "çĪ Ĩ", "ãĢģ ãģĬ", "ï¼Ł âĢĿĊĊ", "ï¼ŁâĢĿ ĊĊ", "éĢ ı", "ç« ŀ", "ни ÑĨÑĮ", "ниÑĨ ÑĮ", "çĽ ĺ", "à¹Ģ à¸Ńà¸ĩ", "à¹Ģà¸Ń à¸ĩ", "ì Łģ", "à¥ĩव ल", "ä¹ĭ åIJİ", "ãĥ« ãĥĪ", "Ġ stru", "Ġs tru", "Ġst ru", "Ġstr u", "Ġ ＿", "Ġï¼ ¿", "Îķ ÎĽ", "h le", "hl e", "ĠÙĨ ÙĪØ´", "ĠÙĨÙĪ Ø´", "ìĿ µ", "ĠÙħ Ùģ", "æĪĸ èĢħ", "Ġö ld", "Ġöl d", "éĢ Ķ", "ãĥ³ ãĥĹ", "ãĥ³ãĥ Ĺ", "íĺ ¼", "Ġu ÄŁ", "ĠÄij á", "Ġvlast nÃŃ", "ĠÙħج ÙĦس", "åį Ķ", "ÏĦ ικήÏĤ", "ÏĦικ ήÏĤ", "ÏĦική ÏĤ", "Ġpo vin", "Ġpov in", "ů l", "ĠاÙĦ ØŃÙĬ", "ĠاÙĦØŃ ÙĬ", "Ġsm lou", "ãĥĥ ãĥģ", "Ġ ÙĥÙĨ", "ĠÙĥ ÙĨ", "Ġch ấp", "èIJ ¬", "ج ب", "? âĢľ", "д ав", "да в", "ร วม", "รว ม", "Ùİ Ø¯", "ĠاÙĦد ÙĪÙĦ", "ĠëĦ¤ ìĿ´íĬ¸", "Ġà¤Ĩ स", "ظ ÙĬÙģ", "ãĥ¼ ãĥ©", "ãĥ¼ãĥ ©", "ãģł ãĤįãģĨ", "ĠÙĪاØŃ Ø¯", "ĠÙĪا ØŃد", "ر ÙĪس", "رÙĪ س", "Ġzákon a", "ĠпеÑĢ еб", "ĠпеÑĢе б", "à¥Ģ -", "à¹Ī à¹Ħà¸Ķ", "为 äºĨ", "ÎĻ ÎĿ", "ĠìĽĶ ìĦ¸", "ส à¸Ńà¸ĩ", "Ġ æīĭ", "Ġæī ĭ", "Ġ ÐĴÑģе", "ĠÐĴ Ñģе", "ĠÐĴÑģ е", "à¹Ĥ ย", "Ġkal dır", "Ġkaldı r", "ÏĦ ÎŃÏĤ", "ÏĦÎŃ ÏĤ", "Ġ ï¿£", "Ġ íĸĪëĭ¤", "Ġíĸ Īëĭ¤", "ĠíĸĪ ëĭ¤", "ãĤģ ãģŁ", "Ġ Äįer", "ĠÄį er", "ĠÄįe r", "c ela", "ce la", "cel a", "üs ü", "ê³ ³", "ìĹIJ ëıĦ", "ز Ø©", "ãģª ãĤĭ", "ÙĪ ÛĮÙĨ", "ÙĪÛĮ ÙĨ", "çī Ľ", "Ġ voj", "Ġv oj", "Ġvo j", "Ġ ëĬIJ", "ĠëĬ IJ", "Ġ ÙĥÙħ", "ĠÙĥ Ùħ", "æ³ ī", "з Ñı", "è£ Ŀ", "ĠØ¢ ÙĦ", "Ġ ανά", "Ġα νά", "Ġαν ά", "Âł ÐĴ", "Ġyap ıl", "Ġyapı l", "æı Ľ", "ĠÑģ ÑĥÑīеÑģÑĤв", "ĠÑģÑĥ ÑīеÑģÑĤв", "ĠÑģÑĥÑīе ÑģÑĤв", "Ġn á»iji", "ÙĪ ئ", "ĠëĦ¤ìĿ´íĬ¸ ìĺ¨", "Ġpolit ik", "Å¡ ka", "Å¡k a", "ebilir siniz", "ld kf", "Ñĥб лÑĸ", "Ġe oq", "Ġeo q", "ĠÙħØŃ ØµÙĪÙĦ", "krv ldkf", "Ġeoq krvldkf", "Ïĥε Ïīν", "بÙĦ غ", "Įĵ ê¸Ģ", "ĠÑģ ÑĢок", "ĠU y", "ĠN ÄĽk", "ĠNÄĽ k", "Ġ див", "Ġд ив", "Ġди в", "ãĤµ ãĤ¤", "Ġ ìĤ¬ìĿ´", "ĠìĤ¬ ìĿ´", "Ġ éĹ", "Ġé Ĺ", "Ġб аÑĤÑĮ", "Ġба ÑĤÑĮ", "Ġп еÑĢÑĸ", "ĠпеÑĢ Ñĸ", " ĸ", "交 éĢļ", "ен з", "ÙĪ ست", "ÙĪس ت", "ีย à¸ļ", "Ġ à¸Īะ", "Ġà¸Ī ะ", "ë¡ Ģ", "üf us", "Ùij ÙIJ", "ç¸ ½", "ัà¸Ķ ส", "ê² Ģ", "ĠÑĤ иÑħ", "ĠÑĤи Ñħ", "ĠØ¢ زÙħ", "Ġآز Ùħ", "Ġ اض", "Ġا ض", "ĠØ§Ø ¶", "ì ¡´", "ì¡ ´", "ÙĴ ت", "æĪ ¸", "ĠìŀĪ ìĿĦ", "Ġ çĶ·", "Ñī Ñĸ", "о ма", "ом а", "ĠاÙģز اÛĮØ´", "Ġ Thông", "ĠTh ông", "ĠاجتÙħاع ÛĮ", "е лÑİ", "ел Ñİ", "ĠÑħоÑĢоÑĪ о", "à¸ł าษ", "Ġ rám", "Ġr ám", "Ġrá m", "å¾ ¡", "ãĥ¼ ãĥĦ", "ãĥ¼ãĥ Ħ", "ĠL Ỽp", "Ġ Ø´ÙĬ", "ĠØ´ ÙĬ", "Ġh iá»ĥm", "Ġhi á»ĥm", "θ ν", "ο ÏħÏĥ", "οÏħ Ïĥ", "å¾ ©", "Ġú zem", "à¹ģ à¸ľ", "å ·¨", "å· ¨", "à¸Ī à¸Ļ", "Ú¯ راÙĨ", "گر اÙĨ", "Ġت ÛĮÙħ", "ĠتÛĮ Ùħ", "Ġ ilet", "Ġi let", "Ġil et", "Ġile t", "า à¸Ĥà¸Ńà¸ĩ", "าà¸Ĥ à¸Ńà¸ĩ", "Ġ تÙĪر", "Ġت ÙĪر", "ĠتÙĪ ر", "Ġдо говоÑĢ", "Ġдог овоÑĢ", "Ġдогов оÑĢ", "Ġt ento", "Ġten to", "Ġtent o", "в Ñĥ", "Ġз ада", "Ġза да", "Ġзад а", "Ġstole tÃŃ", "Ġstol etÃŃ", "Âł Ġ", "âĢĮ اÙĦ", "Ë ĺ", "ÅŁ iv", "ÅŁi v", "н ÑıÑĤи", "нÑı ÑĤи", "нÑıÑĤ и", "ãĤī ãĤĮãģŁ", "ãĤīãĤĮ ãģŁ", "ĠS b", "ĠاÙĦÙħ ص", "ĠУкÑĢаÑĹ Ð½Ñĸ", "ĠУкÑĢаÑĹн Ñĸ", "ĠØ´ Ú©", "iế ng", "iến g", "ÑĮ ÑĤе", "è° ¢", "ĠÙħ تÙĨ", "ĠÙħت ÙĨ", "Ġ ÑĢад", "ĠÑĢ ад", "ĠÑĢаР´", "ĠÑĢа д", "ĠÙħÙĪ اد", "ì± Ħ", "é¡ ¶", "Ġbo ÅŁ", "ت ÙĪر", "تÙĪ ر", "ĠÄij áng", "ĠÄijá ng", "Ġkit ap", "Ġki tap", "Ġkita p", "Ġho din", "Ġhod in", "Ġtarih i", "ãĤĦ ãĤĭ", "Ñģ ÑĤеÑĢ", "ÑģÑĤ еÑĢ", "ÑģÑĤе ÑĢ", "Ġ Ñħод", "ĠÑħ од", "в ание", "ва ние", "ван ие", "ĠоÑģ вÑĸ", "ĠÑģиÑģÑĤем Ñĭ", "़ न", "Ïĩ ο", "Ġ åı°", "Ġåı °", "o ÅĻ", "ç»ı æµİ", "Ġ ä½ľ", "Ġthu áºŃn", "Ľ Ī", "Ġy alnız", "a let", "al et", "ale t", "ì¦Ŀ ê¸Ī", "Ġза Ñī", "Ġе кÑģп", "Ġек Ñģп", "âĦĸ âĦĸ", "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠÚ¯ ÙĪØ´", "ĠÚ¯ÙĪ Ø´", "ãģ« åħ¥", "Ġu dÄĽl", "Ġud ÄĽl", "Ġ áº", "Ġá º", "à¤Ĩ à¤Ī", "âĢĮ دÙĩ", "âĢĮد Ùĩ", "æĤ ª", "Ġtr ò", "æļ Ĺ", "λλ ην", "λλη ν", "ĠпÑĢи зна", "ĠпÑĢиз на", "Ġس ÛĮستÙħ", "ĠسÛĮ ستÙħ", "Ġà¤ħ त", "è o", "è¿ İ", "Ġз Ñĥб", "ĠзаÑģ об", "Ġس Ùģ", "ĠÙħاÙĨ ÙĨد", "Ø® Ø´", "v ajÃŃ", "va jÃŃ", "nit ÅĻ", "æ¯ Ĵ", "æ¤ į", "Ġgir iÅŁ", "ĠÄij áp", "ĠÄijá p", "@ n", "ов аÑĢи", "оваÑĢ и", "ова ÑĢи", "ĠØ® دا", "Ġخد ا", "Ġv ÄĽtÅ¡", "ĠvÄĽt Å¡", "ĠΣ Ïħ", "Ùģ Ø©", "аннÑı м", "ĠÑĩ лен", "æĶ¯ æĮģ", "å¨ ľ", "lar arası", "lara rası", "Ρ Îij", "Ġz iy", "Ġzi y", "Ġ êµIJìľ¡", "ĠêµIJ ìľ¡", "Ġh á»ĵi", "Ġhá»ĵ i", "าà¸Ħ าร", "าà¸Ħา ร", "im leri", "imler i", "è³ ¼", "ĠجÙĩ اÙĨ", "ĠÑĢоз мÑĸ", "Ñħ Ñĸв", "γ ε", "æ¨ ª", "ÎĻ ÎijΣ", "ÎĻÎij Σ", "ç¶ Ń", "Ġbi raz", "Ġbir az", "ĠÑĤак ого", "ĠÑĤа кого", "íĥ Ħ", "ĠбÑĥд ÑĥÑĤ", "ĠбÑĥ дÑĥÑĤ", "ĠбÑĥдÑĥ ÑĤ", "ĠÑĪ вид", "Ġ неÑģ", "Ġн еÑģ", "Ġне Ñģ", "ĠÙħ عÙĦÙĪÙħات", "ĠÙħعÙĦ ÙĪÙħات", "à¥ĩ यर", "à¥ĩय र", "Ġдв ÑĥÑħ", "å¿ħ è¦ģ", "å§ Ĩ", "Ġpo hled", "Ġpoh led", "ìĬ¤ íĦ°", "Ġ åįģ", "Ġåį ģ", "ĠØ£ ب", "веÑĢ дж", "веÑĢд ж", "Ġà¤ľ म", "ल त", "åľ° åĮº", "Ġ |[", "Ġ| [", "Ġв меÑģÑĤ", "ĠÚ© اÙħ", "Ġ ãĥIJ", "Ġãĥ IJ", "ãĥ¼ ãĥĸ", "ãĥ¼ãĥ ĸ", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "Ġ ìĥģíĴĪ", "Ġìĥģ íĴĪ", "à¹Ģล ย", "Äį né", "ĠÑģÑĢед ÑģÑĤва", "ĠÑģÑĢедÑģÑĤв а", "Ġ ÑĤаб", "ĠÑĤ аб", "ĠÑĤа б", "Ġ Ùħار", "ĠÙħ ار", "ĠÙħا ر", "Ġ hled", "Ġh led", "Ġhl ed", "д аÑĤ", "да ÑĤ", "ÙĪ ÛĮد", "ÙĪÛĮ د", "Ġ ãĥ©", "Ġãĥ ©", "ĠØ® د", "è¤ ĩ", "ç§ ĺ", "Ġ برد", "Ġب رد", "Ġبر د", "ĠÏĥ αÏĤ", "Ïİ ÏĥειÏĤ", "æĿ ¯", "λ Ïį", "å® ¿", "Ġ ëĤľ", "ĠëĤ ľ", "ï» Ł", "Ġözel likle", "Ġözellik le", "Ġкон Ñģ", "ĠÙħ غ", "ع ÙĬ", "à¹Į à¸ģ", "Ġ ÙĬت", "ĠÙĬ ت", "ĠÙħ شاÙĩ", "ĠÙħØ´ اÙĩ", "ĠTh anh", "ĠThan h", "ा à¤ľà¤¨", "à¤¾à¤ľ न", "¥ ¤", "Ġv lá", "Ġvl á", "ĠÙģ ض", "Τ ÎĻÎļ", "Ġна Ñĥков", "ĠнаÑĥк ов", "е лем", "ел ем", "еле м", "Ġd Ãłng", "ĠгоÑģп одаÑĢ", "Âł S", "и ÑĩеÑģкиÑħ", "иÑĩеÑģ киÑħ", "иÑĩеÑģки Ñħ", "ĠتÙĨ Ùĩا", "à¤ľ न", "Ġп ан", "Ġпа н", "åĨ ł", "Ġ ëĤĺëĬĶ", "ĠëĤĺ ëĬĶ", "t ÃŃ", "ä¸Ģ èµ·", "Ġlã nh", "Âł v", "ov ým", "ový m", "ز ب", "ĠجÙħع ÛĮت", "Ġ æµ·", "Ġæµ ·", "ĠоÑģ ÑĥÑīеÑģÑĤв", "à £i", "ã i", "ائ ر", "Ġ ë³ij", "Ġë³ ij", "á»ĭ nh", "Ġs á»Ńa", "Ġsá»Ń a", "à¥ĩà¤Ĥ ।", "ÄĽ jÅ¡ÃŃ", "ÄĽj Å¡ÃŃ", "Ġд ÑĸÑĤ", "ĠдÑĸ ÑĤ", "Ġ æĥ", "Ġæ ĥ", "mÄ±ÅŁ tı", "ر ØŃ", "Ġì§Ģ ê¸Ī", "å¦ »", "âĹ ĭ", "Ġ ì§ĢìĹŃ", "Ġì§Ģ ìĹŃ", "ÙĴ ÙĨ", "Ġur Äįit", "ĠurÄį it", "ÙĴ Ùħ", "z ÃŃ", "è ķ", "Ġ Ø´ÙĪر", "ĠØ´ ÙĪر", "ĠØ´ÙĪ ر", "Ġ Không", "ĠKh ông", "ÛĮ زÛĮ", "ÛĮز ÛĮ", "Ġз г", "Ġв не", "Ġpr ávÄĽ", "Ġprá vÄĽ", "Ġpráv ÄĽ", "è« ĭ", "ا ÙĬت", "اÙĬ ت", "ั à¸ģร", "ัà¸ģ ร", "Ġolduk ça", "ãĤģ ãĤĭ", "ĠT ây", "ëĿ¼ ìĿ¸", "èĻ ķ", "Ġs Æ°", "Ġ ник", "Ġн ик", "Ġни к", "Ù ł", "اش ÛĮÙĨ", "اشÛĮ ÙĨ", "e lerde", "el erde", "eler de", "ìĭľ ìķĦ", "Ġ Ñĥмов", "ĠÑĥ мов", "ĠÑĥм ов", "ĠçalÄ±ÅŁ an", "Ġ ë¸Ķ", "Ġë¸ Ķ", "ĠÑĤак им", "ĠÑĤа ким", "ÑĢ ин", "ÑĢи н", "Ġ Ø®ÙĦ", "ĠØ® ÙĦ", "a yd", "ay d", "Ġ ãĥ¡", "Ġãĥ ¡", "ей ÑĩаÑģ", "Ġdo prav", "Ġdop rav", "ãģĵ ãģ¨ãģ¯", "ãģĵãģ¨ ãģ¯", "Ġ ì¶Ķì²ľ", "Ġì¶Ķ ì²ľ", "å» ¶", "Ġ kı", "Ġk ı", "åı ¶", "ÑĢ иг", "ÑĢи г", "íħ ľ", "çĶ³ åįļ", "Ġ веÑĤ", "Ġв еÑĤ", "Ġве ÑĤ", "ĠпомоÑī ÑĮÑİ", "ĠاÙģ راد", "ĠاÙģر اد", "ÏĢ ει", "ÏĢε ι", "à¹Ģ สร", "à¹Ģส ร", "Ġgi ám", "Ġgiá m", "é İ", "h las", "hl as", "man ız", "manı z", "ан гл", "анг л", "Ġmu ž", "Âł K", "ÑĢед иÑĤ", "ÑĢеди ÑĤ", "设 å¤ĩ", "ι Ïĥμ", "ιÏĥ μ", "Ġc ải", "Ġcả i", "Ġ éĢļ", "ĠéĢ ļ", "Ġ Ùĥار", "ĠÙĥ ار", "Ġпод об", "ĠмеÑĤ ал", "ĠÑģам е", "л ÑĥÑĩ", "лÑĥ Ñĩ", "åĤ ³", "ĠÙĪÙĩ ÙĪ", "Ġ éĩį", "Ġéĩ į", "в ий", "ви й", "æ³ ģ", "Ġ æĿİ", "ĠæĿ İ", "ĠiliÅŁ kin", "ĠiliÅŁk in", "Ġεί Ïĩε", "çĬ ¯", "ÅĻej mÄĽ", "èŃ ĺ", "ç¨ ±", "μ μα", "μμ α", "Ġ ÙĦÛĮ", "ĠÙĦ ÛĮ", "Ùĩ اÙĬ", "Ùĩا ÙĬ", "Ġ опиÑģ", "Ġо пиÑģ", "Ġоп иÑģ", "Ú¯ رد", "گر د", "Ġг ÑĢ", "ĠAnimal ia", "ĠAnim alia", "ÐŁ о", "Ġb óng", "ĠдеÑĤ ей", "Ġl âu", "Ġ æķĻ", "Ġæķ Ļ", "Ġпо ÑıÑģ", "ĠاÙĦ Ø¢", "ั à¸Ļà¸ķ", "ัà¸Ļ à¸ķ", "Ġд ев", "Ġде в", "Ġ ÑĨей", "ĠÑĨ ей", "ĠÑĨе й", "ÑĮ в", "æĥ ł", "m aları", "ma ları", "mal arı", "malar ı", "im ler", "à¥Ī ।ĊĊ", "à¥Ī। ĊĊ", "à¥Ī।Ċ Ċ", "Ġ ном", "Ġн ом", "Ġно м", "z v", "Ġ à¸ģร", "Ġà¸ģ ร", "Ġpay laÅŁ", "Âł s", "ि सम", "िस म", "ÑģÑĤв еннÑĭÑħ", "ÑģÑĤвен нÑĭÑħ", "st oup", "sto up", "о нÑĸ", "он Ñĸ", "s tÃŃ", "st ÃŃ", "ĠØŃ Ú©", "ĠÚ¯ رÙģت", "ĠگرÙģ ت", "Ġگر Ùģت", "าà¸Ħ า", "д Ñı", "ÙĦ اث", "ÙĦا Ø«", "Ġzdrav ot", "ä¸Ĭ ãģĴ", "ãģ ¼", "e lere", "el ere", "eler e", "ele re", "ظ Ùħ", "ĠÑģ веÑĤ", "ĠÑģв еÑĤ", "о ÑĢг", "оÑĢ г", "ç« ¥", "ĠпеÑĢ еп", "ĠпеÑĢе п", "Ġम द", "а за", "аз а", "å¦Ĥ ä½ķ", "ÑģÑĮ кÑĸй", "ÑģÑĮк Ñĸй", "ÑģÑĮкÑĸ й", "Ġb Æ°á»Ľc", "Ġger ekli", "Ġgerek li", "大 家", "Ġtr ái", "éģ ©", "ä¸Ń 央", "Ġph ản", "Ġع رض", "ĠÙĥ تاب", "æĭ ©", "ÑĪ его", "ÑĪе го", "å¸ ®", "ĠÙĨÛĮ از", "è¿ ·", "ุ à¸Ľ", "ิ à¸Ľ", "Ġد Ø®", "ÏĦ ικÎŃÏĤ", "ÏĦικ ÎŃÏĤ", "ĠU z", "Ġت ÙĪÙħاÙĨ", "ĠتÙĪ ÙħاÙĨ", "ĠتÙĪÙħ اÙĨ", "ĠÙĪ اÙĦØ£", "ĠÙĪاÙĦ Ø£", "ÅĻ es", "ÅĻe s", "Ñij м", "Ġ å¸Ĥ", "Ġå¸ Ĥ", "ĠÑĤ оже", "ĠÑĤо же", "Ġy apan", "Ġya pan", "Ġyap an", "å½¼ 女", "ĠÙħ در", "ĠÙħد ر", "¶ ģ", "Ġ æŶ", "ĠæĹ ¶", "à¹Ģ à¸ĺ", "à¹Ģภĺ", "Ġ ÙħاÙĦ", "ĠÙħ اÙĦ", "ĠÙħا ÙĦ", "ĠB üyük", "ĠBü yük", "Ġ ÙĦت", "ĠÙĦ ت", "å° ļ", "d eme", "de me", "dem e", "ü b", "ĠÑħ Ñĥд", "Ġlé ka", "çĽ Ľ", "缴 æİ¥", "ниÑĨÑĤ ва", "ĠпÑĢи Ñĩин", "ĠпÑĢиÑĩ ин", "е ÑĢап", "еÑĢ ап", "еÑĢа п", "ĠÑģозд а", "ĠÑģоз да", "æ ¢°", "æ¢ °", "Ġm üz", "Ġmü z", "ç³» åĪĹ", "o uz", "ou z", "Ġà¤ĵ र", "ÑĢ ÑĥÑĩ", "ÑĢÑĥ Ñĩ", "Ġ á½", "Ġá ½", "μÎŃ Î½Î±", "μÎŃν α", "ĠпÑĢед меÑĤ", "Ġ å²", "Ġå ²", "ãĥ³ ãĥģ", "ãĥ³ãĥ ģ", "μÎŃ Î½Î·", "μÎŃν η", "л Ñĥг", "лÑĥ г", "Âł n", "ĠT arih", "ĠTar ih", "Ġ ãĢĪ", "ĠãĢ Ī", "Ġb ana", "Ġban a", "Ġba na", "Ġ cÃŃ", "Ġc ÃŃ", "Ġvý kon", "åĽł æѤ", "Ġt ÅĻi", "ĠtÅĻ i", "า à¸ĭ", "าภĭ", "v ailable", "vail able", "Ġ istem", "Ġis tem", "Ġi stem", "Ġist em", "ãĥ¥ ãĥ¼", "Ðķ ÐĿ", "Ġ гаÑĢ", "Ġг аÑĢ", "οÏħ λ", "ॠĽ", "ĠÙĪ ضع", "ส ะ", "è· Ŀ", "ĠØŃ Ùģ", "ิà¸Ĺย าล", "ิà¸Ĺยา ล", "她 çļĦ", "н ÑĸÑĪ", "нÑĸ ÑĪ", "ж ение", "же ние", "жен ие", "기 ìĹIJ", "Ġ éĺ¿", "Ġéĺ ¿", "ĠÙħ ارس", "ĠÙħا رس", "ĠÙħار س", "ĠçeÅŁit li", "Ġ ÅŁehir", "ĠÅŁ ehir", "ĠÅŁeh ir", "á tor", "át or", "à¹ī à¸Ĺ", "ìĿ´ ëĬĶ", "Ġ è²", "Ġè ²", "é¡ į", "ç ĻĤ", "çĻ Ĥ", "Ġ ниÑĩ", "Ġн иÑĩ", "Ġни Ñĩ", "Ġ ê°Ģì§Ģ", "Ġê°Ģ ì§Ģ", "ä¼ ¦", "r án", "rá n", "o stat", "os tat", "ost at", "osta t", "Ġ ÙĦÙĥ", "ĠÙĦ Ùĥ", "è º", "ĠNg Ãłnh", "Ġस द", "æľ Ĺ", "çĦ¶ åIJİ", "ãĤ¸ ãĤ§", "л еÑĢ", "ле ÑĢ", "ĠÐŀ на", "ĠÐŀн а", "س ÙĪÙĨ", "سÙĪ ÙĨ", "Ïģ ον", "Ïģο ν", "ĠدرÛĮ اÙģت", "ĠدرÛĮا Ùģت", "à¸Ń à¸Ńà¸Ļà¹Ħลà¸Ļ", "Ġ dál", "Ġd ál", "Ġdá l", "ĠмÑĸÑģ ÑĨе", "Ġд ней", "Ġ اÙĦات", "Ġا ÙĦات", "ĠاÙĦ ات", "Ġरह त", "ï¼Į 对", "è³ĩ æĸĻ", "ä»» ä½ķ", "é Ħ", "t aj", "ta j", "β ά", "Ġна до", "Ġнад о", "ĠÑģÑĤ Ñĥд", "ĠÅŁ eh", "ัà¸į à¸į", "à¥ĭ ब", "ãĥ© ãĥ¼", "Û± Ûµ", "Û±Û µ", "e pt", "ep t", "Ġbil dir", "Ġbild ir", "ส à¸ĸาà¸Ļ", "สà¸ĸ าà¸Ļ", "е ÑĤÑĮÑģÑı", "еÑĤÑĮ ÑģÑı", "sk ým", "ský m", "Ġобла ÑģÑĤÑĮ", "ĠоблаÑģ ÑĤÑĮ", "ĠоблаÑģÑĤ ÑĮ", "Ġìŀ ł", "ĠG ör", "ĠGö r", "Ġd ayan", "Ġday an", "Ġda yan", "ĠÛĮ اد", "ĠÛĮا د", "çĶŁ 产", "íĺ ij", "å¾ ģ", "Ġ اجر", "Ġا جر", "Ġاج ر", "Ġп ÑĢе", "ĠпÑĢ е", "ä¸īä¸ī ä¸īä¸ī", "åŁİ å¸Ĥ", "Ġ пÑĢимеÑĢ", "ĠпÑĢ имеÑĢ", "ĠпÑĢи меÑĢ", "ĠпÑĢим еÑĢ", "Äį ást", "èģ ĺ", "ĠÙħرب ÙĪØ·", "æŀ ļ", "åĪ Ģ", "æŁ¥ çľĭ", "Ġ모 ëijIJ", "ìŀIJ ë£Į", "- ､", "Ġê°Ļ ìĿ´", "Ġ ì¡´", "Ġì ¡´", "Ġì¡ ´", "е гоÑĢ", "ег оÑĢ", "его ÑĢ", "e dik", "ed ik", "edi k", "и мÑĥ", "им Ñĥ", "ĠAr th", "ĠArt h", "åºĶ çĶ¨", "m iÅŁti", "miÅŁ ti", "Ġkhá»ı e", "Ġ Ñĸд", "ĠÑĸ д", "λ λη", "λλ η", "â h", "м аг", "ма г", "éļ Ĩ", "ĠвнÑĥ ÑĤÑĢ", "ĠвнÑĥÑĤ ÑĢ", "Ġ بط", "Ġب Ø·", "( æĹ¥", "Ä° Y", "л ик", "ли к", "ĠB ản", "Ġت ÙĪس", "ĠتÙĪ س", "़ त", "a mak", "am ak", "ama k", "åķı é¡Į", "ĠÑģам оÑģÑĤ", "ĠÑģамо ÑģÑĤ", "ï¼¼ Ċ", "Ġ ç¦ı", "Ġç¦ ı", "Ù ¡", "Ġ ÑĦоÑĢми", "ĠÑĦоÑĢм и", "ĠÑĦоÑĢ ми", "Ġ ÑĢозÑĥм", "ĠÑĢоз Ñĥм", "ĠÙħ طاÙĦ", "ĠÙħØ· اÙĦ", "ä¹Ł æĺ¯", "ç¾İ åĽ½", "ëĵľ 립ëĭĪëĭ¤", "Ġl Ä©nh", "ĠпоÑĤ омÑĥ", "ĠпоÑĤом Ñĥ", "Ñı бÑĢÑı", "Ñıб ÑĢÑı", "æ¼ «", "Ġng oại", "à¸Ń ำ", "ÙĬ ÙĨا", "ÙĬÙĨ ا", "Ġm lad", "Ġml ad", "Ïĥ ÏĦά", "ÏĥÏĦ ά", "ا تر", "ات ر", "주 ìĿĺ", "ен нÑĸ", "о за", "оз а", "ÙĤ ات", "ĠÐĴ аÑģ", "è® Ń", "é IJ", "Ñĥ ÑİÑĩи", "ÑĥÑİ Ñĩи", "Ġ کر", "ĠÚ© ر", "Ġ .|", "Ġ. |", "Ġgen ç", "è© ²", "ä» ģ", "о дÑĭ", "од Ñĭ", "ĠØ£ ÙĪÙĦ", "ĠØ£ÙĪ ÙĦ", "Ġ ìĤ¬íļĮ", "ĠìĤ¬ íļĮ", "Ġ à¹Ģส", "Ġà¹Ģ ส", "Ġà¹Ģภª", "ĠëķĮ문 ìĹIJ", "âĢĮ ب", "Ġли ÑĪÑĮ", "ĠлиÑĪ ÑĮ", "Ġи менно", "Ġим енно", "m adı", "ma dı", "mad ı", "Ġ éĤ", "Ġé Ĥ", "ĠÙĪ ارد", "ĠÙĪا رد", "Ġtak ım", "Ġ à¹Ģห", "Ġà¹Ģ ห", "Ġà¹Ģภ«", "Ġ à¸Ńย", "Ġà¸Ń ย", "Ġkon usu", "Ġkonu su", "Ġkonus u", "Ø® ÙĪ", "ĠÑģ ид", "ĠÑģи д", "èµ ¤", "о ÑıÑĤелÑĮ", "оÑıÑĤ елÑĮ", "ëĭ µ", "ε Ïī", "Ñĸ Ñħ", "Ġय द", "ĠÚ© ÛĮÙģ", "ĠÚ©ÛĮ Ùģ", "μ οÏĤ", "μο ÏĤ", "Ġal dı", "Ġald ı", "Ġ íĻį", "ĠíĻ į", "к Ñĥп", "кÑĥ п", "ĠÙĨÙħ اÛĮØ´", "ĠÙĨÙħاÛĮ Ø´", "ãģ ¥", "Ġ íķ©ëĭĪëĭ¤", "Ġíķ ©ëĭĪëĭ¤", "Ġíķ© ëĭĪëĭ¤", "Ġë Įĵê¸Ģ", "б оÑĢа", "бо ÑĢа", "боÑĢ а", "éī Ħ", "Ġ à¹Ģà¸Ī", "Ġà¹Ģ à¸Ī", "Ġà¹ĢภĪ", "à¹ī à¸ģ", "§ Ø·", "ر بÙĩ", "رب Ùĩ", "Ġ Ñĥз", "ĠÑĥ з", "Ġм аÑİÑĤÑĮ", "Ġма ÑİÑĤÑĮ", "Ġby li", "Ġbyl i", "ี à¸ķ", "Ġ ì§ĢìĽIJ", "Ġì§Ģ ìĽIJ", "èĩª çĦ¶", "ù y", "Ġç aÄŁ", "Ġça ÄŁ", "е дин", "ед ин", "еди н", "ë ī´", "åį ±", "Ġпоз волÑı", "Ġпозвол Ñı", "ØŃ Ø§Ø¯", "ĠÑĩ его", "ีย ร", "Ġyön tem", "Ġ ders", "Ġd ers", "Ġde rs", "Ġder s", "Ġ ÑģÑĤоÑı", "ĠÑģÑĤ оÑı", "ĠÑģÑĤо Ñı", "Ġк ÑĢÑĥп", "Ġ ð", "Ġдом аÑĪ", "Ġдома ÑĪ", "е нд", "ен д", "ç» §", "ĠÄij ô", "Ġch tÄĽ", "计 åĪĴ", "ÎŃ Î±", "Ġdob ÅĻe", "ส à¸Ńà¸ļ", "е ление", "ел ение", "еле ние", "елен ие", "ĠÄij ông", "ĠÄijô ng", "ãģ¾ ãĤĬ", "Ġboy unca", "à¥ģ à¤Ĺ", "à¥ģठĹ", "ĠÑĦ из", "ãĤ³ ãĥ³", "Ġde ney", "Ġden ey", "ÑĩеÑģ киÑħ", "Ñĩе ÑģкиÑħ", "ÑĩеÑģки Ñħ", "λ ον", "λο ν", "以 åıĬ", "ا ÙĪت", "اÙĪ ت", "Âł ³³³³", "³³ ³³³", "³³³³ Âł", "³³³ ³³", "Ġ ì¤Ħ", "Ġì¤ Ħ", "ि फ", "िठ«", "ĠÑĤ ол", "ĠÑĤо л", "ĠëĤ´ ê°Ģ", "âĸ ı", "Ġp há", "Ġph á", "ĠÑģп Ñĸв", "Ġ جÙħÙĬع", "ĠجÙħ ÙĬع", "Ġb ezpeÄį", "Ġbez peÄį", "Ġ æĹł", "ĠæĹ ł", "Ġv Å¡e", "ĠvÅ¡ e", "ÑģÑĤ вÑĥ", "ÑģÑĤв Ñĥ", "d ust", "du st", "o Å¡", "Ġت ارÙĬØ®", "ا ØŃØ©", "اØŃ Ø©", "ĠÙħشار ÙĥØ©", "Ġ ακ", "Ġα κ", "ั à¸Ļà¸Ļ", "ัà¸Ļ à¸Ļ", "éģ Ĭ", "Ġ ÑģоÑĤ", "ĠÑģ оÑĤ", "ĠÑģо ÑĤ", "Ġ каз", "Ġк аз", "Ġка з", "ĠÑĤ еÑĩение", "ĠÑĤеÑĩ ение", "ê¸ ´", "acak tır", "ê±° ëĤĺ", "ี ยม", "ีย ม", "ĠÑģ ÑĥÑħ", "ĠÑģÑĥ Ñħ", "ĠëĦĪ 무", "ãģı ãĤĭ", "ĠкоÑĤоÑĢ ой", "ا ÙĤØ©", "اÙĤ Ø©", "y ıl", "yı l", "ãĤ» ãĥĥãĥĪ", "ĠÑį лем", "æģ IJ", "ÙĨ اء", "ÙĨا Ø¡", "åħ ©", "Ġte Äı", "ä¸ ¥", "Ġì§Ī 문", "Ġ 为", "Ġä¸ º", "ìĭľ íĹĺ", "Ġп ÑĢок", "ĠпÑĢ ок", "ĠпÑĢо к", "u jeme", "uj eme", "uje me", "ü cü", "üc ü", "ĠاÙĦÙħ غ", "ĠØŃ Ø³Ø§Ø¨", "ĠØŃس اب", "ãģĹ ãģ¦ãģĦ", "ãģĹãģ¦ ãģĦ", "к ова", "ко ва", "ков а", "ĠÄij Ãło", "Ġп ÑĢиз", "ĠпÑĢ из", "ĠпÑĢи з", "ĠÙĪ ÙħÙĨ", "ĠÙĪÙħ ÙĨ", "Ġ оÑĢ", "Ġо ÑĢ", "à¸ģ à¸ķ", "а ÑĦ", "Ġ à¸ŀร", "Ġà¸ŀ ร", "ÑĨи ей", "æ ª", "Ġ působ", "Ġp ůsob", "Ġpů sob", "åŃ© åŃIJ", "Ġb ánh", "Ġbán h", "ĠÑĦоÑĢм Ñĥ", "ĠÑĦоÑĢ мÑĥ", "Ġ á»ķ", "Ġá» ķ", "Ġмен ее", "Ġмене е", "à¹ī าห", "à¹īา ห", "ни ÑĨа", "ниÑĨ а", "ี Ċ", "Ġв олоÑģ", "Ġвол оÑģ", "Ġار ائÙĩ", "第 ä¸ī", "ëIJĺ ìĹĪ", "Ġkıs m", "Ġkı sm", "ãĥ¼ ãĥĬ", "ãĥ¼ãĥ Ĭ", "ler imiz", "ÙĨ ÙĬÙĨ", "ÙĨÙĬ ÙĨ", "Ġ NgÆ°á»Ŀi", "ĠNg Æ°á»Ŀi", "ĠоÑĤ дел", "ĠоÑĤд ел", "çļĦ æŶåĢĻ", "о нов", "он ов", "Äį an", "i zm", "iz m", "ĠÑģоб ой", "à¹ĩ à¸ķ", "Ġ ÑģлÑĸд", "ĠÑģ лÑĸд", "ĠÑģл Ñĸд", "Ġ à¤ľà¤¹", "Ġà¤ľ ह", "ï¼Į æĪij们", "ï¼ĮæĪij 们", "ãĢĤ ãģĿãģ®", "ÏĢ ÏīÏĤ", "çĨ Ł", "ภ¯", "ëĦ IJ", "æľ ĭ", "Ġë¹Ħ ë°Ģ", "ëį ķ", "Ġm Ãłn", "ĠmÃł n", "ìĿ´ ê³ł", "ëŀľ ëĵľ", "éĤ Ħ", "Ä±ÅŁ ık", "Ä±ÅŁÄ± k", "Ġ 个", "Ġä¸ ª", "Ġn ád", "Ġná d", "б ÑĢа", "æĮĩ å®ļ", "lar ıyla", "ları yla", "ĠÐŀ ни", "ĠÐŀн и", "Ġ hra", "Ġh ra", "Ġhr a", "ĠÑĢе ÑĨеп", "ĠÐłÐ¾Ñģ Ñģий", "å½± åĵį", "Ġ Když", "ĠK dyž", "ĠÃ¶ÄŁ renc", "ĠÃ¶ÄŁren c", "åī µ", "Ġ jist", "Ġj ist", "Ġji st", "èĪ Ī", "è§ ¦", "åıij çİ°", "ม าย", "มา ย", "er ken", "erk en", "Ġзд еÑģÑĮ", "ĠÙħس ئ", "@n ate", "ĠëĤ´ ìļ©", "Ġnab ÃŃd", "ĠnabÃŃ d", "Û Ģ", "Ġмо менÑĤ", "Ġмом енÑĤ", "ãģł ãģĮ", "ί δα", "ίδ α", "T ak", "Ta k", "Ġ ë³´ê³ł", "Ġë³´ ê³ł", ": ::::::::", ":: :::::::", ":::: :::::", ":::::: :::", ":::::::: :", "::: ::::::", "::::: ::::", "::::::: ::", "ÄŁ men", "Ġпо меÑī", "Ġпом еÑī", "ãģ«ãģ¤ ãģĦãģ¦", "ĠÙģ ÙĪÙĤ", "ĠÙģÙĪ ÙĤ", "Ġع ضÙĪ", "ĠÙħ ÛĮاÙĨ", "ĠÙħÛĮ اÙĨ", "Ġm üc", "Ġmü c", "ĠпÑĢо Ñıв", "ÑĩеÑģ ки", "Ñĩе Ñģки", "ãģł ãģĭãĤī", "éĤ ¦", "Ġ ë¶ĦìĦĿ", "Ġë¶Ħ ìĦĿ", "éŁ ©", "į ¨", "ĠD aha", "ĠDa ha", "ĠDah a", "Ġ κÏĮ", "Ġκ ÏĮ", "Ġна Ñĩина", "ĠнаÑĩ ина", "ĠÐŁ оÑĤ", "ĠÐŁÐ¾ ÑĤ", "Ïĥκε Ïħ", "Ïĥκ εÏħ", "Ġ ÑĢан", "ĠÑĢ ан", "ĠÑĢаР½", "ĠÑĢа н", "ÙĪ ÙĬس", "ÙĪÙĬ س", ": :::::::::", ":: ::::::::", ":::: ::::::", ":::::: ::::", ":::::::: ::", "::: :::::::", "::::: :::::", "::::::: :::", "::::::::: :", "Û±Û¹ Û¹", "Ġard ından", "à¹Ĥ à¸Ķ", "ا راÙĨ", "ار اÙĨ", "ارا ÙĨ", "د اد", "دا د", "Ġqu ý", "ĠØ£Ùĥ ثر", "âĹ Ĩ", "ĠØ£ خرÙī", "Ġأخ رÙī", "Ġë§Ī ìĿĮ", "ë¦ ´", "Ġ عÙĦÙĪÙħ", "ĠعÙĦ ÙĪÙħ", "Ġe ÄŁ", "воÑĢ Ñİ", "во ÑĢÑİ", "Ġ ãĥĹ", "Ġãĥ Ĺ", "Ñĥ ÑĩаÑģ", "ÑĥÑĩ аÑģ", "ÑĥÑĩа Ñģ", "Ġب Ø£", "ÏĨ ο", "ни ками", "ник ами", "ника ми", "никам и", "à¹ĥ à¸ķ", "Äįet nÄĽ", "à¸ļ าà¸ĩ", "çī Ļ", "ãĥª ãĤ«", "í Ĵ", "åĩº çīĪ", "γ ι", "ãĢĤ ãģĿãĤĮ", "Ġy ani", "Ġya ni", "Ġyan i", "l ech", "le ch", "lec h", "ĠLu áºŃt", "çļĦ ãģª", "Ġneden iyle", "Ġnedeni yle", "d ej", "de j", "ĠÑģов еÑĢÑĪ", "Ġph á»ķ", "ıs ından", "ısında n", "Ġch ắc", "d eÅŁ", "de ÅŁ", "Ġком ан", "Ġко ман", "æĽ ¿", "Ġp lán", "Ġpl án", "Ġplá n", "Ġd ữ", "ĠêµŃ ê°Ģ", "Ġta kip", "Ġtak ip", "Ġth ủy", "Ġthủ y", "Ñģ лÑĸд", "Ñģл Ñĸд", "âī §", "ĠI IC", "ĠII C", "θ Ïħ", "á vat", "áv at", "Ġ Ñģок", "ĠÑģ ок", "ĠÑģо к", "Ġб агаÑĤо", "Ġбаг аÑĤо", "ĠбагаÑĤ о", ";:;: ;:;:", "Ïģ ιοÏĤ", "Ïģι οÏĤ", "Ïģιο ÏĤ", "il miÅŁtir", "ilm iÅŁtir", "ilmiÅŁ tir", "Ġ znam", "Ġz nam", "Ġzn am", "Ġ Τα", "ĠΤ α", "a maz", "am az", "ama z", "à¹ģ à¸ŀ", "ãĥģ ãĥ£", "Ġkullan ı", "æĶ¾ éĢģ", "д н", "ĠÙĪ اب", "ĠÙĪا ب", "Ġtr ắng", "Ñģ Ñıг", "ÑģÑı г", "Ġار تباط", "Ġв Ñħод", "å·ŀ å¸Ĥ", "Ġ सत", "Ġस त", "Ñĩ аеÑĤÑģÑı", "Ñĩа еÑĤÑģÑı", "ÑĩаеÑĤ ÑģÑı", "íĮĮ íĬ¸", "Ġ Những", "ĠNh ững", "ä¸į åı¯", "å± Ĭ", "Ġ ãĤŃ", "ĠãĤ Ń", "ار ÙĩاÛĮ", "ارÙĩ اÛĮ", "Ġar ÅŁiv", "Ġ اÙĦÙī", "Ġا ÙĦÙī", "ĠاÙĦ Ùī", "ाय à¤ķ", "ãģĹ ãĤĩãģĨ", "ãģĹãĤĩ ãģĨ", "Ġ ulus", "Ġu lus", "Ġul us", "al axy", "ala xy", "기 ê°Ģ", "ãİ¡ (", "μά ÏĦÏīν", "è n", "ù i", "Ġна ÑģÑĤоÑı", "ĠнаÑģÑĤ оÑı", "ĠС в", "ĠоÑģ оби", "ĠоÑģоб и", "к ово", "ко во", "ков о", "ĠÑĢеб енка", "ĠÑĢебен ка", "ĠÑĤ Ñıж", "ĠÑĤÑı ж", "Ġxu á»ijng", "Ġ ê¶Į", "Ġê ¶Į", "о год", "ог од", "ого д", "Ġ ấy", "è² ł", "ว à¸Ļ", "Ġ stanov", "Ġsta nov", "Ġstan ov", "Ġk rál", "Ġkr ál", "Ġà¤ĩ सल", "Ġà¤ĩस ल", "e be", "eb e", "å® ¾", "ĠдоÑģÑĤаÑĤ оÑĩно", "II IK", "III K", "ÏĢ ά", "Ġbir kaç", "ĠاÙĦ ÙħÙĤ", "ĠاÙĦÙħ ÙĤ", "ãĥ ¶", "ĠBaÅŁ kanı", "ĠBaÅŁkan ı", "Ġ첨ë¶Ģ íĮĮìĿ¼", "Ġya rar", "Ġyar ar", "äº ¡", "Ġ ÏĢÏĮ", "ĠÏĢ ÏĮ", "Âł Ñģ", "δ ή", "e lerini", "eler ini", "eleri ni", "elerin i", "Ġs uç", "Ġsu ç", "Ġд ома", "Ġдо ма", "Ġдом а", "Ġна ÑĢÑĥÑĪ", "ĠнаÑĢ ÑĥÑĪ", "Ġ ί", "ĠÎ ¯", "Ġê·¸ ìĿĺ", "çĶµ å½±", "ا بÙĩ", "اب Ùĩ", "к омÑĥ", "ко мÑĥ", "ком Ñĥ", "Ġत ब", "à¥Ī à¤ł", "Ġ모 ì§ij", "Ġ æ±Ł", "Ġæ± Ł", "Ġê²ĥ ìĿĢ", "ον ÏĦαι", "ĠاÙĦ رÙĬاض", "è¨ ±", "Ġhal inde", "Ġاش ارÙĩ", "Ġ кÑĢÑĭ", "Ġк ÑĢÑĭ", "л ений", "лен ий", "ле ний", "lu ÄŁ", "Ġdo bu", "Ġdob u", "s ik", "si k", "à¥ģ à¤Ł", "à¥ģठŁ", "Ġ кÑĸн", "Ġк Ñĸн", "ãģ¨ ãģį", "à¥Ĥ स", "æħ ¢", "ĠdÄ±ÅŁ ında", "ĠdÄ±ÅŁÄ± nda", "ç· ı", "Ġ bÃŃ", "Ġb ÃŃ", "ĠCL IIIK", "ĠIIC III", "Ġh erk", "Ġhe rk", "Ġher k", "ãĤı ãģĽ", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "Âł ³³³³³", "³³ ³³³³", "³³³³ ³³", "³³³ ³³³", "³³³³³ Âł", "اÙĦ د", "Ġdav ran", "Äį er", "Äįe r", "Ġ ØŁ", "ĠØ Ł", "ãģĺãĤĥ ãģªãģĦ", "Ġd air", "Ġda ir", "Ġdai r", "Ġî ¥¤", "ั à¸ĩส", "ัà¸ĩ ส", "Ġ ëĭ´", "Ġëĭ ´", "å¾ ŀ", "ĠÑįÑĤ иÑħ", "ĠÑįÑĤи Ñħ", "è¯ º", "á» ·", "е ÑĢиÑģÑĤи", "еÑĢи ÑģÑĤи", "ов ÑĭÑħ", "Ġ ãĥĩ", "Ġãĥ ĩ", "ض ÙĬ", "Ġà¤ī à¤ł", "Ġnap ÅĻÃŃklad", "è ´Ŀ", "è´ Ŀ", "Ġ Å¡k", "ĠÅ¡ k", "ĠبÙĪد ÙĨد", "ĠبÙĪدÙĨ د", "vů li", "éģ ĩ", "Ġз най", "Ġзна й", "Ġзн ай", "ĠT ham", "ĠTh am", "r ani", "ra ni", "ran i", "ا ØŃت", "اØŃ Øª", "Ø´ Ùĩ", "мÑĸнÑĸ ÑģÑĤÑĢа", "๠ĭ", "ĠÎij να", "ĠÎijν α", "à¥ĭ à¤ļ", "ç»Ħ ç»ĩ", "ÑģÑĤ иÑĤ", "ÑģÑĤи ÑĤ", "im li", "åIJį çĦ¡ãģĹãģķãĤĵ", "åIJįçĦ¡ãģĹ ãģķãĤĵ", "Ùij Ø©", "θ μ", "о лоÑĤ", "ол оÑĤ", "оло ÑĤ", "ย à¸ĩ", "ãĤī ãĤĮãĤĭ", "ãĤīãĤĮ ãĤĭ", "Ġ лиÑĩ", "Ġл иÑĩ", "Ġли Ñĩ", "ов Ñĭе", "éĢ ĥ", "Ġ 广", "Ġå¹ ¿", "ìĬ ¬", "Ùħ ÛĮÙĨ", "ÙħÛĮ ÙĨ", "ĠìłĦ ì²´", "ĠÎŃ Ïĩ", "Ġ ì±ħ", "Ġì± ħ", "Ġ hlas", "Ġh las", "Ġhl as", "е кÑĤив", "ек ÑĤив", "екÑĤ ив", "екÑĤи в", "ĠÏĢ λη", "lu ÄŁu", "luÄŁ u", "好 çļĦ", "ĠÚĨ ÙĪÙĨ", "ĠB eled", "ĠBe led", "ĠBel ed", "Ġen gel", "Ġeng el", "нÑı Ñı", "ĠyaÅŁ an", "Ñĩ ниÑħ", "ار ÙĬØ©", "ارÙĬ Ø©", "म त", "ãĥĭãĥĭ ãĥĭãĥĭ", "åĭ ¢", "Ġ åĨħ", "ĠåĨ ħ", "Ġíı¬ íķ¨", "Ġоб Ñģ", "Ġth ấp", "Ġd ây", "ãĥĸ ãĥ©", "а ÑĤÑĭ", "аÑĤ Ñĭ", "ĠÑģво ей", "ĠÑģвое й", "ãĤī ãģªãģĦ", "åıij çĶŁ", "e rece", "er ece", "ere ce", "Ġod bor", "Ġв неÑģ", "Ġвне Ñģ", "ĠÄIJ ảng", "ĠëıĮ ìķĦ", "ÄĽ li", "ÄĽl i", "ı sında", "ıs ında", "ısı nda", "Ġबद ल", "v nÃŃ", "vn ÃŃ", "ãģ® ãģ«", "Ġпо ÑĤом", "ĠпоÑĤ ом", "im de", "imd e", "a lama", "al ama", "ala ma", "alam a", "âĢ ª", "Ġstej nÄĽ", "е ÑĢе", "еÑĢ е", "éĴ ¢", "æľº æŀĦ", "Ġ è³", "Ġè ³", "åĶ ±", "Ġ ëħ¸ì¶ľ", "Ġëħ¸ ì¶ľ", "Ġли бо", "âĢ Ĭ", "Ġc ez", "Ġce z", "ro mÄĽ", "rom ÄĽ", "ί Ïīν", "ÏĨ ή", "Ġ íĻ©", "ĠíĻ ©", "Ġdlou h", "éª ¨", "åħ¬ éĩĮ", "ä¼ ¸", "Ġ ãĥij", "Ġãĥ ij", "ä» Ļ", "Ġol madı", "Ġolm adı", "Ġolma dı", "е лиÑĩ", "ел иÑĩ", "ели Ñĩ", "ожд ениÑı", "Ġsöy ledi", "Ġsöyl edi", "á tek", "át ek", "áte k", "ìĥ µ", "ย วà¸ģ", "ยว à¸ģ", "Ġ 鼻", "ĠéĽ »", "Ġп ев", "ĠдÑĢÑĥг ие", "át ku", "Ġع ÙĪ", "ov ána", "ová na", "ován a", "ض ر", "Ġëģ Ŀ", "ĠíĨµ íķ´", "Î ĸ", "Ġv ur", "Ġvu r", "åĨ ²", "Ġп ÑĢек", "ĠпÑĢ ек", "ĠпÑĢе к", "Ġप à¤ķ", "Ġ à¹Ģà¸Ĺ", "Ġà¹Ģ à¸Ĺ", "Ġà¹ĢภĹ", "ãģ¨ ãģĭ", "ع ÙĨ", "å® ĩ", "ÏĦ ζ", "Ġn ằm", "ĠÑģ воб", "ĠÑģв об", "ĠÑģво б", "Ġδ Ïį", "çĸ Ĺ", "- й", "é¦Ļ 港", "ت ا", "Ïĥι μο", "íķ Ħ", "Ġ 详æĥħ", "Ġè¯ ¦æĥħ", "ä¸ ¡", "Ùİ Ø§ÙĦ", "Ùİا ÙĦ", "ĠTr Æ°á»Ŀng", "e ného", "en ého", "ené ho", "ĠÑĢекомен дÑĥ", "ÛĮ رÙĩ", "ÛĮر Ùĩ", "า à¸ĸ", "าภĸ", "ĠÚ© اÙħÙĦ", "ĠکاÙħ ÙĦ", "ب Ø·", "ز ÛĮÙĨÙĩ", "زÛĮ ÙĨÙĩ", "Ġдолж на", "Ġë§İ ìĿĢ", "âĹıâĹıâĹıâĹı âĹıâĹıâĹıâĹı", "lep Å¡ÃŃ", "ал ог", "ало г", "ãĤª ãĥ³", "Ġ ë³Ħ", "Ġë³ Ħ", "ı rı", "ır ı", "ĠجاÙħ عÙĩ", "ĠجاÙħع Ùĩ", "æĽ ľ", "o jÃŃ", "oj ÃŃ", "ĠÑĪ лÑıÑħ", "Ġhız lı", "Ġ خصÙĪص", "Ġخص ÙĪص", "ÐIJ ÑĢ", "å ľĺ", "åľ ĺ", "Ġжив оÑĤ", "é ±", "Ġng ữ", "Ġv òng", "èİ «", "Ġза Ñħод", "ĠзаÑħ од", "ìĻ Ħ", "ĠÑģлед ÑĥÑİÑī", "éĹ »", "Ñij ÑĢ", "Ġch vÃŃ", "èĥ ľ", "ãģª ãģĹ", "Ġtek noloj", "Ġtekn oloj", "ej ména", "Ġ ìłĪ", "Ġìł Ī", "ì³ IJ", "æĻ® éĢļ", "Ġvý ro", "Ġay rı", "Ġayr ı", "Ġп ÑĢев", "ĠпÑĢ ев", "ĠпÑĢе в", "Ġgó p", "à¹Ĥ à¸ģ", "à¸Ĺำ à¹ĥห", "åı İ", "åĺ ī", "Ġte lev", "Ġtele v", "Ġtel ev", "ãģ¨ ãģĵãĤį", "ëı Į", "ph yl", "phy l", "ร าะ", "Ġ çĪ", "Ġç Ī", "ÑģÑĤ иÑĤÑĥ", "ÑģÑĤи ÑĤÑĥ", "ÑģÑĤиÑĤ Ñĥ", "ï¼Į è¿ĺ", "ĠÎij γ", "Äį ku", "æı ´", "ाय त", "æı ı", "ãĤĤ ãģĹ", "ĠпеÑĢ еÑģ", "ĠпеÑĢе Ñģ", "Ġìĺģ íĻĶ", "id la", "idl a", "åİ ħ", "ï¼ı :", "ت رÛĮ", "تر ÛĮ", "à¸Ľ à¸ı", "ĠнаÑģ еленнÑı", "Ġam aç", "Ġama ç", "Ġk do", "Ġkd o", "Ġиз веÑģÑĤ", "ÑĪ иÑĢ", "ÑĪи ÑĢ", "ì£ ł", "Å¡ it", "Å¡i t", "Ġt á»ijc", "Ġtá»ij c", "ìŀIJ ìĿĺ", "Ñĩ аÑĤ", "Ñĩа ÑĤ", "åı ĥ", "éĽ ¶", "å° º", "Ġ indir", "Ġin dir", "Ġind ir", "Ġна ÑĨÑĸоналÑĮ", "Ġx anh", "Ġxa nh", "ÛĮ دÛĮ", "ÛĮد ÛĮ", "Ġин ÑĤеÑĢеÑģ", "ĠинÑĤеÑĢ еÑģ", "ĠØ¢ سÛĮ", "Ġآس ÛĮ", "éĤ£ 个", "Ġb ilm", "Ġbi lm", "Ġbil m", "а не", "ан е", "ĠtÄĽch to", "Ñĩ ик", "Ñĩи к", "Ġдо Ñħод", "èĤ¡ 份", "åħ³ ç³»", "ãģ«ãģª ãģ£ãģŁ", "ĠпÑĢед пÑĢи", "Ġgeç en", "Ġب ÙĤ", "Ġvý znam", "Ġ à¹Ģà¸Ħร", "Ġà¹Ģ à¸Ħร", "Ġà¹Ģà¸Ħ ร", "ĠÑħ ÑĤо", "Ø´ ÙĬ", "åıĤ åĬł", "ÑģÑĤв енного", "ÑģÑĤвен ного", "ÑģÑĤвенно го", "ÑĤ ÑĢон", "ÑĤÑĢ он", "ÑĤÑĢо н", "ÂĢÂĢ ÂĢÂĢ", "æ¢ Ŀ", "б ав", "ба в", "Û± Û¶", "Û±Û ¶", "é¡ º", "Ġj az", "Ġja z", "ĠاÙĦ ÙħÙĦ", "ĠاÙĦÙħ ÙĦ", "Ġا ثر", "Ġاث ر", "ĠпÑĢи вод", "ĠпÑĢив од", "а нÑĥ", "ан Ñĥ", "à¥ģ à¤Ń", "à¥ģठŃ", "æĹ §", "ÑĮ е", "ส ล", "л ÑıÑİÑĤ", "лÑı ÑİÑĤ", "ว à¸Ķ", "Æ° Ỽi", "Æ°á» Ľi", "Æ°á»Ľ i", "ÙĬ ÙħØ©", "ÙĬÙħ Ø©", "ãĤ¯ ãĥŃ", "л ий", "ли й", "γ Ïģά", "Ġper forman", "Ġperform an", "Ġperf orman", "Ġperfor man", "è¯ ī", "ä½ł çļĦ", "ìħ Ķ", "н ениÑı", "не ниÑı", "нен иÑı", "á»Ń i", "ÙĪ زÛĮ", "ÙĪز ÛĮ", "éŁ ¿", "à¥Ī द", "Ġëª ¸", "Ġe ser", "Ġes er", "Ġese r", "ĠÙģعاÙĦ ÛĮت", "нÑĸ веÑĢ", "нÑĸв еÑĢ", "κ Ïģα", "è¨ ¼", "Ġn emoc", "Ġnem oc", "Ġyardım cı", "Ġ çī¹", "Ġçī ¹", "Ġ коп", "Ġк оп", "Ġко п", "ĠÐľ ож", "़ à¤ķ", "Ġ ëľ", "Ġë ľ", "ĠÑĢе ак", "Ġp ozor", "Ġpoz or", "Âł ÐIJ", "Ġ ÙĬÙĥ", "ĠÙĬ Ùĥ", "ĠÑģ ад", "Ġ åħ«", "Ġåħ «", "Ġп олÑĮз", "ĠполÑĮ з", "Ġra ÄŁmen", "ter nÃŃ", "tern ÃŃ", "s iyon", "si yon", "Ñģ ÑıÑĩ", "ÑģÑı Ñĩ", "ov aný", "ova ný", "ovan ý", "ĠëĮĢíķľ 민êµŃ", "ĠвÑĸд б", "ĠÐIJ нд", "ĠÐIJн д", "st va", "éĮ Ħ", "Ġ ëij", "Ġë ij", "ิ à¸Ħ", "j ÃŃt", "jÃŃ t", "Ġkullan ıcı", "Ġkullanı cı", "Ġ æŁ¥çľĭ", "ĠæŁ¥ çľĭ", "Ùģ ÙĦ", "Ġ ЯкÑīо", "ĠЯк Ñīо", "çľĭ åĪ°", "ÑĢ еÑħ", "ÑĢе Ñħ", "ĠاÙĦع ربÙĬØ©", "ĠاÙĦعرب ÙĬØ©", "ĠاÙĦعربÙĬ Ø©", "ë¡ľê·¸ ëŀ¨", "Ġब à¤ľ", "Ġп ÑĢип", "ĠпÑĢ ип", "ĠпÑĢи п", "Ġs chop", "Ġsc hop", "Ġsch op", "Ġscho p", "Ġب اÙĦا", "Ġبا ÙĦا", "ĠباÙĦ ا", "å® ħ", "Ġا ÙĦÙħÙĩ", "ĠاÙĦ ÙħÙĩ", "ĠاÙĦÙħ Ùĩ", "α να", "αν α", "à¥ĭ व", "åģ ´", "å¼Ģ åıij", "Ùħ اÙĦ", "Ùħا ÙĦ", "Ġ धर", "Ġध र", "Ġda hil", "Ġdah il", "Ġdahi l", "ãĢģ ãģĵãģ®", "ัà¸Ī à¸Ī", "Ñģп ÑĸлÑĮ", "Ġà¤ķ प", "Ġв еÑĩ", "Ġве Ñĩ", "Ġвид а", "Ġви да", "ĠÙħ عÙĨ", "ĠÙħع ÙĨ", "ĠоÑĤ ли", "i á»ħ", "iá» ħ", "л иÑĪ", "ли ÑĪ", "Ġ ÐŁÐ¾Ñģле", "ĠÐŁÐ¾Ñģ ле", "ãģĵ ãģĵ", "Ġk ültür", "Ġ جر", "Ġج ر", "Ġ æ¼", "Ġæ ¼", "èĩ º", "Ġmev cut", "Ù¾ ÛĮ", "ĠاÙĦ سÙĦاÙħ", "ĠاÙĦس ÙĦاÙħ", "иÑĤ елей", "иÑĤе лей", "Ġ ÑĢоÑģÑĤ", "ĠÑĢ оÑģÑĤ", "ĠÑĢоÑģ ÑĤ", "Ġed il", "Ġedi l", "Ġ å·²", "Ġå· ²", "ç²¾ åĵģ", "ä» ħ", "âĢĻ ye", "âĢĻy e", "à¥Īà¤Ĥ .", "Ġ åĨĨ", "ĠåĨ Ĩ", "ëĪ Ħ", "Ġ ìĻķ", "ĠìĻ ķ", "æĺ Ń", "ĠÎļ ο", "m eden", "med en", "me den", "Ġo lab", "Ġol ab", "Ġola b", "ĠÚ© ÙĪد", "ĠÚ©ÙĪ د", "à¸Ħ าส", "ен наÑı", "æĬ ¼", "yl ül", "Ġsev iy", "Ġd ÄĽti", "ĠdÄĽ ti", "ĠdÄĽt i", "âĢ¬ Ċ", "Ġع ز", "Ġu á»ijng", "Ġس رÙħ", "Ġسر Ùħ", "е не", "ен е", "Ġмал енÑĮ", "Ġ вÑĸдом", "Ġв Ñĸдом", "ĠвÑĸд ом", "ĠвÑĸ дом", "ั à¸ļà¸Ĺ", "ัà¸ļ à¸Ĺ", "ĠTh ái", "Ġà¤Ĩव श", "rove ÅĪ", "çĽ £", "ĠÑı зÑĭ", "ĠO y", "å£ ģ", "в аÑĤÑĮ", "ва ÑĤÑĮ", "ваÑĤ ÑĮ", "л адÑĥ", "ла дÑĥ", "лад Ñĥ", "ا صÙĦ", "اص ÙĦ", "ot ÅĻeb", "د ÙĬØ«", "دÙĬ Ø«", "íı °", "νο μ", "г оÑĢод", "го ÑĢод", "гоÑĢ од", "Ġm uh", "Ġmu h", "âĢĻ l", "ÑģÑĤ воÑĢ", "ÑģÑĤв оÑĢ", "ÑģÑĤво ÑĢ", "åħ Ħ", "Ðķ Ðł", "Ø· ÙĦ", "éľ ĩ", "Ùİ Øª", "Ġb lÃŃ", "Ġbl ÃŃ", "Ġed ildi", "Ġedil di", "éĿ ł", "äºĮ åįģ", "æĹ Ĺ", "Ġç iz", "ĠÄij ảo", "Ġo pat", "Ġop at", "o ÄŁan", "oÄŁ an", "ë² Į", "Ġ éł", "Ġé ł", "Ġseb ep", "Ġsebe p", "Ñĥ ÑĤи", "ÑĥÑĤ и", "åĪ º", "Ø· ب", "ev Å¡ÃŃm", "c hop", "ch op", "cho p", "çĶ ļ", "Ġngh á»ģ", "Ġп аÑĢÑĤ", "ĠпаÑĢ ÑĤ", "ุ à¸Ħ", "Ú© ÛĮÙĦ", "Ú©ÛĮ ÙĦ", "d um", "du m", "Ġor tak", "Ġort ak", "Ġorta k", "ãģŁ ãģĹ", "Ġoby vatel", "Ġv ých", "Ġvý ch", "Ġv eren", "Ġver en", "Ġve ren", "Ġvere n", "Ġв еÑģÑĮ", "ĠвеÑģ ÑĮ", "Ġве ÑģÑĮ", "ĠÐĶ а", "Ġ íķĺì§Ģë§Į", "Ġíķĺ ì§Ģë§Į", "Ġíķĺì§Ģ ë§Į", "å¦Ĥ æѤ", "Ġमह त", "ัà¸ĩà¸ģ ฤษ", "ãĢĤ è¿Ļ", "Ġ гал", "Ġг ал", "Ġsa nat", "Ġsan at", "Ġsana t", "éł Ĩ", "ĠÑģам о", "å Ľ°", "åĽ °", "ี à¸Ń", "ĠBaÅŁ kan", "ÏĦ οÏħÏĤ", "ÏĦοÏħ ÏĤ", "Ġyap tıģı", "Ġyaptı ģı", "Ġyaptıģ ı", "ÅĻ it", "ÅĻi t", "ĠÑģ ÑĸлÑĮ", "ान त", "Ġ ÙĨت", "ĠÙĨ ت", "Ġkh Äĥn", "à¸Ĭ à¸Ļะ", "à¸Ĭà¸Ļ ะ", "м ини", "ми ни", "мин и", "ãĥ¬ ãĥ¼", "ë Ĥ¬", "ëĤ ¬", "éħĴ åºĹ", "ĠاÙĦÙĬ ÙĪÙħ", "ä¹ Ĺ", "à¸Ħรà¸ĩ à¸ģาร", "Ùģ اÙĤ", "Ġ à¤ıस", "Ġà¤ı स", "Ġ æ¡", "Ġæ ¡", "Ú¯ Ø°", "Ġà¤ĩ ल", "е лениÑı", "ел ениÑı", "еле ниÑı", "елен иÑı", "à¸ģ รà¸ĵ", "à¸ģร à¸ĵ", "举 西", "ÎŁ Îľ", "ÎŁÎ ľ", "Ġm áºŃt", "Ġs nÃŃ", "Ġsn ÃŃ", " IJ", "à¹Ģร า", "íķ´ ìķ¼", "Ġ ìĦľë¹ĦìĬ¤", "ĠìĦľ ë¹ĦìĬ¤", "Ġداخ ÙĦ", "Ġth ắng", "íĥ Ī", "а вÑģÑı", "ав ÑģÑı", "Ġ Ñĸм", "ĠÑĸ м", "ا Ùħت", "اÙħ ت", "Ġ ÙĪÙĤت", "ĠÙĪ ÙĤت", "ĠÙĪÙĤ ت", "à¥Ĥ à¤ģ", "Ġ èIJ", "Ġè IJ", "Ġ سÙĦاÙħ", "Ġس ÙĦاÙħ", "ĠسÙĦ اÙħ", "Ġvz dÄĽl", "å¸Į æľĽ", "åŃĺ æ¡£", "Ġ à¸Ĺำ", "Ġà¸Ĺ ำ", "ĠвÑĸй ÑģÑĮ", "а ÑĢан", "аÑĢ ан", "аÑĢа н", "ĠÑĢ Ñĸк", "Ġп иÑģÑĮ", "Ġпи ÑģÑĮ", "ĠпиÑģ ÑĮ", "Ġá¼ IJ", "기 ëıĦ", "ĠпоÑģÑĤ оÑıн", "Ġ åĮĹ京", "ĠåĮĹ äº¬", "ĠNÄĽ m", "Ø´ ÙĨاÙħÙĩ", "Ø´ÙĨ اÙħÙĩ", "Ġdal Å¡ÃŃch", "ĠdalÅ¡ÃŃ ch", "Ġب اع", "Ġبا ع", "Ġpo hy", "Ġpoh y", "ا ÙĦÙģ", "اÙĦ Ùģ", "à¸ŀ วà¸ģ", "é ĭ", "Ġ cih", "Ġc ih", "Ġci h", "Ù ¢", "ä¸ ´", "ãĤ¯ ãĥĪ", "п нÑı", "Ġ дал", "Ġд ал", "Ġда л", "ÙĴ ر", "ãĢĢ ãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢãĢĢĠãĢĢ ĠãĢĢ", "ãĢĢãĢĢĠ ãĢĢĠãĢĢ", "æĬ¥ åijĬ", "ÙĪ دÛĮ", "ÙĪد ÛĮ", "á» £i", "ợ i", "ÑĨ ÑĸÑĶÑİ", "ÑĨÑĸ ÑĶÑİ", "Ġ ãĥĢ", "Ġãĥ Ģ", "ĠÑģÑĤ еп", "r až", "ra ž", "ĠS aÄŁ", "ĠSa ÄŁ", "Ġt uyến", "Ġtuy ến", "Ġal mak", "Ġalma k", "Ġalm ak", "Ġзаболева ниÑı", "Ġ ÏĥÏĩ", "ĠÏĥ Ïĩ", "Ġ íĭ", "Ġí ĭ", "Ġв им", "Ġви м", "ç¡ ¬", "Ġ äºĶ", "Ġi kinci", "Ġik inci", "ุ à¸į", "ส าว", "ĠìĦ¸ ê³Ħ", "ĠÙħ ØŃÙĦ", "ĠÙħØŃ ÙĦ", "ระ หว", "Ġelek tron", "Ġelekt ron", "Ġelektr on", "Ġh ại", "Ġhạ i", "æĹ ¢", "Ġ íĸ¥", "Ġíĸ ¥", "Ġji né", "Ġjin é", "Ġng he", "Ġngh e", "æij ©", "ĠÑģо бÑĸ", "ĠÑģоб Ñĸ", "Æ ¯", "ÑĤ ÑĥÑĢ", "ÑĤÑĥ ÑĢ", "æ±½ 车", "Ø´ اÙĩ", "Ġd Ãłnh", "ä¸ ¹", "ä»Ĭ æĹ¥", "ãĥIJ ãĥ¼", "в аниÑı", "ва ниÑı", "ван иÑı", "Ġ ساÙħ", "Ġس اÙħ", "çݯ å¢ĥ", "ĠاÙĦ ÙħÙĨت", "ĠاÙĦÙħ ÙĨت", "ĠاÙĦÙħÙĨ ت", "ĠÑģеÑĢ д", "éģ ł", "ε ÏĦ", "Ġав ÑĤ", "าà¸ĩ ว", "Ġvz tah", "ru ž", "алÑĮ наÑı", "алÑĮна Ñı", "Ġطر اØŃÛĮ", "à¹Ĥรà¸ĩ à¹ģรม", "ĠÄį asto", "ĠÄįas to", "ĠÄįast o", "Ġ ê¼", "Ġê ¼", "Ïĥ ÏĦÏĮ", "ÏĥÏĦ ÏĮ", "Ġbu rada", "Ġbur ada", "ĠÄ° z", "Ġ ê·¸ëŀĺ", "Ġê·¸ ëŀĺ", "å² Ľ", "ĠØ´ ÙĪÙĨد", "ĠØ´ÙĪ ÙĨد", "Å¡ ek", "Å¡e k", "Ġ ìĿ´ìķ¼", "ĠìĿ´ ìķ¼", "ãĤĮ ãģªãģĦ", "ê· ¹", "la mÄ±ÅŁ", "lam Ä±ÅŁ", "ä» į", "cház et", "cháze t", "Ġ ÑģÑĥÑĤ", "ĠÑģ ÑĥÑĤ", "ĠÑģÑĥ ÑĤ", "æĹł æ³ķ", "æµ ¦", "ÄĽ la", "ÄĽl a", "à¹ĥà¸Ļ à¸Ĭ", "Ġc ân", "Ġcâ n", "ÎŁ Îĵ", "ÎŁÎ ĵ", "Ġz vý", "Ġzv ý", "ĠÙ¾ ار", "Ġپا ر", "Ġк лÑĸ", "Ġкл Ñĸ", "Ġn ové", "Ġno vé", "Ġnov é", "çĶ ĺ", "ë¹ ł", "m á", "Ġ Ñģол", "ĠÑģ ол", "ĠÑģо л", "à¤ķ रण", "à¤ķर ण", "н оÑĩ", "но Ñĩ", "Ġ fik", "Ġf ik", "Ġfi k", "Ġà¤ľ à¤Ĺ", "à¹ĩ à¸Ļà¸ķ", "à¹ĩà¸Ļ à¸ķ", "ĠÙħ تØŃ", "ĠÙħت ØŃ", "Ġph iên", "Ġphi ên", "Ġol sun", "Ġк аб", "Ġка б", "Ġh út", "èĦ ±", "Ġ åĸ", "Ġå ĸ", "ĠH ải", "Ġ tÄĽÅ¾", "Ġt ÄĽÅ¾", "ĠtÄĽ ž", "Ġth ái", "Ġ تاب", "Ġت اب", "Ġتا ب", "- ÐŁ", "Ø« ار", "çĨ Ĭ", "Ġ ними", "Ġн ими", "Ġни ми", "Ġним и", "Ġzp rac", "Ġत ह", "Ġм акÑģим", "Ġмак Ñģим", "m eyi", "me yi", "mey i", "ĠÑģ оÑĨи", "ĠÑģо ÑĨи", "æ² Ĵ", "ĠìķĬ ëĬĶ", "＿ _", "åķ ¦", "ĠاÙĨ ÙĪاع", "æļ ´", "ä¸Ĭ æµ·", "åħ· æľī", "à¥ģ ब", "à¥ģठ¬", "ìķ Ļ", "Ġíģ °", "Ġíŀ ĺ", "Ġtr ánh", "ि यन", "िय न", "ãģ¾ ãģ¾", "п оÑĩ", "по Ñĩ", "m ÄĽr", "mÄĽ r", "å³ °", "ĠÙħ صر", "ĠÙħص ر", "ĠÑįÑĦ ÑĦекÑĤив", "ĠÑįÑĦÑĦек ÑĤив", "Ġ çı", "Ġç ı", "ler iyle", "leri yle", "âĪ ļ", "Ġ ì¶ķ", "Ġì¶ ķ", "Ġ ê²Įìĭľ", "Ġê²Į ìĭľ", "ìĿ ij", "Ġ poÅĻád", "Ġpo ÅĻád", "Ġشب Ú©Ùĩ", "اÙĩ Ø´", "ĠخدÙħ ات", "Ġna Å¡e", "ĠnaÅ¡ e", "ν οÏį", "νο Ïį", "Ġyön elik", "Ġk ork", "Ġko rk", "Ġkor k", "ا ÙĩÙħ", "اÙĩ Ùħ", "è° Ī", "Ġ μη", "Ġμ η", "Ġd olar", "Ġdo lar", "Ġdol ar", "çµ ¦", "ĠÎķ Ïħ", "Ġobdob ÃŃ", "Ġ μÏĮ", "Ġμ ÏĮ", "à¹Ģ à¸Ńà¸ģ", "à¹Ģà¸Ń à¸ģ", "Ġپاس Ø®", "è¡ ¥", "ا عد", "اع د", "ãĤī ãģĦ", "ÎŃ Î»", "и ÑĤÑĭ", "иÑĤ Ñĭ", "Ġ ëħ¼", "Ġëħ ¼", "Ġ^{ [", "ί γ", "æł ij", "l ında", "lı nda", "ĠìŬ 룬", "£ £", "ÅĻ il", "ÅĻi l", "Ġав ÑĤоÑĢ", "ĠавÑĤ оÑĢ", "ÏĦ ικÏĮÏĤ", "ÏĦικ ÏĮÏĤ", "ÏĦικÏĮ ÏĤ", "u dur", "ud ur", "udu r", "Ġc Æ°", "Ġk ıy", "Ġkı y", "Ñģ ем", "Ñģе м", "ĠØ£ بÙĪ", "Ġأب ÙĪ", "ÏĦ ικÏİν", "ÏĦικ Ïİν", "Û± Û·", "Û±Û ·", "è² ¸", "Ġп ÑĢож", "ĠпÑĢ ож", "ĠпÑĢо ж", "ün cü", "Ġ нÑĸÑĩ", "Ġн ÑĸÑĩ", "ĠнÑĸ Ñĩ", "Ġ मत", "Ġम त", "ãģķ ãĤĮãģ¦ãģĦãĤĭ", "ãģķãĤĮ ãģ¦ãģĦãĤĭ", "ãģķãĤĮãģ¦ ãģĦãĤĭ", "ا صر", "اص ر", "Ġع ÙĤ", "ĠкаÑĩе ÑģÑĤве", "ĠÐĵ еÑĢ", "ĠÐĵе ÑĢ", "åº Ĩ", "Ù ¹", "a larda", "al arda", "alar da", "ĠÙ¾ رس", "Ġپر س", "и ÑĩеÑģкой", "иÑĩеÑģ кой", "Ġp him", "Ġph im", "Ġphi m", "ί νη", "ίν η", "ä¸ĩ åĨĨ", "i lerini", "iler ini", "ileri ni", "ilerin i", "ãĢģ 大", "Ġo lsa", "Ġol sa", "æł¹ æį®", "âĢĮ س", "ĠTh ủ", "r oje", "ro je", "roj e", "нÑĮ оÑĹ", "нÑĮо ÑĹ", "Ġs lou", "Ġsl ou", "Ġslo u", "ี ฬ", "ıy orum", "ıyor um", "ÄĽ j", "Ġ خبر", "ĠØ® بر", "è® Ĭ", "Ġ 缸", "ĠçĽ ¸", "e lerinin", "eler inin", "eleri nin", "elerin in", "elerini n", "íķĻ ëħĦëıĦ", "íķĻëħĦ ëıĦ", "ÑĩеÑģ кие", "Ñĩе Ñģкие", "ÑĩеÑģки е", "ĠÅŁ ekl", "ĠÅŁek l", "Ġز ÙħاÙĨÛĮ", "ĠزÙħاÙĨ ÛĮ", "ĠزÙħ اÙĨÛĮ", "Ġ xin", "Ġx in", "Ġxi n", "ัà¸ģ à¸ĩาà¸Ļ", "ĠE kim", "ĠEk im", "æĦ ¿", "Ġод ной", "Ġодно й", "ν ή", "æľĢ æĸ°", "ĩ ¼", "Ġн иж", "Ġни ж", "Ġ ë³¼", "Ġë³ ¼", "è· ij", "Ġна пиÑģ", "Ġнап иÑģ", "èģ ĸ", "Ġ âĢĮ", "ĠâĢ Į", "æłĩ åĩĨ", "Ġv rát", "Ġvr át", "ĠV ì", "Ġ ÙģراÙĨ", "ĠÙģ راÙĨ", "ĠÙģر اÙĨ", "æĿ¥ çļĦ", "å§ ¿", "Ñħ Ñĥ", "ĠبÛĮر ÙĪÙĨ", "Ġд ÑĥÑĪ", "ĠдÑĥ ÑĪ", "в аÑİÑĤ", "ва ÑİÑĤ", "Ġs ebe", "Ġse be", "Ġseb e", "é» ĺ", "Ġkay ıt", "Ġkayı t", "Ïģ θ", "ãģ¨ ãģ®", "ĠпÑĢоÑĨ еÑģÑģ", "ĠпÑĢоÑĨеÑģ Ñģ", "æĮģ ãģ¡", "Ñĸ на", "Ñĸн а", "Ġ ÑĤоÑĤ", "ĠÑĤ оÑĤ", "ĠÑĤо ÑĤ", "ĠÑĤак ие", "ĠÑĤа кие", "The o", "Th eo", "ĠÙĨ ÛĮر", "ĠÙĨÛĮ ر", "ÑĨ Ñĥ", "Ġay ak", "à¸Ļ à¸Ń", "Ġsit esinde", "Ġsites inde", "Ġsitesi nde", "ĠÚ©ÙĨ ÛĮÙħ", "ĠÑģ оÑħ", "ĠÑģо Ñħ", "Ġम à¤ľ", "Ġol uyor", "ç½ij åĿĢ", "ĠÙ¾ زش", "ĠE ylül", "d Ã¼ÄŁ", "dü ÄŁ", "Ġبر Ø®ÛĮ", "Ġبرخ ÛĮ", "ĠÙħع رÙģ", "Ġ obec", "Ġo bec", "Ġob ec", "Ġobe c", "ĠçalÄ±ÅŁ ma", "Ġçal Ä±ÅŁma", "ìĦ¼ íĦ°", "ĠÑģво ÑĶ", "оÑģÑĤ ей", ": ::::::::::", ":: :::::::::", ":::: :::::::", ":::::: :::::", ":::::::: :::", "::: ::::::::", "::::: ::::::", "::::::: ::::", "::::::::: ::", ":::::::::: :", "Ġ алÑĮ", "Ġа лÑĮ", "Ġал ÑĮ", "ç« Ł", "Ġباش ÙĨد", "اÙĦ Ø«", "Ġнай б", "Ġп ока", "Ġпо ка", "Ġпок а", "Î ŀ", "ĠÙĪ Ø¥", "Ġ Ø®ÙĪاÙĨ", "ĠØ® ÙĪاÙĨ", "ĠØ®ÙĪ اÙĨ", "à¥ģप य", "Ġ à¹ĥห", "ĠбÑĭ ÑģÑĤÑĢо", "Ġth á»Ń", "Ġthá» Ń", "ëģ ¼", "Ġ å¤ļ", "Ġå¤ ļ", "两 个", "ม à¸ķ", "ز ارش", "زار Ø´", "زا رش", "Ġ ëŁ", "Ġë Ł", "य ह", "Ñī ина", "Ñīи на", "Ñīин а", "ầ ng", "ần g", "ï½Ĺ ï½Ĺ", "à¹Ģà¸ŀ ลà¸ĩ", "à¹Ģà¸ŀล à¸ĩ", "tv rt", "ĠÑĸн ÑĪÑĸ", "ĠÑĸнÑĪ Ñĸ", "λ εί", "λε ί", "Ġv iá»ĩn", "Ġvi á»ĩn", "ij ¸", "Ġ çĻ½", "ĠçĻ ½", "Ùİ ÙĪ", "Ġch ứa", "Ġchứ a", "st vo", "ĠdoÄŁ r", "Ġ iler", "Ġi ler", "Ġil er", "Ġile r", "à¥ĭ ,", "à¹ĥà¸Ļ à¸Ľ", "Ġر ÙĪست", "ĠرÙĪ ست", "ÙĪ ÙĦÙĪ", "ÙĪÙĦ ÙĪ", "Å¡ lo", "ал иÑģÑĤ", "али ÑģÑĤ", "åħ± åĴĮ", "à¸ŀ ย", "Ġ ìĻĢ", "ĠìĻ Ģ", "ÙĦ ÙĬÙĦ", "ÙĦÙĬ ÙĦ", "ĠÑı кого", "ĠÑıк ого", "е ÑģÑĤÑĮ", "еÑģ ÑĤÑĮ", "еÑģÑĤ ÑĮ", "ĠÑĦ ин", "ĠØ£ ÙĨÙĩ", "ĠØ£ÙĨ Ùĩ", "ĠMü dür", "ĠÎĶ ια", "ĠÎĶι α", "ĠÑĤ ел", "ĠÑĤе л", "ि ,", "Ñĥ ки", "Ñĥк и", "ĠÐł Ф", "ĠMay ıs", "à¹Ī à¸Ńม", "à¹Īà¸Ń ม", "ar ken", "ark en", "æĢ ķ", "ب ÛĮÙĨ", "بÛĮ ÙĨ", "ÑĤ аÑħ", "ÑĤа Ñħ", "e bo", "eb o", "ë³´ ì¦Ŀê¸Ī", "ĠÙ¾ ÙĦ", "Ġг Ñĥб", "Ġв клÑİÑĩ", "Ġвк лÑİÑĩ", "æĶ¿ æ²»", "Ġε ÏĢιÏĥ", "ĠεÏĢ ιÏĥ", "ĠεÏĢι Ïĥ", "ĠÙģار سÛĮ", "ĠÙģارس ÛĮ", "èŃ ī", "ÏĨ η", "( éĩij", "ศ ร", "åī §", "âĢĻ ya", "âĢĻy a", "å¹´ 度", "ĠÙĨ رÙħ", "Ùĥ ÙĪÙħ", "ÙĥÙĪ Ùħ", "è¢ ĭ", "Ġneden le", "à¹īà¸Ńà¸ĩ à¸ģาร", "à¹īà¸Ńà¸ĩà¸ģ าร", "ãĢĮ ãģĤ", "Ġп оÑģÑĤÑĥп", "Ġпо ÑģÑĤÑĥп", "ĠпоÑģÑĤ Ñĥп", "ìľĦ ìĽIJ", "åį ĺ", "èİ ±", "Ġum ož", "p ok", "po k", "Ñĥ ÑģÑĤи", "ÑĥÑģ ÑĤи", "ÑĥÑģÑĤ и", "Ġ éħ", "Ġé ħ", "ĠÑĦ Ñĸз", "å» £", "ิ หาร", "Ġж ÑĥÑĢн", "ĠдÑĸÑĤ ей", "Ñĥ ÑİÑīие", "ÑĥÑİ Ñīие", "ÑĥÑİÑī ие", "ä»Ĭ 天", "ìĿ´ ëĿ¼ê³ł", "ìĿ´ëĿ¼ ê³ł", "ç² ī", "èĴ Ļ", "ĠDün ya", "ĠDüny a", "егод нÑı", "Ġm imo", "Ġmi mo", "Ġmim o", "Ġ вин", "Ġв ин", "Ġви н", "ãģĿ ãģĵ", "æ¯ ķ", "ĠØ£ Ø®", "Ġ åIJĮ", "ĠåIJ Į", "س اÙĨÛĮ", "ساÙĨ ÛĮ", "Ġ kah", "Ġk ah", "Ġka h", "ि यर", "िय र", "ÏĢ οÏĤ", "ÏĢο ÏĤ", "j ez", "je z", "ÙĬ ج", "ĠsaÄŁ lay", "ا جÙĩ", "اج Ùĩ", "Ġ çł", "Ġç ł", "ï ľ", "Ġج ست", "Ġt ức", "Æ° Æ¡i", "Æ°Æ¡ i", "Ø´ Ùģ", "ส à¸ķ", "Ġ ÑĢеÑģ", "ĠÑĢ еÑģ", "ĠÑĢе Ñģ", "Ġ å£", "Ġå £", "Ġbi zim", "Ġbiz im", "Ġbizi m", "Ġ ê·Ģ", "Ġê· Ģ", "ि ब", "िठ¬", "ë¡ľ ìļ´", "ĠÑģ ÑĤал", "ĠÑģÑĤ ал", "ĠÑģÑĤа л", "Ġ ÑĢÑĥÑģ", "ĠÑĢ ÑĥÑģ", "ĠÑĢÑĥ Ñģ", "ĠO cak", "ĠOc ak", "åľ £", "Ġ úÄįast", "Ġú Äįast", "ĠúÄį ast", "ive rz", "iver z", "ëĤĺ ëĬĶ", "о ÑĢоÑĤ", "оÑĢ оÑĤ", "оÑĢо ÑĤ", "Ñĩ инÑĭ", "Ñĩи нÑĭ", "Ñĩин Ñĭ", "Ġihtiy aç", "ÐĿ Ðŀ", "ĠÐĿ ов", "ĠÐĿо в", "ีย à¸Ķ", "ĠпоÑĤÑĢÑĸб но", "Ú¯ ز", "ĠÑģказ ал", "ĠG ia", "ĠGi a", "m esini", "mes ini", "mesi ni", "Ġbulun ur", "æ¸ ¡", "г оÑĤ", "го ÑĤ", "Ġh uku", "Ġhu ku", "ëĦ ·", "ã Ĩ", "Ġ اÙĥ", "Ġا Ùĥ", "Ġد ÙĦÛĮÙĦ", "ĠدÙĦ ÛĮÙĦ", "Ġ اساس", "Ġا ساس", "Ġاس اس", "ìĹ° 구", "ĠÎĺ ε", "Ġس ÙĪر", "ĠسÙĪ ر", "Ġ ì¢Ģ", "Ġì¢ Ģ", "ĠاÙĦ در", "ĠاÙĦد ر", "ĠÑģÑĤÑĢо иÑĤелÑĮ", "Ġ Ñĥк", "ĠÑĥ к", "ĠìĻ ľ", "е лик", "ел ик", "ели к", "O VID", "OV ID", "Ġt emiz", "Ġtem iz", "äº ¦", "Ġth iếu", "Ġthi ếu", "Ġп ÑĥÑĤ", "ĠпÑĥ ÑĤ", "Ñİ Ñīей", "ÑİÑī ей", "Ġur Äį", "Ġ ÄIJây", "ĠÄIJ ây", "æ¥ µ", "μ οÏħ", "μο Ïħ", "Ġ à¹Ģà¸Ļ", "Ġà¹Ģ à¸Ļ", "Ġà¹ĢภĻ", "е веÑĢ", "ев еÑĢ", "Âł ÐĶ", "ì ´Ŀ", "ì´ Ŀ", "è¶ £", "Ġà¤ħ लà¤Ĺ", "Ġà¤ħल à¤Ĺ", "Æ°á»Ŀ n", "Ġ ãĥŃ", "Ġãĥ Ń", "Ġ ê³³", "Ġê³ ³", "é² ģ", "Ġرس ÛĮد", "身 ä½ĵ", "ั à¸ĵà¸ij", "y nÃŃ", "yn ÃŃ", "ج ات", "جا ت", "ì§Ģ 를", "न ल", "ì ķĮ", "ìķ Į", "Ñĸ п", "Ġv Ãłng", "ĠvÃł ng", "Ġпл оÑī", "Ġпло Ñī", "оз мож", "åī ²", "Ġth ảo", "л ади", "ла ди", "лад и", "Ġ åĿ", "Ġå Ŀ", "ĠÐľ и", "Ġдел аÑĤÑĮ", "Ġдела ÑĤÑĮ", "é ij", "Ġh uy", "Ġhu y", "ا ÛĮØ·", "اÛĮ Ø·", "Ġпов ÑĤоÑĢ", "ü len", "ül en", "üle n", "Ġ ÙĪÙģ", "ĠÙĪ Ùģ", "ĠÙĬ تÙħ", "ĠÙĬت Ùħ", "ĠÑĢеж им", "Ġ ìºIJ", "Ġìº IJ", "ĠÃĩ ünkü", "ع دد", "عد د", "ни веÑĢ", "нив еÑĢ", "ĠÐĿ ик", "å¸ ĸ", "Ïį ÏĢ", "an lar", "س تÛĮ", "ست ÛĮ", "Ġbulun maktadır", "à¹ģ à¸ļ", "v ek", "ve k", "Ġгла за", "Ġглаз а", "å¹ ħ", "Ġúda j", "Ġг ÑĢо", "ĠгÑĢ о", "Ġкон кÑĥÑĢ", "Ġd ůležit", "Ġdů ležit", "Ġ Ø·ÙĪر", "ĠØ· ÙĪر", "à¸ĺ าà¸Ļ", "ĠÙĦ ÙĥÙĨ", "ĠÙĦÙĥ ÙĨ", "ر ÙĤ", "Ðļ ÐIJ", "Ġ éĿĴ", "ĠéĿ Ĵ", "Ġ ìĤ¬ëŀij", "ĠìĤ¬ ëŀij", "ĠÑħ воÑĢ", "ĠÑħв оÑĢ", "s unuz", "sun uz", "ĠÙħØ´ خص", "éĻ ¸", "Ġ ढ", "Ġठ¢", "Ġv az", "Ġva z", "交 æĺĵ", "ĠÑĤеÑĢ ÑĢиÑĤ", "ÑĩеÑģ кой", "Ñĩе Ñģкой", "ี à¹Ĥ", "rop oda", "ıl dıģı", "ıldı ģı", "Ġ ëī´", "Ġë ī´", "íķĻ 기", "ë³´ íĹĺ", "Ġз аÑĤем", "ĠзаÑĤ ем", "Âł в", "ãĥ¼ ãĥĨ", "ãĥ¼ãĥ Ĩ", "Ġ ÐŀÑģнов", "ĠÐŀÑģ нов", "ãĨ į", "Ġد ع", "ÐŁ оÑģ", "ÐŁÐ¾ Ñģ", "æ² ī", "Ġ лож", "Ġл ож", "çĶµ åŃIJ", "Ġ رد", "Ġر د", "ĠÑģ ÑĢазÑĥ", "e jte", "ej te", "Ġà¤ij फ", "Ġt Ãłu", "ÃŃ k", "lan ması", "lanma sı", "к аÑĤ", "ка ÑĤ", "าà¸ģ าศ", "ãĤ¢ ãĤ¤", "ÏĦ ιο", "ÏĦι ο", "Ġ å§", "Ġå §", "प त", "E Y", "Ġj mé", "Ġjm é", "Ġod kazy", "Ġê°ľ ìĿ¸", "éģ ¿", "bÄĽ h", "Ðł Ðŀ", "çĥ Ī", "Ġza rar", "Ġzar ar", "Ú¯ ÙĪÙĨÙĩ", "Ú¯ÙĪ ÙĨÙĩ", "Ġtr ì", "Ġm ại", "ен нÑĭм", "ĠÑį коном", "ĠÑįк оном", "éĽ £", "Ġ íĦ", "Ġí Ħ", "æİ ī", "Ġs oru", "Ġso ru", "Ġsor u", "ĠФедеÑĢа ÑĨии", "ĠÑģиÑģÑĤем и", "æĸĻ çĦ¡æĸĻ", "Ġà¤ķ à¤Ń", "ĠÙĩ ÙĨد", "ĠÙĩÙĨ د", "ุà¸ĩ à¹Ģà¸Ĺà¸ŀ", "ĠOsman lı", "ĠпÑĢод олж", "Ġ ÙĪÙĦا", "ĠÙĪ ÙĦا", "ĠÙĪÙĦ ا", "ĠÄįlán ku", "Ġa dım", "Ġad ım", "Ġadı m", "ĠÏĢ αÏģά", "ĠÏĢαÏģ ά", "ĠÏĢα Ïģά", "Ġzá ÅĻÃŃ", "Ġ à¸Īำà¸ģ", "Ġà¸Īำ à¸ģ", "Ġп ен", "m enin", "me nin", "men in", "meni n", "Ġìĺ¤ ëĬĺ", "em iz", "emi z", "οÏį ÏĤ", "- स", "íķĺ ìĭľ", "ĠÑħ ви", "ĠÑħв и", "ãĤ° ãĥ©", "Ġп оÑĪ", "Ġпо ÑĪ", "ĠÐŀдна ко", "ĠÐŀднак о", "Ñĸд но", "íĺ ľ", "Ñī ими", "Ñīи ми", "Ñīим и", "èĥ ¸", "ĠÄ° lk", "ĠÄ°l k", "m ey", "me y", "Ġз да", "Ġзд а", "κ λη", "а лом", "ал ом", "ало м", "à¹Ģศ ษ", "ا ÙĨا", "اÙĨ ا", "Ġ ÎŁÎ¹", "ĠÎŁ ι", "Ġ åıĮ", "Ġåı Į", "ี à¸Ĥ", "Ġ بس", "Ġب س", "è§Ħ å®ļ", "i say", "is ay", "isa y", "uk arı", "uka rı", "æµģ éĩı", "v ÃŃm", "vÃŃ m", "λ Ïİ", "ä¹ Ļ", "Ġल ड", "ĠÙĨد ارد", "ĠÙĨدار د", "е ÑĢом", "еÑĢ ом", "еÑĢо м", "Ġsır asında", "Ġsıras ında", "Ġsıra sında", "Ġr Äĥng", "Æ¡ m", "Ġl ạnh", "Ġlạ nh", "ठĥ", "à¥ģ ण", "à¥ģठ£", "uz ey", "uze y", "Ġ Ñĥва", "ĠÑĥ ва", "ĠÑĥв а", "vÄĽ d", "Ñĭ Ñģ", "Ġ κι", "Ġκ ι", "Ñ ķ", "ÛĮ ا", "à¸ĩ à¸Ħ", "ph ylum", "phy lum", "phyl um", "Ġber aber", "ี à¸Ķ", "æµ ®", "ा सन", "ास न", "o vice", "ov ice", "ovic e", "ovi ce", "è¦ §", "Ġस फ", "å°ij 女", "ан ÑĤи", "анÑĤ и", "é¨ ĵ", "Ġso át", "é¬ ¼", "lan mÄ±ÅŁ", "Ġb ếp", "ÙIJ ÙĦ", "Ġsay ısı", "Ġsayı sı", "ĠÙĤ دÙħ", "ĠÙĤد Ùħ", "à¥Ī म", "ह म", "ĠÑĢ Ñĥки", "ĠÑĢÑĥ ки", "ĠÑĢÑĥк и", "ĠصÙģ ØŃÙĩ", "Å¡ ky", "Å¡k y", "é» Ĵ", "èģ ļ", "ãģĭ ãģ«", "Ġs âu", "ед аг", "ĠÑģÑĤоÑĢ онÑĭ", "ĠÑģÑĤоÑĢон Ñĭ", "Ġ ruk", "Ġr uk", "Ġru k", "âĢĮ âĢĮ", "ĠØ¢ ÙĪر", "Ġع دÙħ", "Ġعد Ùħ", "õ i", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "Ġبا زار", "Ġباز ار", "Ġe deb", "Ġed eb", "Ġv ÄįetnÄĽ", "оп аÑģ", "Ġн ег", "Ġне г", "m ayan", "ma yan", "may an", "maya n", "к оÑģÑĤÑĮ", "ко ÑģÑĤÑĮ", "Ġsv ůj", "ÄŁ ında", "ģı nda", "ģın da", "Ø° ÛĮر", "M á»Ļt", "Ð Ħ", "Ġyap tı", "ि थ", "िठ¥", "ĠÙħ Ùĩر", "ĠÙħÙĩ ر", "Ġд оÑģÑĤи", "Ġдо ÑģÑĤи", "ĠдоÑģÑĤ и", "ĠдоÑģ ÑĤи", "Ġ صÙĪر", "Ġص ÙĪر", "m esine", "mes ine", "mesi ne", "ĠD ân", "ä¸Ģ ä¸ĭ", "çį İ", "ĠÐľ иÑħ", "ĠÐľÐ¸ Ñħ", "Ġо Ñĩи", "ĠоÑĩ и", "ãĤ¦ ãĤ§", "Ġ ÑĸÑģ", "ĠÑĸ Ñģ", "Ġgi ác", "Ġgiá c", "åľ¨çº¿ è§Ĥçľĭ", "Ġاد اÙħÙĩ", "ÑĨ ов", "ÑĨо в", "Ġ комÑĥ", "Ġк омÑĥ", "Ġком Ñĥ", "Ġко мÑĥ", "ĠÄ°ng iliz", "Ġг ÑĢаж", "ĠгÑĢа ж", "ĠгÑĢ аж", "ãģ¦ ãĤĤ", "Ġch ữ", "олÑĮ кÑĥ", "m ÄĽt", "mÄĽ t", "Ñıг ом", "Ñĩ аÑģÑĤ", "Ñĩа ÑģÑĤ", "ÑĩаÑģ ÑĤ", "ìĸ ¼", "Ġkh óa", "Ġkhó a", "ĠÐIJ д", "ĠØ¢ ÙĤ", "Ġkurul uÅŁ", "ά ζ", "Ġж ов", "Ġв ÑģÑĤÑĢе", "ĠвÑģÑĤ ÑĢе", "ĠÙĪ ÙĦÙĥ", "ĠÙĪÙĦ Ùĥ", "Ġt uyá»ĩt", "y ı", "Ġ ÐĴо", "ĠÐĴ о", "Ġv á»įng", "Ġvá» įng", "ع ÙĬØ©", "عÙĬ Ø©", "Ġop ÄĽt", "ا ÙĬد", "اÙĬ د", "à¥Ī .Ċ", "à¥Ī. Ċ", "ĠÑģ ами", "ĠÑģам и", "åª Ĵ", "Ġsv ých", "ĠëĤĺ íĥĢ", "ìĨ IJ", "Ġ ÙĦع", "ĠÙĦ ع", "Ġet kin", "Ġetk in", "Ġetki n", "ĠN á", "Ġsou tÄĽ", "Ġsout ÄĽ", "층 ìĿĺ", "Ġ çŃī", "ĠçŃ ī", "Ġر سÙħ", "Ġرس Ùħ", "Ġ خاÙĨÙĩ", "ĠØ® اÙĨÙĩ", "ĠخاÙĨ Ùĩ", "Ġ 家", "Ġå® ¶", "iá» ģm", "iá»ģ m", "ëħ IJ", "ê° Ī", "ì° ©", "ž il", "ži l", "ÑģÑĤиÑĤ ÑĥÑĤ", "ÑģÑĤиÑĤÑĥ ÑĤ", "or uÄį", "oru Äį", "ĠØ¥ ذا", "Ġإذ ا", "à¹Ħ à¸Ĥ", "ี à¸Ĭ", "ÑĢ аб", "ÑĢаР±", "ÑĢа б", "íķĻ ìĥĿ", "Ġ ìī", "Ġì ī", "r nek", "rn ek", "rne k", "Ġاست خداÙħ", "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", "Ġв Ñģем", "ĠвÑģ ем", "ĠвÑģе м", "Ġìłķ ëıĦ", "Ġvy j", "éĢ ±", "алÑĮ ное", "алÑĮно е", "Ġch uyá»ĩn", "ì§Ģ ìĽIJ", "i lerine", "ile rine", "iler ine", "ileri ne", "ilerin e", "ĠìķĦ 무", "Ġок оло", "ाव न", "à¸Ļ า", "о пÑĢи", "оп ÑĢи", "d rž", "dr ž", "ĠÑģÑĥ ÑģпÑĸлÑĮ", "Ġب Ùĥ", "u ky", "uk y", "Ġ ÏĩÏī", "ĠÏĩ Ïī", "Ġtu ần", "nict vÃŃ", "Ġ ÙĩدÙģ", "ĠÙĩد Ùģ", "Ġch iá»ģu", "ÎĹ ÎĿ", "å°ı å§IJ", "íķĺ ìĺĢ", "Ġk las", "Ġkl as", "Ġkla s", "á»Ļ n", "ĠìĿ´ íĽĦ", "ÙĨ اÙħج", "ÙĨا Ùħج", "ÙĨاÙħ ج", "Äį ast", "Äįas t", "ĠاÙĦ خاص", "ĠاÙĦØ® اص", "l Ä±ÅŁ", "lı ÅŁ", "Ġع Ùħر", "ĠعÙħ ر", "ãĢį Ċ", "иб олее", "ãĤĬ ãģ®", "ãħ ł", "ä¹Ł ä¸į", "к ÑĢеÑĤ", "Ġ ìĶ", "Ġì Ķ", "ÏĦ ια", "ÏĦι α", "ĠÑĥпÑĢав лÑĸннÑı", "æ² ¢", "Ġk esin", "Ġke sin", "Ġkes in", "ì¡Į ëĭ¤", "머 ëĭĪ", "羣 çļĦ", "Ġbak ım", "æĿ± 京", "¾ ¸", "ÙħÙĦ ÙĥØ©", "оÑĤ ÑĢеб", "d ın", "dı n", "Ġ PÅĻi", "ĠP ÅĻi", "ĠPÅĻ i", "Ġm ÄĽli", "ĠmÄĽ li", "ĠmÄĽl i", "Ġδη μο", "å ¯¸", "å¯ ¸", "ĠÙĪ ÙĥاÙĨ", "ĠÙĪÙĥ اÙĨ", "Ġप ढ", "ĠвеÑĢ Ñħ", "Ġе Ñij", "C ách", "Các h", "ä½ľ 为", "ĠÐļ ол", "ĠÐļо л", "Ġ ве", "Ġв е", "Ġ деÑĢж", "Ġд еÑĢж", "ĠдеÑĢ ж", "em oc", "emo c", "ãģ¸ ãģ®", "Ġ аÑĢÑħ", "ĠаÑĢ Ñħ", "Ġk iếm", "Ġ æĺİ", "Ġæĺ İ", "ĠлÑİд ини", "ĠлÑİди ни", "ë ·", "ĠÙĪ اÙĦت", "ĠÙĪاÙĦ ت", "ĠÙĪا ÙĦت", "Ġ è°", "Ġè °", "çģ ¯", "íĻ ķ", "Ġ구 매", "Ġ ç§ij", "Ġç§ ij", "it nÃŃ", "и ÑĩеÑģкие", "иÑĩеÑģ кие", "иÑĩеÑģки е", "ĠÙĨ Ùģس", "ĠÙĨÙģ س", "Ġت ÙĦÙģ", "ĠتÙĦ Ùģ", "ا ÙģÛĮ", "اÙģ ÛĮ", "ĠØŃ Ø³ÙĨ", "ĠØŃس ÙĨ", "âĸ¡ âĸ¡", "ý vá", "ýv á", "ÄŁ ın", "ģı n", "ıyor uz", "ĠCh ÃŃ", "ĠÙ¾ÚĺÙĪÙĩ Ø´", "Ġ ÏĦÎŃ", "ĠÏĦ ÎŃ", "ĠÏĥ Ïĩε", "ĠÏĥÏĩ ε", "о леÑĤ", "ол еÑĤ", "α ιδ", "αι δ", "Ġh ạt", "Ġhạ t", "à¸ł าà¸Ħ", "åĨ °", "Ġrych le", "it eli", "ite li", "itel i", "Âł z", "ย à¸ģ", "æ¨ ¹", "Ġج ÙĪاÙĨ", "ĠجÙĪ اÙĨ", "æĺ Į", "Ġü retim", "Ġür etim", "Ġüret im", "ระ à¸ļ", "à¸Ľà¸£à¸° ม", "ά Ïĥ", "å² ©", "ĠÑĥ ÑģÑĤÑĢой", "ĠÑĥÑģÑĤ ÑĢой", "Ġver ilen", "Ġveri len", "ich ni", "ĠpÅĻÃŃ mo", "ĠpÅĻÃŃm o", "ĠاÙĦØ°Ùĩ اب", "ì½ ľ", "æľ ±", "Ġس Ø®", "Ñĸ ла", "Ñĸл а", "Ñĥ ма", "Ñĥм а", "ห า", "ÛĮ دا", "ÛĮد ا", "å² ¸", "ä¸Ģ å®ļ", "Ġ ä¼ļ", "Ġ ÐŁÑĸд", "ĠÐŁ Ñĸд", "Ġ ÑĩиÑĤ", "ĠÑĩ иÑĤ", "ĠÑĩи ÑĤ", "и Ñİ", "Ġ ÐĹап", "ĠÐĹ Ð°Ð¿", "ĠÐĹа п", "ÑĤ иÑı", "ÑĤи Ñı", "Ġ ê°ľë°ľ", "Ġê°ľ ë°ľ", "ĠÑĤе оÑĢ", "Ñı ÑģÑĮ", "ÑıÑģ ÑĮ", "ĠpÅĻÃŃ prav", "( åľŁ", "Ùħ ÙĬ", "ĠpÅĻed evÅ¡ÃŃm", "ĠTem muz", "Ġпод деÑĢж", "Ġнед оÑģÑĤаÑĤ", "ĠìĿ´ ìľł", "Ġkhá»ı i", "ĠاÙĦ تØŃ", "ĠاÙĦت ØŃ", "ĠÙħÙħ Ú©ÙĨ", "Ġv hod", "Ġvh od", "ев ой", "ево й", "о вал", "ов ал", "ова л", "Ġн алеж", "Ġна леж", "Ġнал еж", "ï¼¼ :", "ย ะ", "ĠÙħ اشÛĮÙĨ", "Ġg á»Ńi", "al ım", "alı m", "Ġìµľ ìłĢ", "Ùij Ùĩ", "á»Ļ p", "à¥Ģ ।Ċ", "à¥Ģ। Ċ", "Ġ пиÑģ", "Ġп иÑģ", "Ġпи Ñģ", "Ġ вÑģÑı", "Ġв ÑģÑı", "ĠвÑģ Ñı", "Ñĩ ем", "Ñĩе м", "o zenÃŃ", "oz enÃŃ", "oze nÃŃ", "Ġ äºļæ´²", "Ġäºļ æ´²", "е ÑĢалÑĮ", "еÑĢ алÑĮ", "еÑĢа лÑĮ", "기 ëĬĶ", "Ġп ÑĢез", "ĠпÑĢ ез", "ĠпÑĢе з", "ĠعÙħ ÙĪÙħÛĮ", "и ÑĩниÑħ", "иÑĩ ниÑħ", "Ġ æ²³", "Ġæ² ³", "od nÃŃ", "åıª æĺ¯", "Ġpo dp", "Ġpod p", "à¹īà¸Ńà¸ĩ à¸ŀ", "ाय द", "ाà¤ĩ ल", "ล à¸Ķ", "ĠÑĢÑĸÑĪ еннÑı", "Ġ ÑĤÑĥÑĢ", "ĠÑĤ ÑĥÑĢ", "ĠÑĤÑĥ ÑĢ", "ÑģÑĮ кÑĥ", "ÑģÑĮк Ñĥ", "Ġsal dır", "ĠÐĽ ÑĮв", "ãĢģ Ċ", "ĠÙ¾ÛĮ ÙĪÙĨد", "åѦ ä¹ł", "λ Ïī", "o vit", "ov it", "ovi t", "ü le", "ül e", "女 æĢ§", " Ł", "e mez", "em ez", "eme z", "Ġh ale", "Ġha le", "Ġhal e", "âī ¦", "ĠÎķ κ", "ÏĦηγοÏģ ία", "k ý", "ìĦ± ìĿĦ", "Ġt ým", "Ġtý m", "à¥ĩ -", "Ġz ejména", "æĻ ¶", "Ġn gon", "Ġng on", "ãĢı ĊĊ", "软 件", "éĤ£ ä¹Ī", "ĠкваÑĢ ÑĤи", "ĠÙħÙĨ ظ", "on ec", "one c", "Ġг ли", "à¥ģर à¤ķ", "ĠS okol", "ĠSo kol", "ĠSok ol", "Ġ ä¿Ŀ", "д ив", "ди в", "ál nÃŃm", "áln ÃŃm", "álnÃŃ m", "ac aģı", "aca ģı", "acaÄŁ ı", "a ÅŁa", "aÅŁ a", "ĠÙħ اÙĦÛĮ", "ĠÙħا ÙĦÛĮ", "ĠÙħاÙĦ ÛĮ", "ĠÃĸ n", "иÑĤ ели", "иÑĤе ли", "ĠØ® رد", "Ġخر د", "Ġkullan ıl", "Ġkullanı l", "Ġ ÙħÛĮÙĦ", "ĠÙħ ÛĮÙĦ", "ĠÙħÛĮ ÙĦ", "Ġ íļ¨", "Ġíļ ¨", "ã n", "Ġ rost", "Ġr ost", "Ġro st", "Ġros t", "Ġëĸ ł", "u bat", "ub at", "uba t", "Ġ åıĤ", "Ġåı Ĥ", "Ġبر اÙĬ", "Ġм енÑĮ", "Ġмен ÑĮ", "ั à¸Ħร", "ัà¸Ħ ร", "Ġпом ог", "ĠØŃض ÙĪر", "Ġthá»ĭ t", "ä¹ ³", "Ġ ìĭłì²Ń", "Ġìĭł ì²Ń", "Ġíĺ Ħìŀ¬", "ĠíĺĦ ìŀ¬", "Ġ ë¹ł", "Ġë¹ ł", "вÑĢоп ей", "Ġne jen", "Ġnej en", "Ñĸ ка", "Ñĸк а", "Ġ ìļ¸", "Ġìļ ¸", "ĠÙħ بار", "ĠÙħب ار", "Ġ Äįek", "ĠÄį ek", "ĠÄįe k", "Ġk alk", "Ġka lk", "Ġkal k", "Ġ amac", "Ġa mac", "Ġam ac", "Ġama c", "اد ت", "ĠÙħ اسÙĩ", "ĠÙħا سÙĩ", "Ġarasında ki", "Ġaras ındaki", "Ġб еÑģ", "ĠоÑĤд елÑĮ", "ĠоÑĤдел ÑĮ", "á½ ¶", "ĠΤ ζ", "v yk", "vy k", "ج ÙĨ", "» ê²Į", "ĠниÑĩ его", "ĠØ´ اÙħÙĦ", "ĠÑĥÑģловиÑı Ñħ", "la ması", "lam ası", "lama sı", "è½ ī", "ç¾ ½", "Ġж ид", "ĠоÑĤ ноÑģ", "ĠздÑĸйÑģ нÑİ", "Ġ VỼi", "ĠV Ỽi", "ÙĪ ÙĦÛĮ", "ÙĪÙĦ ÛĮ", "Ġt isÃŃ", "Ġti sÃŃ", "ĠÏĩ ÏģÏĮ", "Ġprac ovnÃŃ", "Ġpracov nÃŃ", "ĠÙĬ ÙĥÙĪÙĨ", "ĠÙĬÙĥ ÙĪÙĨ", "Ġb eÅŁ", "Ġbe ÅŁ", "ج ز", "ั à¸ļร", "ัà¸ļ ร", "ĠYön et", "Ġشر اÛĮØ·", "ĠتÙĪس عÙĩ", "çĹ ĩ", "à¸ĩ à¹Ģà¸Ľ", "ä¸Ģ 次", "ĠÐłÐ¾ÑģÑģий Ñģкой", "æľĢ é«ĺ", "Ġsp olu", "Ġspo lu", "Ġspol u", "д аеÑĤÑģÑı", "да еÑĤÑģÑı", "даеÑĤ ÑģÑı", "Ñĸ ÑĤÑĥ", "ÑĸÑĤ Ñĥ", "Ġоб ÑĢаÑĤ", "ĠобÑĢа ÑĤ", "e nek", "en ek", "ene k", "Ġ mek", "Ġm ek", "Ġme k", "å¦ Ī", "Ġдоп олниÑĤелÑĮ", "Ġ ç²", "Ġç ²", "ĠÙĦ ÙĦت", "ĠÙĦÙĦ ت", "ĠHaz iran", "æ¸ Ī", "à¹Į à¸Ĥà¸Ńà¸ĩ", "ĠÑĦ он", "Ġê²ĥ ìľ¼ë¡ľ", "Ġnh é", "Ġbu gün", "Ġbug ün", "ov ém", "ové m", "Ġза веÑĢ", "Ġзав еÑĢ", "Ġд виг", "Ġдв иг", "Ġдви г", "ä¼ Ļ", "Ġnu ôi", "меÑĢ ик", "ме ÑĢик", "ĠÙĨÙħ ÙĪÙĨÙĩ", "èį ·", "Ñĥ вала", "Ñĥв ала", "Ñĥва ла", "ç¿ »", "Ġs ân", "ог оÑİ", "ого Ñİ", "ا سÙĬØ©", "اس ÙĬØ©", "اسÙĬ Ø©", "Ñĥн кÑĤ", "Ñĥнк ÑĤ", "á nÃŃm", "án ÃŃm", "ánÃŃ m", "ен ное", "енно е", "Ġph út", "Ġ मर", "Ġम र", "ĠاÙĦ ÙĪØ·", "ĠاÙĦÙĪ Ø·", "Ġлег ко", "Ġ ãĢĭ", "ĠãĢ ĭ", "ë¡ľ ëĵľ", "ĠKas ım", "ÙĬ ÙĦÙĬ", "ÙĬÙĦ ÙĬ", "ĠbaÄŁlantı lar", "ĠÑĤ ÑĢÑĥд", "ĠÑĤÑĢ Ñĥд", "ĠÑĤÑĢÑĥ д", "Ø· Ùĩ", "Ġk vůli", "ÑģÑĤ оÑı", "Ġsat Ä±ÅŁ", "Ġh áºŃu", "ĠبÙĩ ترÛĮÙĨ", "ĠبÙĩتر ÛĮÙĨ", "ĠÑģ елÑĮ", "ĠÑģел ÑĮ", "ั à¸Ļว", "ัà¸Ļ ว", "o su", "os u", "य न", "åĽ ³", "ι δ", "ÛĮ تÛĮ", "ÛĮت ÛĮ", "ĠQu áºŃn", "Ġ ей", "Ġе й", "à¹Ģว ลา", "à¹Ģวล า", "ìĬ¤ íĥĢ", "ìĤ¬ 를", "Ġا ÙĩÙĦ", "ĠاÙĩ ÙĦ", "η γ", "Ġk á»·", "Ġká» ·", "Ġ наÑĤ", "Ġн аÑĤ", "Ġна ÑĤ", "âĢ ¡", "Ñĸ ÑĩниÑħ", "ÑĸÑĩ ниÑħ", "ĠÑĢазвиÑĤи Ñı", "ĠÑĢазви ÑĤиÑı", "e cial", "ec ial", "eci al", "ĠÑħ озÑı", "в аеÑĤ", "ва еÑĤ", "ĠÄIJ á»Ļ", "ĠÄIJá» Ļ", "Ġ éĵ", "Ġé ĵ", "Ġok am", "ĠвÑģ ÑĸÑħ", "ĠвÑģÑĸ Ñħ", "ĠPr aze", "ĠPra ze", "ë¥ ł", "ι κα", "ικ α", "æ¬ ²", "Ġgerçek leÅŁ", "ç¥ ĸ", "Ġод ним", "Âł M", "Ġre nk", "Ġr enk", "Ġren k", "Ġल à¤ķ", "ãĥķ ãĤ§", "ãĥķãĤ §", "ĠÙĨ زد", "å¹ »", "Ġúzem ÃŃ", "æı ¡", "а лиÑģÑı", "али ÑģÑı", "Ġ ÃĶ", "Ġà Ķ", "Ġy orum", "Ġyo rum", "ĠÏĢ ÏģÏī", "ãĥ³ ãĥĩ", "ãĥ³ãĥ ĩ", "éĸĭ å§ĭ", "ãĥ¼ ãĥª", "ãĥ¼ãĥ ª", "Ġìĸ¼ êµ´", "Û± Û±", "Û±Û ±", "lü ÄŁÃ¼", "lÃ¼ÄŁ ü", "ÙĨ Ø´", "à¹Ī ำ", "èĽ ĭ", "ĠØ£ د", "ĠW illi", "ĠWill i", "ĠWil li", "ĠWi lli", "èª ²", "Ġsür dür", "ĠEx ternÃŃ", "Ġp ůvod", "Ġpů vod", "ĠØ® اÙĨÙĪ", "ĠخاÙĨ ÙĪ", "ĠкоÑĤоÑĢ ое", "Ġm ohl", "Ġmo hl", "Ġmoh l", "Ġs tÄĽ", "Ġst ÄĽ", "åĩ ı", "ìĤ ¼", "aban cı", "à¹ģ à¸Ļ", "สำ à¸Ħ", "æĤ £", "ab ilece", "abil ece", "abile ce", "éĺ³ åŁİ", "Îij Îļ", "Ġch ữa", "Ġchữ a", "ĠìķĦ ëĭ", "طبÙĬ ÙĤ", "طب ÙĬÙĤ", "ÎĻ ÎŁÎ¥", "ÎĻÎŁ Î¥", "ÑĢ ование", "ÑĢов ание", "ÑĢо вание", "ÑĢован ие", "ÑĢова ние", "åĩ ½", "Ġ ì¼", "Ġì ¼", "ÑĢ оÑĦ", "ÑĢо ÑĦ", "à¹ĩ à¸Ļส", "à¹ĩà¸Ļ ส", "Ġ ãĤ¦", "ĠãĤ ¦", "ï¼ļ ãĢĮ", "á»ĭ a", "Ġ hPa", "Ġh Pa", "m anı", "man ı", "ál nÃŃho", "álnÃŃ ho", "ÙĪ تÛĮ", "ÙĪت ÛĮ", "ĠлеÑĩ ениÑı", "j te", "jt e", "- д", "åħ¨ åĽ½", "ĠбÑĥд Ñĸв", "Ġz atÃŃm", "Ġzat ÃŃm", "Ġ öyle", "Ġö yle", "ìĿ´ ê°Ģ", "s tal", "st al", "sta l", "i vatel", "iv atel", "ivate l", "iva tel", "Ġ æľª", "Ġpož ad", "ĠÑģ ни", "Ġpos lednÃŃ", "Ġposled nÃŃ", "ĠÑģÑĤ анд", "ĠÑģÑĤан д", "ĠÑģÑĤа нд", "à¥Ģ à¤ıम", "à¥Ģà¤ı म", "Ġ عکس", "Ġع کس", "ÑĢ иÑı", "ÑĢи Ñı", "ã y", "á»ĭ p", "Ġo kul", "Ġok ul", "Ġoku l", "à¸ĩ หมà¸Ķ", "Ġвоз ник", "m ÃŃ", "ç§ Ł", "ĠÄij á»ijc", "Ġp odÃŃ", "Ġpo dÃŃ", "Ġpod ÃŃ", "ĠÅĻÃŃ j", "ĠÑĤак Ñĸ", "ĠÑĤа кÑĸ", "à¸ļ าà¸Ĺ", "Ġ 보기", "Ġë³´ 기", "ล า", "еÑģ ÑĤо", "еÑģÑĤ о", "Ġ çĶ¨", "и нÑĭ", "ин Ñĭ", "ĠÑĢ ÑĥÑħ", "ĠÑĢÑĥ Ñħ", "ĠÑĢаÑģп олож", "Ñī еннÑı", "Ġc á»Ń", "à¹ī à¸ļร", "à¥įयव स", "ï¾ ļ", "Ġд алÑĮ", "Ġда лÑĮ", "Ġдал ÑĮ", "Ġض د", "ÙĦ ÙĬØ©", "ÙĦÙĬ Ø©", "ĠкоÑĤоÑĢ ого", "Ġd ve", "Ġdv e", "Ġnh ạc", "ÑĦ Ñĸка", "ÑĦÑĸ ка", "ÑĦÑĸк а", "à¥Ī à¤Ł", "èĩª çĶ±", "Ġпо ÑĢÑĥÑĪ", "ĠпоÑĢ ÑĥÑĪ", "æľĭ åıĭ", "Ġd ört", "Ġdö rt", "ĠÑĢаÑģп ÑĢоÑģÑĤ", "ãģ§ ãģ¯ãģªãģĦ", "ãģ§ãģ¯ ãģªãģĦ", "ĠпеÑĢ ег", "ĠпеÑĢе г", "Ġ ánh", "Ġá nh", "Ġán h", "ĠV ÃŃ", "ظ Ù¹", "à¥į रण", "à¥įर ण", "Ġb ilim", "Ġbi lim", "Ġbil im", "Ġlid é", "Ġd ÃŃky", "ĠdÃŃ ky", "ĠÄIJ á»ĵng", "Ġ εÏģγ", "Ġε Ïģγ", "Ġzn ovu", "Ïĥ ια", "Ïĥι α", "Ñ ŀ", "स à¤Ń", "e kk", "ek k", "Ġμε ÏĦά", "ÑģÑĤ иÑĩ", "ÑģÑĤи Ñĩ", "ÛĮ ÙĨÚ¯", "ÛĮÙĨ Ú¯", "ĠÑıв лÑıÑİÑĤÑģÑı", "Ġ 建", "Ġå» º", "Ïĥ Ïĥα", "ÏĥÏĥ α", "ав лива", "à¸ģ รม", "à¸ģร ม", "ç¬ Ķ", "Ġ ге", "Ġг е", "Ġ رÙĩ", "Ġر Ùĩ", "Ġм ел", "Ġна пÑĢимеÑĢ", "ĠнапÑĢи меÑĢ", "Ġм ик", "Ġми к", "ĠاÙĦس ÙĥاÙĨ", "æ¤ ľ", "ĠÐļ ÑĢа", "Ġv Ãłi", "ĠvÃł i", "ائ Ùħ", "ĠÏĩ Ïģή", "leÅŁ me", "Ġ jas", "Ġj as", "Ġja s", "ê²Į ìŀĦ", "Ġm aç", "Ġma ç", "Ġì§Ħ íĸī", "à¥ĩद न", "Ġvů bec", "ĠÙĦ ÙĨ", "è« ĩ", "âī¡ âī¡", "л ением", "ление м", "лен ием", "ле нием", "ع ÙĨÛĮ", "عÙĨ ÛĮ", "ãĥŀ ãĥ³", "Ä° Z", "ĠÃĸ ÄŁ", "ĠìŬ ìŀIJ", "y Å¡", "Ġ ÑģÑĤа", "ĠÑģ ÑĤа", "ĠÑģÑĤ а", "Ġ สำหร", "Ġสำ หร", "Ġन व", "ãĢĤ ä½Ĩ", "олÑĮ но", "Ġyan ında", "Ġyanı nda", "è² ´", "Ġjednot liv", "Ġ åİŁ", "Ġåİ Ł", "éłħ 缮", "Ġमद द", "리 ìĹIJ", "ĠÙħ اÙĬ", "ĠÙħا ÙĬ", "ĠÑĩ еÑĢв", "ĠÑĩеÑĢ в", "Ġd áv", "Ġdá v", "ÙĦ ÛĮÙĩ", "ÙĦÛĮ Ùĩ", "? #", "Äį nÃŃm", "ÄįnÃŃ m", "ÑĢ ег", "ÑĢе г", "ĠпÑĢимен Ñı", "ĠпÑĢим енÑı", "ãĤĬ ãģ¨", "ê° Ļ", "Ġtop lam", "Ġtopl am", "i leÅŁ", "il eÅŁ", "ile ÅŁ", "Ġk ategor", "ÑĤ ал", "ÑĤа л", "ãģ«ãĤĪ ãĤĭ", "Ġdom ác", "Ġ ê·ľ", "Ġê· ľ", "ĠÙĩ زار", "ĠpÅĻÃŃ stup", "ĠpÅĻÃŃst up", "ı lıyor", "ılı yor", "ıl ıyor", "ж ди", "жд и", "ĠD Æ°Æ¡ng", "ĠPh áºŃt", "Ġç ünkü", "구 ê¸ĢìĥģìľĦ", "ov aných", "ova ných", "ovan ých", "ovaný ch", "Ġع Ø´", "Ġà¤ķर à¤ķ", "ž ÃŃt", "žÃŃ t", "Ġ vÄĽtÅ¡ÃŃ", "ĠvÄĽt Å¡ÃŃ", "ĠvÄĽtÅ¡ ÃŃ", "ĠاÙħ کاÙĨ", "Ġn ông", "Ġz ám", "Ġzá m", "à¥Į न", "е каÑĢ", "ек аÑĢ", "ека ÑĢ", "Âł Т", "k ami", "ka mi", "ĠÑĢеÑģ ÑĥÑĢ", "п оÑģ", "по Ñģ", "Ùİ ÙĤ", "ί λ", "Ġ سازÛĮ", "Ġس ازÛĮ", "Ġساز ÛĮ", "Ġçık an", "Ġçı kan", "ĠdÃŃ tÄĽ", "Ġتص ÙĪ", "ç¯ ĩ", "н д", "Ġrám ci", "h ong", "ho ng", "hon g", "Ġ ÑģÑĸм", "ĠÑģ Ñĸм", "s ak", "sa k", "к еÑĤ", "ке ÑĤ", "д Ñĸл", "дÑĸ л", "ç¹ Ķ", "Ġth Æ°á»Łng", "Ġне ÑĹ", "з Ñĸ", "ÅĻ ÃŃd", "ÅĻÃŃ d", "ित न", "à¤ı à¤ķ", "Ġs ữa", "ĠÙħ رØŃ", "ĠÙħر ØŃ", "é ŀ", "Ġc Æ°á»Ŀng", ": .:", ":. :", "ÑĤ ен", "ÑĤе н", "èī ¦", "Ġkh ợi", "Ġ 기ì¤Ģ", "Ġ기 ì¤Ģ", "lan ır", "彩 票", "ض ÛĮ", "Ġuz av", "Ġb oh", "Ġbo h", "è m", "Ġ æ£", "Ġæ £", "n ici", "ni ci", "nic i", "( çģ«", "åħ³ äºİ", "Ñĸ ÑĩнÑĸ", "ÑĸÑĩ нÑĸ", "à¸ģ ารà¸ĵ", "à¸ģาร à¸ĵ", "Ġì² «", "ÑĢ ÑĥеÑĤ", "ÑĢÑĥ еÑĤ", "ĠarÅŁiv lendi", "ÑĤ им", "ÑĤи м", "า à¸ł", "าภł", "Ġبر ابر", "Ġ à¹Ģà¸ĭ", "Ġà¹Ģ à¸ĭ", "Ġà¹Ģภĭ", "ĠÄij êm", "è· ³", "Ġyön etim", "Ġyönet im", "Ġ éķ·", "Ġéķ ·", "ãĥĨ ãĥ¬ãĥĵ", "м аÑĤи", "ма ÑĤи", "маÑĤ и", "è´£ ä»»", "ick ým", "ický m", "è ¸", "à¹Ģห à¸ķ", "ëł Į", "Ġ رÙĬ", "Ġر ÙĬ", "ĠвÑĭ дел", "åĩº çİ°", "Ġп еÑģ", "Ġì¢ĭ ìĿĢ", "Ġà¤ī सन", "Ġà¤īस न", "ĠAr alık", "ĠAra lık", "ĠÑĩа ÑģÑĥ", "ĠÑĩаÑģ Ñĥ", "l ava", "la va", "lav a", "Ġ ï½ŀ", "Ġï½ ŀ", "æģ ĭ", "د ÛĮد", "دÛĮ د", "âĢĻ den", "âĢĻd en", "âĢĻde n", "Ġ åĪĿ", "ĠåĪ Ŀ", "ÙĪ دة", "ÙĪد Ø©", "Ñĩ или", "Ñĩи ли", "Ñĩил и", "ĠÑħаÑĢакÑĤ еÑĢиÑģÑĤи", "ا ستاÙĨ", "اس تاÙĨ", "است اÙĨ", "द र", "ĠبÙĪد ÙĨ", "ĠبÙĪ دÙĨ", "Ġп алÑĮ", "Ġпа лÑĮ", "Ġпал ÑĮ", "ĠÑĤ ÑĢади", "ĠÑĤÑĢ ади", "ĠÑĤÑĢа ди", "Ġд еÑı", "Ġде Ñı", "Ġ خش", "ĠØ® Ø´", "Ġpok raÄį", "Ġ구 ê¸Ģ", "к овÑĸ", "ко вÑĸ", "ков Ñĸ", "Ġ tık", "Ġt ık", "Ġh ấp", "Ġza lož", "Ġzal ož", "१ à¥", "Ġëĭµ ë³Ģ", "м еÑĪ", "ме ÑĪ", "íļ ¨", "Ġspol up", "Ġspolu p", "Ë Ĩ", "è¾ ¦", "Ġg á»Ĺ", "Ġ å®ļ", "Ġå® ļ", "ĵ n", "as ından", "asında n", "- ı", "ĠбеÑĢ ез", "大 åѸ", "Ġз нов", "Ġзн ов", "ĠHo Ãłng", "Ġد ÙĪÙĨ", "ĠدÙĪ ÙĨ", "Ġan lay", "ĠÙĪ زار", "ĠÙĪز ار", "ĠعÙĦ ÙħÛĮ", "ĠعÙĦÙħ ÛĮ", "è£ ľ", "Ġdü nya", "Ġdün ya", "Ġdüny a", "Ġза лиÑĪ", "Ġзал иÑĪ", "Ġзали ÑĪ", "д аеÑĤ", "да еÑĤ", "ν ε", "и ÑĩеÑģкого", "иÑĩеÑģ кого", "ìĬ¤ íħľ", "ĠÐij еÑĢ", "Ġ дж", "Ġд ж", "Ġ опаÑģ", "Ġоп аÑģ", "ÏĨ α", "Ġzv lá", "Ġt ô", "б еÑĢ", "бе ÑĢ", "ĠÎľ αÏģ", "Ġξα Ïģ", "ti ÄŁini", "tiÄŁi ni", "ãĥ¬ ãĥ³", "ĠK ho", "ĠKh o", "ĠÑĸн ÑĪ", "Ġ ï¿¥", "ì° ¬", "ï½ ¡", "Ġ ноÑĩ", "Ġн оÑĩ", "Ġно Ñĩ", "è¨ Ĭ", "ÄĽ ti", "ÄĽt i", "å¿ Ļ", "Ġکرد ÙĨد", "ĠکردÙĨ د", "ĠÄij ẩy", "ĠÑģказ ав", "ëĥ ¥", "å± ¬", "Ġश हर", "ĠÚ©Ùħ Ú©", "Âł ÐŁ", "ın ca", "нÑĸвеÑĢ ÑģиÑĤ", "Ġ Ú¯ÙĪÙĨÙĩ", "ĠÚ¯ ÙĪÙĨÙĩ", "ĠÚ¯ÙĪ ÙĨÙĩ", "ĠTop lam", "ĠiÅŁ aret", "ä½ł 们", "Ġd erece", "Ġde rece", "Ġder ece", "Ġdere ce", "Ġderec e", "ĠìĤ¬ ìĭ¤", "Ġ ìŀIJ기", "ĠìŀIJ 기", "å®ŀ çİ°", "çĶŁ çī©", "ãģ® ä¸Ģ", "Ġ ÑĢом", "ĠÑĢ ом", "ÙĪ زÙĩ", "ÙĪز Ùĩ", "Ġ ãģ¨", "íĻ į", "ÙĬ ÙĤ", "Ġ åIJįçĦ¡ãģĹãģķãĤĵ", "ĠåIJį çĦ¡ãģĹãģķãĤĵ", "ĠåIJįçĦ¡ãģĹ ãģķãĤĵ", "ĠÙ¾ ÛĮر", "ĠÙ¾ÛĮ ر", "Ġпол ез", "ì¶ ©", "ĠкоÑĢ п", "IJ ëĭ¤", "á» «a", "ừ a", "Îķ Τ", "Ġжел ез", "ãģ£ãģ ±", "Ġx uyên", "Ġ ë¥", "Ġë ¥", "à¥ĩ ।Ċ", "à¥ĩ। Ċ", "ĠÑģÑĤ али", "ĠÑģÑĤал и", "ĠÑģÑĤа ли", "Ġpomoc ÃŃ", "Ġdurum da", "Ġп ÑĢоÑĪ", "ĠпÑĢ оÑĪ", "ĠпÑĢо ÑĪ", "l enÃŃ", "le nÃŃ", "len ÃŃ", "β ολ", "βο λ", "Ġ æĸĩç«ł", "Ġæĸĩ ç«ł", "tÄĽ z", "d ÃŃl", "dÃŃ l", "Ġdruh é", "ĠÑĤ огда", "Ġh rá", "Ġhr á", "о ÑĤÑĮ", "оÑĤ ÑĮ", "า à¸ģร", "าà¸ģ ร", "Ġتص Ùħ", "ĠÙħد ت", "ка дем", "Ġpat ÅĻÃŃ", "ä¹ĭ åīį", "س بة", "سب Ø©", "Ġпо кÑĢÑĭ", "Ġпок ÑĢÑĭ", "Ġn áp", "Ġná p", "Ġ_ {}", "Ġ_{ }", "ëĵ± íķĻêµIJ", "ĠØ¥ ÙĦÙĬ", "ĠØ¥ÙĦ ÙĬ", "Ġöz g", "çļ Ĩ", "Ġhay van", "ĠN isan", "ĠNi san", "غ از", "Ġت ت", "ĠдÑĥ Ñħов", "ĠдÑĥÑħ ов", "ĠÐŁÐ¾ ÑįÑĤомÑĥ", "ÑĮ огод", "ÑĮого д", "Ġk uÅŁ", "Ġku ÅŁ", "Ġà¤ĩ सम", "Ġà¤ĩस म", "ج ÛĮ", "Ġ ãĤ¿", "ĠãĤ ¿", "Ġв кÑĥÑģ", "Ġвк ÑĥÑģ", "ĠвкÑĥ Ñģ", "ç Ģ", "ĠвÑĭ ÑĪе", "âĢĻ dan", "âĢĻd an", "âĢĻda n", "ĠاØŃ Ùħد", "Ġtal ep", "Ġta lep", "Ġtale p", "Ġ ÏĪ", "ĠÏ Ī", "Ġdol ayı", "Ġdolay ı", "ĠÚ¯ زارش", "б ол", "бо л", "ĠاÛĮÙĨ تر", "ÑĢ оÑĩ", "ÑĢо Ñĩ", ") âĢı", "Ġ ëIJł", "ĠëIJ ł", "Ġk oup", "Ġko up", "Ġkou p", "( æľĪ", "é± ¼", "Ġ огÑĢа", "Ġо гÑĢа", "Ġог ÑĢа", "ĠÑĢаз м", "ĠÑĢа зм", "Ġت ست", "Ġتس ت", "ĠpÅĻÃŃ slu", "í ĽĪ", "ĠëĮĢ íķ´", "à¹ģ à¸Ľ", "ан нÑĭе", "аннÑĭ е", "ĠìĿ¸ íĦ°", "Ġkullan ılan", "Ġkullanı lan", "Ġkullanıl an", "Ġz tr", "æĬĢ è¡ĵ", "ि à¤Ľ", "िठĽ", "ĠاÙĦÙħ ؤ", "ov aly", "ova ly", "oval y", "us tos", "ust os", "usto s", "Ġö rg", "Ġör g", "Ġ 太", "Ġå¤ ª", "ε ιο", "ει ο", "Ġ uÄį", "Ġu Äį", "ĠØ´ Ú©ÙĦ", "ĠØ´Ú© ÙĦ", "建 çŃij", "Ġch ạy", "ĠÏĩ Ïģη", "н ÑĥÑĤ", "нÑĥ ÑĤ", "Ġباع Ø«", "ĠNÄĽk ter", "ÑĥÑĤ ÑĤÑı", "ãģ§ãģĻ ãģĭ", "Ġsay ılı", "Ġsayı lı", "им оÑģÑĤÑĮ", "имо ÑģÑĤÑĮ", "ĠпиÑĤ аннÑı", "Ġk ÃŃnh", "Ġh ran", "Ġhr an", "Ġhra n", "ok rat", "Ġed ilir", "Ġedil ir", "Ġà¤ķह त", "Ġp aci", "Ġpa ci", "Ġpac i", "ाल न", "Ġи де", "Ġид е", "ĠZ em", "ĠZe m", "Ġsluž by", "ÑģÑĤв еннÑĭй", "ÑģÑĤвен нÑĭй", "ĠØ¢ ÙĨاÙĨ", "ĠØ¢ÙĨ اÙĨ", "ĠÑĤ оваÑĢи", "ĠÑĤоваÑĢ и", "ĠÑĤов аÑĢи", "ĠتØŃ ÙħÙĬÙĦ", "ĠY ük", "Ġк аÑĤегоÑĢ", "ĠкаÑĤ егоÑĢ", "íĭ Ģ", "Ġк оÑģ", "Ġко Ñģ", "Ġ обов", "Ġо бов", "Ġоб ов", "Ġобо в", "ĠprostÅĻed ÃŃ", "ĠÑģ оÑģ", "ĠÑģо Ñģ", "ĠÐIJ лекÑģанд", "ĠÐIJлекÑģ анд", "Ġ à¹Ģà¸Ĥà¸ķ", "Ġà¹Ģà¸Ĥ à¸ķ", "å¿ħ é¡»", "ั à¸Ĭ", "ĠÙĦ د", "ãĢģ ä¸Ģ", "ĠÎľ ÎŃ", "Ñĥ ваÑĤиÑģÑı", "Ñĥв аÑĤиÑģÑı", "ÑĥваÑĤи ÑģÑı", "Ñĥва ÑĤиÑģÑı", "æķ ı", "ãĥ¼ ãĥIJ", "ãĥ¼ãĥ IJ", "اÙĦ ÙĦÙĩ", "Ġب Ùĩا", "ĠبÙĩ ا", "åĸ ¶", "è´ µ", "æĸ¹ åIJij", "Ġ ì¸", "Ġì ¸", "Ġ ÙĨاÙħÙĩ", "ĠÙĨ اÙħÙĩ", "ĠÙĨاÙħ Ùĩ", "ÑĮ ко", "Ġv ody", "Ġvo dy", "Ġvod y", "v ÃŃc", "vÃŃ c", "à¹ģ à¸Ī", "Ġع ÙĦÛĮÙĩ", "ĠعÙĦ ÛĮÙĩ", "ĠعÙĦÛĮ Ùĩ", "à¹ģ รà¸ĩ", "ί να", "ίν α", "ãģ ¬", "Ġ Ðŀп", "ĠÐŀ п", "Ġsay f", "ï¼Į çĶ±", "ä¼ ´", "ĠÑĥд об", "ãģ¾ ãģł", "Ġне пÑĢи", "Ġнеп ÑĢи", " İ", "à¤¾à¤ľ प", "pl nÄĽ", "Ġ ìĹĦ", "ĠìĹ Ħ", "Ġrů zn", "Ġrůz n", "Ġx ếp", "ãĥĸ ãĥ«", "ĠзаÑħ иÑģÑĤ", "ĠÙħص رÙģ", "ĠÙħصر Ùģ", "ĠvÅ¡ech no", "ãģ® ãģĬ", "ĠTh á»ĭ", "Ġm ùa", "¿ IJ", "ĠпÑĢин ÑĨип", "ĠاÙĨ ÙĤÙĦ", "г аÑĢ", "га ÑĢ", "Ġmož nost", "ÙĤ ÙĬÙĤ", "ÙĤÙĬ ÙĤ", "Ġotev ÅĻ", "Ġ fak", "Ġf ak", "Ġfa k", "Ġng uy", "Ġngu y", "б ов", "бо в", "l acaÄŁ", "lac aÄŁ", "ا طر", "اط ر", "ãģ« ãĤĪãĤĬ", "ãģ«ãĤĪ ãĤĬ", "æĺ¯ åľ¨", "Ġt ầng", "ìĿ¸ ìĿ´", "a ÅĻ", "ç ¢°", "ç¢ °", "ÏĮ με", "Ġ ê°Ī", "Ġê° Ī", "ĠØ£ ØŃد", "ĠØ£ØŃ Ø¯", "غ راÙģ", "غر اÙģ", "ĠÙĬ ØŃ", "ï½ §", "ĠاÙĦØŃÙĬ اة", "Ġ lep", "Ġl ep", "Ġle p", "Ġ ฮ", "Ġภ®", "t ae", "ta e", "Ġl Æ°Æ¡ng", "è½ ®", "Ġз мÑĸн", "ĠзмÑĸ н", "Ġзм Ñĸн", "ĠÐļи ÑĹв", "ĠмÑĸ ÑģÑı", "ĠмÑĸÑģ Ñı", "к ав", "ка в", "à¸ķ ะ", "Ġm noho", "Ġmn oho", "Ġmnoh o", "ĠNgh á»ĭ", "èĻ İ", "Ġ ãĥŁ", "Ġãĥ Ł", "Ġp ráci", "Ġpr áci", "Ġprá ci", "Ġg á»ijc", "ĠY eni", "ĠYe ni", "ĠYen i", "ا ضÙĬ", "اض ÙĬ", "Ġ èij", "Ġè ij", "Ġк ла", "Ġкл а", "ı ng", "ÏĦ εί", "ÏĦε ί", "Ġb eni", "Ġbe ni", "Ġben i", "Ġ عد", "Ġع د", "Ġ aktu", "Ġak tu", "Ġakt u", "ĠÙĪ ÙĤد", "ĠÙĪÙĤ د", "Ġпод гоÑĤов", "Ġgi ai", "Ġgia i", "( æ°´", "Ġs aç", "Ġsa ç", "ĠÙħÙĨ اسب", "ĠÙħÙĨاس ب", "âĸ ĭ", "ÙIJ Ùĩ", "é į", "à¸Ń à¸Ĺ", "ĠسÛĮ اسÛĮ", "o lit", "ol it", "oli t", "ĠاÙĦ جز", "ĠاÙĦج ز", "Ø· ÙĦب", "Ø·ÙĦ ب", "Ġ sey", "Ġs ey", "Ġse y", "e rence", "er ence", "ere nce", "eren ce", "ì´ Į", "ĠвнÑĥÑĤÑĢ ен", "ĠвнÑĥÑĤ ÑĢен", "Ġ à¸Ļาย", "Ġà¸Ļ าย", "ĠìķĬ ìķĺëĭ¤", "ĠìķĬìķĺ ëĭ¤", "o lik", "ol ik", "oli k", "æľĢ åIJİ", "ä» ª", "Ġ ÑĢÑĸд", "ĠÑĢ Ñĸд", "è¼ ĥ", "Ġ باب", "Ġب اب", "Ġبا ب", "Ñĥ ди", "Ñĥд и", "Ġ ÑģÑĤÑĥп", "ĠÑģÑĤ Ñĥп", "ĠÄij ứng", "ĠÄijứ ng", "ĠÅŁ öyle", "Ġ íķĻìĥĿ", "ĠíķĻ ìĥĿ", "Ġв лаÑģÑĤи", "Ġвла ÑģÑĤи", "ĠвлаÑģ ÑĤи", "Ġh ãng", "Ġhã ng", "à¹ī าว", "à¹īา ว", "ĠÚ© اÙĩØ´", "Ġ ëĵ¯", "Ġëĵ ¯", "ĠجÙħ ÙĦÙĩ", "Ġد کتر", "ad olu", "ado lu", "adol u", "Ġت بد", "Ġتب د", "ظ اÙħ", "Ġz naÄį", "Ġzn aÄį", "Ġد ÙĨÛĮ", "ĠدÙĨ ÛĮ", "Ġs ạn", "å¼ ±", "ÏĢ ι", "Ġ çIJĨ", "ĠçIJ Ĩ", "Ġ ÙģصÙĦ", "ĠÙģ صÙĦ", "и нг", "ин г", "Ðļ Ðŀ", "ĠС ов", "ĠСо в", "Ġziy aret", "Ġ دÙħ", "Ġد Ùħ", "ç« ¹", "Ġsah ibi", "is ayar", "isa yar", "isay ar", "ÄŁ a", "ĠпеÑĢÑĸ од", "Ġs na", "Ġsn a", "( æľ¨", "Ġ нее", "Ġн ее", "Ġне е", "ĠÑĦак ÑĤоÑĢ", "ĠÑĦакÑĤ оÑĢ", "м еж", "ме ж", "åº Ħ", "r áž", "rá ž", "ок ÑĢем", "Ġž al", "ิ à¹Ģศษ", "è± ª", "ou cÃŃ", "ĠU lus", "ĠUl us", "Ġtak že", "ا ÙĪÙĨ", "اÙĪ ÙĨ", "н иÑĤи", "ни ÑĤи", "ниÑĤ и", "нÑĮ о", "ëį ¸", "Ġ Ùĥرة", "ĠÙĥ رة", "ĠÙĥر Ø©", "åľ ³", "ĠArth ropoda", "ĠÑĤ одÑĸ", "ĠÑĤо дÑĸ", "Ġدر صد", "ุ รà¸ģ", "ุร à¸ģ", "ĠÑģв ого", "ĠÑģво го", "说 éģĵ", "Ġc ánh", "Ġcá nh", "Ġcán h", "æĵ Ĭ", "Ġ ä¸ĭè½½", "Ġä¸ĭ è½½", "èī ¾", "Ġnik dy", "Ø® Ø·", "ĠÑģ ейÑĩаÑģ", "ÙĪ ÙĬÙĦ", "ÙĪÙĬ ÙĦ", "a met", "am et", "ame t", "문 ìĿĺ", "ĠE ÄŁitim", "大 ä¼ļ", "Ġb ÅĻez", "за ÑĨÑĸÑı", "Ġty to", "н ай", "на й", "غ Ùħ", "Ġ é©", "Ġé ©", "计 ç®Ĺ", "Türk iye", "Ġmn ož", "åIJĪ ä½ľ", "æľį åĭĻ", "Ġkažd ý", "ĠÑİ ÑĢид", "Ġ βα", "Ġβ α", "à¥Ĥ à¤ļ", "åIJĮ ãģĺ", "Ġ çĭ", "Ġç ĭ", "ί ÏĦ", "ÙĪÛĮ ÙĨت", "ÙĪÛĮÙĨ ت", "ا ÙĨس", "اÙĨ س", "æľĢ 大", "Ġ Từ", "ĠT ừ", "éŃĶ æ³ķ", "Ġб ли", "Ġбл и", "ĠÑĤак ое", "ĠÑĤа кое", "ãģ ľ", "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ìĿ´ ë©°", "ĠÙĤسÙħ ت", "Ġ оÑĨÑĸ", "Ġо ÑĨÑĸ", "н икÑĥ", "ни кÑĥ", "ник Ñĥ", "Ġ Bạn", "ĠB ạn", "ĠоÑĢг анÑĸз", "ĠоÑĢган Ñĸз", "ĠоÑĢганÑĸ з", "ü ph", "üp h", "Ġ izin", "Ġi zin", "Ġiz in", "Ġ ï¾Ĭ", "Ġï¾ Ĭ", "εί ÏĤ", "à¸ĩ à¹ģà¸ķ", "ãģ¡ ãĤī", "ва жа", "важ а", "Ġ 欧", "Ġæ¬ §", "ι Ïİ", "ÏĢ ÎŃ", "Ġк ÑĢеп", "ĠÑĨ иÑħ", "ĠÑĨи Ñħ", "æĦŁ ãģĺ", "çķ «", "Ùĥ ÙĪ", "е мо", "ем о", "ž en", "že n", "å¹³ æĸ¹", "ĠÙħجÙħÙĪع Ùĩ", "ĠÑģво и", "Ġ ãĦ", "à¸Ľà¸£à¸°à¸ģ à¸Ńà¸ļ", "ĠпÑĢ оÑĤи", "ĠпÑĢо ÑĤи", "ĠпÑĢоÑĤ и", "ÙĪ ÛĮÙĩ", "ÙĪÛĮ Ùĩ", "认 为", "ÏĨ ÎŃ", "и ÑĩеÑģкий", "иÑĩеÑģ кий", "иÑĩеÑģки й", "æ¥ ļ", "Ġп ап", "Ġпа п", "δ Ïģο", "Ġkullan ım", "Ġkullanı m", "Ġz bo", "Ġzb o", "Ġú spÄĽ", "Ġ Ùħز", "ĠÙħ ز", "ĠF ak", "ĠFa k", "елÑĮ зÑı", "æ´» åĭķ", "ĠÐŁ ÑĢав", "ĠÐŁÑĢ ав", "ĠÐŁÑĢа в", "¦ y", "åĥ ķ", "æij ĺ", "Ġر ئ", "Ġ ÏĨοÏģ", "ĠÏĨ οÏģ", "м иÑĤ", "ми ÑĤ", "Ġt icaret", "Ġti caret", "Ġtic aret", "æ³ķ å¾ĭ", "å¹´ 代", "ìĪ Ļ", "å ¿ł", "å¿ ł", "à¹ĩ à¸Ļà¸Ĺ", "à¹ĩà¸Ļ à¸Ĺ", "Ġ Ñĥж", "ĠÑĥ ж", "ĠÙħتØŃ Ø¯Ùĩ", "Ġtr á»Ŀi", "Ġ رØŃ", "Ġر ØŃ", "ĠÚ©ÙĪ ÚĨ", "ĠопÑĢед елен", "ĠопÑĢедел ен", "ĠزÙħ ÛĮÙĨÙĩ", "ĠزÙħÛĮÙĨ Ùĩ", "Ġn óng", "Ġnó ng", "Ġng ủ", "Nh ững", "Ġк иÑĪ", "Ġки ÑĪ", "Ġ jde", "Ġj de", "Ġjd e", "Ġ ä¸Ĭæµ·", "Ġä¸Ĭ æµ·", "åĭ ĩ", "Ġt anı", "Ġtan ı", "à¹Į à¹ģละ", "à¹Įà¹ģ ละ", "ĠÑĢа ÑģÑĤвоÑĢ", "ĠÑĢаÑģÑĤ воÑĢ", "ĠÑģÑĢед ÑģÑĤв", "Ġc án", "Ġcá n", "Ġsyst ému", "Ġsystém u", "ÛĮ Ø·", "ĠÑģиÑģÑĤем а", "Ġ ëŀ", "Ġë ŀ", "Ġ ÑĩеÑĤ", "ĠÑĩ еÑĤ", "éĥ¨ éŨ", "å¸ °", "Ġm illet", "Ġmill et", "Ġmil let", "ĠÎķ λλά", "à¥ĩà¤ĸ न", "Ġrepublik y", "ÑĢ ами", "ÑĢа ми", "ÑĢам и", "Ġसम स", "Ġaç ısından", "اد ÙĦ", "Ġб еÑģп", "ĠбеÑģ п", "ãĥ» âĶģ", "åľ Ń", "o cu", "oc u", "k ánÃŃ", "ká nÃŃ", "ÙĪ رش", "ÙĪر Ø´", "ëŀ µ", "Ġ çģ", "Ġç ģ", "è° ģ", "Ġs ám", "Ġν εÏĨ", "Ġνε ÏĨ", "b ilir", "bil ir", "ĠmÃŃst ÄĽ", "Ġ žen", "Ġž en", "Ġže n", "Ġil ç", "Ġ ë§ģ", "Ġë§ ģ", "ãĢij Ċ", "ĠÙħÙĪ ارد", "ĠاÙĦ Ø´ÙĬ", "ĠاÙĦØ´ ÙĬ", "Ġ기 ë¡Ŀ", "Ġt ady", "Ġta dy", "Ġtad y", "à¸Ń าà¸Ī", "ĠÑģ ÑĦ", "ĠspoleÄį nost", "Ġtém atu", "Ùħ اÙħ", "Ùħا Ùħ", "Ùħ ع", "Ġ леж", "Ġл еж", "ĠÚĨ Ø´Ùħ", "ĠiÅŁ let", "ĠÙĨس Ø®", "ä¼ °", "ãģį ãģª", "ãĢ ĥ", "å² Ĺ", "Ġ åŃIJ", "Ġå ŃIJ", "ĠåŃ IJ", "Ġb ảng", "Ġbản g", "çĮ ®", "Ġc ứng", "Ġcứ ng", "Ġк ÑĢай", "ĠкÑĢа й", "Ġ èĭ±è¯Ń", "Ġèĭ± è¯Ń", "Ðł ÐIJ", "ز ÙĨ", "èĥ ŀ", "Ġsür eç", "Ġsüre ç", "ãĥķ ãĥĪ", "ĠкÑĸлÑĮ ка", "ne ÄŁin", "neÄŁi n", "ov ány", "ová ny", "ován y", "л Ñĸн", "лÑĸ н", "Ġvý raz", "ĠÑģÑĩ иÑĤа", "ĠÑģÑĩиÑĤ а", "ĠпÑĢав ило", "ĠпÑĢави ло", "ĠпÑĢавил о", "ĠиÑģполÑĮз Ñĥ", "Ġk éo", "Ġké o", "ĠyaklaÅŁ ık", "ĠÙĪاب ستÙĩ", "ов аÑĤелÑĮ", "Ġ ì²ł", "Ġì² ł", "ĠاÙĦ عاÙħ", "ĠاÙĦع اÙħ", "åĿ ı", "Ġ à¸ī", "Ġภī", "ĠS Æ¡n", "λ ιο", "λι ο", "ì¶Ķ ì²ľ", "Ġsluž eb", "ĠдеÑıÑĤелÑĮ ноÑģÑĤи", "з м", "Ġп ози", "Ġпо зи", "Ġпоз и", ".; .;", "ĠпÑĢоиÑģ ÑħодиÑĤ", "าย à¹ĥà¸Ļ", "çļĦ ãģ«", "Ġà¤ĩस स", "о меÑĤ", "ом еÑĤ", "Ġ αÏģ", "Ġα Ïģ", "ा à¤Ĺर", "ाà¤Ĺ र", "i cÃŃch", "ic ÃŃch", "icÃŃ ch", "Ġpolož ky", "ê³ ¨", "æĥ Ĭ", "Ġö ner", "Ġön er", "Ġöne r", "Ġx ảy", "ĠÙĨظ رÛĮ", "ĠÙĨظر ÛĮ", "Ġngh á»ī", "Ġ à¸ľà¸¥", "Ġà¸ľ ล", "ĠÑĢ олÑĮ", "ĠÑĢ емон", "ĠÑĢе мон", "ص ÙĪر", "V ý", "ĠS á»ij", "ĠÑģ ÑĥÑĩаÑģ", "ĠÑģÑĥ ÑĩаÑģ", "ห ย", "ĠاÙĤ داÙħ", "Ġer kek", "Ġerk ek", "Ġ èį", "Ġè į", "ĠÄij ôi", "ĠÄijô i", "Ġкон кÑĢеÑĤ", "æ¬ Ĭ", "Ġ 缮", "ĠçĽ ®", "ÙĪ Ú©", "lı kla", "lık la", "Ġp azar", "Ġpa zar", "Ġpaz ar", "ά νÏī", "άν Ïī", "Ñĥ ÑģÑĤа", "ÑĥÑģ ÑĤа", "ÑĥÑģÑĤ а", "ãģª ãģŁ", "Ġ ÙĩÙĨÚ¯", "ĠÙĩ ÙĨÚ¯", "ĠÙĩÙĨ Ú¯", "Ю ÐĽ", "Ġв елик", "Ġвели к", "Ġвел ик", "Ġве лик", "Ġnh Ỽ", "Ġ ìĭľíĹĺ", "Ġìĭľ íĹĺ", ") ìĿĺ", "Ùĥ Ùĩ", "Ġ à¹ģล", "Ġà¹ģ ล", "Û² Ûµ", "Ġار ساÙĦ", "Ġ окÑĢем", "Ġок ÑĢем", "ά ÏĤ", "ĠвÑĭ Ñħод", "vÄĽt Å¡ÃŃ", "ĠØ· رÛĮÙĤ", "Ġк оÑĢоÑĤ", "ĠкоÑĢ оÑĤ", "Ġко ÑĢоÑĤ", "н ÑĶ", "ãĤĬ ãģ«", "Ġ ä¹Ł", "Ġä¹ Ł", "ØŃ Øµ", "ع ÙħاÙĦ", "عÙħ اÙĦ", "oloj ik", "oloji k", "Ġر ابط", "Ġرا بط", "çª Ĺ", "Ġg iz", "Ġgi z", "Ġch ết", "Ġchế t", "æ¨ £", "ส à¸ĩ", "ÙĪ تر", "ÙĪت ر", "ĠÑı кÑĥ", "ĠÑıк Ñĥ", "çı¾ åľ¨", "ĠоÑĤ ÑģÑĥÑĤÑģÑĤв", "Ġ ê´ijê³ł", "Ġê´ij ê³ł", "Ñĸ ки", "Ñĸк и", "åĢ ¤", "è® ¢", "Ġ dle", "Ġd le", "Ġdl e", "Ġ åł", "Ġå ł", "æ¨ ©", "è® ¯", "åĶ IJ", "Ġ âĸ²", "Ġâĸ ²", "Ġli stop", "Ġlist op", "Ġlis top", "Ġdat ové", "Ġdato vé", "ÏĦ ÏĮÏĤ", "ÏĦÏĮ ÏĤ", "Ġ оз", "Ġо з", "δ ÏĮ", "èĴ Ĥ", "Û³ Û°", "ãĥª ãĥ¼ãĤº", "ãĥªãĥ¼ ãĤº", "ĠÙħر کز", "ĠÙħرک ز", "ĠпÑĸдÑĤ ÑĢим", "ĠÑģ ез", "é¡ ĺ", "Ġol acaktır", "Ġolacak tır", "æº Ģ", "ĠÏĢεÏģι ο", "ĠÏĢεÏģ ιο", "ĠÏĢε Ïģιο", "ÑĦ а", "ÏĦ ηÏĥη", "ÏĦη Ïĥη", "ç» ĥ", "Ðŀ д", "δ Ïħ", "âĦ ĥ", "Ġl ắp", "ĠëĦ ĺ", "Ø· اÙĨ", "ĠÙ¾ ÙĨج", "ĠÙ¾ÙĨ ج", "ت اÙĨ", "تا ÙĨ", "i lerinin", "iler inin", "ileri nin", "ilerin in", "ilerini n", "à Ī", "ĠØ® ÙĪØ´", "ĠØ®ÙĪ Ø´", "Ġ ìĬ¬", "ĠìĬ ¬", "ĠاÙĦر ئÙĬس", "ẵ n", "Ġ شار", "ĠØ´ ار", "e ru", "er u", "ж ив", "жи в", "à¸Ļ าย", "à¸Ļา ย", "Ġs ẻ", "Ġà¤ī à¤ļ", "ãģ« ãģĭ", "ç¡ Ģ", "Ġyür üt", "ĠС еÑĢг", "ĠСеÑĢ г", "Ġ каÑģ", "Ġк аÑģ", "Ġка Ñģ", "ĠÐij ог", "Ġìĸ´ëĸ »ê²Į", "Ġ çŁ³", "Ġç Ł³", "ĠçŁ ³", "Ġöl dür", "Ġöld ür", "л Ñĸв", "лÑĸ в", "Ġho Ãłng", "ĠhoÃłn g", "Ġb á»Ļt", "Ġbá»Ļ t", "çŀ ¬", "Ġ 침", "Ġì¹ ¨", "N ếu", "Ġne vy", "Ġnev y", "Ġ ìľ¤", "Ġìľ ¤", "Ġsou Äįást", "ıs ıyla", "ısı yla", "Ġtük et", "b ou", "bo u", "Ġд во", "Ġдв о", "س Ø·", "å½ĵ çĦ¶", "ãĥ ¨", "Ġ زادÙĩ", "Ġز ادÙĩ", "Ġزاد Ùĩ", "Ġ éĥ¨", "Ġéĥ ¨", "Ġر ÙĪØŃ", "ĠرÙĪ ØŃ", "Ġ ï¼į", "Ġï¼ į", "ĠмÑĸÑģ ÑĨев", "ĠмÑĸÑģÑĨе в", "θ εν", "θε ν", "ภĨ", "л енÑĸ", "лен Ñĸ", "ле нÑĸ", "çį ²", "ĠH OH", "ĠHO H", "s ın", "sı n", "ิ à¸ķร", "ิà¸ķ ร", "è² ¡", "ĠpÅĻ id", "ĠpÅĻi d", "à¹Ģ หà¸Ļ", "à¹Ģห à¸Ļ", "l ý", "è¨Ģ èijī", "ठĵ", "âĸįâĸįâĸįâĸį âĸįâĸįâĸįâĸį", "ب اب", "با ب", "ãĥ¼ ãĥķ", "ãĥ¼ãĥ ķ", "м оÑĢ", "мо ÑĢ", "è¿ĩ ç¨ĭ", "Ġ ãĥĽ", "Ġãĥ Ľ", "ĠK inh", "ĠKi nh", "ĠKin h", "íķľ êµŃ", "Ġìĸ´ëĸ ¤", "Ġв лиÑı", "Ġf ayd", "Ġfa yd", "Ġص ÙĨع", "ĠصÙĨ ع", "Ġal ır", "Ġet tiÄŁi", "Ġetti ÄŁi", "ά κ", "im izin", "imi zin", "imiz in", "imizi n", "ัà¸ļ à¸ľ", "Ġзем елÑĮ", "ÙĬÙĦ اد", "ÙĬÙĦا د", "æ¶ ¨", "çı ł", "ĠØ£ غ", "Ġz ku", "Ġzk u", "âĢŀ A", "า à¸ķร", "าà¸ķ ร", "a yi", "ay i", "ãĥ© ãĤ¹", "и ло", "ил о", "ĠÄij á»į", "ĠÄijá» į", ". Îķ", "ë ľ", "ĠμÏĢο Ïģεί", "å¸ ¶", "Ġar tır", "Ġart ır", "า à¸į", "าภį", "å¿ ĺ", "ta lya", "tal ya", "Ġpoz dÄĽji", "ĠpozdÄĽ ji", "Ġnep ÅĻ", "Ġ æ¹", "Ġæ ¹", "اÙĩ ÛĮ", "Ġsat ın", "Ġ ë²Į", "Ġë² Į", "ج ÙĪ", "ä¸Ģ 缴", "ìķĦ ìļĶ", "Âł P", "Ġ ØĽ", "ĠØ Ľ", "Ġп ал", "Ġпа л", "表 æĥħ", "Ġc anlı", "Ġcan lı", "æĪIJ 为", "ÙĪ ÙĨا", "ÙĪÙĨ ا", "Ġ âĢ¯", "ĠâĢ ¯", "à¸ģำ ล", "åį ĸ", "Ġ αÏĥ", "Ġα Ïĥ", "и нок", "ин ок", "а мп", "ам п", "ล à¸Ńà¸ĩ", "ÙĤ ÙĤ", "ĠпÑĢо Ñħод", "ĠпÑĢоÑħ од", "ãĤĦãĤĭ 夫", "Ïĩ η", "è² ¨", "ĠÙģ ÙĬÙĩ", "ĠÙģÙĬ Ùĩ", "ÙĬ رÙĬ", "ÙĬر ÙĬ", "Ġвне ÑĪ", "Ġk arak", "Ġka rak", "Ġkar ak", "Ġkara k", "Ø« ÙĦ", "Ùĩ ÙĪرÛĮ", "ÙĩÙĪر ÛĮ", "اÙĪر Ù¾", "ĠÄij á»ı", "ĠÄijá» ı", "ji Å¡tÄĽnÃŃ", "jiÅ¡tÄĽ nÃŃ", "ت بر", "تب ر", "Ġê·¸ ê²ĥ", "Ġg ül", "Ġgü l", "Ġпо кÑĥп", "Ġпок Ñĥп", "l ilik", "li lik", "lili k", "lil ik", "Ġz da", "Ġzd a", "åīį ãģ«", "ĠÙħÙĩ ÙĨد", "Ġ ÎijÎĿ", "ĠÎij ÎĿ", "ĠÚ©ÛĮÙĦ ÙĪÙħتر", "Ġp ÅĻeh", "ĠpÅĻ eh", "ĠpÅĻe h", "а леж", "ал еж", "але ж", "Ġka yn", "Ġkay n", "è® ¿", "Ġì¤ij êµŃ", "ĠÑĪиÑĢ ок", "ĠÑĪи ÑĢок", "ĠÙħشار کت", "âĢ Ĥ", "Ġ íŤ", "ĠíĹ ¤", "Ġìłľ íĴĪ", "ĠØ´ ÛĮر", "ĠØ´ÛĮ ر", "es inden", "esinde n", "esin den", "ÑĢ ÑĸÑĩ", "ÑĢÑĸ Ñĩ", "èı ²", "Ñģ коÑĢ", "Ñģк оÑĢ", "Ñģко ÑĢ", "e tik", "et ik", "eti k", "า à¸ľ", "าภľ", "ĠØ· بÛĮ", "Ġطب ÛĮ", "κ ÎŃ", "ĠìŀĪ ìĸ´", "Ġ dek", "Ġd ek", "Ġde k", "ÑĢ Ñĸй", "ÑĢÑĸ й", "å ĨĴ", "åĨ Ĵ", "nÃŃ ci", "® ¤", "ĠÙħر تب", "Ġy azı", "Ġya zı", "Ġyaz ı", "üs lü", "ìľ¼ ëĤĺ", "e lerine", "eler ine", "ele rine", "eleri ne", "elerin e", "Ġy oÄŁun", "Ġб ак", "Ġба к", "ÎĻ ÎŁ", "ά λÏħ", "άλ Ïħ", "ç´ Ļ", "ĠÑĢÑĥ ками", "ĠÑĢÑĥк ами", "Ġçöz üm", "ìłķ ìĿĦ", "Ġgüç lü", "λ ÏĮ", "Ġb elli", "Ġbe lli", "Ġbel li", "Ġbell i", "ÃŃ Å¡e", "ÃŃÅ¡ e", "ĠÏĮ ÏĢÏīÏĤ", "Ġna Å¡", "Ġp ár", "Ġpá r", "ÑĪ ÑĤ", "Ġ ìĨ¡", "ĠìĨ ¡", "à¥Ĥ रत", "à¥Ĥर त", "ĠÏĢολ Ïį", "ĠÏĢο λÏį", "ç° ¡", "èĤ ¯", "æ¹ ¾", "Ġ äºĭ", "Ġब स", "Ġ무 ë£Į", "д ина", "дин а", "ди на", "èª °", "л еж", "ле ж", "Ġú ÅĻad", "ĠоÑģвÑĸ ÑĤи", "ĠоÑģвÑĸÑĤ и", "ĠвÑĸд Ñĩ", "ĠпÑĢи знаÑĩ", "ĠпÑĢизна Ñĩ", "ĠпÑĢиз наÑĩ", "çĶ³ 请", "' ya", "'y a", "ä¿ Ĭ", "Ġ ÙĬÙĪÙĨ", "ĠÙĬ ÙĪÙĨ", "ĠÙĬÙĪ ÙĨ", "Ġس ع", "Ġ ÐĶаÑĤа", "ĠÐĶ аÑĤа", "ĠÐĶа ÑĤа", "è¨Ģ ãģĨ", "ĠØŃ ØªÛĮ", "ĠJi ÅĻÃŃ", "ĠÐ¥ аÑĢ", "éĻ Ī", "à¹Ī าà¸Īะ", "à¹Īา à¸Īะ", "Ġsay esinde", "ĠÑĤÑĢеб а", "ê°Ģ ì§Ģ", "Ġy emek", "Ġye mek", "Ġyem ek", "Ġyeme k", "è¦ ļ", "ặ n", "ãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", "Ġ 举", "Ġä¸ ľ", "Ġ ÙĪا", "ĠÙĪ ا", "ĠÙħ ÙĪس", "ĠÙħÙĪ س", "Ġком анд", "Ġкоман д", "Ġseç im", "Ñĩ еннÑı", "Ñĩен нÑı", "Ġtot iž", "Ġr á»Ńa", "ı a", "Ø¢ Ùħ", "ÑĨ Ñĸон", "ÑĨÑĸ он", ": :::::::::::", ":: ::::::::::", ":::: ::::::::", ":::::: ::::::", ":::::::: ::::", "::: :::::::::", "::::: :::::::", "::::::: :::::", "::::::::: :::", ":::::::::: ::", "::::::::::: :", "ÐĿ ÐIJ", "ı za", "ız a", "h end", "he nd", "hen d", "Ġफ र", "ัà¸Ķ à¸ģาร", "Ġ Cách", "ĠC ách", "ĠCác h", "ĠпоÑĤ Ñĸм", "Ġá¼ Ģ", "ا ÙĦا", "اÙĦ ا", "á» ¡", "ر ÛĮÙħ", "رÛĮ Ùħ", "å® «", "Ġز ÙħÛĮÙĨ", "ĠزÙħ ÛĮÙĨ", "ÑĢ еÑģÑĤ", "ÑĢе ÑģÑĤ", "ÑĢеÑģ ÑĤ", "б аÑĩ", "ба Ñĩ", "Ùĩر ست", "н ог", "но г", "ï¼Į 大", "ĠëĺIJ íķľ", "Ġz ůst", "Ġzů st", "ĠÐĴ она", "ĠÐĴо на", "å¤ĩ 份", "Ġ اÙģت", "Ġا Ùģت", "ĠاÙģ ت", "o je", "oj e", "Ñģк ÑĸлÑĮки", "Ġnh ẹ", "Ġк еÑĢÑĸв", "ĠкеÑĢ Ñĸв", "á¿ ¦", "æĸ¹ æ¡Ī", "з аÑĨиÑı", "за ÑĨиÑı", "ĠвÑĸдповÑĸд но", "ĠвÑĸдпов Ñĸдно", "ãĤ¤ ãĤ¹", "г ал", "га л", "ĠобÑĭ Ñĩно", "ĠобÑĭÑĩ но", "اÙĪرپ ÙĪÛĮÙĨت", "å® ľ", "l osti", "lo sti", "los ti", "lost i", "è¿Ľ åħ¥", "uyor du", "벤 íĬ¸", "æīĭ ãĤĴ", "ÐŁ од", "ÐŁÐ¾ д", "ĠÙħØŃ Ø¯ÙĪد", "Ġ Ø¢Ùħد", "ĠØ¢ Ùħد", "ĠØ¢Ùħ د", "ar akter", "arak ter", "çļĦ 大", "Ġsı cak", "l ant", "la nt", "lan t", "Ġd ấu", "ĠÙĨ Ú©", "èĢħ ãģ®", "Ġkend ini", "Ġkendi ni", "Ġп аÑĨи", "Ġпа ÑĨи", "Ġ 기íĥĢ", "Ġ기 íĥĢ", "ĠвмеÑģÑĤ е", "в аеÑĤÑģÑı", "ва еÑĤÑģÑı", "ваеÑĤ ÑģÑı", "Ġ ë§ī", "Ġë§ ī", "ĠchvÃŃ li", "Ø® ÛĮ", "ÙĦ ع", "n ÃŃky", "nÃŃ ky", "nÃŃk y", "､ :", "ëIJľ ëĭ¤", "ì§ ķ", "Ġк вÑĸÑĤ", "Ġкв ÑĸÑĤ", "¨ ìĸ´", "l iž", "li ž", "Ġë¹Ħë°Ģ ê¸Ģ", "Ġkh á»iji", "Ġ ë°©ìĨ¡", "Ġë°© ìĨ¡", "e chan", "ec han", "ech an", "echa n", "Ġзакон одав", "Ġа кÑĤ", "Ġак ÑĤ", "문 ìłľ", "ĠN ó", "Ġ çĤ¹", "Ġç Ĥ¹", "ĠçĤ ¹", "hled em", "hle dem", "ĠÑģво ÑĹÑħ", "ĠÑģвоÑĹ Ñħ", "Ġر ÙĤÙħ", "ĠرÙĤ Ùħ", "æĽ ¼", "ि वर", "िव र", "åİ ļ", "ĠÐļ од", "ĠÐļо д", "à¤Ń à¤Ĺ", "ìŀIJ ëĬĶ", "à¸Ļ ม", "Ñĥ Ñģа", "ÑĥÑģ а", "Ġg ünü", "Ġgün ü", "ĠÄij ÃŃch", "Ġtr ữ", "å ·»", "å· »", "éĵ¶ è¡Į", "ØŃ ÙĨ", "è® ¨", "γ Ïĩ", "á½ ¸", "a larında", "alar ında", "aları nda", "aların da", "Ġk af", "Ġka f", "ÙĪ اج", "ÙĪا ج", "ĠиÑģ клÑİÑĩ", "ĠиÑģк лÑİÑĩ", "Ġnh iá»ħ", "á»į t", "ĠìĽ ¹", "Ġ éĿ¢", "ĠéĿ ¢", "ãģ® ãģĮ", "Ġм ало", "Ġмал о", "Ġма ло", "Ñĸ лÑĸ", "Ñĸл Ñĸ", "Ġ biên", "Ġb iên", "Ġbi ên", "n ému", "né mu", "ném u", "пÑĢи меÑĢ", "пÑĢ имеÑĢ", "âĸł âĸł", "Ġk amp", "Ġka mp", "Ġkam p", "Ġ веÑī", "Ġв еÑī", "Ġве Ñī", "Äį em", "Äįe m", "à¥ģ ध", "à¥ģठ§", "æŁ »", "ت ÙĪÙĨ", "تÙĪ ÙĨ", "åıª æľī", "ãģ¯ ãģĦ", "Ġ รวม", "Ġร วม", "ãĤ ŀ", "ãģĻ ãĤĭãģ¨", "ãģĻãĤĭ ãģ¨", "å¾Ī å¤ļ", "à¹Ī à¸ķ", "ĠsvÄĽt a", "ĠsvÄĽ ta", "Ġ ê°Ģ격", "Ġê°Ģ 격", "Ú¯ Ùĩ", "an daÅŁ", "and aÅŁ", "anda ÅŁ", "ãĥª ãĤ¹", "Ïī μα", "ĠØ® ÙĪب", "ĠØ®ÙĪ ب", "ç´ ħ", "Ñĩ иÑģ", "Ñĩи Ñģ", "ì¢ Į", "ĠØŃض رت", "Ġви ÑĢÑĸÑĪ", "Ù¾ ر", "Ġtý d", "Ġkon tro", "Ġkont ro", "д ейÑģÑĤв", "ãģŁãĤģ ãģ«", "ì ī", "мини ÑģÑĤÑĢа", "мин иÑģÑĤÑĢа", "âĢ ¯", "åī ij", "ни ÑĨÑĸ", "ниÑĨ Ñĸ", "å¦ ĩ", "Ġ лиÑĪ", "Ġл иÑĪ", "Ġли ÑĪ", "ãģ£ ãģ¦ãĤĭ", "ãģ£ãģ¦ ãĤĭ", "на ÑĢÑĥж", "наÑĢ Ñĥж", "Ñī иÑħ", "Ñīи Ñħ", "ÏĦ οκ", "ÏĦο κ", "ov áno", "ová no", "ován o", "تر ÙĦ", "ÑĢ ек", "ÑĢе к", "غ ات", "Ġ omez", "Ġo mez", "Ġom ez", "ì ĵ°", "ĠÃľ l", "ï½ Ĵ", "lı ģını", "lıģ ını", "lıģın ı", "Ġv ượt", "Ġb ÄĽÅ¾", "ĠbÄĽ ž", "Ãľ R", "Ġ ãĥ¾", "Ġãĥ ¾", "ĠdoÄŁ al", "Ġh atır", "Ġha tır", "Ġhat ır", "Ġsv ým", "ì§Ģ ëıĦ", "à¹Ģà¸ł à¸Ĺ", "Ġv ay", "Ġva y", "Ġ æĻĤ", "Ġæ ĻĤ", "ĠæĻ Ĥ", "à¥įव प", "Ġp lo", "Ġpl o", "é¢Ħ è§Ī", "Ġçık tı", "Ġ دÙĨ", "Ġد ÙĨ", "n ánÃŃ", "ná nÃŃ", "ê· Ģ", "íĺ Ģ", "à¸ŀ à¸ļ", "m uÅŁtur", "muÅŁ tur", "å®ĺ æĸ¹", "ĠíĶĦ ë¡ľê·¸ëŀ¨", "éĢŁ 度", "ler dir", "lerdi r", "ÑĩеÑģ кого", "Ñĩе Ñģкого", "ĠÄ°n san", "âĶ ĥ", "Ġà¤ĩत न", "С Ð¡Ðł", "Ġا Ùħر", "ĠاÙħ ر", "Ġkö tü", "Ùģ Ø´", "Ġb oj", "Ġbo j", "ĠÑĨÑĸ ÑĶÑĹ", "Ġsöy lem", "Ġsöyl em", "ни ÑĨÑĭ", "ниÑĨ Ñĭ", "ãĢĤ 她", "âĢĿ .Ċ", "âĢĿ. Ċ", "Ġm ilion", "Ġmil ion", "Ġmi lion", "Ġmili on", "Ġson unda", "Ġsonu nda", "з Ñĥ", "à¥į मà¤ķ", "à¥įम à¤ķ", "人 åı£", "n ÄĽÅ¾", "nÄĽ ž", "ĠÑģ моÑĤ", "ĠÑģм оÑĤ", "Ġкомп лекÑģ", "Ġкомплек Ñģ", "Ġзави Ñģим", "Ġиме ÑİÑĤ", "Ġl ạc", "Ġlạ c", "Ġ hangi", "Ġh angi", "Ġhang i", "Ġhan gi", "ëĶ ©", "åĬ ³", "ĠvÄĽ ci", "ĠvÄĽc i", "е ÑĢов", "еÑĢ ов", "еÑĢо в", "κ Ïģι", "Ġdur umu", "Ġdurum u", "Ġ بÙĪاسطة", "ĠبÙĪ اسطة", "ĠØ£ بÙĬ", "Ġأب ÙĬ", "ĠAÄŁ ustos", "ε Ïĩ", "Ġд иÑĤи", "Ġди ÑĤи", "ĠдиÑĤ и", "ÑĦ ика", "ÑĦи ка", "ÑĦик а", "Ġ NÄĥm", "ĠN Äĥm", "Ġ 기ìĪł", "Ġ기 ìĪł", "Ġhlav nÃŃ", "ä¿ ĥ", "Ġलà¤Ĺ त", "ĠO br", "ĠOb r", ". ย", "ко вод", "ков од", "ково д", "o pis", "op is", "opi s", "Ġ ãĥī", "Ġãĥ ī", "Ġبش ÙĥÙĦ", "н ием", "ни ем", "ние м", "Ġtém ÄĽÅĻ", "ĠاÙĦ ØŃر", "ĠاÙĦØŃ Ø±", "ĠÙĦ ازÙħ", "ĠÙĦا زÙħ", "Ġm ái", "Ġmá i", "i liÄŁi", "ili ÄŁi", "ë³ ¼", "Ġy ık", "Ġyı k", "ç½ ²", "ÑĢ ава", "ÑĢа ва", "ÑĢав а", "Ñī ин", "Ñīи н", "ãģ« 対", "ç²¾ ç¥ŀ", "à¹ī ส", "Ġtem sil", "à Ĩ", "ìķ Ķ", "ĠпÑĢавилÑĮ но", "ÑĢ оÑİ", "ÑĢо Ñİ", "Û±Û³ Û¸", "è© ŀ", "اء Ø©", "ÙĪ ارÙĩ", "ÙĪا رÙĩ", "ÙĪار Ùĩ", "ï¼ ħ", "ĠÐľ ик", "ĠÐľÐ¸ к", "æģ ¶", "æı Ĵ", "ा पन", "ाप न", "ĠÚ©ÛĮÙģ ÛĮت", "ĠT Ãłi", "Ġt iá»ĥu", "ov alo", "ova lo", "oval o", "çĿ ¡", "Ñĩ ил", "Ñĩи л", "Ġ лиÑĤ", "Ġл иÑĤ", "Ġли ÑĤ", "λεÏħ ÏĦα", "Ġ окон", "Ġо кон", "Ġок он", ": :|", ":: |", "в ала", "ва ла", "вал а", "ĠÙħرک زÛĮ", "ĠÙħرکز ÛĮ", "Ġ alÄ±ÅŁ", "Ġa lÄ±ÅŁ", "Ġal Ä±ÅŁ", "Ġдолж но", "Ġдол жно", "æĻĤ 代", "Ġ sert", "Ġs ert", "Ġse rt", "Ġser t", "е ÑĤом", "еÑĤ ом", "ัà¸Ļ ย", "åģ ·", "Ġ vÃŃc", "Ġv ÃŃc", "ĠvÃŃ c", "ĠÑħ оÑĤÑı", "ĠÑħоÑĤ Ñı", "a larını", "alar ını", "aların ı", "len mesi", "lenme si", "ãĥ³ ãĥIJ", "ãĥ³ãĥ IJ", "Ġëª ĩ", "Ġá» ¦y", "ĠاÙĦ کتر", "vy Å¡Å¡ÃŃ", "è² ¬", "주 ìĭľ", "á ÅĻe", "áÅĻ e", "Ġy ere", "Ġye re", "Ġyer e", "ãĤ¢ ãĥ³", "ĠاÙĦس عÙĪد", "ĠØ¢ Ø´", "Ġch óng", "Ġchó ng", "Ġ è»", "Ġè »", "г аÑĶ", "га ÑĶ", "Ġ ãģĤ", "ç¨ ³", "δ εÏĤ", "δε ÏĤ", "缮 çļĦ", "Ġce vap", "Ġcev ap", "Ñģ ÑĤе", "ÑģÑĤ е", "é¡ ¿", "म न", "é¡ ¾", "Ġк ÑĢедиÑĤ", "ĠÙħس تÙĤ", "ĠÙħست ÙĤ", "Ġ миÑĤ", "Ġм иÑĤ", "Ġми ÑĤ", "Ġt á»ĵn", "Ġtá» ĵn", "Ġ جÙĦ", "Ġج ÙĦ", "Ä© a", "ĠاÙĦع ÙĦÙħ", "ĠاÙĦعÙĦ Ùħ", "á ků", "ák ů", "Ġ íķĻêµIJ", "ĠíķĻ êµIJ", "à¸Ĺ à¸Ńà¸ĩ", "ห à¸Ļà¸Ķ", "หà¸Ļ à¸Ķ", "ĠлÑĸÑĤ еÑĢаÑĤÑĥ", "ëIJ ł", "ά ÏģÏĩ", "άÏģ Ïĩ", "ĠÙĤد رت", "ĠÙĤدر ت", "à¸Ļ าà¸ĩ", "à¸Ļา à¸ĩ", "Ġa rac", "Ġar ac", "Ġara c", "Ġj ÃŃd", "ĠjÃŃ d", "Ġtür lü", "íĶ ½", "er siz", "ers iz", "е ним", "ен им", "ени м", "Ġyüz yıl", "Ġ ãģĦ", "ĠÎļ Ïħ", "Ġ æļ", "Ġæ ļ", "Ġp ůj", "Ġpů j", "Ġt á»Ļi", "Ġth iên", "Ġthi ên", "Ä° S", "Ġth úc", "Ġthú c", "æĹ ģ", "ìŀIJ ìĿ¸", "Ġöl üm", "ر ÛĮÙģ", "رÛĮ Ùģ", "ÑĢ еж", "ÑĢе ж", "ص اÙĦ", "ر ÙģØ©", "رÙģ Ø©", "i ếp", "iế p", "Ñı ÑĤиÑı", "ÑıÑĤ иÑı", "ÑıÑĤи Ñı", "Ġpou žit", "á tu", "át u", "为 ä»Ģä¹Ī", "ì ģ", "Ġ krát", "Ġk rát", "Ġkr át", "ĠپرÙĪ ÚĺÙĩ", "Ġrozhod nutÃŃ", "ĠÑĥ нивеÑĢ", "Ñĸй но", "Ġ åij¨", "Ġåij ¨", "Ġk iá»ĥu", "缮 åīį", "ä¿ Ħ", "ÏĦ οι", "ÏĦο ι", "ÑĦеÑĢ ен", "uÅŁ tur", "Ġ nÃŃm", "Ġn ÃŃm", "ĠnÃŃ m", "âĢĮ Ø®", "Ġ ủy", "Ġ ÑģÑĤаÑĤи", "ĠÑģÑĤ аÑĤи", "ĠÑģÑĤаÑĤ и", "ĠÑģÑĤа ÑĤи", "ÑĩеÑģ кий", "Ñĩе Ñģкий", "ÑĩеÑģки й", "Ġj estli", "Ġjest li", "ĠÙ¾ ÙĨ", "Ġob ce", "ĠجÙĩ اÙĨÛĮ", "ĠجÙĩاÙĨ ÛĮ", "едаг ог", "ãģ§ ãģ®", "Ġbu á»Ļc", "ì¹´ ì§Ģëħ¸", "à¹ĩ à¸Ħ", "ĠÄį tvrt", "Ġ ника", "Ġн ика", "Ġни ка", "Ġник а", "Ġвп лив", "Ġд иÑĢ", "Ġди ÑĢ", "ĠÑģоб ÑģÑĤвен", "Ġë§İ ìĿ´", "æ¾ ³", "ÑĢ Ñĥб", "ÑĢÑĥ б", "æ£ ĭ", "声 éŁ³", "ä¹ ĥ", "تÛĮ جÙĩ", "å¹ ¼", "o nya", "on ya", "ony a", "ĠPlan tae", "ĠPlant ae", "Ч ÑĤо", "æIJ Ń", "ä½ľ çĶ¨", "ìħ ¨", "Ġк ÑĢÑĥг", "Ġ ÙĪÙģÙĬ", "ĠÙĪ ÙģÙĬ", "ĠÙĪÙģ ÙĬ", "Ġ ï¼ŀ", "Ġï¼ ŀ", "ÑĪ ки", "Âł Ðľ", "ا Ø´ÛĮ", "اش ÛĮ", "ĠÅŀ ubat", "ĠÅŀu bat", "Ġع شر", "Ġعش ر", "l if", "li f", "Ġpou žitÃŃ", "Ġpoužit ÃŃ", "íĨ ¡", "Ġб лок", "Ġбл ок", "èĢ ¶", "ู ร", "Ġv üc", "Ø´ ÙĪد", "Ø´ÙĪ د", "и ма", "им а", "ни ÑĨип", "ниÑĨ ип", "ìĿ´ ëĵľ", "Ġ âĢIJ", "ĠâĢ IJ", "Ġ назнаÑĩ", "Ġна знаÑĩ", "Ġназ наÑĩ", "Ġназна Ñĩ", "Ġstr any", "Ġstran y", "Ġstra ny", "æ® ¿", "ĠاÙĦ رÙĪ", "ĠاÙĦر ÙĪ", "çº ¸", "åĪ ij", "ï¼Į ä»İ", "Ġ ë©´", "Ġë© ´", "ĠпÑĢовед еннÑı", "Ġh ava", "Ġha va", "Ġhav a", "ĠìĹĨ ìĹĪëĭ¤", "ĠìĹĨìĹĪ ëĭ¤", "å¢ŀ åĬł", "Ú ¾", "ç¼ º", "Ġع بار", "Ġعب ار", "Ġt ắc", "Ġin ÅŁa", "er se", "ers e", "ر ÙĬب", "رÙĬ ب", "Ġá»ķ n", "Ø£ Ø©", "ĠÏĢολ ι", "ĠÏĢο λι", "Ġm ắc", "Ñģ ол", "Ñģо л", "æ´ ŀ", "- го", "-г о", "ç¨ĭ 度", "ĠвикоÑĢиÑģÑĤ аннÑı", "âĢŀ ظ", "e lerinde", "eler inde", "eleri nde", "elerin de", "ĠNh Æ°ng", "ĠNhÆ° ng", "st ÅĻed", "Ġhasta lık", "Ġhast alık", "à¹ī à¹Ģà¸Ľ", "Ġd efa", "Ġde fa", "Ġdef a", "Ġ زÙĬ", "Ġز ÙĬ", "اط ÙĤ", "Ġп ÑĢой", "ĠпÑĢ ой", "ĠпÑĢо й", "Ġок ÑĢÑĥг", "ν ια", "νι α", "l adu", "la du", "lad u", "k oli", "ko li", "kol i", "Ġ oÄŁ", "Ġo ÄŁ", "Ġви Ñģок", "ĠвиÑģ ок", "Ð ĩ", "çĽ ĸ", "ãĤı ãģij", "ãĥ¼ ãĥģ", "ãĥ¼ãĥ ģ", "æ¡ ¥", "ĠÅ¡kol y", "ĠÅ¡k oly", "i tom", "it om", "ito m", "Ġت ØŃص", "ĠتØŃ Øµ", "a lara", "al ara", "ala ra", "alar a", "Ġк ал", "Ġка л", "ĠпÑĢи Ñħод", "Ġ é¦ĸ页", "Ġé¦ĸ 页", " į", "ĠÛĮ عÙĨÛĮ", "Ġt ùy", "Ģ ë¡ľ", "볤 ê³ł", "á ze", "áz e", "Ġ ек", "Ġе к", "èħ ¹", "ĠF akat", "ĠFa kat", "ĠFak at", "п о", "ĠÄij á»įc", "ĠÄijá»į c", "å Īĺ", "åĪ ĺ", "áz al", "ÑĤ он", "ÑĤо н", "Ú¯ ÙĪ", "ä¸ Ī", "ìĹ ¼", "ĠÙĦÙĦ Ø£", "ĠE ÄŁer", "åħ±åĴĮ åĽ½", "Ø° ر", "Ġd aÄŁ", "Ġda ÄŁ", "è¡Į ä¸ļ", "ê±°ëŀĺ ê°Ģ", "è´Ł è´£", "C ông", "ĠÑĦ илÑĮ", "ĠÑĦил ÑĮ", "Ġ аÑģ", "Ġа Ñģ", "Ġch ẳng", "ним аÑĤÑĮ", "нима ÑĤÑĮ", "Ġif ad", "Ġi fad", "Ġ ìħ", "Ġì ħ", "çĪ µ", "ĠÅĻe Å¡enÃŃ", "åĽ½ 产", "Ġкак ой", "Ġка кой", "Ġम ध", "ĠY ar", "ĠYa r", "ob raz", "obra z", "Ġon emoc", "Ġ âĤ", "Ġâ Ĥ", "åİŁ åĽł", "ĠÙĥ رد", "ĠÙĥر د", "Ġآز اد", "Ġad lı", "ĠH izmet", "ãĥ¼ ãĥij", "ãĥ¼ãĥ ij", "ÙĨ سÙĬØ©", "ÙĨس ÙĬØ©", "Ġв нÑĥÑĤ", "ĠвнÑĥ ÑĤ", "Ġd ále", "Ġdál e", "Ġdá le", "Îķ Î¥", "Ġ ÑĥÑħ", "ĠÑĥ Ñħ", "Ġ ÑĢев", "ĠÑĢ ев", "ĠÑĢе в", "Ġ меÑĪ", "Ġм еÑĪ", "ĠkoÅŁ ul", "ĠاÛĮ راÙĨÛĮ", "ĠاÛĮراÙĨ ÛĮ", "éĺ µ", "Ġ ëıĻìķĪ", "ĠëıĻ ìķĪ", "à¹Ģ à¸Ł", "à¹ĢภŁ", "ëłĪ 벨", "è¨Ń è¨Ī", "p rak", "pr ak", "pra k", "p oÄį", "po Äį", "اع دة", "اعد Ø©", "Ġas ker", "Ġask er", "ĠÙĪÛĮ ÚĺÙĩ", "ĠÙĪÛĮÚĺ Ùĩ", "ĠТ еÑĢ", "ĠТе ÑĢ", "mak ta", "makt a", "ĠÄįty ÅĻ", "Âł С", "âĢĮÚ©ÙĨ ÙĨد", "ï¼Į 並", "ĠÑĢоÑģ Ñĸй", "Ġu nut", "Ġun ut", "è¿Ļ ä¸Ģ", "o pak", "op ak", "opa k", "èĢ IJ", "Ġз амеÑĤ", "Ġза меÑĤ", "Ġзам еÑĤ", "à¹Į ล", "ب ÙĨ", "Ġ 몰", "Ġëª °", "Ġins anlar", "Ġinsan lar", "åı¯ æĺ¯", "æ¢ ¦", "к од", "ко д", "èĽ Ľ", "kl adnÃŃ", "klad nÃŃ", "ÑĢов од", "ÑĢо вод", "ĠмÑĸ ÑģÑĤа", "ĠмÑĸÑģ ÑĤа", "ĠмÑĸÑģÑĤ а", "åĩº äºĨ", "Ġп аÑģ", "Ġпа Ñģ", "о бов", "об ов", "Ú¯ اÙĩÛĮ", "گاÙĩ ÛĮ", "в ин", "ви н", "à¥įर ध", "Ġком пон", "Ġкомп он", "Ġ аÑĤ", "Ġа ÑĤ", "Ġa det", "Ġad et", "Ġade t", "Ġ ãĥģ", "Ġãĥ ģ", "ĠØ° ات", "ĠØŃ ÙĪ", "Ġtro chu", "à¹ģ หà¸Ļ", "à¹ģห à¸Ļ", "Ġзав жди", "ĠPart isi", "ĠParti si", "ĠS avaÅŁ", "ĠSav aÅŁ", "Ġs ÃŃd", "ĠsÃŃ d", "Ġ Ñģон", "ĠÑģ он", "ĠÑģо н", "ر ÙĬÙģ", "رÙĬ Ùģ", "Ġz cela", "åĺ ´", "ĠÑĦ ÑĥÑĤ", "il erek", "ile rek", "iler ek", "ilere k", "m alıdır", "malı dır", "Ġd á»±a", "Ġdá»± a", "à¸Ĺำ à¸ĩาà¸Ļ", "ĠÙĪÙĦ ÙĥÙĨ", "ĠÙĪÙĦÙĥ ÙĨ", "ãģª ãĤĵãģł", "ãģªãĤĵ ãģł", "ĠÚ© ÙħÛĮ", "ĠÚ©Ùħ ÛĮ", "Ġléka ÅĻ", "Ïģ Ïį", "ج Ùħع", "جÙħ ع", "ın ızı", "ını zı", "ınız ı", "ĠAn adolu", "ãģ«ãĤĪ ãģ£ãģ¦", "Ġê·¸ëŁ¬ ëĤĺ", "Ġ íĮĶ", "ĠíĮ Ķ", "Ñĸ ÑĤÑĮ", "ÑĸÑĤ ÑĮ", "Ġ ¦", "Ġ ¦", "ä¸į è¦ģ", "à¸ĸ ม", "Ġ ÙĬد", "ĠÙĬ د", "ĠpÅĻ ep", "ĠpÅĻe p", "Ġ è¦ģ", "Ġè¦ ģ", "ĠпÑĢо екÑĤ", "ĠпÑĢоек ÑĤ", "ĠÑĢе ги", "ĠÑĢег и", "Ġd ạy", "к ового", "ков ого", "ково го", "Ġ ıs", "Ġı s", "ĠK ı", "ĠÙģÙĬ Ùĩا", "ĠÙģÙĬÙĩ ا", "ÛĮ ات", "ÛĮا ت", "ĠÑģÑĤ ала", "ĠÑģÑĤал а", "ĠÑģÑĤа ла", "æĬ ľ", "Ñĥ ÑĢа", "ÑĥÑĢ а", "ĠÙ¾ اÛĮاÙĨ", "ĠپاÛĮ اÙĨ", "Ġپا ÛĮاÙĨ", "Ġitibar en", "а нÑĸÑĹ", "ан ÑĸÑĹ", "анÑĸ ÑĹ", "Ġо ÑĦоÑĢм", "л еÑĩ", "ле Ñĩ", "ε ξ", "æĶ¿ çŃĸ", "Ġ ç½ij", "Ġç½ ij", "å Ĥ¬", "åĤ ¬", "ĠìĿ´ 룰", "Ġkar deÅŁ", "Ñİ Ñīего", "ÑİÑī его", "л ки", "ĠاÛĮ اÙĦات", "ت Ùĩا", "تÙĩ ا", "Ġпод Ñħод", "ĠØŃ ÙĪÙĦ", "ĠØŃÙĪ ÙĦ", "ĠÑģов ÑĢем", "íĿ ¥", "Ġ 詳細", "Ġè© ³ç´°", "ı yı", "ıy ı", "ĠتÙĤ ÙĪ", "æ¯Ķ è¾ĥ", "Ġαν ÏĦι", "Ġ ΣΤ", "ĠΣ Τ", "ji Å¡Å¥", "yn ı", "Ġpo cházet", "- Ðļ", "Ġзав д", "Ùİ Ø³", "ç»ĵ æŀĦ", "Ùħ ار", "Ùħا ر", "ν οι", "νο ι", "ĠÎł εÏģι", "ĠγεÏģ ι", "èĩ £", "Ġna cházÃŃ", "Ġnach ázÃŃ", "ÏĦ Ïİ", "à¥įय त", "u yu", "uy u", "æķ Ĺ", "e bi", "eb i", "Ġë°Ķ ë¡ľ", "ĠгÑĢ н", "ĠاÙĦ اس", "Ġorg án", "Ġ edin", "Ġe din", "Ġed in", "Ġedi n", "åŁ ĥ", "à¹ģ à¸Ħ", "ĠØŃ Ø¯ÙĪد", "ĠØŃد ÙĪد", "ĠдÑĢÑĥг ой", "ĠдÑĢÑĥго й", "оÑģ ков", "оÑģк ов", "ĠS ợ", "ĠpÅĻ ib", "ĠpÅĻi b", "ä¿Ŀ æĬ¤", "Ùħ بر", "Ġ ãĥĨ", "Ġãĥ Ĩ", "Ġd oz", "Ġdo z", "op tera", "opt era", "ิล à¸Ľ", "د ارÛĮ", "دار ÛĮ", "دا رÛĮ", "æĦŁ è§ī", "代 çIJĨ", "ÙĨ دا", "ÙĨد ا", "ا ÙĬا", "اÙĬ ا", "ص ÙĨ", "Ġce lé", "Ġcel é", "Ġ è©ķ", "Ġè© ķ", "à¸ĩ à¸Ļ", "Ġ leh", "Ġl eh", "Ġle h", "èİ· å¾Ĺ", "ãĢĢ ï¾ī", "ĠìĦł ìĪĺ", "르 ëĬĶ", "à¤Ĩ र", "å§Ķ åijĺ", "æĹł çłģ", "Ġ è·", "Ġè ·", "Ġza jÃŃm", "Ġzaj ÃŃm", "ec ké", "eck é", "æ µľ", "æµ ľ", "ĠÑĥ нÑĸвеÑĢÑģиÑĤ", "ĠбÑİдж еÑĤ", "à¥ĩ .", "Ġv stup", "Ġ оÑī", "Ġо Ñī", "Ġ åľĭ", "Ġåľ ĭ", "ä¸ģ 缮", "Ġв едÑĮ", "Ġвед ÑĮ", "Ġë§IJ ìĿĦ", "Ġtek nik", "Ġtekn ik", "ãĢĢ ï½Į", "ãĢĢï½ Į", "ĠпÑĸд виÑī", "ĠÑģвÑıз и", "ĠÑģвÑı зи", "Ġتر جÙħ", " ī", "ĠÄij âu", "Ñĸ Ñĩного", "ÑĸÑĩ ного", "å°ij å¹´", "e cta", "ect a", "ec ta", "ि लत", "िल त", "ι οÏĤ", "ιο ÏĤ", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "t eg", "te g", "á» īnh", "á»ī nh", "¯ ¿", "Ġne bu", "Ġneb u", "ÙĬ ÙĬÙĨ", "о ÑıÑĤ", "оÑı ÑĤ", "é¤ Ĭ", "ĠاÙĤتص ادÛĮ", "ĠاÙĤتصاد ÛĮ", "âĢĻ nun", "âĢĻn un", "ĠÐĴ Ñĸк", "Ġng Äĥn", "ëĮĢ íķĻêµIJ", "ëĮĢíķĻ êµIJ", "é ı", "़ र", "ا باÙĨ", "اب اÙĨ", "Ùİ Ùĥ", "Ġet kil", "Ġetk il", "Ġetki l", "Ġch ắn", "Ġë°ľ ìĥĿ", "Ġtam amen", "Ġtamam en", "ĠÙħØŃ ÛĮØ·", "ü lü", "ül ü", "åģ¥ 康", "ĠÑĢаÑģÑĤ ениÑı", "ÏĢο ί", "Ġ è¶ħ", "Ġè¶ ħ", "á Äį", "ĠìĻ¸ ë¶Ģ", "ĠØ® ÛĮÙĦÛĮ", "ĠØ®ÛĮ ÙĦÛĮ", "Ġد ÙĪست", "ĠدÙĪ ست", "à¹Ģà¸Ĥ à¸ķ", "Ġk alan", "Ġka lan", "Ġkal an", "ë¨ ¼", "a vÄĽ", "av ÄĽ", "문 íĻĶ", "Ġди аг", "ĠÙĨ ÙĪÙĬس", "ĠÙĨÙĪ ÙĬس", "íķ ij", "à¸ŀ าะ", "ëĭ¤ ê°Ģ", "Ġn iá»ĩm", "Ġس ÙĪÙħ", "ĠسÙĪ Ùħ", "- м", "à¸Ķำ à¹Ģà¸Ļ", "à¹ĩ ว", "ãĢĤ ãģĵãģ®", "ç¯ ī", "Width Space", "Zero WidthSpace", "ائ ÙħØ©", "ائÙħ Ø©", "à¹Ħà¸ĭ à¸ķ", "ä¸ĭè½½ 次æķ°", "ä¼¼ ä¹İ", "ĠÑĤ в", "Ġzá kaz", "Ġج دا", "Ġجد ا", "Ġg ider", "Ġgi der", "Ġgid er", "ãĥ¼ ãĥĵ", "ãĥ¼ãĥ ĵ", "n ů", "Ġë§ģ íģ¬", "ĠdÃ¼ÅŁ ük", "Ñĥ нок", "Ñĥн ок", "Ġt óc", "ĠÑĤ ÑĢÑĥб", "ĠÑĤÑĢ Ñĥб", "ĠÑĤÑĢÑĥ б", "о кÑģ", "ок Ñģ", "Ġtr ải", "Ġtrả i", "Ġm iá»ħn", "Ġth Æ°á»Ľc", "Ġnh áºŃt", "Âł D", "m asının", "mas ının", "masını n", "è¼ ª", "ĠÎĿ ο", "er ç", "Ġdok once", "Ġdokon ce", "ĠG üven", "ĠGü ven", "ov aná", "ova ná", "ovan á", "е зд", "ез д", "Ñĸ нÑĮ", "Ñĸн ÑĮ", "èģ ²", "اÙĦ Ø£", "ï¼Į ä½Ĩæĺ¯", "ï¼Įä½Ĩ æĺ¯", "Ġпол ноÑģÑĤÑĮÑİ", "Port ály", "ĠØŃ Ø§Ùģظ", "à¥Ĥ à¤ķ", "ÑĢ Ñĥн", "ÑĢÑĥ н", "人 çī©", "Ġa çı", "Ġaç ı", "Ġp oru", "Ġpo ru", "Ġpor u", "er iod", "eri od", "ĠAmer ika", "ĠAmerik a", "çĩ Ł", "Ġ رÙĪد", "Ġر ÙĪد", "ĠرÙĪ د", "ĠкÑĢов и", "ÙĪ ÙĤت", "ÙĪÙĤ ت", "éĺ ¶", "ãĥ»âĶģ ãĥ»âĶģ", "ر ÙĬÙħ", "رÙĬ Ùħ", "åİĨ åı²", "ä¸ ¸", "Ġзн овÑĥ", "Ġзнов Ñĥ", "ĠÑģво его", "ĠÑģвое го", "бÑĥд ÑĮ", "ĠØŃ Ø¬Ùħ", "ĠØŃج Ùħ", "ĠδÏį ο", "ìķĪ ëĤ´", "Ġ ãģ§", "à¹Ī ะ", "Ùij Ùı", "çµIJ æŀľ", "âĢĻ i", "à¹Į ,", "åŃĺ äºİ", "Ġरà¤ĸ न", "ĠسرÙħ اÛĮÙĩ", "Ġг лÑĥб", "ĠглÑĥ б", "ĠÑĢаз Ñĸ", "ĠÑĢа зÑĸ", "Ñĩ нÑĸ", "ï¼Į åıĪ", "c ısı", "cı sı", "æľī åħ³", "ãĤ» ãĥ³", "èIJ ¨", "ĠGi áo", "ĠGiá o", "ĠاÙĦØ« اÙĨÙĬ", "ĠÑĢаз ом", "Ġ ÑĤÑĢо", "ĠÑĤ ÑĢо", "ĠÑĤÑĢ о", "Ġaçık lam", "åĨ³ å®ļ", "à¸Ń à¸Ľ", "åĶ ¯", "ĠÅŁ ark", "ĠÅŁar k", "Ġsist emi", "Ġsistem i", "Ġto prak", "Ġtop rak", "èĢĥ ãģĪ", "Ġпоп ÑĥлÑıÑĢ", "Ġ ëĨį", "ĠëĨ į", "ا ÙĬÙĨ", "اÙĬ ÙĨ", "स म", "Ġ ÂĢ", "Ġ Ģ", "Ġed erek", "Ġeder ek", "Ġg ec", "Ġge c", "ìĤ¬ ìĹħ", "ĠÑĢ оки", "ĠÑĢок и", "ĠбеÑĢ ем", "ĠخاÙĨÙĪ ادÙĩ", "Ġ èµ·", "Ġèµ ·", "Ġ ЧÑĤо", "ĠЧ ÑĤо", "Ġo bÄĽ", "Ġob ÄĽ", "и нÑĸ", "ин Ñĸ", "ìĿ´ ìĹĪ", "ĠIn di", "ĠInd i", "Ġд иÑĤ", "Ġди ÑĤ", "ãĥ¶ æľĪ", "Ġнем ного", "Ġzáklad ÄĽ", "à¹Ĥ à¸Ħ", "ĠÑģам ого", "ĠÑģамо го", "Ġب ØŃØ«", "ĠبØŃ Ø«", "Ġ æ¶", "Ġæ ¶", "ов ж", "Ġоб ÑĢаÑī", "ĠобÑĢа Ñī", "à Ĵ", "ว รร", "วร ร", "à¤Ĥ श", "ĠоÑĩ еÑĢед", "ĠÙģ رز", "ĠÙģر ز", "ëĮĢ íķľ", "Ġs izin", "Ġsi zin", "Ġsiz in", "Ġsizi n", "ر Ùģت", "رÙģ ت", "Ñİ Ñīим", "ÑİÑī им", "æ» ij", "a vir", "av ir", "avi r", "ĠÙĪ صÙĦ", "ĠÙĪص ÙĦ", "Ġqu ay", "Ġqua y", "Ġг ип", "ÑĢ ениÑı", "ÑĢе ниÑı", "ÑĢен иÑı", "à¥į वत", "à¥įव त", "ιν Ïīν", "à¤ľ ह", "Ġh Æ¡i", "Ġpo važ", "Ġpov až", "Ġع رب", "м енÑĤа", "мен ÑĤа", "менÑĤ а", "Ġо ÑģÑĤан", "ĠоÑģÑĤ ан", "ä¹ĭ éĹ´", "a cÃŃch", "ac ÃŃch", "acÃŃ ch", "ĠÑģказ ала", "ĠÑģказал а", "ìĿ´ ëĿ¼ëĬĶ", "ìĿ´ëĿ¼ ëĬĶ", "ĠØ´ اخ", "Ġëĭ¹ ìĭł", "ar lar", "arl ar", "Ġмл н", "åĨ ¬", ". :.:.:", ".: .:.:", ".:.: .:", ".:. :.:", ".:.:. :", "Ġ θε", "Ġθ ε", "Ġher kes", "Ġherk es", "л Ñıд", "лÑı д", "ا Ùħا", "اÙħ ا", "Ġë ŃIJ", "Ïĥιμο ÏĢοι", "Ġ obraz", "Ġob raz", "Ġobr az", "Ġobra z", "غ اÙĦ", "B Æ°á»Ľc", "å° Ĭ", "ìŀIJ 를", "æĢ Ĵ", "οÏħ Ïģγ", "å¼ķ ãģį", "Ġkon uda", "Ġkonu da", "ĠاÙĦت ج", "Ġ krit", "Ġk rit", "Ġkr it", "å¿ į", "ĠìłĦìĦ¸ ê°Ģ", "г овоÑĢ", "го воÑĢ", "гов оÑĢ", "Ġist iyor", "о ки", "ок и", "ĠобеÑģп еÑĩ", "ĠобеÑģпе Ñĩ", "Ġay rıca", "Ġayrı ca", "à¹Ģ à¸ľ", "à¹Ģภľ", "а ÑĢод", "аÑĢ од", "Ä° Åŀ", "ĠجÙħ ÙĩÙĪرÛĮ", "ĠجÙħÙĩÙĪر ÛĮ", "ĠÑģво иÑħ", "ĠÑģвои Ñħ", "Ġprov ád", "Ġ ÑĢам", "ĠÑĢ ам", "ĠÑĢаР¼", "ĠÑĢа м", "ĠÙĤ ض", "л иÑĤелÑĮ", "лиÑĤ елÑĮ", "ãĤ± ãĥĥãĥĪ", "оÑģ оÑĦ", "Ġर हन", "Ġरह न", "k ový", "ko vý", "kov ý", "ì° ¸", "γ κα", "γκ α", "λ οι", "λο ι", "μ ÏĢο", "μÏĢ ο", "ĠÄij au", "ĠÄija u", "н иÑİ", "ни Ñİ", "Ġman žel", "Ġ íĺ¼", "Ġíĺ ¼", "ĠÑĤ иÑģ", "ĠÑĤи Ñģ", "ãĥĨ ãĥ«", "ab ilecek", "abil ecek", "abile cek", "abilece k", "н ин", "ни н", "à¸ģรรม à¸ģาร", "éł IJ", "Ġph ê", "j edn", "je dn", "jed n", "交 æµģ", "Ġвним ание", "об ÑĢеÑĤ", "Ġжиз нÑĮ", "ÑĢи ÑģÑĤи", "ÑĢиÑģÑĤ и", "ÑĢиÑģ ÑĤи", "à¥Ī à¤ļ", "Ġyüz den", "Ġyüzde n", "Ġg iy", "Ġgi y", "éļ Ķ", "ä» ²", "Ġ èĻ", "Ġè Ļ", "ĠP arti", "ĠPart i", "ĠPar ti", "Ġ éĸ¢", "Ġéĸ ¢", "ัà¸ļ ส", "Ġnej lepÅ¡ÃŃ", "Ùİ Ùī", "ĠìĿ´ ìłľ", "Ġc ắt", "ÑĢоз Ñĥм", "Ġnej sou", "l ÃŃd", "lÃŃ d", "θ ο", "à¹ĩ à¸ĩ", "ĠÑģп ÑĢоÑģ", "ma mÄ±ÅŁ", "mam Ä±ÅŁ", "Ġ 쪽", "Ġì ª½", "ا ÙģÙĤ", "اÙģ ÙĤ", "ÑĨÑĸй ниÑħ", "Ġ é¦Ļ", "Ġé¦ Ļ", "ĠÙħÛĮÙĦÛĮ ÙĪÙĨ", "å¤ ¢", "ĠÙģ Ùĩرست", "r ý", "Ġпо вÑĸдом", "Ġпов Ñĸдом", "ec eÄŁi", "ece ÄŁi", "ĠзабезпеÑĩ еннÑı", " Ķ", "ãģĹ ãģªãģĦ", "åŁº ç¡Ģ", "ĠÚĨ ÙĨÛĮÙĨ", "ĠÑĢоз ÑĢоб", "ä¸Ģ äºĽ", "ãĥ³ ãģ®", "ĠпÑĢа ÑĨÑĸв", "ĠпÑĢаÑĨÑĸ в", "å¾Ĺ åĪ°", "Ġt ấn", "åŃĺæ¡£ å¤ĩ份", "Ġ íĻĪ", "ĠíĻ Ī", "Ġ à¸Ķาว", "Ġà¸Ķ าว", "ìĭ ±", "л ина", "ли на", "лин а", "ĠвоÑģп ал", "ÄŁ inden", "ÄŁin den", "ÄŁinde n", "аÑĤ елей", "аÑĤе лей", "r ž", "ĠÑĦ Ñĥн", "ĠÐIJ л", "ĠпоÑĩ ÑĤи", "о вÑĸд", "ов Ñĸд", "овÑĸ д", "ا عب", "اع ب", "าะ ห", "Ġвоз ÑĢаÑģÑĤ", "ิà¸ĩ ห", "ĠÙģ ÙĦس", "ĠÙģÙĦ س", "ĠÅ¡ est", "à¸Ĭ าว", "Ġ 골", "Ġê³ ¨", "Ġ oÄį", "Ġo Äį", "ãĤ¸ ãĥ§", "к оÑģÑĤи", "ко ÑģÑĤи", "éĽĨ åĽ¢", "æ± ĩ", "ĠpÅĻÃŃ liÅ¡", "Ġ ìĿij", "ĠìĿ ij", "ди ви", "див и", "ĠдокÑĥм енÑĤа", "ĠдокÑĥменÑĤ а", "ĠCh âu", "Ġm áu", "Ġmá u", "Ġkh ô", "à ķ", "Ñī ий", "Ñīи й", "Ġs ẵn", "Ġкон ÑĦ", "Ġз ÑĥÑģÑĤ", "åĽŀ çŃĶ", "Ġ коÑĢиÑģÑĤ", "ĠкоÑĢ иÑģÑĤ", "Ġко ÑĢиÑģÑĤ", "ĠÏĢεÏģ ί", "ĠÏĢε Ïģί", "ä¸ °", "Ġm ạch", "ан к", "ä¸ĭ æĿ¥", "èµĦ æĸĻ", "ย à¸Ńà¸Ķ", "ĠÏĢ ιο", "à¹ī à¸ĩาà¸Ļ", "à¹īà¸ĩ าà¸Ļ", "Ġum ÃŃst", "æ½ ®", "çªģ çĦ¶", "Ġk ultur", "Ġkul tur", "ĠاÙĦ صÙģ", "ĠاÙĦص Ùģ", "a larının", "alar ının", "aların ın", "alarını n", "ĠÎĶη μο", "Ġвикон аннÑı", "Ġвико наннÑı", "ï½ ¿", "Ġбез опаÑģ", "ĠÑģ аÑħ", "Ġn oh", "Ġno h", "à¹ĥ à¸ļ", "éĥ½ å¸Ĥ", "ÅŁ am", "ÅŁa m", "б ÑĥÑĤ", "бÑĥ ÑĤ", "Ġ모 ìĬµ", "Ġв аг", "Ġва г", "çIJĨ 解", "Ġekonom ik", "Ġkh ắc", "Ġs vat", "Ġsv at", "лиÑĪ ком", "ัà¸ĩ à¸Īาà¸ģ", "iz yon", "èĥ½ å¤Ł", "ί νει", "ίν ει", " Ĭ", "ì¦ Į", "Ġ ÙĩاÛĮÛĮ", "ĠÙĩ اÛĮÛĮ", "ĠÙĩاÛĮ ÛĮ", "ĠkiÅŁ iler", "ĠkiÅŁi ler", "Ġк леÑĤ", "Ġкл еÑĤ", "Ġкле ÑĤ", "íĺ ģ", "à¥ĥ द", "i Å¡", "ëĶĶ ìĺ¤", "ÙĬ راÙĨ", "ÙĬر اÙĨ", "ÙĬرا ÙĨ", "ĠÐĿ Ñĥ", "à¸Ń à¸Ļà¸Ĺ", "à¸Ńà¸Ļ à¸Ĺ", "ĠÑģ оÑĩ", "ĠÑģо Ñĩ", "Ġist eyen", "ĠS ez", "ĠSe z", "Ġ ãĤ»", "ĠãĤ »", "ĠA ç", "âĢĮ ÙĨ", "ĠÑĤ оп", "ĠÑĤо п", "ĠÑĤеÑĢÑĢиÑĤ оÑĢ", "a cılık", "acı lık", "Ġод нÑĥ", "Ġv eri", "Ġver i", "Ġve ri", "ĠÚ© د", "ĠÚ¯ ÙģتÙĩ", "ĠÚ¯Ùģت Ùĩ", "Ġcin sel", "Ġcins el", "олог ии", "ĠpÅĻed mÄĽt", "à¤Ĥ à¤ĺ", "Ġ 空", "Ġç© º", "γ α", "' ye", "'y e", "ت رة", "تر Ø©", "Ġd ÅĻÃŃ", "ĠH Ãłn", "ĠHÃł n", "Ġر شتÙĩ", "Ġv idea", "Ġvi dea", "Ġvid ea", "Ġvide a", "Ġ ног", "Ġн ог", "Ġно г", "æ ·»", "æ· »", "è¿ĺ æľī", "ÙĨ در", "ÙĨد ر", "Ġy erde", "Ġyer de", "Ġk ent", "Ġke nt", "Ġken t", "à¸ļ าล", "Ġд еÑģÑı", "Ġде ÑģÑı", "ä¸ļ åĬ¡", "Ġоб ÑĬек", "ĠобÑĬ ек", "ĠвнÑĥÑĤÑĢ ÑĸÑĪ", "ĠвнÑĥÑĤ ÑĢÑĸÑĪ", "k ola", "ko la", "kol a", "eb nÃŃ", "ี ล", "Ġ ,.", "Ġ, .", "ĠмÑĸж наÑĢод", "ãģªãĤĵ ãģ¦", "ĠS öz", "Ġ chod", "Ġc hod", "Ġch od", "Ġcho d", "Ġtr úc", "Ġtrú c", "ìļĶ ìĿ¼", "Ġph áºŃn", "Ñģ ка", "Ñģк а", "ĠÑħ лоп", "Ñģ ким", "Ñģк им", "Ñģки м", "Ġka pit", "Ġkap it", "ëĵ¤ ìĹIJê²Į", "Ġb Ãło", "ĠbÃł o", "lı ģın", "lıģı n", "lıģ ın", "Ä° ÅŁ", "Äį nÃŃk", "ÄįnÃŃ k", "Ġ NgoÃłi", "ĠNg oÃłi", "Ġب ÛĮاÙĨ", "ĠبÛĮ اÙĨ", "Ġpro Äį", "Ġpr oÄį", "ĠпÑĢоÑĤ Ñıгом", "åĢ ī", "е Ñİ", "Ġ νο", "Ġν ο", "ëĿ¼ ëıĦ", "ì ·¨", "Ġви Ñıв", "Ġпо над", "Ġпон ад", "Ġжов ÑĤ", "Ġ æ¯Ķ", "Ġæ¯ Ķ", "Ġd oby", "Ġdo by", "Ġdob y", "л ам", "ла м", "Ñij л", "Ġ ÑĢаÑħ", "ĠÑĢ аÑħ", "ĠÑĢа Ñħ", "Ġвоз ника", "Ġвозник а", "ниÑĨÑĤ во", "å± ¤", "ĠоÑĤ лиÑĩ", "ĠоÑĤли Ñĩ", "çĤ İ", "é£ ¯", "Ġživ ota", "Ġživot a", "at ör", "Ġce lý", "Ġcel ý", "Ġ aday", "Ġa day", "Ġad ay", "Ġada y", "ر ÙĬÙĥÙĬ", "رÙĬ ÙĥÙĬ", "رÙĬÙĥ ÙĬ", "Ġب ص", "m eyen", "me yen", "meye n", "mey en", "ìļ° ìĬ¤", "ب ÙĪÙĦ", "بÙĪ ÙĦ", "Ġ озна", "Ġо зна", "Ġоз на", "é º¼", "éº ¼", "æĵ ļ", "Ġz kou", "Ġzk ou", "ëĤĺ ìļĶ", "Ġk ry", "Ġkr y", "Ġnem oh", "Ġvyu žÃŃ", "Ġ æľ¨", "Ġад мÑĸнÑĸÑģÑĤÑĢа", "ا Ùĩا", "اÙĩ ا", "à¹ĥ à¸ģล", "＿＿＿＿ ＿＿＿＿", "Ġ гоÑĤ", "Ġг оÑĤ", "ĠدÛĮ گرÛĮ", "ĠدÛĮگر ÛĮ", "Ġл екаÑĢ", "è§ Ģ", "Ġ íĺij", "Ġíĺ ij", "ĠB öyle", "ist rov", "istr ov", "istro v", "女 åŃIJ", "Ġпоп еÑĢед", "ĠÙĨÙĪÙĬس ÙĨدÙĩ", "ÙĴ ÙĦ", "ĠÐŁ ав", "Ġö rnek", "Ġör nek", "Ġп ÑĢик", "ĠпÑĢ ик", "ĠпÑĢи к", "Ġ ÑĪи", "ĠÑĪ и", "üslü man", "ĠÙħÙĤ ابÙĦ", "åįģ äºĮ", "Ġb ekl", "Ġbe kl", "Ġbek l", "Ġver ir", "Ġve rir", "Ġveri r", "ÙĪ Ø°", "ض Ø©", "ÑĢо ÑĤив", "ÑĢоÑĤ ив", "æĮ ij", ". .:", ".. :", "Ġخارج ÙĬØ©", "a dık", "ad ık", "adı k", "ĠÐŁ оÑĩ", "ĠÐŁÐ¾ Ñĩ", "ĠÑħÑĥд ож", "客 æĪ·", "μ ον", "μο ν", "ek tiv", "ekt iv", "Ġt vá", "Ġtv á", "Û² Û²", "Ġl á»įc", "Ġо но", "Ġон о", "ÑĨ иÑĤ", "ÑĨи ÑĤ", "ĠÐĴ Ñģ", "Ġ å¢", "Ġå ¢", "æµ ª", "а ÑĢÑĸ", "аÑĢ Ñĸ", "Ġsür ekli", "Ġ stra", "Ġs tra", "Ġst ra", "Ġstr a", "Ġb ize", "Ġbi ze", "Ġbiz e", "Ġtes pit", "Ġch âu", "ĠاÙĦ ض", "à¹īà¸Ń à¸ĩà¸ģ", "à¹īà¸Ńà¸ĩ à¸ģ", "Ġ èĢħ", "ĠèĢ ħ", "ĠH á»", "Ġкажд Ñĭй", "а Ñİ", "à¸Ļ à¸Ħร", "à¸Ļà¸Ħ ร", "à¸Ĺ ะ", "ĠÙħر اجع", "ĠÙħراج ع", "Ġha line", "Ġhal ine", "δ οÏĤ", "δο ÏĤ", "e ÄŁi", "ĠÙħÛĮ زاÙĨ", "Ġ ÙĩÙĦ", "ĠÙĩ ÙĦ", "Ġb olest", "Ġbo lest", "Ġbol est", "Ġ åľŁ", "Ġåľ Ł", "Ġu zman", "Ġuz man", "ÑĢ ог", "ÑĢо г", "確 èªį", "ĠÑĢÑĸз ниÑħ", "Ġза кÑĢÑĭ", "Ġзак ÑĢÑĭ", "лÑĥ ги", "лÑĥг и", "ĠÑģо веÑĤ", "ĠÑģов еÑĤ", "id di", "idd i", "åIJĪ ãĤıãģĽ", "Ġ åIJī", "ĠåIJ ī", "Ġk iá»ĩm", "ë² ½", "ĠÙħع ÙħÙĪÙĦ", "ĠопÑĢед елÑı", "ĠопÑĢедел Ñı", "Ġmik tar", "Ġ ìŀIJëıĻ", "ĠìŀIJ ëıĻ", "Ġil aç", "л оÑĩ", "ло Ñĩ", "Ġy ılı", "Ġyıl ı", "Ġyı lı", "Ġ ÄIJá»ĥ", "ĠÄIJ á»ĥ", "ĠÄIJá» ĥ", "Ġab ych", "Ġaby ch", "Ġrek lam", "Ġrekl am", "Ġv ypad", "Ġvy pad", "Ġvyp ad", "Ġна ÑĥÑĩ", "à¹Ģà¸Ħร าะห", "Ġ ä»ĸ", "p ovÄĽ", "po vÄĽ", "ï¼Į 让", "ç¥ Ŀ", "ا ÙĪÙĨد", "اÙĪ ÙĨد", "اÙĪÙĨ د", "Ġ: |:", "Ġ:| :", "Ġre ž", "Ġvyb av", "ìľ ¤", "æŃ ´", "огÑĢаÑĦ иÑı", "ez peÄį", "ezpe Äį", "± n", "о вÑĥ", "ов Ñĥ", "Ġд Ñĥма", "ĠдÑĥ ма", "ĠдÑĥм а", "Ġjedn odu", "Ġjedno du", "о Ñīи", "оÑī и", "ĠÙħ شتر", "ĠÙħØ´ تر", "è¦ ³", "Ġyok tur", "Ġyoktu r", "Ġob Äįan", "ĠTr ần", "ım sız", "α ιν", "αι ν", " Į", "ر ÛĮاÙĨ", "رÛĮ اÙĨ", "ĠJe ho", "ĠJeh o", "ĠاÙĦ Ø¢ÙĨ", "ĠاÙĦØ¢ ÙĨ", "ÑģÑĮ ким", "ÑģÑĮк им", "Ġk dyby", "Ġkdy by", "ĠbaÅŁ ına", "Ġprez ident", "Ġ Viá»ĩc", "ĠV iá»ĩc", "åħ ¼", "à¥Į à¤ľ", "Ġ매매 ê°Ģ", "模 å¼ı", "nÃŃ mu", "nÃŃm u", "Ġ åĤ", "Ġå Ĥ", "Ġ deniz", "Ġd eniz", "Ġden iz", "ĺ èĽĽ", "Ġ èĢĮ", "ĠèĢ Į", "ÙĪ ØŃ", "Ñĭ п", "Ġâĸ ¼", "n ul", "nu l", "ĠS ev", "ĠSe v", "Ġ ruh", "Ġr uh", "Ġru h", "Ġh ạ", "Ġ Ñıн", "ĠÑı н", "Ġ기 본", "Ġv elik", "Ġve lik", "Ġvel ik", "ĠT ân", "и лиÑģÑĮ", "ил иÑģÑĮ", "или ÑģÑĮ", "ĠÑħ ÑĢа", "åĤ ·", "Ġà¤Ĩ à¤ı", "Ġn ynÃŃ", "Ġny nÃŃ", "» ØĮ", "ĠØ´ ع", "æĿ Ĥ", "Ġм ÑĭÑĪ", "ĠмÑĭ ÑĪ", "ãģĻ ãģIJ", "Ġ ê³µì§Ģ", "Ġê³µ ì§Ģ", "Ġt á»Ļc", "ãĥ¼ ãĥĩ", "ãĥ¼ãĥ ĩ", "ĠÑģ ело", "ĠÑģел о", "Ġا عÙĦاÙħ", "Ġاع ÙĦاÙħ", "ĠÅŁ imdi", "ĠÅŁi mdi", "ĠاÙĦÙħ ÙĬÙĦاد", "ĠاÙĨÙĤÙĦ اب", "Ġشخص ÙĬØ©", "ĠK ür", "ĠKü r", "Ġ вÑĸÑĤ", "Ġв ÑĸÑĤ", "ĠвÑĸ ÑĤ", "ĠاÙĨد ازÙĩ", "Ġм оÑī", "Ġмо Ñī", "t ernet", "ter net", "tern et", "ĠαÏħ ÏĦή", "ĠÑĢоз ÑĤа", "Ġв ив", "Ġви в", "l ej", "le j", "Ġ 表", "Ġè¡ ¨", "ÏĥÏĥ ÏĮÏĦε", "ĠÙĬ ست", "ĠÙĬس ت", "Ġм аÑĪ", "Ġма ÑĪ", "åĿ ļ", "Ġком наÑĤ", "า หล", "าห ล", "Ġ çĻ¼", "ĠçĻ ¼", "ĠاÙĪÙĦ ÛĮÙĨ", "è¿IJ åĬ¨", "Ġп ÑĥнкÑĤ", "ĠпÑĥнк ÑĤ", "ĠоÑģоб енно", "Ġм ам", "Ġма м", "ç» ©", "￣￣￣￣ ￣￣￣￣", "алÑĮ нÑĭм", "ĠЦ енÑĤ", "ĠЦе нÑĤ", "- Ðľ", "ç· Ĵ", "Ġह à¤ľ", "о ÑĤÑĭ", "оÑĤ Ñĭ", "ãĤ¤ ãĥī", "د ارة", "دار Ø©", "دا رة", "ãģ¨ ãģĹãģŁ", "ั à¸ŀย", "ัà¸ŀ ย", "Ġot áz", "Ġдопом огоÑİ", "à¹ģละ à¸ģาร", "ĠÑĤÑĢанÑģп оÑĢÑĤ", "ĠÑĤÑĢанÑģпоÑĢ ÑĤ", "ĠÙĤر Ø¢ÙĨ", "Ġ 第ä¸Ģ", "Ġ第 ä¸Ģ", "Ġм ил", "Ġми л", "Ġng ôi", "Ġl inh", "Ġli nh", "Ġlin h", "ĠNh ân", "ÑĮогод нÑĸ", "æĢ Ģ", "à¹ī าส", "à¹īา ส", ".:: .::", "Ġbi rey", "Ġbir ey", "æĢĿ ãģĦ", "à¹ĥ à¸Ķ", "веÑĢ д", "ве ÑĢд", "Ġlistop adu", "Ġ à¹ģม", "Ġà¹ģ ม", "г е", "Ġк ÑĥÑħ", "ĠкÑĥ Ñħ", "Ġ íĻľëıĻ", "ĠíĻľ ëıĻ", "Ġ èİ", "Ġè İ", "ĠÐIJ лÑĮ", "ĠÐIJл ÑĮ", "íļĮ ìĿĺ", "ĠÏĢ Ïģα", "Ġv ui", "Ġvu i", "ว ร", "à¤Ĥ व", "Ġg ece", "Ġge ce", "Ġgec e", "ç« ¶", "Ġk uv", "Ġku v", "м еÑī", "ме Ñī", "ĠÑĤеп еÑĢÑĮ", "ĠÑĤепеÑĢ ÑĮ", "à¸Ń à¹Ģม", "åĪ¶ 度", "ĠÑĤ ÑĢеÑĤ", "ĠÑĤÑĢ еÑĤ", "ĠÙĨ تÛĮجÙĩ", "ä»ĺ ãģį", "Ġ ï¾ŀ", "Ġï¾ ŀ", "Ġ Ñĩого", "ĠÑĩ ого", "âĢIJ -", "ĠÅĻÃŃ ká", "à¸ĩ à¹ĥà¸Ļ", "ĠnÄĽkol ika", "ĠnÄĽkolik a", "Ġb una", "Ġbu na", "Ġbun a", "ï¼Į åŃĺäºİ", "ล ำ", "ãĢģ ãģ¨", "Ġn á»Ļp", "ĠاÙĦ جÙĨ", "ĠاÙĦج ÙĨ", "ĠÎł αν", "Ġγα ν", "Ðŀ Ðł", "Ġدخ تر", "Ġúda je", "Ġúdaj e", "Ġ å¼ł", "Ġå¼ ł", "r etim", "re tim", "ret im", "s ınız", "sın ız", "ĠÙĩÙĨ اÙĥ", "ĠÙĩÙĨا Ùĥ", "ÐĽ Ь", "æķ ¬", "Îij Îľ", "页éĿ¢ åŃĺæ¡£å¤ĩ份", "ìĤ¬ ê°Ģ", "Ġt rest", "Ġtr est", "Ġtre st", "Ġtres t", "v iÄį", "vi Äį", "ĠÙ¾ ÛĮدا", "ĠÙ¾ÛĮ دا", "ζ ε", "ĠÐŁ ов", "ĠÐŁÐ¾ в", "ÙĦÙħ ات", "o rex", "or ex", "ore x", "è¬ Ľ", "ĠвÑĸдк ÑĢиÑĤ", "м аÑħ", "ма Ñħ", "ĠÑĩиÑģ ле", "ت بار", "تب ار", "ĠÎŃ Îº", "ìķĦ íĮĮíĬ¸", "r avel", "ra vel", "rav el", "α Ïĥία", "αÏĥ ία", "a Äį", "Ġ à¤ıन", "Ġà¤ı न", "ละ à¹Ģà¸Ń", "Ġз алеж", "Ġза леж", "Ġзал еж", "Ġ æģ", "Ġæ ģ", "Ġмож еÑĤе", "ĠможеÑĤ е", "Ġможе ÑĤе", "Ġпо вед", "Ġпов ед", "ĠبسÛĮ ارÛĮ", "ĠبسÛĮار ÛĮ", "Ġ poÄįet", "Ġpo Äįet", "ĠpoÄį et", "ر بع", "رب ع", "e lez", "el ez", "ele z", "ا ÙĪرÛĮ", "اÙĪ رÛĮ", "اÙĪر ÛĮ", "Ġba ÅŁk", "ĠbaÅŁ k", "å° Ĥ", "Ġhal de", "æĭ Ł", "S au", "Sa u", "о ÑĨи", "ี à¸Ħ", "Ġв лади", "Ġвла ди", "Ġвлад и", "ÙIJ Ùħ", "k ud", "ku d", "à¥Ĥ ब", "å§Ķ åĵ¡", "า รà¸ĵ", "าร à¸ĵ", "o rů", "or ů", "Ġ ÙħÙĪÙĦ", "ĠÙħ ÙĪÙĦ", "ĠÙħÙĪ ÙĦ", "Ġ byt", "Ġb yt", "Ġby t", "ĠpÅĻÃŃslu Å¡", "èĭ± è¯Ń", "éĢ IJ", "Ġvel ké", "Ġvelk é", "Ġà¤Ĩ श", "Ġph iếu", "Ġphi ếu", "à¹ĥ ส", "Ġاس Ù¾", "Ġzbo žÃŃ", "ãģĵ ãĤĵãģª", "ãģĵãĤĵ ãģª", "ĠÙĪÙĩ ÙĬ", "ĠÑĥÑĩа ÑģÑĤÑĮ", "ĠÑĥÑĩаÑģÑĤ ÑĮ", "ĠÑĥÑĩаÑģ ÑĤÑĮ", "à¸Īำ à¸Ļวà¸Ļ", "Ġتر Ú©", "åįģ åĪĨ", "ÎŁ Îł", "ÎŁÎ ł", "κ ολ", "κο λ", "Ġf akat", "Ġfa kat", "Ġfak at", "Ġch á»Ĺ", "éĢļ çŁ¥", "Ġв одÑĥ", "Ġво дÑĥ", "Ġвод Ñĥ", "ĠÎļα ÏĦηγοÏģία", "aca ģını", "acaÄŁ ını", "л ого", "ло го", "ĠmÃ¼ÅŁ ter", "Ġj ednou", "Ġjed nou", "Ġjedn ou", "Ġjedno u", "Ġб аÑĢ", "Ġба ÑĢ", "i dae", "id ae", "ida e", "d ım", "dı m", "è¾ ²", "åIJ ¹", "ëIJ ©ëĭĪëĭ¤", "ĠÅŁekl inde", "e ným", "en ým", "ený m", "ëĵ ¯", "i tÄĽ", "it ÄĽ", "Ġк олÑĮ", "Ġкол ÑĮ", "Ġко лÑĮ", "ëĮĢ íķĻ", "ĠÃĸ r", "Ġ ê½", "Ġê ½", "ĠUB ND", "Ġh ik", "Ġhi k", "ãĤī ãģĹãģĦ", "ãĤīãģĹ ãģĦ", "åĩº åĵģ", "C ó", "Ġ Îŀ", "ĠÎ ŀ", "Ġ åħ¥", "Ġåħ ¥", "ĠNg uyên", "ĠÙ¾ ÙĪØ´", "лÑı ÑĶ", "ĠØ¢ غاز", "Ġnhiá»ħ m", "d ivid", "div id", "di vid", "ç ĺ", "ا ÙģتÙĩ", "اÙģ تÙĩ", "اÙģت Ùĩ", "а меÑĤ", "ам еÑĤ", "нÑĥ лÑģÑı", "нÑĥл ÑģÑı", "ä¼ģ æ¥Ń", "ÑĢоб ÑĸÑĤ", "dü ÄŁÃ¼", "dÃ¼ÄŁ ü", "Ġ کاÙĨ", "ĠÚ© اÙĨ", "à¸Ń à¸ĩà¸Ĺ", "à¸Ńà¸ĩ à¸Ĺ", "й н", "Ġpoh yb", "Ġpohy b", "Ġb iá»ĩn", "Ġbi á»ĩn", "Ġ ï¼Ľ", "Ġï¼ Ľ", "Ùħ ÙĨد", "ÙħÙĨ د", "Ġà¤Ĩ à¤ķ", "ĠÄįlov ÄĽk", "ĠÄįlovÄĽ k", "ãĤĴè¦ĭ ãĤĭ", "ë· °", "ĠÑĥв елиÑĩ", "ĠÑĥвели Ñĩ", "Ġ ê´", "Ġê ´", "Ġyan lÄ±ÅŁ", "éº ¦", "Ġå¤ĸ éĥ¨", "ÏĦ οÏħÏģγ", "ÏĦοÏħ Ïģγ", "Ġп ÑĢоÑĩ", "ĠпÑĢ оÑĩ", "ĠпÑĢо Ñĩ", "ĠÑĢÑĥ ковод", "çĽ ¤", "èľ ĺèĽĽ", "å®ī è£ħ", "ĠУ кÑĢа", "Ġtart Ä±ÅŁ", "ÑĤ аж", "ÑĤа ж", "ĠoluÅŁ an", "ĠRus ya", "Ġк лÑĥб", "Ġкл Ñĥб", "ĠклÑĥ б", "ĠÎł Ρ", "alı dır", "k ın", "kı n", "ĠзмÑĸ ни", "ĠзмÑĸн и", "leÅŁ ik", "еÑĢ п", "об Ñīе", "обÑī е", "Ġqu áºŃn", "Ġप श", "ãĤĴ åıĹ", "à¹Ģล à¸Ĥ", "ا ضر", "اض ر", "Ġuž ivatel", "λ ία", "λί α", "ĠÐĴ они", "ĠÐĴо ни", "ุà¸Ķ à¸Ĺ", "ĠV Ãł", "ãĥ³ ãĤ¿", ") ëĬĶ", "æ¸ Ľ", "Ġ μÏĢ", "Ġμ ÏĢ", "å· §", "ĠÑĪ кол", "ĠÑĪк ол", "Ġì²ĺ ìĿĮ", "ัà¸ģ à¸Ķ", "æ® Ĭ", "Ġnh á»Ŀ", "ĠοÏĢο ία", "à¹ģ à¸Ļว", "à¹ģà¸Ļ ว", "меÑĢик ан", "nÃŃ ka", "nÃŃk a", "Ġíĺ¸ íħĶ", "سب ب", "à¸ĩ ม", "ìŀĪ ëĬĶ", "غ Ø·", "Ùı ÙĦ", "¹ æŀľ", "Ñĩ Ñĸв", "ÑĩÑĸ в", "ÑĪ аÑı", "ÑĪа Ñı", "ĠØ¥ ÙĦا", "ĠØ¥ÙĦ ا", "خص ÙĪص", "ll ll", "ĠÑį ÑĤим", "ĠÑįÑĤ им", "ĠÑįÑĤи м", "Ġz vÃŃ", "Ġzv ÃŃ", "Ġqu án", "Ġquá n", "à¸Ļ à¸ģ", "Ġп олов", "Ġпо лов", "Ġпол ов", "Ġ æ·±", "Ġæ· ±", "Ġm iá»ģn", "Ġmi á»ģn", "人 éĸĵ", "Ġз им", "Ġmey dana", "е ÑĦ", "Ġb á»ģn", "Ġbá»ģ n", "ز ÙĬد", "زÙĬ د", "ĠÐł еÑģп", "ĠÐłÐµ Ñģп", "ÎĻ ΣΤ", "ÎĻΣ Τ", "Ġ æĶ¶", "ĠæĶ ¶", "r aya", "ra ya", "ray a", "ĠتÙĪ اÙĨد", "ĠتÙĪاÙĨ د", "Ġ ister", "Ġis ter", "Ġi ster", "Ġist er", "Ġ ë°Ģ", "Ġë° Ģ", "ĠмеÑħ ани", "Ġ à¸ķำ", "Ġà¸ķ ำ", "Ġд ека", "Ġде ка", "Ġдек а", "à¤Ĥ à¤Ĺल", "à¤Ĥà¤Ĺ ल", "ãĥ¼ ãĤ«ãĥ¼", "Ġnep ÅĻÃŃ", "ĠnepÅĻ ÃŃ", "ĠÑģ ÑĩиÑĤ", "ĠÑģÑĩ иÑĤ", "Ġο μά", "Ġç ift", "ب ÛĮÙĨÛĮ", "بÛĮ ÙĨÛĮ", "بÛĮÙĨ ÛĮ", "m eleri", "me leri", "mel eri", "meler i", "Ġвоз дейÑģÑĤв", "d ou", "do u", "ìĥģ ìĿĦ", "ĠÐĴ олод", "ĠÐĴо лод", "ĠÐĴол од", "ε β", "ÐĿ Ðĺ", "Ñı к", "Ïį ÏĦε", "з ано", "за но", "len ir", "c elik", "ce lik", "cel ik", "ĠÑģоÑģÑĤав лÑıеÑĤ", "ι αÏĤ", "ια ÏĤ", "ĠÐĵ оÑĢ", "ä¹ĭ ä¸Ģ", "Ïĥμ ÏĮÏĤ", "ÏĥμÏĮ ÏĤ", "ãģ« éĸ¢", "Ġв Ñĩ", "Ġп оÑģк", "Ġпо Ñģк", "ĠпоÑģ к", "è¼ ¯", "à¥Ģ श", "ĠØ¢ ثار", "à¸Ħวาม ร", "Ġ един", "Ġе дин", "Ġеди н", "íħ IJ", "å¹³ æĪIJ", "ĠkiÅŁ inin", "ĠkiÅŁi nin", "ãĤ² ãĥ¼ãĥł", "à¥įत व", "Ġkapsam ında", "Ġak tar", "Ġakt ar", "Ġtr ừ", "Ġر شد", "Ġна каз", "Ġнак аз", "ر ÙĬÙĦ", "رÙĬ ÙĦ", "à¸Ń à¸Ħ", "Ġگذ شتÙĩ", "Ġ æ°ij", "Ġæ° ij", "ĠÑĤеб Ñı", "s por", "sp or", "spo r", "Ñİ ÑīаÑı", "ÑİÑī аÑı", "окÑĢем а", "в ад", "ва д", "ĠCh úng", "ĠزÛĮ ادÛĮ", "ĠزÛĮاد ÛĮ", "е ного", "ен ого", "ено го", "ĠÚ© سÛĮ", "à ŀ", "Ġad ına", "Ġadı na", "Ñĥ да", "Ñĥд а", "Ñĸ ÑĶ", "аÑĤ ели", "аÑĤе ли", "Ġnáv Å¡tÄĽ", "çĶ¨ äºİ", "ĠپرÙĪ ÙĨدÙĩ", "ĠÙĨ بÙĪد", "ĠÙĨب ÙĪد", "س ات", "ìĹ ĺ", "ãģ£ ãģ¦ãĤĤ", "ãģ£ãģ¦ ãĤĤ", "Ġ çī©", "Ġçī ©", "Ðĺ з", "åĪ ·", "Ġ íľ´", "Ġí ľ´", "ĠоÑģоб лив", "ãģĹ ãģ¾ãģ£ãģŁ", "ãģĹãģ¾ ãģ£ãģŁ", "a ydı", "ay dı", "ayd ı", "åĩº çļĦ", "ĠìķĦëĭĪ ëĿ¼", "ıs ını", "à¸Ĺาà¸ĩ à¸ģาร", "Ġzv uky", "Ġ 管", "Ġç® ¡", "âĸĭ âĸĭ", "ĠÑĤ елеÑĦ", "ĠÑĤел еÑĦ", "Ġн елÑĮзÑı", "ãĥ« ãģ®", "Ïĥ ÏĢ", "Ġ ç³", "Ġç ³", "åł ¡", "ÑĨ Ñĥз", "ÑĨÑĥ з", "رÙĬ ÙĤØ©", "رÙĬÙĤ Ø©", "à¤¿à¤Ľ ल", "è² ©", "ĠУ кÑĢаÑĹн", "ĠУкÑĢаÑĹ Ð½", "ĠÙħسئ ÙĪÙĦ", "Ġо ÑĩÑĸ", "ĠоÑĩ Ñĸ", "æľĢ å¾Į", "Ġзна Ñİ", "Ġзн аÑİ", "à¹ī à¸Ļà¸Ĺ", "à¹īà¸Ļ à¸Ĺ", "ĠÑĤ еÑĢап", "ĠÑĤеÑĢ ап", "ĠÑĤе ÑĢап", "ĠÑģп ок", "ĠØ®ÙĪد رÙĪ", "éĺ »", "Ġdüz ey", "ä¸Ģ åĢĭ", "ا ÙģÙĩ", "اÙģ Ùĩ", "à¤Ĥ य", "èµĦ 产", "继 ç»Ń", "ĠÑģ лаб", "ĠÑģл аб", "æĦı æĢĿ", "ĠíĻĺ ìĤ°", "ĠÑı ÑĢ", "Ġd ůvod", "Ġdů vod", "çĿ Ľ", "تÛĮ ب", "ĠÙĪ ÛĮر", "ĠÙĪÛĮ ر", "ĠÙĩ زÛĮÙĨÙĩ", "Ġben zer", "Ġbenz er", "ĠÙħ ادÙĩ", "ĠÙħا دÙĩ", "ĠÙħاد Ùĩ", "à¥Į à¤ķ", "Ġ à¹Ģà¸ķ", "Ġà¹Ģ à¸ķ", "Ġà¹Ģภķ", "ãĤĪ ãģı", "ид енÑĤ", "èĭ± èªŀ", "е ÑĢÑĭ", "еÑĢ Ñĭ", "Ġê¸Ī ìķ¡", "Ġ ãĥ¼", "Ġãĥ ¼", "Ġ ëį¤íĶĦ", "Ġëį ¤íĶĦ", "ÑĢ аÑĤÑĮ", "ÑĢа ÑĤÑĮ", "ÑĢаÑĤ ÑĮ", "Ġ åįķ", "Ġåį ķ", "à¹Ģà¸ī à¸ŀาะ", "Ġ æĶ¿", "ĠæĶ ¿", "Ġà¤Ĩ म", "Ġз ни", "Ġзн и", "Ġ ëĿ¼ìĿ´", "ĠëĿ¼ ìĿ´", "æİ Į", "çIJĨ çĶ±", "Ġ اغ", "Ġا غ", "ĠØ§Ø º", "ĠÑģ иг", "ĠÑģи г", "Ġе ÑĦекÑĤив", "ĠÐŁ ÑĢед", "ĠÐŁÑĢ ед", "ãĥ´ ãĤ£", "Ġви ко", "Ġвик о", "Ġt vrd", "Ġtv rd", "ëĤ´ 기", "ãĥĭ ãĤ¢", "ĠÙħشاÙĩ دÙĩ", "Ġस à¤ļ", "l Ã¼ÄŁ", "lü ÄŁ", "è¯ģ åĪ¸", "Ġs iêu", "Ġsi êu", "Ġ оÑĤв", "ĠоÑĤ в", "Ġvyt voÅĻ", "ĠØŃ ÙħÙĦ", "ĠØŃÙħ ÙĦ", "ĠÑĦ ÑĢан", "à¹ī à¸Ķ", "åĮ» éĻ¢", "Ġв лад", "Ġвла д", "غ ÙĦ", "建 ç«ĭ", "os loven", "osl oven", "и лаÑģÑĮ", "ила ÑģÑĮ", "عÙĦ ÙĪÙħات", "عÙĦÙĪÙħ ات", "Ġ ترÛĮÙĨ", "Ġتر ÛĮÙĨ", "ÎŃ Ïģει", "ÎŃÏģ ει", "Ġb áºŃt", "ĠÙħØ´ Ú©", "Ġر ئÙĬس", "Ġرئ ÙĬس", "Ġìłľ ìŀij", "γ η", "Ġн Ñĸк", "ĠнÑĸ к", "Ġ구 ìĦ±", "ĠÄij en", "Ġà¤ļ र", "Ġgeç miÅŁ", "äºĨ 解", "Ġл еÑģ", "Ġqu anh", "Ġqua nh", "Ġquan h", "ãĢĮ æĪij", "ĠNÄĽkter á", "ëŀ į", "Ãħ Ÿ", "à¤Ĥ दर", "à¤Ĥद र", "ìķĦ ìĿ´", "å°ij ãģĹ", "ĠØ´Ùĩر ÛĮ", "ĠØ´Ùĩ رÛĮ", "κ ÏĦη", "ĠâĹ Ħ", "Ġ Ùĥس", "ĠÙĥ س", "è· Į", "à ı", "å·¥ åħ·", "åĬ ĥ", "p om", "po m", "ĠнавÑĩ аннÑı", "Ġ رج", "Ġر ج", "ÑĢ ÑĥеÑĤÑģÑı", "ÑĢÑĥ еÑĤÑģÑı", "ÑĢÑĥеÑĤ ÑģÑı", "Ġν ÎŃ", "ÛĮÙĨ Ú©", "à¹Ĥ à¸ĭ", "åĭ ¤", "ãģĹãģ¾ ãģĨ", "ĠÑģ оглаÑģ", "ĠÑģог лаÑģ", "éĩij èŀį", "ç »¿", "ç» ¿", "ĠС ан", "æķ µ", "Ġпо вÑĸÑĤ", "Ġпов ÑĸÑĤ", "Ġпом оÑīи", "ĠпомоÑī и", "ãĥ¡ ãĥªãĤ«", "ãĤ· ãĤ¢", "ĠÏĢ ÏģοÏĤ", "ĠÏĢÏģο ÏĤ", "èĪª 空", "ĠваÑĢи анÑĤ", "ĠваÑĢиан ÑĤ", "Ġyalnız ca", "ç³» çµ±", "ĠÙģ ÙĪر", "ĠÙģÙĪ ر", "оÑĩ ной", "оÑĩно й", "à¹Ģว à¸Ńร", "ĠкÑĥлÑĮ ÑĤÑĥÑĢ", "ĠкÑĥлÑĮÑĤÑĥ ÑĢ", "Ïĩ ι", "ÄįÃŃ ta", " ĵ", "人 ãģĮ", "κ οÏį", "κο Ïį", "ĠÑĢе ÑĶ", "Ġв ÑģÑİ", "ĠвÑģ Ñİ", "éº Ĺ", "Ġز ÙĨاÙĨ", "ĠزÙĨ اÙĨ", "çĭ Ĥ", "Ġ หม", "Ġห ม", "Ġx úc", "åħ Ĵ", "ÄŁ inin", "ÄŁi nin", "ÄŁini n", "ÄŁin in", "åĸľ 欢", "ĠÑģÑĤ ад", "ĠÑģÑĤа д", "iy esi", "iye si", "ìļ ±", "è Ŀ", "Ġ kus", "Ġk us", "Ġku s", "ÏĦ ολ", "ÏĦο λ", "г Ñĸв", "Ñĸ ли", "Ñĸл и", "ãģĦ ãĤĦ", "é© Ĺ", "ont rol", "ا ÙĦÙĥ", "اÙĦ Ùĥ", "к овиÑħ", "ко виÑħ", "ков иÑħ", "ĠÑģÑĤ ало", "ĠÑģÑĤал о", "ĠÑģÑĤа ло", "ĠÎł αÏģα", "Ġγα Ïģα", "ĠγαÏģ α", "Ġ chy", "Ġc hy", "Ġch y", "Ġcih az", "ĩ ´", "ìŀ¥ ìĿ´", "a ceae", "ace ae", "acea e", "Ø´ Ùĩر", "Ø´Ùĩ ر", "ил аннÑı", "çļĦ å°ı", "Ġth ụ", "Ġthá» ¥", "ÙĪ ÙĨت", "ÙĪÙĨ ت", "л оÑĢ", "ло ÑĢ", "ãĤĴ æĮģ", "ĠÎĶ ι", "Ġ 羣", "Ġçľ Ł", "ÐĽ Ðŀ", "é½ IJ", "çİ Ħ", "ا ÙĪÙĩ", "اÙĪ Ùĩ", "Ġи нÑĤ", "Ġин ÑĤ", "à¥Ģ à¤Łà¤°", "à¥Ģà¤Ł र", "Ġ обÑīе", "Ġоб Ñīе", "ĠобÑī е", "Ġдеп ÑĥÑĤ", "μÎŃν εÏĤ", "ĠÙĥ ÙĬÙģ", "ع ÙħÙĦ", "عÙħ ÙĦ", "ï¼Į å¦Ĥæŀľ", "ï¼Įå¦Ĥ æŀľ", "Ġин ÑĦек", "i tele", "it ele", "ite le", "itel e", "Ġ ãĢĢãĢĢĠãĢĢ", "ĠãĢĢ ãĢĢĠãĢĢ", "ĠãĢĢãĢĢ ĠãĢĢ", "ãĤ¤ ãĥ³ãĥĪ", "ãĤ¤ãĥ³ ãĥĪ", "л ÑĸÑĤ", "лÑĸ ÑĤ", "Ġ ÑģÑİ", "ĠÑģ Ñİ", "Ġz ase", "Ġza se", "Ġzas e", "d ech", "de ch", "dec h", "е ко", "ек о", "è® ĵ", "åı ¬", "з ем", "Îł Îij", "Ġvz du", "า à¸Īาà¸ģ", "าà¸Ī าà¸ģ", "ko liv", "kol iv", "koli v", "zk um", "èģ Ĭ", "Ġì±Ħ ìļ©", "๠į", "Ġ asp", "Ġa sp", "Ġas p", "Û² Û´", "ìĿ¸ ëį°", "ĠkarÅŁÄ± laÅŁ", "ï¼Į åı¯ä»¥", "ï¼Įåı¯ 以", "Ġà¤ĩन à¤ķ", "Ġ ìĬ¤íĥĢ", "ĠìĬ¤ íĥĢ", "éĥ¨ å±ĭ", "åĪ¶ ä½ľ", "ãĥ¼ ãĤ·ãĥ§ãĥ³", "ον ÏĦαÏĤ", "γ ο", "Ġìŀij ìĦ±", "èij £", "oz ÅĻejmÄĽ", "ĠÑĢезÑĥлÑĮÑĤаÑĤ е", "ĠÑĢезÑĥлÑĮÑĤ аÑĤе", "ĠIns ecta", "Ġs kon", "Ġsk on", "o tu", "ot u", "Ġp ÄĽt", "ĠpÄĽ t", "Ñģ ÑĮого", "ÑģÑĮ ого", "ĠÄ°s lam", "Ġl á»ħ", "Ġlá» ħ", "ä¸Ń åľĭ", "ĠÐľÑĸ нÑĸÑģÑĤ", "åIJĪ åIJĮ", "asy onu", "asyon u", "ож еÑĤ", "оже ÑĤ", "èĩª åĬ¨", "ÑģÑĮ коÑİ", "ÑģÑĮк оÑİ", "ÑģÑĮко Ñİ", "ĠkiÅŁ isel", "ĠkiÅŁi sel", "ÏĦ ικοÏį", "ÏĦικ οÏį", "ÏĦι κοÏį", "ÏĦικο Ïį", "Ġ ÑĥÑĩаÑģ", "ĠÑĥ ÑĩаÑģ", "ĠÑĥÑĩ аÑģ", "ĠÑĥÑĩа Ñģ", "ıl mÄ±ÅŁtır", "ılmÄ±ÅŁ tır", "ĠÑı ке", "ĠÑıк е", "Ñī инÑĭ", "Ñīи нÑĭ", "Ñīин Ñĭ", "м аÑĢ", "ма ÑĢ", "Ġso udu", "Ġsou du", "Ġsoud u", "Âł Я", "Ġд ÑĢÑĥ", "ĠдÑĢ Ñĥ", "ãģ¡ ãĤĩ", "à¥ĭ à¥ľ", "ï¾ ij", "Ġ ÏĦÏĮ", "ĠÏĦ ÏĮ", "Ġ ضر", "Ġض ر", "l áš", "lá Å¡", "Ġд Ñĸв", "ĠдÑĸ в", "Ġج دÙĬد", "Ġجد ÙĬد", "Ġнеб олÑĮÑĪ", "ĠнеболÑĮ ÑĪ", "éģ Ń", "ç» į", "ĠKur ulu", "ĠKurul u", "ÑģÑĤÑĢ ÑĥменÑĤ", "ÑģÑĤÑĢÑĥ менÑĤ", "è¿Ļ æĺ¯", "ìĻ Ķëĭ¤", "ìĻĶ ëĭ¤", "м елÑĮ", "ме лÑĮ", "Ġ ä¼Ĭ", "ủ ng", "ĠзавиÑģим оÑģÑĤи", "ëį ¤íĶĦ", "çĩ ĥ", "è¿ĩ åİ»", "ĠзаÑģÑĤоÑģ ÑĥваннÑı", "Ġداخ ÙĦÛĮ", "ĠداخÙĦ ÛĮ", "Ñī Ñij", "ĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂł", "ïº ®", "ĠاÙĦÙħ ÙħÙĦÙĥØ©", "s ında", "sı nda", "sın da", "è³ Ģ", "å± ı", "Ġ ê¿", "Ġê ¿", "Ġdo ktor", "Ġdok tor", "ĠÙĤ اب", "ĠS ist", "ĠSi st", "ĠмеÑģÑĤ е", "ĠÑģоÑħ ÑĢа", "ا شتÙĩ", "اش تÙĩ", "اشت Ùĩ", "Ġ æľŁ", "ĠпоÑģк олÑĮкÑĥ", "Ġp ev", "Ġpe v", "ا گر", "اگ ر", "Ùħ ز", "Ġض ÙħÙĨ", "ॠ©", "g esi", "ge si", "ges i", "a ÄŁa", "aÄŁ a", "解 åĨ³", "ëħ¸ ì¶ľ", "Ġl uyá»ĩn", "Ġкон ÑĤак", "ĠконÑĤ ак", "ภº", "Ġ NgÃły", "ĠNg Ãły", "Ġvý stav", "Ġth uyết", "اÛĮ ع", "Ġ: /:", "Ġ:/ :", "Ġph ạt", "ĠÎij ÏĢÏĮ", "ĠÎijÏĢ ÏĮ", "Ġ muz", "Ġm uz", "Ġmu z", "Ġ ìĥī", "Ġìĥ ī", "ĠÃĩ in", "Ġکار برد", "Ġکاربر د", "ائ د", "ب اد", "با د", "à¥į तम", "à¥įत म", "Ġ ëijĺ", "Ġëij ĺ", "Ġм оз", "Ġмо з", "Å¡ ÃŃch", "Å¡ÃŃ ch", "Ġ มห", "Ġม ห", "ĠØ¢ س", "ĠÑģ лиÑĪком", "èĥ ¡", "è£ ģ", "æĪ »", "ĠìĦ¤ ëªħ", "Ġo tom", "Ġot om", "Ġoto m", "Ġलà¤Ĺ à¤Ńà¤Ĺ", "à¸ĩ à¸ģ", "ا بد", "اب د", "à¸Ļ าม", "à¸Ļา ม", "èĤ ©", "Ġشد ÙĨد", "ĠشدÙĨ د", "ãģĿãģ® ä»ĸ", "ad lo", "ÄĽ n", "ĠÙĦ Ùĩا", "ĠÙĦÙĩ ا", "Ġмин им", "Ġми ним", "Ġd ÅĻev", "ĠTh iên", "ĠThi ên", "ëŀ Ļ", "en gin", "eng in", "à¥Ģ मत", "à¥Ģम त", "ĠÑĥп оÑĤÑĢеб", "âĢĮ تر", "Ġç¥ŀ 马", "ov ánÃŃm", "ová nÃŃm", "ovánÃŃ m", "ován ÃŃm", "Ġд ело", "Ġдел о", "Ġде ло", "Ġ ç¼ĸ", "Ġç¼ ĸ", "ĠاÙĦ ظ", "Ġ вий", "Ġв ий", "Ġви й", "а ÑĤом", "аÑĤ ом", "аÑĤо м", "åħ¬ åijĬ", "ĠÄij em", "ãĤ· ãĥªãĥ¼ãĤº", "ä¸ĭ çļĦ", "l ası", "la sı", "las ı", "ĠвÑĭ боÑĢ", "ĠвÑĭб оÑĢ", "ÑĤ оÑĤ", "ÑĤо ÑĤ", "ëıĦ ë³Ħ", "ĠÑĥ ÑģÑĤан", "ĠÑĥÑģÑĤ ан", "Ġ íŀĪ", "Ġíŀ Ī", "лÑĥ аÑĤа", "Ġth ác", "а нием", "ан ием", "ани ем", "ание м", "ов аÑĤÑĮÑģÑı", "ова ÑĤÑĮÑģÑı", "оваÑĤÑĮ ÑģÑı", "ÑĤ ÑĶ", "ÐŃ ÑĤо", "ï¼Į è¦ģ", "ĠV z", "ĠØŃ ÙĪزÙĩ", "ĠØŃÙĪ زÙĩ", "- к", "V Ỽi", "ent ů", "Ġbulun duÄŁu", "Ġbulundu ÄŁu", "ر ÙĪØ·", "رÙĪ Ø·", "ĠÑĹ Ð¹", "Ġçev r", "Ġ ÅĻed", "ĠÅĻ ed", "ĠÅĻe d", "Ġس اختÙĩ", "Ġساخت Ùĩ", "åĬŀ æ³ķ", "Ġ ÙĤÙĦ", "ĠÙĤ ÙĦ", "i ÅŁi", "iÅŁ i", "ï¼Ŀ ï¼Ŀ", "س اس", "Ġúdaj ů", "å ¬", "æį Ł", "á ct", "ác t", "ĠÎij ÏĢ", "çĪ ·", "Ġ ÅĻád", "ĠÅĻ ád", "Ġl á»Ĺi", "Ġlá» Ĺi", "Ġlá»Ĺ i", "on tent", "ont ent", "onte nt", "ĠÙħ Ø°", "ol oji", "olo ji", "oloj i", "Ġپرد اخت", "à¹ī าà¸ŀ", "à¹īา à¸ŀ", "ĠдейÑģÑĤв иÑı", "Ġmnož stvÃŃ", "ìķĪ ë§Ī", "åģ ¶", "Ġ ÃĶng", "ĠÃĶ ng", "Ġdak ika", "hen dis", "hend is", "Ġb ác", "å¯ ¶", "à¹ĩà¸ģ หà¸į", "noc enÃŃ", "ĠErd oÄŁan", ": ::::::::::::", ":: :::::::::::", ":::: :::::::::", ":::::: :::::::", ":::::::: :::::", "::: ::::::::::", "::::: ::::::::", "::::::: ::::::", "::::::::: ::::", ":::::::::: :::", "::::::::::: ::", ":::::::::::: :", "аÑĤ ем", "аÑĤе м", "d ız", "dı z", "ĠØ£ÙĬ ضا", "ĠØ£ÙĬض ا", "ĠÑįÑĦ ÑĦек", "ãĤĮ ãģ¦ãģĦãĤĭ", "ãĤĮãģ¦ ãģĦãĤĭ", "ĠbaÅŁv uru", "ĠbaÅŁvur u", "ά νει", "άν ει", "ĠÏĦε λεÏħÏĦα", "Ġê²Ģ ìĥī", "ĠÚ©ÙĨ ترÙĦ", "Ġ शà¤ķ", "Ġश à¤ķ", "å¼ ¹", "Ġol muÅŁtur", "Ġolm uÅŁtur", "ĠolmuÅŁ tur", "Ġв ÑģÑĤÑĥп", "ĠвÑģÑĤ Ñĥп", "Ñĩ ила", "Ñĩи ла", "Ñĩил а", "ย า", "ĠØ£ØŃ Ùħد", "os lav", "osl av", "ĠÑĩа Ñģов", "ĠÑĩаÑģ ов", "Ġzá kladnÃŃ", "Ġzáklad nÃŃ", "Ġस व", "д он", "до н", "ĠÅĻÃŃj na", "κ οÏħ", "κο Ïħ", "éĢģ æĸĻçĦ¡æĸĻ", "éĢģæĸĻ çĦ¡æĸĻ", "Ïĥ ίαÏĤ", "Ïĥία ÏĤ", "Ïĥί αÏĤ", "ãĤ´ ãĥª", "Ġв иб", "Ġви б", "å½ Ĵ", "Ġназ ад", "ĠçĻ¾åº¦ æĶ¶å½ķ", "á» Ĩ", "Ġkal dı", "ì¼ ľ", "Ġ íıŃ", "Ġíı Ń", "ĠÑĩи ном", "ĠÑĩин ом", "è ¹", "Ñı л", "ĠÑĢаз дел", "ĠÑĢазд ел", "d G", "ĠT ento", "ĠTen to", "ĠTent o", "Ñı ÑĤÑĮÑģÑı", "ÑıÑĤÑĮ ÑģÑı", "éĿ¢ çļĦ", "ĠÎķ ÏĢι", "ĠÎķÏĢ ι", "ê° ij", "Ġk èm", "ни ÑĨÑı", "ниÑĨ Ñı", "çĸ «", "éĽ Ļ", "ĠÙħر Ùĥز", "Ġна Ñĥк", "å¢ Ĺ", "ĠÑĤе пеÑĢ", "ĠÑĤеп еÑĢ", "ा à¤ł", "ाठł", "à¹ĩà¸ļ à¹Ħà¸ĭà¸ķ", "μβ ÏģίοÏħ", "ĠÑĦÑĸн анÑģов", "ĠÑĦÑĸнанÑģ ов", "Ñĸ ÑĶÑİ", "ÑĸÑĶ Ñİ", "Ïģ ίζ", "Ïģί ζ", "ì¤ Ħ", "ĠباÙĨ Ú©", "t ul", "tu l", "li ÄŁini", "liÄŁi ni", "liÄŁ ini", "liÄŁin i", "ĠпозволÑı еÑĤ", "Ġпозвол ÑıеÑĤ", "Ïĥ ί", "Ġ ìĽĥ", "ĠìĽ ĥ", "à¹Į à¸Ħ", "Ġpol ov", "Ġpo lov", "Ġpolo v", "ìŀ¥ ìĿĦ", "is té", "ist é", "ĠС Ð¡Ð¡Ðł", "á hl", "áh l", "è ¥", "Ġкомп лек", "à¸Ĥ à¸Ļาà¸Ķ", "ั ศ", "ν αν", "να ν", "Ġç¥ŀ马 æĶ¶å½ķ", "ìĭľ ìĺ¤", "Ġé¦ĸ页 第", "ĠçĻ¾åº¦ æµģéĩı", "åij¨ æĶ¶å½ķ", "Ġh atta", "Ġhat ta", "ÐĴ Ñĸд", "ĠвÑĭ ÑģÑĤÑĥп", "Ú© ارÛĮ", "کار ÛĮ", "کا رÛĮ", "K hi", "Kh i", "Ġì°¾ ìķĦ", "Ġn ặng", "éĨ «", "ĠV Å¡", "ĠпеÑĢ ен", "ĠпеÑĢе н", "л ава", "ла ва", "лав а", "ÙĬ ÙħÙĬ", "ÙĬÙħ ÙĬ", "Ġvat andaÅŁ", "Ġ ιÏĥÏĦο", "Ġι ÏĥÏĦο", "Ġ à¸ĵ", "Ġภĵ", "स ल", "г ен", "ге н", "Ġ بÙĪر", "Ġب ÙĪر", "ĠبÙĪ ر", "âĢĮدÙĩ د", "âĢĮد Ùĩد", "l ıklı", "lık lı", "Ġ strate", "Ġst rate", "Ġstr ate", "Ġstrat e", "Ġstra te", "ب ÙĪر", "بÙĪ ر", "ãĢģ ãĤ¢", "Ġson uc", "Ġsonu c", "Ġна иболее", "- в", "Ġвод ой", "oj enÃŃ", "oje nÃŃ", "Ġغ رب", "Ġغر ب", "Ġb eri", "Ġbe ri", "Ġber i", "a dÄĽ", "ad ÄĽ", "Ġd ovol", "Ġdo vol", "Ġdov ol", "âĢĮÚ©ÙĨ ÙĨدگاÙĨ", "âĢĮÚ©ÙĨÙĨد گاÙĨ", "ãģķ ãĤī", "ãĥ³ ãĤº", "ãĤ« ãĥ«", "om etr", "ome tr", "omet r", "åĩ Ģ", "ĠÙģ ÙĪÙĦ", "ĠÙģÙĪ ÙĦ", "ĠÙħ ÙĪسÛĮ", "ĠÙħÙĪ سÛĮ", "ĠÙħÙĪس ÛĮ", "ĠاÙĦÙħغ رب", "e cko", "ec ko", "eck o", "ÙĢÙĢÙĢÙĢ ÙĢÙĢÙĢÙĢ", "ê°Ģ 격", "ÑĢ ÑĥÑĤ", "ÑĢÑĥ ÑĤ", "Ġ ë¶Ģë¶Ħ", "Ġë¶Ģ ë¶Ħ", "ĠpÅĻed pis", "Ġoprav du", "еÑĤ иÑĩ", "еÑĤи Ñĩ", "à¹Ĥ à¸Ħรà¸ĩà¸ģาร", "æ ħ§", "æħ §", "æĭ ľ", "س Ùĥ", "ìŀ¡ ëĭ´", "à¸Ľà¸£à¸°à¸¡ าà¸ĵ", "è´¨ éĩı", "Ġголов Ñĥ", "Ġгол овÑĥ", "л ениÑİ", "лен иÑİ", "ле ниÑİ", "Ġन à¤ı", "Ġprojekt u", "ا Ùģر", "اÙģ ر", "at ivnÃŃ", "ati vnÃŃ", "ativ nÃŃ", "ÎŃ Î½ÏĦ", "ÎŃν ÏĦ", "ãĥī ãĥ©", "Ġted av", "ê ¼", "à¸Ľà¸£à¸°à¸ģ าศ", "Ġt uto", "Ġtu to", "Ġtut o", "Ġch iếu", "Ġchi ếu", "Ġchiế u", "Ġv yz", "Ġvy z", "ÑĢ оÑĪ", "ÑĢо ÑĪ", "åıĸ å¾Ĺ", "Ġм иÑģÑĤ", "Ġми ÑģÑĤ", "ĠмиÑģ ÑĤ", "ĠÑģлÑĥÑĩа ÑıÑħ", "Ġغ Ø°", "ĠÑĥ клад", "ĠÑĥк лад", "ĠÑĥÑģÑĤанов лен", "Ġtes lim", "Ġ ãĢį", "ĠãĢ į", "Ġ è£", "Ġè £", "æ¯ «", "éĬĢ è¡Į", "e cts", "ect s", "ec ts", "k emiz", "kem iz", "ν ηÏĤ", "νη ÏĤ", "è¾ º", "Ġп ÑĢем", "ĠпÑĢ ем", "ĠпÑĢе м", "Ġson ucu", "Ġsonuc u", "Ġsonu cu", "P okud", "Po kud", "Pok ud", "ĠÐŀÑģ об", "è¾ Ľ", "è¼ ¸", "ë³´ ê³ł", "à¸ļ à¸Ħ", "ãĢĤ ãĢį", "ा ।ĊĊ", "ा। ĊĊ", "ा।Ċ Ċ", "ĠÑģамоÑģÑĤ оÑıÑĤелÑĮ", "ÙĦ ÛĮت", "ÙĦÛĮ ت", "λ εκ", "λε κ", "ĠÑĢай она", "ĠÑĢайон а", "ÑĮ и", "à¹Ī าà¸Ĺ", "à¹Īา à¸Ĺ", "Ġ à¸Ľà¸£à¸°à¹Ģà¸Ĺศ", "Ġà¸Ľà¸£à¸° à¹Ģà¸Ĺศ", "ม à¸Ń", "ا Ùĩر", "اÙĩ ر", "Ġви боÑĢ", "Ġвиб оÑĢ", "ÑİÑĩи ÑģÑĮ", "Ġp ovol", "Ġpo vol", "Ġpov ol", "a base", "ab ase", "aba se", "âĢ³ N", "Ú© ÙĪ", "ĠУкÑĢаÑĹ Ð½Ð°", "ĠУкÑĢа ÑĹна", "ĠУкÑĢаÑĹн а", "sta nov", "stan ov", "ĠÑĥÑĩ аÑģÑĤи", "ĠÑĥÑĩа ÑģÑĤи", "ĠÑĥÑĩаÑģÑĤ и", "ĠÑĥÑĩаÑģ ÑĤи", "Ġh lad", "Ġhl ad", "ĠÑĢаÑģÑģ каз", "ãģ¿ ãģŁãģĦ", "á½ °", "Ġ åĽŀ", "ĠåĽ ŀ", "Ġ Æ°Æ¡ng", "α Ïģά", "αÏģ ά", "Ø® ب", "æį ķ", "ÃŃ ÅĻ", "Ġ سÛĮÙĨ", "Ġس ÛĮÙĨ", "ĠسÛĮ ÙĨ", "Âł in", "ĠM ÄĽst", "æķĻ åѦ", "ĠоÑģоб иÑģÑĤ", "ĠоÑģоби ÑģÑĤ", "u ji", "uj i", "çĶ» åĥı", "ĠداÙĨ Ø´ÙĨاÙħÙĩ", "ĠداÙĨØ´ ÙĨاÙħÙĩ", "ìĿ´ ìķ¼", "Ġзап иÑĤ", "ĠÑģво ими", "ĠÑģвои ми", "ĠÑģвоим и", "Û²Û° Û²", "ï¼Į å°Ĩ", "ãĥ¼ ãģ®", "Ġth ÃŃ", "ĠÙħت ÙĪسط", "à¥ĩ Ċ", "å¤ļ å°ij", "ï¼Į çĦ¶åIJİ", "íĹ Ī", "Ġ à¤Ńà¤Ĺ", "Ġà¤Ń à¤Ĺ", "Ġ åı·", "Ġåı ·", "Ġt eor", "Ġte or", "å Ĥ¨", "åĤ ¨", "Ġ ÑĢÑĸÑĩ", "ĠÑĢ ÑĸÑĩ", "ĠÑģÑĤаÑĤ ÑĤÑĸ", "Ġرابط Ùĩ", "Ġ ï¼ľ", "Ġï¼ ľ", "ب اØŃ", "با ØŃ", "ิà¸Ļ à¸Ĺาà¸ĩ", "ิà¸Ļà¸Ĺ าà¸ĩ", "à¥ĩà¤Ĥ Ċ", "ائ ÙĤ", "ĠاÙĦج دÙĬد", "l iÄį", "li Äį", "ا ØŃÙĦ", "اØŃ ÙĦ", "mé nÄĽ", "Ġb ầu", "ĠÐĴ ал", "Ġб лагод", "Ġбла год", "Ġблаг од", "еÑĤ елÑĮ", "еÑĤе лÑĮ", "å¹³ åĿĩ", "м ин", "ми н", "Ġsü rec", "Ġsür ec", "Ġsüre c", "Ġза вод", "Ġзав од", "èį IJ", "ÑĤ ий", "ÑĤи й", "л об", "ло б", "Ġ вок", "Ġв ок", "Ġво к", "l adıģı", "la dıģı", "lad ıģı", "ladı ģı", "ladıģ ı", "اÙĬ ÙĬ", "ê²ł ìĬµëĭĪëĭ¤", "Ġamac ıyla", "Ġamacı yla", "ï¼Į åĽłä¸º", "ï¼ĮåĽł 为", "ãģ§ ãģĤãģ£ãģŁ", "ãģ§ãģĤ ãģ£ãģŁ", "ĠØ´ رÙĪع", "Ġشر ÙĪع", "æŁ Ķ", "' nun", "'n un", "о кол", "ок ол", "око л", "Ġc iddi", "Ġcid di", "Ġb ụ", "Ġyap ılacak", "Ġyapı lacak", "Ġyapıl acak", "ĠÑĩÑĥв ÑģÑĤв", "ìĤ¬ ìĿĺ", "à¸Ń à¸Ļà¸Ķ", "à¸Ńà¸Ļ à¸Ķ", "ΊΤ", "Ġëĭ¤ ìĸij", "ëĭ¤ ë©´", "im izi", "imi zi", "imiz i", "ä¹ Ĥ", "ãģ² ãģ¨", "Ġ éĿŀ", "ĠéĿ ŀ", "âĢĮپدÛĮ ا", "ä¹ ĺ", "ãĥĬ ãĥ«", "ĠпÑĸдпÑĢиÑĶм ÑģÑĤва", "ĠпÑĸдпÑĢиÑĶмÑģÑĤв а", "๠ij", "è¿ Ŀ", "ĠÙħ ÙĨÙĩ", "ĠÙħÙĨ Ùĩ", "ÑĢ ик", "ÑĢи к", "а ÑĢÑĸв", "аÑĢ Ñĸв", "аÑĢÑĸ в", "Ġ кого", "Ġк ого", "Ġко го", "ĠÙĤ ص", "Ġ æĿ¥", "ĠæĿ ¥", "ĠPh òng", "Ġ ово", "Ġо во", "Ġов о", "ĠпеÑĢ ева", "ĠпеÑĢе ва", "ĠпеÑĢев а", "é£ ²", "à¤Ĥ à¤Łà¤°", "à¤Ĥà¤Ł र", "ÙĬ را", "ÙĬر ا", "il diÄŁi", "ildi ÄŁi", "e tin", "et in", "eti n", "Ïĩε ία", "Ïĩεί α", "Ġzah rani", "ÙĪ جد", "ÙĪج د", "Ġ ç¯", "Ġç ¯", "าร ย", "Ġза ко", "Ġзак о", "ĠتÙĤ س", "ãĤ¹ ãĤ¿ãĥ¼", "ãĤ¹ãĤ¿ ãĥ¼", "æĿ °", "Ġ ãĤ°", "ĠãĤ °", "Ġ é»Ħ", "Ġé» Ħ", "Ġ Ðļогда", "ĠÐļ огда", "ॠ«", "Ġ 次", "Ġæ¬ ¡", "ĠвÑĭ ÑĢаж", "Ġch Äĥm", "лÑı ÑĶÑĤÑĮÑģÑı", "лÑıÑĶ ÑĤÑĮÑģÑı", "د ÙĩÙħ", "دÙĩ Ùħ", "Ġv rch", "Ġvr ch", "çº Į", "п оÑĢ", "по ÑĢ", "Ġm aÄŁ", "Ġma ÄŁ", "å¾Ĵ æŃ©", "po dob", "pod ob", "ะ à¹ģ", "éģ¸ æīĭ", "å¸ ¯", "Ġse bou", "Ġseb ou", "in ize", "ini ze", "iniz e", "ĠÐľ ак", "ĠÐľÐ° к", "Ġ æĻ®", "ĠæĻ ®", "ĠÏħÏĢ άÏģÏĩ", "ĠÄIJ Ãł", "ĠBr no", "Ġ Å¡ÃŃ", "ĠÅ¡ ÃŃ", "اÙĦ ص", "Ġngh iêm", "Ġnghi êm", "Ġon ları", "Ġonlar ı", "Ġu žÃŃ", "Ġuž ÃŃ", "èĩªåĪĨ ãģ®", "ĠнаÑħод иÑĤÑģÑı", "Ġj si", "Ġjs i", "Ġस मर", "Ġसम र", "ĠÏĨ Ïī", "Û±Û¹ Û¸", "Ġà¤ľà¤Ĺ ह", "éŃ ļ", "ìĿ¸ ê°Ģ", "ÄIJ iá»ģu", "ĠØ£ عÙĦاÙħ", "Ġأع ÙĦاÙħ", "à¥ĩà¤Ĥ ।Ċ", "à¥ĩà¤Ĥ। Ċ", "å½¢ æĪIJ", "Ġ ikt", "Ġi kt", "Ġik t", "Ġzd roj", "ĠAmer ik", "Ρ Îĵ", "à¸ĩ ส", "ĠíĴ Ģ", "Ñģол ÑİÑĤ", "ÙĪ ÙĬت", "ÙĪÙĬ ت", "Ġgörün tü", "ан нÑĭÑħ", "аннÑĭ Ñħ", "ĠØ£ ÙĤ", "Ġ миÑĢ", "Ġм иÑĢ", "Ġми ÑĢ", "å« Į", "Ġm á»iji", "Ġd erin", "Ġde rin", "Ġder in", "é ĴĪ", "éĴ Ī", "Ġма ÑĪи", "ĠмаÑĪ и", "ì¸ ¡", "ĠجÙĨ ÙĪب", "ĠÑģ ло", "ĠÑģл о", "ãĢĤ ä¸Ģ", "ени ÑıÑħ", "ениÑı Ñħ", "ĠÑĩолов Ñĸк", "Ġy ana", "Ġya na", "Ġyan a", "Ġо кÑĤ", "Ġок ÑĤ", "Ġ неÑĢ", "Ġн еÑĢ", "Ġне ÑĢ", "æĪ ¶", "н ÑĮомÑĥ", "нÑĮ омÑĥ", "нÑĮо мÑĥ", "ĠÑĸ мен", "ĠÑĸм ен", "ãĤı ãģŁãģĹ", "ĠÎĵ ια", "ĠÎĵι α", "ãĢģ ç§ģ", "Ġ kou", "Ġk ou", "Ġko u", "ĠÑĨ еÑĢк", "ĠÑĨеÑĢ к", "lay arak", "ãĢ ĩ", "ا ÙĦس", "اÙĦ س", "Âł T", "Ġд ÑĢÑĥж", "ĠдÑĢÑĥ ж", "ĠдÑĢ Ñĥж", "Ġд воÑĢ", "Ġдв оÑĢ", "Ġдво ÑĢ", "λ ί", "ĠëĨ Ģ", "Ġte plot", "Ġtep lot", "Ùģ ات", "б Ñĸ", "Ġgüven lik", "Ġgüvenli k", "n ÄĽn", "nÄĽ n", "è© ©", "Ġinsan ların", "Ġinsanlar ın", "ĠìĦ¤ ì¹ĺ", "èĵ Ŀ", "a vatel", "av atel", "ava tel", "j ev", "je v", "ĠÚĨ را", "Ġgerek iyor", "ãĥĥ ãĤ°", "ĠÃĩ ok", "Ġ ÙĪجÙĩ", "ĠÙĪ جÙĩ", "ĠÙĪج Ùĩ", "Ġ Ñĥли", "ĠÑĥ ли", "ĠÑĥл и", " ij", "åij Ģ", "ĠоÑĢгани заÑĨии", "ĠоÑĢганиз аÑĨии", "ĠоÑĢганиза ÑĨии", "ĠÑĸÑģ нÑĥ", "Ġneb ude", "Ġnebu de", "Ġë° ¤", "ä¸Ĭ ãģĮ", "Ġध न", "ĠرÙĪ ابط", "γγ ελ", "Ġдо ÑģÑıг", "ĠдоÑģ Ñıг", "ĠاÙĦÙĤ دÙħ", "ĠاÙĦÙĤد Ùħ", "Ġзна Ñħод", "ĠÄįÃŃs lo", "ÅŁ k", "ĠاÙĦد ÙĬÙĨ", "Ġgün lük", "ÙĥÙĬ ÙĬÙģ", "ÎŃ Ïģα", "ÎŃÏģ α", "à¸ķ รว", "à¸ķร ว", "Ġнали Ñĩи", "ا ÙħÛĮÙĨ", "اÙħ ÛĮÙĨ", "اÙħÛĮ ÙĨ", "Ġμ ικ", "Ġdönem de", "à¹Ī à¸Ĺ", "æĥ ij", "à¥ĭà¤Ĥ ,", "Ñĩ Ñı", "ãģ¾ ãĤĭ", "ĠاÙĦ تÙĨ", "ĠاÙĦت ÙĨ", "ÑĢ аг", "ÑĢаР³", "ÑĢа г", "ëĵ¤ ê³¼", "Ń Ķ", "ĠÙħÙĨ Ùĩا", "ĠÙħÙĨÙĩ ا", "ĠTh ế", "éIJ µ", "Ġ ï¾Ħ", "Ġï¾ Ħ", "ĠاÙĦØ¥ سÙĦاÙħ", "ãĤ¦ ãĤ¹", "ÙĬ دÙĬ", "ÙĬد ÙĬ", "Ġ å¾Ĺ", "Ġå¾ Ĺ", "Ġза ÑĢаз", "ãĤ¸ ãĥ¥", "Ġت عد", "Ġتع د", "i ÃŃ", "Ġç ocu", "oz ici", "Ġ ë²Ķ", "Ġë² Ķ", "ĠØ¢Ùħ دÙĩ", "ĠØ¢Ùħد Ùĩ", "ÑĦ ик", "ÑĦи к", "Ġпо ÑģÑĤанов", "ĠпоÑģÑĤ анов", "Ġkrál ov", "¨ ¨", "Ġì¤ij ìļĶ", "ĠG Wei", "ĠGW ei", "Ġvý voj", "Ġboy ut", "Ġ nek", "Ġn ek", "Ġne k", "ا ÙĩاÛĮ", "اÙĩ اÛĮ", "اÙĩا ÛĮ", "Ġst ranÄĽ", "Ġstran ÄĽ", "Ġstra nÄĽ", "и ем", "ие м", "Ġпо ÑĢаж", "ĠпоÑĢ аж", "à¥įर दर", "à¥įरद र", "é¡Ķ ãĤĴ", "ĠY üz", "Ġо знаÑĩа", "Ġозна Ñĩа", "à¹ģล à¸Ļà¸Ķ", "Ġب ÙĩرÙĩ", "ĠبÙĩ رÙĩ", "ен ÑĤÑĥ", "енÑĤ Ñĥ", "ĠÐĿ ад", "ĠÐĿа д", "ĠÐŁ олÑĮ", "ĠÐŁÐ¾ лÑĮ", "ĠÐŁÐ¾Ð» ÑĮ", "ãĥĹ ãĥª", "á¿ ¶", "âĢĮپدÛĮ اÛĮ", "âĢĮپدÛĮا ÛĮ", "ĠÙ¾ اÙĪرپÙĪÛĮÙĨت", "ิà¸ģ า", "Ġε νÏİ", "Ġεν Ïİ", "Ġس اÛĮر", "éģ º", "ãĢģ ä»Ĭ", "ĠL ê", "äºĭ æĥħ", "ĠY er", "ĠYe r", "èħ °", "ĠاÙĦر سÙħ", "ĠاÙĦÙħ ÙĪÙĤع", "ĠاÙĦÙħÙĪ ÙĤع", "Ġh Ãłm", "Ġд ÑĢев", "ĠдÑĢ ев", "á tel", "át el", "áte l", "ĠвÑģ Ñij", "ìĺ ¥", "ĠM ec", "ĠMe c", "ãĤ Ľ", "Ġص اد", "ĠÚ¯ ردد", "Ġگرد د", "Ġگر دد", "Ġkr ás", "èĮĥ åĽ´", "a larına", "alar ına", "aları na", "aların a", "èĻ ļ", "ĠØ¢ ÙĪرد", "ĠØ¢ÙĪر د", "ç¼ ĵ", "ิ à¸ŀ", "Ġ ãĥĭ", "Ġãĥ ĭ", "Ġ æĢ§", "ĠæĢ §", "ĠÙħÙĨ Ø°", "ç· ´", "Ġ ê¶ģ", "Ġê ¶ģ", "в аем", "ва ем", "Ġζ Ïī", "Ġn avr", "Ġna vr", "Ġnav r", "Ïĥ ÏĦαÏĥη", "ÏĥÏĦα Ïĥη", "Ġر Ø£", "Ġd opl", "Ġdo pl", "Ġdop l", "＿ ＿＿", "＿＿ ＿", "çĶļ èĩ³", "Äį el", "Äįe l", "æĦı åij³", "ç¥ Ń", "à ĺ", "ÑģÑĤв еннÑĭе", "ÑģÑĤвен нÑĭе", "è£ ¡", "Ġ ãĢī", "ĠãĢ ī", "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "Ġ вал", "Ġв ал", "Ġва л", "Ġ ẩm", "Ġd iyor", "Ġdi yor", "Ġdiy or", "à¸Ńà¸ĩ à¸Īาà¸ģ", "ĠPh ó", "ĠÐĵ е", "ĠвеÑĢ еÑģ", "Ġве ÑĢеÑģ", "Ġk onz", "Ġko nz", "Ġkon z", "ر ز", "ĠÑģоб оÑİ", "Ġεκ εί", "ìĺģ ìĸ´", "i ag", "ia g", "ĠÑģ енÑĤ", "Ġn ấu", "Ġja ké", "Ġjak é", "Ġro zh", "Ġroz h", "Ġб ог", "Ġбо г", "ÙĨ اد", "ÙĨا د", "ĠاÙħ ÙĪر", "à¹Į à¸ģาร", "à¹Įà¸ģ าร", "ĠY aÅŁ", "ĠYa ÅŁ", "é Ī", "åķ ª", "Ġon ay", "Ġona y", "ìĹ ĩ", "o mu", "om u", "ÑĨ Ñĸйного", "ÑĨÑĸй ного", "ÑĨÑĸйно го", "ĠÑģ ал", "ĠΣ Ïħν", "ĠΣÏħ ν", "Ġsav un", "å¦ Ļ", "à¸Īะ ม", "ãĤ¹ ãĤ¯", "Ġd osy", "Ġdo sy", "Ġdos y", "ľ ĺ", "ë¨ ¹", "Ġmin ul", "Ġmi nul", "ãĢĭ Ċ", "åģ ı", "ĠÐļ аÑĤ", "ĠÐļа ÑĤ", "Ġed ilmesi", "Ġedilm esi", "Ġedil mesi", "ÑĨÑĸ ÑĶн", "ìĦ± ìĿ´", "åĸ Ķ", "Ġв ÑĸÑĢ", "ĠвÑĸ ÑĢ", "è¯ ij", "ाà¤ĩ ड", "ĠÙĪÙĤ تÛĮ", "ĠÙĪÙĤت ÛĮ", "ÄIJ á»ĥ", "Ġ vyÅ¡Å¡ÃŃ", "Ġvy Å¡Å¡ÃŃ", "Äį ila", "Äįi la", "Äįil a", "а дÑĥ", "ад Ñĥ", "çī¹ åĪ¥", "ĠìĿ¸ 기", "u jÃŃcÃŃch", "ujÃŃ cÃŃch", "ujÃŃcÃŃ ch", "ĠPo dle", "ĠPod le", "Ġy avaÅŁ", "Ļ æ±Ł", "Ġka yb", "Ġkay b", "åĬ ª", "ç´ ¹", "Ġоб ÑĢабоÑĤ", "ĠобÑĢа боÑĤ", "Ġм аÑı", "Ġма Ñı", "Ġ åıĬ", "Ġåı Ĭ", "æİ¥ åıĹ", "ÙĨ تÛĮ", "ÙĨت ÛĮ", "Ġ ÏĩÏİ", "ĠÏĩ Ïİ", "ÑĤ ÑĢо", "ÑĤÑĢ о", "Ġu yar", "Ġuy ar", "ĠعÙħÙĦ کرد", "Ġо ÑĨен", "ĠмеÑģÑĤ а", "à¸ķ ลาà¸Ķ", "à¸ķล าà¸Ķ", "Ùħ ÙĤ", "ild ren", "Ġзави ÑģиÑĤ", "Âł ĠÂł", "ÂłĠ Âł", "Ġm ožná", "Ġmož ná", "æĺŃ åĴĮ", "ır ken", "к ин", "ки н", "åĿ Ĥ", "ÏĦ Ïĥι", "ÏĦÏĥ ι", "ĠÑĩ Ñĥд", "Ðļ он", "is lav", "isl av", "ĠÐļ ÑĢаÑģ", "ĠÐļÑĢа Ñģ", "N ej", "Ne j", "Âł b", "r of", "ro f", "Ġ ileri", "Ġi leri", "Ġil eri", "Ġile ri", "Ġiler i", "ĠÐŀ ÑĢ", "ĠCh á»ī", "Ġn üfus", "ĠÑĸ нÑĤ", "ĠÑĸн ÑĤ", "! âĢľ", "Ġन र", "主 ä¹ī", "ĠتÙĨ ظ", "ův odu", "ůvod u", "ĠгоÑĢ ода", "ĠгоÑĢод а", "Ġk ural", "Ġkur al", "Ġku ral", "Ġj edin", "Ġje din", "Ġjed in", "ÑĢ аÑĤег", "ÑĢаÑĤ ег", "åĢ º", "Ġzpůsob em", "ìĿ¸ ìĿĺ", "Ġ ÙĨب", "ĠÙĨ ب", "ĠN ga", "ĠNg a", "ĠÐĿ ай", "ĠÐĿа й", "ĠاÙģ زار", "ĠاÙģز ار", "нÑĥ вÑģÑı", "нÑĥв ÑģÑı", "Ġдв оÑħ", "Ġдво Ñħ", "Ġro zp", "Ġroz p", "ε ίοÏħ", "εί οÏħ", "είο Ïħ", "Ġο ικο", "Ġοι κο", "ĠG eç", "ĠGe ç", " Ĺ", "Ġch iếm", "Ġchiế m", "ĠÑĢаÑģпÑĢоÑģÑĤ ÑĢан", "Ġh Æ°Æ¡ng", "èĩª åĭķ", "ĠÙħÙĪ ÙģÙĤ", "æĮ ¥", "ï¼ģ âĢĿĊĊ", "ï¼ģâĢĿ ĊĊ", "Ïģο ÏĨοÏģ", "èı Į", "ãĥ´ ãĤ¡", "欧 ç¾İ", "ĠÑĤеп ло", "ãģĤ ãģĤ", "ãĤ¦ ãĥ³", "ĠÅŁ eyi", "ĠÅŁey i", "Ġs üt", "Ġsü t", "ãģ¹ ãģ¦", "ãĥ³ ãĥij", "ãĥ³ãĥ ij", "μÎŃν Ïīν", "Ġgenel likle", "Ġدر ÙħاÙĨ", "Ù ª", "Ġak ıl", "ĠÐľ Ñĭ", "Ġet miÅŁ", "Ġetm iÅŁ", "Å¡ la", "Ġвозмож ноÑģÑĤÑĮ", "Ġвозможно ÑģÑĤÑĮ", "Ġgün cel", "Ġná ro", "å½¢ å¼ı", "Ġα ÏĢοÏĦε", "ĠαÏĢο ÏĦε", "ĠмÑĸÑģ ÑĨÑı", "Ġ رض", "Ġر ض", "ä¸į çŁ¥éģĵ", "ä¸įçŁ¥ éģĵ", "r ava", "ra va", "rav a", "ĠÎļ ά", "ิà¸Ļ à¸Ĺร", "ิà¸Ļà¸Ĺ ร", "Ġли ÑģÑĤÑĮ", "ĠлиÑģÑĤ ÑĮ", "èĨ ľ", "ãģ«ãģª ãĤĬ", "Ġ æĿ¾", "ĠæĿ ¾", "å® ı", "Ġм иÑģ", "Ġми Ñģ", "át nÃŃ", "Ġyıl lık", "ĠMerk ezi", "ĠMerkez i", "Ġiç eri", "Ġiçer i", "ÅĻ ÃŃž", "ÅĻÃŃ Å¾", "Ġp ÅĻe", "ĠpÅĻ e", "Ïĩ Ïģι", "Ġ åįĥ", "Ġåį ĥ", "Ġs rp", "Ġsr p", "à¹Ĥ à¸Ĺร", "à¹Ĥà¸Ĺ ร", "ĠK rál", "ĠKr ál", ". Σ", "á val", "áv al", "l éd", "lé d", "Ġ λα", "Ġλ α", "ี ยวà¸ģ", "ีย วà¸ģ", "ียว à¸ģ", "ãģı ãģª", "ĠvÅ¡ ichni", "ĠпÑĢед оÑģÑĤав", "ì ¿", "Ġ 구ê¸ĢìĥģìľĦ", "Ġ구 ê¸ĢìĥģìľĦ", "Ġ구ê¸Ģ ìĥģìľĦ", "Ġà¤īप लब", "в оз", "во з", "ĠëħĦ ëıĦë³Ħ", "､ _", "à¸ļ รร", "à¸ļร ร", "ĠÑģв ÑĸÑĤÑĥ", "ĠÑģвÑĸÑĤ Ñĥ", "ĠÑĢÑĥб лей", "len me", "lÃŃ Äį", "ÏĦ ει", "ÏĦε ι", "Ġ åı¤", "Ġåı ¤", "ĠObr ázky", "Ġìĺģ íĸ¥", "ĠгÑĢаж дан", "í Ĥ¹", "íĤ ¹", "Ġsahip tir", "Ġп оÑĩаÑĤкÑĥ", "ĠпоÑĩ аÑĤкÑĥ", "ĠØ£ÙĬ ض", "ĠÑĤоÑĢ гов", "Ġgel ecek", "Ġgele cek", "Ġ 문íĻĶ", "Ġ문 íĻĶ", "ik leri", "ikler i", "ĠнеобÑħÑĸд но", "Ġ äºij", "o vol", "ov ol", "ovo l", "Ġद ल", "ĠìķĬ ê³ł", "Ġм г", "Ġz jist", "an lı", "ั à¸ĩà¸Ļ", "ัà¸ĩ à¸Ļ", "ÑĢа Ñħов", "ÑĢаÑħ ов", "ι νη", "ιν η", "Ġп лоÑĤ", "Ġпл оÑĤ", "Ġпло ÑĤ", "Ġn itel", "Ġni tel", "Ġnit el", "ìĬ¤ íģ¬", "ĠSon ra", "ĠÑģ боÑĢ", "ĠÑģб оÑĢ", "Ġ ÏĥοÏħ", "ĠÏĥ οÏħ", "Ġol mam", "Ġolm am", "Ġolma m", "Ġan aliz", "Ġanal iz", "à¹Į ว", "Ġm ỹ", "Ġmá» ¹", "ce ae", "cea e", "Ġ ден", "Ġд ен", "Ġде н", "веÑĢ жд", "веÑĢж д", "Ạ¢", "ãģĵ ãģ¨ãĤĤ", "ãģĵãģ¨ ãĤĤ", "ìĤ¬ íķŃ", "è¨Ģ ãģ£ãģŁ", "Ġ ì¹´ì§Ģëħ¸", "Ġì¹´ ì§Ģëħ¸", "ÑĢ иÑĤи", "ÑĢи ÑĤи", "ÑĢиÑĤ и", "Ġch ce", "Ġçev ir", "ÛĮ ÛĮÙĨ", "ä¼ļ è®®", "ัม à¸ŀ", "Ġ åĦ", "Ġå Ħ", "ĠÙ¾ در", "å¼ı ä¼ļ社", "Ġ ÑĨен", "ĠÑĨ ен", "ĠÑĨе н", "ิ à¸ĸ", "Ġji nak", "Ġjin ak", "Ġб лÑİ", "Ġбл Ñİ", "и ÑĨин", "иÑĨ ин", "ÙĴ Ùĩ", "Ú© ÙĪر", "Ú©ÙĪ ر", "Ġ ìķħ", "Ġìķ ħ", "e ksiyon", "ek siyon", "eks iyon", "ĠÑģ веÑĢ", "ĠÑģв еÑĢ", "ĠобÑĢаз ованиÑı", "Ġ ãĥĻ", "Ġãĥ Ļ", "æľī 人", "Ġbilg ileri", "Ġbilgi leri", "Ġbilgiler i", "Ġh ầu", "еÑĢ Ñĸг", "еÑĢÑĸ г", "Ġva Å¡e", "Ġn edir", "Ġne dir", "Ġned ir", "ä¸į å¾Ĺ", "ĠbaÅŁar ılı", "ĠbaÅŁarı lı", "Ġkay bet", "Ġkayb et", "å© ·", "ĠÐĿ ав", "ĠÐĿа в", "Ġê´Ģ íķľ", "Ñģ ÑĤÑİ", "ÑģÑĤ Ñİ", "å®ŀ éĻħ", "k lady", "kl ady", "klad y", "kla dy", "д аÑĤÑĮ", "да ÑĤÑĮ", "даÑĤ ÑĮ", "r aç", "ra ç", "Ġkuv vet", "à¸ģาร à¸Ĺ", "å ļ", "Ġ ÑĢеп", "ĠÑĢ еп", "ĠÑĢе п", "Ġ à¸Ŀ", "ĠภĿ", "ĠDi ÄŁer", "íĶĦ íĬ¸", "Ġnej vÄĽtÅ¡ÃŃ", "Ġìłģ ìļ©", "Ġonemoc nÄĽnÃŃ", "а ка", "ак а", "Ðł аз", "ĠÙģ Ø¥ÙĨ", "ãĤµ ãĤ¤ãĤº", "ãĤµãĤ¤ ãĤº", "Ġv lád", "Ġvl ád", "Ġvlá d", "Ġr ady", "Ġrad y", "Ġra dy", "ãĢģ ãģĵãĤĮ", "ÑģÑĤв ие", "lı ÄŁa", "lıģ a", "å ŃĶ", "åŃ Ķ", "Ġ áo", "Ġá o", "à¸Ń าà¸ģาศ", "Ġ à¤ıम", "Ġà¤ı म", "δ αÏĤ", "δα ÏĤ", "Ġа пÑĢ", "Ġап ÑĢ", "æİ Ľ", "Ġ ç«ĭ", "Ġç« ĭ", "âĸı âĸı", "ĠС м", "Ġne má", "Ġnem á", "Ġ è¢", "Ġè ¢", "νο μα", "νομ α", "ĠÙģ رÙĪد", "ĠÙģر ÙĪد", "ĠÙģرÙĪ د", "Ġül ke", "Ġülk e", "Ġ æĺŁ", "Ġæĺ Ł", "ั à¸Ļà¸ģ", "ัà¸Ļ à¸ģ", "ãģķãĤĵ ãģ®", "eÅŁ il", "ÄŁ iz", "ÄŁi z", "ĠÐij оÑĢ", "Ġt ầm", "ει ÏĦοÏħÏģγ", "Ġ γÏģα", "Ġγ Ïģα", "à¥įष à¤ķ", "Ġv ẻ", "Ġkend isine", "Ġkendisi ne", "ĠìķĮ ê³ł", "Ġêµ Ńìłľ", "ĠêµŃ ìłľ", "ĠnÄĽk do", "Ġ ÛĮÙĩ", "ĠÛĮ Ùĩ", "Ġکار بر", "ãĥĻ ãĥ«", "ï» ´", "Ġt uyên", "Ġtuy ên", "Ġç at", "Ġça t", "âĢIJ âĢIJ", " ı", "Ġ ìĤ¬ìĹħ", "ĠìĤ¬ ìĹħ", "é ĨĴ", "éĨ Ĵ", "æıIJ é«ĺ", "æ· ¡", "Ġ ÄŁ", "ĠÄ Ł", "èĸ ¦", "ãĢĭ ï¼Ī", "æ¡ ĥ", "ìĹ Ħ", "Ġ æŀĹ", "Ġæŀ Ĺ", "Ä Ĥ", "ĠÄĮ ech", "α ιο", "αι ο", "ĠØ· رÙĬÙĤ", "Ġطر ÙĬÙĤ", "Ġзав еÑĢÑĪ", "ĠзавеÑĢ ÑĪ", "تÙĪ بر", "ĠØŃ Ø¬", "ĠÎŃÏĩ οÏħν", "¿ ÃĤ", "Ġd ÄĽtÃŃ", "ĠdÄĽ tÃŃ", "ĠdÄĽt ÃŃ", "Ġiç ine", "Ġiçin e", "Ġiçi ne", "ĠCh úa", "ан нÑĭй", "аннÑĭ й", "ĠÙĪÛĮ Úĺ", "Ġna stav", "Ġnast av", "ıs ına", "ısı na", "ĠÑĹ Ð¼", "п он", "по н", "е нÑı", "ен Ñı", "ĠÙĪ ظ", "Ú¯ ÙĦ", "หล วà¸ĩ", "Ġza stav", "Ġzast av", "а кон", "ак он", "³³³³³³³³³³³³³³³³ ³³³³³³³³³³³³³³³³", "ĠK ır", "ĠKı r", "çµ ¶", "ĠоÑĢганÑĸ заÑĨÑĸÑĹ", "ĠоÑĢганÑĸз аÑĨÑĸÑĹ", "ĠоÑĢганÑĸза ÑĨÑĸÑĹ", "ãģŁ ãĤĬ", "Ø° ÙĬ", "Ġर à¤ķ", "amp iyon", "Ġ æ¸ħ", "Ġæ¸ ħ", "çľ¼ çĿĽ", "Ġìķ ĬìĿĢ", "ĠìķĬ ìĿĢ", "é¹ ¿", "Ġ å¿ĥ", "Ġå¿ ĥ", "ĠпÑĢек ÑĢаÑģ", "ĠÑģ егоднÑı", "Ġ सल", "Ġस ल", "ĠÏħ ÏĢÏĮ", "ĠÏħÏĢ ÏĮ", "ĠÐķ го", "ĠÐĽ и", "ãĤ¨ ãĥ«", "Ġл ÑİÑĤ", "ĠлÑİ ÑĤ", "é¥ °", "Ġvz dál", "¯ ÃĤ", "Ġна Ñıв", "Ġتش Ú©ÛĮÙĦ", "Ġس ÙĪÛĮ", "ĠسÙĪ ÛĮ", "Ġt ái", "Ġtá i", "Ġk apı", "Ġkap ı", "ĠsvÄĽt ÄĽ", "ĠsvÄĽ tÄĽ", "δ ÏĮν", "δÏĮ ν", "æ¼ ¢", "ì į¨", "ĠbaÅŁv ur", "ÑĢ ина", "ÑĢи на", "ÑĢин а", "Ġk elim", "Ġke lim", "Ġkel im", "аÑĤ ок", "аÑĤо к", "Ġκά θε", "ĠYük sek", "à¹ĩà¸Ļ à¸ľ", "éł Ĥ", "åIJĮ æĻĤ", "ÅŁ tır", "ÅŁt ır", "ว à¸ĩศ", "วà¸ĩ ศ", "o ty", "ot y", "Ġ ارد", "Ġا رد", "Ġار د", "ĠìŀIJìĭł ìĿĺ", "ĠÑıн ва", "üyor du", "æĿ ¨", "ĠâĢĵ Ċ", "ï¼Į å®ĥ", "е йн", "ей н", "ĠпеÑĢ еÑĤ", "ĠпеÑĢе ÑĤ", "ĠdeÄŁiÅŁik lik", "ĠогÑĢа ниÑĩ", "ìĦľ ìļ¸", "Ġgel iyor", "ĠÙ¾ Ø°ÛĮر", "åĵ ²", "ey in", "eyi n", "Ġëı Ī", "Ġun iverz", "Ġh ned", "Ġhn ed", "Ġt áºŃn", "vo ÅĻÃŃ", "voÅĻ ÃŃ", "Ġn iên", "Ġni ên", "dÄĽ podob", "ìĤ¬ íļĮ", "ãģĮ ãģĤãĤĬ", "ĠÑģ ÑĸÑĩ", "' '\"", "'' \"", "Ġtop lantı", "ĠÑģ ÑĩеÑĤ", "ĠÑģÑĩ еÑĤ", "åĩĨ å¤ĩ", "ан ÑĸÑı", "анÑĸ Ñı", "Ġ zel", "Ġz el", "Ġze l", "v ala", "val a", "va la", "Ġа пп", "Ġап п", "ĠاÙĦÙħ ÙĦÙĥ", "ĠاÙĦÙħÙĦ Ùĥ", "Ġho ÅŁ", "ĠÐĵ ен", "ĠÐĵе н", "ÑĤ аб", "ÑĤа б", "ĠÄĮesk o", "ĠÄĮes ko", "Ġмай же", "ĠmÄĽ sto", "ĠmÄĽst o", "yo nel", "yon el", "ê±° 리", "Ġìĺ¨ ëĿ¼ìĿ¸", "ç´ ¯", "Ġde rec", "Ġder ec", "Ġdere c", "Ġок ÑĢÑĥж", "Ġy abancı", "Ġ íĦ°", "ĠíĦ °", "Ġ èµĦ", "Ġèµ Ħ", "ÎĻÎļ ÎĹ", "Ġп Ñĭ", "Ġv ÄĽn", "ĠvÄĽ n", "и нки", "ин ки", "ụ p", "æľº 械", "ĠìķĮ 볤", "ëħ ķ", "Ġ λÏĮγ", "Ġλ ÏĮγ", "e yn", "ey n", "Ġ ëIJĺìĹĪëĭ¤", "ĠëIJĺ ìĹĪëĭ¤", "ĠëIJĺìĹĪ ëĭ¤", "æ± ¡", "Ġve dle", "Ġved le", "ĠÙĥ تب", "ë§ ¨", "ĠÙħÙĤ اÙĪ", "å¹´ ãģ«", "ाà¤ĩ à¤ķ", "ĠÑģÑĤ оÑģ", "ĠÑģÑĤо Ñģ", "ĠÏĥ ÏĦοÏħÏĤ", "м еÑĤÑĮ", "ме ÑĤÑĮ", "меÑĤ ÑĮ", "Ġes as", "Ġesa s", "ëIJĺ ê³ł", "ĠkvÄĽt na", "Ġ éľ", "Ġé ľ", "d ük", "dü k", "åŁ ·", "è ªĮ", "èª Į", "Ġm luv", "Ġml uv", "ĠпÑĢи нÑı", "ĠпÑĢин Ñı", "Ġpo té", "Ġpot é", "ĠÚ© ÙĨÙħ", "ĠÚ©ÙĨ Ùħ", "ĠпÑĢед лож", "ĠÐľÐ¾Ñģк ва", "ï¼Į å¦Ĥ", "Ġsv ém", "Ġsvé m", "Ġا ÙħÙĨ", "ĠاÙħ ÙĨ", "ส าย", "ĠÑĥм енÑĮ", "Ġ ãģĵãģ®", "åī Ĥ", "ĠÑģ еÑĢÑĮ", "ĠÑģеÑĢ ÑĮ", "Ġm á»ĩ", "Ġmá» ĩ", "Ġ ä¹Ŀ", "Ġä¹ Ŀ", "Ġза кÑĸн", "Ġзак Ñĸн", "Ġв елиÑĩ", "Ġвели Ñĩ", "Ġвел иÑĩ", "Ġве лиÑĩ", "Ġкон ÑĤÑĢа", "ĠконÑĤ ÑĢа", "ĠконÑĤÑĢ а", "ĠS osyal", "Ġy ukarı", "Ġد ÙĪب", "ĠدÙĪ ب", "ä¾ §", "Ġза мен", "Ġзам ен", "ï» ®", "Ġso bÄĽ", "Ġsob ÄĽ", "ĠТак же", "Ð İ", "ε δ", "Ùħ ارÛĮ", "Ùħا رÛĮ", "Ùħار ÛĮ", "ξ ι", "ì¹ Ń", "Ġп лаÑģÑĤи", "Ġпл аÑģÑĤи", "Ġпла ÑģÑĤи", "Ïĥ οÏħν", "Ïĥο Ïħν", "ÏĥοÏħ ν", "èľĺèĽĽ è¯į", "ÙĪ ÛĮزÛĮ", "ÙĪÛĮ زÛĮ", "Ġnap ÅĻ", "ĠÑĤип а", "ĠÑĤи па", "à¥Ĥ à¤Ľ", "ĠÅŁ ah", "л ÑıÑĤи", "лÑı ÑĤи", "ب ÛĮر", "بÛĮ ر", "ระ ยะ", "ĠболÑĮ ÑĪин", "ĠболÑĮÑĪ ин", "ÏĦη ÏĦα", "Ġíıī ê°Ģ", "Ġpro jev", "Ġproj ev", "Ġproje v", "ò i", "Ġк нÑı", "ÏĨ εÏģ", "е ÑĢÑĥ", "еÑĢ Ñĥ", "Ñį н", "ĠعÙħ ÙĦÛĮ", "ĠعÙħÙĦ ÛĮ", "à¤ł न", "ãĥ³ ãĤ¯", "ĠìķĦ ëŀĺ", "Î Ī", "Ġب است", "Ġبا ست", "Ġ تÙĥ", "Ġت Ùĥ", "a ÄįnÃŃ", "aÄį nÃŃ", "ĠлÑĸ кÑĥваннÑı", "ĠлÑĸк ÑĥваннÑı", "à¸Ħ à¹Ĥà¸Ļ", "Ġ èĥ½", "Ġè ĥ½", "Ġèĥ ½", "θ λη", "len miÅŁ", "Ġl á»Ļ", "Ġlá» Ļ", "Ġsi lah", "Ġsil ah", "ĠA ustr", "ĠAust r", "ĠAus tr", "ĠAu str", "ØŃ ÙĤ", ".*** .***", "ì ©", "Ġg Ãł", "Ġباز بÛĮÙĨÛĮ", "ĠÄij Ãłn", "ÃŃ ky", "ÃŃk y", "ĠÎķ ν", "ض Ùħ", "å§ ĵ", "Ġ ÙĨÙĪÛĮس", "ĠÙĨ ÙĪÛĮس", "ĠÙĨÙĪ ÛĮس", "Ġskup iny", "Ġس ÛĮد", "ĠسÛĮ د", "Ġal dıģı", "Ġald ıģı", "Ġaldı ģı", "m eli", "me li", "mel i", "в иж", "ви ж", "ì¹ĺ ëĬĶ", "ов аÑħ", "ова Ñħ", "Ġ æ©", "Ġæ ©", "Ø´ÙĨ اسÛĮ", "Ø´ÙĨاس ÛĮ", "Ġn imi", "Ġni mi", "Ġnim i", "ĠÐĵ ÑĢи", "íĹ Į", "Ġк в", "éŁ ĵ", "Ġ íĽĦ기", "ĠíĽĦ 기", "Ġ stÅĻÃŃ", "Ġst ÅĻÃŃ", "ĠstÅĻ ÃŃ", "ĠкÑĸлÑĮ кÑĸÑģÑĤÑĮ", "ĠBakan lıģı", "ĠменÑĮ ÑĪе", "ا ÙĪÛĮ", "اÙĪ ÛĮ", "Ġار ÙĪÙ¾", "Ġ èī²", "Ġèī ²", "ĠÚ©ÙĪÚĨ Ú©", "ĠA ynı", "Ġ äºĨ", "Ġس Ùģر", "ĠسÙģ ر", "ĠÑĤе аÑĤ", "Ġ vÄĽd", "ĠvÄĽ d", "а ÑĢов", "аÑĢ ов", "Ġоб меж", "ĠìķĬ ìķĺ", "追 åĬł", "éł Ī", "dÄĽ lenÃŃ", "dÄĽl enÃŃ", "dÄĽlen ÃŃ", "Ġk ims", "Ġki ms", "Ġkim s", "Ġ èı²", "Ġèı ²", "Ġг ÑĢÑĥн", "ĠгÑĢÑĥ н", "ĠгÑĢ Ñĥн", "ĠØ¢ ÙĦÙħاÙĨ", "ĠØ¢ÙĦ ÙħاÙĨ", "Ġав г", "ĠÑī оÑģÑĮ", "ĠÑīо ÑģÑĮ", "Ġ å¾·", "Ġå¾ ·", "ĠÐĿа ÑĨÑĸоналÑĮ", "æĪIJ ç«ĭ", "ูà¸Ļ ย", "ãĥ¼ ãĥ«ãĥī", "ãĥ¼ãĥ« ãĥī", "éĽ ²", "ĠT á»ķ", "cı lık", "ĠAlma nya", "ĠAlman ya", "Ġov Å¡em", " ĭ", "ĠÏĩÏģη ÏĥιμοÏĢοι", "Ġörg üt", "िस स", "èĹ Ŀ", "ĠGi ải", "Ġsv ob", "Ġsvo b", "Ġrůzn ých", "Ġrůz ných", "Ġsmlou vy", "ÑĢ еÑģÑģ", "ÑĢеÑģ Ñģ", "ี à¹Ģà¸Ķ", "ĠاÙħ رÙĪز", "ĠاÙħر ÙĪز", "ãĤ ħ", "åĿ ¦", "à¹ī à¸Ħ", "Ġ каж", "Ġк аж", "Ġка ж", "å¼ Ĺ", "Ñĩ ноÑĹ", "Ñĩно ÑĹ", "åľ Ī", "ĠØ¢ ÙĩÙĨÚ¯", "ëª °", "Ġ æº", "Ġæ º", "Ġ èĦ", "Ġè Ħ", "ä¸Ģ æŃ¥", "оÑĩ ка", "Ġpro stor", "Ġpros tor", "Ġprost or", "Ġng ắn", "Ġ ç·", "Ġç ·", "н аÑĢ", "на ÑĢ", "Ġà¤ľ व", "ĠнаÑĩ алÑĮ", "Ġне дел", "Ġнед ел", "ĠÑģиÑģÑĤем Ñĥ", "ج ÙĬ", "اد ات", "ادا ت", "Ġ æ¢", "Ġæ ¢", "ĠجاÙħ عة", "ĠجاÙħع Ø©", "Ġ ä»İ", "Ġà¤ħ फ", "èĸ Ħ", "Ġب اÙĤ", "Ġبا ÙĤ", "ب ÙĬع", "بÙĬ ع", "ãģķ ãĤĮãģ¦", "ãģķãĤĮ ãģ¦", "ĠÃĩ alÄ±ÅŁ", "Ø®ÙĪ است", "ãĥĥ ãĤ·ãĥ¥", "ĠØŃ Ø³ÛĮÙĨ", "ĠØŃس ÛĮÙĨ", "Ġоб наÑĢÑĥж", "в Ñĸдом", "вÑĸ дом", "вÑĸд ом", "Ġh ôm", "л анд", "ла нд", "лан д", "Ġव à¤ľà¤¹", "س ÙĬÙĨ", "سÙĬ ÙĨ", "æł ı", "Ġna vÃŃc", "Ġnav ÃŃc", "ãĤµ ãĤ¤ãĥĪ", "ãĤµãĤ¤ ãĥĪ", "ĠÑı комÑĥ", "ĠÑıк омÑĥ", "Ġí Ľ", "ĠY ani", "ĠYan i", "ĠYa ni", "ãĤĵ ãģ§ãģĻ", "ãĤĵãģ§ ãģĻ", "Ġг ÑĢÑĥп", "ĠгÑĢÑĥ п", "ĠгÑĢ Ñĥп", "Äį ný", "ÑĨ ик", "ÑĨи к", "ÙĪ ÙĬر", "ÙĪÙĬ ر", "Ġ Xã", "ĠX ã", "Ġf yz", "Ġfy z", "Ġ ï½ī", "Ġï½ ī", "âĢĮ ترÛĮÙĨ", "âĢĮتر ÛĮÙĨ", "à¤Ł à¤ķ", "ÑĦоÑĢм и", "ÑĦоÑĢ ми", "ĠO yun", "ĠOy un", "åł´ æīĢ", "ØŃ Ø«", "ĠìķĮ ìķĦ", "ÑĢав илÑĮ", "ÑĢави лÑĮ", "ï¼Į âĢĿ", "b oru", "bo ru", "bor u", "ĠK ullan", "ĠKul lan", "ĠKay nak", "Ġê° ĸ", "ç´ Ķ", "ï¼Į æ¯ı", "ÎĹ Î¡", "Ġp ůl", "Ġpů l", "Ġг оÑģÑĤ", "ر ÙĪÙħ", "رÙĪ Ùħ", "ï¼Į åį³", "Û² Û³", "ĠÙĨØ® ست", "ĠÚ© سب", "Ġ à¹Ģà¸ļ", "Ġà¹Ģ à¸ļ", "Ġà¹Ģภļ", "Ġy azar", "Ġya zar", "Ġyaz ar", "j ekt", "je kt", "à¹Ĥล ย", "Ġдоб ÑĢе", "Ġپزش Ú©ÛĮ", "ĠتÙĩ ÛĮÙĩ", "ç¾İ åľĭ", "но ÑģÑıÑĤ", "ноÑģ ÑıÑĤ", "ноÑģÑı ÑĤ", "ëłĪ ìĬ¤", "åĹ ¯", "Ġr Ãłng", "ĠÎķ ξ", "а ÑĤаÑĢ", "аÑĤ аÑĢ", "аÑĤа ÑĢ", "k ova", "ko va", "kov a", "ĠÅŁey ler", "Ø® اص", "ĠìķĪ ìłĦ", "Ñī ей", "Ñīе й", "Ġë° Ŀ", "âĢĮتÙĪاÙĨ د", "ãģĪ ãģ°", "Ġv ữ", "Ġvá» ¯", "ĠÑģ ама", "ĠÑģам а", "ĠобоÑĢ Ñĥд", "Ġобо ÑĢÑĥд", "âĢĮ باشد", "à¹Į à¸Ń", "Ġdet ay", "æĤ ²", " Ī", "ãĤ¦ ãĤ£", "ĠпÑĢав ила", "ĠпÑĢави ла", "ĠпÑĢавил а", "kr ét", "à¹Į ร", "åĮ ¹", "Ġ åħį", "Ġå ħį", "Ġåħ į", "ĠÑģилÑĮ но", "ĠиÑģ ÑĤоÑĩ", "ĠиÑģÑĤ оÑĩ", "ĠsaÄŁ lar", "Ġ æѦ", "ĠæŃ ¦", "íĸ ĪìĬµëĭĪëĭ¤", "íĸĪ ìĬµëĭĪëĭ¤", "Kh ông", "à¹Īาà¸ĩ à¹Ĩ", "Û° Û°Û°", "Û°Û° Û°", "Ġ رÙĤ", "Ġر ÙĤ", "âĢĻ ÑıÑĤ", "âĢĻÑı ÑĤ", "åĽ ²", "à¹ģ à¸Ķà¸ĩ", "Ġžád né", "c ouz", "co uz", "cou z", "à ĭ", "ĠпÑĸд гоÑĤов", "Ġ ëĮĢíķĻ", "ĠëĮĢ íķĻ", "Ġdüny anın", "èĢģ å¸Ī", "èģĮ ä¸ļ", "Ġy eri", "Ġye ri", "Ġyer i", "à¥ĭ à¤ķर", "à¥ĭà¤ķ र", "ĠبÙĩ تر", "ëĭĪ ìķĦ", "ìĿĮ ìĿĦ", "Ġ æĮĩ", "ĠæĮ ĩ", "ãĢį ï¼Ī", "ĠÑģооÑĤвеÑĤÑģÑĤв ии", "æĬ ĵ", "à¹Ĥ à¸Ĺ", "Ġtr á»ĵng", "ĠпÑĢа ÑĨÑĸ", "Ġ ëĨĵ", "ĠëĨ ĵ", "à¤ĩ न", "Ġìłķ ë§IJ", "ãĢ ķ", "Ġc áºŃn", "åĸ Ŀ", "Ġê³Ħ ìĨį", "Ġ ä¸İ", "Ġä¸ İ", "å¥ ı", "Ġع اÙĦÙħ", "Ġvys vÄĽt", "Ġдо ÑĢог", "ĠдоÑĢ ог", "Ġн еÑĢв", "ĠнеÑĢ в", "Ġб еÑĤ", "Ġп ÑĢиÑĤ", "ĠпÑĢ иÑĤ", "ĠпÑĢи ÑĤ", "ов Ñĭй", "å· ¡", "Ùģ اع", "Ðļ Ðĺ", "à¸ķ รวà¸Ī", "à¸ķรว à¸Ī", "ĠÐľ ай", "ĠÐľÐ° й", "ëıĦ ë¡ľ", "Ġz lat", "ĠsaÄŁ lam", "Ïģ αν", "Ïģα ν", "à¸Ĭ ร", "å¹´ ãģ®", "à¸Ħ รà¸Ńà¸ĩ", "à¸Ħร à¸Ńà¸ĩ", " ħ", "Ġho á", "Ġдов олÑĮно", "Ġol maz", "Ġolm az", "Ġolma z", "ĠpodmÃŃn ky", "ĠÑħозÑı й", "æĻ ´", "ÑĢ ова", "ÑĢов а", "ÑĢо ва", "Ġl ược", "ान न", "Ġкап иÑĤ", "ĠÚĺ اÙĨ", "æľī äºĽ", "ĠповеÑĢÑħ ноÑģÑĤи", "ĠÑĨ Ñĸн", "ĠÑĨÑĸ н", "ü yle", "üy le", "Ġj azy", "Ġja zy", "Ġjaz y", "ĠPh ú", "Ġ सन", "Ġस न", "åĩº åĶ®", "Âł д", "Ġ ãĤ¯", "ĠãĤ ¯", "çĶ± äºİ", "à¥į पत", "à¥įप त", "ĠاÙĦØ® اÙħ", "Ġاص ÙĦاØŃ", "ĠاصÙĦ اØŃ", "Ġ تÛĮ", "Ġت ÛĮ", "Ġt ato", "Ġta to", "Ġtat o", "å¹ ¹", "æ³ ½", "à¸Ńà¸ģ à¸Īาà¸ģ", "Ñĥ лÑİ", "Ñĥл Ñİ", "Ġв Ñģп", "ĠвÑģ п", "m ekte", "me kte", "mek te", "à¥Ģ फ", "ĠÚĺ ÙĪئ", "Ġl á»ĩnh", "Ġlá»ĩ nh", "âĢĮ کرد", "âĢĮÚ© رد", "íı¬ ì¸ł", "an ki", "ank i", "Ġëĵ±ë¡Ŀ ëĮĢíĸī", "Ġ ãĤĿ", "ĠãĤ Ŀ", "Ġار زش", "Ġارز Ø´", "Ġth ú", "Ġ ấn", "è¡Į 为", "ĠÑģ нова", "ê ¾¸", "Ġsou hlas", "Ġв озв", "Ġвоз в", "Ġво зв", "ÏģÎŃ ÏĢει", "ĠнÑĸ Ñĩого", "ĠнÑĸÑĩ ого", "н ож", "но ж", "ÑĤ ик", "ÑĤи к", "ãģ© ãģĵ", "ĠоÑģнов е", "ãĤ ¥", "à¸Ľà¸£à¸° à¸Īำ", "Ġ à¸Ĺà¸Ńà¸ĩ", "Ġà¸Ĺ à¸Ńà¸ĩ", "Ġek sik", "Ġeks ik", "ĠÙĦ Ø¥", "ãģĭ ãģ®", "Ġ ãģª", "- प", "Ïģ ει", "Ïģε ι", "ĠìłĦ 문", "า à¸ģล", "าà¸ģ ล", "β ε", "íĬ¹ ë³Ħ", "íķĺ ë©´ìĦľ", "íķĺë©´ ìĦľ", "à¸Ħà¹Ĥà¸Ļ à¹Ĥลย", "Ġ 好", "Ġå¥ ½", "Ġy ayım", "Ġyay ım", "ë§Į ëĤ¨", "ĠкиÑģ лоÑĤ", "ĠкиÑģл оÑĤ", "ĠÑį неÑĢг", "çĸ ¾", "Ġد Ø´", "Ġsor uml", "Ġsoru ml", "Ġза клад", "Ġзак лад", "à¸Ĭ à¸Ńà¸ļ", "ĠÙģرÙĩÙĨÚ¯ ÛĮ", "Ġà¤ı ल", "Ġë¹Ħ êµIJ", "l erce", "ler ce", "Ġ Ø·ÙĦب", "ĠØ· ÙĦب", "ĠØ·ÙĦ ب", "ãģ« ãģĹãģ¦", "ĠÑı коÑĹ", "ĠÑıк оÑĹ", "ĠاÙĦب تÙĩ", "ĠÐľ аÑĤ", "ĠÐľÐ° ÑĤ", "åį ĵ", "Ġ åħ¬åı¸", "Ġåħ¬ åı¸", "Ġsöy ley", "Ġsöyl ey", "ĠìĥĪ ë¡ľìļ´", "ĠÑĦ аÑĢ", "Ġalt ına", "Ġaltın a", "Ġaltı na", "Ġsta vu", "Ġstav u", "âĢĻ ı", "al izace", "aliz ace", "Ġви ÑģÑĤÑĥп", "æķĻ å¸Ī", "à¥Ģ à¤ıस", "à¥Ģà¤ı स", "o dÄĽ", "od ÄĽ", "ĠÑĨ Ñĸл", "ĠÑĨÑĸ л", "ĠëĮĢ ìĥģ", "ĠкоÑĤоÑĢ ом", "ĠкоÑĤ оÑĢом", "Ġظ رÙģ", "éİ ®", "اÙģ ÙĬØ©", "اÙģÙĬ Ø©", "Ġ ìĹĨìĿ´", "ĠìĹĨ ìĿ´", "ĠμÏĮ νο", "ĠC Æ¡", "å¯ »", "ÏĦ ιÏĥ", "ÏĦι Ïĥ", "Ġ ãĤĦ", "ĠãĤ Ħ", "Ġjed noho", "Ġjedn oho", "Ġjedno ho", "ا ا", "Ø§Ø §", "et ler", "Ġव स", "ĠÑĢазлиÑĩ нÑĭÑħ", "Ġج غراÙģ", "Ġth ừa", "Ġthá» «a", "ĠгÑĢомад Ñıн", "ॠ°", "ĠاÙĦØ£ Ø®", "Ġнаг ÑĢÑĥз", "ç¸ ¾", "à¥Ĥ ह", "ĠпÑĢÑıм о", "â Ĭ", "ĠاÙĦØ£ÙĪÙĦ Ùī", "æĸ° èģŀ", "Ġìĥģ íĻ©", "it esi", "ite si", "ites i", "ëį° ìĿ´íĬ¸", "æŃ ·", "ï¼ĮèĢĮ ä¸Ķ", "ãģ¯ ãģļ", "产 çĶŁ", "æ°Ĺ ãģĮ", "y slu", "ys lu", "ysl u", "ìĸ´ ëĤĺ", "ا Ú©Ùħ", "اک Ùħ", "âĢ ĥ", ") ìĿĢ", "Ġجست ارÙĩاÛĮ", "ÙĪ Ø«", "ãħ İ", "Ġkav ram", "v ál", "vá l", "æľ Ń", "æĤ ł", "ìħ Ģ", "h rad", "hr ad", "hra d", "Ġت ÙĥÙĪÙĨ", "ĠتÙĥ ÙĪÙĨ", "ĠH òa", "å¹´ çļĦ", "Ġç arp", "Ġça rp", "Ġy olu", "Ġyo lu", "Ġyol u", "Ġdub na", "ĠÐĴ елик", "ĠÐĴели к", "Ġt ôn", "Ġtô n", "æ ķĮ", "æķ Į", "Ġc oi", "Ġco i", "Ġnak onec", "ĠÑį ÑĤÑĥ", "ĠÑįÑĤ Ñĥ", "íĨµ ëł¹", "ÑĪ ел", "ÑĪе л", "Ġneb yl", "Ġneby l", "in ç", "ب اÙĦØ¥ÙĨجÙĦÙĬزÙĬØ©", "باÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©", "ï¼ ¡", "о нÑĮ", "он ÑĮ", "Ġне маÑĶ", "Ġнем аÑĶ", "Ġê³ł ê°Ŀ", "ĠÙĤ طع", "ĠÙĤØ· ع", "ĠÑĤеÑĢиÑĤоÑĢ ÑĸÑĹ", "人 ãģ¯", "ĠΣ α", "éĤ£ äºĽ", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ios per", "í Ĥ¨", "íĤ ¨", "r aki", "ra ki", "rak i", "اÛĮ ج", "Âł C", "Ġан алÑĸз", "ãĤı ãĤĬ", "ĠìķĦëĭ Į", "ĠاÙĦعÙħÙĦ ÙĬØ©", "ĠاÙĦعÙħ ÙĦÙĬØ©", "l ament", "la ment", "lam ent", "é» ¨", "u jÃŃcÃŃm", "ujÃŃ cÃŃm", "ujÃŃcÃŃ m", "Ġr ẻ", "ä¸į åĪ°", "Ġrez erv", "ĠاÙĦØ° ÙĬÙĨ", "ĠاÙĦØ°ÙĬ ÙĨ", "æĭ ¥", "Ðĺ н", "Ġतह त", "r esi", "re si", "res i", "Ġ ãĥ¢", "Ġãĥ ¢", "л ев", "ле в", "ãĢĢ r", "Ġ ä»Ĭ", "Ġö dem", "Ġpot rav", "ĠêµIJ ìĪĺ", "ÑĢ еди", "ÑĢед и", "ÑĢе ди", "ĠÎļ ÎijÎĻ", "Ġ наÑĩала", "ĠнаÑĩ ала", "Ġиз б", "ĠbÅĻez na", "Ġle dna", "Ġled na", "ÑĢ ÑĥÑİÑĤ", "ÑĢÑĥ ÑİÑĤ", "Ġ моÑĤ", "Ġм оÑĤ", "Ġмо ÑĤ", "åıĹ åĪ°", "ĠÑĢÑĥ кÑĥ", "ĠÑĢÑĥк Ñĥ", "Ỽ m", "ad ele", "ade le", "adel e", "ĠÑĢоз глÑı", "åħ IJ", "Ġر ÙĪاÙĨ", "ĠرÙĪ اÙĨ", "а ков", "ак ов", "Ñĥ ÑĢÑĭ", "ÑĥÑĢ Ñĭ", "Ġaz al", "ĠÑĥ кÑĢа", "ĠÑĥк ÑĢа", "пи он", "ĠÄįlov ÄĽ", "äºĮäºĮ äºĮäºĮ", "ا بÙĬ", "اب ÙĬ", "Ġas lında", "ë¹ Ī", "Ġв ÑĢаÑĩ", "ĠвÑĢа Ñĩ", "ë£ ¹", "Ġген еÑĢа", "à¸ģาร ส", "ĠÑģов Ñģем", "ÙĪ ÙĦا", "ÙĪÙĦ ا", "Ġश ब", "ाà¤ĸ ण", "ست اÙĨÛĮ", "ستاÙĨ ÛĮ", "æĬ ½", "Ġrů z", "ĠíĮIJ 매", "à¸ģาร à¸ķ", "ائ ÛĮ", "a sal", "as al", "asa l", "ĠÑĢабоÑĤ Ñĥ", "ĠÑĢаб оÑĤÑĥ", "ĠÑĢабо ÑĤÑĥ", "à¥ĭल न", "Ġ 马", "Ġé© ¬", "Ġl ai", "Ġla i", "ó i", "v ap", "va p", "ëħĦ ìĹIJëĬĶ", "ëħĦìĹIJ ëĬĶ", "ĠпеÑĢед баÑĩ", "Ġп леÑĩ", "Ġпл еÑĩ", "id det", "idd et", "ĠÑĩ оÑĢ", "i yan", "iy an", "iya n", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", "ĠØŃر ÙģÙĩ", "ĠØŃرÙģ Ùĩ", "大 éĺª", "Ñĩ ого", "Ġ ки", "Ġк и", "ا ÙĪÙĬ", "اÙĪ ÙĬ", "ĠbaÅŁ lan", "Ġmerk ezi", "Ġmerkez i", "© ©", "Ġر است", "Ġرا ست", "Ġ ëĬĶ", "ĠëĬ Ķ", "ĠÑģ ÑĢав", "ĠвнÑĥ ÑĤÑĢи", "ĠвнÑĥÑĤÑĢ и", "ĠвнÑĥÑĤ ÑĢи", "ãĢĢ ãĥİ", "åĿ Ľ", "Ġв ÑĤ", ": :/", ":: /", "Ġsöz leÅŁ", "Ġver diÄŁi", "Ġverdi ÄŁi", "ิ ยม", "ิย ม", "ĠÐŁ ÑĢоÑĤ", "ĠÐŁÑĢ оÑĤ", "ĠÐŁÑĢо ÑĤ", "Ùĥ ار", "Ġب ÙĨدÛĮ", "ĠبÙĨ دÛĮ", "ĠبÙĨد ÛĮ", "Ùı ÙĪ", "缴 æĴŃ", "ĠÙħ ÙĦÙĬ", "ĠÙħÙĦ ÙĬ", "Ġnut né", "ะà¹ģ à¸Ļà¸Ļ", "ĠM ã", "Ġ ì´", "Ġì ´", "à¹Ī าม", "à¹Īา ม", "м оÑģ", "мо Ñģ", "Ġпо Ñıви", "ĠпоÑıв и", "Ġn ghi", "Ġng hi", "Ġngh i", "Ġ ëIJĺëĬĶ", "ĠëIJĺ ëĬĶ", "Ñģ клад", "Ñģк лад", "à¤Ĺ ल", "ĠC á»Ļng", "çŁ¥ è¯Ĩ", "Ġ taj", "Ġt aj", "Ġta j", "Ġع بر", "Ġعب ر", "éĻĦ è¿ij", "ü ÄŁ", "Ġê³µ ê³ł", "è£ ķ", "âĢĮ Ø´ÙĨ", "âĢĮØ´ ÙĨ", "Ġgerç ekten", "Ġgerçek ten", "n un", "nu n", "Ùħ Ø´", "ê°Ģ ëĬ¥", "ãĥ© ãĥ³ãĥī", "ãĥ©ãĥ³ ãĥī", "ay acak", "aya cak", "åįģ ä¸Ģ", "ĠB ảo", "Ġyet erli", "Ġyeter li", "ž iv", "ži v", "ĠÙĬÙĨ اÙĬر", "Ġb ýval", "Ġbý val", "ìĽĶ ê¹Įì§Ģ", "Ġn ợ", "Ġ ê´Ģê³Ħ", "Ġê´Ģ ê³Ħ", "Ġ íĿ¬", "ĠíĿ ¬", "а ÑİÑĤÑĮ", "аÑİÑĤ ÑĮ", "аÑİ ÑĤÑĮ", "Ġgö tür", "Ġваж но", "Ġва жно", "æµ ©", "ĠìĿ¼ ë¶Ģ", "ÑĨÑĸй ний", "ëł¥ ìĿĦ", "Ġл еÑĩение", "ĠлеÑĩ ение", "éĸ¢ ä¿Ĥ", "ĠT üm", "ìĻ Ķ", "éģ Ĺ", "ĠD ön", "Ġ ÑģпÑĸлÑĮ", "ĠÑģп ÑĸлÑĮ", "ĠÑģпÑĸл ÑĮ", "ãĥģ ãĤ§", "н ÑıеÑĤÑģÑı", "нÑı еÑĤÑģÑı", "нÑıеÑĤ ÑģÑı", "il tere", "ilter e", "ilt ere", "Ġ íĮĢ", "Ġí ĮĢ", "ĠíĮ Ģ", "è¨Ń å®ļ", "Ġro din", "Ġrod in", "Ġrodi n", "ĠاÙĤتص اد", "алÑĮ не", "à¥į à¤ķर", "à¥įà¤ķ र", "Ġvý bÄĽ", "Ġteh lik", "âĶ IJ", "Ġ çĶ°", "Ïģί ÏĤ", "iy el", "iye l", "Ġth iá»ĩu", "Ġthi á»ĩu", "ÏĪ ηÏĤ", "ÏĪη ÏĤ", "Ġд ве", "Ġдв е", "ĠEl ekt", "ĠEle kt", "à¸ģ à¸İ", "о ÑĢÑĥж", "оÑĢ Ñĥж", "оÑĢÑĥ ж", "a ÅŁÄ±", "aÅŁ ı", "è© ³ç´°", "Ġات ÙģاÙĤ", "Ġg ắn", "æ²Ĵ æľī", "ĠÙħطاÙĦ عÙĩ", "ÏĦ ιν", "ÏĦι ν", "Ġok res", "Ñ ľ", "ê° Ķëĭ¤", "Ðł оз", "å¾ĭ 宾", "ï¼ī ï¼Ī", "Ġìļ´ìĺģ ìŀIJ", "ãĤ« ãĥĨ", "l aÄį", "la Äį", "à¥ĩब स", "Ġo Äįi", "ĠoÄį i", "- б", "e lerden", "eler den", "elerde n", "k ových", "kov ých", "kový ch", "ĠÄ°z mir", "สม าà¸Ĭ", "lad atel", "Ġ æ»", "Ġæ »", "éĶĢ åĶ®", "ĠдоÑģлÑĸд женнÑı", "ĠлÑĸ каÑĢ", "ĠлÑĸка ÑĢ", "ĠлÑĸк аÑĢ", "Ġодна ко", "ĠV ác", "Ġ è«", "Ġè «", "éĢ² è¡Į", "以 å¤ĸ", "é³ ¥", "Ġ ÙĨج", "ĠÙĨ ج", "ĠbaÅŁ kan", "ĠbaÅŁka n", "ĠbaÅŁk an", "Ġopat ÅĻenÃŃ", "ا رش", "ار Ø´", "ض اÙģØ©", "ضا ÙģØ©", "ãĤ¹ ãĥ¬", "ή ν", "ÄĽ tÃŃ", "ÄĽt ÃŃ", "ว ย", "Ġرس ÙĪÙĦ", "ÅĻ ich", "ÅĻi ch", "ĠpÅĻ ih", "ĠpÅĻi h", "ÑĮ ми", "çĦ¶ èĢĮ", "Ġth ẳng", "l amaz", "la maz", "lam az", "lama z", "ÙĢ ÙĢÙĢ", "ÙĢÙĢ ÙĢ", "Ġì°¸ ìŬ", "ĠÙĨÙĪ شتÙĩ", "ĠÙĨÙĪØ´ تÙĩ", "ĠÑģÑĤ ек", "ãģ® ãģ¿", "ĠÙĪ اÙĦع", "ĠÙĪاÙĦ ع", "ĠÙĪا ÙĦع", "æķ ¢", "à¥Ģà¤Ĥ ,", "ÐŀÑģ нов", "им оÑģÑĤи", "имо ÑģÑĤи", "ĠÄĮesk á", "ĠÄĮes ká", "Ñĸ Ñĩний", "ÑĸÑĩ ний", "าม ารà¸ĸ", "ekk ür", "Âł h", "ι κη", "ικ η", "Ġتع ÛĮÛĮÙĨ", "к оÑģÑĤÑĸ", "ко ÑģÑĤÑĸ", "ĠMust afa", "Ġì¦ ī", "ãģ§ ãģĤãĤĬ", "ãģ§ãģĤ ãĤĬ", "å·¥ ä¸ļ", "ov ÃŃd", "ovÃŃ d", "ÐĿ о", "Ġس پس", "Ġسپ س", "Ú¯ÛĮ رد", "Ú¯ÛĮر د", "Ġп едагог", "Ġ کارÛĮ", "ĠÚ© ارÛĮ", "Ġکار ÛĮ", "ĠÑĪ ÑĤÑĥ", "ĠÑĪÑĤ Ñĥ", "æĮ Ĥ", "Ø¢ Ùħد", "Ø¢Ùħ د", "羣 æĺ¯", "Ġ ابت", "Ġا بت", "Ġاب ت", "Ġرئ ÛĮس", "Ġد ÛĮÙĨ", "ĠدÛĮ ÙĨ", "ÏĪ ε", "Ġse zon", "Ġsez on", "Ġ çĨ", "Ġç Ĩ", "स न", "ãĥ» ãĤ¢", "Ġ åħŃ", "Ġåħ Ń", "Ġ è±", "Ġè ±", "Ġìłľ 목", "ĠÙħ عد", "ĠÙħع د", "ĠÙģ ÙĤد", "ĠÙģÙĤ د", "éĤ Ĭ", "Ω Σ", "Ġ å¡", "Ġå ¡", "Ġob vyk", "ĠìĿ´ ëłĩê²Į", "ĠбоÑĢ оÑĤÑĮ", "Û² Û±", "Ġ á»ijng", "Ġá» ijng", "è¯ Ĺ", "Ġ ÄIJá»iji", "ĠÄIJ á»iji", "ĠбеÑĢез нÑı", "Ġs oÄŁ", "Ġso ÄŁ", "Ġ ï¾į", "Ġï¾ į", "ãĤĴ ãģ¤", "ãģĹ ãĤĥ", "еÑĢ еÑĩ", "еÑĢе Ñĩ", "ãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ãĢĢãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", "æĪ ª", "ĠاÙĦسعÙĪد ÙĬØ©", "ĠëĤ¨ ìŀIJ", "ĠAng iosper", "???????? ????????", "Ġpr ům", "Ġprů m", "ĠплоÑī ад", "Ġ ÏĦÏģα", "ĠÏĦ Ïģα", "д аÑİÑĤ", "да ÑİÑĤ", "Ġsı nav", "Ġsın av", "Ġm ặc", "æ°´ å¹³", "Ġви глÑı", "Ġвиг лÑı", "Ġn ást", "Ġná st", "Ġnás t", "ĠобÑĭ Ñĩ", "ĠìĿ´ìķ¼ 기", "ë¹ Ľ", "ĠB aÄŁ", "ĠBa ÄŁ", "ĠاÙĦØ« اÙĦØ«", "Ġser vis", "Ġserv is", "Ġservi s", "Ġ 룬", "ĠëŁ ¬", "ом ина", "ί θ", "Ġ Ấ", "ĠẠ¤", "ê²½ 기", "Ġì¡ ¸", "ี à¸ļ", "Ġà¤ĺ à¤Łà¤¨", "Ġ à¸Ļาà¸ĩ", "Ġà¸Ļ าà¸ĩ", ". Îł", "ìķ ķ", "r ün", "Ġon ların", "Ġonları n", "Ġonlar ın", "Ġзб ÑĸлÑĮÑĪ", "à¹ģ à¸Ł", "ĠìŬ 기", "Ġ ëĮĢíijľ", "ĠëĮĢ íijľ", "ĠÑģи лÑĥ", "ĠÑģил Ñĥ", "à¹Ĥ à¸Ľ", "Ġت ÙĤد", "ĠتÙĤ د", "ĠÐŁ ом", "ĠÐŁÐ¾ м", "ĠмаÑģ ла", "Ġ ìĺģìĥģ", "Ġìĺģ ìĥģ", "н ение", "не ние", "нен ие", "λα μβ", "ĠB yl", "ĠBy l", "æĬ µ", "æİ ª", "Ġκαθ ÏİÏĤ", "m ızı", "æĸ° çļĦ", "éĩį è¤ĩ", "ั à¸Ľ", "çŃ Ĩ", "ĠÑĤ ка", "ĠзнаÑĩ еннÑı", "Ġзна ÑĩеннÑı", "л аÑĤи", "ла ÑĤи", "лаÑĤ и", "Ġv liv", "Ġvl iv", "ÐIJ н", "ĠÚĨ اپ", "ĠпиÑĤ анÑĮ", ": ï½ī", "æķĻ æİĪ", "Ġì¹ľ 구", "Ġtr ao", "Ġtra o", "à¥įयà¤ķ त", "ุà¸Ħ à¸Ħล", "ĠرÙĪ Ø´ÙĨ", "ĠرÙĪØ´ ÙĨ", "ĠعÙĦÙĬ Ùĩا", "ĠعÙĦÙĬÙĩ ا", "ãĢģ ãģĦ", "ëħĦ ìĹIJ", "éĢ Ĩ", "Ġмаг аз", "ï¾ŀ ï¾ŀ", "Ġs ice", "Ġsi ce", "Ġsic e", "âĢĻ te", "âĢĻt e", "ĠاÙĦÙĦ غة", "á u", "èĩª 身", "Ġng Å©", "ĠÑģк ладÑĥ", "ĠÑģклад Ñĥ", "Ġz ru", "Ġtr uy", "Ġ ilan", "Ġi lan", "Ġil an", "ĠÙ¾ اÛĮÙĩ", "ĠپاÛĮ Ùĩ", "Ġپا ÛĮÙĩ", ": :::::::::::::", ":: ::::::::::::", ":::: ::::::::::", ":::::: ::::::::", ":::::::: ::::::", "::: :::::::::::", "::::: :::::::::", "::::::: :::::::", "::::::::: :::::", ":::::::::: ::::", "::::::::::: :::", ":::::::::::: ::", "::::::::::::: :", "f ak", "fa k", "ÑĤ еÑħ", "ÑĤе Ñħ", "Ġt aky", "Ġta ky", "Ġtak y", "Ġìĸ¸ ìĸ´", "ed enÃŃ", "eden ÃŃ", "ede nÃŃ", "Ġà¤ļ लत", "Ġà¤ļल त", "Ġë°° ìļ°", "Ġjmé no", "ĠÙĦ Ø£ÙĨ", "ĠÙĦØ£ ÙĨ", "α νά", "αν ά", "к ÑĥлÑĮ", "кÑĥ лÑĮ", "кÑĥл ÑĮ", "ĠØŃÙģ ظ", "ĠآزÙħ ÙĪÙĨ", "иÑĤелÑĮ нÑĭе", "ĠÐŀ лекÑģанд", "èį £", "Ġà¤ľà¤¬ à¤ķ", "Ġr odi", "Ġro di", "Ġrod i", "Ġبرخ ÙĪرد", "Ġhaf ta", "Ġhaft a", "λ ικά", "λι κά", "λικ ά", "à¸ķ à¸Ļ", "ĠбеÑĢ ег", "αν δ", "- С", "Ġprav idel", "ĠбÑĸ лÑı", "ĠбÑĸл Ñı", "íĴ į", "ĠпÑĢед ÑĥÑģ", "ĠмÑĥ ниÑĨип", "åĮĸ åѦ", "ĠتÙħ اس", "Ġà¤ī ल", "Ðĵ Ðŀ", "غ ر", "r adan", "ra dan", "rad an", "rada n", "ĠëĤĺ ìĺ¤", "è¨ Ĥ", "à¹Ģà¸ĺ à¸Ń", "âĢĮ سÛĮ", "âĢĮس ÛĮ", "ĠобÑıз аÑĤелÑĮно", "ĠобÑıзаÑĤелÑĮ но", "о ÑĤе", "оÑĤ е", "à¹Į à¸Ĭ", "çĶ¨ çļĦ", "Ġalt ın", "Ġaltı n", "ĠÑģоÑĤ ÑĢÑĥд", "Ñĸ нки", "Ñĸн ки", "озмож но", "Î IJ", "ë¹ Į", " ķ", "ĠÑĤ оÑĩно", "ĠÑĤо Ñĩно", "ĠÑĤоÑĩ но", "Ġj men", "Ġjm en", "اÙĦ ÛĮا", "اÙĦÛĮ ا", "èĪ į", "ch odu", "cho du", "chod u", "ê³ ¤", "ick ém", "ické m", "ĠÙħ ÙĪر", "ĠÙħÙĪ ر", "ãĥª ãĥ³ãĤ¯", "ãĥªãĥ³ ãĤ¯", "Ġa ÅŁam", "ĠaÅŁ am", "Ġ иÑĤ", "Ġи ÑĤ", "Ġन य", "Ġ μο", "Ġμ ο", "éķ ľ", "ĠبÙĨ ابر", "ĠبÙĨا بر", "Ġت خصص", "Ġส à¸ŀ", "ĠпÑĢоÑĦеÑģ Ñģи", "Ġp uan", "Ġpu an", "ĠÙģر ÙħاÙĨ", "ĠÙģرÙħ اÙĨ", "ëĮĢ íļĮ", "Ġп ÑıÑĤ", "ĠÙħ ÙĪب", "ĠÙħÙĪ ب", "ĠvÄĽ ku", "Ġ ëĥ", "Ġë ĥ", "ec ký", "eck ý", "ĠìĪĺ ëıĦ", "Ġth ao", "Ġtha o", "Ġk apat", "Ġka pat", "Ġkap at", "ĠзаÑħ воÑĢÑİ", "Ġ åħī", "Ġåħ ī", "ر اÙĨÛĮ", "راÙĨ ÛĮ", "را ÙĨÛĮ", "éĢł æĪIJ", "ĠÑģв Ñĸй", "ĠдоÑģ иÑĤÑĮ", "Ġmil yar", "Ġener ji", "Ġenerj i", "Ġк ип", "Ġки п", "Ġì¢ĭ ìķĦ", "Ġب Ø¥", "ê²Į ìĭľ", "ĠL Æ°u", "ĠÙħÙĨظ ÙĪر", "Ïī μά", "ζ ί", "ım da", "Ġ ìĿ´ë¥¼", "ĠìĿ´ 를", "๠Ĵ", "Ġв важ", "Ġвв аж", "Ġga zet", "Ġgaze t", "Ġgaz et", "à¥įत न", "à¹īำ หà¸Ļ", "åľŁ åľ°", "Ġसद स", "ت بة", "تب Ø©", "Ġpo ÄįÃŃta", "Ġìĭľ ìĬ¤íħľ", "ร à¸Ħ", "Ġed ecek", "ĠتØŃ ÙĦÛĮÙĦ", "æĮī çħ§", "åĿ ª", "Ġê·¸ ê°Ģ", "ت ÙĩÙħ", "تÙĩ Ùħ", "Ġб аж", "Ġба ж", "ا Ùģع", "اÙģ ع", "éĢļ 常", "ĠТ и", "γ νÏī", "ì¹ Ļ", "Ġznam ená", "ï¼¼ ï¼¼", "α ÏĢÏĮ", "åĨĻ 羣", "Ġ ï¼¼Ċ", "Ġï¼¼ Ċ", "åĬł å·¥", "èĤ¡ä»½ æľīéĻIJåħ¬åı¸", "Ñı ÑĤий", "ÑıÑĤ ий", "ÑıÑĤи й", "Ġh âl", "Ġç ab", "Ġça b", "ĠØŃ Ø§Ø¶Ø±", "P ÅĻ", "ĠاÙĦ تÙĤ", "ĠاÙĦت ÙĤ", "ξ ηÏĤ", "ξη ÏĤ", "б е", "Ġkh ám", "Ġkhá m", "Ġ âĮĴ", "Ġâ ĮĴ", "Ġ éķ¿", "Ġéķ ¿", "Ġ âĢ¦Ċ", "ĠâĢ¦ Ċ", "द म", "ĠSt udi", "ĠStud i", "Ġk odu", "Ġko du", "Ġkod u", "Ġkom unik", "Ġkomun ik", "Ġkat kı", "n ete", "ne te", "net e", "Ġr apor", "Ġrap or", "Ġra por", "éĨ ´", "ãĤī ãģĽ", "ĠнеÑģк олÑĮ", "Ġhá»į p", "ï¿£ ￣￣", "￣￣ ï¿£", "º ¼", "è£ Ĥ", "ед ÑĮ", "Ġا ÙĦاØŃ", "ĠاÙĦ اØŃ", "l adık", "la dık", "lad ık", "ladı k", "Ġfot oÄŁraf", "æĹ¥ ãģ®", "ĠØŃ Ø§ÙĦت", "ĠØŃاÙĦ ت", "ĠØ« ÙĦاث", "а ÑĤов", "аÑĤ ов", "аÑĤо в", "ey se", "Ġê°IJ ìĤ¬", "á že", "áž e", "Ġн ада", "Ġна да", "Ġнад а", "Ġà¤ķ हन", "Ġà¤ķह न", "Ġ ãĥĿ", "Ġãĥ Ŀ", "ãģ« ãģĤãĤĭ", "ãģ«ãģª ãģ£ãģ¦", "ÙĪ دÙĩ", "ÙĪد Ùĩ", "Ġpo Å¡k", "太 éĺ³åŁİ", "ç»ı éªĮ", "æĴŃ æĶ¾", "Ġma jet", "Ġmaj et", "Ñħ о", "ĠÑĤ еÑģÑĤ", "ĠÑĤе ÑģÑĤ", "ï¼ı Ċ", "Ïĥε ÏĦε", "ĠТ омÑĥ", "ĠТо мÑĥ", "ĠТом Ñĥ", "Ùİ ØŃ", "ĠìŀĪ ìľ¼ë©°", "Ġза знаÑĩ", "éļ IJ", "Ġд ÑĸÑĹ", "ĠдÑĸ ÑĹ", "к ÑĤив", "кÑĤ ив", "кÑĤи в", "ÙĪ ÙģÙĬ", "ÙĪÙģ ÙĬ", "Ġt á»Ŀ", "Ġtá» Ŀ", "à¸¹à¸Ľ à¹ģà¸ļà¸ļ", "ĠÑĢ едак", "ĠÑĢед ак", "Ġa teÅŁ", "Ġat eÅŁ", "Ġate ÅŁ", "Ġkh iá»ĥn", "Ġkhi á»ĥn", "ü ny", "ün y", "ี ยà¸ģ", "ีย à¸ģ", "ĠÑĩа Ñīе", "Ġt uy", "Ġtu y", "γ Ïīν", "γÏī ν", "ร à¸Ńà¸ļ", "Ġtr ùng", "à¹ģà¸Ĺ à¸Ļ", "Ġα κÏĮ", "Ġακ ÏĮ", "ĠÐĴеÑĢ Ñħов", "à¹ĥ à¸Ļส", "à¹ĥà¸Ļ ส", "ãĢģ ä½ķ", "åĩ ¦", "Ġ ç»ı", "Ġç» ı", "æ¨ ĵ", "اÙĨÚ¯ ÙĦÛĮسÛĮ", "Ġ lepÅ¡ÃŃ", "Ġlep Å¡ÃŃ", "Ġ å¼Ģå§ĭ", "Ġå¼Ģ å§ĭ", "éĻ º", "ĠÑĩ еÑĤÑĭ", "ĠÑĩеÑĤ Ñĭ", "ĠС еÑĢ", "оÑİ Ð·", "Ġx ung", "Ġxu ng", "åĵģ çīĮ", "Ġìĥģ íĥľ", "ĠÙĨ صب", "ĠÙĨص ب", "ĠÑĩ омÑĥ", "Ġتر Ú©ÛĮ", "Ġترک ÛĮ", "- ли", "o vÃŃ", "ov ÃŃ", "Ġا ÙĨج", "ĠاÙĨ ج", "çµ ¡", "Ġت ÙĪص", "ĠتÙĪ ص", "Ġ ì¿ł", "Ġì ¿ł", "Ġvar sa", "Ġva rsa", "Ġvars a", "ĠÑĢаз ÑĢабоÑĤ", "à¸Ĥ à¸Ńà¸ĩà¸Ħ", "à¸Ĥà¸Ńà¸ĩ à¸Ħ", "éŃ Ĥ", "Ġà¤Ĭ पर", "æĿ¥ 说", "ĠÑĨенÑĤ ÑĢалÑĮ", "ĠÑĨенÑĤÑĢ алÑĮ", "ĠÑĨенÑĤÑĢа лÑĮ", "ĠTak ım", "Ġon lar", "Ġسر عت", "好 åĥı", "Ġbu á»ķi", "ĠÐij ел", "Âł c", "Ø£ ت", "à¸Ĥ à¸ĵะ", "ãģ« åĩº", "Ġ+ **************", "ÏĦη κε", "ا جر", "اج ر", "Ġ âĢ²", "ĠâĢ ²", "ãĥ¼ ãĥ¬", "ãĥ¼ãĥ ¬", "é¥ Ń", "Ġج ÙĦس", "ĠجÙĦ س", "Ġب ستÙĩ", "Ġبس تÙĩ", "ว าà¸ĩ", "Ġ βά", "Ġβ ά", "Ġа меÑĢикан", "ĠPr emi", "ĠPre mi", "ĠPrem i", "m ae", "ma e", "ĠÑģ ÑĢеди", "ĠÑģÑĢед и", "Ạł", "Ġв ÑĢед", "ãĢĤ èĢĮ", "åĴ ²", "Ġê³µ ê°ľ", "èĤ ¥", "з виÑĩай", "Ġpro cent", "Ġproc ent", "и лоÑģÑĮ", "ил оÑģÑĮ", "ило ÑģÑĮ", "श न", "é łģ", "éł ģ", "е кÑĤи", "ек ÑĤи", "екÑĤ и", "د اشت", "دا شت", "íķĻ íļĮ", "ãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢ", "ĠÙħد ÙĬÙĨØ©", "िल न", "Ġ èĹ", "Ġè Ĺ", "м иÑĢ", "ми ÑĢ", "Ġн оÑĢ", "Ġно ÑĢ", "Ġ íķĺì§Ģ", "Ġíķĺ ì§Ģ", "в еÑī", "ве Ñī", "nÄĽ m", "е ÑĢами", "еÑĢ ами", "еÑĢа ми", "Ġpra cov", "Ġprac ov", "ĠبÙĬ اÙĨات", "ĠÏĥ Ïįν", "ĠÏĥÏį ν", "Ġج Ø°", "ãģĦ ãģ§", "ĠB ÃŃ", "è± Ĩ", "Ġh mot", "Ġhm ot", "il eceÄŁi", "ilece ÄŁi", "Ġت اث", "Ġتا Ø«", "è´ ´", "Ġ ê¸ī", "Ġê¸ ī", "Ġm ysl", "Ġmy sl", "Ġmys l", "ĠìĿ´ íķ´", "Ġ기 ëĬ¥", "ĠТ ам", "ĠТа м", "ĠнаÑģ елениÑı", "ĠM ez", "ĠMe z", "Ġ모 르", "íĻĶ 를", "ĠÙĨسخ Ùĩ", "ĠتÙĦ ÙĪÛĮزÛĮ", "ĠÄįerv na", "Æ°á» ¡ng", "ص ØŃ", "ĠÑĤ ÑĢен", "ĠÑĤÑĢ ен", "Õ ¡", "Ġce lou", "Ġcel ou", "Å© i", "ìĹĨ ìĿ´", "nÃŃ ku", "nÃŃk u", "Ġprogram u", "à¥į पन", "à¥įप न", "Ġп ÑĢеж", "ĠпÑĢ еж", "ĠпÑĢе ж", "ا رب", "ار ب", "æľŁ éĸĵ", "Ġ μά", "Ġμ ά", "ëįĶ ëĭĪ", "ụ n", "ĠпеÑĢ еÑģÑĤ", "ĠпеÑĢе ÑģÑĤ", "ĠпеÑĢеÑģ ÑĤ", "对 äºİ", "è¿IJ è¡Į", "ĠÑĤ ан", "ĠÑĤа н", "Ġ ìĤ¬ìĿ´íĬ¸", "ĠìĤ¬ ìĿ´íĬ¸", "ĠìĤ¬ìĿ´ íĬ¸", "ĠQu ảng", "ĠQuản g", "Ġst ojÃŃ", "Ġsto jÃŃ", "ãĥŃ ãĥ¼", "Ú¯ ار", "Ġе неÑĢг", "Ġkter ým", "Ġkterý m", "ĠпÑĢи мÑĸ", "ĠпÑĢим Ñĸ", "ĠкаÑĢÑĤ и", "ĠкаÑĢ ÑĤи", "Ġz engin", "Ġzen gin", "ï¼Į åĨį", "Ġت رب", "Ġتر ب", "ĠÑĨенÑĤ ÑĢ", "ĠÑĨен ÑĤÑĢ", "ĠsaÄŁ lamak", "ĠsaÄŁlam ak", "ëĭ Ŀ", "ãģ® åŃIJ", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "Ġs Æ¡n", "ĠsÆ¡ n", "z ı", "ÑĤ аки", "ÑĤа ки", "ÑĤак и", "ÄĽ stÃŃ", "ÄĽst ÃŃ", "Ġ à¥", "Ġà ¥", "é ®", "åŁ¹ è®Ń", "Ġ ìĶ¨", "ĠìĶ ¨", "Ġbel ki", "ĠìĿ´ 벤íĬ¸", "ëĶĶ ìĸ´", "Ġs Ãłn", "ни кам", "ник ам", "ника м", "a lim", "al im", "ali m", "対 å¿ľ", "ĠS á»±", "éģĵ è·¯", "é«ĺ æ¸ħ", "Ġd õi", "ĠÙĦ ÙĢ", "Ġ èĤ¡", "ĠèĤ ¡", "ν ι", "âĢŀ J", "' nde", "'n de", "Îij Îĵ", "ãģ¨ ãģªãĤĭ", "ãģ¨ãģª ãĤĭ", "çĪ ¸", "ع ÙĦÛĮ", "عÙĦ ÛĮ", "Ïģι ÏĥÏĦ", "ÏģιÏĥ ÏĦ", "Ġe ÄŁit", "ĠeÄŁ it", "Ġзов нÑĸÑĪ", "Ġп ÑĢим", "ĠпÑĢ им", "ĠпÑĢи м", "س Ùħبر", "سÙħ بر", "ĠmÄĽst ÄĽ", "ĠÏĢεÏģι ÏĥÏĥÏĮÏĦε", "ÐIJ Ðł", "æĦŁ åĪ°", "Ġ문 ìĦľ", "ãģĭ ãĤĭ", "ÙĤÙĬ ÙĤØ©", "ÙĤÙĬÙĤ Ø©", "Ġв ÑĤÑĢа", "ĠвÑĤ ÑĢа", "Ġ à¸Ńำ", "Ġà¸Ń ำ", "Ñģ кÑĥÑİ", "Ñģк ÑĥÑİ", "د ÙĩاÛĮ", "دÙĩ اÛĮ", "ĠÄ° st", "ĠÄ°s t", "ĠÐĹ Ð°Ð²", "ĠÐĹа в", "Ġ éĥ½", "Ġé ĥ½", "Ġéĥ ½", "ÑĪ ем", "ÑĪе м", "Ġе ÑīÑij", "ĠÐľÐ¸Ñħ ай", "ĠÑĥпÑĢав лениÑı", "л еннÑĭе", "лен нÑĭе", "ĠzaÄį al", "æ¡ Į", "Ġп Ñĸз", "л ÑıÑĤÑĮÑģÑı", "лÑı ÑĤÑĮÑģÑı", "лÑıÑĤÑĮ ÑģÑı", "Ġ ìŀIJë£Į", "ĠìŀIJ ë£Į", "ãĢĢ ãĢĢĠ", "ãĢĢãĢĢ Ġ", "ĠK ral", "ĠKr al", "ĠKra l", "èĪ ī", "Ġà¤Ń व", "Ġ Ø®Ùħ", "ĠØ® Ùħ", "Ġа кадем", "Ġ isten", "Ġis ten", "Ġi sten", "Ġist en", "ĠиÑģ кÑĥÑģ", "ĠиÑģк ÑĥÑģ", "ĠعÙĨد Ùħا", "Ġا ÙĦاÙħ", "ĠاÙĦ اÙħ", "is mus", "ism us", "ismu s", "Ġayr ıntı", "Ġ Що", "ĠЩ о", "ĠÙĩ ÙĪØ´", "ĠÙĩÙĪ Ø´", "د ÙĪاج", "دÙĪ اج", "л аж", "ла ж", "ĠÚ©ÙĨ ار", "Âł R", "æĢ§ çļĦ", "Ñģ Ñĸм", "ÑģÑĸ м", "ĠM üz", "ĠMü z", "ÑĢ овиÑĩ", "ÑĢов иÑĩ", "ÑĢо виÑĩ", "Ġ Ω", "ĠÎ ©", "Ġìĸ´ ëĶĶ", "س ÙħØ©", "سÙħ Ø©", "Ġ ÑĢÑı", "ĠÑĢ Ñı", "Ġt Æ°Æ¡i", "ĠtÆ° Æ¡i", "ĠÑĢаÑģ Ñħод", "åı° çģ£", "ĠاÙĦ ÙĪÙĤت", "ĠاÙĦÙĪ ÙĤت", "بر اÛĮ", "ĠзÑĢоб иÑĤи", "Ġб ÑĥÑĢ", "ĠбÑĥ ÑĢ", "ĠÄįin nosti", "ĠÄįinnost i", "Ġص اØŃ", "ĠصÙĨ عت", "ĠصÙĨع ت", "Ġ Ø·ÙĦ", "ĠØ· ÙĦ", "ξ Ïį", "ĠtisÃŃ c", "ĠFr ansa", "ĠFran sa", "ì¦ ĺ", "è» ½", "Ñ ĺ", "ÏĮÏĦη ÏĦαÏĤ", "ÏĮÏĦηÏĦα ÏĤ", "ĠM illet", "ĠMill et", "ĠMil let", "ãĢĢ ãĥ¾", "ĠпÑĢ ием", "ĠпÑĢи ем", "ĠترجÙħ Ùĩ", "Ġس ÙĪد", "ĠسÙĪ د", "ĠsouÄįást ÃŃ", "ÐĶ о", "Ġtr ụ", "足 çIJĥ", "à¸Ľ à¸ģ", "Ġu stanov", "ÎŁ ÎĻ", "ÎŁÎ Ļ", "Ðŀ н", "Ġн еж", "Ġне ж", "к ог", "ко г", "ä¸Ģ çĤ¹", "Ġد ÙĪراÙĨ", "ĠدÙĪ راÙĨ", "ĠدÙĪر اÙĨ", "å½± éŁ¿", "el idir", "eli dir", "âĢŀ N", "es iyle", "esi yle", "ÑĢем енно", "ÑĢе менно", "Ġilet iÅŁim", "ม à¹Ģà¸ķ", "以 åīį", "ãĥĭ ãĥ¼", "鼻 話", "à¹Ĥ à¸ŀ", "ov ky", "Ġза мÑĸ", "Ġзам Ñĸ", "Ġव à¤ķ", " Ļ", "ĠвÑĸй ни", "Ġol madıģı", "Ġolm adıģı", "Ġolmadı ģı", "Ġolma dıģı", "æ¢ ģ", "ĠТ еп", "ĠТе п", "nÄĽ te", "nÄĽt e", "èħ ķ", "ìĤ¬ ëĬĶ", "m amak", "ma mak", "mam ak", "Ġc iz", "Ġci z", "æ£ Ĵ", "Ġ ï¼ı:", "Ġï¼ı :", "éģĭ åĭķ", "ĠÙĩ ÙĨا", "ĠÙĩÙĨ ا", "Ġ ê°ij", "Ġê° ij", "ĠÙĩÙĨÚ¯ اÙħ", "ĠuÄŁ ra", "å½ ¦", "Ġob jekt", "Ġobj ekt", "ãģ¨ ãģĻãĤĭ", "åĽ½ åĨħ", "ĠдеÑĢжав и", "ĠдеÑĢж ави", "Ġ èĮ", "Ġè Į", "Ġulus lararası", "Ù £", "Ġmut lak", "Ġз обов", "Ġ γεν", "Ġγ εν", "Ġγε ν", "à¹Ħà¸Ł à¸Ł", "Ġözg ür", "íĦ ¸", "Ġвипад кÑĥ", "Ġà¤ķ ब", "ĠاÙĦ خط", "ĠاÙĦØ® Ø·", "θη καν", "ï¼Į æĬĬ", "ÑıÑĤ ÑĤÑı", "Ġolmadı ģını", "Ġolma dıģını", "Âłk W", "ĠnÄĽkter ých", "ãĥĩ ãĥ«", "æ¤į çī©", "μι λοÏĤ", "ÐIJÑĢ ÑħÑĸв", "ĠТ о", "èĸ ¬", "ÑģÑĤв иÑı", "Ġ Ø®ÙĪاست", "ĠØ®ÙĪ است", "олог ÑĸÑĹ", "ÙĪ اÙĩد", "ÙĪاÙĩ د", "ÙĪا Ùĩد", "Ġ нак", "Ġн ак", "Ġна к", "ĠкоÑĤоÑĢ ÑĥÑİ", "Ġद à¤ķ", "âĢŀ M", "λ ια", "λι α", "æŃ ²", "第 åĽĽ", "à¤¾à¤ľ स", "Ġ( «", "Ġth ẻ", "､ Ċ", "ç£ ģ", "Ġ ÙĦÙĤ", "ĠÙĦ ÙĤ", "Ġ ìķĶ", "Ġìķ Ķ", "Ġн ового", "Ġнов ого", "ĠìķĦ 주", "Ġ ëIJĺìĸ´", "ĠëIJĺ ìĸ´", "Ġo lun", "Ġol un", "à ¾", "Ġkar iy", "Ġkari y", "ĠØŃ Ø³Ø¨", "ĠØŃس ب", "ĠìĿĺ 미", ". Ðľ", "Ġoz naÄį", "ÙĦ سÙĦ", "ÙĦس ÙĦ", "ĠÐĴ ид", "ĠÐĴи д", "ë¡ľ ëĤĺ", "à¥įà¤Ł म", "í ľ´", "Ġbilg isayar", "ìĿ¸ ì§Ģ", "Ġв ов", "Ġво в", "nict vÃŃm", "nictvÃŃ m", "า à¸Ńย", "าà¸Ń ย", "Ġشخص ÛĮ", "п Ñĸон", "æľ¬ å½ĵ", "Ġب ÙĢ", "ĠмаÑģ ло", "ĠPh át", "Ġ ба", "Ġб а", "алÑĮ номÑĥ", "алÑĮно мÑĥ", "алÑĮном Ñĥ", "社 åĮº", "Ġ Ò", ": ::|", ":: :|", "::: |", "ê ´", "Ġ ä¸ĥ", "Ġä¸ ĥ", "ĠÙĪ اÙĦد", "ĠÙĪاÙĦ د", "ни ке", "ник е", "à¸Ń ลล", "à¸Ńล ล", "Ġyer leÅŁ", "Ġkom bin", "Ġkomb in", "u Å¡", "Ġо ÑĤÑĢи", "ĠоÑĤ ÑĢи", "ä¹ Į", "iÅŁ ti", "Ġs óng", "Ġsó ng", "λ ηÏĤ", "λη ÏĤ", "Ġк ÑĥÑĢÑģ", "ĠкÑĥÑĢ Ñģ", "à¹Ī าà¸Ħ", "à¹Īา à¸Ħ", "Ġ ÙĬس", "ĠÙĬ س", "Ġ داÙħ", "Ġد اÙħ", "çĴ° å¢ĥ", "Ñĩ енко", "Ñĩен ко", "ãĢį ãģ®", "ĠmÃŃ sta", "ĠmÃŃst a", "ĠÑĦ оÑĤ", "ĠpÅĻÃŃ zn", "ĠÑĢ аза", "ĠÑĢаз а", "ĠÑĢа за", "ç´ «", "lá da", "lád a", "ĠÑģпеÑĨи алиÑģÑĤ", "ĠبÛĮ ÙħارÛĮ", "ĠبÛĮÙħ ارÛĮ", "ĠبÛĮÙħار ÛĮ", "Ġëĵ £", "çĭ Ĺ", "ÙĪ ÙĪ", "ан ÑĸÑĤ", "анÑĸ ÑĤ", "ĠدÙĨ باÙĦ", "ĠÙħجÙħÙĪع Ø©", "ÃŃ na", "ÃŃn a", "ĠH alk", "ĠHa lk", "ĠHal k", "á jem", "áj em", "enÃŃ ze", "Ġim z", "« ng", "Ġ ÎķÎł", "ĠÎķ Îł", "ĠÙħ Ùĩد", "ĠÙħÙĩ د", "ìľĦìĽIJ íļĮ", "Ġìľł íĺķ", "ाप स", "Ġje ž", "ан Ñĸз", "анÑĸ з", "иÑĤ ай", "иÑĤа й", "á¿ ĸ", "ir ler", "irl er", "기 ê°Ħ", "Ġ воÑĢ", "Ġв оÑĢ", "Ġво ÑĢ", "Ġ Ïİ", "ĠÏ İ", "Ġpo zn", "Ġpoz n", "Ġ ساÙĨ", "Ġس اÙĨ", "å ¯¿", "å¯ ¿", "æĸ¯ çī¹", "Ġtu rist", "Ġtur ist", "ĠìŀIJ ìľł", "à¥Ģ à¤ĸ", "μ με", "μμ ε", "an sı", "ans ı", "ìĨĮ ëħĦ", "Ġted avi", "Ġtedav i", "Ñĩ еÑģÑĤва", "Ñĩе ÑģÑĤва", "å£ ĵ", "о ве", "ов е", "ï¼Į çľĭ", "ĠпоÑģл Ñĥг", "ĠпоÑģ лÑĥг", "ĠÑĤÑĢ анÑģ", "ĠÑĤÑĢан Ñģ", "Ġz áz", "Ġzá z", "æĪ ´", "Ġм она", "Ġмон а", "Ġмо на", "ิ à¹Ģà¸Ħราะห", "ĠÙĨ ÛĮÙħ", "ĠÙĨÛĮ Ùħ", "ĠìĤ¬ëŀĮ ìĿ´", "a hat", "ah at", "aha t", "Ïħ κ", "ĠоÑĤ каз", "ĠоÑĤк аз", "ĠÐĴолод ими", "ĠС к", "िà¤ķ त", "å¦ ĸ", "Ġëĭ¤ìļ´ ë¡ľëĵľ", "ìĺģ ìĥģ", "Ġन à¤Ī", "c ete", "ce te", "ĠгÑĢи б", "ĠгÑĢ иб", "ece ÄŁini", "eceÄŁi ni", "Ġç oÄŁu", "ĠçoÄŁ u", "ĠмаÑĤеÑĢи ала", "ĠмаÑĤеÑĢиал а", "ứ t", "Ġz aten", "Ġza ten", "Ġzat en", "ĠF RA", "ĠFR A", "ĠBir liÄŁi", "Ġs itesi", "Ġsit esi", "Ġsite si", "Ġsites i", "Ġ åĶ", "Ġå Ķ", "ĠÐĴ ол", "ĠÐĴо л", "Âł PS", "ÂłP S", "ा लत", "ाल त", "Ġ баÑĩ", "Ġб аÑĩ", "Ġба Ñĩ", "алÑĸ заÑĨÑĸÑĹ", "алÑĸз аÑĨÑĸÑĹ", "ĠS lov", "ĠSl ov", "ĠSlo v", "ç³ ĸ", "ĠговоÑĢ иÑĤ", "Ġв вед", "Ġвв ед", "ุà¸ķ à¸ļà¸Ńล", "ãģĨ ãģ¡", "Ġyap tık", "Ġyaptı k", "Ġìłķ ì¹ĺ", "ê°ľ 를", "à¥Ī सल", "à¥Īस ल", "ج ÙĬÙĦ", "جÙĬ ÙĦ", "ĠзаÑģÑĤоÑģ ов", "è¿ «", "ĠKur ul", "ĠNas ıl", "ĠнапÑĢÑı м", "Ġ ä½į", "à¹Į à¸ļ", "Ġ éģĵ", "Ġéģ ĵ", "Ġни же", "Ġниж е", "Ġк оÑģÑĤ", "Ġко ÑģÑĤ", "ĠкоÑģ ÑĤ", "ظ Ùĩر", "ظÙĩ ر", "Т а", "ì§ Ŀ", "Ġön ünde", "ж Ñĸ", "Ġاجر اÛĮ", "Ġاجرا ÛĮ", "ĠоÑĢган Ñĸв", "ĠоÑĢганÑĸ в", "v ise", "vis e", "vi se", "Ġ ìĿĦ", "ĠìĿ Ħ", "à¸ķ รà¸ĩ", "à¸ķร à¸ĩ", "Ú©ÙĨ ÙĪÙĨ", "Ġdlou ho", "Ġdlouh o", "Ðŀ ÐĿ", "Ġ ìľ¡", "Ġìľ ¡", "缮 æłĩ", "ë¯ Ģë¡ľ", "ï¼ıï¼ıï¼ıï¼ı ï¼ıï¼ıï¼ıï¼ı", "ĠпоÑĩ емÑĥ", "æķħ äºĭ", "ÑĤ еÑģÑĮ", "ÑĤе ÑģÑĮ", "ĠÙĤ ÙĦب", "ĠÙĤÙĦ ب", "Ġت جÙĩ", "Ġتج Ùĩ", "i lendir", "il endir", "ilen dir", "Ġи гÑĢа", "Ġиг ÑĢа", "ĠÐĶ он", "ĠÐĶо н", "ĠpÅĻÃŃ jem", "è¦ Ĩ", "С п", "- ни", "on se", "ons e", "и ной", "ин ой", "о Ñĩного", "оÑĩ ного", "оÑĩно го", "ا ساÙĨ", "اس اÙĨ", "ĠполÑĥÑĩ иÑĤÑĮ", "ĠполÑĥ ÑĩиÑĤÑĮ", "ÑĤ ап", "ÑĤа п", "ĠL ý", "ĠÃĤ u", "Ġh üc", "e bek", "eb ek", "ebe k", "ĠY ayın", "æĹ ĭ", "ัà¸Ļ à¸Ĺร", "ัà¸Ļà¸Ĺ ร", "Ġвикон ав", "Ġвико нав", "Ġs ông", "à¥ģ à¤ľ", "à¥ģठľ", "ĠÐĹ Ð°Ð³", "ĠÐĹа г", "¤ ëĭ¤", "Ġc Å©", "ĠÚ¯ رÙħ", "Ġگر Ùħ", "ä¼ ı", "ãģ« ãģĻãĤĭ", "- Ф", "Ġ ÙĤÙħ", "ĠÙĤ Ùħ", "Ġo lacaÄŁ", "Ġol acaÄŁ", "æĿ¥ äºĨ", "æĭĽ èģĺ", "ĠÐĿаÑģ еленнÑı", "Ġ ìĺģìĸ´", "Ġìĺģ ìĸ´", "Ġ æѤ", "ĠæŃ ¤", "Ġب دÙĨ", "Ġبد ÙĨ", "Û² Û¸", "оÑĢ аÑĤив", "оÑĢа ÑĤив", "ï¼ ³", "Ġneby lo", "Ġnebyl o", "ĠÑĥ ÑĩиÑĤ", "ĠÑĥÑĩ иÑĤ", "æĿ ľ", "Ġд анÑĸ", "Ġда нÑĸ", "Ġдан Ñĸ", "Ġsp otÅĻeb", "Ġspot ÅĻeb", "ãĥ¼ ãĥĨãĤ£", "ãĥ¼ãĥĨ ãĤ£", "ен нÑĥÑİ", "ê¹Į ìļĶ", "v em", "ve m", "P ÅĻÃŃ", "PÅĻ ÃŃ", "Ġy andan", "Ġyan dan", "é¼ ĵ", "Ġدست ÙĪر", "Ġدس تÙĪر", "Ġhaf if", "h ů", "Ġv áž", "Ġvá ž", "ĠìķĦ ì§ģ", "Ùı ر", "Ġ ла", "Ġл а", "ëł ī", "า à¸Ľà¸£à¸°", "à¸²à¸Ľ ระ", "lık lar", "lıkla r", "ĠÑģÑĤанд аÑĢÑĤ", "à¸Ń à¹ĥห", "å¥ ´", "ĠоÑĤ п", "âĪ ł", "ãĥ¼ ãĥĢ", "ãĥ¼ãĥ Ģ", "ch áze", "cház e", "Ġê·¸ ëłĩê²Į", "Ġê·¸ëłĩ ê²Į", "os tel", "ost el", "oste l", "Ġгал Ñĥз", "â k", "еÑĨ ÑĤ", "ëŀij ìĬ¤", "ĠÄį ist", "ĠÄįi st", "ÑĢ ана", "ÑĢа на", "ÑĢан а", "Ġv ững", "Ġvữ ng", "Ġs eni", "Ġse ni", "Ġsen i", "Ġg óc", "Ġgó c", "ÏĨ ÏĮ", "á nu", "án u", "Ġ öt", "Ġö t", "Ġs óc", "Ġsó c", "ãģĦ ãģ®", "ĠÑģк лада", "ĠÑģклад а", "ÐIJÑĢÑħÑĸв овано", "ĠìĿ´ ë²Ī", "ãĤ¹ ãģ®", "il ebilir", "ile bilir", "ï½Ģ ãĥ½", "ีย à¸į", "Ġκα ὶ", "Ġë ¯¿", "æĽ´ å¤ļ", "ıs ının", "ısını n", "ĠGi ám", "ĠGiá m", "æŃ£ å¼ı", "Ïĥ μÏĮ", "Ïĥμ ÏĮ", "Ġarch it", "Ġarc hit", "Ġ ï½²", "Ġï½ ²", "Ñĩ аÑİÑĤÑģÑı", "Ñĩа ÑİÑĤÑģÑı", "ÑĩаÑİÑĤ ÑģÑı", "ë²Ħ ì§Ģ", "ãĤ¤ ãĥ¤", "é«ĺ æł¡", "è¨ ³", "ĠÙħ ÛĮÚ©", "ĠÙħÛĮ Ú©", "Ġ æĥħ", "Ġæĥ ħ", "Ġ pha", "Ġp ha", "Ġph a", "太 éĥİ", "à¸ŀระ ราà¸Ĭ", "ÙĤ ÙĬØ©", "ÙĤÙĬ Ø©", "ĠÑĥ лÑĥÑĩ", "ĠÑĥл ÑĥÑĩ", "ÑģÑĤв ÑĥеÑĤ", "ÑģÑĤвÑĥ еÑĤ", "Ġk eÅŁ", "Ġke ÅŁ", "é«ĺ çŃī", "Ġs Ỽm", "Ïģ κε", "Ïģκ ε", "μ οÏģ", "μο Ïģ", "Ġzá stup", "o zÃŃ", "oz ÃŃ", "Ġm ili", "Ġmil i", "Ġmi li", "Ġмог ли", "Ġз ÑĢозÑĥм", "Ġباش ÛĮد", "Ġak ci", "Ġд ÑĢа", "ĠдÑĢ а", "Ġα Ïģι", "ĠαÏģ ι", "ãģĭ ãĤīãģ®", "ãģĭãĤī ãģ®", "å¯ Ĵ", "ĠZ aman", "ĠZa man", "ĠZam an", "ĠÑĸ де", "ĠÑĸд е", "Ġ ãĢĢĠ", "ĠãĢĢ Ġ", "Ġk lu", "Ġkl u", "ak lı", "à¥ĩ à¤ļ", "à¥ĩठļ", "ĠÑģвоб од", "س اÙħ", "Ġ ов", "Ġо в", "Ġu byt", "Ġub yt", "éĩĩ çĶ¨", "Ġdavran Ä±ÅŁ", "ĠnabÃŃ zÃŃ", "ĠÐij Ñĥд", "Ġ Ïī", "ĠÏ ī", "ĠاÙĦ رØŃ", "ĠاÙĦر ØŃ", "ั à¸ķà¸Ļ", "ัà¸ķ à¸Ļ", "и ме", "им е", "Ġت ÙĦÙĥ", "ĠتÙĦ Ùĥ", "ت Ùħع", "تÙħ ع", "Ġад миниÑģÑĤÑĢа", "Ġzor unda", "Ġzorun da", "ĠÙĨ سبة", "ĠÙĨسب Ø©", "ĠÙĨس بة", "ĠصÙĨع تÛĮ", "ĠصÙĨعت ÛĮ", "ĠÑĦÑĥн да", "éı ¡", "Ġpo tom", "Ġpot om", "Ġп ÑĢеÑģÑĤ", "ĠпÑĢ еÑģÑĤ", "ĠпÑĢе ÑģÑĤ", "ĠпÑĢеÑģ ÑĤ", "Ġsı rada", "Ġsır ada", "Ġsıra da", "Ġ ayar", "Ġa yar", "Ġay ar", "ا ÙĤÙĦ", "اÙĤ ÙĦ", "æº ª", "ĠØ¢ÙĤ اÛĮ", "ĠпеÑĢе Ñħод", "ĠпÑĢакÑĤи ÑĩеÑģки", "é» ĥ", "ĠÑĥ Ñħод", "ĠÑĥÑħ од", "ĠÙħ تÙģ", "ĠÙħت Ùģ", "Ġsiyas i", "Ġпо ÑĤен", "ĠпоÑĤ ен", "Ùİ Ùģ", "ĠÐĽ Ñĥ", "ĠконÑĤÑĢ олÑĮ", "ĠÑģказ аÑĤÑĮ", "à¥Ģ à¤ķरण", "à¥Ģà¤ķ रण", "åħ¨ çIJĥ", "Û² Û¶", "Ġt oto", "Ġto to", "Ġtot o", "Ġ ÙĪد", "ĠÙĪ د", "ãĤ¿ãĤ¤ ãĥĹ", "åľ į", "å¼ķ çĶ¨", "ï¼ £", "èĬ ¸", "ä»ĭ ç»į", "ĠÑĤеÑĢÑĢиÑĤоÑĢ ии", "æĹ¥ ãģ«", "m ÃŃt", "mÃŃ t", "am ız", "amı z", "ìĿ´ ìĸ´", "Ġyar Ä±ÅŁ", "Ġyarı ÅŁ", "Ġg üc", "Ġgü c", "Ġ Ïĩα", "ĠÏĩ α", "ัà¸Ļย ายà¸Ļ", "ãĤĴ è¡Į", "Ġm illi", "Ġmill i", "Ġmil li", "Ġmi lli", "Ġ çı¾", "Ġçı ¾", "K dyž", "m azon", "ma zon", "maz on", "ë³´ ëĤ´ê¸°", "ĠÑĤÑĢÑĥ дов", "ĠÑĤÑĢÑĥд ов", "é£ ¾", "Ġви ник", "Ġвин ик", "ĠÙĪز ارت", "ĠÙĪزار ت", "éĩĮ çļĦ", "м аз", "ма з", "ĠR US", "ĠRU S", "е кÑĤÑĥ", "ек ÑĤÑĥ", "екÑĤ Ñĥ", "Ġع اش", "Ġk once", "Ġko nce", "Ġkon ce", "ãĤĪãģĨ ãģ§ãģĻ", "Ġмал ÑĭÑĪ", "m eni", "me ni", "men i", "е Ñģа", "еÑģ а", "ا ضÛĮ", "اض ÛĮ", "Ġb rat", "Ġbr at", "Ġbra t", "ĠвÑĸд ноÑģ", "θ εÏģ", "θε Ïģ", "ĠЧ ем", "æij ĩ", "ĠÙħ ادر", "ĠÙħا در", "ĠÙħاد ر", "çĶ¨ åĵģ", "ĠÙħØŃ Ø§Ùģظ", "Ġm yÅ¡", "Ġmy Å¡", "ج ع", "Ġis im", "Ġi sim", "Ġisi m", "æ³ Ĭ", "ıl maz", "ĠÎĽ α", "å¯ ©", "Ġay ır", "е ними", "ен ими", "ени ми", "еним и", "à¥ĩह तर", "åľ Ĩ", "ãģ¾ ãģ£ãģŁ", "çĶ¢ åĵģ", "ĠÑĸнÑĦоÑĢма ÑĨÑĸÑĹ", "Ġt ủ", "Ġtá» §", "สม à¸ļ", "Ġst ÅĻ", "Ġë°ľ íijľ", "а ÑĢÑĮ", "аÑĢ ÑĮ", "ĠC ao", "ĠCa o", "Ρ ÎĻ", "à¸ģาร à¸Ī", "Ġпод Ñĥм", "ä»ķ äºĭ", "ĠÐļ ÑĢоме", "Ġ ìĹĶ", "ĠìĹ Ķ", "Ġ Ñĥда", "ĠÑĥ да", "ĠÑĥд а", "ĠавÑĤом аÑĤи", "Ġ à¸Ħà¸ĵะ", "Ġà¸Ħ à¸ĵะ", "ĠK iÅŁ", "ĠKi ÅŁ", "ĠÑģоÑģÑĤоÑı ние", "l isi", "li si", "lis i", "Ġëĸ ¨ìĸ´", "oot ball", "Ġ íį¼", "Ġí į¼", "Ġ лим", "Ġл им", "Ġли м", "Ġç erç", "ÙĪÙĦ ÙĬÙĪ", "ÙĪÙĦÙĬ ÙĪ", "Ġs lož", "Ġsl ož", "Ġslo ž", "Ġ 먼", "Ġë¨ ¼", "ร à¸Ńà¸ĩ", "ÑĪ ее", "ÑĪе е", "âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦", "ãģĵ ãģ¡ãĤī", "о ÑĢÑĭ", "оÑĢ Ñĭ", "çĥ Ł", "Âł F", "а ного", "ан ого", "ано го", "Ø« ÛĮر", "çı į", "å¸Ĥ åł´", "vÄĽ dom", "vÄĽd om", "ì² ¨ë¶Ģ", "ĠìĤ¬ ê±´", "ï¾ Į", "à¹ĥ à¸Ļว", "à¹ĥà¸Ļ ว", "Ġzvlá Å¡t", "ÏĦ εÏħ", "ÏĦε Ïħ", "Ġкак ие", "Ġка кие", "ÏĨοÏģ ά", "ÏĨο Ïģά", "åĦ Ħ", "Ġzp ÄĽt", "íķľ íħĮ", "Ġz vol", "Ġzv ol", "Ġ çĹ", "Ġç Ĺ", "ÑĢа нениÑı", "ÑĢан ениÑı", "ĠسÛĮ است", "ĠÐļ оли", "ĠÐļол и", "ĠÐļо ли", "ĠоÑĢганиз ма", "ĠоÑĢганизм а", "ĠÑıнва ÑĢÑı", "Ġد ادÙĨ", "Ġداد ÙĨ", "п ÑĢа", "пÑĢ а", "ï¼Į ä»ĸ们", "ï¼Įä»ĸ 们", "æijĺ è¦ģ", "Ġqu ần", "ÙĬ ÙĪÙĨ", "ÙĬÙĪ ÙĨ", "Ġви Ñħов", "Âł à¹Ģà¸Ķ", "Ġ елем", "Ġе лем", "eb ilecek", "Ġд оÑĩ", "Ġдо Ñĩ", "Ġб лаг", "Ġбл аг", "Ġбла г", "ĠÑı й", "ad nÃŃ", "Ġzá roveÅĪ", "en stvÃŃ", "âĢĮ اÙĨ", "ãģķãĤĵ ãģ¯", "/ |", "ĠاÙĦع اÙħØ©", "ĠاÙĦعاÙħ Ø©", "éł ¼", "Ġخدا ÙĪÙĨد", "Ġخد اÙĪÙĨد", "н ам", "на м", "ĠÑģ лиз", "ĠÑģл из", "æ¶ ī", "ร ษ", "e ÅŁtir", "eÅŁ tir", "ĠÙĨ دار", "ĠÙĨد ار", "ร าà¸Ħ", "è¨Ģ ãĤı", "Ġ èŃ", "Ġè Ń", "Ġк ÑĢиÑĤ", "ĠкÑĢи ÑĤ", "ĠвоздÑĥ Ñħа", "ĠвоздÑĥÑħ а", "Ġà¤Ĺ त", "Ġprá vo", "Ġpráv o", "à¥ĭष ण", "Ġs ắp", "íı Ń", "Ġص رÙģ", "ĠراÛĮ گاÙĨ", "ĠоÑĤ к", "ëĨ ĵ", "ĠÑģек ÑĢеÑĤ", "Ä° n", "on avir", "ona vir", "ĠV ys", "ĠVy s", "ĠbaÅŁ lat", "ĠMu ham", "ĠлиÑģÑĤ оп", "ĠT icaret", "ĠTi caret", "ĠTic aret", "Ġad landır", "ĠÐĶ миÑĤ", "Ïĥμ οÏį", "Ïĥμο Ïį", "ä¾ µ", "ìĭľ ëĬĶ", "à¹ģà¸Ľ ลà¸ĩ", "ın ıza", "ını za", "ınız a", "- г", "и ÑĩноÑĹ", "иÑĩ ноÑĹ", "иÑĩно ÑĹ", "Ñĥ ÑĢи", "ÑĥÑĢ и", "U Z", "ìĽ ł", "Ġتبد ÛĮÙĦ", "æ º«", "æº «", "ĠÑĢам каÑħ", "Ġn ét", "Ġné t", "æ² ¿", "Ġroz Å¡ÃŃ", "Ġस प", "ĠÑĤак е", "ĠÑĤа ке", "ÑĢ аÑĩ", "ÑĢа Ñĩ", "ĠاÙĦ ÙĤد", "ĠاÙĦÙĤ د", "íķĻ ê³¼", "Ñĥв аннÑıм", "ÑĥваннÑı м", "Ġm ám", "Ġmá m", "ë¡ ¯", "á½ IJ", "Ġet kili", "Ġetk ili", "Ġetkil i", "Ġetki li", "Ġار تÙģ", "Ġtechn olog", "Ġtechno log", "Ġ ì½ĺ", "Ġì½ ĺ", "Ġت ÙĥÙĬÙĬÙģ", "ĠpÅĻ ece", "ĠpÅĻe ce", "家 åºŃ", "Ġ ãģı", "âĶ ´", "íģ ¼", "ĠÎľ ά", "à¹Ģ à¸ķร", "à¹Ģà¸ķ ร", "ĠÑģÑĤанов иÑĤÑģÑı", "ç«ĭ ãģ¡", "Ġ éĸĭ", "Ġéĸ ĭ", "ĠÄ° yi", "ĠnÄĽkter é", "ĠÑĢоб оÑĤ", "ĠÄIJ Æ°á»Ŀng", "ĠاÙĦ اج", "Ġsp eci", "Ġspec i", "Ġspe ci", "çī¹ åĪ«", "åŃ Ŀ", "âĢĮ گذ", "âĢĮÚ¯ Ø°", "a lıģı", "al ıģı", "alı ģı", "Ġм иÑĢа", "Ġми ÑĢа", "ĠмиÑĢ а", "í İĺìĿ´ì§Ģ", "íİĺ ìĿ´ì§Ģ", "Ø® Ùģ", "ãĤª ãĥª", "Ġس ÛĮÙħ", "ĠسÛĮ Ùħ", "Ġìĸ´ ëĬIJ", "алÑĮ нÑĥ", "Ñĩ ний", "ümüz de", "æĻº èĥ½", "ý n", "ĠتÙĤÙĪ ÛĮت", "Ġп ÑĢиг", "ĠпÑĢ иг", "ĠпÑĢи г", "ĠгÑĢÑĥпп Ñĭ", "am ı", "γ οÏį", "γο Ïį", "оÑĢ ÑĤÑĥ", "оÑĢÑĤ Ñĥ", "ĠG iang", "ĠGi ang", "ĠGian g", "ĠGia ng", "ÅĻ en", "ÅĻe n", "Ġokol ÃŃ", "产 ä¸ļ", "Ġ зм", "Ġз м", "Ġ é¾", "Ġé ¾", "ÙĬ ار", "ÙĬا ر", "ĠاÙĦØ´ÙĬ Ø®", "иÑĤелÑĮ нÑĭй", "Ġ اÙĩÙħ", "Ġا ÙĩÙħ", "ĠاÙĩ Ùħ", "ĠباÙĦ رÙĬاض", "ĠÙ¾ÛĮ اÙħ", "Ġk redi", "Ġkr edi", "Ġkre di", "Ġkred i", "ĠA rap", "ĠAr ap", "ĠAra p", "Ġ ÑĢаб", "ĠÑĢ аб", "ĠÑĢаР±", "ĠÑĢа б", "ĠнекоÑĤоÑĢ ÑĭÑħ", "ĠØŃاÙģظ Ùĩ", "иÑĤелÑĮ ного", "иÑĤелÑĮно го", "Ġgerek mektedir", "ĠD eniz", "ĠDen iz", "ĠتÙĦ اش", "st agram", "sta gram", "stag ram", "áv ky", "åĬł åħ¥", "oz or", "ozo r", "Ġdurum unda", "Ġdurumu nda", "Ġíıī ëĭ¹", "Ġ ë´ī", "Ġë´ ī", "Ġp enÄĽ", "Ġpe nÄĽ", "Ġpen ÄĽ", "Ú¯ اÙĨÛĮ", "گاÙĨ ÛĮ", "ĠK up", "ĠKu p", "Ġ ÑĨеÑĢ", "ĠÑĨ еÑĢ", "ĠÑĨе ÑĢ", "ul ması", "âij ł", "ĠÑģÑĸÑĩ нÑı", "ım ıza", "ımız a", "ımı za", "å®ļ çļĦ", "Âł ÑĤ", "åĬŀ åħ¬", "ìľ¼ ëĭĪ", "ĠاÙĦ Ø¥ÙĨ", "ĠاÙĦØ¥ ÙĨ", "Ġ çĥ", "Ġç ĥ", "ãĢį ï¼Į", "ÑĹ Ð½Ð°", "ĠпÑĢигоÑĤов лениÑı", "Ð ħ", "ĠÑģ олн", "ĠÑģол н", "Ġë¶Ģ ìĤ°", "æħ ¶", "ãĤ ¾", "v oje", "vo je", "voj e", "ÛĮ دÙĨ", "ÛĮد ÙĨ", "ìĥĿ ëĭĺ", "ç¹ ģ", "á du", "ád u", ": ::::::::::::::", ":: :::::::::::::", ":::: :::::::::::", ":::::: :::::::::", ":::::::: :::::::", "::: ::::::::::::", "::::: ::::::::::", "::::::: ::::::::", "::::::::: ::::::", ":::::::::: :::::", "::::::::::: ::::", ":::::::::::: :::", "::::::::::::: ::", ":::::::::::::: :", "س ÙĨÚ¯", "سÙĨ Ú¯", "éĶ ĭ", "Ġ звиÑĩай", "Ġз виÑĩай", "å§Ķåijĺ ä¼ļ", "ĠμÎŃ Ïĥα", "ĠÑĢ ождениÑı", "æĪIJ 人", "Ġ dÃŃl", "Ġd ÃŃl", "ĠdÃŃ l", "ĠÐĶ об", "ĠÐĶо б", "Ġ à¹ĥà¸Ĭ", "ÏĢ ί", "g amber", "gam ber", "ĠÙĪÛĮÚĺ Ú¯ÛĮ", "Ġ èĬ±", "ĠèĬ ±", "Ġb Ãły", "ĠbÃł y", "ĠжовÑĤ нÑı", "åħ¬ å¼Ģ", "ĠÑĤ оÑĩки", "ĠÑĤо Ñĩки", "ĠÑĤоÑĩ ки", "ãģĤ ãģ®", "а лÑĸв", "ал Ñĸв", "алÑĸ в", "Ġch arakter", "Ġchar akter", "ĠÎĴ α", "Ġzku Å¡en", "Ġà¤Ńà¤Ĺ व", "Ñĩ ика", "Ñĩи ка", "Ñĩик а", "à¥Ģà¤Ĥ ।", "è£ ı", "åijĬ è¯ī", "iy atı", "iya tı", "iyat ı", "ĠÑĨ елÑĮ", "ĠÑĨе лÑĮ", "ĠÑĨел ÑĮ", "Ġ ìĬĪ", "ĠìĬ Ī", "а ÑĢд", "аÑĢ д", "ĠÃľl ke", "Ġpro since", "Ġpros ince", "ĠÙĨ گاÙĩ", "ĠÙĨÚ¯ اÙĩ", "ãĢĮ ãģĬ", "ÎŁ Τ", "ÎŁÎ ¤", "ìĦľ ëĬĶ", "ÙĪ گر", "ض اÙĨ", "ضا ÙĨ", "Ġdů sled", "çIJ ´", "à¸ķำ à¹ģหà¸Ļ", "к ÑĤÑĸв", "кÑĤ Ñĸв", "lád á", "á¿ Ĩ", "ĠD oÄŁu", "ĠDo ÄŁu", "ĠDoÄŁ u", "ãģij ãĤĮãģ°", "缮 ãĤĴ", "Ġ 缴", "ĠçĽ ´", "æ Ľ°", "æĽ °", "ĠвÑĤоÑĢ ой", "Ġг лÑĥ", "ĠìĿ ½", "기 ì¤Ģ", "Ġma dde", "Ġmad de", "Ġmadd e", "Ġjed né", "Ġjedn é", "Ġо ÑĦÑĸ", "ìĭĿ ìĿĦ", "Ġch út", "Ġchú t", "åĩº ãģĹãģŁ", "åĩºãģĹ ãģŁ", "и ÑĩеÑģкаÑı", "иÑĩеÑģ каÑı", "Ġ лок", "Ġл ок", "Ġal tı", "Ġalt ı", "ëĵľ ëĬĶ", "ey gamber", "ĠÑģв ое", "ĠÑģво е", "ĠtaÅŁ ım", "ĠÑĤо Ñīо", "Ġgeç ti", "Ġpr emi", "Ġpre mi", "Ġprem i", "ĠMeh met", "ï¼Į åĽłæѤ", "ï¼ĮåĽł æѤ", "ί κη", "ίκ η", "Ġönce ki", "Ġönc eki", "Ġ à¤ķन", "Ġà¤ķ न", "ĠÑĤемп еÑĢаÑĤÑĥÑĢа", "ĠÑĤемпеÑĢаÑĤÑĥ ÑĢа", "éĺ ´", "Ġìĸ¼ ë§Ī", "Ø´ ب", "á ky", "ák y", "ãĢĢ V", "воÑĢ еннÑı", "l asyon", "las yon", "Ġд оказ", "Ġдо каз", "Ġдок аз", "Ġëľ »", "Ġоб лиÑĩ", "Ġобл иÑĩ", "ÎĻ ÎijÎļ", "ÎĻÎij Îļ", "Ġ ÑĢазд", "ĠÑĢаз д", "ĠÑĢа зд", "ï¼Į 为", "å® ½", "Ġk orum", "Ġko rum", "Ġkor um", "åķĬ åķĬ", "ĠÅĻe kla", "ĠÅĻekl a", "ĠÅĻek la", "ãĥĹ ãĥ¬", "Ġв аÑĢÑĤ", "ĠваÑĢ ÑĤ", "ĠпÑĢоблем Ñĭ", "Ġ ä½ł", "Ġth Æ¡m", "Ġta kové", "Ġtak ové", "Ġtako vé", "л енÑĭ", "лен Ñĭ", "ле нÑĭ", "Ġ åĪ¶", "ĠåĪ ¶", "Ġji ných", "Ġjin ých", "Ġjiný ch", "ĠÙĨ ص", "ĠгÑĢÑĥд нÑı", "Ġ ãģĹ", "иÑĤелÑĮ ной", "иÑĤелÑĮно й", "ĠاØŃ ØªÙħ", "Ñİ ÑĢ", "ÏĨ Ïħ", "Ġ Ø´ÙħاÙĦÛĮ", "ĠØ´Ùħ اÙĦÛĮ", "ĠØ´Ùħا ÙĦÛĮ", "ĠØ´ÙħاÙĦ ÛĮ", "Ġ ì»´", "Ġì» ´", "acaÄŁ ız", "acaģı z", "ì§Ģ ë§ī", "ĠÑĦин анÑģов", "Ġ ê·¹", "Ġê· ¹", "ĠÚĨ ÛĮزÛĮ", "ĠÚĨÛĮز ÛĮ", "à¥Ģ à¤Ľ", "ص ات", "ान म", "Ġв озможно", "Ġвозмож но", "è¨ İ", "çĦ ¦", "ĠاÙĦبÙĦ د", "Ġ çĶ³åįļ", "ç¥ ¥", "Ġë°Ķ ëĿ¼", "Ú¯ ÛĮر", "Ú¯ÛĮ ر", "Ûµ Û°", "μι οÏħÏģγ", "ĠpÅĻed sed", "ç»ı èIJ¥", "å§ ij", "e mey", "em ey", "eme y", "ĠÙĨ ÙĪÙģ", "ĠÙĨÙĪ Ùģ", "å¾ ½", "Ġprá va", "Ġpráv a", "Ġво обÑīе", "Ġ íĭ°", "Ġíĭ °", "Ġب Ø£ÙĨ", "Ġبأ ÙĨ", "ĠFr anti", "ĠFran ti", "ĠP aÅŁa", "ĠPa ÅŁa", "ĠÙ¾ ست", "Ġپس ت", "k ân", "ĠÑģиг н", "Ġd ần", "æ IJľ", "æIJ ľ", "Ġr oky", "Ġro ky", "Ġrok y", "Ùĥ ÙĪر", "ÙĥÙĪ ر", "ĠÎĶ ή", "али заÑĨии", "ализ аÑĨии", "ализа ÑĨии", "ä¼ł å¥ĩ", "ı da", "lÃŃ b", "ĠÑĢÑĸв нÑı", "Ġн оÑı", "Ġно Ñı", "bÄĽ hu", "bÄĽh u", "ิà¸ĩห าà¸Ħม", "ï¼Į åį´", "Ġ ÑĩеÑģ", "ĠÑĩ еÑģ", "lan mÄ±ÅŁtır", "lanmÄ±ÅŁ tır", "Ġ Æ°á»Ľc", "áv acÃŃ", "ีฬ า", "δ ÎŃ", "âĢĮØ´ ÙĪÙĨد", "Ġ ÑĢобÑĸÑĤ", "ĠÑĢоб ÑĸÑĤ", "Ġ å·´", "Ġå· ´", "ĠM ev", "ĠMe v", "ĠÙħرØŃ ÙĦÙĩ", "Ġвз ÑĢоÑģ", "ç½ ļ", "Ġب اÙĦÙħ", "Ġبا ÙĦÙħ", "ĠباÙĦ Ùħ", "Ġиз гоÑĤов", "ĠS por", "ĠSp or", "ĠSpo r", "åĦ Ģ", "ĠاÙĦ Ø£ÙĨ", "ĠاÙĦØ£ ÙĨ", "à¹Īา à¸ĩà¸ģ", "à¹Īาà¸ĩ à¸ģ", "л аÑģÑĤи", "ла ÑģÑĤи", "лаÑģ ÑĤи", "ÎŁ Îļ", "ÎŁÎ ļ", "Ġ Ú©ÛĮ", "ĠÚ© ÛĮ", "åij½ 令", "ØŃ Ø¯Ø«", "ØŃد Ø«", "ÙĬ ÙĥÙĬ", "ÙĬÙĥ ÙĬ", "ĠпеÑĢв Ñĭй", "ãĤ¹ ãĤ³", "ĠÅ¡ pat", "ĠÅ¡p at", "Ġnik do", "ั à¸ĩม", "ัà¸ĩ ม", "èµ «", "æĺ ¨", "Ġв Ñĥли", "ĠвÑĥл и", "ĠÐļ а", "à¹Ī ละ", "Ġsa mot", "Ġsam ot", "Ġsamo t", "ĠобеÑģп е", "ĠÙħعرÙģ ÛĮ", "ĠÙħØŃصÙĪÙĦ ات", "в анов", "ва нов", "ван ов", "вано в", "ĠÙħستÙĤ ÛĮÙħ", "å¢ Ļ", "Âł Ðļ", "Ġд оÑĤ", "Ġдо ÑĤ", "z im", "zi m", "ÙIJ ر", "Ġ Ø´ÙĪ", "ĠØ´ ÙĪ", "åľ¨ åľ°", "Ġ çİ°", "Ġçİ °", "Ġ åĮĸ", "ĠåĮ ĸ", "ز ÙĪ", "Ġyay gın", "Ġо ÑĢиг", "ĠоÑĢ иг", "Ùı ÙĨ", "Ġev rop", "Ġ ï½ľ", "Ġï½ ľ", "Ġëħ¸ì¶ľ ëĵ±ë¡Ŀ", "åĩ Ŀ", "л еннÑĭÑħ", "лен нÑĭÑħ", "Ġje nom", "Ġjen om", "Ġ ЧÑĤобÑĭ", "ĠЧ ÑĤобÑĭ", "ĠЧÑĤо бÑĭ", "ĠìĹĨ ëĭ¤", "ĠìŬ ìĦ±", "Ġres mi", "im álnÃŃ", "缮 ãģ®", "s ian", "si an", "-ни бÑĥдÑĮ", "ο κ", "çĭ¬ ç«ĭ", "ÅŁ ehir", "åIJ IJ", "åĪ¶ éĢł", "Ġ ÎĶεν", "ĠÎĶ εν", "ĠÎĶε ν", "ãĥĭ ãĥ¥", "иÑĤелÑĮ нÑĭÑħ", "Ġ ÙĥاÙħ", "ĠÙĥ اÙħ", "Ïģ κ", "Ġr au", "Ġra u", "ĠÑģм еÑĢÑĤи", "ĠÑģмеÑĢ ÑĤи", "ĠÏĮ ÏĦαν", "Ġ Tại", "ĠT ại", "Ġ رب", "Ġر ب", "ε νο", "εν ο", "ر دد", "رد د", "Ġ à¸ģระ", "Ġà¸ģ ระ", "Ġà¸ģร ะ", "Ïĥ μο", "Ïĥμ ο", "Ġ æ¼Ķ", "Ġæ¼ Ķ", "ิà¸Ī à¸ģรรม", "ĠÑĢаз ви", "ĠÑĢазв и", "ãĤ¹ ãĥļ", "Ñĸ ÑĩноÑĹ", "ÑĸÑĩ ноÑĹ", "lá Å¡enÃŃ", "láš enÃŃ", "اب عة", "ابع Ø©", "ov ými", "ový mi", "ovým i", "а нг", "ан г", "Ġкап ÑĸÑĤ", "ãĢģ âĢĭ", "íĸĪ ëįĺ", "ĠÑĥ ÑģÑĸ", "ĠÑĥÑģ Ñĸ", "ย าว", "ยา ว", "Ø£ Ùħ", "ãĥ© ãĥĥãĤ¯", "Ġë ķ", "ĠسÙĨ ÙĪات", "ĠÑģÑĤаÑĤ ÑĮи", "ĠÑģÑĤаÑĤÑĮ и", "ÑĹ Ñħ", "Ïģο Ïĩή", "ĠØ£Ùĥ تÙĪبر", "lan ma", "Ġmal zem", "ç £¨", "ç£ ¨", "Ġб окÑĥ", "Ġбо кÑĥ", "Ġбок Ñĥ", "åŃĹ å¹ķ", "ĠоÑĢганÑĸ за", "ĠоÑĢганÑĸз а", "ãĥ© ãĤ¤ãĥ³", "ãĥ©ãĤ¤ ãĥ³", "ĠÙħع دÙĨ", "ĠÙħعد ÙĨ", "çĶ· åŃIJ", "Ġ æĤ", "Ġæ Ĥ", "Ạ¾", "Ġmez iná", "Ġmezi ná", "и ваÑİÑĤ", "ив аÑİÑĤ", "ива ÑİÑĤ", "ĠطبÛĮ عÛĮ", "èĻ ij", "à¤Ł र", "Ġпод Ñģ", "ĠÅŁ aÅŁ", "à¸Ļ à¹Ĩ", "ĠÅ¡ p", "v ÄĽÅĻ", "vÄĽ ÅĻ", "з ÑĮ", "ëĿ¼ ë§Ī", "ุ à¸ĺ", "âĢ¦ Ø·", "리 ì§Ģ", "âĦĸâĦĸ âĦĸâĦĸ", "Ġb ức", "ĠSp oj", "ĠSpo j", "ĠиÑģполÑĮзов ани", "ĠиÑģполÑĮз овани", "å·¦ åı³", "en ler", "ĠоÑī ÑĥÑī", "Ġоб лÑĸ", "Ġобл Ñĸ", "ظ ËĨ", "ÙĦ ÛĮس", "ÙĦÛĮ س", "æıIJ åįĩ", "ĠговоÑĢ иÑĤÑĮ", "ĠговоÑĢиÑĤ ÑĮ", "Ġk ür", "Ġkü r", "Ġλ ειÏĦοÏħÏģγ", "ла га", "лаг а", "ĠÑģÑĥ дÑĥ", "ĠÑģÑĥд Ñĥ", "Ġ 측", "Ġì¸ ¡", "θε Ïĥη", "Ġ нен", "Ġн ен", "Ġне н", "Ġbiç imde", "Ġbiçim de", "ÑĨÑĸй ноÑĹ", "ÑĨÑĸйно ÑĹ", "à¹Ģà¸Ħ ย", "ĠDal Å¡ÃŃ", "Ġи меÑĤÑĮ", "Ġим еÑĤÑĮ", "Ġиме ÑĤÑĮ", "èĭ Ĺ", "ĠÙħع رÙĪÙģ", "Ġt ạp", "Ġm eÅŁ", "Ġme ÅŁ", "Âł N", "оÑĢ они", "оÑĢон и", "оÑĢо ни", "ع Ùģ", "à¹Ĥ รà¸ĩà¹Ģร", "à¹Ĥรà¸ĩ à¹Ģร", "âĶ ¬", "Ġ à¹Ģà¸ŀราะ", "Ġà¹Ģà¸ŀ ราะ", "Ġèı² å¾ĭ宾", "ÑģÑĤв енное", "ÑģÑĤвен ное", "ÑģÑĤвенно е", "Ġاز دÙĪاج", "ĠÑĦ ев", "éł »", "Ġ สล", "Ġส ล", "à¸ķ à¸Ńà¸Ļ", "Ġ 기ê°Ħ", "Ġ기 ê°Ħ", "ä½ ©", "ÏĦ ην", "ÏĦη ν", "ëĤ¬ ëĭ¤", "ĠQ uy", "ĠQu y", "Ġë¶ Ļ", "ĠС Ñĥд", "и ж", "Ġ à¹Ģà¸ģม", "Ġà¹Ģà¸ģ ม", "ĠÑģв ÑıÑĤ", "ĠÑģвÑı ÑĤ", "et ooth", "eto oth", "ε Ïģο", "εÏģ ο", "ÙĦ ÙħØ©", "ÙĦÙħ Ø©", "Ø´ ÙĪر", "Ø´ÙĪ ر", "Ġd omu", "Ġdo mu", "Ġdom u", "èį Ĵ", "m î", "ëıĦ 를", "ĠÑĢекомендÑĥ еÑĤÑģÑı", "Ġsonra sında", "Ġsonrası nda", "Ġд нÑĸв", "Ġç al", "Ġça l", "ãĤ«ãĥĨ ãĤ´ãĥª", "Ġ еж", "Ġе ж", "Ġìķ ī", "èī² çļĦ", "âĢĻ nde", "âĢĻn de", "Ġ ÏĢÏīÏĤ", "ĠÏĢ ÏīÏĤ", "ĠÑĩеÑĤ веÑĢ", "k ili", "ki li", "kil i", "æĢ§ èĥ½", "اد ÙĬØ©", "ادÙĬ Ø©", "çº ¯", "ĠاÙĦ تش", "ĠاÙĦت Ø´", "ĠÑĤ ела", "ĠÑĤе ла", "ĠÑĤел а", "Ġоб ÑĬем", "ĠобÑĬ ем", "å²Ĺ ä½į", "Ġkon krét", "Ġa rada", "Ġar ada", "Ġara da", "ìĭľ ìĹIJ", "Ġor anı", "Ġoran ı", "ر Ùĥ", "ÐĽ ÐIJ", "Ġ ménÄĽ", "Ġmé nÄĽ", "ج ÙĪÛĮ", "جÙĪ ÛĮ", "Ġv ợ", "Ġvá» £", "ĠAngiosper mae", "èĥ İ", "Ġh ôn", "äºĭ æ¥Ń", "ĠоÑĤ веÑĢ", "ĠоÑĤв еÑĢ", "Ġs rd", "Ġsr d", "Å¡ li", "ส à¸ģ", "æ¼ ı", "ĠØ´ رØŃ", "Ġشر ØŃ", "ÑĨ Ñıми", "ÑĨÑı ми", "Ġs lav", "Ġsl av", "Ġsla v", "Ġc eny", "Ġce ny", "Ġcen y", "à¸Ń à¹Ģร", "Ġ ÙĪÙĦد", "ĠÙĪÙĦ د", "Ġк оÑĢа", "ĠкоÑĢ а", "Ġко ÑĢа", "Ġб ÑĢон", ": .:.:.:.:", ":.:.: .:.:", ":.: .:.:.:", "Ġne mus", "Ġnem us", "è¿Ļ æł·çļĦ", "è¿Ļæł· çļĦ", "Ġبر ÙĨاÙħج", "Ġú plnÄĽ", "ีà¸Ļ าà¸Ħม", "Ġë°Ľ ìķĦ", "με Ïģα", "μεÏģ α", "ç¼ ©", "Ġn ắm", "ĠобÑĬ ÑıÑģ", "ĠконÑĤÑĢ олÑİ", "á vajÃŃcÃŃ", "ávajÃŃ cÃŃ", "Ġk um", "Ġku m", "çĶ· 人", "Ġv nitÅĻ", "Ġب دÙĩ", "Ġبد Ùĩ", "Ġأب رÙĬÙĦ", "人æ°ij åħ±åĴĮåĽ½", "Ġyap ılır", "Ġyapıl ır", "Ġna Å¡ÃŃ", "ĠnaÅ¡ ÃŃ", "ãĥ¼ ãĥŃ", "ãĥ¼ãĥ Ń", "Ġt ạm", "Ġhen üz", "Ġz emi", "Ġze mi", "Ġzem i", "Ġkh áng", "Ġkhá ng", "åħ¬ åħ±", "Ġ èĢģ", "ĠèĢ ģ", "ĠعÙĪ اÙħÙĦ", "Âł V", "à¹ī à¹ģà¸ģ", "άν ÏĦα", "ĠÑĤÑĢав нÑı", "Ġη μÎŃ", "è´ ¸", "ส à¸Ķ", "Ġس Ùħت", "ĠسÙħ ت", "ĠØ® اک", "ĠÑĤак ий", "ĠÑĤа кий", "Ġet tik", "Ġett ik", "Ġetti k", "ĠÏĮ λ", "Ġп оли", "Ġпо ли", "Ġпол и", "Ġ нож", "Ġн ож", "Ġно ж", "غ اÙĨ", "ÙĨ دÙĬ", "ÙĨد ÙĬ", "ĠÄįty ÅĻi", "ĠÄįtyÅĻ i", "ĠPh Æ°Æ¡ng", "ĠÙĪر زش", "ĠÙĪرز Ø´", "ãģĦ ãģĭ", "r vé", "rv é", "Ġतर फ", "Ġन à¤Ĺर", "m asında", "ma sında", "mas ında", "ması nda", "е виÑĩ", "ев иÑĩ", "еви Ñĩ", "ve ÅĻej", "ä¿Ŀ æĮģ", "æĬĢ èĥ½", "æݨ èįIJ", "l âm", "Ġ Ïį", "ĠÏ į", "å¢ŀ éķ¿", "Ġاص ÙģÙĩ", "ĠÐĹак онÑĥ", "ĠÐŁ ÑĢез", "ĠÐŁÑĢ ез", "Ġpod por", "Ġpodp or", "기 íĥĢ", "Ġ íıIJ", "Ġíı IJ", "Ġ ëĭĪ", "Ġëĭ Ī", "lar ınız", "ların ız", "larını z", "ãĥĸ ãĥŃ", "ĠÑĦÑĢан ÑĨÑĥз", "ãĥĬ ãĥ¼", "Ġb eled", "Ġbe led", "Ġbel ed", "Ġbele d", "ัà¸Ļว าà¸Ħม", "ĠÙģ رÙĪ", "ĠÙģر ÙĪ", "ÑĦ ÑĢов", "ĠìĿ´ 룬", "ượ u", "Ġê³µ ìĭĿ", "Ġbird en", "Ġbir den", "Ġз елен", "Ġзел ен", "çĴ ĥ", "Ġh á»ĵng", "Ġhá»ĵ ng", "ĠÅ¡ kola", "ĠÅ¡kol a", "ĠÅ¡k ola", "ĠÑģам ом", "ĠÑģамо м", "an lık", "anlı k", "空 éĹ´", "åįĹ çľģ", "л еÑĢг", "ле ÑĢг", "леÑĢ г", "Ñĸз неÑģ", "Âł A", "ãĢį ãĤĴ", "Ġkend ine", "Ġkendi ne", "Ġ اÙĪÙĨ", "Ġا ÙĪÙĨ", "ĠاÙĪ ÙĨ", "ãĢ Ķ", "ĠΣ Ïį", "à¹Ģ à¸Ħล", "à¹Ģà¸Ħ ล", "å¥ ¶", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "Ġú Äįet", "ĠúÄį et", "Ñĥ ла", "Ñĥл а", "éĢļ ä¿¡", "Ġì¦ IJ", ". čĊĊ", ".čĊ Ċ", "ĠÐľ ед", "ا عÙĬ", "اع ÙĬ", "Ġjeho ž", "ĠGü ney", "ĠGün ey", "ĠÎij ÏĢο", "ĠÎijÏĢ ο", "Ġп олÑĸ", "Ġпо лÑĸ", "Ġпол Ñĸ", "ü me", "üm e", "ho dob", "hod ob", "ĠÎĿ α", "ĠØ¢ ÙĦات", "ĠØ¢ÙĦ ات", "ĠpÅĻ iz", "ĠpÅĻi z", "Ġt avs", "Ġta vs", "Ġtav s", "Ġتب ÙĦÛĮ", "ãĥ³ ãĥĶ", "ãĥ³ãĥ Ķ", "Ø® رج", "خر ج", "Ġак кÑĥ", "Ġú prav", "ĠاØŃ Ø³Ø§Ø³", "ì¹´ ëĿ¼", "ı mızı", "ım ızı", "ımız ı", "ımı zı", "Ġд окÑĥменÑĤ", "Ġдок ÑĥменÑĤ", "ĠдокÑĥм енÑĤ", "Ġ اصÙĦ", "Ġا صÙĦ", "Ġاص ÙĦ", "ظ Ùĩ", "ĠìĿ¸ ê°Ħ", "Ġج رÛĮاÙĨ", "Ġجر ÛĮاÙĨ", "Î¥ ÎĿ", "ÑĩеÑģ каÑı", "Ñĩе ÑģкаÑı", "ÙĬ ÙĨÙĬØ©", "ÙĬÙĨ ÙĬØ©", "ÙĬÙĨÙĬ Ø©", "åĴ ¨", "æĹħ 游", "Ġ à¸Īำà¸Ļวà¸Ļ", "Ġà¸Īำ à¸Ļวà¸Ļ", "Ġ анг", "Ġа нг", "Ġан г", "Ïħ Ïĩ", "èĻ «", "ĠÙħ ÙĤر", "ĠÙħÙĤ ر", "ĠÙħÙĪسÛĮ ÙĤÛĮ", "ut ut", "ĠÐĽ е", "ĠÐŁ ÑĸÑģлÑı", "ãĤŃ ãĥ¼", "ุล าà¸Ħม", "åĩ ¡", "ÏĢ οÏį", "ÏĢο Ïį", "ĠÃĸ dül", "Ïĥ κ", "Ġ ÑĨÑİ", "ĠÑĨ Ñİ", "Ñĭ ваÑı", "Ñĭв аÑı", "Ñĭва Ñı", "ï½ŀ ï½ŀ", "ĠØ´ ÙħاÙĦ", "ĠØ´Ùħ اÙĦ", "ĠØ´Ùħا ÙĦ", "è¿ ħ", "ĠبÙĦ Ú©Ùĩ", "çİ Ľ", "Ġì§Ģ ëĤĺ", "ĠÙģ کر", "ĠÑģÑĤеп ени", "Ġна Ñĥки", "ĠнаÑĥк и", "çī© çIJĨ", "ÄĽ le", "ÄĽl e", "Ġо ÑģкÑĸлÑĮки", "ĠкÑĥлÑĮÑĤÑĥ ÑĢи", "ĠкÑĥлÑĮÑĤÑĥÑĢ и", "èĢĥ è¯ķ", "Ġmateri ál", "ĠÑģÑĤ воÑĢеннÑı", "ĠÑģÑĤвоÑĢ еннÑı", "Ġà¤ħ द", "æıIJ åĩº", "Ġè©ķ 価", "ÙĴ د", "Ġë§Įëĵ¤ ìĸ´", "duÄŁu nu", "duÄŁ unu", "ÙĬ ÙĨÙĩ", "ÙĬÙĨ Ùĩ", "åĵ ¦", "оÑĩ нÑĭÑħ", "ĠÙħ ض", "is mu", "ism u", "Ġ Ñĩай", "ĠÑĩ ай", "ĠÑĩа й", "ÙĪ رÙĪب", "ÙĪر ÙĪب", "Ġ англ", "Ġан гл", "Ġанг л", "oÄŁun luk", "ĠпÑĢед пол", "ĠÎŃ ÏīÏĤ", "ส à¸ĸ", "ĠÎķλλά δα", "ĠBi lg", "ĠBil g", "Ġ بات", "Ġب ات", "Ġبا ت", "ĠÐĽÑĮв Ñĸв", "Ġyap ılması", "Ġyapıl ması", "æ£Ģ æŁ¥", "æķ° åѦ", "Ġ :.:", "Ġ: .:", "Ġ:. :", "Ġ çİ©", "Ġçİ ©", "Îļ α", "à¹Ģà¸Ĺ à¸Ħà¹Ĥà¸Ļà¹Ĥลย", "Ġساخت ÙħاÙĨ", "ĠìĨĮ 리", "é¼ »", "Ġs mr", "Ġsm r", "Ġëĭ¤ìĸij íķľ", "Ġjed nánÃŃ", "Ġjedn ánÃŃ", "Ġjedná nÃŃ", "Ġserv isi", "Ġservi si", "Ġservis i", "Ġey lem", "Ġм али", "Ġмал и", "Ġма ли", "Ġvý hod", "éĻ¤ äºĨ", "ĠпоÑĢÑıд кÑĥ", "Ġn ový", "Ġno vý", "Ġnov ý", "å¤ ķ", "ĠнекоÑĤоÑĢ Ñĭе", "Ġ^ {}", "Ġ^{ }", "γÏī γ", "Ñĥ ÑĪки", "ÑĥÑĪ ки", "Ġp sik", "Ġps ik", "Ġpsi k", "ĠíĶĦ 리", "Ø´ اء", "Ġ ван", "Ġв ан", "Ġва н", "Ġس ÙĥاÙĨ", "ç¢ ¼", "ĠÎľ η", "ĠÑĥÑĢов енÑĮ", "ãĤµ ãĥ¼", "ĠاÙĦب ØŃر", "Ġd nÃŃ", "Ġdn ÃŃ", "à¸ģาร ศ", "e diÄŁi", "edi ÄŁi", "Ġbelir li", "Ùĭ ØĮ", "ĠzamÄĽst nan", "ĠzamÄĽstn an", "æŁ ±", "ا ÙģÙĬ", "اÙģ ÙĬ", "Ġh ải", "æĢĿ æĥ³", "Ġn eler", "Ġne ler", "Ġnel er", "Ġرس ÙħÛĮ", "ĠرسÙħ ÛĮ", "Ñģ еÑĢ", "Ñģе ÑĢ", "ãģĵãģ¨ ãģ§", "ĠZá kladnÃŃ", "л ова", "ло ва", "лов а", "к ÑĤÑĥ", "кÑĤ Ñĥ", "ÙĪس Ùģ", "Ñĸб лÑĸ", "Ì Ĥ", "ÑĢ д", "éĻ ³", "æį ·", "ĠyaÅŁ ayan", "à¥ģ à¤ļ", "à¥ģठļ", "ÑĸÑĤ ÑĤÑı", "Ġb á»ģ", "ëĤĺ ëĿ¼", "Ġм ÑıÑģ", "Ġ{ [%", "Ġ{[ %", "θ α", "Ġдоз волÑı", "Ġдозвол Ñı", "Ġ åIJĦ", "ĠåIJ Ħ", "ĠÐŁ еÑĢв", "ĠÐŁÐµÑĢ в", "ĠSaÄŁ lık", "ÑģÑĤоÑĢ иÑı", "Ġbun lar", "Ġs á»ķ", "़ à¥į", "Ġ åĪ©", "ĠåĪ ©", "ĠÑģ поÑģ", "ĠÑģп оÑģ", "Ġyap tır", "Ġyaptı r", "Ġt Æ°á»Ŀng", "ÙĪ ÙĨØ©", "ÙĪÙĨ Ø©", "Ġ еп", "Ġе п", "ãģ§ãģį ãģªãģĦ", "Ùģ تÙħ", "Ùģت Ùħ", "ĠÐĵ ол", "íķĺ ì§Ģë§Į", "íķĺì§Ģ ë§Į", "Ġì§Ħ ì§ľ", "Ġob jedn", "Ġobj edn", "Ġизмен ениÑı", "女 人", "Ġпл ани", "Ġпла ни", "Ġплан и", "ĠFak ült", "Ġt zv", "Ġtz v", "ĠобÑıз аÑĤелÑĮ", "Ġблиз ÑĮко", "r ası", "ra sı", "ras ı", "ĠεÏĢί ÏĥηÏĤ", "ĠÑĦак ÑĤи", "ĠÑĦакÑĤ и", "ĠÄIJ ặc", "ĠAlt ın", "л иÑĤ", "ли ÑĤ", "Ġл ÑĸÑģ", "ĠлÑĸ Ñģ", "çī §", "Ġп ÑĥÑģÑĤ", "ĠпÑĥ ÑģÑĤ", "Ġком ÑĸÑģ", "ä¿Ŀ éļľ", "åħ· ä½ĵ", "- ÑĤ", "Ġtr hu", "Ġtrh u", "Ġâī Ī", "Ġдека бÑĢÑı", "ĠÑĦоÑĢм Ñĭ", "ĠÑĦоÑĢ мÑĭ", "Ng oÃłi", "Ġdo hod", "رÙĬ ÙĥÙĬØ©", "رÙĬÙĥ ÙĬØ©", "رÙĬÙĥÙĬ Ø©", "ĠØ¢ÙħÙĪز Ø´ÛĮ", "ĠØ¢ÙħÙĪزش ÛĮ", "ĠzajÃŃm av", "Ġkat ılım", "Ġkatıl ım", "ä¸ ĺ", "Ġko num", "Ġkon um", "Ġkonu m", "Ġм оÑĩ", "Ġмо Ñĩ", "ãĥ³ ãĥķ", "ãĥ³ãĥ ķ", "диви дÑĥ", "Ġ äºļ", "Ġ æĴ", "Ġæ Ĵ", "γÏģά ÏĨ", "ãĥIJ ãĤ¹", "Ġп Ñĥнк", "ĠBir leÅŁik", "Ġqu en", "Ġque n", "Ġq uen", "Ġв каз", "Ġвк аз", "à¥ĩ शà¤ķ", "à¥ĩश à¤ķ", "ĠY unan", "ĠYu nan", "ĠYun an", "ãģł ãģ¨", "Û±Û¹ Û·", "á ty", "át y", "Ġ ÙĪص", "ĠÙĪ ص", "Ġнег аÑĤив", "ãģ¤ ãģ®", "Ġ åĬ¨", "ĠåĬ ¨", "ãĥį ãĥĥãĥĪ", "Ġд Ñĸй", "ĠдÑĸ й", "ĠbaÅŁ ında", "Ġtr Æ°ng", "Ġm akin", "Ġma kin", "Ġmak in", "Ġ æĦĽ", "ĠæĦ Ľ", "м еÑĩ", "ме Ñĩ", "Ġ è¿ij", "Ġè¿ ij", "ÙĤ در", "ÙĤد ر", "Ġاست اÙĨد", "ĠاستاÙĨ د", "Ġinform acÃŃ", "ार à¤ķ", "è¬ Ŀ", "ÑĢаб аÑĤ", "Ġ çŃĶ", "Ġç ŃĶ", "ĠçŃ Ķ", "Ġ èĩ³", "Ġèĩ ³", "Ġп олÑĮ", "Ġпо лÑĮ", "Ġпол ÑĮ", "ĠÙĩÙĨ ر", "ëĮĢ ë¹Ħ", "Ġخارج ÛĮ", "r act", "ra ct", "rac t", "ãĢĤ ãģĵãĤĮ", "ĠØ´ÙĪر اÛĮ", "л енно", "лен но", "Ġh isset", "Ġhis set", "Ġhiss et", "Ġc Ãłi", "ĠcÃł i", "ĠÑĦ оÑĤо", "ĠÑĦоÑĤ о", "æģ Ĵ", "Ġмед иÑĨин", "Ġмеди ÑĨин", "ÑģÑĤ вÑĸ", "ÑģÑĤв Ñĸ", "ĠاÙĦ عÙĦ", "ĠاÙĦع ÙĦ", "ĠпиÑģÑĮ мен", "ãĢĤ ãģ¾ãģŁ", "Ġvlast nÄĽ", "Ġп ода", "Ġпо да", "Ġпод а", "Ïģ οι", "Ïģο ι", "Ġ ìĦĿ", "ĠìĦ Ŀ", "ĠìĿ¼ ìĿ´", "Ġ ìĽĮ", "ĠìĽ Į", "ок Ñģи", "окÑģ и", "Ġos oby", "Ġosob y", "ÐŁÐ¾Ñģ ле", "ĠÑĸÑģÑĤоÑĢ ÑĸÑĹ", "ع ÙĦÙī", "عÙĦ Ùī", "н ка", "ت Ùħبر", "تÙħ بر", "à¥ĩ हर", "à¥ĩह र", "ĠJ ana", "ĠJan a", "ĠJa na", "ÙĦ ÙĬات", "ÙĦÙĬ ات", "ĠмаÑĢ ÑĤа", "ĠÐļи ÑĶ", "ĠÑĢоб оÑĤÑĥ", "ĠÑĢобоÑĤ Ñĥ", "Ġnh ấn", "и Ñģлов", "иÑģ лов", "ëŁ Ń", "Ġo dv", "Ġod v", "ĠT á»īnh", "âĢľ ê·¸", "ãģ» ãģĨ", "é² ľ", "м еÑĨÑĮ", "ме ÑĨÑĮ", "า ศาสà¸ķร", "าศ าสà¸ķร", "à¥ģ à¤ĵ", "à¥ģठĵ", "ิ à¸Ļà¸Ĺ", "ิà¸Ļ à¸Ĺ", "m ada", "ma da", "mad a", "ز اÙħ", "زا Ùħ", "ĠÙĥ بÙĬر", "å®ŀ æĸ½", "ze ÅĪ", "Ġl ái", "Ġlá i", "Ïĥ μα", "Ïĥμ α", "ا سات", "اس ات", "ÑĦ ÑĤ", "è° ±", "çĮ ľ", "Ġpro bÃŃ", "Ġprob ÃŃ", "æľĢ è¿ij", "ÑĢ ад", "ÑĢаР´", "ÑĢа д", "ãĤ½ ãĥ³", "Ġ клад", "Ġк лад", "Ġкл ад", "Ġкла д", "à¥ľ à¤ķ", "é v", "ล าย", "ลา ย", "èİ İ", "ĠμÎŃ ÏĩÏģι", "Ġ кÑĥÑģ", "Ġк ÑĥÑģ", "ĠкÑĥ Ñģ", "Ġ íĻĺê²½", "ĠíĻĺ ê²½", "Ñĩ оÑĹ", "åıĺ åĮĸ", "Ġب تÙĪاÙĨ", "Ġبت ÙĪاÙĨ", "Ġt ắt", "Ġgöster en", "а лÑİ", "ал Ñİ", "Ġкоман ди", "Ġкоманд и", "Ġ 컨", "Ġì» ¨", "Ñĥ нд", "Ñĥн д", "Ġج ÙĦÙĪ", "ĠجÙĦ ÙĪ", "åŃIJ çļĦ", "ĠÑģ б", "ĠÐł аÑģ", "ĠÐłÐ° Ñģ", "P CP", "PC P", "ĠCumhur baÅŁ", "од аÑĤелÑĮ", "ÃŃ sto", "ÃŃs to", "ÃŃst o", "Ġo znám", "Ġoz nám", "ãĥ¼ ãĥĭ", "ãĥ¼ãĥ ĭ", "Ġok uy", "Ġoku y", "o phy", "op hy", "oph y", "า à¸Ļà¸Ħร", "าà¸Ļ à¸Ħร", "ĠÎķ θν", "ay ım", "ayı m", "Ùİ Ø£", "æİ ¡", "Ġfunk ce", "Ġfunkc e", "æļ ĸ", "Ø· ار", "ĠÐĿ аг", "ĠÐĿа г", "Ġ ä¸ĩåĨĨ", "Ġä¸ĩ åĨĨ", "Ġ íĴį", "ĠíĴ į", "Ġ ä½ı", "Ġ ï¼İ", "Ġï¼ İ", "Ñĭ ваÑİÑĤÑģÑı", "Ñĭв аÑİÑĤÑģÑı", "Ñĭва ÑİÑĤÑģÑı", "ÑĭваÑİÑĤ ÑģÑı", "ĠP la", "ĠPl a", "ا ÙĬÙĦ", "اÙĬ ÙĦ", "Ġ무 ìĹĩ", "Ġкон еÑĩно", "к м", "à¤Ĥ पर", "à¤Ĥप र", "Ġ ìłķë¶Ģ", "Ġìłķ ë¶Ģ", "ĠëĤ´ 볤", "ãĤ° ãĥ«", "çģ °", "Ġc yk", "Ġcy k", "Ġжел Ñĥд", "ĠëĨĴ ìĿĢ", "çĶŁ åij½", "æµ ´", "Ġart Ä±ÅŁ", "Ġ Ðĩ", "ĠÐ ĩ", "ï¼ ²", "e kim", "ek im", "eki m", "ĠÑĦ едеÑĢа", "ĠвеÑĢеÑģ нÑı", "н иÑĤе", "ни ÑĤе", "ниÑĤ е", "ĠÄ°ÅŁ te", "ĠÙĪضع ÛĮت", "ãģķ ãģ¾", "ĠtÅĻ etÃŃ", "ĠtÅĻet ÃŃ", "u luÄŁ", "ulu ÄŁ", "ĠCumhur iyet", "ä¼ Ł", "Ġ ë§Ŀ", "Ġë§ Ŀ", "Ġver mek", "Ġverm ek", "Ġn alez", "Ġna lez", "Ġnal ez", "Ġnale z", "çĵ ¶", "Ġd iÅŁ", "Ġdi ÅŁ", "ĠH á»ĵng", "ĠHá»ĵ ng", "غ ÙĬرة", "غÙĬر Ø©", "å© Ĩ", "н ив", "ни в", "Ġr út", "' nda", "'n da", "Ġh roz", "Ġhr oz", "à¥ī प", "Ġза коном", "Ġзак оном", "Ġзакон ом", "Ġзако ном", "Ġjed nu", "Ġjedn u", "ĠKa dın", "ĠKad ın", "in dir", "ind ir", "indi r", "س ازÛĮ", "åĮº åŁŁ", "ĠkonuÅŁ tu", "Ġز ÙĨد", "ĠزÙĨ د", "ा ĊĊ", "ाĊ Ċ", "ĠÐIJ з", "à¸ĩ à¸Ĥà¸Ńà¸ĩ", "à¸ĩà¸Ĥ à¸Ńà¸ĩ", "ĠÑģвой ÑģÑĤва", "Ġìŀij íĴĪ", "пе ки", "Ġ å°±", "Ġå° ±", "ев ого", "ево го", "ĠtaÅŁ ıy", "ĠÙħÙĨ Ø·ÙĤØ©", "ĠÙħÙĨØ· ÙĤØ©", "ĠÃĩ ocuk", "Û² Û·", "ĠÏĥÏħ μÏĢ", "é£Ł åĵģ", "h á", "ï¼ ¯", "ÙĦ ÙħÙĩ", "ÙĦÙħ Ùĩ", "ãģ¨ãģª ãģ£ãģŁ", "о ÑĢÑĸ", "оÑĢ Ñĸ", "° }", "ĠtaÅŁ ın", "çŁ ¿", "ĠÑĩаÑģÑĤ ини", "ĠÑĩаÑģÑĤи ни", "ĠدÙĬ سÙħبر", "Ġ èī¯", "Ġèī ¯", "st ÅĻÃŃ", "Ġ ÑĨик", "ĠÑĨ ик", "ĠÑĨи к", "âĢķâĢķ âĢķâĢķ", "ĠÄ°ng iltere", "ĠÑģÑĤ ÑĢаÑĤег", "ĠÑģÑĤÑĢ аÑĤег", "ÃĦ Ÿ", "и Ñĩного", "иÑĩ ного", "иÑĩно го", "ÃŃ rk", "ÃŃr k", "ĠÎij Ïģ", "! âĢľĊĊ", "!âĢľ ĊĊ", "Ġ 깨", "Ġê¹ ¨", "à¥ģà¤Ĩ त", "ĠدÙĨÛĮ ا", "ĠدÙĨ ÛĮا", "l ÃŃn", "lÃŃ n", "Ġà¤ķ ड", "ĠÙħ بت", "ĠÙħب ت", "ем ÑĭÑħ", "о би", "об и", "ย à¸Ļà¸ķ", "ยà¸Ļ à¸ķ", "à¤Ĥध न", "ÚĨ ÛĮ", "Ġ çŁ¥", "Ġç Ł¥", "ĠçŁ ¥", "ĠXu ân", "a daki", "ad aki", "ada ki", "Ġ orta", "Ġor ta", "Ġort a", "æł¹ æľ¬", "åħ± åIJĮ", "н ений", "не ний", "нен ий", "ب ÙĬرة", "بÙĬ رة", "بÙĬر Ø©", "çŃ ĭ", "ïº Ķ", "âĢĮ ÙĩاÙĬ", "âĢĮÙĩا ÙĬ", "Ġö deme", "Ġödem e", "ĠØ¢ÙĨ ÚĨÙĩ", "Ġза Ñıви", "ĠзаÑıв и", "ĠÙĨÙĤ Ø´Ùĩ", "ĠÙĨÙĤØ´ Ùĩ", "Ġ ç³»", "Ġç ³»", "Ġç³ »", "à¥ĭ ।", "Ġì§Ģ ìłķ", "Ġin sp", "Ġins p", "Ġ ÑĤен", "ĠÑĤ ен", "ĠÑĤе н", "Ġت Ø·", "Ġqu ảng", "Ġquả ng", "Ġquản g", "åī £", "ãģı ãģ®", "ĠÑĨ им", "ĠÑĨи м", "k ovi", "ko vi", "kov i", "i yah", "iy ah", "iya h", "Ġ ëIJľëĭ¤", "ĠëIJľ ëĭ¤", "ص Ùĩ", "ĠÄij u", "Ġsu á»ijt", "ı ma", "ım a", "ì§Ģ ê³ł", "Ì ĥ", "à¸ļ าย", "ĠCert if", "ĠCer tif", "ĠÑĥÑģ ÑĸÑħ", "ĠÑĥÑģÑĸ Ñħ", "à¸ķะ ว", "εί ÏĦε", "Ġ č", "Ġмож ливÑĸÑģÑĤÑĮ", "Ġможлив ÑĸÑģÑĤÑĮ", "Ġ -âĢIJ", "Ġ- âĢIJ", "Ġ íĺ¹", "Ġíĺ ¹", "ìĤ¬ ì§Ħ", "Ġд аниÑħ", "Ġда ниÑħ", "Ġдан иÑħ", "Ġzah áj", "주 ëĬĶ", "Ġг ид", "n iž", "ni ž", "Ġ^{ °}", "Ġk ro", "Ġkr o", "Äį en", "Äįe n", "ÏĨ ι", "ımız da", "Ġ æ¹ĸ", "Ġæ¹ ĸ", "Ġпов ÑĢежд", "Ġì¡´ ìŀ¬", "à¸Ļ าà¸Ļ", "à¸Ļา à¸Ļ", "μÎŃ Î½Î¿ÏĤ", "μÎŃν οÏĤ", "μÎŃνο ÏĤ", "æ½ ľ", "ï¼Į 使", "Ġd osp", "Ġdo sp", "Ġdos p", "Ġl iá»ģn", "Ġli á»ģn", "ัà¸ļ à¸Ħวาม", "ัà¸ļà¸Ħ วาม", "ĠÑĢабоÑĤ е", "ĠÑĢаб оÑĤе", "ĠÑĢабо ÑĤе", "Ġмай бÑĥÑĤ", "à¹Ģà¸ģ ษ", "B aÅŁ", "Ba ÅŁ", "Ġ æĿ±äº¬", "ĠæĿ± 京", "наÑĩ ала", "δ ει", "δε ι", "à¥Ī प", "Ñĸ мÑĸ", "Ñĸм Ñĸ", "Ġf izik", "Ġfi zik", "Ġfiz ik", "ว ล", "ä¼ į", "Ġ à¸Ĭà¸Ļะ", "Ġà¸Ĭ à¸Ļะ", "' ÑıÑĤ", "'Ñı ÑĤ", "н ил", "ни л", "и нов", "ин ов", "ĠÄijo án", "รว à¸Ī", "f et", "fe t", "à¹Į à¹Ĥ", "Ġ маÑĤи", "Ġм аÑĤи", "ĠмаÑĤ и", "Ġма ÑĤи", "é¨ İ", "Ðļ Т", "à¹Ģส à¸Ļà¸Ń", "à¹Ģสà¸Ļ à¸Ń", "Ġм ав", "Ġма в", "lı ģına", "lıģı na", "lıģ ına", "lıģın a", "Ġпо Ñĩина", "ĠпоÑĩ ина", "ู à¸ķร", "ูà¸ķ ร", "ÑĨ еÑĢ", "ÑĨе ÑĢ", "uj ete", "uje te", "ujet e", "Ġtah min", "Ġвим ог", "า à¸Ł", "าภŁ", "е дж", "ед ж", "ÏĦ εÏį", "ÏĦε Ïį", "ad la", "ĠÄij Æ°Æ¡ng", "Ġد استاÙĨ", "Ġbas ın", "Ġba sın", "ĠÑħ в", "Ġ reak", "Ġre ak", "ĠоÑĤ меÑĤ", "æ³ ¥", "Ġm áte", "Ġmá te", "Ġmát e", "Ġzo run", "Ġzor un", "ãģ¨æĢĿ ãģĨ", "Ġدر جة", "ĠвÑĸд ÑģÑĥÑĤ", "Ġع اÙħÙĦ", "ĠعاÙħ ÙĦ", "èĶ µ", "Ġson raki", "Ġsonra ki", "Ġmoh li", "Ġmohl i", "и ваеÑĤ", "ив аеÑĤ", "ива еÑĤ", "ĠпÑĸд ÑģÑĤав", "Ġost rov", "Ġostr ov", "ान व", "âĢŀ P", "Ġви знаÑĩа", "ĠвизнаÑĩ а", "Ġprav dÄĽpodob", "Ġz az", "Ġza z", "ìĿ´ 를", "Ġдж еÑĢ", "ĠÐł ад", "ĠÐłÐ° д", "ĠÑģеÑĢÑĮ ез", "Ġ дем", "Ġд ем", "Ġде м", "ÏĢ ή", "ĠÐĦ вÑĢоп", "ĠÐĦв ÑĢоп", "ĠÄįesk é", "ĠÄįe ské", "ï¾ ı", "Ġ ØŃÙĬ", "ĠØŃ ÙĬ", "ì¼ ĢìĿ´", "ì¼Ģ ìĿ´", "ĠØ® ÙĪÙĨ", "ĠØ®ÙĪ ÙĨ", "Âł L", "ãģĦ ãģ«", "из неÑģ", "ĠÙħ ÙĤاÙħ", "ĠÙħÙĤ اÙħ", "ĠاÙĦ ØŃÙĦ", "ĠاÙĦØŃ ÙĦ", "ëĨ į", "ĠØ¢ ÛĮا", "ĠØ¢ÛĮ ا", "ç¿ ¼", "ï¼ ½", "æ¸ IJ", "ли вÑĸ", "лив Ñĸ", "ãģĦ ãģ¦ãģĦãĤĭ", "ãģĦãģ¦ ãģĦãĤĭ", "Ġ ÎijÎł", "ĠÎij Îł", "ĠиÑģполÑĮз ÑĥеÑĤÑģÑı", "ĠиÑģполÑĮзÑĥ еÑĤÑģÑı", "Ġm át", "Ġmá t", "Ġμε γά", "Ġμεγ ά", "ëħ ¼", "æµ· éģĵ", "ĠÙħØ´Ú© ÙĦات", "ĠÙħØ´Ú©ÙĦ ات", "Ñĩ на", "'; ';", "Ġ μία", "Ġμ ία", "Ïģ Ïİν", "ÏģÏİ Î½", "Ġby ste", "ĠÑįлек ÑĤÑĢи", "ĠÑįлекÑĤÑĢ и", "ĠY ardım", "ĠYard ım", "ĠYar dım", "Ġh át", "Ġhá t", "ĠÐĶ еÑĢжав", ". С", "Ġo rada", "Ġor ada", "Ġora da", "Ġal anı", "Ġalan ı", "åľ° åŁŁ", "ĠدÙĩ ÙĨد", "мен ÑĪ", "ĠоÑĢг анов", "ĠоÑĢган ов", "Ġع ص", "ู à¸ĩส", "ูà¸ĩ ส", "ĠØ´ عر", "Ġشع ر", "Ġìĸ »", "Ġά λλ", "Ġάλ λ", "Ġg ói", "Ġgó i", "ĠÙĨ اØŃ", "å¼ ĺ", "à¥įथ ल", "i lim", "il im", "ili m", "ëIJĺ ì§Ģ", "Ġкон ÑĨе", "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", "Ġì¤Ģ ë¹Ħ", "Ġostat nÃŃ", "Ġvlá dy", "Ġvlád y", "ĠÑģо биÑĢа", "ĠÑģоб иÑĢа", "ĠìĹŃ ìĤ¬", "à¹ģ à¸ģรม", "à¹ģà¸ģ รม", ". ï¼ı", "Ùı ÙĪÙĨ", "ÙıÙĪ ÙĨ", "Ù¾ س", "ĠW ikip", "ĠWi kip", "ĠWiki p", "ĠWik ip", "Ġ æ¾", "Ġæ ¾", "Ġж аÑĢ", "容 æĺĵ", "ĠprostÅĻed nictvÃŃm", "Ġž eny", "Ġže ny", "Ġžen y", "Ġèı²å¾ĭ宾 çĶ³åįļ", "а ÑİÑĤÑģÑı", "аÑİÑĤ ÑģÑı", "Ġm iêu", "Ġmi êu", "Ġp enÃŃze", "δ ιά", "δι ά", "ol dur", "old ur", "ĠпÑĢимеÑĢ но", "ĠìŀĪ ê³ł", "à¸ĩ à¸Ńย", "к овий", "ко вий", "ков ий", ". ÎŁ", "à¹ĥ à¸Ħร", "çĭ ł", "ĠÐŁ Ñĸв", "æĶ¹ éĿ©", "ĠÐĿаÑģ еление", "Å¡et ÅĻ", "ÙĴ ب", "Ġ âĶĢ", "ĠâĶ Ģ", "غ ÙĬÙĦ", "ĠдÑĸÑıлÑĮ нÑĸÑģÑĤÑĮ", "ĠÙĦ ÙĬس", "ĠÙĦÙĬ س", "Ġìĭľ ìŀ¥", "ãĥŁ ãĥ¥", "ĠÚ© ÙĪت", "ĠÚ©ÙĪ ت", "ĠÎĵ ι", "ิ à¹Ģว", "e ktor", "ek tor", "ekt or", "ĠбÑĥд Ñĥ", "ĠбÑĥ дÑĥ", "но важ", "нов аж", "нова ж", "Ñī аеÑĤÑģÑı", "Ñīа еÑĤÑģÑı", "Ġng ôn", "ĠvÄĽ c", "å¾ IJ", "à¸Ńà¹Ģม ร", "ัà¸į à¸Ĭ", "ĠиÑģполÑĮз ÑĥÑİÑĤ", "ĠиÑģполÑĮзÑĥ ÑİÑĤ", "r ubu", "ru bu", "rub u", "Ġnh á»±a", "ãģĮ ãģĬ", "ĠÐĵ аÑĢ", "о ÑĢе", "оÑĢ е", "Ġз олоÑĤ", "æ Ł³", "æŁ ³", "Ġ ÙĬØ´", "ĠÙĬ Ø´", "Ġповин нÑĸ", "اÙĤ تص", "ÙĦ ØŃ", "ĠокÑĤ ÑıбÑĢÑı", "ĠnÄĽk dy", "Ġо бÑĢа", "Ġоб ÑĢа", "ست Ú¯ÛĮ", "符 åIJĪ", "Ġth iá»ĥu", "æĺ¯ ä»Ģä¹Ī", "Ġro zs", "Ġroz s", "ì½ľ 걸", "Ġк аÑĦ", "Ġка ÑĦ", "åIJĮ æŃ¥", "ì¼ ĵ", "ÏĢ ÏĦÏħ", "à¸ł ายà¹ĥà¸Ļ", "ι ÏĥÏĦή", "ιÏĥ ÏĦή", "ĠدÙĪÙĦ ار", "ĠÙħا ÙĬÙĪ", "ĠÙħاÙĬ ÙĪ", "Ġ peÄį", "Ġp eÄį", "Ġpe Äį", "ัà¸ļ ม", "ÎĻ ÎĶ", "ı ydı", "ıy dı", "ัà¸ģ à¸Ĺ", "à¸Ľà¸£à¸° à¸ĸม", "κ αι", "κα ι", "Ġpro dej", "Ġprod ej", "ĠиÑİ Ð»Ñı", "Ġv Å©", "é© ±", "Ġh vÄĽ", "Ġhv ÄĽ", "æĥ³ è¦ģ", "ç¯ Ħ", "ç ak", "ça k", "Ġм Ñıг", "ım ın", "ımı n", "Ġdisp ozici", "Ġu kaz", "Ġuk az", "r acak", "ra cak", "rac ak", "Ġболез ни", "ว à¹Ĥม", "Ġз ел", "ĠÐĴ ики", "ĠÐĴи ки", "ĠÐĴик и", "ĠÐł од", "ูà¸ģ à¸Ħ", "í ij¸", "Ġth ải", "ĠbaÄŁ ımsız", "ĠÑĢоÑģ Ñģий", "ĠÐļ ам", "ĠÐļа м", "ĠиÑģполÑĮзов аниÑı", "ĠиÑģполÑĮз ованиÑı", "ĠиÑģполÑĮзовани Ñı", "ĠØŃ Ø°", "Âł ³³³³³³³³", "³³ ³³³³³³³", "³³³³ ³³³³³", "³³³ ³³³³³³", "³³³³³³³³ Âł", "³³³³³³³ ³³", "³³³³³ ³³³³", "³³³³³³ ³³³", "ĠاÙĨت ÙĤاÙĦ", "Ġаб ÑģолÑİÑĤ", "Ġ Ä±ÅŁÄ±k", "ĠÄ±ÅŁÄ± k", "ÏĦο γÏģαÏĨ", "ĠболÑĮÑĪ ой", "Ġعب ارت", "Ġعبار ت", "ÃŃ Å¾", "Ġدر ست", "Ġدرس ت", "ĠÑģл ово", "ĠÑģлов о", "ĠÑģло во", "à¥Ī Ċ", "ب ÙĪب", "بÙĪ ب", "ĠÐĴ оÑĤ", "ĠÐĴо ÑĤ", "ว à¹Ħà¸Ľ", "Ġbil inen", "Ġbilin en", "Ġ ÙĤÙĬ", "ĠÙĤ ÙĬ", "Ġbun ların", "Ġbunlar ın", "Ġbunları n", "Ùij ت", "Ġbas it", "Ġba sit", "ë¦ ¿", "ائ رة", "ائر Ø©", "Ġp ů", "Ġed ilmiÅŁ", "Ġedilm iÅŁ", "Ġedil miÅŁ", "Ġ ä½IJ", "ĠYön etim", "ĠYönet im", "Ùħ ÛĮر", "ÙħÛĮ ر", "Ġsp ou", "Ġspo u", "æ·± åľ³", "Ġвза ÑĶм", "ÎĻ ÎĽ", "Ð ĥ", "ĠдеÑĢжав ноÑĹ", "Ġ mrt", "Ġm rt", "Ġmr t", "ĠDe mir", "ĠDem ir", "é» İ", "ĠÑĢег ÑĥлÑıÑĢ", "Ġник огда", "å¼ ¾", "à¥ī ड", "Ġг лаз", "Ġгла з", "ĠÙħÛĮ Ú©ÙĨ", "ĠÙħÛĮÚ© ÙĨ", "éĻIJ å®ļ", "Ġнав к", "Ġпод ÑĤ", "ĠتصÙĪ ÛĮر", "ĠاÙĦØŃ Ø¯ÙĬØ«", "Ġdo Å¡lo", "нÑİ Ñİ", "ĠÑģ Ñħод", "ĠÑģÑħ од", "Ø· ÙĤØ©", "ĠÑģенÑĤ ÑıбÑĢÑı", "çī¹ æ®Ĭ", "à¸ģาร à¹ģà¸Ĥ", "á zd", "áz d", "ÑĶ ÑĤе", "ĠΣ ε", "ĠÙĦ ÙĥÙĦ", "ĠÙĦÙĥ ÙĦ", "åIJį åŃĹ", "اÙĨ ÛĮا", "اÙĨÛĮ ا", "Ġc ins", "Ġcin s", "Ġci ns", "기 ìĹħ", "Ġ éŁ³", "Ġé Ł³", "ĠéŁ ³", "éł ĥ", "ย าย", "ยา ย", "ìļ ķ", "ĠvÃŃ tÄĽz", "à¥įर ब", "Ġشر ÙĤÛĮ", "ĠشرÙĤ ÛĮ", "ĠbezpeÄį nost", "Ġçerç ev", "Ġ 맼", "Ġë§ Ľ", "c ky", "ck y", "ĵ ¨", "ĠÑĥм оваÑħ", "ĠÑĥмов аÑħ", "л иÑħ", "ли Ñħ", "m eniz", "men iz", "meni z", "Ġب Ú¯ÛĮر", "Ġبگ ÛĮر", "ÙĨ Ùī", "Ġ à¸ģารà¹ģà¸Ĥ", "Ġà¸ģาร à¹ģà¸Ĥ", "ι Ïĥε", "ιÏĥ ε", "âĢ³ E", "Ġdönem inde", "리 ì¹´", "Ġ åĪ°", "ĠåĪ °", "Ġhu kuk", "Ġhuku k", "а ÑĤоÑĢа", "аÑĤ оÑĢа", "аÑĤоÑĢ а", "аÑĤо ÑĢа", "ĠاÙĦ عÙĨ", "ĠاÙĦع ÙĨ", "ïº ĺ", "ün üz", "ünü z", "Ñģ оÑĤ", "Ñģо ÑĤ", "ุ ษ", "Ġd Æ°Æ¡ng", "ov ny", "Ġ ÑĦоÑĢма", "ĠÑĦоÑĢм а", "ĠÑĦоÑĢ ма", "ãģĹ ãģ®", "ãģĹãģ ®", "ز ÙĬز", "زÙĬ ز", "ĠاÙĦÙĨ اس", "Ġ Ñĩим", "ĠÑĩ им", "ĠÑĩи м", "大 人", "Ú¯ ÙĬ", "ĠÐĵ оÑģп", "é¢Ĩ 导", "Ġn inh", "Ġni nh", "Ġnin h", "Ġร าà¸Ħา", "Ġราà¸Ħ า", "ÙĤ اء", "ìī ¬", "ĠìĿ´ ìłĦ", "ĠÃ¶ÄŁret men", "ĠÑĨвеÑĤ а", "ен ноÑģÑĤÑĮ", "енно ÑģÑĤÑĮ", "大 ãģį", "ĠмиÑģÑĤ еÑĨÑĤ", "ر ÙĪت", "رÙĪ ت", "p oÅĪ", "po ÅĪ", "ĠÅŀ irket", "ĠкÑĢаÑģ ив", "ĠÑĢеÑģ ÑĥÑĢÑģ", "ĠÑĢеÑģÑĥÑĢ Ñģ", "ä¹ ¾", "Ġ ÙģÙĩ", "ĠÙģ Ùĩ", "ĠY Ãĸ", "èĬ ³", "μ ÏīÏĤ", "ÄĽ ji", "ÄĽj i", "Ġв лаж", "Ġвла ж", "ĠÑĥв ели", "ا ذا", "اذ ا", "ãĢĤ å¦Ĥæŀľ", "ĠпÑĢи ÑģÑĥÑĤÑģÑĤв", "ĠẤ n", "æĢ ĸ", "ĠÐľ еÑĤ", "Ġje dna", "Ġjed na", "Ġjedn a", "Ġc ục", "Ġcụ c", "ĠاÙĨت شار", "Ġз окÑĢема", "и ÑĩеÑģки", "иÑĩеÑģ ки", "ĠкÑĢаÑĹ Ð½Ð¸", "ĠкÑĢаÑĹн и", "и ÑĢÑĥ", "иÑĢ Ñĥ", "ĠÑĸн ÑĤеÑĢ", "ĠÑĸнÑĤ еÑĢ", "Ġан алог", "Ñ Ľ", "ี à¸ĭ", "н Ñĥли", "нÑĥ ли", "нÑĥл и", "ĠN inh", "ĠNi nh", "ĠNin h", "еÑĢ аÑĤоÑĢ", "еÑĢа ÑĤоÑĢ", "Ġr uce", "Ġru ce", "ĠÑĪ кÑĸ", "ĠÑĪк Ñĸ", "تر ÙĨت", "Ġson rası", "Ġsonra sı", "Ġ æį", "Ġæ į", "ÑĨен ÑĤÑĢа", "ÑĨенÑĤ ÑĢа", "Ġà¸Ńำ à¹Ģà¸ł", "Ø· ÙĬ", "ï¼Į å½ĵ", "ĠÑĤ ÑĢеÑħ", "ĠÑĤÑĢ еÑħ", "Âł H", "æ´ ª", "ãĥ³ ãĥĦ", "ãĥ³ãĥ Ħ", "ĠвÑĸдповÑĸд алÑĮ", "âĢĻ daki", "âĢĻd aki", "âĢĻda ki", "á ÅĻi", "áÅĻ i", "ĠpÅĻ em", "ĠpÅĻe m", "t uk", "tu k", "ĠÙģر ÙħÙĪد", "ĠÙģرÙħ ÙĪد", "Ġ ìĿ¸ì¦Ŀ", "ĠìĿ¸ ì¦Ŀ", "สำ à¸Ļ", "ìĥģ ìĿĺ", "ÅĻ ÃŃm", "ÅĻÃŃ m", "æ¾ ¤", "Ġ ÑĢей", "ĠÑĢ ей", "ĠÑĢе й", "ĠлÑİб ой", "u jte", "uj te", "ë³µ ì§Ģ", "Ġ درس", "Ġد رس", "Ġدر س", "ĠÐĴ лади", "ĠÑģво им", "ĠÑģвои м", "ĠìĿ¸íĦ° ëĦ·", "è± Ĭ", "Ġн алог", "Ġнал ог", "ãĤĪ ãģ³", "ĠØ® اطر", "Ġ ìŀħëĭĪëĭ¤", "Ġìŀħ ëĭĪëĭ¤", "ãĢĤ ãģĹãģĭãģĹ", "л аг", "ла г", "å° ĸ", "ëĭ ¥", "ìĬ¤ ëĬĶ", "ìĭł ì²Ń", "ãĥĩ ãĥ¼ãĤ¿", "ĠÑĥÑĢов нÑı", "Ġ무 ìĬ¨", "ĠاÙĦØ£ رض", "à¹ī à¸ķ", "Ỽ t", "ĠÙĨÛĮ رÙĪ", "ĠÙĨÛĮر ÙĪ", "å¢ ¨", "ãĤ¶ ãĥ¼", "r uba", "ru ba", "rub a", "ĠÙĨØ´ دÙĩ", "и лÑı", "ил Ñı", "a cÃŃm", "ac ÃŃm", "acÃŃ m", "ãĥ© ãĤ¯", "X H", "Ġس رد", "Ġسر د", "Ġद स", "t ember", "tem ber", "ĠDoÄŁ um", "ĠDoÄŁu m", "ĠпÑĢ оÑĢ", "ĠпÑĢо ÑĢ", "θ οÏĤ", "θο ÏĤ", "ĠiÅŁ e", "à¸Ń à¸Ł", "л аÑĪ", "ла ÑĪ", "اص ÙĦÙĩ", "اصÙĦ Ùĩ", "l ivÄĽ", "li vÄĽ", "liv ÄĽ", "ë¶Ģ ë¶Ħ", "н ак", "на к", "åįģ ä¸ī", "ส าห", "à¸Ľà¸£à¸°à¹Ģà¸Ĺศ à¹Ħà¸Ĺย", "ãĤŃ ãĥ³ãĤ°", "ĠмеÑĤ оÑİ", "Ġkullan arak", "âij ¡", "ÛĮز ات", "ĠÙħÙĪب اÛĮÙĦ", "ĠзнаÑĩ иÑĤ", "Ġзна ÑĩиÑĤ", "Ġorgan izace", "Ġorganiz ace", "ÑĢ ии", "ÑĢи и", "ov na", "Ġ ê²½ìłľ", "Ġê²½ ìłľ", "ãĢģ å½¼", "Ġम स", "Ġ à¹Ĥà¸Ľà¸£", "Ġà¹Ĥ à¸Ľà¸£", "L ARI", "LA RI", "LAR I", "æĩ Ĥ", "Ġ ва", "Ġв а", "ĠÙĥ ÙĨت", "ĠÙĥÙĨ ت", "ĠÑĢабоÑĤ а", "ĠÑĢаб оÑĤа", "ĠÑĢабо ÑĤа", "Âł ĠÂłĠÂł", "ÂłĠ ÂłĠÂł", "ÂłĠÂł ĠÂł", "好 äºĨ", "ĠzamÄĽst n", "ж енÑĮ", "же нÑĮ", "жен ÑĮ", "Ġu kon", "Ġuk on", "nÄĽ né", "nÄĽn é", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "ĠاÙĦخاص Ø©", "ĠÄį asu", "ĠÄįas u", "å°ı 说", "ĠØŃر کت", "æij Ħ", "Ïĩ Ïī", "ĠÑģв еж", "æĸ° éĹ»", "Ġ ìĭ±", "Ġìĭ ±", "Ġe ÄŁer", "ĠeÄŁ er", "Ġsitu ace", "Ġ ç·¨", "Ġç ·¨", "Ġç· ¨", "f ik", "fi k", "ë§ Īëĭ¤", "ë§Ī ëĭ¤", "Îķ Îļ", "Ġê°ľ ìµľ", "Ġc Ãł", "ا دث", "اد Ø«", "Ġsay ıda", "Ġsayı da", "ĠØ£ ÙģضÙĦ", "ĠØ£Ùģ ضÙĦ", "æ³ķ éĻ¢", "Ġ .,", "Ġ. ,", "ĠTh Æ°Æ¡ng", "Ïģ ÏĮÏĤ", "ÏģÏĮ ÏĤ", "ãģĹ ãĤĪãģĨ", "Ç İ", "æ ij¸", "æij ¸", "Ġ éĻ³", "ĠéĻ ³", "¥ IJ", "ฤ à¸Ķ", "Ġgi ảng", "Ġgiả ng", "Ġgiản g", "ĠлÑİ Ð±Ð¾Ð²", "ĠлÑİб ов", "Ġek ran", "о пиÑģ", "оп иÑģ", "еж дÑĥ", "Ġназ ва", "æĭ ĵ", "ı f", "à¹Ī à¸ģ", "и ÑĩнÑĸ", "иÑĩ нÑĸ", "Ġ ê³Ħíļį", "Ġê³Ħ íļį", "à¸ł าà¸Ħม", "à¸łà¸²à¸Ħ ม", "Ġ اپ", "Ġا Ù¾", "리 ìĿĺ", "ãģ§ãģĻ ãģĮ", "Ġkon ci", "Ġکار خاÙĨÙĩ", "Ġ ä½ķ", "ĠÑĤ ва", "ĠÑĤв а", "ĠÐŁ оÑģÑĤ", "ĠÐŁÐ¾ ÑģÑĤ", "ĠÐŁÐ¾Ñģ ÑĤ", "ĠапÑĢ елÑı", "ĠاÙĦع راÙĤ", "ä¸Ń åįİ", "à¹ĩ à¸Ńà¸ģ", "à¥įत à¤ķ", "Ġz ájem", "Ġzá jem", "Ġدر جÙĩ", "Ġब à¥ľ", "ĠÑģÑĤ ÑĢан", "ĠÑģÑĤÑĢ ан", "ĠÑģÑĤÑĢа н", "èѦ å¯Ł", "Ġyer leÅŁtir", "ĠyerleÅŁ tir", "ĠV Å©", "ç¾İ åħĥ", "Ġì¡° ê¸Ī", "Ġ รà¸Ńà¸ĩ", "Ġร à¸Ńà¸ĩ", "Ġak adem", "Ġaka dem", "à¸Ħ à¸ĵะ", "Ġpoz it", "Ġkon eÄį", "Ġkone Äį", "è°ĥ æŁ¥", "Ġ ãģĭ", "ĠÄįerv ence", "ĠOd kazy", "ĠëıĦ ìĭľ", "ั สà¸Ķ", "ัส à¸Ķ", "Ġg ái", "ĠÐł об", "Ġб оÑı", "Ġбо Ñı", "æī ©", "å¼Ģ å±ķ", "a nik", "an ik", "ani k", "Ġvy ž", "ĠbaÅŁ lay", "Ġbak Ä±ÅŁ", "ek ce", "ÑģÑĤ ика", "ÑģÑĤи ка", "еÑĢа ÑĤÑĥÑĢа", "еÑĢаÑĤÑĥ ÑĢа", "Ġë¶Ħ ë¥ĺ", "ĠPo Äįet", "od áÅĻ", "ëĭĺ ìĿĺ", "Ġk lid", "Ġkl id", "Ġkli d", "Û² Û¹", "ĠÚĨ ÛĮز", "m ür", "Ġs ứ", "ÙĬا ÙĨØ©", "ÙĬاÙĨ Ø©", "åĬ ±", "Ġ oku", "Ġo ku", "Ġok u", "Ġв оди", "Ġво ди", "Ġвод и", "ĠزÛĮر ا", "ĠزÛĮ را", "大 åĪ©", "ĠÙĦ ÛĮÙĨÚ©", "ĠÙĬ جب", "ĠÙĬج ب", "Ùħ ÛĮÙĦ", "ÙħÛĮ ÙĦ", "ĠÏĥ ÏĦÏģα", "æĻ ĵ", "ิ สà¸ķ", "ิส à¸ķ", "ĠÅŁ iddet", "ĠÑĢекомен да", "Ġpožad av", "Ġп ÑĸÑģ", "åħ¬ å¼ı", "Ġ Ú¯ÛĮرÛĮ", "ĠÚ¯ ÛĮرÛĮ", "ĠÚ¯ÛĮ رÛĮ", "ĠÚ¯ÛĮر ÛĮ", "к ÑĤа", "кÑĤ а", "ĠÙħÙĨ اطÙĤ", "Ġfirm y", "Ġfir my", "Ġ à¹Ħà¸Ľ", "Ġà¹Ħ à¸Ľ", "Ġ ÎŃÏģγ", "ĠÎŃ Ïģγ", "å¿« éĢŁ", "ãģĮ ãģªãģĦ", "н еÑģÑĤи", "не ÑģÑĤи", "неÑģ ÑĤи", "Ġ ç²¾", "Ġç² ¾", "ÑĢ ади", "ÑĢа ди", "ÑĢад и", "ãĤĴ ãģĭ", "ïº ª", "ky nÄĽ", "Ġह त", "t ak", "ta k", "ĠÙĬÙĪÙĨ ÙĬÙĪ", "ö ÄŁ", "Ġ ÑĢÑĥк", "ĠÑĢ Ñĥк", "ĠÑĢÑĥ к", "åľĭ éļĽ", "Ñİ ÑģÑĮ", "Ġдав но", "Ġp opis", "Ġpop is", "Ġpo pis", "ĠB Ä°L", "ĠÙĨ ÙĤد", "ĠÙĨÙĤ د", "ĠÑģп ож", "ÑĨион нÑĭÑħ", "ĠÑĪ п", "Ñĥ ÑİÑīиÑħ", "ÑĥÑİ ÑīиÑħ", "ÑĥÑİÑī иÑħ", "ĠвоздÑĥ Ñħ", "ÑĤ ие", "ÑĤи е", "ĠU ž", "ÏĮ δ", "à¸ģร าà¸Ħม", "Ġalan ında", "Ġalanı nda", "Ġs ắt", "ãĥIJ ãĤ¤", "Ng Ãły", "Ġ ë¹Į", "Ġë¹ Į", "ï¼ī ãģ¯", "Ġ ä¿¡", "Ðķ С", "ĠT ato", "ĠTa to", "ĠTat o", "Ġún ora", "e rap", "er ap", "era p", "Ä ł", "ĠT áºŃp", "Ġкомп ании", "ãĥ© ãĤ¤ãĥĪ", "ãĥ©ãĤ¤ ãĥĪ", "éľĢ æ±Ĥ", "Ġت ÙĪÙĤ", "ĠتÙĪ ÙĤ", "âĢĻ âĢĻ", "ëŀį ëĭĪëĭ¤", "ĠквÑĸÑĤ нÑı", "Ġoyun cu", "ÂĢÂĢÂĢÂĢ ÂĢÂĢÂĢÂĢ", "åĨ Ĭ", "Ġyap mÄ±ÅŁ", "ัà¸ĩ à¹Ħม", "Ġзап аÑħ", "á la", "ál a", "ĠÑĤеÑħ ниÑĩеÑģ", "Ġ ØŃص", "ĠØŃ Øµ", "ร à¸Ķ", "å¼ Ħ", "ĠÚ¯ÛĮ اÙĩ", "اÙĩ رة", "اÙĩر Ø©", "Ġà¤ı ड", "ним аеÑĤ", "нима еÑĤ", "ا دÙĨ", "اد ÙĨ", "Îľ Îij", "Ġ 社", "Ġç¤ ¾", "аÑĢ Ñĩ", "ت ز", "æ¶ ¦", "in izin", "ini zin", "iniz in", "inizi n", "Ġbey az", "Ġ بÙĪÙĦ", "Ġب ÙĪÙĦ", "ĠبÙĪ ÙĦ", "åĿ ¡", "ãģ® ãĤĪãģĨãģ«", "Ġyap tıģ", "Ġyaptı ÄŁ", "Ġd aģı", "Ġda ģı", "ĠdaÄŁ ı", "ĠbaÅŁ arı", "ĠbaÅŁar ı", "Ġ ÏĢά", "ĠÏĢ ά", "ĠпÑĢод аж", "B á»Ļ", "Ġत त", "Ġpod stat", "Ġpods tat", "Ġ æµģ", "Ġæµ ģ", "Ġzdrav ÃŃ", "Ġ ç¡", "Ġç ¡", "Ġ opak", "Ġo pak", "Ġop ak", "Ġhá»į a", "æĭ Ķ", "Ñĥ жд", "Ñĥж д", "Ġtr ứng", "ÙĪر ÙĬØ©", "ÙĪرÙĬ Ø©", "Ñĭ л", "um suz", "ums uz", "Ġ سبب", "Ġسب ب", "许 å¤ļ", "å®ŀ éªĮ", "Ġб оли", "Ġбол и", "Ġбо ли", "Ġd uyá»ĩt", "áºŃ c", "ĠÐij ез", "ĠبÙĦ ÙĨد", "м м", "ÑĢ ел", "ÑĢе л", "N Ä°", "Ġ ãĥ¯", "Ġãĥ ¯", "éĭ ¼", "ĠÑģв Ñı", "Ġ åIJİ", "ĠåIJ İ", "Ġmu ht", "Ġmuh t", "ĠпÑĢоблем и", "ĠÑĤÑıж ел", "ĠС ем", "ฤษ à¸łà¸²à¸Ħม", "à¹Ī าà¸ķ", "à¹Īา à¸ķ", "ör ü", "üy orum", "üyor um", "ĠاÙĦØ£ ØŃ", "ĠÑģÑĤÑĢ аÑĪ", "ĠÑģÑĤÑĢа ÑĪ", "h oo", "ho o", "ध र", "Ġt lak", "Ġtl ak", "Ġsrp na", "ifik ace", "Ġ reh", "Ġre h", "Ġr eh", "Ġм инÑĥ", "Ġмин Ñĥ", "Ġми нÑĥ", "ãĢĢ j", "ĠгÑĢÑĥ пи", "ĠгÑĢÑĥп и", "Ġ άλ", "Ġά λ", "Ġolur sa", "λογ ία", "ĠÐĴ ик", "ĠÐĴи к", "Ġmüc adel", "Ġz ávÄĽ", "Ġzá vÄĽ", "Ġzáv ÄĽ", "ĠÑĦев ÑĢа", "Äį ná", "à¹Į à¹Ģà¸ĭ", "ĠÙĦ ÙĦØŃ", "ĠÙĦÙĦ ØŃ", "ÑĢ ип", "ÑĢи п", "Ġб Ñĥк", "ĠбÑĥ к", "ãģĪ ãģªãģĦ", "Ġpo rad", "Ġpor ad", "Ġsa mostat", "Ġsam ostat", "Ġsamo stat", "Ġt esis", "Ġte sis", "Ġtes is", "اب ÙĤÙĩ", "ابÙĤ Ùĩ", "Ġجد ÙĬدة", "ĠجدÙĬد Ø©", "éĢ Ĵ", "âĶģ âĶ", "س ÛĮÙĨ", "سÛĮ ÙĨ", "Ġgerek tiÄŁini", "ียà¸Ļ à¸ļ", "è¨Ģ ãģ£ãģ¦", "ĠÑĸн ÑĤеÑĢеÑģ", "ĠÑĸнÑĤеÑĢ еÑģ", "ĠÑı ким", "ĠÑıк им", "Ġ æĢ»", "ĠæĢ »", "k ovou", "ko vou", "kov ou", "Ġd emek", "Ġde mek", "Ġdem ek", "اÙĨ ÙĬا", "اÙĨÙĬ ا", "Ġdom ů", "Å¡ nÃŃ", "ate ÅĻ", "åĢ «", "δο Ïĥη", "Ġ 기ìĹħ", "Ġ기 ìĹħ", "åĶ ĩ", "ì¹ ł", "Ñĸ дÑĥ", "Ñĸд Ñĥ", "린 ìĿ´", "æľĢ åĪĿ", "è ¸ı", "è¸ ı", "æĥ³ åĪ°", "à¥į बर", "à¥įब र", "Ġ ìŀĶ", "Ġìŀ Ķ", "ĠÑĢаз нÑĭÑħ", "k rom", "kr om", "ι αν", "ια ν", "Ġд ÑĢÑĥз", "ĠдÑĢÑĥ з", "ĠдÑĢ Ñĥз", "ä »¿", "ä» ¿", "Ġê·¸ ëłĩ", "Ġд алÑĸ", "Ġда лÑĸ", "Ġдал Ñĸ", "æķĪ æŀľ", "Ġह व", "è¼ Ŀ", "Ġì°¸ ê³ł", "Ġ ìĨĶ", "ĠìĨ Ķ", "Ġz nal", "Ġzn al", "ĠпеÑĢ Ñģ", "ÙIJ Ùij", "ĠÑĤ еж", "ĠÑĤе ж", "åĭ Ł", "ι θ", "Äį ů", "Ġe kip", "Ġek ip", "Ġk hung", "Ġkh ung", "Ġkhu ng", "éĹ ĺ", "ĠتصÙħ ÛĮÙħ", "о иÑĤ", "ĠÑħ ол", "æĬ ŀ", "a mam", "am am", "ama m", "Ġâĸ ³", "ãģ ĩ", "Ġع ÙĨÙĩ", "ĠعÙĨ Ùĩ", "Ġì°¸ ê°Ģ", "ĠÎļ ÏĮ", "åı¤ å±ĭ", "к овоÑĹ", "ков оÑĹ", "ково ÑĹ", "ศ à¸Ī", "олог иÑı", "ĠÙħØ« بت", "ĠÐļÑĢа ÑĹна", "ĠмеÑģÑı ÑĨев", "Ġalın an", "ĠÏĢÏģα γμα", "Ġ ìŀ¡ëĭ´", "Ġìŀ¡ ëĭ´", "Ġп лод", "Ġпл од", "Ġпло д", "ĠÑĤка ни", "ÑģÑĭ лки", "ÑģÑĭл ки", "سط س", "ra nÄĽ", "ran ÄĽ", "к аж", "ка ж", "е маÑĤи", "ем аÑĤи", "ема ÑĤи", "Ġز ÛĮست", "ĠزÛĮ ست", "æ¿ Ł", "Ġpop lat", "Ġpo plat", "γ ÎŃν", "íĨł íĨł", "Ġt ây", "Ġìµľ ê·¼", "ãĥ© ãĥ³ãĤ¹", "ãĥ©ãĥ³ ãĤ¹", "Ġgün eÅŁ", "Ġ ÙģÙĤ", "ĠÙģ ÙĤ", "ĠsaÄŁ layan", "ĠsaÄŁlay an", "ĠØŃ Ø²Ø¨", "à¥ģल न", "ĠB ilim", "ĠBi lim", "ĠBil im", "ĠB atı", "ĠBa tı", "ĠBat ı", "æł· çļĦ", "δ ικ", "δι κ", "α ÏģίοÏħ", "αÏģ ίοÏħ", "Ġ ìĽĢ", "ĠìĽ Ģ", "Ġl á»Ńa", "ÙĨ ÙĪع", "çİ ²", "а ном", "ан ом", "ано м", "Ġst átnÃŃ", "Ġstát nÃŃ", "Ġ äºİ", "Ġm ùi", "ĠÄij á»Ļt", "ĠÄijá»Ļ t", "æ² ĥ", "åħ¬ åľĴ", "ĠÑģ ÑĮогоднÑĸ", "но Ñģи", "ноÑģ и", "Z a", "Ġд ли", "ĠÏĥÏħν ÎŃ", "ĠV á»ĭ", "m av", "ma v", "ĠM üslüman", "/ ï¼ı", "ĠзаÑī иÑĤ", "é ĸī", "éĸ ī", "Ġ çģ«", "Ġçģ «", "Ġ å·Ŀ", "Ġå· Ŀ", "Ġ аж", "Ġа ж", "è¿ĩ æĿ¥", "à¸Ĺ าà¸Ļ", "ĠAr aÅŁtır", "ĠAra ÅŁtır", "Õ¡ Õ", "Ġpo mÄĽr", "Ġpom ÄĽr", "Ġd ům", "Ġdů m", "å¦ ®", "Ġhlav nÄĽ", "Ġfin ans", "Ġfinan s", "Ġ γνÏī", "Ġγ νÏī", "ÏĥÏĦη μα", "ï¼Į çĶ¨", "ìĭŃ ìĭľìĺ¤", "ĠÙħ ثاÙĦ", "ĠÙħØ« اÙĦ", "- Ðij", "ÑĨÑĸй нÑĸ", "Ġد ستÙĩ", "Ġدست Ùĩ", "Ġدس تÙĩ", "à¥ī स", "ÑĢ Ñĸп", "ÑĢÑĸ п", "ĠpÅĻi pom", "ĠpÅĻip om", "Ġ ÙĪÙĦÙĬ", "ĠÙĪ ÙĦÙĬ", "ĠÙĪÙĦ ÙĬ", "ĠÙĪ زÙĨ", "ĠÙĪز ÙĨ", "Ġelekt rik", "Ġelektr ik", "ĠQu ân", "i vé", "iv é", "Ġl ẽ", "ç®Ģ åįķ", "Ġon lara", "Ġonlar a", "оÑģ лав", "ìĭľ íĤ¤", "ëª ¬", "ĠÙħÙĤ دار", "ĠÙħÙĤد ار", "ĠOr ta", "ĠOrt a", "ĠS eç", "ĠSe ç", "ĠÙĨÙĪÙģ Ùħبر", "ุà¸Ļ ายà¸Ļ", "ĠÑĥм ови", "ĠÑĥмов и", "Ġपर म", "Ġ strom", "Ġst rom", "Ġstr om", "Ġstro m", "ĠкÑĢа Ñīе", "ç§ ¦", "缸 æīĭ", "鼻 è¦ĸ", "Ġuygu lama", "Ġuygulam a", "Ġ ÑĢиз", "ĠÑĢ из", "æĪ ²", "य र", "ĠH lav", "Ġ ìĭ¸", "Ġìĭ ¸", "Ġли пнÑı", "ÅĪ ujÃŃ", "ÑĢ из", "ÑĢи з", "é«ĺ éĢŁ", "缸 å½ĵ", "k enin", "ke nin", "ken in", "Ġо ÑģÑĤанов", "ĠоÑģÑĤ анов", "ĠоÑģÑĤан ов", "Ġbit k", "Ġbi tk", "ova ného", "ovan ého", "ované ho", "ĠÐľ аÑĢи", "ĠÐľÐ°ÑĢ и", "ĠÐľÐ° ÑĢи", "èµ ¶", "ì½ ©", "Ġölç ü", "ĠС еÑĢед", "ĠСеÑĢ ед", "ĠTh á»Ŀi", "Ïī να", "Ïīν α", "ÙĪ بة", "ÙĪب Ø©", "Ġch ụp", "âĢĮ د", "Ġch áy", "ĠÐĴ ели", "Ġоб ÑģÑĤ", "ĠобÑģ ÑĤ", "Ġìĭľ ì¦Į", "د ÙħØ©", "دÙħ Ø©", "п од", "по д", "l ue", "lu e", "ĠдÑĸ лÑıн", "ĠÙ¾ ÙĪست", "ĠاÙĦ ÙĨس", "ĠاÙĦÙĨ س", "èĤ Į", "ìĪĺ 를", "Ġú rov", "ĠÙħØ´ Ú©ÙĦ", "ĠÙħØ´Ú© ÙĦ", "éĩįè¤ĩ éĩįè¤ĩ", "н ез", "не з", "Ġdop oruÄį", "Ġtas arım", "Ġtasar ım", "íģ¬ 기", "ìĿ´ ìħĺ", "Ġde set", "Ġdes et", "Ġdese t", "ĠÙħرتب Ø·", "ัà¸Ĵ à¸Ļา", "ัà¸Ĵà¸Ļ า", "' ı", "Ñĩ ки", "ĠìŀĪ ëįĺ", "ÑĪ ка", "n ám", "ná m", "ÑģÑĤ ÑĢов", "ÑģÑĤÑĢ ов", "ÑģÑĤÑĢо в", "à¥į सर", "à¥įस र", "нÑĥ лаÑģÑĮ", "нÑĥла ÑģÑĮ", "ãģ¡ãĤĩ ãģ£ãģ¨", "Ġ å¦", "Ġå ¦", "γ ÏĮ", "Ġ é»ij", "Ġé» ij", "X em", "Ġt á»ĩ", "Ġtá» ĩ", "ĠëĮĢ íĨµëł¹", "기 ê´Ģ", "æīį èĥ½", "è¯Ń è¨Ģ", "ed eyse", "ĠТ Ñĭ", "ĠÑģо един", "ĠìĹĨ ìĬµëĭĪëĭ¤", "Ñı ÑİÑĤ", "à¹ģ หล", "à¹ģห ล", "Ġì§Ģ ë°©", "Ġosob nÃŃ", "ÛĮ ÙĦÛĮ", "ÛĮÙĦ ÛĮ", "Ġавг ÑĥÑģÑĤа", "Ñī ик", "Ñīи к", "Ġvý Å¡e", "g th", "gt h", "ĠÏĢ αν", "ĠÏĢα ν", "ج ار", "جا ر", "Ġвид ов", "Ġви дов", "ìĿ´ ìĬĪ", "ĠÐij аÑĢ", "ĠÏĮ ÏĢοÏħ", "æ¤ ħ", "Ġع اÙĦÛĮ", "ĠQ uyết", "ĠQuy ết", "Ãľ M", "ãĥĿ ãĤ¤ãĥ³ãĥĪ", "Ġ ê¹Į", "Ġê¹ Į", "Ġкан ди", "k ového", "kov ého", "kové ho", "ĠMerk ez", "Ġy iy", "Ġyi y", "ĠpÅĻÃŃ spÄĽ", "ĠÑĤемпеÑĢаÑĤÑĥ ÑĢÑĭ", "ĠÙ¾ ÙĬ", "ฤ ศà¸Ī", "è°ĥ çĶ¨", "ĠÑģÑĤоÑĢ онÑĥ", "ĠÑģÑĤоÑĢон Ñĥ", "à¹ī à¸Ĭ", "好 ãģį", ". Åŀ", "Ġп ÑĢоз", "ĠпÑĢ оз", "ĠпÑĢо з", "ÙĨت اج", "鼻 åŃIJ", ".: .:.", ".:.: .", ".:. :.", "è¨ ĵ", "и ÑĩеÑģкое", "иÑĩеÑģ кое", "Ġн оги", "Ġно ги", "Ġног и", "Ġ λÎŃ", "Ġλ ÎŃ", "Ġsık ıntı", "Ġê°Ģ 족", "ĠتÙĨ ظÙĬÙģ", "ĠتÙĨظ ÙĬÙģ", "Ġö dül", "ĠaÅŁaģı daki", "Ġž elez", "Ġže lez", "ĠاÙĦع دÙĬد", "غ ÙĨ", "Ġокон Ñĩ", "ÑĢем Ñı", "ÑĢе мÑı", "L Ä°", "Ġne jd", "Ġnej d", "Ġ ÏĢλα", "ĠÏĢ λα", "Ñģ ко", "Ñģк о", "Ġ ìĪĻ", "ĠìĪ Ļ", "ĠÙ¾ ÙĪÙĦ", "θεν ήÏĤ", "Ġ주 ìļĶ", "Ġ æĬ¥", "ĠæĬ ¥", "ĠÙħ Ùħا", "ĠÙħÙħ ا", "Ðł Ð¡Ðł", "ĠÑĢа дÑĸ", "ĠÑĢад Ñĸ", "ä¸Ģ ç§į", "é¾ Ħ", "Ġsö yl", "Ġsöy l", "Ïģκε ια", "Ïģκ εια", "Ġзем лÑĸ", "Ġve Äįer", "g eç", "ge ç", "س تÙħ", "ست Ùħ", "Ġse fer", "ĠÑģ вÑĸд", "ĠÑģв Ñĸд", "ï»Ł ï»", "а лов", "ал ов", "ало в", "ìĬ¤ 를", "âī ¥", "ĠتÙĦ ÙģÙĨ", "ĠتÙĦÙģ ÙĨ", "åİ» äºĨ", "़ à¥ĭà¤Ĥ", "़à¥ĭ à¤Ĥ", "ĠÑĦоÑĢм е", "ĠÑĦоÑĢ ме", "d üm", "dü m", "åħ ģ", "ÑĢ ап", "ÑĢаР¿", "ÑĢа п", "ĠV Æ°Æ¡ng", "à¸Ńะ à¹Ħร", "ัà¸ģษ à¸ĵ", "Ġ åį³", "Ġåį ³", "ĠاÙĦ رÙħ", "ĠاÙĦر Ùħ", "ĠзаÑħиÑģÑĤ Ñĥ", "° E", "o dÃŃ", "od ÃŃ", "Ġव न", "ĠÄij èn", "Ġ åıĹ", "Ġåı Ĺ", "èIJ½ ãģ¡", "Ġ zim", "Ġz im", "Ġzi m", "리 ì¦Ī", "èĪ Ĵ", "Ġзб ÑĸÑĢ", "Ġ ä»·æł¼", "ĠлÑİ Ð´Ð¸Ð½Ð°", "ĠлÑİд ина", "ĠлÑİди на", "ĠÐŁÐ¾Ñģ иланнÑı", "и Ñī", "ĠÎ ¨", "ิà¸ģ ายà¸Ļ", "ิà¸ģา ยà¸Ļ", "Ġbu dete", "Ġbud ete", "Ġbude te", "Ġз ÑĢоÑģÑĤ", "Ġ vyk", "Ġv yk", "Ġvy k", "ĠÐĹ ÐµÐ¼", "ĠиÑİ Ð½Ñı", "ĠmÄĽ lo", "ĠmÄĽl o", "ÙĦ اÙģ", "ÙĦا Ùģ", "Ġ ÙĪØ´", "ĠÙĪ Ø´", "ĠÑģп ÑĢави", "ĠÑģпÑĢав и", "ãģĻ ãģİ", "ĠгÑĢа дÑĥ", "ĠгÑĢ адÑĥ", "R oz", "Ro z", "ι νή", "ιν ή", "Ġch á»ĵng", "ä¸Ģ åį·", "Ġ Xem", "ĠX em", "ĠÑģимв ол", "ĠÑģим вол", "Ġod mÃŃt", "ĠÑĢÑıд ом", "ĠÑĢÑı дом", "ĠÑĩеÑĢв нÑı", "à¸ģระ à¸Ĺ", "人 人", "æ°Ĺ æĮģãģ¡", "un daki", "und aki", "unda ki", "åľĭ 家", "εÏģ μαν", "Ġ лÑĮ", "Ġл ÑĮ", "ĠN üfus", "Ġм еÑĢе", "ĠмеÑĢ е", "بر اÙĬر", "н аннÑı", "Ġ наÑĢ", "Ġн аÑĢ", "Ġна ÑĢ", "Ġt ấm", "æĸ½ å·¥", "é¡ ¯", "Ġh è", "æĺİ çĻ½", "Ġдо гов", "Ġдог ов", "ĠÙģ رÙħ", "ĠÙģر Ùħ", "èĢ Ĺ", "ìĬ¤ ìĿĺ", "ìĦ¸ ëĮĢ", "è¯ ļ", "Ġнеб олÑĮ", "Ġ à¸Ľà¸£à¸°à¸ģ", "Ġà¸Ľà¸£à¸° à¸ģ", "Ġì¹ ¼", "Ġov liv", "Ġ NGC", "ĠN GC", "ĠNG C", "ãĢĤ ä¸į", "ا ÙĦÙī", "اÙĦ Ùī", "æī £", ". ÐIJ", "ÑĢа ÑģÑĤа", "ÑĢаÑģ ÑĤа", "ÑĢаÑģÑĤ а", "ĠÃĩ ev", "ãģ£ ãģ¡", "ãģ£ãģ ¡", "ï¼Į éĥ½", "Ġrov nÄĽÅ¾", "ĠÏĩÏģÏĮ νια", "Ġì¡° ìĦł", "ĠØ¢ باد", "Ġآب اد", "ĠÐľ аÑģ", "ĠÐľÐ° Ñģ", "çĻ¼ å±ķ", "ä» Ķ", "Ġkend isini", "Ġkendisi ni", "à¹Īà¸Ńà¸ĩ à¹Ģà¸Ĺ", "ĠV ÄĽ", "Ġr ượu", "Ġm áme", "Ġmá me", "Ġmám e", "ĠоÑĩеÑĢед ÑĮ", "Ġسب تÙħبر", "Ġб ок", "Ġбо к", "ì§Ģ ìĹŃ", "Ġتا Ø«ÛĮر", "Ġتاث ÛĮر", "Ġl isans", "Ġli sans", "Ġlis ans", "Ġger ektir", "Ġgerek tir", "Ġs izi", "Ġsi zi", "Ġsiz i", "Ñĸ но", "Ñĸн о", "ĠM Ã¼ÅŁ", "ĠMü ÅŁ", "ãģı ãĤīãģĦ", "ãģıãĤī ãģĦ", "Ġза клÑİÑĩ", "Ġзак лÑİÑĩ", "ãģĵãģ¨ ãģ«", "è¨Ģ ãģĦ", "ãĢģ å°ı", "Ġet mektedir", "Ġetm ektedir", "åł± åijĬ", "Ġkar Ä±ÅŁ", "Ġоб лад", "Ġобла д", "Ġобл ад", "å¥ ij", "ra cat", "rac at", "ĠارتÙģ اع", "μ αι", "μα ι", "íĶ Ī", "ĠÙĪ ÙĦÙħ", "ĠÙĪÙĦ Ùħ", "ëĬĶ ì§Ģ", "lom ou", "Ġли ÑĨа", "ĠлиÑĨ а", "ĠìĿĮ ìķħ", "Ġhod nÄĽ", "èĭ± æĸĩ", " Ħ", "à¹ī าà¸Ĥà¸Ńà¸ĩ", "à¹īา à¸Ĥà¸Ńà¸ĩ", "Ġê³Ħ ìķ½", "åIJĦ ç§į", "ĠÙħر Ú¯", "éĶ ģ", "Ġन द", "ãĥĭ ãĥ¡", "Ġ ем", "Ġе м", "Ġe leÅŁtir", "Ġel eÅŁtir", "Ġele ÅŁtir", "Ġ íĬ¹ë³Ħ", "ĠíĬ¹ ë³Ħ", "ĠÎ¥ ÏĢο", "Å¡ ker", "Å¡k er", "L ERÄ°", "LER Ä°", "æ² Ī", "l ikleri", "lik leri", "likle ri", "likler i", "ĠÙħÙĩÙĨد سÛĮ", "ĠbaÄŁ ır", "dı ģını", "dıģ ını", "ĠاÙĦ تد", "ĠاÙĦت د", "à¸¸à¸Ľ à¸ģรà¸ĵ", "ĠÑģлед ÑĥÑİÑīие", "ĠÑģледÑĥÑİÑī ие", "Ġì§ģ ìłij", "å° ¤", "ĠоÑģнов Ñĸ", "Ġt ÄĽla", "ĠtÄĽ la", "ĠtÄĽl a", "Ġп ак", "Ġпа к", "iz ace", "iza ce", "Ġná rod", "Ġnáro d", "a ný", "an ý", "ĠÑį п", "Ġüç üncü", "Î¥ Ρ", "éĨ´ éĨ´", "à¹Ģà¸ģ à¸Ńร", "âĢĮاÙĨ بار", "ç¶ Ļ", "Îij Îł", "ı lıģı", "ılı ģı", "ıl ıģı", "ılıģ ı", "ĠÃľ rün", "Ġдоз вол", "Ġ íĥĪ", "Ġíĥ Ī", "Ġà¤ĵ वर", "è« ¸", "èĺ ĩ", "ĠпÑĢоÑģÑĤ ÑĢан", "éĿĴ å¹´", "ãģ® æĸ¹", "ĠÚĨ Ú¯ÙĪÙĨÙĩ", "ÙĦ Ø·", "âĢľ æĪij", "Ġëĭ¤ìļ´ ë°Ľ", "ा .Ċ", "ा. Ċ", "Ġmüc adele", "Ġmücadel e", "Ġc ÃŃt", "ĠcÃŃ t", "à¹Īวม à¸ģ", "ÄŁ ına", "ģı na", "ģın a", "ê°ľ ë°ľ", "ĠÏĢ αιδ", "ĠÏĢα ιδ", "ĠÏĢαι δ", "ض اÛĮ", "ضا ÛĮ", "Ġbor ç", "íĬ ľ", "ĠخدÙħ ت", "Ġخد Ùħت", "Ġu dál", "Ġud ál", "Ġ виг", "Ġв иг", "Ġви г", "Ġ ë°°ìĨ¡", "Ġë°° ìĨ¡", "å¹ ¾", "Ùİ Ø¬", "Ġ ìĹĺ", "ĠìĹ ĺ", "çĢ ¬", "ï Ģ", "ĠÎij θή", "пÑĢи клад", "ĠпÑĢи Ñĩина", "ĠпÑĢиÑĩин а", "ĠпÑĢиÑĩ ина", "ĠÙģ شار", "æ »¿", "æ» ¿", "Ġd ostat", "Ġdo stat", "Ġdos tat", "Ġdost at", "Ġ졸 ìĹħ", "Ġا رز", "Ġار ز", "ÙĪÙĦ ÙĪج", "ÙĪÙĦÙĪ ج", "س ÙĪ", "æĺł çĶ»", "Ġth ôi", "Ġ ³³³", "ĠÂł ³³", "Ġ³³ Âł", "à¹ģ à¸Ļะ", "à¹ģà¸Ļ ะ", "è¨Ń åĤĻ", "Ġмног ие", "ÑĤ оÑĦ", "ÑĤо ÑĦ", "i Å¡tÄĽ", "iÅ¡ tÄĽ", "à¤Ĺ ढ", "Ġин дивидÑĥ", "Ġ ìĥĿíĻľ", "ĠìĥĿ íĻľ", "Ġзов ÑģÑĸм", "íĥ ķ", "çľ ł", "ĠêµŃ ëĤ´", "e ptal", "ep tal", "ept al", "r aci", "ra ci", "rac i", "è¡ ¡", "ãĦ ·", "ĠSt ÅĻed", "اÙĦ ÙĬا", "اÙĦÙĬ ا", "Σ Τ", "Ľ °", "ãĥī ãĥ«", "á zÃŃ", "áz ÃŃ", "Ġа Ñģп", "ĠаÑģ п", "ĠdÄ±ÅŁ arı", "ĠвиÑĢоб ниÑĨÑĤва", "e za", "ez a", "ï¼Į ä¸įè¿ĩ", "ï¼Įä¸į è¿ĩ", "çĥ ¦", "ãĥ³ ãĤ°ãĥ«", "ãĥ³ãĤ° ãĥ«", "Ġroz voj", "ĠÙħÙĨت شر", "ĠÑĥÑĤ еп", "Ġد ÙĬÙĨ", "ĠدÙĬ ÙĨ", "ĠзаÑģоб Ñĸв", "Ng Æ°á»Ŀi", "ãĤ· ãĥ¼", "ĠFran sız", "ÎĻ Τ", "ائ Ùģ", "ι Ïĩ", "ี à¹Ģม", "à¥į मन", "à¥įम न", "à¥įम à¤ļ", "Ġس عر", "Ġسع ر", "ï¾ Ŀ", "ë°© ë²ķ", "ĠС о", "Ġà¤ĸ बर", "ìĨĮ ê°ľ", "Ġsl ova", "Ġslo va", "Ġslov a", "Q PCP", "QP CP", "ĠK ız", "ĠKı z", "Ø· ÙģاÙĦ", "Ø·Ùģ اÙĦ", "Ġк оÑĢм", "ĠкоÑĢ м", "ĠìĹħ ëį°ìĿ´íĬ¸", "es poÅĪ", "esp oÅĪ", "à¸Ķ าว", "à¸Ķา ว", "о ÑĢом", "оÑĢ ом", "оÑĢо м", "ĠгÑĢа ÑĦ", "ĠгÑĢ аÑĦ", "Ġп ÑĸÑĪ", "Ġ ë¿IJ", "Ġë ¿IJ", "ý v", "С ам", "Ġk rev", "Ġkr ev", "Ġkre v", "ĠB unu", "ĠBu nu", "ĠBun u", "Ġz obraz", "Ġسخ ÙĨ", "Ġ æĶ¯", "ĠæĶ ¯", "лÑİ Ð±", "Ùİ Ø§ÙĨ", "Ùİا ÙĨ", "маÑĤ ÑĢива", "λ εÏį", "λε Ïį", "Ġпо Ñħод", "ĠпоÑħ од", "Ġг ÑĢе", "ĠгÑĢ е", "çľĭ çĿĢ", "à¸Īำ à¸ģ", "ัà¸ĩà¸Ħ ม", "Ġseç enek", "Ä° stanbul", "ĠвÑĸд мов", "m iyor", "mi yor", "Ġm ụn", "ìĿ´ ìĹIJ", "ĠNh Æ°", "Âł tom", "Âłt om", "lık ları", "lıkla rı", "lıklar ı", "Âł Äij", "ãĥ» ãĥŀ", "Ġ Ùģت", "ĠÙģ ت", "ĠFakült esi", "ìłĦ íŀĪ", "éª ij", "Ġìŀij ìĿĢ", "ç¼ ĺ", "ìº IJ", "Ġmü zik", "Ġmüz ik", "а лÑĭ", "ал Ñĭ", "Ġp ozem", "Ġpo zem", "Ġpoz em", "çĥ §", "Ġ 常", "Ġå¸ ¸", "Å¡ il", "Å¡i l", "à¤Ĩ प", "à¸ģำ หà¸Ļà¸Ķ", "Ġگرد Ø´", "λ ιά", "λι ά", "Ġö den", "åıª è¦ģ", "ĠÄIJ o", "Ġstrat ej", "Ġstra tej", "Ġstrate j", "ĠÙĩ تÙĦ", "ÙĤ Ùģ", "Ġkullan ılır", "Ġkullanıl ır", "ĠÑģп оÑģÑĤ", "ĠÑģпоÑģ ÑĤ", "ĠnÄĽ ho", "ĠÐŁ еÑĢед", "ĠÐŁÐµÑĢ ед", "Ġиз меÑĢ", "] ]>", "]] >", "ĠнÑĸк оли", "Ġha yal", "Ġhay al", "Ġhaya l", "Ġдод аÑĤков", "Ġन à¤ķ", "Ġins anın", "Ġinsan ın", "ุม à¸łà¸²à¸ŀ", "ograf ie", "в об", "во б", "ĠاÙĨ ساÙĨÛĮ", "ĠاÙĨساÙĨ ÛĮ", "Ġm ük", "Ġmü k", "ĠÑĥ меÑĢ", "ĠÑĥм еÑĢ", "оÑĩ нÑĭе", "ëıĦ ìĿĺ", "Ġ ara", "Ġa ra", "Ġar a", "Ġë¹ ¨", "Ġκ Ïį", "л ой", "ло й", "Ñģи он", "Ġroz dÃŃl", "ay ıf", "ayı f", "ĠÙĪاØŃ Ø¯Ø©", "ĠÙĪاØŃد Ø©", "ĠÙĪا ØŃدة", "о ÑĢалÑĮ", "оÑĢ алÑĮ", "оÑĢа лÑĮ", "Ġpo chop", "Ġpoc hop", "éļ ¨", "à¹īà¸Ń à¸ĩà¸Ļ", "à¹īà¸Ńà¸ĩ à¸Ļ", "Ġ ÙĪاÙĨ", "ĠÙĪ اÙĨ", "ĠÙĪا ÙĨ", "Îľ ε", "Ġ μον", "Ġμ ον", "Ġμο ν", "Ñĥ ÑĪка", "ÑĥÑĪ ка", "or dum", "ord um", "æ¸ħ æ¥ļ", "ĠDe ÄŁ", "ÏĢ Ïģο", "ĠÙĪاÙĦ تÙĬ", "ĠÙĪاÙĦت ÙĬ", "Ġp okus", "Ġpo kus", "Ġpok us", "íĽĦ 기", "é¥ ®", "æĹħ è¡Į", "Ġжен Ñīин", "ĠdoÄŁru dan", "Ġ Ñıб", "ĠÑı б", "Ġza ÄįÃŃ", "ĠzaÄį ÃŃ", "Ġë³´ ìŬ", "- CP", "-C P", "åIJ ¨", "à¥ĭ à¤ĸ", "ÑĢ огÑĢа", "ÑĢо гÑĢа", "ÑĢог ÑĢа", "ler di", "ìĬ ´", "Ùı ÙĪا", "ÙıÙĪ ا", "Ġustanov enÃŃ", "Ġд оÑģÑĤав", "Ġдо ÑģÑĤав", "ĠдоÑģÑĤ ав", "Ġfır sat", "ĠاÙĦÙħÙĩ ÙĨØ©", "ĠвеÑī еÑģÑĤва", "ĠвеÑīеÑģÑĤв а", "Ġн еÑģп", "Ġне Ñģп", "ĠнеÑģ п", "ĠاÙĦکتر ÙĪÙĨ", "t aÅŁ", "ta ÅŁ", "æĪ Ĵ", "Ġy urt", "Ġyu rt", "Ġgir di", "ĠÐļ Ñĥб", "Ġ 를", "Ġë¥ ¼", "ุ à¹Į", "ãģĿãģĨ ãģª", "à¹ī Ċ", "ĠвÑĭ бÑĢа", "ĠвÑĭб ÑĢа", "k ovÄĽ", "ko vÄĽ", "kov ÄĽ", "ĠS iz", "ĠSi z", "Ġ گاÙĩ", "ĠÚ¯ اÙĩ", "ĠЧ аÑģ", "Ġзг Ñĸдно", ". ÐŁ", "å§ Ĭ", "ĠÐļ ÑĥÑĢ", "ĠìĿĺ íķ´", "Ġet raf", "Ġк аÑĪ", "Ġка ÑĪ", "ĠØ· ÛĮ", "ξ ει", "ξε ι", "ç² Ĵ", "ĠØ¢ Ø°", "Ġböl ge", "Ġbölg e", "Ġम à¤ľà¤¬", "Ġà¤®à¤ľ ब", "ÙIJ Ùĥ", "Ġvál ky", "ãģł ãĤĪ", "Ġmes aj", "Ġmesa j", "ĠpÅĻ ist", "ĠpÅĻi st", "Ġtyp u", "Ġty pu", "ĠкиÑĪ еÑĩ", "ãĤī ãģ®", "Ġkend isi", "Ġkendi si", "ĠвÑĸдб Ñĥва", "ĠвÑĸдбÑĥ ва", "ä¾ ¯", "Ġди за", "ãĢĢ Ċ", "ĠпÑĢоÑĨеÑģ Ñĥ", "ĠÑįлек ÑĤÑĢ", "_P US", "Ġмног иÑħ", "Ġk ém", "Ġké m", "æŀ ª", "çİ° 代", "Ġ éħį", "Ġé ħį", "Ġéħ į", "ë¡ Ń", "ÑĤи ÑģÑı", "Ġl ục", "ĠÙĪ اÙĦØŃ", "ĠÙĪاÙĦ ØŃ", "ĠÙĪا ÙĦØŃ", "p tal", "pt al", "pta l", "ẵ ng", "ẵn g", "ÏĢ λ", "Ġd olu", "Ġdo lu", "Ġdol u", "Ġt òa", "Ġин огда", "ĠпоÑĢÑıд ок", "Як Ñīо", "âĶ ĺ", "Ġغ ربÛĮ", "Ġغرب ÛĮ", "Ġغر بÛĮ", "移 åĬ¨", "ยà¸Ļ à¸ķร", "ยà¸Ļà¸ķ ร", "H DATA", "HD ATA", "_PUSH DATA", "_PUS HDATA", "ĠØ« ابت", "åĮħ åIJ«", "ĠÏĢ ÏģÎŃÏĢει", "़ à¥ĭ", "åIJį åīį", "ÑĤ еÑĢи", "ÑĤе ÑĢи", "ÑĤеÑĢ и", "ï½ ¯", "Ġ åħĪ", "Ġåħ Ī", "н ед", "не д", "Ïģ οÏįν", "Ïģο Ïįν", "ÏģοÏį ν", "в ей", "ве й", "èĤ ĸ", "ĠÅĻed itel", "Ġth ép", "Ġthé p", "ĠÙĩ ÙģتÙĩ", "ĠÙĩÙģت Ùĩ", "ĠдÑĢÑĥг а", "ĠдÑĢÑĥ га", "ER Ä°", "Ġ Ả", "ĠẠ¢", "ĠпеÑĢ еÑĢ", "ĠпеÑĢе ÑĢ", "Ġж еÑģÑĤ", "Ġже ÑģÑĤ", "ĠÄij ẳng", "ç¦ ®", "алÑĮ ном", "алÑĮно м", "िष य", "ид енÑĤа", "иденÑĤ а", "Ġآخر ÛĮÙĨ", "Ġ æĵ", "Ġæ ĵ", "Ġ มหาว", "Ġมห าว", "ĠлÑİ ÑĤого", "ĠлÑİÑĤ ого", "Ġб ÑĸзнеÑģ", "gı ç", "Ġng á»ĵi", "оÑĩ нÑĭй", "Ġo Äįek", "ĠoÄį ek", "ĠÙħ رة", "ĠÙħر Ø©", "Ġt var", "Ġtv ar", "Ġsam ozÅĻejmÄĽ", "ĠBeled iye", "Ġв ода", "Ġво да", "Ġвод а", "Ġ Ú¯ÛĮرد", "ĠÚ¯ÛĮ رد", "ĠÚ¯ÛĮر د", "Ġг одÑĭ", "Ġгод Ñĭ", "ãģ« è¡Į", "æĺ¯ æĪij", "ÑĪ или", "ÑĪи ли", "Ġ åĽ½äº§", "ĠåĽ½ 产", "ủ i", "ĠбÑĥд ÑĥÑĤÑĮ", "ĠбÑĥдÑĥÑĤ ÑĮ", "ĠбÑĥдÑĥ ÑĤÑĮ", "ĠÑĢай онÑĥ", "ĠÑĢайон Ñĥ", "Ġì ĵ", "ĠÙĪ اس", "ĠÙĪا س", "ĠاÛĮ شاÙĨ", "ενο δο", "Ġнез алеж", "ĠÙ¾ شت", "Ġپش ت", "Ġgir iÅŁim", "ĠgiriÅŁ im", "Ġд еле", "Ġдел е", "Ġде ле", "ĠاصÙģÙĩ اÙĨ", "à¸Ķ วà¸ģ", "ĠاÙĦ ÙĤÙĬ", "ĠاÙĦÙĤ ÙĬ", "à¹Į à¸Ī", "ëª »", "Ġd ru", "Ġdr u", "è¿ ¹", "ад женнÑı", "адж еннÑı", "Ùģ ÙĨ", "Ïĩ οÏĤ", "Ïĩο ÏĤ", "à¹Ĥ à¸Ī", "e yle", "ey le", "å¡ ij", "Ġu prav", "Ġup rav", "Ġз даÑĤ", "Ġзд аÑĤ", "Ġзда ÑĤ", "Ġvid ÄĽt", "Ġ à¸Ľà¸£", "Ġà¸Ľ ร", "Ġ ÑĦеÑĢ", "ĠÑĦ еÑĢ", "ÐĨ н", "Ġ ìµľìĭł", "Ġìµľ ìĭł", "l oha", "lo ha", "loh a", "ĠиÑģп ÑĭÑĤ", "Ġ avan", "Ġa van", "Ġav an", "Ġava n", "γ οÏħ", "γο Ïħ", "ĠGi ấy", "ãĤ»ãĥ³ ãĤ¿ãĥ¼", "éģ į", "е ÑĢаÑħ", "еÑĢ аÑħ", "еÑĢа Ñħ", "Ġê°Ģ ì§Ģê³ł", "Ġê°Ģì§Ģ ê³ł", "Ġ ид", "Ġи д", "Ġmnoh em", "æ£Ģ æµĭ", "Ġet me", "Ġetm e", "Ġ تÙħر", "Ġت Ùħر", "ĠتÙħ ر", "ĠbaÅŁ layan", "ĠbaÅŁlay an", "ãģı ãĤĮ", "à¹ĩà¸Ļ à¸ģาร", "ĠÑħаÑĢакÑĤеÑĢ из", "Ġanlam ına", "Ùı Ùĩ", "ĠÑģеÑĢ пнÑı", "çķª çµĦ", "Ġ msgid", "Ġmsg id", "Ġms gid", "Ġzv ÃŃÅĻ", "ĠzvÃŃ ÅĻ", "ĠíļĮ ìĽIJ", "Ġya par", "Ġyap ar", "ä¼ĺ åĬ¿", "ен нÑĭми", "еннÑĭм и", "ĠØ£ Ø«", "ì² Ļ", "Ġji ného", "Ġjin ého", "Ġjiné ho", "Ġد Ùģاع", "ĠدÙģ اع", "ĠØŃÚ© ÙĪÙħ", "Ġr izik", "Ġri zik", "ά λι", "άλ ι", "à¸ĩ à¸Ĥ", "èµ ¢", "Ġ ÎķÎĽ", "ĠÎķ ÎĽ", "Ġok um", "Ġoku m", "æĶ¶ åħ¥", "ĠÚĨ ÛĮÙĨ", "æľī çļĦ", "ÑĨ ами", "ÑĨа ми", "d ÄĽnÃŃ", "dÄĽ nÃŃ", "ĠкоÑĢ аб", "Ġко ÑĢаб", "ĠкоÑĢа б", "Ġa landa", "Ġal anda", "Ġalan da", "ส à¸Ļาม", "สà¸Ļ าม", "ï¼ī ãģ®", "ı sız", "ıs ız", "ısı z", "ÙĬ ÙĬر", "Ùĥ ÙĬØ©", "ÙĥÙĬ Ø©", "Ġnebo Å¥", "Ġbit ir", "Ġbi tir", "Ġ ãĥľ", "Ġãĥ ľ", "Ùij ا", "ï¼ Ĩ", "ĠاÙĦت ارÙĬØ®", "มห าà¸Ļà¸Ħร", "at ürk", "ãĤ¹ãĥĨ ãĥł", "θή κη", "Ġ καν", "Ġκ αν", "Ġκα ν", "ĠS ür", "ĠSü r", "Ġd Ä±ÅŁÄ±", "ĠdÄ±ÅŁ ı", "Ġk ancel", "Ġkan cel", "ĠÙ¾ خش", "h Pa", "ĠÄį t", "ĠпÑĢ оÑħ", "ĠпÑĢо Ñħ", "à¹ī à¸Ī", "Ġê±° ìķ¼", "ĠдеÑĢжав ного", "èĤ¡ 举", "ìĿ´ íģ¬", "Ùĥ تÙĪر", "Ùĥت ÙĪر", "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "è¨ º", "Ġب Ùħا", "ĠبÙħ ا", "ĠноÑĢм аÑĤив", "ç iler", "çi ler", "à¸ĩ ศ", "éĽĨ ä¸Ń", "ÑĢ иÑģ", "ÑĢи Ñģ", "Ñĩ аÑĶ", "Ñĩа ÑĶ", "li ÄŁin", "liÄŁi n", "liÄŁ in", "ãĥ¼ ãĤ¿ãĥ¼", "ãĥ¼ãĤ¿ ãĥ¼", "а ÑĢаÑĤ", "аÑĢ аÑĤ", "аÑĢа ÑĤ", "åĬĽ éĩı", "ĠÑģÑħ ем", "åħ¥ åı£", "离 å¼Ģ", "ÏģοÏĨοÏģ ίεÏĤ", "ĠÐĹ Ð°ÑĤем", "ĠkarÅŁ ısında", "ĠkarÅŁÄ± sında", "ĠاÙĨت ظ", "ï½ Ĭ", "Ġ eÅŁit", "ĠeÅŁ it", "Ġyaz ılı", "Ġyazı lı", "Ðļ ом", "ا زÙĬ", "از ÙĬ", "Ġki mse", "Ġkim se", "Ġkims e", "ÑĢа Ñīи", "ÑĢаÑī и", "ัà¸ģ ส", "Ġkan un", "Ġka nun", "Ġ ëIJĺìĹĪ", "ĠëIJĺ ìĹĪ", "Ġι ÏĥÏĩ", "Ġм еди", "Ġмед и", "æ° §", "ï¼Į åħ¶ä¸Ń", "ï¼Įåħ¶ ä¸Ń", "Ġyok tu", "Ġ ãĤ½", "ĠãĤ ½", "ĠпÑĢи обÑĢеÑĤ", "ÙĪ ÛĮØ´", "ÙĪÛĮ Ø´", "ãħł ãħł", "Ġکرد Ùħ", "Ġکر دÙħ", "Ġdu var", "Ġ ç¸", "Ġç ¸", "ıs ır", "ısı r", "Ġïº į", "ĠÐłÐ¾Ñģ ÑģиÑı", "à¹ī à¹ĥà¸Ļ", "Ġ iÅŁi", "Ġi ÅŁi", "ĠiÅŁ i", "d ol", "do l", "ĠÙħØŃ ÙħÙĪد", "ĠÑģам ÑĭÑħ", "ĠبÙĨابر اÛĮÙĨ", "ãĤĮ ãģ©", "ุà¸ķ สาห", ". »", "ู à¸Ĭ", "ĠT ep", "ĠTe p", "ãģı ãĤĵ", "Ġ å¸ĥ", "Ġå¸ ĥ", "Ġत ल", "Ġs erm", "Ġse rm", "Ġser m", "λ ÏĮγ", "λÏĮ γ", "ĠÅŀ imdi", "Ġà¤ľà¤¨ त", "- ÐĴ", "è¨ ª", "ĠвÑĸд пов", "ิ à¸Ļà¸Ķ", "ิà¸Ļ à¸Ķ", "ι ÏĥμÏĮÏĤ", "ιÏĥμ ÏĮÏĤ", "Ω Τ", "âĨĴ âĨĴ", "ικο ί", "ĠÑģп ÑĢава", "ĠÑģпÑĢав а", "æľº åħ³", "Ġ ÃĿ", "Ġà Ŀ", "Ġм ова", "Ġмо ва", "Ġмов а", "Ġмог ла", "Ġд лиÑĤелÑĮ", "ãģĹ ãģ¦ãĤĤ", "ãģĹãģ¦ ãĤĤ", "Ġβ Ïģί", "Ġж од", "éĹ ª", "ĠмÑĸ ÑģÑĮкоÑĹ", "η Ïģε", "çł Ĥ", "Ġkter ých", "Ġkterý ch", "ĠÐĵ олов", "ĠÐĵол ов", "Ġh á»Ļp", "Ġhá»Ļ p", "Ġpa nÃŃ", "Ġpan ÃŃ", "تÙħ اد", " ľ", "åįģ åħŃ", "κ οÏĤ", "κο ÏĤ", "ев ÑĭÑħ", "æĭ Ĵ", "ĠÑģÑĤ оÑĢон", "ĠÑģÑĤоÑĢ он", "ĠÑģÑĤо ÑĢон", "Ġph óng", "ĠÑĥлÑĥÑĩ ÑĪ", "m rt", "mr t", "m par", "mp ar", "ĠS lav", "ĠSl av", "Ġ kov", "Ġk ov", "Ġko v", "ìĿ¸ ìĿĢ", "Ġ åºĶ", "Ġåº Ķ", "ั à¸ļà¸Ħ", "ัà¸ļ à¸Ħ", "Ġk ì", "Ġa Å¥", "ÅĻ ÃŃt", "ÅĻÃŃ t", "ì° Į", "Ùħ ÙĨت", "ÙħÙĨ ت", "ıyor lar", "æŃ£ 常", "н ÑıÑĤÑĤÑı", "нÑıÑĤ ÑĤÑı", "r acÃŃ", "ra cÃŃ", "rac ÃŃ", "ĠпиÑĤ аниÑı", "à¸Īะ à¹Ģà¸Ľ", "ĠاÙĦÙĩ ÙĨد", "ĠD ost", "ĠDo st", "ĠDos t", "ĠÐĴаÑģ илÑĮ", "Ġ íĥĦ", "Ġíĥ Ħ", "Ġn ạn", "à¹Īà¸Ń à¹Ħà¸Ľ", "رÙĪ ض", "± ظ", "Ġbych om", "à¸Ļ วย", "à¸Ļว ย", "ãģł ãģ£ãģ¦", "ĠÐĺ Ñģп", "ĠÐĺÑģ п", "à¸Ħร à¸ļ", "Ġ สà¸ĸาà¸Ļ", "Ġส à¸ĸาà¸Ļ", "ĠëĤ ®", "j iÅ¡tÄĽ", "ji Å¡tÄĽ", "ĠÙģ ÙĪت", "ĠÙģÙĪ ت", "ĠCh Æ°Æ¡ng", "ĠìĿ´ 루", "ĠpÅĻÃŃ tom", "t ual", "tu al", "b ette", "be tte", "bet te", "bett e", "Ġsa bah", "Ġsab ah", "μ ί", "Ġm á»ĩnh", "Ġmá»ĩ nh", "ãģ® ãģłãĤįãģĨ", "ãģ®ãģł ãĤįãģĨ", "Ġzam ÄĽÅĻ", "åįģ äºĶ", "ĠìķĬ ìĿĦ", "اÙĨ ÙĪ", "е нÑĥ", "ен Ñĥ", "ĠÑĥ год", "ĠÑĥг од", "ĠV Æ°á»Ŀn", "Ġëĵ± ìĿĦ", "Ġbelirt ilen", "æŁ Ħ", "Ġtek lif", "¬ Ĥ", "Ġпод аÑĤков", "ĠاÙĦ ÙĨÙĩ", "ĠاÙĦÙĨ Ùĩ", "ï¼ ´", "ìĽ ĥ", "Ġ हल", "Ġह ल", "Ġ имÑĥ", "Ġи мÑĥ", "Ġим Ñĥ", "ĠкоÑĤоÑĢ Ñĭм", "ï¼Į 以åıĬ", "ï¼Į以 åıĬ", "ĠÑĤаб ли", "ा :", "Ġب رج", "Ġبر ج", "ĠÎŃ Î½Î±Î½", "ĠÎŃνα ν", "ĠÎŃν αν", "ĠÙĬ ÙĪÙĦÙĬÙĪ", "ý Å¡", "Ġ ÙĬج", "ĠÙĬ ج", "ĠÑĤÑĢо Ñħи", "æŀ Ŀ", "Ġd Ãły", "ĠBur ada", "ĠBu rada", "ĠÏĥÏħ μβ", "ĠÎij ÏģÏĩ", "ĠÎijÏģ Ïĩ", "Ġsoci álnÃŃ", "Ġ Ú¯ÙĪ", "ĠÚ¯ ÙĪ", "Ġyan ıt", "Ġyanı t", "ãģ¯ ãģªãģĦ", "ãģ® ä¸Ĭ", "Ġn úi", "ĠرÙģت ار", "ĠÙħ رات", "ĠÙħر ات", "ز ÙħاÙĨ", "زÙħ اÙĨ", "าà¸Ī ารย", "ĠÑĩиÑģ лÑĸ", "Ġس ÙĨت", "ĠسÙĨ ت", "ĠÃĸzel likle", "ì ĩ¼", "ìĩ ¼", "ĠÄį ÃŃm", "AD DE", "ADD E", "ãģ® ãĤĪãģĨãģª", "ÙĪÙĦÙĪ ÚĺÛĮ", "ĠíĻľ ìļ©", "ãĢģ ãģ©ãģĨ", "ĠÎł ÏģÏī", "çĻ» åł´", "Ġнад аннÑı", "Ġм еÑĢеж", "ĠмеÑĢ еж", "ĠмеÑĢе ж", "Ġ ìĿµ", "ĠìĿ µ", "jÃŃ cÃŃch", "jÃŃcÃŃ ch", "it ou", "ito u", "ÙĤ ÙĪÙĦ", "Ùħ ج", "Ġب ÙĨد", "ĠبÙĨ د", "Ġön üne", "Ġ ï½°", "Ġï½ °", "з в", "Ġе ÑģÑĤе", "Ðł Ðĺ", "ÑĢ ол", "ÑĢо л", "a yla", "ay la", "Ġк лÑĥ", "Ġкл Ñĥ", "æݨ èĸ¦", "ĠÑĢоз ÑĢаÑħ", "Ġ ìĥģëĭ´", "Ġìĥģ ëĭ´", "ĠÙĨ سÙħØ©", "ĠÙĨس ÙħØ©", "Ġви Ñħод", "à¥Ģ à¤Ĩà¤Ī", "ĠпÑĢи ÑģÑĤÑĥп", "ÙĴ ع", "ĠteÅŁ ekkür", "дÑı ки", "Ġfi kir", "Ġfik ir", "ัศ à¸Ļ", "ĠآزÙħ اÛĮØ´", "Ġb izi", "Ġbi zi", "Ġbiz i", "ÏĨ αÏģ", "ÏĨα Ïģ", "æľª æĿ¥", "æIJ º", "Ġδ Ïħνα", "ĠδÏħ να", "Ġ رÙĪÙħ", "Ġر ÙĪÙħ", "ĠرÙĪ Ùħ", "Ġb undan", "Ġbund an", "Ġbun dan", "ĠÙĤ اÙĦب", "ĠÙĤاÙĦ ب", "Ġ haft", "Ġh aft", "Ġha ft", "Ġhaf t", "å¿ ½", "ĠÐľ оÑĢ", "Ġzá pas", "Ġzáp as", "Ġ ë¹Ľ", "Ġë¹ Ľ", "å» ·", "äºĪ ç´Ħ", "Ġkh uyến", "Ġ ÎijÎĵ", "ĠÎij Îĵ", "Ġìŀij ìĹħ", "ड र", "Ġjednodu ch", "à¥ī म", "ĠdeÄŁ ildi", "ĠdeÄŁil di", "Ġk olo", "Ġko lo", "Ġkol o", "Ġد ÙĤÛĮ", "л ами", "ла ми", "лам и", "ĠH á»įc", "ĠHá»į c", "Ġप स", "ĠÎł ÏģÏĮ", "ĠâĹ ij", "Ġ наÑģлÑĸд", "Ġна ÑģлÑĸд", "ĠнаÑģ лÑĸд", "Ġ диви", "Ġди ви", "Ġдив и", "ĠpÅĻes nÄĽ", "ĠТак им", "ĠТа ким", "Ġru kou", "Ġruk ou", "ä¸Ģ åĪĩ", "ĠÑģ пÑĢи", "ĠÑģп ÑĢи", "en ské", "ens ké", "æĹ ¦", "ĠÙĤ ÙĨ", "Ġú stav", "िश त", "à¹Į )", "ĠT rang", "ĠTr ang", "ĠTra ng", "ĠTran g", "Ġmoh la", "Ġmohl a", "ĠÎķ λλην", "Ġп оки", "Ġпо ки", "Ġпок и", "ĠØ¢ Ùħار", "ĠØ¢Ùħ ار", "åIJ ¾", "ĠÑĢ еÑģп", "ĠÑĢе Ñģп", "ĠÑĢеÑģ п", "Ġta kdir", "Ġtak dir", "Ġrahat sız", "éŁ³ ä¹IJ", "Ġ âĶĥ", "ĠâĶ ĥ", "i lis", "il is", "ili s", "ĠÙĪ اÙĦØ¥", "ĠÙĪاÙĦ Ø¥", "å® Ļ", "Ñĥ мов", "Ñĥм ов", "ĠÐĽ иÑĤ", "ĠÐĽÐ¸ ÑĤ", ": :::|", ":: ::|", ":::: |", "::: :|", "åħ ½", "ĠÙĨزد ÛĮÚ©", "е лÑĸв", "ел Ñĸв", "елÑĸ в", "θ οÏįν", "θο Ïįν", "ìĹIJìĦľ ëıĦ", "èµĦ æł¼", "çIJĨ 论", "ĠKe mal", "ĠKem al", "Ġк еÑĢ", "ษ ายà¸Ļ", "Ġ åįİ", "Ġåį İ", ") ìĹIJ", "Ġ ëĬĺ", "ĠëĬ ĺ", "ãĥĿ ãĥ¼ãĥĪ", "ĠÐĹ Ð´", "اص ÙĬÙĦ", "Ġk atı", "Ġka tı", "Ġkat ı", "ãĤĤãģĹ ãĤĮãģªãģĦ", "Ġкажд ого", "Ġ дÑĢ", "Ġд ÑĢ", "Ġfut bol", "ÙĦ ÙĬÙģ", "ÙĦÙĬ Ùģ", "Ġì§Ģ ëĤľ", "ĠÙ¾ÛĮØ´ ÙĨÙĩ", "ü lük", "ül ük", "ülü k", "Ġ à¸ķำà¸ļล", "Ġà¸ķำ à¸ļล", "Ġb áºŃc", "Ġ åĽł", "ĠåĽ ł", "ik ler", "Ïģ ιά", "Ïģι ά", "Ġв важа", "Ġвваж а", "Ġvy pl", "Ġvyp l", "Ġв низ", "í Ģ", "çľ ¾", "ĠÑģ ила", "ĠÑģи ла", "ĠÑģил а", "ĠналиÑĩи и", "Ġع راÙĤ", "ĠاÙĦÙħ Ùĥ", "å°± ä¼ļ", "Ġм Ñĸг", "ĠмÑĸ г", "ĠÎĮ μιλοÏĤ", "Ñī его", "Ñīе го", "Ġíĸī ìłķ", "Âł mph", "Âłm ph", "Ġma lé", "Ġmal é", "ĠÛĮ اÙģتÙĩ", "ĠÛĮا ÙģتÙĩ", "ĠÛĮاÙģت Ùĩ", "Ġmn oha", "Ġmnoh a", "γ ά", "Ġпо ÑģÑĤÑĢо", "ĠпоÑģ ÑĤÑĢо", "ĠпоÑģÑĤ ÑĢо", "ĠاÙĦÙħ ÙĪس", "ĠاÙĦÙħÙĪ س", "Ġol ma", "Ġolm a", "ëī´ ìĬ¤", "Ġt utar", "Ġtu tar", "Ġtut ar", "ãĥ¼ãĥĵ ãĤ¹", "à¥įथ न", "-ли бо", "æ¥Ń åĭĻ", "ĠоÑģоб ливо", "ĠоÑģоблив о", "è® Ģ", "ÙģÙĩ ÙĪÙħ", "Ġk ẻ", "Ġ Å¡tÄĽ", "ĠÅ¡ tÄĽ", "ĠÅ¡t ÄĽ", "Ġc ầm", "ĠÄįlán ky", "ĠÄIJ iá»ĩn", "( =", "OV Ãģ", "ul du", "uld u", "a ft", "af t", "Ġl ãi", "Ġlã i", "Ġd oldur", "Ġdol dur", "³³ ³³³³³³³³³", "³³³³ ³³³³³³³", "³³³ ³³³³³³³³", "³³³³³³³³ ³³³", "³³³³³³³ ³³³³", "³³³³³ ³³³³³³", "³³³³³³ ³³³³³", "³³³³³³³³³ ³³", "β ι", "ãģ£ãģ¦ ãģįãģŁ", "ì¶ľìŀ¥ ìķĪë§Ī", "å¯ Ŀ", "Ġë¶Ģ íĥģ", "ĠاÙĦ اخ", "Ġγ Ïħνα", "à¤ı म", "à¥Į ल", "ع ادة", "عا دة", "عاد Ø©", "Ġ κοÏħ", "Ġκ οÏħ", "Ġκο Ïħ", "ĠÙħØ· رØŃ", "ĠÑĩелов еÑĩ", "Ġn umar", "Ġnum ar", "Ġnu mar", "Ġnuma r", "Ġ дина", "Ġд ина", "Ġди на", "ÏĦ ÏģÎŃ", "ÏĦÏģ ÎŃ", "λ ικ", "λι κ", "Ġдол го", "Ġnh iêu", "ĠвоÑģ ÑģÑĤанов", "ap ı", "Ġ kanı", "Ġk anı", "Ġkan ı", "ĠK ế", "ãĤī ãģļ", "Ġhar ek", "Ġha rek", "Ġhare k", "ãģłãģij ãģ§", "æ» ħ", "Ġo hled", "Ġoh led", "е ÑĢим", "еÑĢ им", "еÑĢи м", "ĠØŃ ÙĬÙĨ", "ĠØŃÙĬ ÙĨ", "ĠÙĤ Ùĩر", "Ġब à¥Ŀ", "اپ ÛĮÙħ", "è¶ħ è¿ĩ", "Ġ æħ", "Ġæ ħ", "Ġت Ùģس", "ĠتÙģ س", "as ıyla", "ası yla", "б иÑĤ", "би ÑĤ", "ĠØŃ Ø§Ø¬", "ĠÑĤÑĢеб ованиÑı", "Ġ æݨ", "Ġæİ ¨", "Ġ ç±³", "Ġç± ³", "ãĤ³ ãĥ¼ãĥī", "ĠÑĥ Ñģи", "ĠÑĥÑģ и", "Ġاخ ÙĦاÙĤ", "Ġdo stup", "Ġdost up", "Ġع ÙĦاÙĤ", "ĠعÙĦ اÙĤ", "िव स", "Ġ оди", "Ġо ди", "Ġод и", "t ej", "te j", "Ġthá» ıa", "ัà¸ģษ à¸ĵะ", "ัà¸ģษà¸ĵ ะ", "ĠÑĢаÑģ к", "ĠÑĢа Ñģк", "ĠÐĿ аÑĢод", "ĠÐĿа ÑĢод", "Ġза кÑĥп", "Ġзак Ñĥп", "o že", "ož e", "Ġاج را", "Ġاجر ا", "ê´ij ê³ł", "аÑĢÑĤ ам", "Ġп еÑĢеж", "ĠпеÑĢ еж", "ĠпеÑĢе ж", "èij£ äºĭ", "ĠÑı коÑģÑĤÑĸ", "ĠÑıк оÑģÑĤÑĸ", "Ġв Ñĥл", "м он", "мо н", "Ġch lap", "Ġ ÑįÑĤомÑĥ", "ĠÑįÑĤ омÑĥ", "ĠÑįÑĤо мÑĥ", "ĠÑįÑĤом Ñĥ", "а ÑĤÑĸ", "аÑĤ Ñĸ", "Ġ íĴĪ", "Ġí ĴĪ", "ĠíĴ Ī", "è¡Ĺ éģĵ", "س د", "ÙĪ رÙĩ", "ÙĪر Ùĩ", "ĠزÛĮ اد", "åľ¨çº¿ è§Ĩé¢ij", "ا ÙĪÙĬØ©", "اÙĪ ÙĬØ©", "اÙĪÙĬ Ø©", "ï¼Į å°±æĺ¯", "ï¼Įå°± æĺ¯", "e lerinden", "eler inden", "elerin den", "elerinde n", "ÑĢ ажд", "ÑĢа жд", "ÑĢаж д", "Ġп озд", "Ġпо зд", "Ġпоз д", "Ġзна ÑĤÑĮ", "Ġзн аÑĤÑĮ", "ัà¸ļ สà¸Ļ", "ัà¸ļส à¸Ļ", "à¥ĩà¤ĸ त", "Ġ æĽ°", "Ġæ Ľ°", "ĠæĽ °", "ê³¼ ìłķ", "é® ®", "ĠV iá»ĩn", "ĠVi á»ĩn", "Ġd voj", "Ġdv oj", "ίν εÏĦαι", "Ġosob nÃŃch", "ĠosobnÃŃ ch", "Ġ âĢª", "ĠâĢ ª", "éĻ µ", "ĠØ®ÙĪد Ø´", "ĠاÙĨ ر", "ĠпÑĢоÑĦеÑģÑģи оналÑĮ", "k ám", "ká m", "ĠÙħ ÙĥاÙĨ", "ĠÙħÙĥ اÙĨ", "ĠاÙĦØ£ د", "Ġ ê³µë¶Ģ", "Ġê³µ ë¶Ģ", "ĠÄij ức", "ĠÄijứ c", "ĠCumhur iyeti", "ĠCumhuriyet i", "åĩº ãģĹ", "д ами", "да ми", "дам и", "ĠìĪĺ ìĥģ", "ĠÙģ براÙĬر", "Ġsü resi", "Ġsür esi", "Ġsüre si", "Ġب ج", "Ġ æĶ¾", "ĠæĶ ¾", "ØŃ ÛĮ", "çłĶ究 æīĢ", "åĩºçīĪ 社", "ĠÙħÙĪ تÙĪر", "&& &&", "ĠпеÑĢ ей", "ĠпеÑĢе й", "Ġ ìĦłê±°", "ĠìĦł ê±°", "ĠúspÄĽ Å¡", "ار Ú©", "Ġet tir", "Ġett ir", "Ġetti r", "Ġ ì¶ľìŀ¥", "Ġì¶ľ ìŀ¥", "ĠKa nun", "ĠKan un", "ĠÑĥменÑĮ ÑĪ", "ĠзаÑĤ веÑĢдж", "ĠاÙĦد ÙĪÙĦÙĬ", "ĠاÙĦدÙĪÙĦ ÙĬ", "Ġ ãĥĵ", "Ġãĥ ĵ", "ĠB azı", "ĠBa zı", "ĠBaz ı", "åŃIJ ãģ®", "åĩ ¯", "Ġse beb", "Ġseb eb", "Ġsebe b", "Ġ åħ±", "Ġåħ ±", "Ġd nů", "Ġdn ů", "ä½į äºİ", "ĠZ d", "æī ±", "Ġتج ربÙĩ", "ÃĶ NG", "Ġìĺ¬ ëĿ¼", "Ïī ÏĦεÏģ", "ĠÑģ вид", "ĠÑģв ид", "ĠÑģви д", "æ¯Ķ èµĽ", "ãģ« åIJij", "ìľĦ 를", "ãģĹ ãģ¾ãģĹãģŁ", "ãģĹãģ¾ ãģĹãģŁ", "Ġd á»ĭ", "ĠÐł ÑĥÑģ", "Ġv á»ı", "Ġvá» ı", "à¤Ĥड ल", "Ġп иÑī", "Ġпи Ñī", "Ġsmr ti", "Ġsmrt i", "à¸Īาà¸ģ à¸ģาร", "ĠÑģаÑħ аÑĢ", "Ġtho át", "ج ÙħØ©", "جÙħ Ø©", "Ġпоз вол", "ĠاÙĦØ« اÙĨÙĬØ©", "ĠاÙĦثاÙĨÙĬ Ø©", "ز ادÙĩ", "زا دÙĩ", "ãĢģ ä¸Ń", "ή μεÏģα", "æ¦ ľ", "l acaģı", "lac aģı", "lacaÄŁ ı", "Ġна ÑĪиÑħ", "ĠнаÑĪ иÑħ", "ìĶ Ģ", "ĠÐĺ ÑģÑĤоÑĢиÑı", "ün deki", "ünd eki", "ünde ki", "ĠпеÑĢ ел", "ĠпеÑĢе л", "Ġ목 ìĨĮ", "ĠÑģÑĤаÑĤ ÑĥÑģ", "о вали", "ов али", "ова ли", "овал и", "ÅĻ az", "ĠдÑĢÑĥг ого", "ĠдÑĢÑĥго го", "ÙĥÙĪÙħ Ø©", "ÙĥÙĪ ÙħØ©", "Ñĩ иÑģÑĤ", "Ñĩи ÑģÑĤ", "ÑĩиÑģ ÑĤ", "μ μ", "åıį åºĶ", "ic ari", "ica ri", "ĠÙ¾ اک", "Ġپا Ú©", "алÑĮ ним", "ĠB una", "ĠBu na", "ĠBun a", "и ÑĤив", "иÑĤ ив", "иÑĤи в", "ÑĦ ÑĢа", "ãĥ¼ ãĥĸãĥ«", "ãĥ¼ãĥĸ ãĥ«", "ĠÑĤоб ÑĤо", "룬 ìĬ¤", "ĠاÙĦ اع", "åħ¬ éĸĭ", "å¥ ī", "ÙĪÙĦ د", "åIJį çĦ¡ãģĹ", "æ°ij 主", "à¥ģ à¤ľà¤°", "à¥ģà¤ľ र", "ìĤ¬ 무", "Ġön celik", "Ġönce lik", "Ġönc elik", "Ġ å¨", "Ġå ¨", "Ñı б", "çľ ī", "à¥įव य", "ĠH ình", "çļĦ åľ°æĸ¹", "çļĦåľ° æĸ¹", "ĠاÙĦ تس", "ĠاÙĦت س", "ä¸Ī 夫", "Ġп ÑĥблÑĸ", "ĠnÄĽjak é", "ÄIJ á»iji", "ĠÑģоÑģÑĤоÑı ниÑı", "à¥Ģ )", "ĠÄij áºŃu", "j ed", "je d", "ê ¶ģ", "Ġs enin", "Ġse nin", "Ġsen in", "Ġseni n", "ĠH óa", "âĻ ł", "лÑı ÑİÑĤÑĮ", "лÑıÑİÑĤ ÑĮ", "éĹ ²", "ìĿ¸ íĬ¸", "ت بÙĩ", "تب Ùĩ", "Ġरà¤ĸ त", "ĠÑģлов ами", "ĠÑģлова ми", "ĠÑģло вами", "Ġطب ÙĤ", "Ġuy du", "ุà¸ĩà¹Ģà¸Ĺà¸ŀ มหาà¸Ļà¸Ħร", "ĠSan at", "ĠSa nat", "à¹ī าà¸Ĭ", "à¹īา à¸Ĭ", "Ġкни ж", "Ìģ c", "ا Ùħج", "اÙħ ج", "δ Ïİ", "Å ®", "Ġb inh", "Ġbi nh", "Ġbin h", "è¾ Ĩ", "n eÄŁi", "ne ÄŁi", "Ø· ÙĨ", "å¸ ķ", "Ġ ìĩ¼", "Ġì ĩ¼", "оÑģ ÑĢед", "ĠοÏĢο ίο", "k ır", "kı r", "à¥Ī श", "Ġ à¸ĩาà¸Ļ", "Ġà¸ĩ าà¸Ļ", "Ġd ruž", "Ġdru ž", "em atik", "ema tik", "emat ik", "a dıģ", "ad ıģ", "adı ÄŁ", "è¾ ŀ", "ĠpoužÃŃ vá", "Ġkur tar", "ĠsaÄŁ lan", "ãĢı ï¼Ī", "Ġmůže me", "Ġmůž eme", "Ġ باد", "Ġب اد", "Ġبا د", "æľŁ éĹ´", "ا تÙģ", "ات Ùģ", "Ġyaz ılım", "Ġyazılı m", "ĠìĹ° ê²°", "ÙĬ ÙģØ©", "ÙĬÙģ Ø©", "Ġ emin", "Ġe min", "Ġem in", "ĠнеÑģколÑĮ киÑħ", "Û´ Û°", "å¯ §", "ί ζει", "ίζ ει", "Ġd él", "Ġdé l", "ver iÅŁ", "価 æł¼", "Ġاست اد", "Ġал ког", ".H CM", "ί οÏĤ", "ίο ÏĤ", "α κ", "Ø· ع", "ãģ£ ãģį", "ãģ£ãģ į", "Ñı еÑĤÑģÑı", "ÑıеÑĤ ÑģÑı", "л ика", "ли ка", "лик а", "Ġ ÑĨÑı", "ĠÑĨ Ñı", "Ġë§Ī ì§Ģë§ī", "ĠаÑĢ ми", "Ġγ λÏİ", "E NÃį", "EN Ãį", "ë ®¤", "ŃIJ ï¸ı", "Ġ æ¯ı", "Ġæ¯ ı", "Ġ æĸ¼", "Ġæĸ ¼", "Ġκα λÏį", "ĠТ ом", "ĠТо м", "ul ur", "ulu r", "Ġak ce", "ĠÙħÙĪ جب", "ĠÙħÙĪج ب", "e siz", "es iz", "esi z", "н Ñıв", "нÑı в", "алÑĮ нÑĥÑİ", "алÑĮнÑĥ Ñİ", "ал ÑĸÑģÑĤ", "алÑĸ ÑģÑĤ", "Ġв аÑĢÑĸ", "ĠваÑĢ Ñĸ", "Ġва ÑĢÑĸ", "ĠÙħؤ س", "ĠÙħ اÛĮÙĦ", "ĠÙħا ÛĮÙĦ", "ĠμεÏĦα ξÏį", "åĩº ãģĻ", "Ġv á»Ŀi", "Ġvá» Ŀi", "ëŁ ´", "ï¼ ĭ", "æ¯ İ", "Ġt abi", "Ġtab i", "Ġta bi", "âĤ ĥ", "æ£ĭ çīĮ", "Ġ ÃIJ", "Ġà IJ", "ĠпÑĢоÑĦеÑģ Ñĸй", "Ñĥв аннÑĸ", "Îľ Îł", "Ġж ил", "Úĺ ÙĨ", "л ÑĥÑĪ", "лÑĥ ÑĪ", "á½ ´", "о веÑĢ", "ов еÑĢ", "ове ÑĢ", "è¾¼ ãģ¿", "ĠÐľ акÑģим", "ĠÐľÐ°Ðº Ñģим", "Ġвз глÑıд", "Ġн аÑĤÑĥ", "Ġна ÑĤÑĥ", "ĠнаÑĤ Ñĥ", "म à¤ķ", "ĠÑħ ими", "ĠÑĢозÑĤа ÑĪ", "ÙĪ راÙĨ", "ÙĪر اÙĨ", "ÙĪرا ÙĨ", "ĠØ´Ùĩر ÙĩاÛĮ", "æ© Łèĥ½", "æ©Ł èĥ½", "Ø® Ø°", "ĠÑģво ÑĶÑĹ", "ĠÑģвоÑĶ ÑĹ", "н ÑıеÑĤ", "нÑı еÑĤ", "Ġgh ế", "ĠpÅĻed ch", "ÑĶ ÑĪ", "огÑĢаÑĦ ÑĸÑı", "Ġ à¸Ĺำà¹ĥห", "Ġà¸Ĺำ à¹ĥห", "åĿ Ĭ", "Ïģ Ïīν", "ÏģÏī ν", "า ระ", "าร ะ", "ĠK ết", "ĠKế t", "Ġch ặt", "Ġ éĻĪ", "ĠéĻ Ī", "ĠdÄĽ lat", "ĠdÄĽl at", "ĠбÑĥд ÑĥÑī", "ĠбÑĥдÑĥ Ñī", "ĠAç ık", "æłª å¼ıä¼ļ社", "ĠÐŁ аÑĢ", "ĠK hu", "ĠKh u", "ãĢģ æĸ°", "Ġб ой", "Ġбо й", "ë§Ī íĬ¸", "ĠÑģоп ÑĢов", "س اب", "н иÑģÑĤ", "ни ÑģÑĤ", "å¼ ĥ", "Ġ Ø´ÙĨاس", "ĠØ´ÙĨ اس", "ен ном", "енно м", "Ġ 项", "Ġé¡ ¹", "èīº æľ¯", "о зем", "оз ем", "ĠÑĢеÑĪ ениÑı", "l ady", "la dy", "lad y", "ĠвÑģ ей", "ĠвÑģе й", "æĶ» åĩ»", "Ġê²° ìłķ", "ãĢĢ ï¾ŀ", "Ġê°IJ ëıħ", "- ÐIJ", "Ġm ÃŃr", "ĠmÃŃ r", "à¥ģप à¤ı", "нÑĸ ÑĨип", "б ом", "бо м", "Ġ Å¡t", "ĠÅ¡ t", "éľ į", "ĠÑĢеÑĪ ение", "Ġдиаг ноÑģÑĤи", "i par", "ip ar", "ipa r", "ا ÛĮز", "اÛĮ ز", "ã ng", "ãn g", "ั วร", "ัว ร", "ĠÑĨ аÑĢ", "Ġs ly", "Ġsl y", "ν Ïİ", "ĠK uzey", "رÛĮ ب", "Ġc enu", "Ġce nu", "Ġcen u", "Ġcert if", "Ġcer tif", "ĠÑĤ ÑĢеÑĤÑĮ", "ĠÑĤÑĢ еÑĤÑĮ", "ĠÑĤÑĢеÑĤ ÑĮ", "ิà¸Ķ à¸Ĥ", "Ġпа ÑĨÑĸÑĶн", "ÅĻ iv", "ÅĻi v", "èĦ Ĥ", "¢ °", "ĠPh ần", "ĠмеÑĤод и", "ĠмеÑĤ оди", "Ạ¤", "ìĨ Ķ", "åIJĮ åѦ", "Ġ åĢĭ", "ĠåĢ ĭ", "моÑĤ ÑĢÑı", "моÑĤÑĢ Ñı", "Ġuv ád", "Û±Û¹ Û¶", "éģ¸ æĬŀ", "! »", "ë ĺIJ", "ĠÛĮ ÙĪتÛĮ", "ĠاÙĦØŃ Ø±Ø¨", "ĠاÙĦØŃر ب", "олог ÑĸÑı", "n ila", "ni la", "nil a", "ĠÄij ảng", "á zi", "áz i", "ÑĢ оÑī", "ÑĢо Ñī", "Ġort adan", "Ġorta dan", "Ġاخ بار", "Ġà¤ħ à¤ľ", "Ġ매 ìļ°", "Ġп ой", "Ġпо й", "Ġ جÙĬ", "Ġج ÙĬ", "к ÑĥваÑĤи", "кÑĥ ваÑĤи", "Ġá» ŀ", "Ġب شر", "Ġبش ر", "Ġ ÙĥÙĬÙĦ", "ĠÙĥ ÙĬÙĦ", "Ñī еÑģÑĤво", "Ñīе ÑģÑĤво", "ÑīеÑģÑĤв о", "ĠìŬ íĸī", "ا ÙħÙĬ", "اÙħ ÙĬ", "в ÑĸлÑĮ", "вÑĸ лÑĮ", "ĠPr vnÃŃ", "Ġ ÙĪسÛĮ", "ĠÙĪ سÛĮ", "ĠÙĪس ÛĮ", "ĠÄIJ á»", "æĪ¿ éĹ´", "åľ¨çº¿ éĺħ读", "æķ ·", "Ġt rai", "Ġtr ai", "Ġtra i", "ä¿ Ĺ", "ĠÑģамоÑģÑĤоÑıÑĤелÑĮ но", "ĠÑĤÑĢеб ÑĥеÑĤÑģÑı", "ĠÑĤÑĢебÑĥеÑĤ ÑģÑı", "δ Ïģα", "ĠÑĢеÑĩ ов", "Ġв Ñĸк", "ĠвÑĸ к", "Ġ ÑĢÑĥÑĩ", "ĠÑĢ ÑĥÑĩ", "ĠÑĢÑĥ Ñĩ", "å¥ §", "ĠolduÄŁ una", "ĠolduÄŁu na", "ев Ñĭе", "Ġ à¸Ħล", "Ġà¸Ħ ล", "ا ÙĦÙĤ", "اÙĦ ÙĤ", "ĠÑĸм енÑĸ", "ĠÑĸмен Ñĸ", "æĶ» æĴĥ", "ĠÑĥнивеÑĢ ÑģиÑĤ", "Ġth Äĥm", "ĠлиÑģÑĤоп ада", "२ ०", "Ø® ÙĬ", "Îķ Îł", "Ġart tır", "Ġس خت", "Ġسخ ت", "ï¼Ī æĺŃåĴĮ", "ĠÎŁ Ïħ", "и ваниÑı", "ив аниÑı", "ива ниÑı", "Ġstav eb", "âħ ¥", "γÏī γή", "γÏīγ ή", "Ù ©", "ĠиÑģÑģлед ованиÑı", "åĢĭ 人", "Ġëĭ¤ìļ´ë°Ľ 기", "ĠÏĦ ελ", "ĠÏĦε λ", "° N", "ĠباÙĦ ÙĨ", "à¹Į à¸ŀ", "Ġnem ůže", "Ġголов а", "Ġгол ова", "à¹Į à¹ģ", "æ¢ ¯", " ĺ", "δ ηÏĤ", "δη ÏĤ", "ìĿ¸ ì¦Ŀ", "l ayın", "lay ın", "á½ ·", "ĠÙĨت اÛĮج", "ĠÑģоб лÑİд", "Ġдви жениÑı", "Ġдвиж ениÑı", "ì Į", "Ġ povÄĽ", "Ġp ovÄĽ", "Ġpo vÄĽ", "Ġpov ÄĽ", "Ġ ìłĦìĹIJ", "ĠìłĦ ìĹIJ", "å¦Ĥ ä¸ĭ", "ĠاÙĦÙħ در", "ĠاÙĦÙħد ر", "ï¼Į æĪĸ", "ا را", "ار ا", "æ°ij æĹı", "Ġب رÙĤ", "Ġبر ÙĤ", "Ġзап аÑģ", "à¸Ļ à¹ĥà¸Ī", "é f", "Ġ à¸Łà¸£", "Ġà¸Ł ร", "Ġë³´ ëĤ´", "Ġ 欧ç¾İ", "Ġ欧 ç¾İ", "- ÑĤаки", "-ÑĤ аки", "é© ļ", "ÑĢ ÑĸÑı", "ÑĢÑĸ Ñı", "æŁ ı", "ĠповÑĸÑĤ ÑĢÑı", "çµĦ ç¹Ķ", "d aÅŁ", "da ÅŁ", "Ġहम ल", "ĠÑĢеÑĶ ÑģÑĤÑĢа", "ά β", "ĠÎł ο", "Ġê·¸ 림", "Ñĩ аÑİÑĤ", "Ñĩа ÑİÑĤ", "à¸ĩ à¸ķ", "íĥ ĢìĿ´", "íĥĢ ìĿ´", "æī ¬", "Ġpo jist", "Ġpoj ist", "Ġ çłĶ", "Ġçł Ķ", "Ġ åıĸ", "Ġåı ĸ", "Ġüzer indeki", "Ġüzerinde ki", "j Å¡ÃŃch", "jÅ¡ÃŃ ch", "à¥Ģद व", "æª ¢", "ĠмаÑĤеÑĢи алов", "ĠмаÑĤеÑĢиал ов", "и ваннÑı", "ив аннÑı", "Ġ å°Ĩ", "Ġå° Ĩ", "л л", "Ġнаб лÑİд", "ĠнаблÑİ Ð´", "ĠG öz", "ĠGö z", "Ġв зÑı", "Ġвз Ñı", "çĶµ è§Ĩ", "Ġв ак", "Ġва к", "ç¿ Ķ", "Ġвза им", "Ġg itti", "Ġgi tti", "Ġgit ti", "it eleri", "ite leri", "itel eri", "itele ri", "ä»· åĢ¼", "ĠاÙĦ تص", "ĠاÙĦت ص", "िन à¤ķ", "éĢļ ãĤĬ", "ĠÑģ ÑĦеÑĢ", "ĠÑģÑĦ еÑĢ", "çĻº 売", "âĿ ¤", "ĠÚ¯ÙĪØ´ ÛĮ", "ĠÚ¯ÙĪ Ø´ÛĮ", "аг аÑĤо", "ĠÏĥÏħ γκ", "ав иÑģ", "ави Ñģ", "æĤ£ èĢħ", "ĠØ® اÙħ", "ÎĻÎļ ÎĹΣ", "ÎĻÎļÎĹ Î£", "ınız da", "pan ÄĽl", "pa nÄĽl", "ĠÄIJ á»ĭa", "à¹ģละ ส", "Ġ ãĤĤ", "ĠãĤ Ĥ", "Ġsonuc unda", "Ġsonucu nda", "ìĿ į", "e less", "el ess", "ele ss", "ĠN ha", "ĠNh a", "Ġzak áz", "Ġв оÑģÑĤ", "Ġво ÑģÑĤ", "ĠвоÑģ ÑĤ", "ĠvzdÄĽl ávánÃŃ", "- ม", "Ġmet rů", "ĠپاÛĮ ÛĮÙĨ", "Ġپا ÛĮÛĮÙĨ", "ĠÑĢаÑģÑĤ ение", "Ġmu á»iji", "èµĦ éĩij", "ĠÅŁ üph", "ÙĬ ÙĦÙħ", "ÙĬÙĦ Ùħ", "ĠdÃ¼ÅŁÃ¼n c", "Ġк Ñĸм", "ĠÏĩÏī ÏģίÏĤ", "áz ev", "áze v", "ĠDe ÄŁer", "ĠDeÄŁ er", "å·¥ æ¥Ń", "Ġر Ùħز", "ĠرÙħ ز", "Ġal espoÅĪ", "ĠпÑĢе ÑģÑĤÑĥп", "ĠпÑĢеÑģÑĤ Ñĥп", "ĠعÙĦ اÙĪÙĩ", "Ġme rak", "Ġmer ak", "à¹Į :", "çİ° åľº", "ÑĨ веÑĤ", "Ġप à¥ľ", "Ġëĭ¤ìĿĮ ê³¼", "u dic", "ud ic", "udi c", "ĠL ep", "ĠLe p", "Ġод нÑĸ", "Ġa larak", "Ġal arak", "å®ī æİĴ", "Ġ à¸Ĥà¸Ļาà¸Ķ", "Ġà¸Ĥ à¸Ļาà¸Ķ", "re zent", "rez ent", "is inden", "isin den", "isinde n", "ر ÙĪÛĮ", "رÙĪ ÛĮ", "Ġp lu", "Ġpl u", "ç«ĭ ãģ¦", "Ñĭ ваниÑı", "Ñĭв аниÑı", "Ñĭва ниÑı", "Ġr ast", "Ġra st", "Ġras t", "Ġdüzen lem", "je zd", "jez d", "Ġве ÑīеÑģÑĤв", "ĠвеÑī еÑģÑĤв", "ĠдиÑĢ екÑĤоÑĢ", "ÑĦ ÑĦ", "t ainment", "tain ment", "ĠاÙĦ ÙĪز", "ĠاÙĦÙĪ ز", "l anda", "land a", "la nda", "lan da", "ĠÙĨÚ¯ Ùĩد", "ĠпÑĢоÑĤив оп", "ãģ£ ãģı", "ãģ£ãģ ı", "ãģ¨ãģª ãĤĬ", "Ġë°ľ 견", "i ctor", "ic tor", "ict or", "ãĤ¸ ãĤª", "ÎŁ Φ", "ÎŁÎ ¦", "ĠÑģклад Ñĸ", "Ġob sahuje", "Ġobsah uje", "ĠUkr a", "ĠUk ra", "æķ ¦", "ĠÏĩ αÏģα", "ĠÏĩα Ïģα", "ĠÑĢег Ñĥли", "俺 ãģ¯", "ัà¸ķ ว", "éĦ ī", "Ġب اÛĮ", "Ġبا ÛĮ", "éĬ ·", "ĠN ẵng", "л од", "ло д", "ا رÙģ", "ار Ùģ", "æ´ ģ", "ĠëıĻ ìĿ¼", "ÑĤив ного", "âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ", "Ġ- :-", "Ġ-: -", "ì» ¬", "ĠÑĪ аг", "ìłĦ ìŀIJ", "çļĦ äºĭæĥħ", "çļĦäºĭ æĥħ", "ĠÑĢег Ñĸ", "िय ल", "ĠÐĿ аз", "ĠÐĿа з", "ĠÐĻ ого", "ĠÐł ом", "ĠÃĸr neÄŁin", "Ġп ÑĢеÑģ", "ĠпÑĢ еÑģ", "ĠпÑĢе Ñģ", "u luÄŁu", "ulu ÄŁu", "uluÄŁ u", "Ġза дов", "Ġзад ов", "ÅĻ eh", "ÅĻe h", "æ¯ķ ä¸ļ", "Ġth áºŃp", "ëĤ ¸", "Ġdlou hodob", "Ġdlouho dob", "дÑĸ лÑĥ", "дÑĸл Ñĥ", "a lat", "al at", "ala t", "ä» °", "о ком", "ок ом", "око м", "ĠÑĦ ÑĸлÑĮ", "ĠÑĦÑĸл ÑĮ", "ĠNg ân", "Ġ ترÙĥ", "Ġت رÙĥ", "Ġتر Ùĥ", "ĠÑĤ Ñī", "ر ÙĪد", "رÙĪ د", "ç uk", "çu k", "ra nÃŃ", "ran ÃŃ", "Ġdo laÅŁ", "Ġdol aÅŁ", "ĠQ uang", "ĠQu ang", "ĠpÅĻed pok", "Ġnám ÄĽstÃŃ", "ой Ñĩив", "çĭ Ģ", "Ġб изнеÑģ", "ãģŁ ãģı", "ĠìĿ¸ ì²ľ", "о ÑĢо", "оÑĢ о", "ĠKü rt", "ĠKür t", "ê·¸ 룬", "ÑĨ аÑĤÑĮ", "ÑĨа ÑĤÑĮ", "ĠB ên", "Ġ acı", "Ġa cı", "Ġac ı", "Ú© Ø´", "ï¼Ī å¹³æĪIJ", "Ġ èģĶ", "Ġèģ Ķ", ") ãĢģ", "d iler", "di ler", "Ñĩ иÑĤÑĮ", "ÑĩиÑĤ ÑĮ", "Ñĩи ÑĤÑĮ", "Ư á»", "éĻ ¶", "il eceÄŁini", "ilece ÄŁini", "ileceÄŁi ni", "Ġv Å¡em", "ĠvÅ¡ em", "ĠvÅ¡e m", "å¼Ģ å¥ĸ", "è§Ħ 模", "ul muÅŁ", "Ġ åĪĺ", "Ġå Īĺ", "ĠåĪ ĺ", "е о", "еР¾", "ĠпеÑĢев ÑĸÑĢ", "åĪĨ åĪ«", "Ġjed ná", "Ġjedn á", "li ÄŁe", "liÄŁ e", "ĠرÙħ ضاÙĨ", "ık lı", "ıkl ı", "Ùĩ ÙĢ", "éĩį çĤ¹", "Ñĩ иваеÑĤÑģÑı", "Ñĩи ваеÑĤÑģÑı", "Ñĩив аеÑĤÑģÑı", "Ñĩива еÑĤÑģÑı", "ë¡ľ ìĦľ", "ÏĦ εÏģο", "ÏĦε Ïģο", "ÏĦεÏģ ο", "åľ° ä¸ĭ", "д наннÑı", "дн аннÑı", "Ġng ược", "ॠª", "ĠÎij λ", "Ġa lacak", "Ġal acak", "Ġ à¹Ģà¸ĩ", "Ġà¹Ģ à¸ĩ", "Ġà¹Ģภĩ", "اÛĮ ÙĨد", "اÛĮÙĨ د", "Ġh Ãłi", "ÑĢо из", "ĠЧ и", "Ġ ÑıÑģ", "ĠÑı Ñģ", "خر ÛĮد", "Ġhu deb", "Ġhud eb", "åľ §", "Ġ ìĦ¼", "ĠìĦ ¼", "åĶ¯ ä¸Ģ", "Ġ вÑĸлÑĮ", "Ġв ÑĸлÑĮ", "ĠвÑĸ лÑĮ", "ĠباÙĦ اتر", "ĠباÙĦا تر", "à¸Ńà¸ģ าส", "Ġ Tôi", "ĠT ôi", "ม à¸Ĥ", "o mor", "om or", "omo r", "ĠO lomou", "Ġx ong", "Ġxo ng", "Ġdomác ÃŃ", "Ġ اختÛĮ", "Ġاخ تÛĮ", "Ġاخت ÛĮ", "ĠÑĤеÑħ нÑĸÑĩ", "ĠÑĤеÑħнÑĸ Ñĩ", "ĠiÅŁ te", "à¥Į द", "Ġнад еж", "Ø®ÛĮ ص", "åĬª åĬĽ", "ĠتجÙĩ ÛĮزات", "Ġv ole", "Ġvo le", "Ġvol e", "k inci", "kin ci", "Ġhes ab", "ĠÑģ еÑģÑĤ", "Ú© ا", "ÑĤеÑĢ н", "ร รà¸Ħ", "รร à¸Ħ", "åıĤ èĢĥ", "ĠÐļ аб", "ĠÐļа б", "ĠÄ° mpar", "Ġnáv rh", "Ġnávr h", "åĴ¨ 询", "à¸ĸ าม", "Ġye rel", "Ġyer el", "Ġyere l", "ĠÃĸ l", "çĮ Ľ", "ĠاÙĦÙĪØ· ÙĨÙĬ", "Ġ ìĿ´ìĸ´", "ĠìĿ´ ìĸ´", "ิà¸Ĺย าศาสà¸ķร", "ิà¸Ĺยา ศาสà¸ķร", "ĠA ÅŁ", "Ġзем лÑİ", "ĠдомаÑĪ ниÑħ", "ĠÑĥ веÑĢ", "ĠÑĥв еÑĢ", "A LI", "AL I", "г ан", "га н", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "Ġdo stan", "Ġdos tan", "Ġdost an", "ez pe", "ãģĭ ãģĦ", "ر ÙģتÙĩ", "رÙģ تÙĩ", "رÙģت Ùĩ", "Ġм ÑĥÑģ", "ĠмÑĥ Ñģ", "à¹Į à¸Ł", "è¦ º", "али за", "ализ а", "ĠÑĥÑĩ ÑĢежд", "ĠÚ© اÙĦ", "Ġetk isi", "Ġetki si", "ä½Ĩ æĺ¯", "Ġsou vis", "ĠSav aÅŁÄ±", "ĠSavaÅŁ ı", "Ġب سبب", "ÎŁ ι", "ÎŁÎ ¹", "è ļ", "Ġ æ®", "Ġæ ®", "Ġìĺģ êµŃ", "ا سÛĮÙĪÙĨ", "اسÛĮ ÙĪÙĨ", "ĠاÙĦات ØŃاد", "Ġ глÑı", "Ġг лÑı", "à¹ĩà¸ģ à¸ĭ", "Ġج ÙĪÙĨ", "ĠجÙĪ ÙĨ", "ĠاÙĦرسÙħ ÙĬ", "Âł G", "ĠÑĤо бÑĸ", "ĠÑĤоб Ñĸ", " ĩ", "Ġ ëĮĢíĸī", "ĠëĮĢ íĸī", "çĬ¶ æħĭ", "Ġê·¸ ëĥ¥", "Ġи мп", "Ġим п", "ĠتÙĨظ ÛĮÙħ", "ÙĦ اÛĮÙĨ", "ÙĦا ÛĮÙĨ", "ÑģÑĤв еннÑĭм", "ÑģÑĤвен нÑĭм", "о пол", "оп ол", "ر ÙĪج", "رÙĪ ج", "Ġ à¸ĩ", "Ġภĩ", "Ġ çĤº", "ĠçĤ º", "ĠUlus lararası", "à¥Į à¤Ĥ", "ãĢģ ãģĿãģĨ", "Ġس ادÙĩ", "ÎŃ Î±ÏĤ", "ÎŃα ÏĤ", "Ġà¤Ĩ ल", "- ÑĦ", "ĠÎłÎ¿Î» ι", "ĠÎłÎ¿ λι", "Ġно ÑıбÑĢÑı", "ĠноÑı бÑĢÑı", "ÙĪ ÙĦÙĬ", "ÙĪÙĦ ÙĬ", "æĽľ æĹ¥", "æĮģ ç»Ń", "Ġê¼ Ń", "ece ÄŁiz", "eceÄŁi z", "ĠÛĮ اÙģت", "ĠÛĮا Ùģت", "Ġ åı¸", "Ġåı ¸", "ाà¤Ĺ त", "Ġ æķħ", "Ġæķ ħ", "Ġал леÑĢг", "Ġt uz", "Ġtu z", "еÑĢ ÑĤи", "еÑĢÑĤ и", "Ġth ầu", "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", "- à¤ħ", "Ġим мÑĥ", "ÑĢ ай", "ÑĢаР¹", "ÑĢа й", "主 義", "ĠbaÅŁ lar", "Ġä¸Ĭ 涨", "ع ا", "ĠÎĻ Ïī", "ียà¸ĩ à¹ĥหม", "ĠاÙĦÙħد ÙĬÙĨØ©", "Ñģ ÑĮко", "ÑģÑĮ ко", "ÑģÑĮк о", "ĠتارÛĮØ® ÛĮ", "at ÃŃm", "âĢļ Ø·", "Ø¢ خر", "ĠëĦ £", "ĠÙĨÙħ اÛĮد", "ĠÙĨÙħاÛĮ د", "ãģķãĤĵ ãģĮ", "Ġb ò", "Ġ à¸ķาม", "Ġà¸ķ าม", "ë³´ ìķĺëĭ¤", "а ÑĤÑĸв", "аÑĤ Ñĸв", "аÑĤÑĸ в", "ĠÑĦ ил", "Ġkısm ı", "iá»ĩ ng", "iá»ĩn g", "Ġay dın", "éģķ ãģĦ", "е ви", "ев и", "Ġ å¾®", "Ġå¾ ®", "( íģ¬ê¸°", "Ġ Ú¯ÛĮر", "ĠÚ¯ ÛĮر", "ĠÚ¯ÛĮ ر", "ìķĦ ìĦľ", "Ġδη μιοÏħÏģγ", "ãģ«ãģĬ ãģĦãģ¦", "ĠÃľ NÄ°", "и ÑĤом", "иÑĤ ом", "ع ÙĦاÙħ", "عÙĦ اÙħ", "åIJİ çļĦ", "Ġp lá", "Ġpl á", "à¸Ľà¸£à¸° à¹Ĥย", "ç¢ İ", "Ġ éĺ²", "Ġéĺ ²", "ëĬ Ķëĭ¤", "ëĬĶ ëĭ¤", "Ġ æĹ¥æľŁ", "ĠæĹ¥ æľŁ", "Ġgeç erli", "л аÑĤÑĭ", "ла ÑĤÑĭ", "лаÑĤ Ñĭ", "Ġmutlak a", "ÙĪ غ", "à¹Ģ ฮ", "à¹Ģภ®", "Ġï» £", "e deki", "ed eki", "ede ki", "à¹Į à¹Ģà¸Ļ", "Ġнайб ÑĸлÑĮÑĪ", "ĠнайбÑĸлÑĮ ÑĪ", "ï¼ Ĭ", "Ġ à¹Ĥรà¸ĩ", "Ġà¹Ĥ รà¸ĩ", "Ġfot bal", "Ġ éĢģ", "ĠéĢ ģ", "âĢĮاÙĦ ÙħÙĦ", "Ïīμά ÏĦιο", "Ġú kol", "åįļ 士", "d ub", "du b", "ı lıģ", "ılı ÄŁ", "ıl ıģ", "ëĵľ 를", "çĭ IJ", "α λλ", "αλ λ", "æŃ» 亡", "ĠпÑĢед поÑĩ", "çµ µ", "Ġм ÑĥзÑĭ", "ĠмÑĥ зÑĭ", "ĠмÑĥз Ñĭ", "еÑĢÑĤ в", "ĠÙĥ ÙĨد", "ĠÙĥÙĨ د", "Ġu lož", "Ġul ož", "ÎŁÎ¥ ÎĽ", "g ili", "gi li", "gil i", "üs tü", "üst ü", "н ки", "ĠÙĤ ÙĪاÙĨ", "ι ακ", "ια κ", "ĠÅŁ er", "ĠкиÑģ л", "Ġки Ñģл", "Ùģ ضÙĦ", "ĠÐIJ ÑĦ", "γ εν", "γε ν", "Ġdo stal", "Ġdos tal", "Ġdost al", "ĠsaÄŁ lıklı", "ĠsaÄŁlık lı", "家 æĹı", "ÄIJ T", "е ÑĢин", "еÑĢ ин", "еÑĢи н", "ĠìĿ´ëŁ¬ íķľ", "Ġdüny ada", "Ġdünya da", "Ġnh ắc", "Âł ÂłĊ", "³³ Ċ", "ν ηÏĥη", "νη Ïĥη", "γÏģα μμα", "Ġtak son", "ĠTürk çe", "ĠÙģراÙĨ سÙĩ", "天 åłĤ", "æº ¶", "Ġ oto", "Ġo to", "Ġot o", "èµ µ", "ch yb", "chy b", "Ġ å¾Ĵ", "Ġå¾ Ĵ", "ÏĦ Ïį", "áh nout", "à¥į पर", "à¥įप र", "Ġv las", "Ġvl as", "Ġíļ¨ ê³¼", "Ġt hang", "Ġth ang", "Ġthan g", "Ġtha ng", "Ġol masına", "Ġolm asına", "Ġolması na", "ĠпоÑĢÑĥÑĪ еннÑı", "Ġqu ỹ", "ĠíĿ IJ", "ĠìĪ ¨", "Ġ ë²Ī째", "Ġë²Ī 째", "ẹ n", "Ġз год", "Ġзг од", "Ġ تز", "Ġت ز", "Ġاخ تص", "Ġاخت ص", "ĠзÑĥÑģÑĤ ÑĢÑĸ", "Ġt ặng", "ῶ ν", "Ġ ì½ľ", "Ġì½ ľ", "ов аниÑħ", "ова ниÑħ", "овани Ñħ", "ован иÑħ", "âĢĮ شد", "âĢĮØ´ د", "Ġa raya", "Ġar aya", "Ġara ya", "Ġaray a", "r ové", "ro vé", "rov é", "Ġاخ تÙĦ", "Ġاخت ÙĦ", "ли вий", "лив ий", "Ġات ØŃاد", "Ġak ÅŁam", "ĠÚ©ÙĦ اس", "ãĤ¢ ãĥĥãĥĹ", "Ġz ih", "Ġzi h", "å ĩĮ", "åĩ Į", "å±± å¸Ĥ", "Ġçev res", "Ġçevr es", "Ġçevre s", "Ġог ÑĢом", "ĠØ¢ دÙħ", "ĠtÄĽ lo", "ĠtÄĽl o", "ï¼Į æľ¬", "ĠÚĺاÙĨ ÙĪÛĮÙĩ", "Ġkr aje", "Ġkra je", "Ġkraj e", "μ ία", "μί α", "èħ ¿", "âĢŀ To", "決 å®ļ", "ì ĩ", "Ġ éĴ", "Ġé Ĵ", "ĠΣ ÏĦα", "ĠجÙħ ÙĩÙĪر", "ĠGen ç", "r ám", "rá m", "ĠÐł ез", "ĠÐłÐµ з", "Ġvyt vá", "ĠпÑĢоизвод ÑģÑĤва", "ĠÙħ Ø°Ùĩ", "ĠÙħØ° Ùĩ", "Ġihtiy ac", "ãĤ¯ ãĤ»", "Ġn êu", "å¾ ³", "Ġ ëĵĿ", "Ġëĵ Ŀ", "н аÑĩе", "на Ñĩе", "наÑĩ е", "ĠÏĥÏħ μμε", "ÏĨ Ïīν", "в авÑģÑı", "ва вÑģÑı", "вав ÑģÑı", "Ġви ÑĤами", "ĠвиÑĤ ами", "Ìģ t", "Ġfinan ÄįnÃŃ", "åıĬ åħ¶", "âĢ ħ", "çĭ ¼", "ัà¸ļ à¸ķ", "ãģĽ ãĤĭ", "ÎĻÎļ ÎŁ", "λ λι", "λλ ι", "ÑĤ оÑİ", "ÑĤо Ñİ", "ا عÙĬØ©", "اع ÙĬØ©", "اعÙĬ Ø©", "vÃŃ ce", "vÃŃc e", "о нÑĸв", "он Ñĸв", "онÑĸ в", "ì£ Ħ", "å» ł", "ĠØ´ÙĬ Ø¡", "ĠТ ем", "ĠТе м", "Ġاب زار", "ĠTH PT", "γ γÏģαÏĨ", "ĠëĮĢ íķ´ìĦľ", "ĠëĮĢíķ´ ìĦľ", "ĠPh ạm", "ÑĨион ной", "| /", "Ġ ãĤ¸ãĥ£", "ĠãĤ¸ ãĥ£", "ÑĮ ÑİÑĤ", "ÑĮÑİ ÑĤ", "Ñĥ зÑĭ", "Ñĥз Ñĭ", "ĠÙħ اد", "ĠÙħا د", "ĠmÄĽ ly", "ĠmÄĽl y", "Ġ çĪ±", "ĠçĪ ±", "Ġr ád", "Ġrá d", "à¸Ħว à¸ļà¸Ħ", "à¥Ī ?", "Ġl idi", "Ġli di", "Ġlid i", "m amız", "mam ız", "Ġ à¹ģà¸ģ", "Ġà¹ģ à¸ģ", "ãĤ¯ ãĤ·ãĥ§ãĥ³", "à¸Ńำ à¸Ļวย", "es át", "Ġv iêm", "Ġvi êm", "è¡Į åĬ¨", "มาà¸ģ à¸ģว", "ĠØ®ÙĪ اب", "Ġser best", "ÅĻÃŃ z", "ĠíĺĦ ëĮĢ", "ãĢĮ ãģĿãģĨ", "çĤ ¸", "om ik", "omi k", "ĠÄ° ran", "Ġer iÅŁ", "ĠÑģ ела", "ĠÑģел а", "Ġار زÛĮ", "Ġارز ÛĮ", "ãĥĪ ãĥª", "ĠB ÄĽ", "е кÑĥ", "ек Ñĥ", "Ч ÑĤобÑĭ", "ЧÑĤо бÑĭ", "Ġanlam da", "Îij Îĺ", "ĠLINE AR", "ĠLIN EAR", "æľī çĤ¹", "ÑĤ аÑĢ", "ÑĤа ÑĢ", "it ler", "itle r", "Ġn ÃŃž", "ĠnÃŃ Å¾", "ĠС ÑģÑĭлки", "å ¶", "Ġв пол", "Ġвп ол", "ĠدÙĤÛĮ ÙĤÙĩ", "ĠدÙĤÛĮÙĤ Ùĩ", "Ġ ä½ĵ", "ر Ùī", "ëĶ °", "Ġà¤ķ व", "Ġж иÑĢ", "æij Ĩ", "Ġì¤ij ìĭ¬", "Ġк Ñĥб", "ĠкÑĥ б", "Ġz lep", "ĠÑĢÑĭ б", "é³ ´", "à¹ģà¸ľ à¸Ļ", "Ġ íĢ", "Ġí Ģ", "ĠÐĿ еÑĤ", "ĠÐĿе ÑĤ", "ž itÄĽ", "žit ÄĽ", "ži tÄĽ", "Ġb Äĥng", "ĠH ava", "ĠHa va", "ĠHav a", "Ġ모 ëį¸", "ĠH ãy", "ĠìĿ´ ê²ĥ", "Ġìĥģ ìĦ¸", "me miÅŁ", "mem iÅŁ", "ĠθÎŃ Ïĥη", "ण न", "ĠskuteÄį nÄĽ", "ĠTarih i", "Ġtext u", "Ġtex tu", "ï¼Į éĢĻ", "ĠاÛĮÙĨتر ÙĨتÛĮ", "ĠÙ¾ اد", "Ġپا د", "ิà¸Ļ à¸ģาร", "ĠNg á»įc", "ĠÑĢоб иÑĤи", "íĸĪ ê³ł", "Ġम ण", "ÐĽ Ðĺ", "Ġпо ÑĤеÑĢ", "ĠпоÑĤ еÑĢ", "Ñģ ом", "Ñģо м", "ĠاÙĪ ÙĦÛĮÙĩ", "ĠاÙĪÙĦ ÛĮÙĩ", "éĽ ij", "ĠGi á", "Ġk anal", "Ġkan al", "Ġka nal", "Ġavant aj", "Ġavan taj", "Ġr yb", "Ġry b", "Ø® تÙĩ", "خت Ùĩ", "ĠÙĪ رÙĪد", "ĠÙĪر ÙĪد", "ÐĴ ÑĤ", "Ïī Ïĥε", "기 ë¡ľ", "ĠÐĽ Ñĸ", "Ġt ảng", "Ġtả ng", "Ġص ÙĦÙī", "ĠÑĥ лÑĭ", "ĠÑĥл Ñĭ", "Ġcu á»ijn", "ĠÐIJ нг", "ĠÐIJн г", "Ġد اÙĪ", "ĠÑĪлÑıÑħ ом", "ĠÄįlovÄĽk a", "ĠÄįlovÄĽ ka", "d ete", "de te", "det e", "ÑĬ ем", "à¹Į à¹ĥà¸Ļ", "à¤ķ न", "åĪ ¤æĸŃ", "åĪ¤ æĸŃ", "ĸ ìĹIJ", "ÏĦ ÏīÏĥη", "ÏĦÏī Ïĥη", "ĠÙģÙĨ اÙĪرÛĮ", "ĠyaÅŁ ında", "ĠÏĥÏĩ ÎŃ", "Ġ yı", "Ġy ı", "Ġp ÅĻen", "ĠpÅĻ en", "ĠpÅĻe n", "ĠÑĦоÑĢм ÑĥваннÑı", "ĠÑĦоÑĢмÑĥ ваннÑı", "ü mÃ¼ÅŁ", "üm Ã¼ÅŁ", "ümü ÅŁ", "Ġ δο", "Ġδ ο", "ımız ın", "ımızı n", "Ġ é¢Ħ", "Ġé¢ Ħ", "оÑģÑĤ ÑĮÑİ", "оÑģÑĤÑĮ Ñİ", "ĠоÑĤкÑĢÑĭ ÑĤ", "Ġأغ سطس", "ĠA sp", "ĠAs p", "ĠÑĥ зн", "ĠÑĥз н", "ĠÙĪ است", "ĠÙĪا ست", "ĠÙĪاس ت", "e lerle", "eler le", "èķ ī", "Ġت Ú©ÙĨ", "Ġتک ÙĨ", "Ñĥ мÑĥ", "Ñĥм Ñĥ", "à¹Į à¸ĭ", "ाद न", "ĠâĢĭ âĢĭâĢĭ", "ĠâĢĭâĢĭ âĢĭ", "Ġa lıyor", "Ġal ıyor", "Ġî ¡", "Ùħ دة", "Ùħد Ø©", "Ġ Ïĥει", "ĠÏĥ ει", "ĠÏĥε ι", "Ġ è¿Ļ", "Ġè¿ Ļ", "ĠÅŀ ehir", "ен ÑĤами", "енÑĤ ами", "енÑĤа ми", "ãĤ¿ ãĥ«", "ห าย", "หา ย", "ай ÑĤ", "Ġh arc", "Ġhar c", "Ġha rc", "ãĢĤ ãģĬ", "Ġتأ Ø«ÛĮر", "า à¸Ĭà¸Ļ", "าà¸Ĭ à¸Ļ", "Ġth áºŃm", "Ġ æ¿", "Ġæ ¿", "Ġm Å©i", "Ġprv nÃŃm", "ĠprvnÃŃ m", "Ġбаг аÑĤÑĮ", "ĠбагаÑĤ ÑĮ", "ãģķãĤī ãģ«", "b iên", "bi ên", "åºĶ å½ĵ", "ìĿ´ ë²Ħ", "Ġpou žÃŃt", "ĠpoužÃŃ t", "Ġokam ž", "e sin", "es in", "esi n", "v ÄĽl", "vÄĽ l", "Ġ ضÙĪ", "Ġض ÙĪ", "è» Ł", "- з", "à¥Ī त", "è¨Ī ç®Ĺ", "r abilir", "ra bilir", "rab ilir", "ĠÐłÐ¾Ñģ ÑĸÑĹ", "Ġpla tÃŃ", "Ġplat ÃŃ", "Ġdosp ÄĽl", "Ġر ضا", "Ġرض ا", "Ġn ového", "Ġnov ého", "Ġnové ho", "Ġна ÑĨионалÑĮ", "ĠÐIJ б", "ãģĮ ãģĤãģ£ãģŁ", "Ġ ë¹Ī", "Ġë¹ Ī", "âĢĮ Ùħ", "å±ŀ äºİ", "Ġt ane", "Ġta ne", "Ġtan e", "ÙĬ اÙĩ", "ÙĬا Ùĩ", "Ġ βο", "Ġβ ο", "Ġ ëĬ¥", "ĠëĬ ¥", "ãĥĩãĤ£ ãĥ¼ãĤ¹", "Ġ Ø°Ùĥر", "ĠØ° Ùĥر", "Ġobvyk le", "Ġbir inci", "ĠاÙĦ زر", "ĠاÙĦز ر", "ìĿ´ ë¹Ħ", "ĠØ¥ د", "ĠE kon", "ĠEk on", "ÐŁ ол", "ÐŁÐ¾ л", "ĠвеÑĢ оÑıÑĤ", "Ġyarar lan", "Ġа ÑĢом", "ĠаÑĢ ом", "Ġ éĦ", "Ġé Ħ", "Ġ iddi", "Ġid di", "i Äįka", "iÄį ka", "struk ce", "mÃ¼ÅŁ tür", "Ïħ ÏĦÏĮ", "ë¡ ±", "Ġal maktadır", "Ġalmak tadır", "ени Ñıми", "ениÑı ми", "ียà¸Ļ ร", "à¹ĩ à¸Ļว", "à¹ĩà¸Ļ ว", "и кÑĥ", "ик Ñĥ", "е нка", "ен ка", "âĢĻ yi", "âĢĻy i", "Ġpo hod", "Ġpoh od", "Ġ زر", "Ġز ر", "Ġx ấu", "Ġ à¸łà¸²à¸©", "Ġà¸ł าษ", "Âł Ðŀ", "Ġ δικ", "Ġδ ικ", "Ġδι κ", "Ġназ ива", "åıª èĥ½", "大 éĩı", "ĠÄij ế", "Ġ 第äºĮ", "Ġ第 äºĮ", "ĠkiÅŁ ilerin", "ĠkiÅŁi lerin", "ĠkiÅŁiler in", "Ġdob ré", "Ġdobr é", "é© ¾", "Ġdůležit é", "ë¡ ¤", "μÎŃ Î½Î¿Ïħ", "μÎŃν οÏħ", "μÎŃνο Ïħ", "Ġtr ú", "Ġbiç im", "Ġ ÐĿÐIJ", "ĠÐĿ ÐIJ", "Ġ å¾Į", "Ġå¾ Į", "Ġdu yg", "Ġduy g", "åŀ Ĥ", "ÐĨ ÐĨ", "Ġet meye", "Ġetm eye", "Ġetme ye", "ĠÙĦب اس", "Ġд вÑĸ", "Ġдв Ñĸ", "Ġ 긴", "Ġê¸ ´", "ÑĨ Ñĸйно", "ÑĨÑĸй но", "κ ÏĦή", "ï½ Ŀ", "ĠÑĦевÑĢа лÑı", "å¯ «", "Ġ 겨", "Ġê² ¨", "Ġyıl larda", "Ġyıllar da", "Ġз Ñĥп", "Ġobchod nÃŃ", "Ġاض اÙģÙĩ", "в еÑĢж", "веÑĢ ж", "Ġ æłĩ", "Ġæł ĩ", "ج اج", "جا ج", "Ġر ÙĪسÛĮ", "ĠرÙĪ سÛĮ", "Ġstand art", "Ġstan dart", "é ru", "ér u", ") ìĿĦ", "д екÑģ", "де кÑģ", "Ġ âĪļ", "ĠâĪ ļ", "ĠÄ°ngiliz ce", "èĬ Ŀ", "身 ä¸Ĭ", "ØŁ ØŁ", "Ġm ẽ", "Îij ÎĶ", "енÑģ ив", "âĢĻ ta", "âĢĻt a", "à¹ī าà¸ģ", "à¹īา à¸ģ", "ÎŁÎĽ ÎŁÎĵ", "ä»ĺ ãģij", "Ġs Ãłng", "ĠsÃłn g", "Ġह à¤Ł", "ÑĭÑĪ лен", "ĠØ® طر", "Ġخط ر", "Ġнай ÑĤи", "缸 ä¿¡", "Ïī δ", "ठĶ", "Ġdo pad", "Ġdop ad", "à¹Ħà¸Ł ล", "æ ģµ", "æģ µ", "í Ĥ¬", "íĤ ¬", "Ä±ÅŁ ma", "ãģı ãĤĮãģŁ", "ãģıãĤĮ ãģŁ", "Ġnap rost", "ĠÑģоÑģÑĤав е", "Ġ ÙĪسط", "ĠÙĪ سط", "ĠÙĪس Ø·", "๠ķ", "éĸĭ çĻº", "ĠдеÑĢ ева", "ĠдеÑĢев а", "- ÐĶ", "à¸ĩ à¸Ĭ", "ิà¸ķ ย", "ĠاÙĦÙĤ اÙĨÙĪÙĨ", "ãĤ¹ ãĤ«", "l ÃŃž", "lÃŃ Å¾", "Ġан ализ", "Ġproblém y", "æĸĩ åѦ", "çĹħ éĻ¢", "Ñģ ед", "Ñģе д", "ï¼Į å°ı", "Ġعش ÙĤ", "ãģ° ãģĭãĤĬ", "Ġع ÙĤد", "ĠعÙĤ د", "ØŃ ÙĬØ©", "ØŃÙĬ Ø©", "Ġë°Ķ ëŀįëĭĪëĭ¤", "inc lu", "incl u", "Ġ ëĵľë¦½ëĭĪëĭ¤", "Ġëĵľ 립ëĭĪëĭ¤", "åį« çĶŁ", "Ġвид Ñĥ", "Ġви дÑĥ", "ุ à¸ļาล", "ุà¸ļ าล", "ÑĢ ÑĥкÑĤ", "ÑĢÑĥ кÑĤ", "ÑĢÑĥк ÑĤ", "ĠоÑģ вÑĸÑĤ", "ĠоÑģвÑĸ ÑĤ", "Ġvel ký", "Ġvelk ý", "Ġch tÄĽl", "ĠchtÄĽ l", "æīĵ å¼Ģ", "Ġзакон одаÑĤелÑĮ", "ан Ñģи", "анÑģ и", "ì¶ ĺ", "ĠÙħر اج", "åģľ æŃ¢", "Ġво но", "ìłķ ìĿ´", "Ġroz sah", "Ġrozs ah", "Ġ æĻ´", "ĠæĻ ´", "Ġza jist", "Ġzaj ist", "Âł м", "tı ģını", "tıģ ını", "Ġhizmet i", ". Îij", "ĠÙħعÙħÙĪÙĦ ا", "Ġ ži", "Ġž i", "Ġg á»įn", "èĮ Ĥ", "Ġh uz", "Ġhu z", "ζ ει", "ζε ι", "à¥ī à¤Ł", "Ġиз дел", "ìŀ ĸ", "ĠëĶ° 른", "Ġk ia", "Ġki a", "Ġz nÄĽnÃŃ", "Ġzn ÄĽnÃŃ", "ĠоÑĢгани за", "ĠоÑĢганиз а", "از ات", "Ġrež im", "Ġв енÑĤи", "b ách", "Ġод номÑĥ", "Ġодно мÑĥ", "Ġодном Ñĥ", "Ġkit ab", "Ġki tab", "Ġkita b", "Ġfran couz", "Ġfranc ouz", "ĠØ£ ÙĦ", "Ġس رÙĪ", "Ġسر ÙĪ", "Ùij ÙĦ", "Ġ ман", "Ġм ан", "Ġма н", "ë° į", "Ġк Ñĥда", "ĠкÑĥ да", "Ùı س", "ãĢĤ æѤ", "ا شة", "اش Ø©", "à¸Ĥà¸Ńà¸ĩ à¸ľ", "主 ä»»", "ив ÑĪи", "Ġà¸ģ รà¸ģ", "Ġà¸ģร à¸ģ", "ек Ñģи", "екÑģ и", "иÑĤ еÑĤ", "иÑĤе ÑĤ", "ĠØ£ ÙĦÙģ", "ĠØ£ÙĦ Ùģ", "а ними", "ан ими", "ани ми", "ãĥļ ãĥ¼ãĤ¸", "ĠпÑĢав ил", "ĠпÑĢави л", "åªĴ ä½ĵ", "Ñİ Ñīее", "ÑİÑī ее", "ä¸Ģ 人", "β ο", "ìĭ ¸", "о зна", "оз на", "å¤ī æĽ´", "ĠÙħØ´ Ùĩد", "æ³ķ 人", "ĠBa kanı", "ĠBak anı", "ĠBakan ı", "ĠÑħоÑĩ а", "Ġα ξ", "Ġver ilm", "Ġveri lm", "Ġk onus", "Ġkon us", "Ġkonu s", "με νη", "μεν η", "Ġ 馬", "Ġé ¦¬", "Ġé¦ ¬", "Ġìĭ¤ ìłľ", "Ġjed no", "Ġjedn o", "Ġб аб", "Ġба б", "åĥ į", "æĺ¯ ä¸Ģ个", "æĺ¯ä¸Ģ 个", "- е", "ĠpÅĻek vap", "à¸Ń à¸ŀ", "ĠY ol", "ĠYo l", "ĠÑĥÑģÑĤан авлива", "ê² ¼", "Ġ 件", "اÙĦ Ø´", "Ġоб ÑĥÑĩ", "åĺ Ľ", "ĠÑħоÑĩ Ñĥ", "ĠÐķ в", "ÑĦ оÑĢÑĤ", "ÑĦоÑĢ ÑĤ", "Ġर न", "âĢŀ V", "èľ ľ", "Ġd oma", "Ġdo ma", "Ġdom a", "æĶ¯ æı´", "Ġ اخت", "Ġا خت", "Ġاخ ت", "å¾ ª", "à¥Ĥ à¤ļन", "à¥Ĥà¤ļ न", "ा हन", "ाह न", "Ġ å¤ı", "Ġå¤ ı", "ĠاÙĦØ£ Ùħر", "ĠاÙĦØ£Ùħ ر", "ĠбеÑĢем енноÑģÑĤи", "ĠTh á»±c", "é£İ éĻ©", "Ġül kemiz", "Ġülk emiz", "çķª åı·", "ÑģÑĤ ÑĢе", "ÑģÑĤÑĢ е", "ÑĪ ло", "ĠصاØŃ Ø¨", "ι νε", "ιν ε", "ĠK ıs", "ĠKı s", "ĠPr ahy", "ĠPra hy", "æ¹ ¿", "Ġv ým", "Ġvý m", "çĽ Ĵ", "ÎŁ ÎĶ", "ÎŁÎ Ķ", "ãģł ãģª", "ĠpÅĻÃŃ ležit", "Ġìĸ¸ ìłľ", "ĠÑĪвид ко", "Ġsitu aci", "åħĥ ç´ł", "Ä°T ESÄ°", "ĠV ak", "ĠVa k", "Ġner edeyse", "i iii", "ii ii", "iii i", "ÑĢа зд", "ÑĢаз д", "Ġп олиÑĤ", "Ġпо лиÑĤ", "Ġпол иÑĤ", "Ġполи ÑĤ", "Ġп огод", "Ġпо год", "Ġпог од", "ĠпÑĢоÑĨеÑģ Ñģе", "ĠпÑĢоÑĨеÑģÑģ е", "Ġмен ÑĪе", "äºĮ 人", "ĠÙħÙĪ اط", "Ġp ÅĻik", "ĠpÅĻ ik", "ĠpÅĻi k", "è· ¡", "Ġs erg", "Ġse rg", "Ġser g", "ĠÑĢаÑģ ÑģÑĤоÑı", "и Ñĩно", "иÑĩ но", "ĠÎĶ ÎĹÎľ", "¨ Ø·", "ص بØŃ", "صب ØŃ", "สะ à¸Ķวà¸ģ", "د رÛĮ", "در ÛĮ", "k ům", "ků m", "ç§ģ ãģ¯", "Ġt vor", "Ġtv or", "à¥įव व", "Ġp ÅĻiv", "ĠpÅĻ iv", "ĠpÅĻi v", "Ġ íı´", "Ġíı ´", "Ġst átu", "Ġstát u", "Ġed ilmiÅŁtir", "Ġedilm iÅŁtir", "Ġedil miÅŁtir", "ĠedilmiÅŁ tir", "ØŃ Ùħ", "Ġб ÑĥÑħ", "ĠбÑĥ Ñħ", "สำ à¹Ģร", "ĠتÙĪ ضÛĮ", "ãģĿãĤĮ ãģ¯", "Ġà¤ħव ध", "é ŀĭ", "éŀ ĭ", "âĤ¬ Ċ", "Ġ éº", "Ġé º", "ĠÄĮ es", "Ġpop rvé", "ï¼Į åĽł", "Ġal mÄ±ÅŁ", "Ġalm Ä±ÅŁ", "l al", "la l", "ĠØ® ÙĪبÛĮ", "ĠØ®ÙĪ بÛĮ", "ĠØ®ÙĪب ÛĮ", "Ġκ οÏģ", "Ġκο Ïģ", "ìļ´ ëıĻ", "m ayın", "may ın", "mayı n", "Ġak tif", "Ġakt if", "ĠاÙĨج ÙħÙĨ", "ĠÑģ ÑĤак", "ĠÑģÑĤ ак", "ĠÑģÑĤа к", "ĠÑģÑĤ аÑĢа", "ĠÑģÑĤаÑĢ а", "ĠÑģÑĤа ÑĢа", "ÙĦ ÙģØ©", "ÙĦÙģ Ø©", "Ġparç ası", "ĠкоÑĢп ÑĥÑģ", "ãĢģ é«ĺ", "! ..", "!. .", "ĠÎł ÎijÎĿ", "ĠÙĩÙĨ ÙĪز", "ion álnÃŃ", "Ġprá vnÃŃ", "Ġpráv nÃŃ", " Ŀ", "Ġت ÛĮر", "ĠتÛĮ ر", "Ġ åŁİ", "ĠåŁ İ", "Ġзг ад", "Ġsaldır ı", "æŁ¥çľĭ æijĺè¦ģ", "é« ª", "Ùģ صÙĦ", "ãģĻ ãģ¹ãģ¦", "е во", "ев о", "ê´Ģ리 ìŀIJ", "Ġìĺ Ĩ", "udic ots", "ÙĪر ÙĨ", "Ġcel kem", "ãĤ¤ ãĤº", "ìĬ¤ ê°Ģ", "販 売", "ĠíĮĮìĿ¼ 첨ë¶Ģ", "ë ¢°", "Ġe nergie", "Ġenerg ie", "Ġener gie", "es idir", "esi dir", "Ġm iá»ĩng", "éĻ ·", "Ġг аÑĢа", "ĠгаÑĢ а", "Ġb iliyor", "Ġbil iyor", "çį² å¾Ĺ", "еÑĤ еÑĢб", "à¹Īา à¹Ģà¸Ľ", "Ġμα ζί", "Ġzprac ovánÃŃ", "Ñģ м", "Ġh ala", "Ġha la", "Ġhal a", "Ġز ÙĪج", "ĠвÑĸд нов", "à¹Ģหม าะ", "ĠÐłÐµÑģп Ñĥбли", "åĩºåĵģ èĢħ", "Ñī ини", "Ñīи ни", "Ñīин и", "ัà¸Ļ à¹Ģà¸Ľ", "Ġtý den", "Ġtýd en", "Ġب ÙĬت", "ĠبÙĬ ت", "Ñģ комÑĥ", "Ñģк омÑĥ", "Ñģком Ñĥ", "Ñģко мÑĥ", "ĠÙĩÙĪ اپÛĮÙħ", "оÑģ нов", "é¸ Ł", "Ġsou krom", "Ġfa iz", "Ġdem ok", "Ġdemo k", "Ġkter ém", "Ġkteré m", "Ġëħ ¹", "л аÑĩ", "ла Ñĩ", "ĠоÑĤвеÑĤ ÑģÑĤвен", "Ġ ï¼¼:", "Ġï¼¼ :", "Ġ λο", "Ġλ ο", "ÄĮ esk", "ê°Ģ ìļĶ", "Ġ ãĥĬ", "Ġãĥ Ĭ", "Ġnhu áºŃn", "ĠÑģ или", "ĠÑģи ли", "ĠÑģил и", "ĠÐľ он", "Ġç ap", "Ġça p", "ĠRow Box", "Ġм аÑģÑĤ", "ĠмаÑģ ÑĤ", "Ġма ÑģÑĤ", "ĠÐľ а", "ĠдÑĢÑĥг о", "ĠдÑĢÑĥ го", "ĠØ£ Ø´", "ë°© ìĨ¡", "ĠпÑĸд пиÑģ", "èĩ ¨", "åī ©", "Ġh iá»ĥn", "Ġhi á»ĥn", "ĠÙĤر ارد", "ĠÙĤرار د", "ist rat", "istr at", "istra t", "ÐŁ Ñĸд", "ÏĦε Ïģα", "ÏĦεÏģ α", "Ġpoz dÄĽ", "ĠbaÅŁ ta", "夫 人", "л ини", "ли ни", "лин и", "Ġка ÑĩеÑģÑĤва", "ĠкаÑĩе ÑģÑĤва", "Ġkur tul", "Ġ ì¢Į", "Ġì¢ Į", "ãģ«ãģĬ ãģijãĤĭ", "åľ° åįĢ", "ĠÑĩа Ñģом", "ĠÑĩаÑģ ом", "ìµľ ê³ł", "Ġn gang", "Ġng ang", "Ġnga ng", "ا Ùĩد", "اÙĩ د", "ĠШ ев", "ĠpÅĻ itom", "ĠpÅĻi tom", "Ġch ấm", "ĠÐľ еÑģÑĤо", "ĠÑģовеÑĢÑĪ енно", "ÃŃ cÃŃ", "ÃŃc ÃŃ", "Ń å·ŀ", "åĪĽ æĸ°", "äºĶ æľĪ", "Ġا عÙħاÙĦ", "Ġاع ÙħاÙĦ", "Ġвозмож ноÑģÑĤи", "Ġвозможно ÑģÑĤи", "ĠпÑĢод овж", "n ÄĽt", "nÄĽ t", "ĠÐĿа пÑĢимеÑĢ", "ĠاÙĦ دÙħ", "ĠاÙĦد Ùħ", "Ġ à¹ģà¸ļà¸ļ", "Ġà¹ģ à¸ļà¸ļ", "çĶŁ çļĦ", "ĠÑħ аÑĢÑĩ", "ĠSon uç", "Ġrůzn é", "Ġrůz né", "Ġ اذ", "Ġا Ø°", "ĠØ§Ø °", "à¸ķ à¸Ńà¸ļ", "P ÅĻed", "PÅĻ ed", "ĠдеÑĢев Ñıн", "ë´ IJ", "ĠëĬIJ ëĤ", "جÙħ ÙĬع", "ĠBöyle ce", "èµ ı", "Ġب سÙĬ", "Ġبس ÙĬ", "ĠÃĩ aÄŁ", "Ġت اÛĮ", "Ġتا ÛĮ", "Ġnej vyÅ¡Å¡ÃŃ", "èĸ ©", "Ïĩε δÏĮν", "Ġëĵ± ìĿĺ", "e yh", "ey h", "æĸĻ çIJĨ", "ا تÙĩ", "ات Ùĩ", "æī «", "Ġ å©", "Ġå ©", "ĠпÑĢи вед", "ĠпÑĢив ед", "æī ¶", "Ġ 견", "Ġê² ¬", "Ġا ÙħÛĮر", "ĠاÙħ ÛĮر", "ाय ल", "æ¡ ij", "à¸Ļ à¹Ģà¸ķ", "ила кÑĤи", "家 ä¼Ļ", "Ġbulun uyor", "y sa", "ys a", " Ĩ", "ĠB Ä°R", "íĨ ¤", "à¤Ĥà¤Ĺ à¤łà¤¨", "ÎĶ εν", "à¥Į à¤ķर", "à¥Įà¤ķ र", "éĸĵ ãģ«", "Ġм об", "Ġмо б", "ĠMo rav", "ĠMor av", "è§Ħ åĪĴ", "ĠÑģвÑĸÑĤ Ñĸ", "ul ts", "ult s", "Ġze mÃŃ", "Ġzem ÃŃ", "Âł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", "ÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", "ĠÐŁ оп", "ĠÐŁÐ¾ п", "ãģĤ ãģĴ", "Ġpom oci", "Ġpomoc i", "ĠзмÑĸ ÑģÑĤ", "Ġзм ÑĸÑģÑĤ", "主 人", "ĠS ı", "ãĥĽ ãĥĨãĥ«", "ĠÑĥва гÑĥ", "å» ³", "à¹Ģม à¸ķร", "est li", "Ġlo ạt", "ãĤ¢ ãĥ¼", "ĠÎĶ ε", "Ġbun ları", "Ġbunlar ı", "Ġ çĤ¹åĩ»", "ĠçĤ¹ åĩ»", "Ġ BÃłi", "ĠB Ãłi", "ĠBÃł i", "Ġ ä¸ĸ", "Ġä¸ ĸ", "Ġê³ł ê°ľë¥¼", "ĠÐŃ ÑĤоÑĤ", "ĠÐŃÑĤо ÑĤ", "Ġmem nun", "Ġ ।Ċ", "Ġ। Ċ", "ĠиÑģÑĤоÑĢ ии", "Ġ ì°©", "Ġì° ©", "१ ९", "१ॠ¯", "ĠÐŀд нак", "ĠÐŀдна к", "Ġv ede", "Ġve de", "Ġved e", "ÏĨ ÎŃÏģει", "ÏĨÎŃ Ïģει", "â b", "çĬ¶ åĨµ", "åįı è®®", "Ġ ê°Ŀ", "Ġê° Ŀ", "е вид", "ев ид", "еви д", "j mu", "jm u", "Ġколи ÑĩеÑģÑĤва", "ĠколиÑĩ еÑģÑĤва", "ĠколиÑĩе ÑģÑĤва", "à Ľ", "i Äįe", "iÄį e", "Ġfirm alar", "Ġfir malar", "Ġfirma lar", "èĢ Ģ", "к Ñĸн", "кÑĸ н", "ĠêµŃ 민", "Ġ목 ë¡Ŀ", "ĠÎļ αÏģ", "ĠÎļα Ïģ", "Ġhis sed", "Ġhiss ed", "ï¼ «", "Ġ Tên", "ĠT ên", "ĠÑĤÑĭ ÑģÑıÑĩ", "ØŃÙĬ ØŃ", "Ġвпол не", "ĠS ınıf", "ĠSın ıf", "Ġμ ην", "Ġμη ν", "Ġ íij¸", "Ġí ij¸", "ĠاÙĦ طبÙĬ", "ĠاÙĦØ· بÙĬ", "ĠاÙĦطب ÙĬ", "ĠزÛĮ ب", "Ġп Ñĥ", "Ġp raž", "Ġpr až", "Ġpra ž", "ìĹĨ ëĬĶ", "θ ÏģÏī", "Ġi çi", "Ġiç i", "Ġб Ñĸл", "ĠбÑĸ л", "Ðł Ñij", "Ġì¶ķ 구", "Ġl ạ", "Ġ ãĥķãĤ¡", "Ġãĥķ ãĤ¡", "Ġ èĸ", "Ġè ĸ", "μα ÏĦο", "éĩij å±ŀ", "á li", "ál i", "ĠÙģ Ø£", "ĠKar lov", "ĠKarl ov", "ĠZ áp", "ĠZá p", "ãĥª ãĥ³ãĤ°", "ãĥªãĥ³ ãĤ°", "ab ilmek", "abil mek", "ĠС и", "Ġc ÃŃrk", "ĠcÃŃ rk", "Ġk á»ĭp", "Ġà¤ij नल", "ĠÙĪ ØŃدة", "ĠÙĪØŃ Ø¯Ø©", "ãĥĭ ãĥĥãĤ¯", "Ġn Æ°á»Ľng", "Ġа кÑĤÑĥ", "Ġак ÑĤÑĥ", "ĠакÑĤ Ñĥ", "å¸Ŀ åĽ½", "Ġn ázev", "Ġnáz ev", "ĠÑĢемон ÑĤ", "ĠÑĢ инкÑĥ", "ĠÏĢ άνÏī", "ĠÏĢά νÏī", "ÏĦ ικο", "ÏĦικ ο", "ÏĦι κο", "ĠìĤ¼ ìĦ±", "ĠÑģимпÑĤом Ñĭ", "ĠÑĢа нÑĸÑĪе", "ĠJ á", "ĠÑģÑĩиÑĤа еÑĤÑģÑı", "ĠÑģÑĩиÑĤ аеÑĤÑģÑı", "Ġп оÑĢÑĸв", "Ġпо ÑĢÑĸв", "ĠпоÑĢ Ñĸв", "ĠÐľ ал", "ĠÐľÐ° л", "éĿ¢ 积", "ĠÙĦ غ", "Ġج Ø´ÙĨ", "Ġнед ели", "Ġнедел и", "Ġì¦Ŀ ê°Ģ", "ãĨį ëıĻ", "Ġl ượt", "ĠÄIJ á»ĭnh", "Ġ à¸Ńà¸Ńà¸Ļà¹Ħลà¸Ļ", "Ġà¸Ń à¸Ńà¸Ļà¹Ħลà¸Ļ", "Ġyap arak", "Ġyapar ak", "ĠÄij ai", "ĠÄija i", "Ġо ÑĦиÑĨи", "Ġε μÏĢ", "ξ ειÏĤ", "ξει ÏĤ", "Ġкон ÑĦеÑĢен", "Ġ arası", "Ġa rası", "Ġar ası", "Ġaras ı", "Ġara sı", "à¸ķ า", "Ġ ë´IJ", "Ġë´ IJ", "о вана", "ов ана", "ова на", "ован а", "ì§Ģ ê°Ģ", "ĠV ám", "ि à¤ľà¤¨", "à¤¿à¤ľ न", "Ġ ç¼ĸè¾ij", "Ġç¼ĸ è¾ij", "ζ ÏĮ", "ĠÏĦ ÏģÏĮ", "Ġücret siz", "ĠکاÙħ ÙĦا", "ĠکاÙħÙĦ ا", ": ::/", ":: :/", "::: /", "à¹Į ĊĊ", "à¹ĮĊ Ċ", "Ġéĸ¢ éĢ£", "Ġ kara", "Ġk ara", "Ġka ra", "Ġkar a", "Ġбез пеки", "ĠzmÄĽ ny", "ĠzmÄĽn y", "Ġê¿ Ī", "v rd", "vr d", "li ÄŁine", "liÄŁi ne", "liÄŁ ine", "liÄŁin e", "ĠاÙĨتخاب ات", "ĠдоÑģ вÑĸд", "Ġkter ého", "Ġkteré ho", "ен ÑĤом", "енÑĤ ом", "ê³µ ë¶Ģ", "ìł Ŀ", "Ġë§Į 족", "Ġ æij", "Ġæ ij", "åĩº åı£", "建 è®®", "о ÑĤÑı", "оÑĤ Ñı", "Ġ Òij", "ĠÒ ij", "íĶĦ ë¡ľ", "Ġg ió", "Ġgi ó", "ãĤ· ãĤ§", "Ġλ εÏĢ", "íķĺ 볤", "Ġyok sa", "Ġist ih", "ï¼ ¶", "ĠاÙĦ عÙħ", "ĠاÙĦع Ùħ", "Ġکار گرد", "à¹Ģà¸ŀ ราะ", "Ġn ových", "Ġnov ých", "Ġnový ch", "ĠÑģ на", "Ġs ana", "Ġsa na", "Ġsan a", "व त", "Ä±ÅŁ man", "Ä±ÅŁma n", "åı¦ å¤ĸ", "ì¶ľìŀ¥ ìĥµ", "å© ¦", "ĠкоÑĪ ÑĤÑĸв", "ĠÙĪاÙĦ ÙĨ", "Ġب اÙĦØ¥", "ĠباÙĦ Ø¥", "Ġ æĬĢ", "ĠæĬ Ģ", "Ġмн оже", "à¥Ĥ ड", "ĠC ục", "Ġe vet", "Ġev et", "Ġeve t", "èģĶ åIJĪ", "Ġ³³Ġ³³ Ġ³³Ġ³³", "çļĦ å¿ĥ", "Ġd áng", "Ġdá ng", "Ġdán g", "اÛĮ سÙĩ", "Ġ erken", "Ġer ken", "Ġerk en", "æ³ ¡", "ائ ب", "Ġyap ıldı", "Ġyapıl dı", "ĠQu ản", "æŶ 代", "ìĽ ¨ìĸ´", "ìĽ¨ ìĸ´", "Ġг ÑĸÑĢ", "ok oj", "oko j", "Ùĥ رة", "Ùĥر Ø©", "Ñİ Ðº", "Ġvý j", "Ġhod iny", "Ġhodin y", "Ġелек ÑĤÑĢон", "m ıyor", "ĠìŀĪ ëĭ¤ëĬĶ", "ĠìŀĪëĭ¤ ëĬĶ", "à¹ī à¹ī", "иÑĤелÑĮ ное", "иÑĤелÑĮно е", "Ġyıl lar", "Äı te", "ĠÄįin nost", "ุà¸ĵ à¸łà¸²à¸ŀ", "í ĵ¨", "н г", "ู รà¸ĵ", "ูร à¸ĵ", "ĠпоÑĢÑıд ке", "Ġëĭ¹ ìĭľ", "ĠÐľ оÑģков", "ĠÐľÐ¾Ñģк ов", "Ġk red", "Ġkr ed", "Ġkre d", "u rum", "ur um", "uru m", "Ġ ÑĤÑı", "ĠÑĤ Ñı", "Ú© ÙĨاÙĨ", "Ú©ÙĨ اÙĨ", "д ии", "ди и", "ÑĢи мÑĸн", "ÑĢим Ñĸн", "ĠоÑĢгани зм", "ĠоÑĢганиз м", "Ġ éĽĨ", "ĠéĽ Ĩ", "ι ÏĥÏĦο", "ιÏĥ ÏĦο", "ä¿¡ çĶ¨", "åįģ åĽĽ", "à¹Ī à¹ĥà¸Ĭ", "ĠÑĥ вид", "ĠÑĥв ид", "ัà¸ĩ à¸ģล", "ัà¸ĩà¸ģ ล", "åı¦ ä¸Ģ", "ãĥ« ãĥķ", "ัà¸ļ à¸Ľà¸£", "ĠÃľ st", "説 æĺİ", "в ай", "ва й", "а Ñĩе", "аÑĩ е", "æ¬ £", "Ġkat ıl", "Ġkatı l", "ĠC em", "ĠCe m", "ĠاÙĦ جÙĩ", "ĠاÙĦج Ùĩ", "Ġг ÑĢÑĥз", "ĠгÑĢÑĥ з", "ĠгÑĢ Ñĥз", "Ġза ÑģÑĤав", "ĠзаÑģÑĤ ав", "cı lar", "ĠÑħоÑĤ ел", "Ġs nÃŃm", "Ġsn ÃŃm", "ĠsnÃŃ m", "ï¼Į 被", "Ġ виÑī", "Ġв иÑī", "Ġви Ñī", "Ġdem okrat", "Ġdemok rat", "à¥ĩ à¤Łà¤°", "à¥ĩà¤Ł र", "åij¨ å¹´", "Ġod pad", "Ġodp ad", "Ġda ÅĪ", "Ġ 代", "à¹ĩ à¸Ļà¸Ń", "à¹ĩà¸Ļ à¸Ń", "ĠÑģк олÑĮко", "Ġα ÏĨ", "ĠpÅĻes vÄĽd", "Ġ åĵģ", "Ġåĵ ģ", "ĠинÑĦоÑĢм аÑĨии", "ĠинÑĦоÑĢма ÑĨии", "çĽ Ĺ", "ãģ¾ ãģ¨", "ĠÑģам ов", "ĠÑģамо в", "Ġpo cit", "Ġpoc it", "Ġíݸ ì§ij", "ĠÑģм еÑģÑĮ", "Ġpo jiÅ¡tÄĽnÃŃ", "ãģ® ãĤĤ", "à¹Ī าà¸ģาร", "à¹Īา à¸ģาร", "à¹Īาà¸ģ าร", "ĠÛĮ ÙĪÙĨ", "Ġ기 ìĸµ", "ick ými", "ický mi", "ickým i", "a lace", "al ace", "ala ce", "鼻 å½±", "Ñİ Ð²Ð°Ð½Ð½Ñı", "缸 åIJĮ", "Ġ ãĢĥ", "ĠãĢ ĥ", "ĠдокÑĥм енÑĤÑĸв", "ĠдокÑĥменÑĤ Ñĸв", "ï¼ ¹", "åĪ° åºķ", "ó z", "ĠAh met", "ĠÙħس اØŃت", "Ġhl avou", "Ġhlav ou", "ül ebilir", "üle bilir", "ãĢĤ ä½ł", "à¹ĩà¸ģ à¸Ĭาย", "¤ ¤", "Ġ æĦı", "ĠæĦ ı", "Ġch áºŃm", ". д", "Ġ cca", "Ġc ca", "Ġcc a", "Ġol umsuz", " ŀ", "çĬ ¬", "ĠпоÑģÑĤоÑıн но", "Ġ.************** Ċ", "Ġا ستر", "Ġاست ر", "Ġاس تر", "ĠдалÑĮ ней", "ů r", "ä¿Ŀ èŃ·", "боÑĢ аÑĤоÑĢ", "боÑĢа ÑĤоÑĢ", "à ·", "Ïĥ ÏĦαν", "ÏĥÏĦ αν", "ÏĥÏĦα ν", "ĠÙģ ÙĬÙĦÙħ", "ĠÙģÙĬ ÙĦÙħ", "ç ek", "çe k", "ìŀIJ 기", "Ġ æ¥Ń", "Ġæ¥ Ń", "н Ñĸп", "нÑĸ п", "èī ĩ", "Ġm oci", "Ġmo ci", "Ġmoc i", "ìľ µ", "리 ê·¸", "ĠÐļ о", "éĤ£ éĩĮ", "ĠС ÑĤаÑĢ", "ĠСÑĤ аÑĢ", "ĠСÑĤа ÑĢ", "ĠتÙĪاÙĨ ÛĮد", "Ġng uyá»ĩn", "Ġnguy á»ĩn", "Ġ สามารà¸ĸ", "Ġส ามารà¸ĸ", "Ñĸ Ñĩна", "ÑĸÑĩ на", "Ġ 被", "Ġè¢ «", "ุà¸ķสาห à¸ģรรม", "Ġع صر", "Ġعص ر", "ĠÃľNÄ° VERS", "Ġteh dy", "ĠÙĪصÙĦ ات", "ĠÙĪص ÙĦات", "ä¿Ŀ è¯ģ", "ĠE udicots", "ĠÎł ÎŃ", "建 è¨Ń", "ĠìłĦ êµŃ", "Ġ ØŃÛĮ", "ĠØŃ ÛĮ", "ãĤ¤ ãĥĦ", "ĠØŃ Ø§ØµÙĦ", "ĠجÙĨ ÙĪبÛĮ", "ĠجÙĨÙĪب ÛĮ", "ãĢģ æĹ¥æľ¬", "à Ļ", "Ġ à¸Ĺาà¸ĩ", "Ġà¸Ĺ าà¸ĩ", "ĠÙĨØŃ ÙĪ", "اÙĩ ÙĬÙħ", "å¾Į ãģ«", "à¸Īะ à¹Ħà¸Ķ", "åĩł 个", "à¥ģ à¤ģ", "à¥ģठģ", "ëĮĢ ìĿĺ", "Ġl Ãłn", "ĠlÃł n", "ìĽĶ ë¶ĢíĦ°", "Æ ł", "Ġ еди", "Ġе ди", "Ġs pis", "Ġsp is", "Ġspi s", "æľī ä»Ģä¹Ī", "Ġneb yla", "Ġneby la", "Ġnebyl a", "Ġíķ´ ìĻ¸", "ë¡ľ ë¶ĢíĦ°", "аÑĢ Ñħ", "l ili", "li li", "lil i", "Ġíķĺ 루", "ma ması", "mam ası", "Ñĩ аеÑĤ", "Ñĩа еÑĤ", "ĠØŃ Ø§ÙĦØ©", "ĠØŃاÙĦ Ø©", "ĠBöl üm", "缸 éĹľ", "ĠдÑĢÑĥг ими", "ĠдÑĢÑĥгим и", "çĽ£ çĿ£", "à¥Ī à¤ľ", "Ġعبد اÙĦÙĦÙĩ", "ĠعبداÙĦ ÙĦÙĩ", "Ġ è¿ŀ", "Ġè¿ ŀ", "ĠÐľ ин", "ĠÐľÐ¸ н", "Ġê¸ °ëĭ¤", "Ġ기 ëĭ¤", "Ġê³µ 격", "è¡Į åĭķ", "ा मà¤ķ", "ाम à¤ķ", "æ±Ĥ è´Ń", "模 åŀĭ", "Ñģ оÑĢ", "Ñģо ÑĢ", "r ane", "ra ne", "ran e", "à¹ĩà¸Ī à¸ŀระ", "ĠÙħس ÛĮر", "è£ħ ç½®", "ìķ ¤", "nÄĽ jÅ¡ÃŃch", "nÄĽjÅ¡ÃŃ ch", "αλ ÏįÏĦε", "ĠH akk", "ĠHa kk", "ĠHak k", "访 éĹ®", "ĠÑĤ еÑĩ", "ĠÑĤе Ñĩ", "ĠL á»ĭch", "Ġدش ÙħÙĨ", "Î Į", "Ġ ÏĢε", "ĠÏĢ ε", "Ġза мов", "Ġзам ов", "Ġb irim", "Ġbi rim", "Ġbir im", "Ġbiri m", "ãĤ· ãĤ¹ãĥĨãĥł", "ĠÏĢÏģο ÏĬ", "Ĭ ìĿĢ", "в иг", "ви г", "Ġëıħ ìĿ¼", "ĠÑĢев олÑİ", "Ġ é¦Ļ港", "Ġé¦Ļ 港", "Ġ lez", "Ġl ez", "Ġle z", "ĠبÛĮ Ùħار", "ĠبÛĮÙħ ار", "Ġduy gu", "Ġduyg u", "Ġë Ľ°", "Ġa macı", "Ġam acı", "Ġama cı", "Ġamac ı", "à¥įय प", "ĠìŀIJ ìĦ¸", "اÙĪ ÛĮر", "اÙĪÛĮ ر", "Ġs pole", "Ġsp ole", "Ġspo le", "Ġspol e", "Ãĸ L", "Ġ جع", "Ġج ع", "ÙĦ ÛĮÙħ", "ÙĦÛĮ Ùħ", "ãģªãģ© ãģ®", "à¸Ľà¸£à¸°à¸ª à¸ļ", "ĠnaÅ¡ ich", "ĠпÑĢедÑģÑĤав лÑıеÑĤ", "Ġзд об", "Ġo bou", "Ġob ou", "Ø® ÙĪاÙĨ", "Ø®ÙĪ اÙĨ", "ãĥ¬ ãĥĥãĥĪ", "о дейÑģÑĤв", "од ейÑģÑĤв", "Ú© رÛĮ", "کر ÛĮ", "Ġات اÙĤ", "ĠÑįкÑģп лÑĥаÑĤа", "ï½ ¢", "ĠÙĦÙĦ Ø¥", "ĠاÙĦÙĨ ظاÙħ", "ĠíĶĦ ëŀijìĬ¤", "ıs ıt", "ısı t", "åŃ Ļ", "Ġžád ný", "ÙĤ Ùī", "ัà¸ģ à¹Ģร", "Ġë²ł ìĬ¤íĬ¸", "Ġ ãĥ«", "Ġãĥ «", "åı Ķ", "n ické", "nic ké", "nick é", "Ġε ιÏĥ", "Ġει Ïĥ", "ãĥ« ãĥī", "Ġدار Ùħ", "Ġг ем", "Ġге м", "Ġ åѸ", "ĠåŃ ¸", "ान सà¤Ń", "ानस à¤Ń", "али зи", "ализ и", "ов анÑĸ", "ова нÑĸ", "ован Ñĸ", "Ġо бо", "Ġоб о", "ìłĦ ìĹIJ", "ĠS inh", "ĠSi nh", "ĠSin h", "Ġ ÙĨع", "ĠÙĨ ع", "Ġоб лаÑģ", "Ġобла Ñģ", "Ġобл аÑģ", "Ïħ ÏĢ", "èĥ ¶", "Ġaz alt", "Ġazal t", "åħ¨ éĿ¢", "ĠK romÄĽ", "ĠKro mÄĽ", "ĠC z", "æĬ¥ åIJį", "Ġnásled ujÃŃcÃŃ", "Ġна пÑĢиклад", "ĠнапÑĢи клад", "ãģª ãģijãĤĮãģ°", "à¸Ń าย", "çľĭ çľĭ", "Ġà¸ģร à¸ģà¸İ", "Ġà¸ģรà¸ģ à¸İ", "ed nou", "edn ou", "ا زÙĦ", "از ÙĦ", "ãĢģ æľ¬", "е Ñģи", "еÑģ и", "Ġta rz", "Ġtar z", "ãĢĢ ï¾Ĭ", "Ġroz um", "ãĤ« ãĥ¼ãĥī", "ãĤ«ãĥ¼ ãĥī", "Ġà¤ĩ à¤ķ", "Ġpros tÄĽ", "Ġprost ÄĽ", "ĠÎĵ κ", "ç© ´", "ĠH ük", "la vÃŃ", "lav ÃŃ", "ê ¿", "é¸ ¡", "Ġвозник аеÑĤ", "Ġвозника еÑĤ", "ÑŁ ÑŁÑŁ", "ÑŁÑŁ ÑŁ", "Ġпо нима", "Ġпон има", "ÐŁ Ðŀ", "ãģĶãģĸ ãģĦãģ¾ãģĻ", "ãģ ħ", "Ġtr val", "Ġдал еко", "ĠÙĨ ÙĬز", "ĠÙĨÙĬ ز", "ĠвÑĭ Ñıв", "ิà¸Ĺย า", "ิà¸Ĺ ยา", "Ġl á»Ĺ", "Ġlá» Ĺ", "à¹Ģ สà¸Ļ", "à¹Ģส à¸Ļ", "ĠÑģÑĤ енÑĭ", "ĠÑģÑĤен Ñĭ", "à¥įड ल", "Ġjednotliv ých", "ĠпÑĢиб лиз", "i kat", "ik at", "ika t", "Ġп одав", "Ġпо дав", "Ġпод ав", "Ġпода в", "ر ÛĮز", "رÛĮ ز", "ĠØ¢ÙĨ جا", "社 æľĥ", "Ġà¤ľà¤¨ वर", "Ġa ile", "Ġai le", "Ġail e", "ี à¸Ľ", "Ġ èħ", "Ġè ħ", "ãģ§ ãģĹãĤĩãģĨ", "С Ðŀ", "ãĢģ ãĢĬ", "ìĿ¼ 본", "ov anou", "ova nou", "ovan ou", "ν ÏĮ", "å± ¥", "ع ÙĦÙĤ", "عÙĦ ÙĤ", "Ġìī ½", "Ġгли б", "Ġê²ĥ ìŀħëĭĪëĭ¤", "ĠнеобÑħодим оÑģÑĤи", "ĠнеобÑħодимо ÑģÑĤи", "Ġتخصص ÛĮ", "ا سر", "اس ر", "ï¼Į 说", "ĠÐĿ Ñĸ", "Ġvy rob", "ÑĪ ÑĥÑİ", "ÑĪÑĥ Ñİ", "æĪ¿ å±ĭ", "Âł ÐĹ", "à¹Ģ à¸ŀล", "à¹Ģà¸ŀ ล", "åĨħ éĥ¨", "ĠدÙĦ ار", "Ġп ÑĤи", "Å¡ ti", "Å¡t i", "ĠaraÅŁtır ma", "Ġзна ком", "Ġε λλην", "Ġ ấm", "ÑĢ ак", "ÑĢаРº", "ÑĢа к", "ãĤŃ ãĥ¥", "Ġth áºŃn", "èŃ ľ", "ëªħ ìĿĺ", "Ġy eter", "Ġyet er", "Ġye ter", "Ġна Ñģлед", "ĠнаÑģ лед", "ĠÐļ ан", "ĠÐļа н", "ĠвÑĭ биÑĢа", "ĠвÑĭб иÑĢа", "ĠΣ Ïĩ", "ĠÑĤеÑĢ мÑĸн", "Ġ æ´»", "Ġæ´ »", "ĠاÙĦ تÙģ", "ĠاÙĦت Ùģ", "ĠJ apon", "ĠJa pon", "ĠJap on", "éĤ ª", "ë¶Ħ ìĦĿ", "Ġли ÑĨо", "ĠлиÑĨ о", "Ġm ê", "à¸Ħ วร", "à¸Ħว ร", "Ġà¤ħ à¤Ĺल", "Ġà¤ħà¤Ĺ ल", "ĠÙĩ ج", "룬 ìļ´", "Ġвой нÑĭ", "اÙĪر زÛĮ", "ĠÑģп ÑĢÑı", "çĦ ¼", "è¢ ĸ", "Ġiç eren", "Ġiçer en", "Ġëħ¸ ëŀĺ", "ĠЧеÑĢ ез", "ÙĪج ÙĪد", "Ñı ÑĤие", "ÑıÑĤ ие", "ÑıÑĤи е", "à¸Ńลล าร", "è ·¨", "è· ¨", "ĠM illi", "ĠMill i", "ĠMil li", "ĠMi lli", "件 äºĭ", "Ġ æľĿ", "βολ ή", "βο λή", "Ġ ков", "Ġк ов", "Ġко в", "ĠØ´Ùĩ ÛĮد", "ä¸ĭ åİ»", "Ġìłķ ìĭł", "оÑĩ кÑĥ", "ï¼Į 便", "γ κε", "γκ ε", "ĠÙħ باش", "ĠÙħب اش", "Ġay ında", "Ġ ä»»", "ÑģÑĤоÑĢ ÑĸÑı", "ä¸Ń åѦ", "ç¸ ®", "ĠÑĦ Ñĸл", "ãĢģ ãĤĦ", "Ġ æĺ¥", "Ġæĺ ¥", "Ġter ör", "Ġповин ен", "Ġmilion ů", "ĠÙģ ارس", "ĠÙģار س", "Ġв вод", "Ġвв од", "Ø· اÙĦ", "Ġê¶ģ ê¸Ī", "Ġuk áz", "çĶ ľ", "æļ Ĥ", "ص ت", "Ðļ огда", "Ġम ल", "ά να", "άν α", "Ġдок ÑĤоÑĢ", "Ġком мÑĥ", "ĠпÑĸд Ñģ", "Ġà¸ģรà¸ģà¸İ าà¸Ħม", "Âł г", "Ġö ne", "Ġön e", "ĠÄIJ á»ģ", "ĠÄIJá» ģ", "äºĭ åĭĻ", "Ġs rov", "Ġsr ov", "Ġ άν", "Ġά ν", "ëıĦ ê°Ģ", "acaÄŁ ım", "acaģı m", "к ол", "ко л", "Ġb á»ĵi", "Ġپرد از", "Ġ ä¸ļ", "Ġä¸ ļ", "ëĭ¤ ìļ´", "Ġп ÑĢедел", "ĠпÑĢед ел", "ĠпÑĢе дел", "ĠÑĦедеÑĢа лÑĮ", "ĠاÙĦ Ø£Ùĥ", "ĠاÙĦØ£ Ùĥ", "ãĢĢ ãĢĢãĢĢãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢ", "Ġtr ấn", "Ġд лин", "Ġдли н", "ĠÑĸ мп", "ĠÑĸм п", "ĠsmÄĽ rem", "° ëĭ¤", "Ġr ừng", "ici álnÃŃ", "è¡ Ĩ", "μ ιο", "μι ο", "Ġاد ارÙĩ", "ĠÑĤ ÑĢÑĮ", "ĠÑĤÑĢ ÑĮ", "ĠÄ° li", "ĠÄ°l i", "มà¸Ļ à¸ķร", "à¥įव à¤ļ", "е ÑĢо", "еÑĢ о", "ĠK UR", "sk ými", "ský mi", "ským i", "δ ί", "u tin", "ut in", "uti n", "Ġver iler", "Ġveri ler", "สà¸ĸาà¸Ļ à¸Ĺ", "ĠзаÑħод Ñĸв", "ĠÙģرÙĪد گاÙĩ", "Ġ çĶ±", "ู à¹ģล", "éĥ ij", "ĠJ ako", "ĠJa ko", "ĠJak o", "ĠÑĢазвиÑĤи е", "ĠÑĢазви ÑĤие", "à¤ī न", "ÙĬ دا", "ÙĬد ا", "Ġà¸ŀ à¸¤à¸©à¸łà¸²à¸Ħม", "물 ìĿĦ", "ë łĢ", "ëł Ģ", "- ÐĽ", "ãĢĤ ãģĤ", "Ġпод в", "ï¼ī ï¼ļ", "论 åĿĽ", "ائ ع", "ãĤĴ ãģĻãĤĭ", "ĠØ£ ص", "Ñĩ ики", "Ñĩи ки", "Ñĩик и", "ĠÑģÑĤ ил", "ley ici", "Ñģ илÑĮ", "Ñģи лÑĮ", "Ġbulun du", "ĠÑģеÑĢед ови", "à¤Ĥ र", "ĠاÛĮÙĨ جا", "åľŃ åľŃ", "ĠmyÅ¡ len", "ĠÑĢозвиÑĤ ок", "Ġiy ileÅŁ", "Ġiyi leÅŁ", "Ġв Ñĸз", "ĠвÑĸ з", "ëĤĺ 무", "æĦı è§ģ", "ι ÏĥÏĦη", "ιÏĥ ÏĦη", "ãĥĥ ãĥĦ", "äºĭ æķħ", "m adıģı", "ma dıģı", "mad ıģı", "madı ģı", "Ġà¤ħ पर", "Ġà¤ħप र", "ĠÚĨ رخ", "Ġп лав", "Ġпл ав", "Ġпла в", "以 æĿ¥", "Ġë© Ģ", "T uy", "Tu y", "ãĥ¼ ãĥį", "ãĥ¼ãĥ į", "Ġиз ÑĥÑĩ", "ĠstÅĻed nÃŃ", "课 ç¨ĭ", "Ġê·¸ëħĢ ëĬĶ", "ĠдоговоÑĢ Ñĥ", "Ġдогов оÑĢÑĥ", "ĠÄij á»ĭch", "ĠÄijá»ĭ ch", "Ġkar arı", "Ġkarar ı", "Ġkara rı", "åIJ ´", "Ùĥ اÙħ", "ĠпоÑĤ ол", "в ок", "во к", "ĠD üz", "Τ α", "å µ", "âĢĻ na", "âĢĻn a", "а дж", "ад ж", "ĠdÅĻÃŃ ve", "æ¢ ¨", "ĠAv ust", "åĬĽ ãĤĴ", "à¹Ģ à¸ģล", "à¹Ģà¸ģ ล", "Ġпоб ед", "Ġп ÑĢиÑĩ", "ĠпÑĢ иÑĩ", "ĠпÑĢи Ñĩ", "ĠÐij Ñĸ", "åŃ ¤", "ĠÐł ег", "ĠÐłÐµ г", "Ġyet iÅŁ", "Ġн еÑİ", "Ġне Ñİ", "Ġb ÃŃl", "ĠbÃŃ l", "ìĹĨ ìĿĮ", "ĠÄ° talya", "ÐĴ Ñģе", "å¾Į ãģ®", "Ġje jÃŃm", "Ġjej ÃŃm", "ĠjejÃŃ m", "ĠвиглÑı дÑĸ", "о гÑĢад", "ог ÑĢад", "огÑĢа д", "Ġbo hat", "Ġboh at", "Ġ åħĭ", "Ġåħ ĭ", "ĠдиÑĤи ни", "ĠдиÑĤ ини", "лÑı ÑĤоÑĢ", "ма га", "маг а", "ëĭĪ ìĬ¤", "ĠÐł ади", "ĠÐłÐ°Ð´ и", "ĠÐłÐ° ди", "ÏĢ οÏħÏģγ", "ÏĢοÏħ Ïģγ", "& ZeroWidthSpace", "Ġ struk", "Ġst ruk", "Ġstr uk", "Ġstru k", "æIJ ŀ", "Ġ ãģĿãģ®ä»ĸ", "ìĿ¸ ìĿĦ", "ĠпÑĢо веÑģÑĤи", "ĠпÑĢов еÑģÑĤи", "漫 çĶ»", "Ġçİ© 家", "ĠÙĪ رز", "ĠÙĪر ز", "ĠÑģвоÑĹ Ð¼", "ĠL RV", "ĠLR V", "ิà¸ķ à¸ł", "स त", "ĠíĿ Ķ", "âĹıâĹıâĹıâĹıâĹıâĹıâĹıâĹı âĹıâĹıâĹıâĹıâĹıâĹıâĹıâĹı", "Ġt voÅĻÃŃ", "ĠtvoÅĻ ÃŃ", "Ġ ÐŁÐŀ", "ĠÐŁ Ðŀ", "é«ĺ 度", ".h wp", ".hw p", "à¸ķำ à¸ļล", "Ġد س", "ìĪĺ ê°Ģ", "ìĶ ©", "ï¼ī ãĢĤĊ", "ï¼īãĢĤ Ċ", "æĭ ³", "Ġl ô", "ĠK ültür", "اط عة", "Ġku chy", "Ġst roj", "Ġstr oj", "Ġstro j", "μ ενο", "με νο", "μεν ο", "ĠконÑģÑĤÑĢÑĥк ÑĨии", "å°ı åѦ", "Ġ åįļ", "Ġåį ļ", "Ġ èĢĥ", "ĠèĢ ĥ", "Ġas ıl", "æĪij åĢij", "خر اج", "ĠO nun", "ĠOn un", "Ġ ç¾İåĽ½", "Ġç¾İ åĽ½", "à¥Ĥ बर", "à¥Ĥब र", "Ġmu ži", "Ġmuž i", "å§ «", "Ġв б", "Ġдо ме", "Ġдом е", "Ġ ам", "Ġа м", "Ġk uru", "Ġkur u", "Ġku ru", "æ± Ĺ", "l ediÄŁi", "le diÄŁi", "ledi ÄŁi", "Ġv ẽ", "å¾ ĵ", "ĠгÑĥб еÑĢ", "ĠÑģÑĤанов иÑĤÑĮ", "ĠzemÄĽ dÄĽl", "ÙĦ ÙĦ", "Ġr amen", "Ġra men", "Ġram en", "Ġprů bÄĽhu", "Ġb lok", "Ġbl ok", "Ġblo k", "ý val", "ýv al", "v ou", "vo u", "ν ά", "ëĶĶ ìĭľ", "ÑĨион нÑĭе", "Ġê²Įìĭľ íĮIJ", "ãĥ³ ãĥĩãĤ£", "ãĥ³ãĥĩ ãĤ£", "ä¸Ģ 级", "и Ñĩа", "иÑĩ а", "ĠسرÛĮ اÙĦ", "i lin", "il in", "ili n", "ा यन", "ाय न", "ÙĨ ÙĪÛĮس", "ĠÐĶ и", "Ġاد بÛĮ", "ĠÑĥ дов", "ĠÑĥд ов", "ĠÐĹ Ð°Ð¼", "ĠÐĹа м", "à¥ģà¤Ń व", "Ñģ ок", "Ñģо к", "ĠÑĢай оне", "ĠÑĢайон е", "Ġ EK", "ĠE K", "æĤ ī", "Ġsor umlu", "Ġsoruml u", "Ġzv yÅ¡", "à¹Ģà¸ĭ à¸Ńร", "in áÅĻ", "iná ÅĻ", "Ġu drž", "Ġud rž", "но вид", "нов ид", "ĠspoleÄį nÄĽ", "Ġspole ÄįnÄĽ", "æĪIJ äºĨ", "ï¼ ¤", "ัà¸ŀ à¸Ĺ", "а ÑĪа", "аÑĪ а", "ĠÙĨ ادÙĬ", "à¹ĥ à¸Ļà¸Ĺ", "à¹ĥà¸Ļ à¸Ĺ", "å¡ ļ", "Ġس Ú©", "ãĥģ ãĥ¥", "ĠмаÑĢ ÑĪ", "а леннÑı", "ал еннÑı", "ĠØŃÙħ اÛĮت", "ãĥ³ ãĤ¸", "รษ à¸IJ", "Ġк ÑĢем", "ĠK ažd", "ê ½", "Ġpar lament", "Ġparl ament", "ĠÅŁ un", "ĠÅŁu n", "Ġk ys", "Ġky s", "ÏĦ ÏĤ", "ê°ľ ìĿĺ", "Ġve lice", "Ġvel ice", "Ġce stu", "Ġces tu", "Ġcest u", "ظ Ø©", "è¯ Ĭ", "Ġ út", "Ġú t", "ĠØ® ÙĪر", "ĠØ®ÙĪ ر", "ĠТ е", "Ġобла ÑģÑĤ", "ĠоблаÑģ ÑĤ", "Ġобл аÑģÑĤ", "à¹Ī à¸Ńà¸ķ", "à¹Īà¸Ń à¸ķ", "ĠAc adem", "ĠAcad em", "ãĢĤ æľ¬", "Ġ 風", "Ġé¢ ¨", "Ñģ ен", "Ñģе н", "ãĥ¢ ãĥĩãĥ«", "Ġзавд аннÑı", "ãģ¾ ãĤĮ", "моÑĤ ÑĢеÑĤÑĮ", "моÑĤÑĢ еÑĤÑĮ", "Ġkh á»ķ", "à¹Ī ร", "د رس", "در س", "ĠÄĮesk osloven", "Ġ 计", "Ġè® ¡", "ĠÑĤак ом", "ĠÑĤа ком", "ĠÙĦ اعب", "ĠÙĦا عب", "ĠMuham med", "ĠÙħ ÙĦÙģ", "ĠÙħÙĦ Ùģ", "ĠÙĪس ÙĦÙħ", "ãĤ·ãĥ£ ãĥ«", "Ġо кÑĢа", "Ġок ÑĢа", "à¥ģ मत", "à¥ģम त", "ĠëĪĦ 구", "Ġned eni", "Ġneden i", "ĠëĤł ì§ľ", "/ km", "/k m", "Ġд емон", "Ġде мон", "Ġдем он", "ĠصÙĨ اÛĮع", "m asından", "mas ından", "masında n", "åīį ãģ®", "æĪIJ 绩", "ल à¤Ĺ", "Ġ åĮħ", "ĠåĮ ħ", "à¸Ńà¸ģà¸Īาà¸ģ à¸Ļ", "ا دا", "اد ا", "Ġay lık", "ĠÙħ ÙĤد", "ĠÙħÙĤ د", "Ġönemli dir", "ĠìĪľ ê°Ħ", "Ġd inh", "Ġdi nh", "Ġdin h", "Ġná kup", "ist ické", "istic ké", "åº Ł", "ìĬ¤ íĨł", "Ġd ny", "Ġdn y", "ĠìŀĪ ëıĦë¡Ŀ", "ìĽIJ ìĿĺ", "ãĥķ ãĥ¬", "p oz", "po z", "Ġ ев", "Ġе в", "ĠdÃ¼ÅŁ ür", "à¥įर à¤ļ", "Ġê²° íĺ¼", "Ġ ÑĨенÑĤÑĢа", "ĠÑĨенÑĤ ÑĢа", "ĠÑĨен ÑĤÑĢа", "ĠÑĨенÑĤÑĢ а", "åŁ ĭ", "ï¿£ ï½Ģ", "æѦ åĻ¨", "à¹Īาà¸Ļ มา", "Ġर व", "Ùij د", "μÎŃ Î½Î¿Î¹", "μÎŃν οι", "μÎŃνο ι", "Ġë§IJ ìĶĢ", "Ġpo ÅĻad", "Ġب غ", "ĠÏĮ λα", "ĠÏĮλ α", "à¹ī à¹Ħà¸Ĥ", "à¹Ģà¸ģ าะ", "Ġb ạc", "Ġd á", "d ÄĽla", "dÄĽ la", "dÄĽl a", "Ġt eb", "Ġte b", "Ġk èo", "ãĤı ãĤĮ", "Ġist iyorum", "Ġistiyor um", "λ ήÏĤ", "λή ÏĤ", "ÐIJ в", "Ġa sla", "Ġas la", "Ġperform ans", "Ġperfor mans", "Ġperforman s", "ĠVác lav", "Ïģ ίαÏĤ", "Ïģί αÏĤ", "Ïģία ÏĤ", "Ġ tÄĽl", "Ġt ÄĽl", "ĠtÄĽ l", "æĮ Ļ", "о ба", "об а", "ãģij ãĤĮãģ©", "ĠëĶ ¸", "ÙĪ اء", "ÙĪا Ø¡", "ĠÚ©ÙĪد کاÙĨ", "ĠÚ©ÙĪدک اÙĨ", "Ġп лиÑĤ", "Ġпл иÑĤ", "Ġ bilir", "Ġb ilir", "Ġbil ir", "Ñĥ же", "Ñĥж е", "ÏĦÎŃ Î»Îµ", "Ġà¤Ĩ à¤ķर", "Ġà¤Ĩà¤ķ र", "ĠÑĤÑĢ Ñĥда", "ĠÑĤÑĢÑĥ да", "ĠÑĤÑĢÑĥд а", "Ġدر ÛĮا", "ĠدرÛĮ ا", "Ì §", "Ġng á»įt", "ÙĨس ا", "а ÑģÑĤи", "аÑģ ÑĤи", "аÑģÑĤ и", "ï½ £", "Âł на", "ем Ñĭе", "Ġس عÙĪد", "Ġسع ÙĪد", "Ġ alım", "Ġal ım", "è´ «", "åĪ° çļĦ", "Ġkesin likle", "Ġzá sad", "Ġ ìĬ¤íĬ¸", "ĠìĬ¤ íĬ¸", "Ġd ahi", "Ġda hi", "Ġdah i", "t é", "åįģ åħ«", "Ġz ayıf", "Ø° ار", "ذا ر", "Ġا ÙĬراÙĨ", "ĠاÙĬ راÙĨ", "Ġhod nocenÃŃ", "D ST", "DS T", "Ġìĸ ĺ", "æĺ ĩ", "éĻ £", "Ġк ле", "Ġкл е", "Ġu plat", "Ġup lat", "ĠاÙĦتع ÙĦÙĬÙħ", "ÏĢοί ηÏĥη", "ек ÑĤоÑĢа", "екÑĤ оÑĢа", "екÑĤоÑĢ а", "Ġë§IJ ìĿ´", "ĠÙģ رÙĬÙĤ", "ĠÙģر ÙĬÙĤ", "帮 åĬ©", "çĶŁ ãģį", "åĨħ ãģ®", "èģĶ 缣", "г ÑĢад", "гÑĢа д", "Ġch uyến", "ãĤĤ ãĤĬ", "ĠÑĩаÑģÑĤ ина", "ĠÑĩаÑģÑĤи на", "ãģª ãģıãģª", "ãģªãģı ãģª", "ÑĶ в", "ĠÑĦ аÑħ", "k uk", "ku k", "çĶ· æĢ§", "ĠÙħÛĮÙĦ ادÛĮ", "Ġb eden", "Ġbe den", "Ġbed en", "ê°Ģ 를", "म र", "Ġìĸ´ 머ëĭĪ", "èģĶ ç½ij", "Âł mi", "Âłm i", "Ġzah rn", "æ² ĸ", "Ġkhu ẩn", "Ġo práv", "Ġop ráv", "Ġopr áv", "ाह à¤ķ", "ĠÚ©ÙĪت اÙĩ", "Ġо бол", "Ġоб ол", "Ġобо л", "Ġph úc", "r ánÃŃ", "rá nÃŃ", "rán ÃŃ", "à¥įर थ", "æݪ æĸ½", "Ġв олод", "Ġво лод", "Ġвол од", "Ġsp ÃŃÅ¡e", "Ġm Æ¡", "ÑĬ ек", "ng ör", "à¤ī त", "k siyon", "ks iyon", "ksi yon", "а ÑĤе", "аÑĤ е", "Ġجز Ø¡", "áv ka", "ÐĴ С", "laÅŁ ma", "Ġ ç¿", "Ġç ¿", "à¸Ń าà¸Ĭ", "ни ÑĨÑĥ", "ниÑĨ Ñĥ", "Ġ หาà¸ģ", "Ġห าà¸ģ", "ãģĭ ãģĹ", "íı ´", "Ġг аÑĢан", "ĠгаÑĢ ан", "ĠгаÑĢа н", "Ġ Ïĥαν", "ĠÏĥ αν", "Ġдобав иÑĤÑĮ", "ĠÑĢаз ÑĢеÑĪ", "á ¾", "æĺ¯ ä¸ª", "μ ÎŃÏĤ", "μÎŃ ÏĤ", "ĠÄ°mpar ator", "æ¨Ļ æºĸ", "Ñģ ÑĤÑĭ", "ÑģÑĤ Ñĭ", "Ġg ücü", "Ġgü cü", "Ġgüc ü", "Ġ íĥĢìĿ´", "Ġíĥ ĢìĿ´", "ĠíĥĢ ìĿ´", "Ġ åħ¶ä»ĸ", "Ġåħ¶ ä»ĸ", "Ġt ông", "Ġtô ng", "Ġtôn g", "Ġv edenÃŃ", "Ġved enÃŃ", "Ġvede nÃŃ", "ëĵľ ë¡ľ", "Ġm esel", "Ġme sel", "Ġmes el", "Ġ Äįe", "ĠÄį e", "j de", "jd e", "Ïģ εια", "Ïģε ια", "Ïģει α", "ãĤĪ ãģŃ", "Ðł ÐĿ", "è·Ŀ 离", "ĠÙĤ ائÙħØ©", "า à¸ļาล", "าà¸ļ าล", "ĠÑģай ÑĤÑĸ", "Ġर स", "ĠÙĤر ÙĨ", "Ġná vr", "Ġnáv r", "Ú© Ùħ", "çļĦ æīĭ", "Ġsor unu", "Ġsorun u", "Ġsoru nu", "/N ÄIJ", "nut ÃŃm", "nutÃŃ m", "ĠØ® ÙĪرد", "ĠØ®ÙĪ رد", "ĠØ®ÙĪر د", "Ġng á»Ŀ", "Ġ: .|", "Ġ:. |", "Ġbudou c", "i Äįky", "iÄį ky", "Ġد رد", "Ġدر د", "ÑĢо ниÑĩеÑģ", "ÑĢон иÑĩеÑģ", "ç¾ Ĭ", "ĠìķĦ ë²Ħì§Ģ", "ĠKan unu", "ĠKanun u", "ĠпÑĢивод иÑĤ", "άλÏħ ÏĪηÏĤ", "ĠVlad im", "Ġal ıp", "Ġе ÑĤап", "Ġà¤Ĺ लत", "ĠراÙĩ ÙĨÙħ", "Ġpoz isyon", "Ġgö ç", "èµ ŀ", "Ġм ой", "Ġмо й", "ĠÎł ά", "Ġ ìĪł", "ĠìĪ ł", "ĠØ¢ÛĮ ÙĨدÙĩ", "a ná", "an á", "举 çľģ", "ĠÙħت عدد", "Ġ åįĬ", "Ġåį Ĭ", "ãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ãĢĢãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", "Ġth á»Ŀ", "Ġthá» Ŀ", "Ġвд ÑĢÑĥг", "п аÑĤ", "па ÑĤ", "ĠпÑĢовед ениÑı", "ÙĨ ز", "ĠاÙĦب ØŃØ«", "æģ ¢", "Ġbak tı", "Ġ è·¯", "Ġè· ¯", "Ġзаболева ний", "ĠÐķ вÑĢоп", "ĠÐķв ÑĢоп", "Ġtarih li", "ê¹ ¨", "ĠÚ© ÙĪÙĩ", "ĠÚ©ÙĪ Ùĩ", "Ġìĸ´ 볤", "Ġtit ul", "Ġti tul", "Ġvyd ánÃŃ", "éĺ¶ æ®µ", "à¸Īะ à¸ķ", "Ġм оÑı", "Ġмо Ñı", "ĠкоÑĢ ол", "Ġко ÑĢол", "Ġб анк", "Ġбан к", "วรร à¸ĵ", "วร รà¸ĵ", "ĠÙĥس ارة", "ĠK hoa", "ĠKh oa", "ĠKho a", "ĠÑĥнÑĸвеÑĢÑģиÑĤ еÑĤ", "ãģ«éĸ¢ ãģĻãĤĭ", "r uary", "ru ary", "Ġ à¸Ĥาย", "Ġà¸Ĥ าย", "Ġsv az", "ĠØ´ رÙĤ", "Ġشر ÙĤ", "Ġд ÑĭÑħ", "Ġиз бав", "Ġизб ав", "ĠÑı кÑĸй", "ĠÑıк Ñĸй", "ĠÑıкÑĸ й", "ĠÎľ ον", "Ġg ön", "Ġgö n", "ĠUkr aj", "ĠUk raj", "ĠUkra j", "ัà¸Ļ à¸Ńà¸Ńà¸ģ", "ัà¸Ļà¸Ń à¸Ńà¸ģ", "Ġม à¸ģราà¸Ħม", "и ÑĤов", "иÑĤ ов", "Ġanal ý", "Ġana lý", "ĠоÑĤ меÑĩ", "Ġبر اÙī", "âĪ ı", "ัà¸ģ à¸ģ", "æĭ¥ æľī", "ĠÑĸнÑĪ ого", "Ġкомп анÑĸÑĹ", "Ġk ÅĻes", "ĠÑĢаб оÑĩ", "ĠÑĢабо Ñĩ", "a dÃŃ", "ad ÃŃ", "ìł ł", "à¹Ħ หà¸Ļ", "à¥ģब ह", "âĢĻ deki", "âĢĻd eki", "âĢĻde ki", "çħ ¤", "ĠпаÑĢ Ñĥ", "Ġпа ÑĢÑĥ", "ìĦ Ń", "Ġнеп оÑģÑĢед", "ĠÄ° b", "Ġà¸ŀ ฤศà¸Ī", "íĭ ´", "Ġ ëłĪìĿ´", "ĠëłĪ ìĿ´", "ĠTh á»ķ", "Ñı еÑĤ", "ائ ج", "» çĴĥ", "ÐĴ Ðŀ", "åĸ Ĭ", "Ġ 第ä¸ī", "Ġ第 ä¸ī", "Ġвок ÑĢÑĥг", "Ñĩ енÑĮ", "Ñĩе нÑĮ", "Ñĩен ÑĮ", "Ġolan ak", "Ġola nak", "t ura", "tu ra", "tur a", "Ġ ÙħÙĬÙĦ", "ĠÙħ ÙĬÙĦ", "ĠÙħÙĬ ÙĦ", "ey di", "ĠÙħد ÙĬر", "Ġnel ze", "ัว à¸Ńย", "ìħ ľ", "Ġhlav u", "Ġkor uy", "ÑĨ ин", "ÑĨи н", "ĠдиÑģ ÑĨип", "ĠÙħ اÙĨد", "ĠÙħا ÙĨد", "ĠÙħاÙĨ د", "Ġпод ÑĢоб", "Т Ðŀ", "ÙĤر ار", "à¹ģà¸Ļะ à¸Ļำ", "문 ìĿĦ", "æĮ¯ ãĤĬ", "P ÅĻi", "PÅĻ i", "Ġy ên", "श à¤ķ", "Âł je", "ĠÐļон ÑģÑĤиÑĤÑĥ", "à¥ģ ह", "à¥ģठ¹", "ĠÙ¾ ا", "ìĨĮ 를", "Ġд ела", "Ġдел а", "Ġде ла", "к ид", "ки д", "à¹Ĥ à¸Ĭ", "커 ìĬ¤", "dÄĽ len", "dÄĽl en", "à¤Ķ र", "äºİ æĺ¯", "ĠÙĩÙħ ÛĮØ´Ùĩ", "ĠbaÅŁ lam", "Ġ ìĽ¨", "ĠìĽ ¨", "Ġden eyim", "Ġdeney im", "Ġü ye", "Ġüy e", "Ġ νÏĮ", "Ġν ÏĮ", "Ġà¤ĸ ड", "n ÄĽl", "nÄĽ l", "ĠÑģÑĦ еÑĢÑĸ", "ĠÑģÑĦеÑĢ Ñĸ", "à¸Ńà¸Ķ à¸ł", "ä¸Ģ å¹´", "Ġvur gu", "Äŀ Ä°", "âĢĻ Ċ", "ĠÑĸн ÑĪими", "ĠÑĸнÑĪ ими", "Ġз менÑĪ", "Ġठĭ", "Ġв ека", "Ġве ка", "ĠØŃÚ©ÙĪÙħ ت", "ĠتÙħ اÙħÛĮ", "ĠتÙħاÙħ ÛĮ", "Ġs mrt", "Ġsm rt", "Ġsmr t", "Ġh ủy", "Ġyap ılmÄ±ÅŁ", "Ġyapıl mÄ±ÅŁ", "à¹ī à¸ľ", "ĠY en", "ĠYe n", "Ġ Ñĥл", "ĠÑĥ л", "ĠS vÄĽt", "ĠSv ÄĽt", "ั à¸Ħ", "ĠmÄĽsÃŃ ců", "д енÑĤи", "ден ÑĤи", "Ġ ï¾ĺ", "Ġï¾ ĺ", "Ġпол иÑĤи", "Ġполи ÑĤи", "ĠполиÑĤ и", "s kyt", "sk yt", "sky t", "ä¹Ł æľī", "Ġê°Ļ ìĬµëĭĪëĭ¤", "Ġê·¸ëŀĺ ìĦľ", "ÏĦε Ïģη", "ÏĦεÏģ η", "Ñĩ еÑĢ", "Ñĩе ÑĢ", "ĠÃľNÄ°VERS Ä°TESÄ°", "ส à¸ł", "Ġ สร", "Ġส ร", "ान द", "ĠaÅŁ ırı", "λ ίοÏħ", "λί οÏħ", "Ġ ÙĦÙģ", "ĠÙĦ Ùģ", "ÃŃ nu", "ÃŃn u", "à¸Ń าร", "ÑĤ ÑĥÑĢа", "ÑĤÑĥ ÑĢа", "ÑĤÑĥÑĢ а", "ĠÄįesk ých", "ĠÄįe ských", "ĠÄįeský ch", "Ġph ức", "以 为", "ÏģÏī ÏĢα", "ĠاÙĨر ÚĺÛĮ", "» )", "a lardan", "alar dan", "alarda n", "ĠÑģÑĤ воÑĢÑİ", "ĠÑģÑĤвоÑĢ Ñİ", "Ġt ráv", "Ġtr áv", "ॠ¬", "ãģĬ ãĤĪãģ³", "ïľ ĭ", "ad il", "adi l", "ĠΤ ι", "Ġ ëIJ©ëĭĪëĭ¤", "ĠëIJ ©ëĭĪëĭ¤", "Ġε μÏĨ", "Ġ구 ì¡°", "ìĹŃ ìĭľ", "ĠاÙĦ جاÙħ", "ĠاÙĦج اÙħ", "主 é¢ĺ", "ãĤ¹ ãĥĿ", "Ġ ìĹŃìĭľ", "ĠìĹŃ ìĭľ", "ĠÚ©Ùħ تر", "ĠSp oleÄį", "ол оÑĪ", "оло ÑĪ", "ĠSur iye", "Ч еÑĢ", "æĪĺ æĸĹ", "Ġz ávis", "Ġzá vis", "Ġzáv is", "æĽ¸ 館", "Ġmus el", "Ġmu sel", "Ġmuse l", "Ġ çĿ", "Ġç Ŀ", "Ùħ Ùħ", "ĠاÙĦØ® ارج", "Ġ ÐĵÐŀ", "ĠÐĵ Ðŀ", "ĠваÑĢ ÑĤо", "ĠваÑĢÑĤ о", "Ïģα β", "Ġपह à¤ļ", "ub lice", "ublic e", "ubl ice", "ÑĨион ного", "è Į¨", "èĮ ¨", "ĠدÙģ تر", "Ġ Ùģس", "ĠÙģ س", "Ġन à¤ľà¤°", "t arı", "ta rı", "tar ı", "Ġоб ÑĢоб", "ĠÐł а", "ĠاÙĦ صÙĨ", "ĠاÙĦص ÙĨ", "Ø´ Ø©", "ĠìĹĨ ìĹĪ", "ož ná", "æľĢ çµĤ", "Ù ¥", "r ech", "re ch", "rec h", "ĠاÙĦØ£ سر", "ĠاÙĦأس ر", "Ġм ови", "Ġмо ви", "Ġмов и", "Ġì¡° êµIJ", "Ñĸ меÑĩ", "Ñĸм еÑĩ", "ãĥ¯ ãĥ¼", "б ÑĥÑĢг", "бÑĥ ÑĢг", "Ġس ÙĦس", "ĠسÙĦ س", "åѦ ä¼ļ", "Ġ ë¦", "Ġë ¦", "åħĭ æĸ¯", "æĸĩ çĮ®", "Ġx Æ°Æ¡ng", "Ġyo lc", "Ġyol c", "Ġ ìĤ¬ë¬´", "ĠìĤ¬ 무", "ãĤı ãģļ", "ĠÑĢаÑģÑĤ ений", "ĠÙģ ضاÛĮ", "ĠÙģض اÛĮ", "Ġna opak", "Ġnao pak", "ĠпÑĢи вÑĭ", "ĠпÑĢив Ñĭ", "Ġد ÛĮدÙĩ", "ĠدÛĮ دÙĩ", "ĠدÛĮد Ùĩ", "à¸ģาร à¹ĥà¸Ĭ", "Ġ åŀ", "Ġå ŀ", "çij Ł", "以 åIJİ", "ĠpÅĻib liž", "ĠdÃ¼ÅŁ man", "Ġt emin", "Ġte min", "Ġtem in", "ĠÑĥÑģл Ñĥг", "ĠÑĥÑģ лÑĥг", "Ġद ब", "ĠìĥĪ ê¸Ģ", "ĠÑĥÑģÑĤÑĢой ÑģÑĤва", "ĠТ ÑĥÑĤ", "ÏĦ ίοÏħ", "ÏĦί οÏħ", "ĠÄ°s lâm", "Ù ¤", "åıĤ ä¸İ", "Ġк ÑĥÑģÑĤ", "ĠкÑĥ ÑģÑĤ", "ĠкÑĥÑģ ÑĤ", "éĻIJ åĪ¶", "ت ÙĬÙĨ", "تÙĬ ÙĨ", "ĠоÑģÑĤ аннÑĸ", "ĠоÑģÑĤан нÑĸ", "ic ations", "ication s", "ا Ú©ÛĮ", "اک ÛĮ", "но ÑģÑı", "ноÑģ Ñı", "ÄŁ an", "ÄŁa n", "ãģı ãĤĮãĤĭ", "ãģıãĤĮ ãĤĭ", "Ġyap ıyor", "Ġyapı yor", "Ġê°ķ ëĤ¨", "Ùħ ÙĬÙħ", "ÙħÙĬ Ùħ", "æ ŃIJ", "æŃ IJ", "Ġر ع", "Ġb oÄŁ", "Ġbo ÄŁ", "ĠиÑģ Ñħод", "èª ł", "æł· åŃIJ", "Ġbu deme", "Ġbud eme", "Ġbude me", "ĠÑģ еÑĤ", "ι ÏĥμοÏį", "ιÏĥμ οÏį", "Ġ å¾ĴæŃ©", "Ġå¾Ĵ æŃ©", "u álnÃŃ", "ĠاÙĦع ÙĤ", "Ġسب Ú©", "ĠاÙĦØ£ خرÙī", "ĠاÙĦأخ رÙī", "E FA", "EF A", "åĽº å®ļ", "Ġ ãĤ¬", "ĠãĤ ¬", "ĠìŀIJ ìĹ°", "ยว à¸Ĥ", "ب س", "un ma", "Ġза ним", "Ġзан им", "à¹ĥà¸Ļ ร", "èĢĥ èĻij", "æ·· åIJĪ", "å° ĭ", "Ġçık Ä±ÅŁ", "Ġm aliyet", "Ġmal iyet", "éľ Ĭ", "ãģŁãĤģ ãģ®", "ĠÙ¾ Ø´", "Ġз лоÑĩ", "Ġvý Å¡i", "Ġsch vál", "ĠÙĨÙħ ÙĪدÙĩ", "ĠÙĨÙħÙĪد Ùĩ", "Î Ĩ", "Ġz ách", "Ġzá ch", "Ġ Ïĥκ", "ĠÏĥ κ", "ãĤ¹ ãĥŀ", "ĠÙħس ائÙĦ", "ĠاÙĦاج تÙħاع", "åľ° çĤ¹", "ا ÛĮاÙĨ", "اÛĮ اÙĨ", "ĠÐŀ к", "ê¸ Ķ", "e lease", "el ease", "ele ase", "Ġطب ÙĤÙĩ", "ĠطبÙĤ Ùĩ", "éij ij", "Ġì½Ķ ë¡ľëĤĺ", "é¼ ł", "大 åħ¨", "ĠпÑĢи веÑģÑĤи", "ĠпÑĢив еÑģÑĤи", "Ġاب تد", "Ġابت د", "리 ë¡ľ", "ĠÑģÑĤÑĢ анÑĭ", "ĠÑģÑĤÑĢа нÑĭ", "ĠÑģÑĤÑĢан Ñĭ", "ĠzatÃŃm co", "Ġh uyết", "Ġhuy ết", "سÛĮ ÙĪÙĨ", "Ġsor du", "âĢĮ رس", "ĠÑĦ ÑĢон", "Ġed ip", "Ġedi p", "ÙĨ Ú¯ÛĮ", "ÙĨÚ¯ ÛĮ", "Ġк иÑĢ", "Ġки ÑĢ", "Ġ íķ´ìķ¼", "Ġíķ´ ìķ¼", "ì» ´", "ÑĨик лоп", "ĠпÑĢимен ениÑı", "Ġоб л", "éļ ª", "Ġk romÄĽ", "Ġkro mÄĽ", "æł¸ å¿ĥ", "ra him", "rah im", "о ÑĢд", "оÑĢ д", "Ġl Ãłnh", "ĠlÃł nh", "ĠlÃłn h", "Ġо ÑģÑĤÑĢов", "ĠоÑģÑĤ ÑĢов", "; |", "b uz", "bu z", "Ġ ÏĦÏģο", "ĠÏĦ Ïģο", "ĠÐĴ аÑĢ", "æī İ", "ı lÄ±ÅŁ", "ılı ÅŁ", "ıl Ä±ÅŁ", "éĿ¢ ç©į", "身 份", "é¢Ĩ åŁŁ", "ĠاÙĦÙĤر ÙĨ", "Ġ пÑĢиклад", "ĠпÑĢи клад", "ĠпÑĢик лад", "ãĥģ ãĥ¼ãĥł", "Ġสà¸ŀ à¸Ľ", "Ġо ÑĩиÑģÑĤ", "ĠоÑĩ иÑģÑĤ", "ĠоÑĩи ÑģÑĤ", "Ġмил ли", "а ÑĨÑĸÑĹ", "ี à¹Ģà¸Ń", "Ġt anın", "Ġtan ın", "Ġtanı n", "çĪ¶ 亲", "Ġmsg str", "Ġmsgs tr", "ĠØ´ ÛĮÙħÛĮ", "ĠØ´ÛĮ ÙħÛĮ", "ĠÙģر اÙĩÙħ", "Ġ 맥", "Ġë§ ¥", "ãĢĤ å½ĵ", "Ġкон ÑĨенÑĤÑĢа", "êµIJ íļĮ", "ãĤī ãĤĮãģ¦", "ãĤīãĤĮ ãģ¦", "Ġya sak", "Ġyas ak", "ĠÐij ол", "Ġ æ¾³", "Ġæ¾ ³", "çĩ ķ", "Ġ جا", "Ġج ا", "ëij ĺ", "Ġدر Ø®ÙĪاست", "ĠmÃŃst nÃŃ", "ÂĤ ÃĮ", "Ġbas kı", "Ġu çak", "Ġuç ak", "ä» ĵ", "Ġìľł ì§Ģ", "Ġп оба", "Ġпо ба", "Ġпоб а", "Ġz eptal", "Ġze ptal", "ç»Ļ æĪij", "ĠAt atürk", "ĠÙħÙĨ اس", "Ñ Ĵ", "Ġar acı", "Ġarac ı", "Ġara cı", "лÑİ ÑĶ", "Ġnit elik", "Ġnitel ik", "ĠM ezi", "ĠMe zi", "ĠMez i", "ĠÎŃ Î½Î±ÏĤ", "ĠÎŃνα ÏĤ", "ĠÎŃν αÏĤ", "Ïİν ÏĦαÏĤ", "v až", "va ž", "Ġk uzey", "Ġ ÏİÏģα", "ĠÏİ Ïģα", "ĠÑĢоз пов", "ĠÑĢозп ов", "à¹Ī าà¸ģ", "à¹Īา à¸ģ", "ãĢģ ä¸ī", "ĠÑģÑĤ аÑĢи", "ĠÑģÑĤаÑĢ и", "ĠÑģÑĤа ÑĢи", "Ġhak kı", "Ġhakk ı", "ĠØ¢Ùħ ادÙĩ", "íĮ Ķ", "о мÑĸ", "ом Ñĸ", "Ġ âĢł", "ĠâĢ ł", "ãģĭ ãĤı", "ãĢĮ ä½ł", "æ³ķ åĽ½", "ÙIJ ÙĬÙĨ", "ÙIJÙĬ ÙĨ", "æī ķ", "н или", "ни ли", "нил и", "ĠÑĥÑģÑĤанов ки", "Ġl ông", "Ġlô ng", "त म", "ÙĪ ÙĨÙĬØ©", "ÙĪÙĨ ÙĬØ©", "ÙĪÙĨÙĬ Ø©", "ÙĬ تÙĬ", "ÙĬت ÙĬ", "Ġê²Įìĭľ 물", "Ġve Å¡ker", "ÎŃ Ïģγ", "ÎŃÏģ γ", "ĠÑĥ Ñģе", "ĠÑĥÑģ е", "Ġk ıl", "Ġkı l", "Ġil gi", "Ġilg i", "μ Ïīν", "Ġз вÑĸлÑĮ", "Ġзв ÑĸлÑĮ", "Ġön lem", "à¸ģà¸İ หมาย", "ĠH iá»ĩp", "Ġг оÑĢм", "ĠгоÑĢ м", "лÑı ÑİÑĤÑĮÑģÑı", "лÑıÑİÑĤÑĮ ÑģÑı", "la maya", "lam aya", "lama ya", "ĠÑģпоÑģоб ом", "ãģ¸ ãģ¨", "ç¦ģ æŃ¢", "ĠÑĢаÑħ Ñĥнок", "ĠоÑĤвеÑĢ ÑģÑĤи", ".: .:.:.", ".:.: .:.", ".:.:.: .", ".:.:. :.", "Ġmü da", "о наÑħ", "он аÑħ", "она Ñħ", "Ì£ c", "Ġyap acak", "Ġн азвание", "Ġназ вание", "Ġназва ние", "对 æĸ¹", "ëĮĢ íijľ", "çĪ Ń", "в ана", "ва на", "ван а", "ह न", "ĠпÑĢоблем а", "Ġжен ÑīинÑĭ", "ĠженÑīин Ñĭ", "èŀ º", "Ġhosp odáÅĻ", "ĠСÑĤ еп", "ĠodpovÄĽ d", "ĠS á»Ń", "e view", "ev iew", "evi ew", "åĩł ä¹İ", "çŁ ¢", "æĿ¥ ãģŁ", "Ġп олоÑģ", "Ġпол оÑģ", "ĠÑģ ел", "å± Ĩ", "ĠпеÑĢв ой", "ĠпÑĢоÑĨеÑģ Ñģа", "ĠпÑĢоÑĨеÑģÑģ а", "ãĢĢ ãĤĿ", "ت اÙħبر", "تا Ùħبر", "и лаÑģÑı", "ила ÑģÑı", "ï¼Į æĹł", "ĠвлаÑģ ноÑģÑĤÑĸ", "íķĺ ìŀIJ", "аÑĤ ки", "ĠB Ãł", "ĠK arel", "ĠKar el", "ĠKa rel", "ĠKare l", "è· µ", "ر ÛĮÙĩ", "رÛĮ Ùĩ", "ĠëĤĺ 를", "ĠобеÑģпеÑĩ ива", "ĠобеÑģпе Ñĩива", "à¥įर पत", "ãģĹ ãĤĩ", "åį Ĵ", "Ġ 奥", "Ġå¥ ¥", "ĠпÑĢ оÑĤе", "ĠпÑĢо ÑĤе", "ĠпÑĢоÑĤ е", "Ġ æĭĽ", "Ġæĭ Ľ", "ĠСÑĤ ÑĢана", "ĠÑĢабоÑĤ аÑĤÑĮ", "ĠÑĢабоÑĤа ÑĤÑĮ", "Ġتش Ø®ÛĮص", "ек ÑģÑĥ", "екÑģ Ñĥ", "Ġ 리그", "Ġ리 ê·¸", "Ġص اÙĦØŃ", "ĠbaÅŁ lamÄ±ÅŁ", "ĠbaÅŁlam Ä±ÅŁ", "ĠÙ¾ÛĮ اÙħبر", "ĠÙ¾ÛĮاÙħ بر", "ز ا", "Ġм аÑģÑģ", "ĠмаÑģ Ñģ", "ĠÎł αÏģ", "Ġγα Ïģ", "ëĿ¼ íĶ¼", "Ġy arı", "Ġya rı", "Ġyar ı", "ĠÑĤип Ñĥ", "Ðŀ п", "ãģij ãģªãģĦ", "e mem", "em em", "eme m", "ĠnÄĽ mu", "ĠnÄĽm u", "ĠÙĨ شر", "ĠÙĨØ´ ر", "ĠÎijθή να", "Ùģ راÙĨ", "Ùģر اÙĨ", "Ġ 網", "Ġç¶ ²", "ĠпÑĢом иÑģлов", "ĠBu gün", "ĠBug ün", "ìŀ Ķ", "ĠжÑĸн ок", "Ġ à¸Ľà¸£à¸°à¹Ģà¸łà¸Ĺ", "Ġà¸Ľà¸£à¸° à¹Ģà¸łà¸Ĺ", "ĠвикоÑĢиÑģÑĤов ÑĥваÑĤи", "ĠТ им", "ĠТи м", ") 를", "еж аÑĤÑĮ", "Ġs ona", "Ġso na", "Ġson a", "Ø´ÙĨ بÙĩ", "Ġnich ž", "åī Ľ", "ĠÙģ تØŃ", "ĠÙģت ØŃ", "ĠÙħÙĤ دÙħ", "ĠÙħÙĤد Ùħ", "ĠGüven lik", "e um", "eu m", "ç»ı è¿ĩ", "è·Ŀ éĽ¢", "Âł не", "Ġا صÙĪÙĦ", "Ġاص ÙĪÙĦ", "ĠzaÄį átku", "ิà¹Ģว à¸ĵ", "Ġà¤ķ à¤Ł", "Ġk riz", "Ġkr iz", "Ġp án", "Ġpá n", "ĠбоÑĢ ÑĮ", "Ġбо ÑĢÑĮ", "ظ ÙħØ©", "ظÙħ Ø©", "Ġê²½ ë¶ģ", "ĠاÙĦÙĬ ÙħÙĨ", "ĠاÙĦعرب ÙĬ", "Ġh lub", "Ġhl ub", "Ġch á»Ŀ", "è¥ ²", "ëĵľ 리", "ãĥĸ ãĥª", "ĠÑģÑĤол ÑĸÑĤÑĤÑı", "ر بÙĬØ©", "رب ÙĬØ©", "Ġ æ°¸", "Ġæ° ¸", "Ġê±° ìĿĺ", "Ġβ αÏĥ", "Ġβα Ïĥ", "Ġa rz", "Ġar z", "ãĥ¢ ãĥ³", "ĠÑĢÑĸв енÑĮ", "ä¸į çŁ¥", "导 èĩ´", "ا ÙĬØ´", "اÙĬ Ø´", "ĠпÑĢев ÑĭÑĪ", "Ġп н", "ĠÎĴ ÏģοÏĩή", "Ġ 身", "Ġè º«", "ĠÄIJ ầu", "ĠÏĮ μÏīÏĤ", "j ÃŃž", "jÃŃ Å¾", "Ġλ ίγ", "ĠÑĪк оли", "ĠÑĪкол и", "ãģ£ãģ± ãģĦ", "z dy", "zd y", "Ġê³ §", "t eÅŁ", "te ÅŁ", "ÑĢ еÑī", "ÑĢе Ñī", "κ ει", "κε ι", "sah uje", "Ġà¤īस स", "ĠTan rı", "ä¸į 好", "éĥ Ń", "ĠвÑĭ глÑıд", "Ġç oÄŁ", "Ġин ÑģÑĤÑĢÑĥменÑĤ", "r ej", "re j", "èĪ Į", "ãģĭ ãĤīãģªãģĦ", "ãģĭãĤī ãģªãģĦ", "ĠнепÑĢи ÑıÑĤ", "Ġк ÑĢоме", "ζ η", "Ġл ог", "ा वर", "ाव र", "ëħķ íķĺìĦ¸ìļĶ", "ाह रण", "ाहर ण", "Ġgüven ilir", "T ại", "ĠØ´Ùĩر د", "ĠØ´Ùĩ رد", "ĠΤ ε", "о ÑĢаз", "оÑĢ аз", "оÑĢа з", "Ġl Ãłng", "ĠlÃł ng", "ĠlÃłn g", "ï¼ ©", "æĬķ 注", "Ġsiyas et", "ÐĽ Ñİ", "Ġt ÅĻet", "ĠtÅĻ et", "ĠÏĢÏģÏİ ÏĦη", "ĠÑĥлÑĭ б", "ĠL âm", "ÑĥлÑĮÑĤ а", "ÑĥлÑĮ ÑĤа", "åŁº åľ°", "Ġskup ina", "æ°¸ ä¹ħ", "лÑĥ гов", "лÑĥг ов", "Ġ ÑĨÑĸй", "ĠÑĨ Ñĸй", "ĠÑĨÑĸ й", "ĠP oh", "ĠPo h", "i д", "ĠTr uy", "ĠTru y", "çļĦ ä¸Ģ个", "çļĦä¸Ģ 个", "ë²Ħ ìłĦ", "Ġx ứ", "à¸ĩ à¹ģรà¸ģ", "à¸Ħ à¸Ńม", "Ġelektron ik", "ĠaÄŁ aç", "Ġà¤ľ य", "ĠповеÑĢÑħ ноÑģÑĤÑĮ", "ĠاÙĩÙħ ÛĮت", "ли виÑħ", "лив иÑħ", "ĠolduÄŁ undan", "ï¼ī :", "ÑĨи ÑıÑħ", "ÑĨиÑı Ñħ", "製 ä½ľ", "à¸Ĺ รà¸ĩ", "à¸Ĺร à¸ĩ", "ey im", "eyi m", "Ġná klad", "c ilik", "ci lik", "cil ik", "ĠÐĵ лав", "ĠUy gu", "ĠÑĢег ÑĥлÑİ", "à¤Ĥ à¤ľà¤¨", "à¤Ĥà¤ľ न", "Ġkayn aģı", "à¹ī าà¸Ń", "à¹īา à¸Ń", "Ġgör mek", "ĠíĮ ¬", "Ġ å®Į", "Ġå® Į", "Ø« ÙħاÙĨ", "ĠÑĤак аÑı", "ĠÑĤа каÑı", "ĠÑĤака Ñı", "Ġне из", "Ġzpráv y", "ĠاÙĦØ´ خص", "Ġìĺ¤ íĽĦ", "ĠاÙĦ طب", "ĠاÙĦØ· ب", "atır ım", "ر ÙĬر", "رÙĬ ر", "ĠÙħع ÙħارÛĮ", "Ãľ RK", "ÃľR K", "ĠÒ IJ", "ĠìĦ ¬", "æīĭ ãģ«", "Ġë³Ģ íĻĶ", "u lace", "ul ace", "ula ce", "Ġs ợ", "ÑĢ иÑĩ", "ÑĢи Ñĩ", "มห าว", "Ġk â", "ĠÑģп ÑĢоб", "Ùĩ رÙĩ", "Ùĩر Ùĩ", "ाध न", "ĠÏĢ αι", "ĠÏĢα ι", "ب عد", "بع د", "ĠاÙĦ تÙĪ", "ĠاÙĦت ÙĪ", "ç»ı çIJĨ", "p ůsob", "æ¬ ł", "ĠзаÑħвоÑĢÑİ Ð²Ð°Ð½Ð½Ñı", "Ø® Ø©", "ÚĨ ار", "Ġboz uk", "] âĢı", "ĠSoc orro", "Ġ hrad", "Ġh rad", "Ġhr ad", "Ġhra d", "над леж", "ĠÑĥÑĩаÑģÑĤ ие", "ĠÑĥÑĩаÑģ ÑĤие", "ĠÑĥÑĩаÑģÑĤи е", "å¤ī ãĤı", "Ġy ans", "Ġya ns", "Ġyan s", "ĠØ¥ ÙĦ", "Ø® بر", "خب ر", "ÑĨиклоп ед", "ι Ïİν", "ιÏİ Î½", "Ïĥ ÏĦÏģο", "ÏĥÏĦ Ïģο", "Ġb anka", "Ġbank a", "Ġban ka", "ĠsoÄŁ uk", "Ġün lü", "é¢ ľ", "Ġر Ùģع", "ĠرÙģ ع", "çIJ ³", "ĠÑģоÑģÑĤоÑı нии", "ν ονÏĦαÏĤ", "νον ÏĦαÏĤ", "Ġа кÑĤи", "Ġак ÑĤи", "ĠакÑĤ и", "ĠÏĢολ Ïħ", "ĠÏĢο λÏħ", "Ġм оÑĹ", "Ġмо ÑĹ", "Ġ æł¼", "Ġæł ¼", "ç² Ĺ", "ĠÑģлÑĥÑĩ ай", "ĠÑģлÑĥ Ñĩай", "ĠÑģлÑĥÑĩа й", "ìĿ¼ ìĹIJ", "ĠÑĤÑĢеб ÑĥеÑĤ", "Ġ åıĤèĢĥ", "ĠåıĤ èĢĥ", "an gl", "ang l", "am ik", "ami k", "Ġ Ä°Åŀ", "ĠÄ° Åŀ", "æ¹ ¯", "ĠÄij áo", "ĠÄijá o", "ละ à¸Ħร", "Ñģ о", "Âł ob", "Ġk lim", "Ġkl im", "Ġkli m", "èĥ Ĩ", "ìĥĿ íĻľ", "ãĥij ãĥ³", "- ब", "Ġк ад", "Ġка д", "à¹Ī สามารà¸ĸ", "ĠÙħس ÙĦÙħاÙĨ", "ç¿ °", "ĠB ütün", "ĠK raj", "ĠKr aj", "ĠKra j", "ĠпеÑĢ Ñģп", "ĠпеÑĢÑģ п", "Ġener j", "ãģķ ãģĽãĤĭ", "ãģķãģĽ ãĤĭ", "è¾¾ åĪ°", "ा à¤Ĭ", "ाठĬ", "ĠگرÙģ تÙĨ", "ĠگرÙģت ÙĨ", "ÑĪ кÑĥ", "ĠÐŁ ло", "ÃŃ ny", "ÃŃn y", "ĠH ra", "ĠÚĨ ÙĨاÙĨ", "Ġ à¹Ħà¸Ĺย", "Ġà¹Ħ à¸Ĺย", "vise jÃŃcÃŃ", "Û³ Û³", "ĠÐľÑĸнÑĸÑģÑĤ еÑĢ", "à¹Ĥ à¸Ń", "ĠدÙĩ ÛĮد", "æ¯Ķ ä¾ĭ", "Ïĥι εÏį", "Ç IJ", "ãĢģ ãģª", "Ġत स", "ĠÄ° t", "ĠìłĦ ìŁģ", "à¹Ģ à¸Īร", "à¹Ģà¸Ī ร", "Ġelek tr", "Ġelekt r", "Ġd Æ°", "â ĶĶ", "âĶ Ķ", "Ġ ìĥ¤", "Ġìĥ ¤", "ä» ®", "à¸ģาร à¹Ģล", "Ġм ÑĥлÑĮ", "ĠмÑĥ лÑĮ", "Ġ 度", "Ġåº ¦", "ĠH uyá»ĩn", "в ен", "ве н", "Ġl Æ°á»Ľi", "Ġprovoz u", "Ñĥ ÑĢÑĥ", "ÑĥÑĢ Ñĥ", "ÑĢ ÑĸÑĹ", "ÑĢÑĸ ÑĹ", "Ġçocu ÄŁ", "ัà¸IJ à¸ļาล", "ÙĦ ÙĬÙĩ", "ÙĦÙĬ Ùĩ", "Ġ[âĢ¦] ...Ċ", "åİŁ å§ĭ", "Ġs klad", "Ġsk lad", "Ġskl ad", "Ġسپ تاÙħبر", "ĠTom áš", "Ġس ÙĪاÙĦ", "ĠسÙĪ اÙĦ", "çģ Ń", "ãĤĵ ãģ©", "на знаÑĩ", "ĠÄij Ä©a", "ĠudÄĽl at", "Ġà¤Ĩ दम", "Ġà¤Ĩद म", "ï¼ ¬", "ι νÏĮ", "ιν ÏĮ", "iÅŁ leri", "ÄIJ ây", "Ġرس اÙĨÙĩ", "ع اÙħ", "عا Ùħ", "ãĥ¼ãĥij ãĥ¼", "Ġdo prov", "Ġdop rov", "ĠмÑĸÑģ ÑĤо", "ĠмÑĸÑģÑĤ о", "ï¼ ¥", "ел Ñĸг", "елÑĸ г", "ائ ز", "ä¸į äºĨ", "ĠÐIJлекÑģанд ÑĢ", "ĠвÑĢем ен", "Ġdve ÅĻe", "Ġch ảy", "Ġ otel", "Ġo tel", "Ġot el", "èĤ¯ å®ļ", "ĠÑĥÑĤ веÑĢжд", "ĠÐļом п", "ĠÐļо мп", "Ġ ëĤĺëĿ¼", "ĠëĤĺ ëĿ¼", "ĠвÑĸдбÑĥва ÑĶÑĤÑĮÑģÑı", "ãĢģ ãĢİ", "ĠkarÅŁÄ± lık", "Ġl ẫn", "çħ Ļ", "ع کس", "å¼ ¥", "Ġte cr", "Ġtec r", "Ġne od", "Ġneo d", "æĪIJ çĤº", "åħ¥ ãĤĬ", "ĠÐŁ ÑĢод", "ĠÐŁÑĢ од", "ĠÐŁÑĢо д", "ĠÏĢ Ïģά", "ื à¸Ńà¸Ķ", "ืà¸Ń à¸Ķ", "ÑģÑĤ аÑĤи", "ÑģÑĤа ÑĤи", "е ноÑĹ", "ен оÑĹ", "ено ÑĹ", "Ñĩи Ñģл", "ÑĩиÑģ л", "羣 æŃ£", "Ġ ราà¸Ħ", "Ġร าà¸Ħ", "Ñĥ ÑĢе", "ÑĥÑĢ е", "ĠØ´ اÙĩد", "ĠشاÙĩ د", "ا عر", "اع ر", "Ġê²½ íĹĺ", "à¸Ļ à¸Ħ", "ãĥį ãĥ«", "ÏĢοÏħ λοÏĤ", "Ġम à¤Ī", "ìĬ¤ ì½Ķ", "itel né", "å¼Ģ æĶ¾", "ç į¨", "çį ¨", "ĠpÅĻ ech", "ĠpÅĻe ch", "ú Äįast", "å¢ ĵ", "Ġ å½±", "Ġå½ ±", "ÙĨ ساÙĨ", "ÙĨس اÙĨ", "ÙĨسا ÙĨ", "Ġд вад", "Ġдв ад", "Ġдва д", "Ġи деÑĤ", "Ġиде ÑĤ", "Ġид еÑĤ", "Ġпод клÑİÑĩ", "Ġподк лÑİÑĩ", "íĬ¹ë³Ħ ìĭľ", "B Ãłi", "Å¡ ku", "Å¡k u", "i lerden", "iler den", "åıĺ å¾Ĺ", "ëıĻ ìķĪ", "Ġpostup nÄĽ", "ĠиÑĤ ог", "Ġd ůvodu", "Ġdůvod u", "siz lik", "ÙĦ اÙĨ", "ÙĦا ÙĨ", "éĤ£ ç§į", "ĠÑĩа Ñģа", "ĠÑĩаÑģ а", "ä¸į æĸŃ", "ĠØ®ÛĮ اباÙĨ", "ĠاÙĦد اخ", "ĠÑģÑĤоÑĢ Ñĸн", "Ġì¶ľ ìĹ°", "æ² Ł", "Ġh ry", "Ġhr y", "ĠG Ãľ", "ĠìĿ¸ 구", "l ied", "li ed", "lie d", "Ġع اÙĦÙĬØ©", "ĠпÑĢед ваÑĢ", "ан ной", "åı¥ è¯Ŀ", "éł ĵ", "ë°Ķ ìĿ¼", "ï¼ı /", "ĠÙħخت صات", "ëŀ «", "ĠçalÄ±ÅŁ maları", "ĠçalÄ±ÅŁmalar ı", "ĠçalÄ±ÅŁma ları", "Ġrepublik a", "Ġ ì³", "Ġì ³", "ा )", "Ġê±´ ê°ķ", "Ġê³µ ëıĻ", "èħ ¦", "ĠìĦľ ë¡ľ", "ĠпÑĢовод иÑĤÑĮ", "ĠдейÑģÑĤв иÑĤелÑĮно", "v eç", "ve ç", "Ø« اÙĦ", "Ġgöster ir", "ır lar", "ĠÑģам Ñĭм", "á lo", "ál o", "é¢ij 次", "à¥Ī à¤Ĺ", "ا دÙħ", "اد Ùħ", "çĮ ª", "ĠS ản", "Ġ çı", "Ġç ı", "Ġl ety", "Ġle ty", "Ġlet y", "Ġrep ublice", "Ġrepublic e", "æĿ¥ èĩª", "Ġv ết", "Ġbi rik", "Ġbir ik", "Ġbiri k", "Ġm ekt", "Ġme kt", "Ġmek t", "ĠاÙĦ ÙĪÙģ", "ĠاÙĦÙĪ Ùģ", "Ġj ich", "Ġji ch", "ä¸Ģ 覧", "éľ² åĩº", "ĠH iá»ĩn", "ĠHi á»ĩn", "Ġd iá»ĩt", "ĠÑħ ÑĢиÑģÑĤи", "åĪļ æīį", "k ate", "ka te", "kat e", "Ġb azen", "Ġba zen", "Ġbaz en", "ĠurÄįit ÄĽ", "ĠurÄį itÄĽ", "Ġumož ÅĪuje", "é¡ĺ ãģĦ", "/Q ÄIJ", "Ġmen Å¡ÃŃ", "ÏĥκεÏħ ή", "ĠÑĨеÑĢк ов", "ĠÑĨеÑĢ ков", "Ġ è´Ń", "Ġè´ Ń", "ок ÑĢаÑĤи", "ĠÑĢоз к", "α νοÏħ", "αν οÏħ", "Ġyön etic", "Ġyönet ic", "Ġol madan", "Ġolm adan", "Ġolma dan", "åĨľ ä¸ļ", "Ġë°Ķ ëŀĮ", "çĵ ľ", "ÑĪ аеÑĤÑģÑı", "ÑĪа еÑĤÑģÑı", "ĠÐļ оÑģÑĤ", "ĠÐļо ÑģÑĤ", "ĠÙħ عت", "ĠÙħع ت", "Ġ à¸ŀล", "Ġà¸ŀ ล", "ĠÙħتÙģ اÙĪت", "ãĤī ãģı", "èĪ Ĺ", "Ġتع رÛĮÙģ", "éīĦ éģĵ", "Ġpé Äįe", "ì» µ", "Ġпод ÑĢаз", "Ġбан кÑĥ", "Ġбанк Ñĥ", "Ä°S Ä°", "æ¡ IJ", "à¹Ĥ รà¸Ħ", "à¹Ĥร à¸Ħ", "ĠØŃØ° Ùģ", "Ġ ë£", "Ġë £", "л иж", "ли ж", "Ġ ìĤ°ìĹħ", "ĠìĤ° ìĹħ", "ĠпÑĢи ÑĩинÑĭ", "ĠпÑĢиÑĩин Ñĭ", "ĠпÑĢиÑĩ инÑĭ", "Ġна зна", "Ġназ на", "ãĥª ãĤ¹ãĥĪ", "ãĥªãĤ¹ ãĥĪ", "ìłķ ë¶Ģ", "Ïĥ ÏĨα", "ÏĥÏĨ α", "å¦ ĥ", "Ġголов и", "Ġгол ови", "ëIJĺìĹĪ ìĬµëĭĪëĭ¤", "Ġεν ÏĮÏĤ", "ãĤ¤ ãĥ³ãĤ¿", "ãĤ¤ãĥ³ ãĤ¿", "Ġs lun", "Ġsl un", "Ġslu n", "ëł ´", "ĠÑģÑĥÑīеÑģÑĤв ÑĥеÑĤ", "ĠÑģÑĥÑīе ÑģÑĤвÑĥеÑĤ", "з аб", "за б", "æĽ´ åĬł", "Ġблагод аÑĢÑı", "ĠëĮĢ 구", "è¾ ħ", "ห าà¸ģ", "หา à¸ģ", "Ġ æİ¥", "Ġæİ ¥", "ëĮĢ 를", "人 ç±»", "j eme", "je me", "jem e", "åĪĨ å¸ĥ", "ìŀ¥ ìĿĢ", "Ġдопом оги", "ìĻĦ ë£Į", "o sy", "os y", "èĭ± éĽĦ", "Ġ ÙĦس", "ĠÙĦ س", "म ह", "Ġ à¸ģำ", "Ġà¸ģ ำ", "Ġداش تÙĨ", "Ġداشت ÙĨ", "Ń ìłľ", "Ä° ng", "Ä°n g", "ĠTh Æ°á»Ŀng", "íĻ Ģ", "Ñį ÑĦ", "íķ´ ìļĶ", "ĠÐľ Ñĸж", "ĠÐľÑĸ ж", "еÑĢÑĸ га", "еÑĢÑĸг а", "Ġε á¼", "à¹ģ สà¸ĩ", "à¹ģส à¸ĩ", "ãĥĢ ãĤ¤", "Ġc esty", "Ġce sty", "Ġces ty", "Ġcest y", "Ġpr ázd", "Ġprá zd", "第 ä¸Ģ次", "第ä¸Ģ 次", "ĠÙĩÙħ سر", "Ġz ev", "Ġze v", "Âł E", "ĠBeled iyesi", "ĠBelediye si", "ĠпÑĢоп ози", "Ġanlay Ä±ÅŁ", "Âł Ùħ", "ĠÑĢаÑģÑģ ÑĩиÑĤ", "ĠاÙĦØ£Ùħ رÙĬÙĥÙĬØ©", "ĠاÙĦØ£ÙħرÙĬÙĥÙĬ Ø©", "Ġž ena", "Ġže na", "Ġžen a", "d eniz", "den iz", "Ġn oci", "Ġno ci", "Ġnoc i", "Ġst ál", "ุ ย", "주 ìĨĮ", "Ġз еÑĢ", "Ġ ìĨĮê°ľ", "ĠìĨĮ ê°ľ", "Ġkh ẳng", "at ıcı", "atı cı", "ÄĽ ž", "ĠÑĩ ÑĥÑĤÑĮ", "Ġc áºŃu", "ĠاطÙĦ اع", "æµ ħ", "Ġst rav", "Ġstr av", "Ġstra v", "ĠSan ayi", "Ġ طبÙĬ", "ĠØ· بÙĬ", "Ġطب ÙĬ", "Ġhız la", "Ïİ Î½Î±", "Ïİν α", "à¤¿à¤ľ ल", "ÙħØŃ Ùħد", "à¸ļ à¸ģ", "Ġvzdál en", "ĠÑĤак ими", "ĠÑĤа кими", "ĠÑĤаким и", "ãĢĤ ãģĿãģĹãģ¦", "Ġka lp", "Ġkal p", "Ġкож ного", "Ðł µ", "ÙĦع اب", "ĠÙħ ÙĪÙĨ", "ĠÙħÙĪ ÙĨ", "ĠìĿ¼ ìĿĦ", "Ġ ë°ĶìĿ´", "Ġë°Ķ ìĿ´", "Ġme kan", "Ġmek an", "ĠجاÙħ ع", "Ġجا Ùħع", "ĠÙĨ Ùģت", "ĠÙĨÙģ ت", "ĠاÙĦ سÙħ", "ĠاÙĦس Ùħ", "л ÑĭÑħ", "лÑĭ Ñħ", "èĥĮ æĻ¯", "Ġê²ĥ ëıĦ", "ĠìĤ´ ìķĦ", "y dı", "yd ı", "Ġна веÑĢ", "Ġнав еÑĢ", "åŃIJ ãģ¯", "l uluk", "lu luk", "Ġhá»Ĺ n", "Ġ Ø´Ùģ", "ĠØ´ Ùģ", "Ġع ÙĦت", "ĠعÙĦ ت", "à¸Ħร าม", "ĠÎļ ÏįÏĢ", "Ġà¹Ģม ษายà¸Ļ", "ÙĨد ÙĤ", "ĠÑĥ ÑģÑĤÑĢа", "ĠÑĥÑģÑĤ ÑĢа", "ĠÑĥÑģ ÑĤÑĢа", "ĠÎĵ εν", "ĠÐĨ ван", "ĠP hong", "ĠPh ong", "ĠPhon g", "家 çļĦ", "ĠÐIJ лекÑģ", "ĠÐIJле кÑģ", "ĠÐIJл екÑģ", "Ġзб еÑĢÑĸг", "ĠÅŁark ı", "ĠÅŁar kı", "ĠظرÙģ ÛĮت", "ĠÙħ عÙĨÛĮ", "ĠÙħع ÙĨÛĮ", "ĠÙħعÙĨ ÛĮ", "Ġ лов", "Ġл ов", "ĠìĤ ¶", "èħ IJ", "Ġ å¯Į", "Ġå¯ Į", "E RG", "ER G", "ĠÑģÑĤо имоÑģÑĤÑĮ", "ÅĻ et", "ÅĻe t", "à¥ī य", "à¹Ī าร", "à¹Īา ร", "ĠارÙĪÙ¾ ا", "Ġб ÑĢоÑģ", "ĠоÑĤ ноÑģÑıÑĤ", "ĠоÑĤноÑģ ÑıÑĤ", "ĠÎŁ κ", "ÑĨÑĮ кий", "ÏĬ κ", "ãģĤãĤĬ ãģ¾ãģĽãĤĵ", "ĠÑĥ ник", "ĠÄij iá»ĥn", "ĠÄiji á»ĥn", "Ġvý zkum", "Ġh ứ", "Ġhá» ©", "Ġ ÙĪات", "ĠÙĪ ات", "ĠÙĪا ت", "Ġ å¹³æĸ¹", "Ġå¹³ æĸ¹", "Ïħ μ", "ãĤĴ 使", "εί ÏĦαι", "两 人", "Ġ åĮ»", "ĠåĮ »", "ÑĢаÑĤ иÑĤÑĮ", "ÑĢаÑĤи ÑĤÑĮ", "ĠاÙĦ اÙĨت", "ĠاÙĦاÙĨ ت", "ãģ® 人", "ر Ø´", "ĠТ ÑĥÑĢ", "r nÄĽ", "rn ÄĽ", "天 天", "ม าร", "มา ร", "Ġort alama", "Ġorta lama", "ĠпеÑĢе пиÑģ", "ĠпеÑĢеп иÑģ", "ĠìĥĿ ìĤ°", "å¿ Ĩ", "í ĩ´", "ï¼Į 该", "éĮ ¢", "ÏĢα ίδ", "ĠмеÑĢ опÑĢи", "Ġг ÑĢав", "ĠгÑĢа в", "ĠгÑĢ ав", "ÃĶ ng", "Ġ æ¤", "Ġæ ¤", "ĠاÙĦد ÙĪÙĦØ©", "ĠاÙĦدÙĪÙĦ Ø©", "Ġ оÑģÑĮ", "Ġо ÑģÑĮ", "ĠоÑģ ÑĮ", "å¥ Ķ", "Ġgüven li", "íķĺ ìĭł", "Ġ éĬ", "Ġé Ĭ", "éŁ³ æ¨Ĥ", "Ġmed ya", "Ġب ÙĨا", "ĠبÙĨ ا", "а ма", "ам а", "Ġ ãĤŃãĥ£", "ĠãĤŃ ãĥ£", "èĹ ¥", "l arım", "lar ım", "ları m", "ĠT iếng", "iyor lar", "ï¼ ¢", "æĶ Ŀ", "Ñĸй ÑģÑĮкоÑĹ", "Ġyet iÅŁtir", "ĠyetiÅŁ tir", "ĠÙ¾ سر", "Ġپس ر", "ãĤī ãģĹ", " ļ", "ìĥ ¤", "à¸Ķ าห", "à¸Ķา ห", "ĠتØŃص ÛĮÙĦ", "Ġб енз", "éģ £", "Ġнаб лÑİ", "ä½ĵ ç³»", "ãĥ¯ ãĤ¤ãĥĪ", "Âł ÂłĠ", "³³ Ġ", "书 è®°", "ĠMü hendis", "p lor", "pl or", "l az", "la z", "лÑı ли", "Ġpom áh", "Ġб лиж", "Ġбл иж", "Ġбли ж", "ĠÑĩиÑģ ла", "Ġubyt ovánÃŃ", "ÑĢаÑĤ но", "Ġtr Äĥm", "Ġاب راÙĩ", "át ka", "Ġiç indeki", "Ġiçin deki", "Ġiçinde ki", "ั à¸ļà¸Ļ", "ัà¸ļ à¸Ļ", "ĠاÙħ ÛĮد", "n ave", "na ve", "nav e", "e cut", "ec ut", "å°± åľ¨", "Ġt radi", "Ġtr adi", "Ġtrad i", "Ġtra di", "Ø· ÙĦÙĤ", "Ø·ÙĦ ÙĤ", "ãĤ¦ ãĤ©", "Ġkhu ôn", "ìĬ¤ ë¡ľ", "ÏĦ ÎŃÏģα", "ÏĦÎŃ Ïģα", "ĠÏĥ κο", "ĠÏĥκ ο", "ë§ Ľ", "ĠÙģ ÙĨÛĮ", "ĠÙģÙĨ ÛĮ", "à¹Į à¹Ģà¸ŀ", "ĠاÙĦع ظ", "Ġth ôn", "기 ìĿĺ", "Ġภ¿", "Ñĥ ÑİÑĤÑģÑı", "ÑĥÑİÑĤ ÑģÑı", "ĠÙħ کاÙĨ", "ĠÙħÚ© اÙĨ", "Ġ âĹİ", "ĠâĹ İ", "Ġ çľģ", "Ġçľ ģ", "Ġ åį¡", "Ġåį ¡", "ĠпеÑĢ ÑĪий", "ĠпеÑĢÑĪ ий", "ĠíĽĦ ë³´", "Ġآر اÙħ", "ãģĮ ãģĦ", "ย าà¸Ļ", "ยา à¸Ļ", "μ ει", "με ι", "ĠM áy", "Ġz ů", "Ġpodp oru", "Ġpodpor u", "ì» ¨", "Ñģ ÑĤÑĢи", "ÑģÑĤ ÑĢи", "ÑģÑĤÑĢ и", "ÏĢ ÏĦÏīÏĥη", "Ф ÐĽ", "åĵª éĩĮ", "ĠпеÑĢв ÑĥÑİ", "Ġyer inde", "Ġyeri nde", "ĠزÛĮ با", "ĠزÛĮب ا", "Ġodst ran", "à¥Ģ à¤Ĺ", "ĠÑĢÑĸз нÑĸ", "Ïģ ηÏĥη", "Ïģη Ïĥη", "âĢĮاÙĦÙħÙĦ ÙĦÛĮ", "ع اد", "عا د", "à¥įप ष", "ÑŁ N", "ï½ Ľ", "ãĥ¼ ãĥľ", "ãĥ¼ãĥ ľ", "è´Ń ä¹°", "ĠìĿ¸ê¸° ê¸Ģ", "ĠÙħÛĮ Ø´ÙĪد", "ĠбезопаÑģ ноÑģÑĤи", "ĠνεÏĨ οκ", "ãģ« ãģ¨", "ĠÑĨеÑĢк ви", "ت Ùĥ", "ĠH Ãłng", "ĠHÃł ng", "ĠHÃłn g", "ĠÙĦ ÙĦس", "ĠÙĦÙĦ س", "ĠνεÏĨοκ άλÏħÏĪηÏĤ", "r aman", "ra man", "ram an", "rama n", "Ġvy vol", "n iÄį", "ni Äį", "ر اÙĨÙĩ", "راÙĨ Ùĩ", "را ÙĨÙĩ", "Ġp eÅŁ", "Ġpe ÅŁ", "ãĥ« ãĤ¯", "å´ ĩ", "Ġim kân", "åĮ» çĸĹ", "Ġप à¥Ŀ", "άν νηÏĤ", "Ġ جÛĮ", "Ġج ÛĮ", "Ġp roje", "Ġpro je", "Ġpr oje", "Ġproj e", "Ġül kenin", "Ġülk enin", "Ġülke nin", "ĠK ew", "ĠKe w", "ĠاÙĦÙħ Ùģ", "Ø£ Ùĥ", "çĻº 表", "Ġ δÏħ", "Ġδ Ïħ", "Ġ åĽ½å®¶", "ĠåĽ½ 家", "ĠKiÅŁ isel", "ãĥ³ ãĤ¬", "Ġzpráv a", "V iá»ĩc", "e rif", "er if", "eri f", "Ġstrán ky", "éļ ł", "è¼ ķ", "к оз", "ко з", "Ġस à¤ľ", "Ùĩد اÙģ", "l oub", "lo ub", "lou b", "à¸łà¸²à¸ŀ ยà¸Ļà¸ķร", "Ġíķł ìĿ¸", "ĠÄIJ Ãło", "ĠÄIJÃł o", "ĠÙĨاØŃ ÛĮÙĩ", "(= )", "ĠÅŀ ampiyon", "Ġp iÅŁ", "Ġpi ÅŁ", "Ġ Ø°Ùĩ", "ĠØ° Ùĩ", "ॠ¯", "ĠÑģÑĢед ÑģÑĤво", "ĠÑģÑĢедÑģÑĤв о", "Ġ à¹Ģวลา", "Ġà¹Ģว ลา", "ĠÑĩ Ñĥж", "Ġver ileri", "Ġveri leri", "Ġveriler i", "ĠÚ© ارت", "Ġکار ت", "а ви", "ав и", "Ġà¤ķर व", "Ġres tau", "Ġrest au", "Ġresta u", "ê°ľ ìĽĶ", "Ġм иÑĢов", "Ġми ÑĢов", "ĠмиÑĢ ов", "ì° ®", "ĠnÄĽjak ý", "Ġses siz", "Ġsess iz", "اء ات", "ĠÐĹ Ð°Ñħ", "ĠÐĹа Ñħ", "Ñı ÑīиÑħ", "ÑıÑī иÑħ", "п ÑĢ", "Ġпод алÑĮ", "Ġпода лÑĮ", "ĠопÑĢедел иÑĤÑĮ", "ॠŃ", "Ġ رÙģ", "Ġر Ùģ", "幸 ç¦ı", "à »", "Ġ vÄĽdom", "ĠvÄĽ dom", "ĠvÄĽd om", "ĠÑģвид еÑĤелÑĮ", "ĠÎĵ οÏħ", "ılıģı yla", "ılıģ ıyla", "çĻ» éĮ²", "Ġä¸ĭ è·Į", "Ġп лÑİ", "Ġпл Ñİ", "н од", "но д", "ĠØ£ جÙĦ", "Ġأج ÙĦ", "Ġà¤ķ थ", "éĥ½ ä¸į", "Ġs ene", "Ġse ne", "Ġsen e", "Ġp ÄĽ", "è¨Ī åĬĥ", "Ġа Ñĥд", "Ġод ном", "Ġодно м", "Ġ ä¸ĩåħĥ", "Ġä¸ĩ åħĥ", "ĠÙĪ Ùħا", "ĠÙĪÙħ ا", "ĠÐĶ ÑĢÑĥг", "èµ· ãģĵ", "в аÑİÑĤÑģÑı", "ва ÑİÑĤÑģÑı", "ваÑİÑĤ ÑģÑı", "л аÑĤÑĥ", "ла ÑĤÑĥ", "лаÑĤ Ñĥ", "Ġ تÙĪÙĨ", "Ġت ÙĪÙĨ", "ĠتÙĪ ÙĨ", "Ñī аÑı", "Ñīа Ñı", "ή λ", "ĠÐŁ ÑĢа", "ĠÐŁÑĢ а", "Ġاست رات", "Ġاستر ات", "ิà¸Ļ à¹Ģà¸Ķ", "à¥įà¤Ĺ त", "Âł з", "Ġп олоÑĤ", "Ġпо лоÑĤ", "Ġпол оÑĤ", "æ® ĸ", "æ¡ Ĩ", "ĠS istem", "ĠSi stem", "ĠSist em", "Ġr uku", "Ġru ku", "Ġruk u", "ãĥĥ ãĤ«ãĥ¼", "ĠобÑıз ан", "Ġkö ÅŁ", "Ġad ını", "Ø´Ùħ اÙĦÛĮ", "na ÄįenÃŃ", "naÄį enÃŃ", "Ġ .ï¼ı", "Ġ. ï¼ı", "Ġ å®ĺ", "Ġå® ĺ", "Ġtoplum sal", "èª ¤", "ĠبÙĩ بÙĪد", "ÑģÑĤв еннаÑı", "ÑģÑĤвен наÑı", "ĠØ¢ Ù¾", "ĠجÙĦ سÙĩ", "ĠجÙĦس Ùĩ", "ãĢĢ ï½", "åĵ Ń", "æīĢ å±ŀ", "æĴ ®", "ì¢ Ģ", "Ġ ει", "Ġε ι", "ì¹ĺ 를", "Ġ ê³¼ìłķ", "Ġê³¼ ìłķ", "u uml", "uum l", "uu ml", "δ ά", "Ġ زد", "Ġز د", "ìĽIJ ìĿĦ", "ĠvÄĽ cÃŃ", "ĠvÄĽc ÃŃ", "د Ø«", "Ġs anki", "Ġsan ki", "Ġsank i", "åĥı æĺ¯", "л аÑĢа", "ла ÑĢа", "ìĤ¬ ìĿ´", "ãĤı ãĤĮãģŁ", "ãĤıãĤĮ ãģŁ", "ĠÄij ón", "ĠÄijó n", "åIJ¯ åĬ¨", "Ġgi Ãłnh", "ĠgiÃł nh", "Ġkır mızı", "Ø® Ùħ", "æIJ į", "åĪĩ ãĤĬ", "ãĤµ ãĥ¼ãĥĵãĤ¹", "Ùĩ ار", "Ùĩا ر", "Ø° Ùĥر", "о ÑĢоз", "оÑĢ оз", "оÑĢо з", "à¥Īà¤Ĥ ।ĊĊ", "à¥Īà¤Ĥ। ĊĊ", "à¥Īà¤Ĥ।Ċ Ċ", "ĠíĻĪ íİĺìĿ´ì§Ģ", "ĠÙĥ بÙĬرة", "ĠÙĥبÙĬر Ø©", "н ина", "ни на", "нин а", "íķĺ ìļ°", "å¼ķçĶ¨ é¢ij次", "ॠ®", "ĠбаÑĤÑĮ кÑĸв", "à¸Ł à¸Ńร", "ี .", "ìłĿ íĬ¸", "éĺħ读 次æķ°", "Ġit ir", "Ġi tir", "ÑĪ ин", "ÑĪи н", "ĠV áºŃy", "çĤ ®", "ла год", "лаг од", "Ø´ÙĨ اس", "á» IJ", "ĠÑı год", "Ġì¤ij ìķĻ", "ر ÙĬØ·", "رÙĬ Ø·", "ĠìĪĺ íĸī", "Ġ ä¸ĢèĪ¬", "Ġä¸Ģ èĪ¬", "ĠÑħви лин", "ĠÐľÐ¾Ð¶ но", "ĠнаÑĩ але", "Ġод нов", "Ġодно в", "ĠÃľ ç", "ÑĨион нÑĭй", "Ġ ìļķ", "Ġìļ ķ", "æ¼ Ĥ", "å² ³", "ت دÙī", "تد Ùī", "κ ηÏĤ", "κη ÏĤ", "âĢĻ nda", "âĢĻn da", "ï¼IJ ï¼IJ", "èª ī", "é§ħ å¾ĴæŃ©", "ĠÙģرز ÙĨد", "åħ¬ è·¯", "α ÏĥίαÏĤ", "αÏĥ ίαÏĤ", "αÏĥία ÏĤ", "าà¸ĵ าà¸Ī", "ëij ¥", "Ġ ÏĢοι", "ĠÏĢ οι", "ĠÏĢο ι", "Ġب داÙĨ", "Ġبد اÙĨ", "к ап", "ка п", "ĠìŀĪ ëĬĶëį°", "ĠìŀĪëĬĶ ëį°", "ï¼Į æѤ", "à¸Ľà¸£à¸°à¹Ĥย à¸Ĭà¸Ļ", "ĠÚ©Ø´ÙĪر ÙĩاÛĮ", "ุ ส", "ãģ¹ ãģį", "ĠÑģам Ñĭй", "Ġп лÑı", "Ġпл Ñı", "Ġб ед", "人 æīį", "ส หร", "ู à¸ķ", "Ġkullan ımı", "Ġkullanım ı", "íķĻ ëħĦ", "æ²» çĸĹ", "ãĢĤ ä¸įè¿ĩ", "ãĢĤä¸į è¿ĩ", "æ£ ļ", "ëĤ¨ ëıĦ", "ĠØ¢ تش", "Ïĩ ÎŃÏĤ", "Ġfunk ci", "Ġfunkc i", "н ообÑĢаз", "но обÑĢаз", "à¥ĭ फ", "Ġk aps", "Ġka ps", "Ġkap s", "าษ à¸İ", "( ع", "ï¼Į åĬł", "à¹Ĭ à¸ģ", "ĠÙĩ Ø´", "Ġدر ÙĪÙĨ", "Ġ меÑĩ", "Ġм еÑĩ", "ĠпÑĢеж де", "à¹Ī ย", "Ġار شد", "า à¹Ģล", "æ¯Ķ è¼ĥ", "ĠØ° کر", "Ġ æĿ¡", "ĠæĿ ¡", "Ð Ĭ", "Ñĥ кÑĢаÑĹн", "ÙĬÙĨ ات", "ÙĬÙĨا ت", "ì¢ ĭ", "д иÑı", "ди Ñı", "ÏĦ Ïģι", "ÏĦÏģ ι", "ĠÐļ аз", "ĠÐļа з", "ÙĤ ÙĦاÙĦ", "ÙĤÙĦ اÙĦ", "_ ,,", "_, ,", "ĠÚĨ ت", "ĠìĿ¼ ìłķ", "ĠÐŁ ÑĢоÑĦ", "ĠÐŁÑĢ оÑĦ", "ĠÐŁÑĢо ÑĦ", "æ³ Ľ", "Ġdruh ý", "Ñĩ Ñĥк", "ÑĩÑĥ к", "l edik", "le dik", "led ik", "ledi k", "Ġhey ec", "Ñĭ вал", "Ñĭв ал", "Ñĭва л", "ĠD üny", "ĠDün y", "Ġ çĻº", "ĠçĻ º", "ĠpÅĻ átel", "β άλ", "βά λ", "Ġ غر", "Ġغ ر", "ëĭ¨ ì²´", "ìĽ¨ ëĶĶìĭľ", "ÑĢаÑī ениÑı", "н ÑĨиклопед", "Ġpodnik atel", "Ġìĭł ìŀħ", "ĠÙģر Ø¢", "и лиÑģÑı", "или ÑģÑı", "Ġol umlu", "à¥įष मत", "ĠÙħت خصص", "й ом", "ؤ اÙĦ", "ĠÐĿ аÑĤ", "ĠÐĿа ÑĤ", "ìĺ¤ ëĬĶ", "ĠMüdür lÃ¼ÄŁÃ¼", "ĠH Ãłnh", "ĠHÃł nh", "ĠHÃłn h", "Ġس ابÙĤ", "ï¼ī çļĦ", "ĠQu ý", "lád ánÃŃ", "ládá nÃŃ", "Ġ ìļ´ëıĻ", "Ġìļ´ ëıĻ", "ĠÐĺ Ñħ", "è« ¾", "lıģ ının", "lıģını n", "lıģın ın", "l il", "li l", "u Äį", "ĠÑĩем пÑĸон", "ÑĤ ож", "ÑĤо ж", "Ġ ä½Ľ", "ни ÑĨе", "ниÑĨ е", "ĠпеÑĢв ого", "Ġ Ñģом", "ĠÑģ ом", "ĠÑģо м", "Ïĩ Ïİ", "ÅĻ ik", "ÅĻi k", "иÑĤелÑĮ ÑģÑĤва", "ĠÄ° ki", "Ġask eri", "Ġasker i", "c isi", "ci si", "cis i", "Ġjed nÃŃm", "Ġjedn ÃŃm", "Ġsta nice", "Ġstan ice", "èĤ¡ 票", "à¸ľ ม", "T ừ", "Å¡ ak", "ÏĦ ία", "ÏĦί α", "м ами", "ма ми", "ãģĮ åĩº", "μο ί", "м аÑĶ", "ма ÑĶ", "ëł¥ ìĿ´", "ãĤĦ ãģ£ãģ¦", "Ġ å¼µ", "Ġå¼ µ", "ØĮ Ċ", "Ġ »Ċ", "Ġ» Ċ", "ا جات", "اج ات", "á½ ³", "æĻĤ ãģ®", "Ġп окол", "Ġпо кол", "Ġпок ол", "ÑĸÑĤ еÑĤ", "Ġíķ´ ê²°", "Ġde dim", "Ġded im", "Ġdedi m", "ĠÑĤ веÑĢд", "Ġжен Ñīина", "ĠженÑīин а", "ед ини", "еди ни", "един и", "ĠÙ¾ ÛĮÚ©", "ĠÙ¾ÛĮ Ú©", "iver site", "ivers ite", "iversit e", "ĠآسÛĮ اب", "ĠÑħаÑĢакÑĤеÑĢиÑģÑĤи ки", "ĠØ£ÙĨ Ùĩا", "ĠØ£ÙĨÙĩ ا", "ĠÑĥкÑĢаÑĹн ÑģÑĮкоÑĹ", "ĠاختÙĦ اÙģ", "Ġاخت ÙĦاÙģ", "Ġt ez", "Ġte z", "Ïģ εÏħ", "Ïģε Ïħ", "Ġkon umu", "Ġkonu mu", "Ġkonum u", "ĠÑĤеÑħ нÑĸ", "м Ñĸв", "мÑĸ в", "èĬ ¯", "ĠÏĥ ελ", "ĠÏĥε λ", "Ä ¢", "μ ιÏĥ", "μι Ïĥ", "ี à¹īĊ", "ีà¹ī Ċ", "Ġm ne", "Ġmn e", "ĠоÑĤв еÑĩ", "ĠÎ ī", "Ġ éĩİ", "Ġéĩ İ", "Ġg ấp", "ĠпÑĢодÑĥк ÑĤÑĭ", "ĠпÑĢодÑĥкÑĤ Ñĭ", "ĠС ÑĢед", "Ñĸл лÑı", "à¸ļ à¸Ńà¸ģ", "ĠtÅĻÃŃ dy", "Ġth á»ķ", "Ġthá» ķ", "ãĥĩãĤ£ ãĤ¢", "ÏĢοι η", "ν ει", "νε ι", "æĪij们 çļĦ", "Ġprofes yonel", "ĠRa kou", "ĠRak ou", "Ġвид но", "Ġz by", "Ġzb y", "ĠØŃ Ø§ÙĦÛĮ", "ĠØŃاÙĦ ÛĮ", "Ġ é£Ł", "Ġé£ Ł", "ĠL Ãłm", "ĠÚ¯ ست", "ĠТ ип", "ĠТи п", "θ ι", "á vis", "áv is", "ÙIJ ب", "åı¯èĥ½ æĢ§", "ĠÑģем ей", "ãĤī ãĤĮãģ¦ãģĦãĤĭ", "ãĤīãĤĮ ãģ¦ãģĦãĤĭ", "ãĤīãĤĮãģ¦ ãģĦãĤĭ", "ìĥģ íĴĪ", "Ġ οÏħ", "Ġο Ïħ", "Ġà¤ħà¤Ĺ स", "о лом", "ол ом", "оло м", "γ ον", "γο ν", "ĠÑģв ÑıÑī", "ĠÑģвÑı Ñī", "æĵ ¦", "Ïĥ ÏĦηκε", "ÏĥÏĦη κε", "èĢħ çļĦ", "- à¤ķ", "ÑĤ ии", "ÑĤи и", "ĠвизнаÑĩ еннÑı", "åıij åĩº", "д аÑħ", "да Ñħ", "ĠмоÑĢ Ñı", "Ġмо ÑĢÑı", "æī¾ åĪ°", "ÙĦ ÙĪب", "ÙĦÙĪ ب", "èĬ Ļ", "ĠÑĦак ÑĤ", "æ¯į 亲", "id lo", "idl o", "ĠSt ad", "ĠSta d", "Ñį й", "ìĽIJ ìĿ´", "à¤ı न", "æķ´ 个", "Ġf ık", "ĠÙħ ات", "ĠÙħا ت", "ÏĢ ον", "ÏĢο ν", "Ġê²½ 기ëıĦ", "Ġ경기 ëıĦ", "Ġα δ", "Ġvz pom", "Ġn á»ĵi", "ĠÙĨÙĤ اط", "ожд ение", "Ġз алÑĸз", "Ġзал Ñĸз", "Ġr ủi", "è¾ °", ".:.:.:.:.:.:.:.: .:.:.:.:.:.:.:.:", "ĠM Ãľ", "Ġk ari", "Ġka ri", "Ġkar i", "ĠÑģ обÑĭ", "ĠÑģо бÑĭ", "ĠÑģоб Ñĭ", "ìĸ´ ì§Ħ", "ر ÙĬس", "رÙĬ س", "u bu", "ub u", "ĠØ® ÙĦÙģ", "ĠØ®ÙĦ Ùģ", "ظٹ Ø·", "æĿ ī", "Ġ æĻ®éĢļ", "ĠæĻ® éĢļ", "ĠÙħÙĪاط ÙĨØ©", "ĠÑģÑĤ анÑĥ", "ĠÑģÑĤан Ñĥ", "ĠÑģÑĤа нÑĥ", "Ġê·¸ëħĢ ìĿĺ", "ĠÙĦ Ùĥرة", "ĠÙĦÙĥ رة", "Ġo sm", "Ġos m", "ĠÑĥ ÑĢож", "е га", "ег а", "Ġf else", "Ġfe lse", "Ġfel se", "æĢĿ èĢĥ", "ãĢĮ ãģĪ", "Ġн овиÑħ", "Ġнов иÑħ", "Ġно виÑħ", "๠IJ", "ü ml", "üm l", "ĠíĶ¼ íķ´", "ìĿ¼ ë°ĺ", "Ġtür ü", "ĠмÑĸ ÑģÑĤÑĸ", "ĠмÑĸÑģ ÑĤÑĸ", "ĠмÑĸÑģÑĤ Ñĸ", "Ġkažd é", "ĠÙħس جد", "ấ c", "ĠÙģ Ú©ÛĮ", "Ġ yasal", "Ġy asal", "Ġya sal", "Ġyas al", "å°± ç®Ĺ", "ĠоблиÑĩ ÑĩÑı", "ĠÙĦ دÙĬ", "ĠÙĦد ÙĬ", "ا بات", "اب ات", "ĠÑģп аÑģ", "êµ° ìļĶ", "Ġп ад", "Ġпа д", "Ġб ÑĢаÑĤ", "ĠбÑĢа ÑĤ", "éĩį 大", "Ġdüzen lenen", "G ün", "Ġaplik ace", "à¸Ń ห", "Ġ çħ", "Ġç ħ", "ĠÑģоÑģÑĤ оиÑĤ", "è¯Ħ ä»·", "ĠD uy", "ĠDu y", "Ø· اÙĤ", "ĠпÑĢид еÑĤÑģÑı", "Ġt olik", "Ġto lik", "Ġtol ik", "Ġob rov", "Ġobr ov", "ĠpÅĻip oj", "Ġ Ä±ÅŁÄ±", "Ġı ÅŁÄ±", "Ú¯ ÙĪÛĮ", "Ú¯ÙĪ ÛĮ", "æľŁ å¾ħ", "ип лом", "Ġ ince", "Ġin ce", "Ġi nce", "Ġinc e", "ĠС об", "ĠСо б", "ен ÑĮÑİ", "енÑĮ Ñİ", "è§Ĵ èī²", "Ġ à¸ķร", "Ġà¸ķ ร", "Ġb ại", "Ġê°ĢëĬ¥ íķľ", "ĠblÃŃ zk", "Ġt ách", "Ġtá ch", "Ġtác h", "Ġвид Ñĭ", "Ġви дÑĭ", "и Ñĩна", "иÑĩ на", "Ġvyž ad", "ĠìĨIJ ìĿĦ", "ĠÐĿ ÑĸмеÑĩ", "ĠÐĿÑĸ меÑĩ", "åŁº äºİ", "ĠÐļ ÑĢи", "Ġعز ÛĮز", "t iler", "til er", "ti ler", "tile r", "е вÑĸ", "ев Ñĸ", "Ġmož nosti", "Ġmožnost i", "ب از", "با ز", "ĠìĤ¬ ë§Ŀ", "Ġz ÅĻejmÄĽ", "íĹ ¤", "Ġürün leri", "ĠÎł λη", "а ки", "ак и", "ãĤĴ éĸĭ", "a nou", "an ou", "ano u", "åĽ½ ãģ®", "ĠyaÅŁ anan", "ĠyaÅŁan an", "ĠÑģ евеÑĢ", "Ġ æ©Ł", "Ġæ© Ł", "มาà¸ģ มาย", "Ġíijľ íĺĦ", "ร ส", "Ġض ربÙĩ", "Ġضر بÙĩ", "ĠE vet", "ĠEv et", "ĠEve t", "æĨ ¶", "Ġد ÙĤÛĮÙĤ", "ĠدÙĤÛĮ ÙĤ", "Ġвозник нов", "ìľł 머", "Ġíijľ ìĭľ", "ÛĮ Ø´ÙĨ", "ÛĮØ´ ÙĨ", "ãĥĹ ãĥ©", "ÑĤ Ñİ", "ÙĪ سÛĮ", "ÙĪس ÛĮ", ") ìĿ´", "è¯ģ æĺİ", "ãģ§ãģį ãģ¾ãģĻ", "ìĪĺ ìĿĺ", "çĸ Ĩ", "ĠÙħ ÙģÙĩÙĪÙħ", "оÑĩ аÑĤкÑĥ", "ाल à¤ķ", "æ¡ Ĥ", "ĠоÑħ оÑĢони", "ĠارزÛĮ ابÛĮ", "Ġìµľ ëĮĢ", "Ġtho ải", "ĠЦенÑĤ ÑĢалÑĮ", "Ġ çķĻ", "Ġçķ Ļ", "à¸Ľà¸£à¸° à¹Ģà¸łà¸Ĺ", "æµ· å¤ĸ", "ĠÅŀ u", "íĻľ ëıĻ", "ĠdvÄĽ ma", "istrov stvÃŃ", "Ġarac ılıģıyla", "Ġtr á»Ļn", "» :", "íĭ ±", "ĠÙĦÛĮ Ú¯", ". Ðļ", "ĠÙħÙĤ اÛĮسÙĩ", "Ġв мÑĸ", "ر ÙĪب", "رÙĪ ب", "ĠاÙĦ Ø´Ùħ", "ĠاÙĦØ´ Ùħ", "Ġden nÄĽ", "Ġdenn ÄĽ", "Ñĥ Ñĩа", "ÑĥÑĩ а", "åħ ¹", "Ñī им", "Ñīи м", "ĠíĬ¹ íŀĪ", "ĠاستاÙĨد ارد", "à¥Ģ ध", "ãĤ¸ ãĤ¢", "à¹ĩ à¹ĩ", "иÑģ Ñģ", "Ġkazan ç", "ĠzÃŃsk al", "åĽŀ æĿ¥", "Ġп ÑıÑĤÑĮ", "ĠпÑıÑĤ ÑĮ", "ĠÄij ãi", "ĠÄijã i", "Ġ ÙĪرد", "ĠÙĪ رد", "ĠÙĪر د", "Ġ ìķķ", "Ġìķ ķ", "ุ à¸Ĺร", "ุà¸Ĺ ร", "åĬ¨ çī©", "Ġp ublik", "Ġpub lik", "Ġpubli k", "æĪIJ æľ¬", "æĪIJ åijĺ", "ãĤ¤ ãĤ¯", "شر ÙĥØ©", "á¿Ĩ ÏĤ", "Ġy ola", "Ġyo la", "Ġyol a", "üyor uz", "Ġк ÑĥÑĢи", "ĠкÑĥÑĢ и", "ĠкÑĥ ÑĢи", "ĠпоÑħ ож", "Ġìłľ ê°Ģ", "िय त", "ائ ÙĦØ©", "ائÙĦ Ø©", "Ġ ãģ¾", "़ à¥ĩà¤Ĥ", "़à¥ĩ à¤Ĥ", "ÑģÑĮ кими", "ÑģÑĮк ими", "ÑģÑĮким и", "âĢľ ä½ł", "imiz de", "ìµľ ìĭł", "Ạ¬", "è Ł", "à¸Ħ รà¸Ńà¸ļ", "à¸Ħร à¸Ńà¸ļ", "ãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "ت غ", "ĠVÅ¡ ech", "à¸±à¸Ľ à¸Ķาห", "Ġa td", "Ġat d", "в оÑİ", "во Ñİ", "Ġyap ım", "Ġyapı m", "olog ické", "ologic ké", "Ġп лен", "Ġпл ен", "Ġlaz ım", "r ung", "ru ng", "run g", "ìĦľ ê´Ģ", "Ġji ný", "Ġjin ý", "Ġtr òn", "Ġtrò n", "ĠполÑĸÑĤи ки", "ا ÙĥÙħ", "اÙĥ Ùħ", "دÛĮ گر", "à¥Īà¤Ĥ .Ċ", "à¥Īà¤Ĥ. Ċ", "Ġ اÙĩد", "Ġا Ùĩد", "ĠاÙĩ د", "Ġ ãĥį", "Ġãĥ į", "ĠпÑĢодÑĥк ÑĤов", "ĠпÑĢодÑĥкÑĤ ов", "æĤ Ł", "ĠpÅĻÃŃpad ech", "ĠzaÄį ala", "ĠzaÄįal a", "åħ¥ ãĤĮ", "ĠÑĢÑĸв нÑĸ", "æĦŁ æĥħ", "ĠΧ α", "ì £½", "ì£ ½", "ิà¸Ī ารà¸ĵ", "Âł б", "Ñĸ ÑĹв", "ÑĸÑĹ Ð²", "ب Ø´", "çļĦ éĹ®é¢ĺ", "Ġza stup", "Ġzast up", "볤 ìļĶ", "ãģ§ãģĻ ãģŃ", "âĢĮ داÙĨ", "âĢĮد اÙĨ", "ï¼Į æĤ¨", "Ġu vÄĽdom", "ãģ¦ ãĤĭ", "ìĤ¬ ëŀĮ", "l un", "lu n", "éĽĨ åIJĪ", "ë§ ¹", "Ġž id", "Ġži d", "ठĬ", "Ġt rp", "Ġtr p", "л ениÑħ", "лен иÑħ", "ле ниÑħ", "＿ __", "＿_ _", "Ðľ Ðŀ", "å¼ ĭ", "λÎŃ Î¿Î½", "ĠÄij òi", "Ġк ÑĢок", "lay ıcı", "ì¶ľìŀ¥ ë§ĪìĤ¬ì§Ģ", "åij Ī", "éľ ŀ", "Ġпо глÑıд", "Ġпог лÑıд", "ت رÙĥ", "تر Ùĥ", "ĠتÙģ اÙĪت", "Ġ å®®", "Ġå® ®", "ĠدÙĪر بÛĮÙĨ", "æĶ¾ åľ¨", "ĠÑģлÑĥÑĩа ев", "ĠÑģлÑĥÑĩае в", "ĠÏħÏĢ ηÏģε", "ë§ ŀ", "ãģĻ ãģĻ", "ê²ł ëĭ¤", "ราย à¸ģาร", "ĠÏĢ Ïģιν", "ĠÑģ меÑĪ", "ĠÑģм еÑĪ", "å§ ī", "Ġvýsled ky", "Ġpot vr", "åıij è¡Į", "Ġt úi", "Ġtú i", "ĠìĤ¬ ëĿ¼", "ç«Ļ åľ¨", "Ġja ký", "Ġjak ý", "Ġ à¸ļาà¸ĩ", "Ġà¸ļ าà¸ĩ", "Ġdik kate", "Ġdikkat e", "Ġدر Ø¢Ùħد", "æİĴ åIJį", "r álnÃŃ", "rál nÃŃ", "ê³¼ ìĿĺ", "ä½ µ", "о лаг", "ол аг", "ола г", "is iyle", "isi yle", "Ġ æ½", "Ġæ ½", "Ġ तम", "Ġत म", "Ġd ij", "Ġdi j", "Ġnh ánh", "ĠR ek", "ĠRe k", "设 æĸ½", "ĠpodmÃŃn ek", "并 ä¸į", "к ÑĥÑĤ", "кÑĥ ÑĤ", "Ġê³ł 볤", "çļĦ å£°éŁ³", "æĪĺ äºī", "д аÑı", "да Ñı", "Ġê´Ģ ìĭ¬", "ĠÑĦÑĸн анÑģ", "ĠK öy", "ĠKö y", "Ġж ал", "ĠÑģлÑĥж би", "м ена", "мен а", "ме на", "ت ÙĬار", "تÙĬ ار", "ĠÑĩем пион", "ÏĢ ιÏĥ", "ÏĢι Ïĥ", "landır ma", "mak tan", "makta n", "makt an", "Ġ 丶", "Ġä¸ ¶", "à¹Ī à¸Ńส", "à¹Īà¸Ń ส", "ĠmÃ¼ÅŁ teri", "ĠmÃ¼ÅŁter i", "Ġص ÙĨد", "ĠصÙĨ د", "Ġet mesi", "Ġetm esi", "Ġetme si", "Ġп оÑĢÑĤ", "ĠпоÑĢ ÑĤ", "ν ονÏĦαι", "νον ÏĦαι", "Ġ ãħĭãħĭ", "ĠK AR", "ĠKA R", "Ġ uch", "Ġu ch", "Ġuc h", "ĠØ® ÙĦÙĤ", "ĠØ®ÙĦ ÙĤ", "าษà¸İ ร", "æŃ ¡", "Ġи мени", "Ġим ени", "Ġиме ни", "ãģł ãģijãģ©", "ãģłãģij ãģ©", "Ġìĭ¤ ìĭľ", "ÏĥÏī ÏĢ", "Ġ ì£", "Ġì £", "t ÄĽÅ¾", "tÄĽ ž", "Ġözel likleri", "Ġözellik leri", "Ġözellikle ri", "Ġب Ù¾", "Ġиз обÑĢаж", "ÙĬÙħ ÙĥÙĨ", "Ġ ãĥĶ", "Ġãĥ Ķ", "ĠÐĶ ив", "ĠÐĶи в", "ĠØ¥ ÙĬ", "Ùĥ ÙĬÙĦ", "ÙĥÙĬ ÙĦ", "ĠÅŁ ik", "ĠÅŁi k", "Ġà¤Ĩ à¤ĸ", "lar ınızı", "ların ızı", "larını zı", "larınız ı", "ĠвÑĸд ÑĢÑĸз", "ĠÑĢоб оÑĤа", "ĠÑĢобоÑĤ а", "Ġta rif", "Ġtar if", "Ġ اÙĪت", "Ġا ÙĪت", "ĠاÙĪ ت", "ın ma", "é£Ł ãģ¹", "Ġuzav ÅĻ", "ë£ ¸", "çĽij çĿ£", "Ġ: ï¼¼", "θ Ïħν", "θÏħ ν", "à¸Ķ ร", "a larından", "alar ından", "aların dan", "alarında n", "èĩª æĭį", "Ġro ÄįnÃŃ", "ाà¤ĩ व", "Ġ ÙĥÙĪر", "ĠÙĥ ÙĪر", "ĠÏĦ αιν", "ĠÏĦα ιν", "ĠÑĸн див", "r ve", "rv e", "ĠνεÏĨ ÏİÏĥειÏĤ", "Ġb á»ijn", "Ġbá»ij n", "Ġ å¿«", "Ġå¿ «", "ĠÑģ олÑĮ", "ĠÑģо лÑĮ", "ĠÑģол ÑĮ", "li ÄŁinde", "liÄŁi nde", "liÄŁ inde", "liÄŁin de", "िन à¤Ł", "a htar", "ah tar", "Ġneb ezpeÄį", "æĹ¢ çĦ¶", "ĠëĮĢ ìłĦ", "ĠÙĨÚ¯Ùĩد ارÛĮ", "ĠzÃŃsk at", "Ġнали Ñĩие", "ĠналиÑĩи е", "Ġ aks", "Ġa ks", "Ġak s", "ï¼ī ãĢĤĊĊ", "ï¼īãĢĤ ĊĊ", "ï¼īãĢĤĊ Ċ", "Ġrod iny", "Ġrodin y", "Ġrodi ny", "Ġза ÑħÑĸд", "ĠзаÑħ Ñĸд", "å¾® ç¬ij", "ÂłÐĶ а", "r adu", "ra du", "rad u", "ī nh", "p les", "pl es", "ple s", "ĠK ons", "ĠKon s", "ĠKo ns", "ิ à¹Ĥล", "ิà¹Ĥ ล", "ĠاÙĦ ÙĪص", "ĠاÙĦÙĪ ص", "åIJ¬ åĪ°", "ĠÑģпоÑĢ ÑĤив", "ĠÑģ айÑĤе", "ĠÑģай ÑĤе", "Ġ اظ", "Ġا ظ", "ĠØ§Ø ¸", "lar ındaki", "ların daki", "larında ki", "Ġtá»ķ n", "ÐĿ ÐĨ", "Ġned ost", "ĠÑĤоÑĢ гÑĸв", "Ġ اÛĮت", "Ġا ÛĮت", "ĠاÛĮ ت", "Ġاختص اص", "ĠÃľ y", "ĠS adece", "ĠSad ece", "ĠÙħØ® رÙĪØ·", "Ä ģn", "Äģ n", "ç esi", "çe si", "Ġ çĬ", "Ġç Ĭ", "ãĤĤ ãģ£ãģ¨", "Ġ éŁĵ", "ĠéŁ ĵ", "èµ ĸ", "ĠполÑĥÑĩ ениÑı", "Ġë ĺ", "âĢĻ ÑĹ", "b ÃŃr", "bÃŃ r", "Ġб ÑĸблÑĸ", "ĠD á»±", "же неÑĢ", "жен еÑĢ", "ç½ij åĪĬ", "Ġल à¥ľà¤ķ", "ĠÑĥÑĩ нÑĸв", "èĪ °", "ĠÃĸÄŁ ren", "Ġ ola", "Ġo la", "Ġol a", "Ġ। âĢĿĊĊ", "ระ à¹Ģà¸ļ", "á½ ²", "Ġ رز", "Ġر ز", "е и", "еР¸", "Ñı Ñĩи", "ÑıÑĩ и", "ØŃ Ø¨", "æĴ ¤", "ãģ¾ãģŁ ãģ¯", "б ина", "би на", "бин а", "ĠÎł εÏģ", "ĠоÑĤноÑģ иÑĤÑģÑı", "åīį çļĦ", "Ġ Å¡Å¥", "ĠÅ¡ Å¥", "Ġyıl da", "Ġyı lda", ": ::::|", ":: :::|", ":::: :|", "::: ::|", "::::: |", "us til", "ust il", "اÙĦ Ø¥", "ĠsouÄįas né", "ĠÙĨÛĮ رÙĪÛĮ", "ĠÙĨÛĮر ÙĪÛĮ", "ĠÙĨÛĮرÙĪ ÛĮ", "ÑĩеÑģ кое", "Ñĩе Ñģкое", "ظ Ùģ", "ĠÙ¾ÛĮØ´ ÛĮÙĨÙĩ", "Ġع ÙģØ´", "Ġrost lin", "ç½ijåĪĬ ä¸ĭ载次æķ°", "ĠпÑĢигоÑĤов иÑĤÑĮ", "ãĥ Į", "ĠÙĪ Ùħع", "ĠÙĪÙħ ع", "Ġb ecer", "Ġbe cer", "Ġbec er", "Ġ ãĤ±", "ĠãĤ ±", "Ïĩ ήÏĤ", "Ïĩή ÏĤ", "о ÑģÑĤÑĥп", "оÑģÑĤ Ñĥп", "Ġë°ľ 매", "Ñĸй ного", "Ñĸйно го", "Ġh rd", "Ġhr d", "ĠпÑĢепаÑĢа ÑĤÑĭ", "ĠÙģ رض", "ĠÙģر ض", "ĠTy to", "Ġ кÑĢаÑĹн", "ĠкÑĢаÑĹ Ð½", "Ġز اد", "Ġikt idar", "ì§ ĵ", "Ùij ر", "ÑĢÑı дÑĥ", "ÑĢÑıд Ñĥ", "к Ñĸй", "кÑĸ й", "âĶ £", "Ġко жи", "Ġкож и", "Ġت ازÙĩ", "Ġتا زÙĩ", "o bec", "ob ec", "obe c", "in ae", "ina e", "Ġvyj ád", "Ġ رÙģتÙĩ", "Ġر ÙģتÙĩ", "ĠرÙģت Ùĩ", "ĠرÙģ تÙĩ", "Щ о", "ĠBy lo", "ĠByl o", "оÑĤ в", "ĠденÑĮ ги", "é§ Ĩ", "Ġма ÑĪин", "ĠмаÑĪ ин", "ĠмаÑĪи н", "ĠØ£ ج", "ì´Ī ëĵ±íķĻêµIJ", "dı ģında", "dıģı nda", "dıģ ında", "б аÑģ", "ба Ñģ", "Ġ æł¹", "Ġæł ¹", "ÎijÎĿ Τ", "ÙĴ ØŃ", "Ġjejich ž", "ìĹIJìĦľ ìĿĺ", "Ġад же", "Ġì ı", "Ïĥ οÏħ", "Ïĥο Ïħ", "et leri", "etler i", "Ġبعد ÛĮ", "Ġبع دÛĮ", "ĠìŀIJëıĻ ì°¨", "ิà¸į à¸į", "Ġt isk", "Ġti sk", "ãĥ¼ ãĤ¹ãĥĪ", "ãĥ¼ãĤ¹ ãĥĪ", "Ġमत लब", "ê³Ħ íļį", "ãĤ¦ ãĥĪ", "Ġ à¹Ģมà¸ķร", "Ġà¹Ģม à¸ķร", "Ġop siyon", "Ġops iyon", "ĠÑĢав но", "ĠبÛĮ ÙħÙĩ", "ĠبÛĮÙħ Ùĩ", "Ġ먼 ìłĢ", "иÑĤелÑĮ нÑĭм", "ĠнÑĸ би", "Ġде ÑģÑıÑĤ", "ĠдеÑģÑı ÑĤ", "ĠÑģиÑĤÑĥа ÑĨии", "еÑĢ ÑĪе", "еÑĢÑĪ е", "Ä ¾", "ุ à¸ķร", "ุà¸ķ ร", "Ġyönet imi", "Ġyönetim i", "éIJ ĺ", "ĠÙħÛĮ تÙĪاÙĨ", "Ġز ÙĨدÙĩ", "ĠزÙĨ دÙĩ", "ĠزÙĨد Ùĩ", "ãĥŃ ãĥ³", "ĠK BS", "ĠKB S", "ìĦľ ë¹ĦìĬ¤", "ï» ł", "eck ého", "ecké ho", "ĠÙĤابÙĦ ÛĮت", "ĠÙĤاب ÙĦÛĮت", "ãĢĤ ä»Ĭ", "ÃŃ nÄĽ", "ÃŃn ÄĽ", "ĠÑģм ог", "ĠÑģл ÑĭÑĪ", "ÙĴ Ùģ", "po ÅĻád", "елÑĮ но", "Ġεί Ïĩαν", "-ÐŁ еÑĤеÑĢб", "ĠCh iến", "ĠChi ến", "é ry", "ér y", "ĠÑĸн ÑģÑĤиÑĤÑĥÑĤ", "ç»Ĩ èĥŀ", "Ñĭ ÑŁN", "Ġv ua", "Ġvu a", "Ġà¤ħ श", "ÑĢоÑģ ÑĤо", "ÑĢоÑģÑĤ о", "Ġvů Äįi", "ë ¿IJ", "Ġl iá»ĩt", "Ġíķ µ", "Ġ اÙģر", "Ġا Ùģر", "ĠاÙģ ر", "ĠTek nik", "Ġr oli", "Ġro li", "Ġrol i", "Ġпоп ÑĭÑĤ", "аÑĤ кÑĸв", "Ġün iversit", "аÑĤ оÑĢÑĭ", "аÑĤоÑĢ Ñĭ", "аÑĤо ÑĢÑĭ", "ÑİÑīиÑħ ÑģÑı", "Ġت ض", "лÑİ ÑĩаеÑĤÑģÑı", "лÑİÑĩ аеÑĤÑģÑı", "лÑİÑĩа еÑĤÑģÑı", "Ġíĸī ë³µ", "Ġayrıntı lı", "ĠкиÑĢ п", "æĭ ¼", "ëģ Ķ", "л аÑĤа", "ла ÑĤа", "лаÑĤ а", "Ġkho án", "Ġhâl â", "Ïĥ Ïħ", "ог лаÑģ", "æİ¥ çĿĢ", "éĿ© åij½", "Ġp ÅĻeb", "ĠpÅĻ eb", "ĠpÅĻe b", "à¹Ģà¸ī ล", "ĠاÙĦÙħÙĦ ÙĦÛĮ", "åł Ĩ", "íı IJ", "à¸ķล à¸Ńà¸Ķ", "° С", "ìĤ¬ ëŀij", "Ġг иб", "ë²Ī 째", "æĶ¹ åıĺ", "表 çİ°", "и ÑĩеÑģким", "иÑĩеÑģ ким", "иÑĩеÑģки м", "สม à¹Ģà¸Ķ", "å±ħ æ°ij", " Ľ", "ĠìķĦìĿ´ ëĶĶ", "ĠмеждÑĥ наÑĢод", "Ġy em", "Ġye m", "Ġm ül", "Ġmü l", "Ġا ÛĮست", "ĠاÛĮ ست", "Ġ ãĥ´", "Ġãĥ ´", "ัà¸Ļ à¹Ħà¸Ķ", "à¥Ģ ण", "åħ¶ å®ŀ", "Ġgel enek", "Ġgele nek", "Ġgelen ek", "ë¶ģ ëıĦ", "à¹ī าà¸ķ", "à¹īา à¸ķ", "Ġ ìī¬", "Ġìī ¬", "Ġ ÏĢÎŃ", "ĠÏĢ ÎŃ", "ĠÙĥ اÙħÙĦ", "ĠÙĥاÙħ ÙĦ", "Ġتع ÙħÛĮر", "è¨ ´", "ë¹ Ļ", "iy im", "iyi m", "å° ¿", "éĤ£ æł·", "êµŃ ìĿĺ", "ãģĹãģ¦ ãģĬãĤĬ", "Ġ niž", "Ġn iž", "Ġni ž", "Ġκ ον", "Ġκο ν", "à¹Ī าà¸Ń", "à¹Īา à¸Ń", "Ġ γε", "Ġγ ε", "ĠС евеÑĢ", "edi álnÃŃ", "ãģŁãģ¡ ãģ®", "m ayacak", "may acak", "maya cak", "Ñ Ļ", "ĠÑĥ гл", "ĠÑĥг л", "Ġk apas", "Ġka pas", "Ġkap as", "Ñĥв алиÑģÑı", "Ñĥва лиÑģÑı", "Ñĥвали ÑģÑı", "ĠмеÑģÑı ÑĨа", "á» ¯u", "ữ u", "ิ ลล", "ิล ล", "ãĤĪãĤĬ ãĤĤ", "à¥ĩ ण", "à¥ĩठ£", "Ġ 客", "Ġå® ¢", "ĠdeÄŁ erli", "ĠdeÄŁer li", "ÙĪ از", "ÙĪا ز", "ี à¸Ńย", "ีà¸Ń ย", "Ġ åıĪ", "Ġåı Ī", "Ġ à¸Ķร", "Ġà¸Ķ ร", "ĠÙĨ اب", "ĠتÙĦÙĪÛĮزÛĮ ÙĪÙĨ", "Ġol anlar", "Ġolan lar", "ä¼ĺ ç§Ģ", "Ùĥ اÙĦ", "ĠдеÑģÑı ÑĤи", "ĠдеÑģÑıÑĤ и", "m án", "má n", "ĠÑĢ анÑĮ", "ĠÑĢа нÑĮ", "ĠÑĢан ÑĮ", "Ġìłľ ì¶ľ", "è³ ¢", "а бо", "аб о", "Ġtechn ik", "Ġtech nik", "ĠK iá»ĥm", "ĠKi á»ĥm", "t eki", "te ki", "tek i", "á ¹", "Ġm nÄĽ", "Ġmn ÄĽ", "Ġê³µ ê°Ħ", "ĠM ek", "ĠMe k", "Ġاع تÙħاد", "à¹Į à¹Ħà¸Ķ", "ε ÏģÏĮ", "εÏģ ÏĮ", "ĠÑĥд аÑĢ", "ĠÑĥда ÑĢ", "оÑĩ ÑĮ", "æ¦Ĥ 念", "ÑĢ ал", "ÑĢаР»", "ÑĢа л", "алÑĮ нÑĭми", "алÑĮнÑĭм и", "à¥ģर स", "r áci", "rá ci", "Ġ ÙĤÙĪÙĦ", "ĠÙĤ ÙĪÙĦ", "Ġद व", "ĠпÑĢав да", "Ġ å¿ħ", "Ġå¿ ħ", "Ġdos ud", "нÑĥ ÑĤÑĮÑģÑı", "нÑĥÑĤÑĮ ÑģÑı", "N Äĥm", "à¸ĺ à¸Ļ", "Ġdok un", "Ġ åľ¨çº¿", "Ġåľ¨ 线", "ู à¹Ħ", "ụ y", "Ġн овÑĭÑħ", "Ġнов ÑĭÑħ", "Ġmez un", "ĠC ần", "à¸ģาร à¸ŀ", "ĠìĺĪ ìłķ", "Ïĥ ή", "à¹Īà¸Ļ à¹Ģà¸ģม", "ĠÙĪ اÙĦس", "ĠÙĪاÙĦ س", "ĠÙĪا ÙĦس", "ãĥ³ ãĥĨãĤ£", "ãĥ³ãĥĨ ãĤ£", "çľĭ è§ģ", "Ġس اÙĦÙħ", "ĠساÙĦ Ùħ", "ĠбагаÑĤÑĮ оÑħ", "ĠÄij Ãłi", "Ġد ستÛĮ", "Ġدست ÛĮ", "Ġدس تÛĮ", "à¸ŀ à¸Ń", "еп ÑĤи", "ĠìłĦ íĻĶ", "æĻĤ ãģ«", "ĠSe znam", "ĠSez nam", "мÑĸ нÑĥ", "мÑĸн Ñĥ", "; ?#", "à¥Ģ सर", "à¥Ģस र", "ĠÚĨ ÛĮست", "νο ια", "νοι α", "ั à¸Ļà¸Ń", "ัà¸Ļ à¸Ń", "Ġ à¸Ħำ", "Ġà¸Ħ ำ", "Ġë³´ íĺ¸", "Ġid dia", "Ġiddi a", "Ġβ ιβ", "é«ĺ ä¸Ń", "Ù ¨", "ÐĴ аж", "ĠиÑģп олн", "ÑĪ ÑĤов", "ÑĪÑĤ ов", "ĠT aÅŁ", "ĠTa ÅŁ", "ìĽ ħ", "åĬ ¹", "Ġ åıĥ", "Ġåı ĥ", "Ġprost oru", "Ġprostor u", "ĠÑģп ад", "е ÑĢина", "еÑĢ ина", "еÑĢи на", "еÑĢин а", "ĠpÅĻek lad", "ĠpÅĻe klad", "Å¡ ov", "ĠÙģ ÙĩÙħ", "ĠÙģÙĩ Ùħ", "æĬ ij", "Ġابت دا", "Ġابتد ا", "ãĤĴ ãģĬ", "l ikler", "lik ler", "likle r", "ĠÙħ اÙĥ", "ĠÙħا Ùĥ", "Ġko nut", "Ġkon ut", "Ġkonu t", "ĠداÙĨØ´ جÙĪÛĮ", "Ġоп ÑĤим", "Ġб Ñĥма", "ĠбÑĥ ма", "ĠлÑİд Ñıм", "Ġл Ñĸка", "ĠлÑĸ ка", "ĠлÑĸк а", "ĠÑĢоз повÑĸд", "ĠÑĢозп овÑĸд", "ĠÑĢозпов Ñĸд", "nes enÃŃ", "Ġ à¸łà¸²à¸ŀ", "Ġà¸ł าà¸ŀ", "и Ñĩний", "иÑĩ ний", "ا Ø·ÙĦ", "اط ÙĦ", "Ñİ Ñīими", "ÑİÑī ими", "ÑİÑīим и", "ãģı ãģ¨", "éŃ ¯", "ĠجÙĨ سÛĮ", "Ðĺ Т", "र ल", "ĠÚ©ÙĪد Ú©", "о лиÑĤ", "ол иÑĤ", "оли ÑĤ", "ĠÑģÑĤÑĢÑĥкÑĤÑĥ ÑĢ", "ve kili", "vek ili", "Ġब य", "Ġgel miÅŁ", "िर फ", "Ġнай кÑĢа", "ĠÐĶж он", "Ġ ãĥĹãĥŃ", "ĠãĥĹ ãĥŃ", "ĠyaÅŁ lı", "Ġkar Ä±ÅŁtır", "ĠkarÄ±ÅŁ tır", "ĠvÄĽtÅ¡ inou", "Ġvaz geç", "à¹ī าà¸Ħ", "à¹īา à¸Ħ", "lendir me", "Ġ ç¨ĭ", "Ġç¨ ĭ", "说 è¯Ŀ", "ĠíķĦìļĶ íķľ", "aÅĻ ilo", "Ġle žÃŃ", "ĠAmer ikan", "ĠAmerika n", "ĠAmerik an", "ãĤĦ ãģĻ", "va jÃŃcÃŃ", "vajÃŃ cÃŃ", "ÐĿ Я", "ĠìĹĦ ë§Ī", "Ġ åĥ", "Ġå ĥ", "r ál", "rá l", "Ġç ay", "Ġça y", "tu ÄŁ", "ุà¸į าà¸ķ", "ĠÑģ лив", "ĠÑģл ив", "ν οÏħ", "νο Ïħ", "ĠO v", "ĠC HP", "ĠCH P", "ĠZe mÄĽ", "ĠZem ÄĽ", "ĠÄįesk ý", "ĠÄįe ský", "ĠTh ánh", "иÑĤелÑĮ ноÑģÑĤÑĮ", "иÑĤелÑĮно ÑģÑĤÑĮ", "æĦı ä¹ī", "à¥įरम ण", "Ġди амеÑĤ", "Ġk lin", "Ġkl in", "Ġkli n", "Ġ کرÛĮ", "ĠÚ© رÛĮ", "Ġکر ÛĮ", "ãģ§ãģ¯ ãģªãģı", "飯 åºĹ", "Ġk ênh", "Ġkê nh", "ĠÑĢанÑĮ ÑĪе", "ãĤĴ ãģĹãģŁ", "ĠпÑĢи боÑĢ", "ĠпÑĢиб оÑĢ", "Ġà¤ĸ तर", "Ġ yu", "Ġy u", "é§ IJ", "ĠÑĢ або", "ĠÑĢа бо", "ĠÑĢаб о", "ĠС ÐłÐ¡Ðł", "èĬ ¬", "ž ila", "ži la", "žil a", "еÑĢ ÑĤа", "еÑĢÑĤ а", "и ÑģÑĤÑĢа", "иÑģ ÑĤÑĢа", "иÑģÑĤ ÑĢа", "Ġкни ги", "ĠFranc ie", "ĠFran cie", "ĠÚĺ اپ", "ĠÎļ οÏħ", "ĠÎļο Ïħ", "ัว à¹Ģà¸Ńà¸ĩ", "Ġl ắng", "Ġ нами", "Ġн ами", "Ġна ми", "Ġнам и", "Ġпод ой", "д ÑĢом", "дÑĢ ом", "o bus", "ob us", "ÐĴ Ñĸн", "Ġst alo", "Ġsta lo", "Ġstal o", "Ġà¤ı à¤ľ", "ĠL inh", "ĠLin h", "ĠLi nh", "ebilir iz", "Ġзав ÑĤÑĢа", "μ εÏģο", "με Ïģο", "μεÏģ ο", "Ġ ÎŃν", "ĠÎŃ Î½", "ÑıÑĤ но", "Ġд оÑĢож", "Ġдо ÑĢож", "ĠдоÑĢ ож", "åıĤ çħ§", "Ïĥ ιο", "Ïĥι ο", "à¹ī à¹Ģà¸ģ", "a ných", "an ých", "aný ch", "ç· ł", "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", "åĬĽ çļĦ", "ĠS ır", "ĠSı r", "Ġ ì§ĢëıĦ", "Ġì§Ģ ëıĦ", "ç· Ĭ", "ĠpoÄį tu", "ï¼Į ä¸İ", "ä¸ĸ ç´Ģ", "ем ого", "емо го", "Ġhus us", "Ġhu sus", "Ġölçü de", "Ġtr ục", "Ġtrụ c", "à¸Ľà¸¥ à¸Ńà¸Ķà¸ł", "Âłp ÅĻÃŃ", "ĠBöl gesi", "м ом", "мо м", "ãģ« ãģ¦", "Ġ쪽 ì§Ģ", "ÄĽt Å¡", "ĠìĦ± ê³µ", "र त", "ur du", "urd u", "ĠìĽĢ ì§ģ", "ÑŁ ÐŃ", "nÃŃ kem", "nÃŃk em", "ĠskuteÄį nosti", "Ġ даÑĤ", "Ġд аÑĤ", "Ġда ÑĤ", "n eum", "ne um", "ĠÑĤаб леÑĤ", "j vu", "Ġs edm", "Ġse dm", "Ġsed m", "س ÙĬØ©", "سÙĬ Ø©", "ĠкоÑĢ об", "Ġко ÑĢоб", "em mel", "emm el", "emme l", "ãģ¤ ãģij", "é¦Ļ èķī", "Ġشخص ÛĮت", "ĠشخصÛĮ ت", "ä¸Ĭ äºĨ", "ÙĪ را", "ÙĪر ا", "ĠаÑĤ моÑģ", "Ġ лей", "Ġл ей", "Ġz prav", "Ġzp rav", "Ġëķ ħ", "ู à¸Ĺ", "Ġ اسر", "Ġا سر", "Ġاس ر", "ĠAy dın", "ĠعÙħ ÙĦÙĬØ©", "ĠعÙħÙĦ ÙĬØ©", "Ġд ÑĸÑĶ", "ĠдÑĸ ÑĶ", "Ġd ök", "Ġdö k", "Ġफ ल", "ĠìĤ¬ëŀĮ ëĵ¤ìĿ´", "ĠнаÑĤÑĥ ÑĢалÑĮ", "æŁ ľ", "温 度", "Ġk les", "Ġkl es", "Ġkle s", "Ġин веÑģÑĤи", "s üz", "æĴ °", "Ġ ãĤ¢ãĥ«", "ĠãĤ¢ ãĥ«", "Ġ èĴ", "Ġè Ĵ", "ад ки", "Ġk lÃŃÄį", "Ïĩ εί", "Ïĩε ί", "ĠTh iết", "ĠThi ết", "ĠسرÛĮ ع", "ĠÏĢεÏģιο Ïĩή", "ÙĪ ÙĤÙģ", "ÙĪÙĤ Ùģ", "Ïģ ÏīÏĥη", "ÏģÏī Ïĥη", "ĠسÙĦ اÙħت", "ĠسÙĦاÙħ ت", "ëĵ¤ ëıĦ", "ĠveÅĻej né", "Ġvi tam", "Ġvit am", "Ġvita m", "ĠبازÛĮ گر", "ĠÑĢеÑĨеп ÑĤ", "ĠìľĦ íķ´ìĦľ", "ĠìľĦíķ´ ìĦľ", "ĠØ£Ùĥ بر", "Ġk üt", "Ġkü t", "민 주", "Ġté ž", "Ġ å¼ķ", "Ġå¼ ķ", "ÑĩаÑģ но", "çļĦ åľ°", "Ġarchit ekt", "Ġбак ÑĤеÑĢ", "Ġ ãģį", "Ġ одеÑĢж", "Ġо деÑĢж", "Ġод еÑĢж", "Ġتج ارÛĮ", "éĿ Ī", "Ġre cep", "Ġrec ep", "Ġrece p", "é© ¶", "Ġد ÙĩÙĩ", "ĠدÙĩ Ùĩ", "è² Į", "çµIJ å©ļ", "ılı ç", "ãģĭãĤī ãģ¯", "å¿ĥ éĩĮ", "æĬķ è³ĩ", "è² Ŀ", "ĠкÑĥлÑĮÑĤÑĥ ÑĢÑĭ", "ĠкÑĥлÑĮÑĤÑĥÑĢ Ñĭ", "Ġ å°ij", "Ġå° ij", "à¹ģ à¸ŀร", "à¹ģà¸ŀ ร", "γ κÏĮ", "γκ ÏĮ", "ar ım", "arı m", "Ġاس اسÛĮ", "Ġاساس ÛĮ", "Ġposled nÃŃch", "ĠposlednÃŃ ch", "ĠÙħ ÙħÙĨ", "ĠÙħÙħ ÙĨ", "Ġпоз иÑĤив", "Ġпози ÑĤив", "ìł ¤", "Ñĥ вавÑģÑı", "Ñĥв авÑģÑı", "Ñĥва вÑģÑı", "Ñĥвав ÑģÑı", "Ġجز ئ", "ìĿ´ ìŀIJ", "Ġин ÑģÑĤÑĢÑĥк", "Ġη λεκ", "Ġde mir", "Ġdem ir", "Ġdemi r", "ä¸Ńæĸĩ åŃĹå¹ķ", "Ġعاش ÙĤ", "Ġب اÙĦÙĤ", "Ġبا ÙĦÙĤ", "ĠباÙĦ ÙĤ", "Ġ maz", "Ġm az", "Ġma z", "ά νι", "άν ι", "Ġ dÃ¼ÄŁ", "Ġd Ã¼ÄŁ", "Ġdü ÄŁ", "Ġ κÏģα", "Ġκ Ïģα", "ĠбÑĥд ÑĤо", "ç¦ı åĪ©", "ĠпÑĢед назнаÑĩ", "Ùħ ÙĦØ©", "ÙħÙĦ Ø©", "ĠбÑĥд инкÑĥ", "Å¥ an", "íķ Ģ", "ç´¹ ä»ĭ", "Ú© ز", "ĠкаÑĦ ед", "ãģ« è¦ĭ", "าà¸ķร à¸IJาà¸Ļ", "ë¡ľ ëĬĶ", "i vÄĽ", "iv ÄĽ", "èĥ½ æºIJ", "ï¼Į åħ¨", "ĠÑĥ див", "ĠÑĥд ив", "Ġë§Į ëĤĺ", "ÐĴ ÐIJ", "ĠG ür", "ĠGü r", "ĠдÑĢÑĥг им", "Ïĥ Ïį", "Ġ oÄŁlu", "ĠoÄŁ lu", "Ġê°Ģ ê¹Į", "ĠзнаÑĩ иÑĤелÑĮно", "ĠзнаÑĩиÑĤ елÑĮно", "о зÑĸ", "оз Ñĸ", "Ġm á»±c", "ĠB eÅŁ", "ĠBe ÅŁ", "Ġ jezd", "Ġje zd", "á vÄĽ", "áv ÄĽ", "ÏĦη Ïĥε", "ãģ¦ãģĦ ãģªãģĦ", "ĠСв ÑıÑĤ", "Ġम श", "ĠΤ οÏħ", "ĠΤο Ïħ", "声 ãĤĴ", "ĠÑģам ое", "ĠÑģамо е", "Ġ åĮº", "ĠåĮ º", "ĠìĤ¬ëŀĮ ìĿĢ", "ĠÙħ ÙĦت", "ĠÙħÙĦ ت", "Ġj oker", "Ġjo ker", "Ġjoke r", "Ġne ob", "Ġneo b", "ĠÑĤ ака", "ĠÑĤак а", "ĠÑĤа ка", "ĠÙĩ Ùģت", "Ġδε δο", "ĠзаÑħ оп", "ĠاÙĦÙħ خت", "ез да", "езд а", "Ġíķľ ë²Ī", "Ġع اÙħØ©", "ĠعاÙħ Ø©", "Ġdo state", "Ġdost ate", "Ġdostat e", "Ġp lav", "Ġpl av", "Ġpla v", "楽 ãģĹ", ".;.; .;.;", "в аÑĶ", "ва ÑĶ", "Ġbụ i", "ĠÄij ỡ", "ĠÄijá» ¡", "Ġmys lÃŃ", "Ġmysl ÃŃ", "ĠÙĨ ار", "Ġn út", "Ġм ала", "Ġмал а", "Ġма ла", "Τ Ρ", "ĠاÙĦرÙħ زÙĬØ©", "la dım", "lad ım", "ladı m", "ä¸Ģ ç·Ĵ", "ĠiÅŁ ç", "l ivé", "li vé", "liv é", "르 ê²Į", "ан наÑı", "ظËĨ Ø·", "Ġd ừng", "ÙĦÙĥ تر", "çŃĶ æ¡Ī", "ĠÙħÙĪÙĤع ÛĮت", "ĠÑĸн озем", "ĠиÑģ Ñĩ", "Ġнеп ÑĢавилÑĮ", "b akan", "ba kan", "bak an", "Ġ çīĪ", "Ġçī Ī", "ен нÑİ", "à¸ĩ à¹Ģศ", "à¸Ħวาม à¸Ħ", "% .Ċ", "%. Ċ", "à¹Ī à¹Ģà¸Ľ", "ĠØ¢ بÛĮ", "Ġآب ÛĮ", "Ġst áty", "Ġstát y", "Ġتر تÛĮب", "Äįem ž", "Ġ é¹", "Ġé ¹", "Ġ ÙģاÙĦ", "ĠÙģ اÙĦ", "Ġbelir len", "ĠâĨ ĺ", "èĩ³ å°ij", "ĠBun lar", "Ġ ä¸ĵ", "Ġä¸ ĵ", "ĠÙħØŃ Ø§Ø³", "ĠìĦľ ë²Ħ", "Ġc anh", "Ġcan h", "Ġca nh", "ĠпÑĢоÑĤ Ñıж", "ĠнÑĸ меÑĨÑĮ", "à¥Īà¤ł à¤ķ", "ëĭ ī", "Ġна неÑģ", "Ġвоз ÑĢаÑģÑĤа", "ĠвозÑĢаÑģÑĤ а", "Ġ[âĢ¦ ]Ċ", "Ġ[âĢ¦] Ċ", ". à¸ŀ", "ิ ศาสà¸ķร", "ิศ าสà¸ķร", "çģ ½", "ê°Ļ ìĿĢ", "ล à¸ĩà¸Ĺ", "ลà¸ĩ à¸Ĺ", "ãĤ± ãĥ¼ãĤ¹", "Ġ ãĤ¢ãĤ¤", "ĠãĤ¢ ãĤ¤", "Ñģ Ñİ", "ĠÙĦ ر", "ãģĭ ãģ£ãģ¦", "Ġ기 ë°ĺ", "Ġ !:", "Ġ! :", "ĠÑģ ÑĬ", "Ġ Ø´ÙĨاسÛĮ", "ĠØ´ÙĨ اسÛĮ", "ĠØ´ÙĨاس ÛĮ", "ĠìķĦ 침", "Ġعب اس", "Ġ à¸ķà¸Ńà¸Ļ", "Ġà¸ķ à¸Ńà¸Ļ", "ĠмеÑĤал ли", "ÑĪ ила", "ÑĪи ла", "Ġpod rob", "Ġpodr ob", "ÑĸÑģ но", "Ġ 赤", "Ġèµ ¤", "c iler", "ci ler", "cil er", "o zem", "oz em", "oze m", "ĠоÑģнов нÑĭÑħ", "Âł à¤ķ", "à¸ĸ à¸Ļà¸Ļ", "ан ÑĤаж", "анÑĤ аж", "анÑĤа ж", "ĠD ÃŃky", "Ġگذ ارÛĮ", "æľº ä¼ļ", "οÏħ λίοÏħ", "οÏħλ ίοÏħ", "оÑĩ ек", "Ġнап иÑĤ", "ĠبÛĮØ´ ترÛĮ", "ĠبÛĮشتر ÛĮ", "ä¾ į", "ĠاÙĦ ÙħÙħ", "ĠاÙĦÙħ Ùħ", "ÙĪز ÙĬع", "Ġgöz lem", "è°ĥ æķ´", "Âłm iles", "Âłmi les", "Ġk oc", "Ġko c", "ัà¸į ห", "æ³ ³", "ĠÎij γγ", "ĠÎijγ γ", "ĠÙĨÙħ از", "ุ à¸Ĺ", "ãĥı ãĤ¤", "Ġth ù", "к ÑĥлÑı", "кÑĥ лÑı", "кÑĥл Ñı", "ĠпÑĥÑĤ ем", "èĩº çģ£", "Ġver gi", "Ġverg i", "åł´åIJĪ ãģ¯", "ĠÑĤÑĢÑĮ оÑħ", "Ġë³´ ë©´", "âĸ ²", "Ïħ γ", "ĠдоÑĤ ÑĢим", "æľ µ", "Ġum ÄĽnÃŃ", "èī¯ ãģĦ", "Âł à¸Ļาà¸ĩ", "Ðİ ÑĭÑŁN", "ä¸ī 个", "ียร à¸ķ", "ï¼Į åIJĮæŶ", "ĠÑĢозÑĢаÑħ Ñĥн", "ĠD ers", "ĠDe rs", "ĠDer s", "ãģª ãģ®", "Ġê·¸ 를", "d ikleri", "dik leri", "Ġhay ata", "Ġhaya ta", "Ġhayat a", "è§Ħ èĮĥ", "ç»ĵ åIJĪ", "Ġs cé", "Ġsc é", "Ġc Æ¡m", "ĠcÆ¡ m", "åѸ éĻ¢", "ĠÐĦ в", "ĠÄįlán ek", "ĠдоÑģÑĤ иг", "ĠдоÑģÑĤи г", "ा à¤ĩस", "ाà¤ĩ स", "εÏħ Ïĥη", "éģ© çĶ¨", "Ïĥ ον", "Ïĥο ν", "ıl maktadır", "ëªħ ìĿĦ", "ı b", "Ġstar Å¡ÃŃ", "Ġch ÃŃn", "ĠchÃŃ n", "ä¸Ģ 个人", "ä¸Ģ个 人", "ĠFranti Å¡ek", "n ÄĽji", "nÄĽ ji", "ï» ¨", "ĠÙĦÙĦ د", "Ġp okoj", "Ġpok oj", "Ġj ih", "Ġji h", "ãĢį ãĢĤ", "Ġعبد اÙĦ", "ãĤĵãģ§ ãģĦãĤĭ", "Ġмод елÑĮ", "ĠteÅŁ kil", "ĠÄĮ er", "à¹Ģà¸Ķ à¸Ńร", "' na", "'n a", "λο γή", "λογ ή", "Ġ kola", "Ġk ola", "Ġko la", "Ġkol a", "ãĥĢ ãĥ¼", "иÑĤ елем", "иÑĤе лем", "ĠÏĥÏħ νο", "ĠÏĥÏħν ο", "ĠK urum", "ĠKur um", "ĠKu rum", "Ġsnad no", "ĠاÙĦÙĤر Ø¢ÙĨ", "ĠV á»ģ", "é«ĺ ãģĦ", "Ġyıl dız", "Ġbir isi", "Ġbiri si", "Ġkh úc", "ÙĪ ÛĮÙĦ", "ÙĪÛĮ ÙĦ", "æľĢ ä½³", "Ġส าà¸Ĥ", "ĠÐŁ ок", "ĠÐŁÐ¾ к", "âī ł", "à¹Ĥà¸Ľà¸£ à¹ģà¸ģรม", "à¥įय यन", "èij ¡", "Ġn ovÄĽ", "Ġno vÄĽ", "Ġnov ÄĽ", "ay ıp", "ayı p", "ĠSing ap", "ĠSin gap", "è° ĵ", "ãĤ¶ ãĤ¤ãĥ³", "Ġн овÑĭе", "Ġнов Ñĭе", "Ġh ảo", "Ġ èŤ", "ĠèĹ ¤", "ãĥ³ ãĥĸ", "ãĥ³ãĥ ĸ", "Âł ĊĊ", "ÂłĊ Ċ", "θ εια", "θε ια", "Ġпоп ада", "ĠëĶĶ ìŀIJìĿ¸", "Ġداشت ÙĨد", "ĠداشتÙĨ د", "ĠØ´ÙĨ اختÙĩ", "Ïĥ μαÏĦα", "Ïĥμα ÏĦα", "å¹³æĸ¹ åħ¬éĩĮ", "Ġg öl", "Ġgö l", "ек оÑĤоÑĢ", "еко ÑĤоÑĢ", "Ġm álo", "Ġmá lo", "Ġاج ازÙĩ", "Ú© اراÙĨ", "کار اÙĨ", "کا راÙĨ", "ĠпÑĸдпÑĢиÑĶм ÑģÑĤв", "ä¸ī å¹´", "ĠسÙģ ÛĮد", "ĠμÎŃ ÏģοÏĤ", "ÐĻ ÐĻ", "Ġh Æ°", "س ÙĪب", "سÙĪ ب", "ĠÙĦ ذا", "Ġnem ovit", "Ġd ÃŃv", "ĠdÃŃ v", "Ä° s", "¶ ¶", "Ġph Æ°á»Ŀng", "ĠÙĨØŃ ÙĪÙĩ", "ĠÙĨØŃÙĪ Ùĩ", "Ð ĭ", "Ġz byt", "Ġzb yt", "Ġzby t", "ed ii", "edi i", "n ech", "ne ch", "ĠадмÑĸнÑĸÑģÑĤÑĢа ÑĤив", "Ġne vÄĽ", "Ġnev ÄĽ", "Ġ ож", "Ġо ж", "ĠÄIJ ó", "à¸Ľà¸£à¸° ว", "Ġvhod né", "Ġum ÄĽl", "ĠÑĢазлиÑĩ нÑĭе", "ĠpÅĻi roz", "Ġبخ Ø´ÛĮ", "Ġبخش ÛĮ", "ãģ® 大", "ĠاÙĦ ÙĥÙĩ", "ĠاÙĦÙĥ Ùĩ", "ec ká", "eck á", "Ġzorun lu", "ĠÐľÐ¸Ðº ола", "Ġ amel", "Ġa mel", "Ġam el", "к овÑĭе", "ков Ñĭе", ": :::/", ":: ::/", ":::: /", "::: :/", "ä¸įåIJĮ çļĦ", "ĠÙĪÙĥ اÙĨت", "ĠÙĪÙĥاÙĨ ت", "à¸Ń à¸Ń", "lá sil", "ĠпÑĢедпол аг", "ï½ ±", "Ġ νε", "Ġν ε", "Ġн овÑĭй", "Ġнов Ñĭй", "Ġìĺģíĸ¥ ìĿĦ", "Ġê°Ģ ì§Ħ", "åĥ ħ", "Y D", "Ġب اغ", "Ġبا غ", "ĠØ´Ú© ست", "Ġgü ney", "Ġgün ey", "и ÑģÑĮ", "иÑģ ÑĮ", "ãģĭ ãģªãģĦ", "ãģĭãģª ãģĦ", "ĠT òa", "Ġگرد ÛĮد", "Ġگر دÛĮد", "ØŃ ÙĦ", "lu vÃŃ", "luv ÃŃ", "v éd", "vé d", "Ġìĺ ·", "Ġε ÏĢα", "ĠεÏĢ α", "ĠÑĤи ÑģÑıÑĩ", "ĠÑĤиÑģ ÑıÑĩ", "Ġê½ ĥ", "ĠP US", "ĠPU S", "ĠдÑĥм кÑĥ", "Ġ âĢĿĊ", "ĠâĢĿ Ċ", "ĠìĬ¤ íı¬ì¸ł", "Ùĩ Ùĩ", "Ġg ắng", "Ġgắn g", "ิ à¸łà¸²à¸ŀ", "éĩĮ éĿ¢", "br ıs", "Ġz áb", "Ġzá b", "κ αÏĤ", "κα ÏĤ", "ĠåıĮ 线", "ล ล", "ĠÄIJ Ãłi", "ĠÄIJÃł i", "åѸ æł¡", "ĠÑĢаÑģп ÑĢед", "ĠÑģÑĤан еÑĤ", "ĠÑģÑĤа неÑĤ", "Ġл ак", "Ġла к", "Ġпод к", "Ġg ören", "Ġgö ren", "Ġgör en", "Ġgöre n", "르 ê³ł", "ĠÑĦ ÑĢÑĥкÑĤ", "íĵ¨ íĦ°", "ãģĻ ãĤĮãģ°", "ãĤĴ ä½ľ", "à¸Ńà¸Ńà¸ģ à¹ģà¸ļà¸ļ", "Ġku lak", "Ġkul ak", "ĠíĶĮ ëłĪìĿ´", "ĠØŃ Ø¯ÙĬØ«", "ĠØŃد ÙĬØ«", "ãģĨ ãĤĵ", "Ġм Ñĸк", "ĠмÑĸ к", "à¤ĩस à¤ķ", "ĠÑĥ ÑĤоÑĩ", "ĠÑĥÑĤ оÑĩ", "ĠÙĥ Ø«ÙĬر", "ĠY ine", "ĠYi ne", "ĠYin e", "ัว หà¸Ļ", "н ÑĸÑĹ", "нÑĸ ÑĹ", "åį ¢", "Ñĥ Ñģлов", "ÑĥÑģ лов", "ìĽĮ íģ¬", "Ġà¤ħ à¤ĸ", "ĠÑĨ Ñĸка", "ĠÑĨÑĸ ка", "ìĦł ìĿĦ", "ĠØ£ ر", "гал ÑĤеÑĢ", "angl icky", "ĠÑģ оÑģÑĥд", "ĠÑģоÑģ Ñĥд", "ĠÑĥ Ñıв", "ĠпÑĢодÑĥк ÑĨÑĸÑĹ", "Ġc hua", "Ġch ua", "Ġchu a", "Ġd án", "Ġdá n", "ाम à¤Ĺ", "ئ ت", "ĠФ ед", "Ġh rom", "Ġhr om", "íķ´ ë³´", "ĠØ¢ÙĨ ÙĦاÛĮÙĨ", "- пÑĢав", "-п ÑĢав", "Ġì¤ijìļĶ íķľ", "Ġв кÑĥ", "Ġвк Ñĥ", "Ġ 大éĺª", "Ġ大 éĺª", "Ġt erk", "Ġte rk", "Ġter k", "Ġпод Ñĸб", "ĠвÑĸд вÑĸд", "à¥Į à¤Ł", "è³ £", "Ġب تÙĨ", "Ġبت ÙĨ", "Ġبع ضÛĮ", "Ġبعض ÛĮ", "ãģª ãģĬ", "ä»ĸ åĢij", "Ġtavs iye", "ĠM ısır", "ĠØ¥ Ø°", "Ġ æIJ", "Ġæ IJ", "íķĺ ëĤĺ", "ĠÙĪ Ø®", "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", "ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", "Ġta kový", "Ġtak ový", "Ġtako vý", "Ġबन न", "Ġз ÑĢениÑı", "ĠÙĪ ÙģÙĤ", "ĠÙĪÙģ ÙĤ", "ë¹Ħ ìķĦ", "Ġпом ожеÑĤ", "åĮĹ å¸Ĥ", "dık ları", "Ġ éĵģ", "Ġéĵ ģ", "Ġakt uálnÃŃ", "Ġaktu álnÃŃ", "Ġв в", "ãĤĤ ãģªãģĦ", "íĨµ ìĭł", "ÏĦα Ïĥη", "Ġìĥģ ëĮĢ", "Ġ æł¡", "Ġæł ¡", "ãĢĤ éĤ£", "ĠرÙĪسÛĮ Ùĩ", "Ġtelev izyon", "å¹´ é¾Ħ", "ĠÐijоÑĢ иÑģ", "리 ìĸ´", "Ġz veÅĻej", "ж но", "ĠÐŀ ÑģÑĤ", "ĠÐŀÑģ ÑĤ", "ĠмÑĥж Ñĩин", "Ġy eÅŁil", "ĠСов еÑĤ", "ĠСо веÑĤ", "ĠB ÃĸL", "ĠТак ож", "Ġob nov", "ĠпÑĢи надлеж", "ĠвиÑģ нов", "Ø· Ùħ", "ĠìĹĨ ìĸ´", "ĠM ùa", "ä½ı å®ħ", "åĮ» åѦ", "Ġна ÑĢез", "ĠнаÑĢ ез", "ãĥĭ ï¾Ĩ", "ĠM ặt", "Ġvu ông", "ä¸Ģ åĮº", "ĠẢ nh", "ÑĢ иÑĦ", "ÑĢи ÑĦ", "ä¿Ŀ éĻ©", "ĠÏĩÏģή Ïĥη", "åIJĮ æĦı", "Ġ æīĵ", "Ġæī ĵ", "e tÄĽ", "et ÄĽ", "ĠÙĪ Ø°ÙĦÙĥ", "ĠÑĤ иж", "ĠÑĤи ж", "ĠÎŁ ικο", "ĠÎŁÎ¹ κο", "ĠмÑĸÑģ ÑĨÑĸ", "ĠÑĢебен ок", "ĠÅŀ ah", "عÙĦ ÙĪÙħ", "l adıģ", "la dıģ", "lad ıģ", "ladı ÄŁ", "Ġg iden", "Ġgi den", "Ġgid en", "лив оÑģÑĤÑĸ", "ливо ÑģÑĤÑĸ", "ÙĴ س", "ĠT HB", "ĠTH B", "Ġmes lek", "Âł ÐĿе", "ÂłÐĿ е", "μÏĨ Ïīνα", "Ġ ÙĪاج", "ĠÙĪ اج", "ĠÙĪا ج", "на ÑģлÑĸд", "æĺŁ æľŁ", "ÐĶ ж", "ĠÑĢабоÑĤ аеÑĤ", "ĠÑĢабоÑĤа еÑĤ", "Ġs ánh", "ìļ° 리", "Ġا بÙĪ", "Ġاب ÙĪ", "çļĦ æĥħ", "ĠìĻ¸ êµŃ", "Ġk abil", "Ġka bil", "Ġkab il", "еÑĢв Ñĭе", "Ġgi Ãłu", "ĠgiÃł u", "Ġt á»ı", "Ġtá» ı", "Âł Ðij", "å®Į æķ´", "Ġmuž ů", "ĠpomÄĽr nÄĽ", "ĠÙħ خصÙĪص", "ĠÐĶ ем", "ãĤı ãĤĮãĤĭ", "ãĤıãĤĮ ãĤĭ", "ĠпÑĢи бÑĭ", "ĠпÑĢиб Ñĭ", "ĠکاÙħ Ù¾ÛĮ", "ï¼ Ń", "Ġt rh", "Ġtr h", "ĠÐij олÑĮÑĪ", "´ :", "и ваеÑĤÑģÑı", "ив аеÑĤÑģÑı", "ива еÑĤÑģÑı", "иваеÑĤ ÑģÑı", "Ġ ìĤ¬íķŃ", "ĠìĤ¬ íķŃ", "è¿Ľ ä¸ĢæŃ¥", "ÑĨ ей", "ÑĨе й", "ãģ¾ ãģļ", "аÑĤ елем", "аÑĤе лем", "éĮ ¯", "Ġžal ob", "ÑĨ ез", "ÑĨе з", "и нÑĥв", "ин Ñĥв", "инÑĥ в", "Ġver ze", "Ġve rze", "Ġverz e", "åĽŀ åĪ°", "Ġd ược", "ائ ÙĬÙĦ", "ائÙĬ ÙĦ", "sto upil", "stoup il", "论 æĸĩ", "ĠÐŁ аÑĢи", "ĠÐŁÐ°ÑĢ и", "Ġдек оÑĢаÑĤив", "اخ تÛĮ", "اخت ÛĮ", "ĠÑģÑĤ ÑĢем", "ĠÑģÑĤÑĢ ем", "ãĥ»âĶģãĥ»âĶģ ãĥ»âĶģãĥ»âĶģ", "ĠÑģам ой", "ĠÑģамо й", "Ñĩ ÑĤо", "ìĥģ ëĭ´", "âī ¤", "ÑĤ ого", "ÑĤо го", "ëIJ ¨", "ı lacak", "ıl acak", "ä¸Ń ãģ«", "ĠÏħÏĢάÏģÏĩ οÏħν", "ĠвÑĸд бÑĥ", "ĠвÑĸдб Ñĥ", "çİ »çĴĥ", "Ġвп еÑĢед", "ĠPl zeÅĪ", "Ú¯ اب", "à¹Ģศ รษà¸IJ", "ï¼Į æľĢ", "Ùħ ÙĨÛĮ", "ÙħÙĨ ÛĮ", "çħ§ çīĩ", "缮 å½ķ", "ÑĢиÑĤ ÑĤÑı", "âĢĮ اش", "Ġ ëĮĢíļĮ", "ĠëĮĢ íļĮ", "ĠÅĻ adu", "- ÑĤеÑħ", "-ÑĤ еÑħ", "Ġ ÙĬÙĪ", "ĠÙĬ ÙĪ", "Ġ à¹ģà¸ŀ", "Ġà¹ģ à¸ŀ", "ا ÙĥÙĨ", "اÙĥ ÙĨ", "Ġ기 ìŀIJ", "Ġг Ñĸд", "Ġìļ° 리ëĬĶ", "Ġìļ°ë¦¬ ëĬĶ", "Ø´ ÙħارÛĮ", "Ø´Ùħ ارÛĮ", "Ġt icari", "Ġtic ari", "âij ¢", "ĠاÙĦ بد", "ĠاÙĦب د", "ĠÑĢаÑģ Ñĩ", "Ġ اÙĦÛĮ", "Ġا ÙĦÛĮ", "ĠاÙĦ ÛĮ", "Ġsü rede", "Ġsür ede", "Ġsüre de", "Ġاع تر", "Ġпо нÑıÑĤÑĮ", "Ġпон ÑıÑĤÑĮ", "γ κο", "γκ ο", "ï¼Į æ¯Ķ", "ĠS eb", "ĠSe b", "Ġìĭł ê·ľ", "æĶ¶ çĽĬ", "ĠÙ¾ÛĮØ´ÙĨÙĩ اد", "Îľ ÎijΤ", "ÎľÎij Τ", "ë°Ķ ìĿ´", "ä¾Ľ åºĶ", "б ин", "би н", "人 æ°Ĺ", "ãģı ãĤī", "Ġsk vÄĽl", "Ġëĵ± ìŀ¥", "æĭħ å½ĵ", "Ġim kan", "æ Ļ¨", "æĻ ¨", "ï¼Į çİ°åľ¨", "Ġsrd ce", "ìĤ° ìĹħ", "Ġмод ели", "æľ¬å½ĵ ãģ«", "а нка", "ан ка", "анк а", "Ġyür üy", "ĠоÑĩ евид", "ĠØŃ Ø³ÙĬÙĨ", "ĠØŃس ÙĬÙĨ", "Ñī аÑİÑĤ", "Ñīа ÑİÑĤ", "lé dl", "léd l", "ÑĨ о", "ĠcÃŃ sa", "ãģĭ ãģij", "èĹ į", "ĠØ®ÙĪاÙĩ ÙĨد", "Ġmu že", "Ġmuž e", "Ġна коп", "Ġнак оп", "di ÄŁini", "diÄŁi ni", "er seniz", "ers eniz", "ersen iz", "ĠпÑĢаÑĨÑĸв никÑĸв", "д лÑı", "Ġα ÏĥÏĦ", "ĠαÏĥ ÏĦ", "æ¶Ī è´¹", "Ġ è¨Ģ", "Ġè¨ Ģ", "Ġb át", "ĠØ´ ÙĥÙĦ", "ĠÑģп иÑĢ", "ÏĢο ÏĦε", "Ġس اÙĦÙĩ", "ĠساÙĦ Ùĩ", "e kil", "ek il", "eki l", "à¹ģ à¸Ĭม", "ĠÏĥ ÏĦι", "ĠÙħ Ø·ÙĦب", "ĠÙħØ· ÙĦب", "Ġìłķ ì±ħ", "ê´Ģ ê³Ħ", "å¹¹ ç·ļ", "Ġ 京", "éĢļ éģİ", "ĠدÛĮ گراÙĨ", "ĠدÛĮگر اÙĨ", "ĠØ£ Ùħا", "ĠØ£Ùħ ا", "æĺ¯ ä¸į", "ĠëĮĢ ëĭµ", "ĠE rk", "ĠEr k", "p erty", "per ty", "pert y", "ĠнаÑĩина еÑĤ", "Ġê·¸ 리", "ë£ ¡", "ĠìĽ¹ ìĤ¬ìĿ´íĬ¸", "ार न", "æĦı è¯Ĩ", "ĠС ÐŁ", "Ġب اÙĬد", "Ġبا ÙĬد", "Ġbakım ından", "/ TT", "/T T", "ĠÙģ اصÙĦÙĩ", "ĠÙħØ« ÙĦا", "ĠÙħØ«ÙĦ ا", "Ġк вад", "Ġкв ад", "ĠØ´ اÛĮد", "ĠuÄį itel", "çĪ ½", "Ġعرض Ùĩ", "Ġ 交", "ĠÑĩ еÑģÑĤÑĮ", "ĠÑĩеÑģ ÑĤÑĮ", "à¥Ī ?Ċ", "à¥Ī? Ċ", "ĠخاÙĨ Ùħ", "et iyle", "eti yle", "Ġε γκα", "ĠÑģÑĥ Ñīе", "ĠìĿ¼ ìĸ´", "ĠÐĽ ени", "ĠÐĽÐµ ни", "Ġ 声", "Ġå£ °", "á lie", "ál ie", "áli e", "ãĥ¡ ãĥ¼ãĤ¸", "à¥Ģ तर", "à¥Ģत र", "г алÑĸ", "га лÑĸ", "гал Ñĸ", "ĠмÑĸ нÑĸм", "ĠE ÅŁ", "ĠпÑĢоиз оÑĪ", "ÐĿ аÑģ", "ÐĿа Ñģ", "Ġب ÙĨÛĮ", "ĠبÙĨ ÛĮ", "让 æĪij", "ĠпоÑģÑĤ еп", "ĠìļĶ 구", "ılı p", "ıl ıp", "Ġج ÙĪر", "ĠجÙĪ ر", "ĠëĮĢ ë¶Ģë¶Ħ", "à¹ĩ à¸ķาม", "à¹ĩà¸ķ าม", "ĠÑĦ аÑģ", "Ġìłķ ê·ľ", "ла менÑĤ", "лам енÑĤ", "ÄŁ en", "ÄŁe n", "à¥ĩà¤Ĥ ĊĊ", "à¥ĩà¤ĤĊ Ċ", "ĠÐĺ ванов", "ĠØŃ Ú©Ùħ", "ĠØŃÚ© Ùħ", "Ġ ï¾ļ", "Ġï¾ ļ", "ï¼ »", "Ġne vid", "Ġnev id", "Ġла боÑĢаÑĤоÑĢ", "à¸ŀย าà¸ļาล", "Ġed iyorum", "Ġediyor um", "Ġhl avy", "Ġhlav y", "ĠEvrop ské", "Ġph ái", "Ġphá i", "ãĥĵ ãĥ¼", "ê´ij ìĹŃìĭľ", "äº ľ", "ØŃد اث", "ĠпÑĢоÑĦ илакÑĤи", "ro stÅĻed", "rost ÅĻed", "Ġм алÑĮ", "Ġмал ÑĮ", "Ġма лÑĮ", "Ġmü dür", "ا ساس", "اس اس", "ĠгалÑĥз Ñĸ", "ี à¸Ł", "Ġغذ اÛĮÛĮ", "åŃIJ ä¾Ľ", "Ġbah sed", "ĠKrál ové", "åį »", "Ġ %,", "Ġ% ,", "ç½Ĺ æĸ¯", "ë ļ", "Ġ çij", "Ġç ij", "Ġξε ÏĦα", "ĠÐŃ ÑĤи", "ĠíĨµ íķ©", "Ġاک تبر", "ĠmÄĽsÃŃ ce", "ìĪĺ ë¡ľ", "ÑĦ Ñĸк", "ÑĦÑĸ к", "ĠÐĴ оз", "ĠÐĴо з", "ÑĩеÑģ ким", "Ñĩе Ñģким", "ÑĩеÑģки м", "ìļ´ ëĵľ", "Ġná klady", "Ġnáklad y", "ĠпоÑĤ ÑĢап", "ĠÑĢÑĥ каÑħ", "ĠÑĢÑĥк аÑħ", "ι λο", "ιλ ο", "ĠG ül", "ĠGü l", "ë© ĺ", "à¹ī ย", "m akt", "ma kt", "mak t", "ãĥ³ ãĥIJãĥ¼", "ãĥ³ãĥIJ ãĥ¼", "Ġн ÑĸÑı", "ĠнÑĸ Ñı", "ĠоÑĤ ÑĤен", "m esinin", "mes inin", "mesi nin", "mesini n", "ĠвÑģп ом", "Ġ ìĿ´ëĬĶ", "ĠìĿ´ ëĬĶ", "dy by", "ãĤ¿ ãĥ³", "âĹ İ", "à¹īา หà¸Ļ", "à¹īาห à¸Ļ", "اد Ú¯ÛĮ", "Ïĩ ία", "Ġsna žÃŃ", "Ġà¤ļ à¤ķ", "μή μα", "Ġ Ùĥر", "ĠÙĥ ر", "Ġκ οι", "Ġκο ι", "éĢ ¸", "Ġne ust", "Ġneu st", "ĠÙĨظ اÙħÛĮ", "ĠÙĨظاÙħ ÛĮ", "åįļ çī©", "Ġ ë²½", "Ġë² ½", "á½ ±", "Ġì¶ľ ìĭľ", "Ġar má", "Ġarm á", "ĠÙĩÙħ کارÛĮ", "çļĦ æĥħåĨµ", "çļĦæĥħ åĨµ", "ÙĤ اÙħ", "ÙĤ ب", "Ġ éĤ£", "ĠéĤ £", "Ġë§ ¡", "Ġo lası", "Ġol ası", "Ġola sı", "β ÎŃÏģ", "ä½ķ ãģĭ", "ĠÑĥÑĩ еб", "Ġв Ñĥз", "Ġبر گز", "Ġبرگ ز", "' yi", "'y i", "Ġп ÑĢазд", "ĠпÑĢа зд", "ĠÐŀ ÑĢг", "ĠÐŀÑĢ г", "Ġ 并", "Ġå¹ ¶", "ĠÑģ ви", "ĠÑģв и", "ĠÙħÛĮ داÙĨ", "ĠnaÅ¡e ho", "ĠBA Åŀ", "å» Ĭ", "Ì Ī", "ãģĵ ãģĿ", "à¹ĩà¸Ļ à¸ŀ", "οÏģ ειο", "Ġбаг аÑĤ", "γ ει", "γε ι", "μ είο", "με ίο", "à¹Īà¸ĩ à¸Ĭาà¸ķ", "ĠHizmet leri", "ĠAfr ika", "ĠAf rika", "Ġted bir", ", ､", "ä¸ī 级", "ÐİÑĭÑŁN ÐİÑĭÑŁN", "ĠÐļ ÑĢÑĸм", "Ġa ray", "Ġar ay", "Ġara y", "Ġböyle ce", "к оÑĤ", "ко ÑĤ", "éĻ °", "åĽ½ éļĽ", "t ÄĽl", "tÄĽ l", "Ġp olis", "Ġpol is", "Ġpo lis", "Ġu vol", "Ġuv ol", "ĠìĪĺ ê°ķ", "çĶµ èĦij", "Ġs ami", "Ġsa mi", "Ġsam i", "Ġشاخ Ùĩ", "Ġв ÑģÑĮого", "ĠвÑģ ÑĮого", "ĠØŃد اÙĤÙĦ", "Ġ iken", "Ġi ken", "Ġik en", "ãĤ¯ãĥ© ãĥĸ", "Ġzá vod", "Ġzáv od", "ब ल", "ë°° ìĨ¡", "éĩĩ è´Ń", "ëł ¬", "Ġ ।ĊĊ", "Ġ। ĊĊ", "Ġ।Ċ Ċ", "Ġê°ģ ê°ģ", "Ġм ак", "Ġма к", "Ïģα Ïĥη", "ĠiÅŁlem i", "ãģĹ ãģ¦ãģĦãģ¾ãģĻ", "ãģĹãģ¦ ãģĦãģ¾ãģĻ", "ãģĹãģ¦ãģĦ ãģ¾ãģĻ", "ĠP ek", "ĠPe k", "Ñİ Ð½", "Ġvel kou", "Ġvelk ou", "åĬŀ çIJĨ", "å®ĥ 们", "Ġ èIJ¬", "ĠèIJ ¬", "ĠнаÑĢод Ñĥ", "ĠнаÑĢ одÑĥ", "Ġch ó", "ĠH iç", "ĠHi ç", "Û³ Ûµ", "Ġ รà¸Ńà¸ļ", "Ġร à¸Ńà¸ļ", "Û³ Û¶", "à¸Ĥ ว", "ä½į æĸ¼", "ĠС ÑĤа", "ĠСÑĤ а", "ั à¸Ļม", "ัà¸Ļ ม", "ाप à¤ķ", "ĠÑĥ ÑĢок", "ãĤ¢ ãĥ¡ãĥªãĤ«", "Ġз мож", "Ġзм ож", "sk ému", "ské mu", "ském u", "Ġ è»Ĭ", "Ġè» Ĭ", "ĠاختÛĮ ار", "ĠP Åĺ", "л Ñıв", "лÑı в", "Ġ маз", "Ġм аз", "Ġма з", "Ġözel liÄŁi", "åij¼ ãģ°", "Ġbir inin", "Ġbiri nin", "Ġод не", "Ì Ĩ", "ä»ĸ ãģ®", "建 ç¯ī", "поÑģ еÑĢед", "ห ลà¸Ķ", "หล à¸Ķ", "å¤ļ ãģĦ", "ÏĦή ÏĥειÏĤ", "Ġر ÙĪÙĨد", "ĠرÙĪ ÙĨد", "èģ ½", "ì¤ij ìĹIJ", "ìĬ¤ íĭ°", "Ġз вÑĸÑĤ", "Ġзв ÑĸÑĤ", "ĠаÑĢ ÑĤи", "Ġc Æ°á»Ŀi", "ĠcÆ° á»Ŀi", "ın dır", "ınd ır", "Ġг олод", "Ġгол од", "ا زد", "از د", "à¹Īาว ว", "ãĥ¡ ãĥ©", "عÙĨ ÙĪاÙĨ", "% )Ċ", "%) Ċ", "ĠÑħолод илÑĮ", "人 们", "C Ä°", "ÐĹ Ð°Ð¿", "ÐĹа п", "ĠpÅĻ isp", "ĠpÅĻi sp", "Ġdurum larda", "ÑĢ Ñĸд", "ÑĢÑĸ д", "Âł У", "Ġε ÏĨαÏģ", "Ġs prav", "Ġsp rav", "Ġspr av", "ĠоÑĤÑĢим аннÑı", "ï¼Į 没æľī", "о вала", "ов ала", "ова ла", "овал а", "Ġng ại", "ãĢĤ 大", "Ġ даеÑĤ", "Ġд аеÑĤ", "Ġда еÑĤ", "ĠpÃŃs em", "ÑĨ ÑıÑĤÑĮ", "ÑĨÑı ÑĤÑĮ", "ov nÄĽ", "ë¦ ī", "Ġê² ģ", "Ñģ ÑĤин", "ÑģÑĤ ин", "ÑģÑĤи н", "ĠS ayı", "ĠSa yı", "ĠSay ı", "ãĢĭ çļĦ", "Ġyol uyla", "Ġyolu yla", "елеÑĦ он", "Ġr áno", "Ġrá no", "Ġíĸī ëıĻ", "ĠاÙĦخاÙħ سة", "Ġповин на", "ÅĻ ila", "ÅĻi la", "ÅĻil a", "Ġà¤ļ रण", "Ġà¤ļर ण", "Ġبرگ زار", "Ġبرگز ار", "ìļ´ ëį°", "à¹Ģà¸Ľ à¸Ńร", "Ġdal eko", "led nÃŃ", "åIJį 稱", "лив ÑĸÑģÑĤÑĮ", "ливÑĸ ÑģÑĤÑĮ", "Ġ몸 ìĿĦ", "о ÑĢÑĸв", "оÑĢ Ñĸв", "оÑĢÑĸ в", "Ц е", "بد Ø£", "ë°ĺ 기", "k rát", "kr át", "ä¸į 足", "Ġolduk ları", "len iyor", "Ġìĭľ íĸī", "ĠпÑĢи нимаÑĤÑĮ", "à¸Ĥà¸Ńà¸ĩ ร", "ÏĪ ει", "ÏĪε ι", "Ġ ẩn", "ت س", "ĠÑĤ ай", "ĠÑĤа й", "Ġнев озможно", "åıĬ ãģ³", "r oti", "ro ti", "rot i", "ï½ Ń", "д ом", "до м", "ой но", "å£ Ĭ", "说 çļĦ", "Ġsk oro", "ni ÄįnÃŃ", "niÄį nÃŃ", "ĠProf es", "ĠÑħ ÑĢониÑĩеÑģ", "Ġ주 문", "ĠZ n", "ĠÑģ лой", "ĠÑģл ой", "ĠÑģло й", "Îł Ïģο", "æĮĩ æķ°", "ĠпеÑĢ еÑĪ", "ĠпеÑĢе ÑĪ", "à¥ģà¤ķ स", "Ġê°Ģ ìłķ", "Ġ íķĺë©´", "Ġíķĺ ë©´", "Û±Û¹ Û´", "к Ñĥл", "кÑĥ л", "ÙĬ ÙĦا", "ÙĬÙĦ ا", "ĠدÙĪب ارÙĩ", "| l", "ĠÐľ Ñĥ", "н ила", "ни ла", "нил а", "ãģ¦ ãģĦãģ¾ãģĻ", "ãģ¦ãģĦ ãģ¾ãģĻ", "m acı", "ma cı", "mac ı", "ãģŁ ãģ¡ãģ¯", "ãģŁãģ¡ ãģ¯", "ĠاÙĦÙĥ تاب", "移 åĭķ", "λ μ", "_ ï¼ı", "Ġê°Ģ ìŀħ", "èħ ¾", "ĠпÑĢез иденÑĤ", "Ġë¶Ħ ìķ¼", "a hy", "ah y", "Å¡et ÅĻenÃŃ", "Å¡etÅĻ enÃŃ", "éĵ º", "ĠpÅĻÃŃ ro", "Ðķ Т", "ĠìļĶ ì²Ń", "Ġmoh lo", "Ġmohl o", "å¿ĥ çIJĨ", "Ġvysok é", "& uuml", "ÏĦ ικα", "ÏĦικ α", "ÏĦι κα", "ìĹħ ì²´", "ãģ§ ãģĤ", "ราย à¸ĩาà¸Ļ", "ĠpÅĻÃŃspÄĽ v", "Ġet miÅŁtir", "Ġetm iÅŁtir", "ĠetmiÅŁ tir", "她 们", "ÏĢ λα", "ÏĢλ α", "ứ a", "Ġ 说", "Ġè¯ ´", "ĠÑģо Ñģед", "ĠÑģоÑģ ед", "åĩ ī", "ĠÐł е", "åİŁ æĿ¥", "ĠÐIJÑĢ Ñħ", "ب ÙĬÙĨ", "بÙĬ ÙĨ", "åľ° 说", "Ġ ört", "Ġö rt", "Ġör t", "ĠΣ εÏĢ", "ĠΣε ÏĢ", "ÂŃ ÙĩاÛĮ", "ĠاÙĦ اÙĤتص", "å°½ 管", "ÑĤ Ñĭй", "ÑĤÑĭ й", "t ains", "ta ins", "tain s", "ÙĢ ÙĦ", "ç§ijæĬĢ æľīéĻIJåħ¬åı¸", "æı ®", "ัà¸ķ à¸ĸ", "á»Ĺ ng", "ล าà¸Ķ", "ลา à¸Ķ", "æļ ®", "ĠÙĨÙģ سÙĩ", "ĠÙĨÙģس Ùĩ", "Ġ çľĭ", "Ġçľ ĭ", "Ġ ãģ¿", "Ġt arım", "Ġtar ım", "Û±Û¹ Ûµ", "ĠÎ Ĭ", "Ġkom plex", "Ġkomple x", "Ġkomp lex", "ĠNh Ä©", "è´¹ çĶ¨", "Ġکاربر اÙĨ", "ÅĪ ovánÃŃ", "Ġ ků", "Ġk ů", "д ап", "да п", "Îķ Χ", "ê·¸ ëŀĺ", "Ġdön dü", "人 åĵ¡", "ĠT iá»ĥu", "ĠÙĪÛĮر اÛĮØ´", "Ġö ngör", "ĠÙĪ غÙĬر", "ĠÑģ кÑĢÑĭ", "ĠÑģк ÑĢÑĭ", "âĢIJ '", "Ġ немÑĥ", "Ġн емÑĥ", "Ġне мÑĥ", "Ġнем Ñĥ", "ĠH á»ĩ", "ĠHá» ĩ", "Ġdüzen li", "ĠsoutÄĽ že", "ãĢģ ãĥŀ", "ÏĦο μα", "ÄĽ lÃŃ", "ÄĽl ÃŃ", "ĠØ£ ÙĦÙħاÙĨ", "ĠØ£ÙĦ ÙħاÙĨ", "çł ²", "Ġtr Ãł", "Ġ ä¸ĸçķĮ", "Ġä¸ĸ çķĮ", "ay ız", "ayı z", "ım lı", "ĠاÙĦØ£ Ùģ", "íķĺ ëĬĶëį°", "íķĺëĬĶ ëį°", "в ано", "ва но", "ван о", "ĠpÅĻi Äįemž", "Ùĥ ÙĬب", "ÙĥÙĬ ب", "ĠмаÑĤ емаÑĤи", "м ени", "мен и", "ме ни", "ĠпÑĢо екÑĤÑĥ", "ĠпÑĢоек ÑĤÑĥ", "ĠпÑĢоекÑĤ Ñĥ", "ี à¹Ĥà¸Ń", "ีà¹Ĥ à¸Ń", "о Ñĥ", "ĠاÙĦ شرÙĥØ©", "ĠاÙĦشر ÙĥØ©", "æ³ £", "ÙĪÙĤ ÙĬت", "ÑĪ ив", "ÑĪи в", "Ġperson el", "Ġpersone l", "Ġperso nel", "Ø´ تر", "شت ر", "à¸Ķ า", "Ġë ª½", "Ġëª ½", "åĿIJ åľ¨", "о ке", "ок е", "Ġë§Ī ë²ķ", "ĠØ£ ÙĨا", "ĠØ£ÙĨ ا", "ëł µ", "ĠÙħب اÙĨÛĮ", "èĭ ¹æŀľ", "Ġ ศร", "Ġศ ร", "ĠÐĽ ÑĥÑĩ", "ĠÐĽÑĥ Ñĩ", "ÎŁ ΥΣ", "ÎŁÎ¥ Σ", "ĠÄį á", "ãģĽ ãģ¦", "Ġk Ä±ÅŁ", "Ġkı ÅŁ", "ÑĪ ев", "ÑĪе в", "æĮĩ 导", "à¹ģละ ม", "Ġvol eb", "Ġvole b", "ĠÑģи лÑĭ", "ĠÑģил Ñĭ", "Ġdruh ou", "Ġdru hou", "Ġ ì°¬", "Ġì° ¬", "ĠìŀĪ ìĿĮ", "Î¥ Σ", "ä¸į å®ī", "Ġ ìĹĨìĿĮ", "ĠìĹĨ ìĿĮ", "Ġde term", "Ġdet erm", "Ġdeter m", "ĠاÙĦÙħ عÙĦÙĪÙħات", "íĺ ¹", "âĻ ¡", "à¥įब न", "Ġخش Ú©", "ĠN ová", "ĠNo vá", "ĠNov á", "ĠÑĦÑĥнда менÑĤ", "ĠпÑĢогÑĢам и", "ĠпÑĢогÑĢа ми", "ĠпÑĢог ÑĢами", "ĠعÙĦ ÙĬÙĥ", "ĠعÙĦÙĬ Ùĥ", "। ĊĊ", "।Ċ Ċ", "Ġver iyor", "Ġveri yor", "Ġ ÑĶв", "ĠÑĶ в", "ĠìŀĪ ëĭ¤ê³ł", "ĠìŀĪëĭ¤ ê³ł", "ĠاÙĦØ£Ùħ رÙĬÙĥÙĬ", "ĠاÙĦØ£Ùħر ÙĬÙĥÙĬ", "Ġå¤ĸéĥ¨ ãĥªãĥ³ãĤ¯", "Ġ ä¿®", "Ġп ÑĥÑĤи", "ĠпÑĥÑĤ и", "ĠпÑĥ ÑĤи", "Ġο Ïģγ", "ĠоÑģнов ном", "Ġ наÑĢÑĥж", "Ġна ÑĢÑĥж", "ĠнаÑĢ Ñĥж", "Ġми ÑĢе", "ĠмиÑĢ е", "o vÄĽt", "ov ÄĽt", "ovÄĽ t", "Ġíĥ IJ", "Ġsok ak", "Ġspolup ráci", "ÐĶ Ðļ", "Ġ åĺ", "Ġå ĺ", "âĸįâĸįâĸįâĸįâĸįâĸįâĸįâĸį âĸįâĸįâĸįâĸįâĸįâĸįâĸįâĸį", "Ġ ³³³³", "ĠÂł ³³³", "Ġ³³ ³³", "Ġ³³³ Âł", "Ġhay ır", "Ġ ìĻĶ", "ĠìĻ Ķ", "æĤ¨ çļĦ", "æĮ º", "Ġ 민주", "Ġ민 주", "Ġhot elu", "Ġhotel u", "ี à¸ľ", "ìŀIJ ëıĻ", "ä¼¼ çļĦ", "ÎŃν ÏĦÏģο", "ÎŃνÏĦ Ïģο", "Ø´ ÙĪ", "Ġ é¤", "Ġé ¤", "Ġ λι", "Ġλ ι", "Ġol maktadır", "Ġolmak tadır", "ĠоÑģ веÑī", "Ġв ина", "Ġви на", "Ġвин а", "Ġخاص Ø©", "r ana", "ra na", "ran a", "γÏģα ÏĨή", "γÏģαÏĨ ή", "ÑĨ еÑģ", "ÑĨе Ñģ", "ĠdoÄŁru lt", "ĠdoÄŁr ult", "ĠÙĤرار داد", "ĠÙĤرارد اد", "ĠÐļ ал", "ĠÐļа л", "ê²½ ìłľ", "Ïĩ ÏĮ", "Ñĥ ÑİÑīий", "ÑĥÑİ Ñīий", "ÑĥÑİÑī ий", "ëĭ ĺìĿ´", "ëĭĺ ìĿ´", "ë Į", "л аз", "ла з", "Ġng ừng", "i sku", "is ku", "isk u", "ìĦł ê±°", "ĠÑįлек ÑĤÑĢон", "ĠÑįлекÑĤÑĢ он", "ĠV oj", "ĠVo j", "н Ñıми", "нÑı ми", "ĠÙĪ Ø£ÙĨ", "ĠÙĪØ£ ÙĨ", "äº Ń", "绣 计", "ĠÅŁ iÅŁ", "ĠÅŁi ÅŁ", "ãĢį çļĦ", "æŃ ¯", "Ġкол лек", "Ġд виж", "Ġдв иж", "Ġдви ж", "Ġn á»Ńa", "Äįas ÃŃ", "Ġs onu", "Ġso nu", "Ġson u", "ĠмеÑħ анÑĸз", "ž ený", "že ný", "žen ý", "Ġза ÑģÑĤÑĥп", "ĠзаÑģÑĤ Ñĥп", "ê´Ģ 볨", "ĠÑĤоваÑĢ Ñĸв", "ĠÑĤов аÑĢÑĸв", "Ġ ì¼ĢìĿ´", "Ġì¼ ĢìĿ´", "à¥ģà¤Ĺ त", "Ġzá sob", "мов ÑĸÑĢ", "u fac", "uf ac", "ů ležit", "Ġви гоÑĤов", "Ġвиг оÑĤов", "ĠاÙĦÙĨ ÙĪ", "Ġع اÙħا", "ĠعاÙħ ا", "æģ ¨", "ĠìĿ´ë¯¸ ì§Ģ", "Ġt voÅĻ", "Ġtv oÅĻ", "Ġvyu žitÃŃ", "Ġgel iÅŁim", "ĠgeliÅŁ im", "ì³ ¤ëĭ¤", "หà¸Ļ à¸Ńà¸ĩ", "ĠìĿ¸ ìłķ", "à¥į दर", "à¥įद र", "ĠпеÑĢед а", "ĠпеÑĢе да", "ĠздÑĸйÑģ неннÑı", "ÙĨ ع", "è¡£ æľį", "Ġl oa", "Ġlo a", "íĻ Ī", "èĭ± åĽ½", "ĠD ruh", "ĠDr uh", "Ø® اÙĨ", "д ам", "да м", "аÑĤелÑĮ нÑĭÑħ", "θ ÏģÏİ", "ĠØ£ Ùħر", "ĠØ£Ùħ ر", "ĠÅĻ ada", "Ġbul uÅŁ", "ĠÑĤÑĢанÑģп оÑĢ", "ĠÑĤÑĢанÑģ поÑĢ", "ĠÙĤ تÙĦ", "ĠTa rif", "ĠTar if", "R us", "Ru s", "ĠзаÑģ Ñĸд", "ĠÄ° h", "l eyin", "ley in", "Ġvy rá", "ĠD ÄĽ", "иб ли", "a vou", "av ou", "avo u", "ĠÐĵеÑĢ м", "н емÑĥ", "не мÑĥ", "нем Ñĥ", "Ġкон ÑĨеп", "ĠконÑĨе п", "ĠÙĤ ادر", "Ġsou bor", "Ġl á»iji", "Ġ çµIJ", "Ġçµ IJ", "л еннÑĭй", "лен нÑĭй", "κ Ïħ", "Ġдопом аг", "à¸ŀวà¸ģ à¹Ģà¸Ĥ", "Ġqu ang", "Ġq uang", "Ġqua ng", "Ġquan g", "ĠØ· ÙĦا", "ĠØ·ÙĦ ا", "Ġ éĩĮ", "Ġé ĩĮ", "Ġéĩ Į", "ĠÙĨÙħÙĪد ار", "ĠÅŁ ar", "ĠÑģп Ñĸл", "ÂŃ n", "ì§Ģ ìļĶ", "åīį å¾Ģ", "åħ³ éĶ®", "å®ŀ åľ¨", "éŁ³ 楽", "ĠÙħسئ ÙĦÙĩ", "Ġy eme", "Ġye me", "Ġyem e", "ĠÑĪ аÑħ", "기 ìĪł", "Ġ สำà¸Ļ", "Ġสำ à¸Ļ", "ĠÙĪرزش ÛĮ", "ĠÙĪرز Ø´ÛĮ", "ãģĹ ãģŁãĤī", "ãģĹãģŁ ãĤī", "ί ÏĥÏī", "ίÏĥ Ïī", "о кон", "ок он", "око н", "ãģŁ ãĤī", "ĠØ¥ ÙĦÙĬÙĩ", "ĠØ¥ÙĦÙĬ Ùĩ", "ĠØ¥ÙĦ ÙĬÙĩ", "Ġآذ رب", "Ġr á»Ŀi", "Ġod ak", "Ġм огÑĥ", "Ġмог Ñĥ", "Ġмо гÑĥ", "ĠÚ¯ ÙĨ", "è² ¼", "ed la", "edl a", "Ġоп ÑĭÑĤ", "la maktadır", "lamak tadır", "å°¼ äºļ", "éĥ½ ä¼ļ", "ĠÎĺε ÏĥÏĥα", "Ġв ог", "Ġво г", "ç»Ī äºİ", "ĠÑĥÑĢов не", "Ġv lak", "Ġvl ak", "ĠØ¢ ÙĦØ©", "ĠØ¢ÙĦ Ø©", "Ġε ιδ", "Ġει δ", "â ĩ", "д ÑĥÑĤ", "дÑĥ ÑĤ", "Ñĸ нг", "Ñĸн г", "ĠØ£Ùħ رÙĬÙĥÙĬ", "ĠØ£Ùħر ÙĬÙĥÙĬ", "از ÙĨد", "Ġب اÙĦØ£", "ĠباÙĦ Ø£", "Ġत न", "Ġkay det", "룬 리", "Ġ drž", "Ġd rž", "Ġdr ž", "Ġп енÑģ", "Ġпен Ñģ", "ĠpÅĻÃŃ Äį", "ĠТ олÑĮко", "Ġб аÑĤаÑĢ", "Ġба ÑĤаÑĢ", "éĵģ è·¯", "ĠÙ¾ÛĮ ÚĨ", "ĠÎĵ εÏī", "ĠαÏħ ÏĦά", "Äŀ I", "ĠакÑĤив но", "ÎĹ ÎľÎij", "ÎĹÎľ Îij", "Ġvar lık", "Ġ åıª", "Ġåı ª", "ĠзаÑī иÑĤÑĭ", "ĠзаÑīиÑĤ Ñĭ", "л им", "ли м", "ĠÙħشاÙĩ دة", "и ком", "ик ом", "Ġì¡° ìĤ¬", "о ген", "ог ен", "Ġm ấy", "g ii", "gi i", "èĽ ĩ", "ĠØ® ÙĪÛĮØ´", "ĠØ®ÙĪ ÛĮØ´", "Ġn ová", "Ġno vá", "Ġnov á", "к овой", "ков ой", "ково й", "Ġkan ıt", "Ġkanı t", "éĿ¢ è®®", "ĠرÙĪست ا", "ìĸ´ ê°Ģ", "ĠоÑĤноÑĪ ениÑı", "Ġhodnot y", "ÙĪ رات", "ÙĪر ات", "ÙĪرا ت", "ĠpÅĻ ÃŃst", "ĠpÅĻÃŃ st", "Ġth á»į", "Ġthá» į", "Ġçık art", "Ġçıkar t", "Ġçı kart", "о обÑĢаз", "Ġnem ÄĽl", "Âł ro", "ĠدÙĪÙĦ تÛĮ", "ĠدÙĪÙĦت ÛĮ", "ี ,", "ä¸Ģ 度", "ia omi", "iao mi", "åĹ İ", "Ùı ع", "ĠваÑĢи ан", "Ġpod aÅĻilo", "ĠëĤĺ ê°Ģ", "èIJ¥ ä¸ļ", "ĠабÑģолÑİÑĤ но", "Ġë¸Į ëĿ¼", "ĠгоÑĢ из", "a ģın", "aÄŁ ın", "aģı n", "Ġyer ini", "Ġyeri ni", "à¹īา à¸Ļà¸Ķ", "à¹īาà¸Ļ à¸Ķ", "æIJ ¬", "Ġb alık", "Ġbal ık", "Ġba lık", "ĠÅŁ ans", "认 è¯Ĩ", "Ġistedi ÄŁiniz", "Ġjist ÄĽ", "Ġ ìĪĺê°Ģ", "ĠìĪĺ ê°Ģ", "ï¼Į ä¸Ĭ", "à¤ľ ब", "Ġви Ñıви", "ĠвиÑıв и", "ë§ ¥", "ãģĹ ãģ¦ãĤĭ", "ãģĹãģ¦ ãĤĭ", "ÙĬÙĥ ا", "ĠH üs", "c ının", "Ġश त", "ĠÑĢаÑģп олаг", "ĠÑģпÑĢав ж", "ืà¸Ń à¸ĸ", "ĠвеÑĢ ÑĤик", "Ġvy stav", "ĠÑĢе алÑĸзаÑĨÑĸÑĹ", "в ами", "ва ми", "ãĤ¹ ãĥĨãĤ£", "ãĤ¹ãĥĨ ãĤ£", "ëħ ģ", "ĠÑĢе ÑĩÑĸ", "ĠÑĢеÑĩ Ñĸ", "Ùģ اÙĦ", "िà¤ķ à¤Ł", "ĠвозÑĢаÑģÑĤ е", "к аÑģ", "ка Ñģ", "ĠÐĺ Ñģ", "Ġл Ñĸк", "ĠлÑĸ к", "ĠÏĥη μαν", "м енÑĤÑĥ", "мен ÑĤÑĥ", "менÑĤ Ñĥ", "н ÑıÑİÑĤ", "нÑı ÑİÑĤ", "æŁ ´", "Ġθ εÏī", "Ġθε Ïī", "çĬ¯ 罪", "ĠÙĤ طر", "ĠÙĤØ· ر", "ÐĶ ÐIJ", "- |", "Ġ ÑģÑĤÑĸ", "ĠÑģ ÑĤÑĸ", "ĠÑģÑĤ Ñĸ", "Ġu yum", "Ġuy um", "Ġpot ÅĻeba", "ĠpotÅĻeb a", "ĠعÙħÙĦ ÛĮات", "ĠعÙħÙĦÛĮ ات", "å¥ ª", "ا خر", "اخ ر", "ĠÚ© ساÙĨÛĮ", "ت Ùħر", "تÙħ ر", "ÑĮ еÑĢ", "ÑĮе ÑĢ", "ĠN ez", "ĠNe z", "íļĮ ìĤ¬", "ĠBank ası", "е гÑĢа", "ег ÑĢа", "à¸Ĥà¸ĵะ à¸Ĺ", "åIJĪ æł¼", "ĠìŬ룬 ë¶Ħ", "y asal", "ya sal", "yas al", "Ġ è¡ĮæĶ¿", "Ġè¡Į æĶ¿", "åĬ ī", "dık tan", "ãĤ¢ãĥ« ãĥIJ", "ĠاÛĮÙĨ ÚĨ", "Ġdij ital", "å° ĺ", "ĠÑĢаз меÑī", "ĠÑĢазм еÑī", "ĠкÑĸлÑĮ коÑģÑĤÑĸ", "ĠEv ropy", "ĠEvrop y", "ĠÑĢоз ви", "ÑİÑī ÑĥÑİ", "Ġ ong", "Ġo ng", "Ġon g", "Ġhe psi", "Ġhep si", "v ailability", "vail ability", "Ġتص ÙħÙĬÙħ", "ĠتصÙħ ÙĬÙħ", "Ñĥ йÑĤе", "Ñĥй ÑĤе", "ह ल", "ĠÅ¡ iro", "Ġp ás", "Ġpá s", ";; ;;;;", ";;;; ;;", ";;; ;;;", "éħį åIJĪ", "ĠاÙĦعاÙĦÙħ ÙĬØ©", "ÐĴ о", "h af", "ha f", "l áv", "lá v", "Ġb ì", "Ġm ůj", "Ġmů j", "ê»ĺ ìĦľ", "ÂłB f", "ĠÑģпÑĢоÑģ ил", "âĢĮÚ©ÙĨ ÙĨدÙĩ", "âĢĮÚ©ÙĨÙĨد Ùĩ", "ÙĨد ÙĬØ©", "ÙĨدÙĬ Ø©", "çī¹ èī²", "Ġìķ ¨", "ุษ ย", "ĠФ оÑĢ", "пиÑģ ок", "пи Ñģок", "u žel", "ım lar", "çĬ¶ æ³ģ", "Ġãĥ¬ ãĥĩãĤ£ãĥ¼ãĤ¹", "Ñħ ови", "Ñħов и", "Ñħо ви", "ÂłK Äį", "Ñĩ им", "Ñĩи м", "Ġت ÙĪÙħ", "ĠتÙĪ Ùħ", "à¹Ģà¸ģษ à¸ķร", "Ġìĭ± ê¸Ģ", "Ùħ ارات", "Ùħا رات", "Ùħار ات", "ê nh", "ên h", "ĠÅĻ id", "æĬ ¬", "Ñģ иÑİ", "Ñģи Ñİ", "æħ İ", "Ġçev re", "Ġçevr e", "ãĥĪ ãĥ«", "Ġyıl dır", "Ġzá znam", "Ġzáz nam", "æľº åľº", "Ġпо ÑĶ", "ĠвÑĭ ÑĢаÑīи", "Ġ Ùģع", "ĠÙģ ع", "ë »", "Ġدار ÛĮÙħ", "ï¼Į æĽ´", "Ġзем ли", "اب ÙĤات", "ابÙĤ ات", "Ġm á»Ŀi", "Ġmá» Ŀi", "k ých", "ký ch", "ÙĦ اة", "ÙĦا Ø©", "å¸ ½", "بر اÙĩÙĬÙħ", "Ġпо баÑĩ", "Ġпоб аÑĩ", "Ġпоба Ñĩ", "ाà¤ĩ म", "à¹Īาà¸ĩ à¸Ľà¸£à¸°à¹Ģà¸Ĺศ", "ĠìĦ¸ ìĥģ", "Ġпомог аеÑĤ", "ĠÏĦÏĮ Ïĥο", "æĸ ·", "ĠÙģر اÙĪ", "à¹Ħà¸Ľ ย", "erg isi", "Ġ éĻIJ", "ĠéĻ IJ", ". xz", ".x z", "ĠÑģл ÑĥÑħ", "ĠÑģлÑĥ Ñħ", "е коном", "ек оном", "еко ном", "ĠNh ất", "± Ø·", "ĠëĪĪ ìĿĦ", "Ġ íļĮìĤ¬", "ĠíļĮ ìĤ¬", "Ñ ĵ", "Ġ åIJįçĦ¡ãģĹ", "ĠåIJį çĦ¡ãģĹ", "Ġομά δα", "ĩ Į", "li ÄŁinin", "liÄŁi nin", "liÄŁ inin", "liÄŁini n", "liÄŁin in", "ع اÙĨ", "عا ÙĨ", "Ġز ÙĨÛĮ", "ĠزÙĨ ÛĮ", "T ôi", "Ġet ki", "Ġetk i", "ĠìĹ° ëĿ½", "Ġкон ÑĨа", "è° ĭ", "Ġзем лÑı", "íĻĺ ê²½", "ĠÙħÚ© اÙĨÛĮ", "ĠÙħکاÙĨ ÛĮ", "çĸ ²", "Ġ ç¢", "Ġç ¢", "Ġkur ulan", "Ġkurul an", "Ġkuru lan", "ؤ ÙĪÙĦ", "د Ùī", "ĠاÙĦÙħÙĨ Ø·ÙĤØ©", "Ġn ắng", "ÐŁ Ðļ", "ол ай", "ола й", "Y K", "åij Ĩ", "λ αν", "λα ν", "西 çľģ", "ĠÎĴ αÏĥ", "ĠÎĴα Ïĥ", "ĠíĻķ ìĭ¤", "Z D", "п Ñĸд", "Ġ наÑĩе", "Ġн аÑĩе", "Ġна Ñĩе", "ĠнаÑĩ е", "Ġ ÏĦά", "ĠÏĦ ά", "å½ »", "âĢŀ D", "Ġ èĩº", "Ġèĩ º", "Ġна ÑĪей", "ĠнаÑĪ ей", "ĠtÃŃm to", "Ġت سÙħ", "Ġتس Ùħ", "Ïģθ Ïģο", "令 人", "ĠP azar", "ĠPa zar", "ĠPaz ar", "ãĤĵ ãģ¨", "ç«ĭ åĪ»", "Âģ @", "Ġb ắc", "ìĬ¤ íħĮ", "Ġkadın lar", "fig ur", "ãģ¤ ãģ¶", "Ġæµ Ļæ±Ł", "Ġдек ÑĸлÑĮ", "è¡ Ŀ", "ยà¸Ļ à¹ģà¸Ľà¸¥à¸ĩ", "o let", "ol et", "ole t", "Ġned ok", "n amen", "name n", "na men", "nam en", "åħĦ å¼Ł", "ืà¸Ń à¸Ĥ", "èĤ ĥ", "Ġb üny", "Ġbü ny", "ĠÑĢад Ñıн", "ãĢģ äºĮ", "ан нÑİ", "Ġ æīĭæľº", "Ġæīĭ æľº", "ĠоÑģ лож", "Ġо глÑı", "Ġог лÑı", "Ġسب ز", "Ġaktiv it", "Ġà¤ı प", "ç« ľ", "Ġd iren", "Ġdi ren", "Ġdir en", "Ġdire n", "i в", "ĠY atırım", "ÑĨÑĸй на", "Ġдо мов", "Ġдом ов", "ẳ n", "ĠC oÄŁraf", "Ùģ ÙĪ", "æ°Ĺ ãģ«åħ¥", "ç§ģ ãģ®", 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биÑĤ", "Ġб иÑĤ", "Ġби ÑĤ", "ĠΣ ÏĦο", "à¸ĩà¹Ģศ ส", "åİŁ æľ¬", "Ġk nÃŃ", "Ġkn ÃŃ", "äºĴ èģĶç½ij", "ĠÑĩеловеÑĩ еÑģ", "çŃ Ĵ", "à¸Īำ หà¸Ļ", "åĩº åİ»", "ãĤ¢ ãĥĭãĥ¡", "å±ķ 示", "r ych", "ry ch", "à¤ħ ब", "o ÅĪ", "jÃŃ cÃŃm", "jÃŃcÃŃ m", "ا ØŃØ«", "اØŃ Ø«", "ĠÙĪاÙĤع ÛĮ", "ĠФедеÑĢа лÑĮ", "ĠФед еÑĢалÑĮ", "Ñģ ам", "Ñģа м", "Ġ ìĺ¥", "Ġìĺ ¥", "åľ° çIJĥ", "Ġs uyu", "Ġsu yu", "Ġsuy u", "s eniz", "sen iz", "à¥ī फ", "Ġê°Ļ ëĭ¤", "ĠпÑĢизна ÑĩеннÑı", "ĠпÑĢизнаÑĩ еннÑı", "ĠS ın", "ĠSı n", "ĠاÙħÙĨ ÛĮت", "Ġl átky", "ĠÐij и", "Ġsür eci", "Ġsüre ci", "Ġsürec i", "·· ··", "Ġê²½ ì°°", "Ġк алÑĮ", "Ġка лÑĮ", "Ġкал ÑĮ", "Ġник ÑĤо", "Ùij Ùħ", "ĠدÙĬ گر", "Ġalın ması", "л еннÑĸ", "лен нÑĸ", "ิว à¹Ģà¸ķà¸Ńร", "à¸Ľà¸ģ à¸Ħรà¸Ńà¸ĩ", "Ġзаконодав ÑģÑĤва", "ãĢĢ ãĤ¤", "Ġëħ¸ íķĺìļ°", "ĠD Ã¼ÅŁ", "Ġг ÑĥÑģÑĤ", "ĠÐĴ аÑĪ", "ĠاÙħ تÛĮ", "Ġpar amet", "Ġparam et", "Ġpara met", "ĠÎłÎ±Î½ εÏĢ", "à¹Į à¸ģร", "à¹Įà¸ģ ร", "ζ α", "ĠëįĶ ìļ±", "ÙĪ ÙĦات", "ÙĪÙĦ ات", "ÙĪÙĦا ت", "в аÑĤиÑģÑı", "ва ÑĤиÑģÑı", "ваÑĤи ÑģÑı", "Ġk ök", "Ġkö k", "ÙĨ ب", 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"<|reserved_special_token_247|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128253": { "content": "<|reserved_special_token_248|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128254": { "content": "<|reserved_special_token_249|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128255": { "content": "<|reserved_special_token_250|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128256": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128257": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "128258": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true } }, "bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", "clean_up_tokenization_spaces": true, "eos_token": "<|end_of_text|>", "legacy": true, "model_input_names": [ "input_ids", "attention_mask" ], "model_max_length": 1000000000000000019884624838656, "pad_token": "", "padding_side": "right", "processor_class": "LlavaProcessor", "tokenizer_class": "LlamaTokenizer", "unk_token": "", "use_default_system_prompt": false } ================================================ FILE: comfy/text_encoders/long_clipl.py ================================================ def model_options_long_clip(sd, tokenizer_data, model_options): w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None) if w is None: w = sd.get("clip_g.text_model.embeddings.position_embedding.weight", None) else: model_name = "clip_g" if w is None: w = sd.get("text_model.embeddings.position_embedding.weight", None) if w is not None: if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: model_name = "clip_g" elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: model_name = "clip_l" else: model_name = "clip_l" if w is not None: tokenizer_data = tokenizer_data.copy() model_options = model_options.copy() model_config = model_options.get("model_config", {}) model_config["max_position_embeddings"] = w.shape[0] model_options["{}_model_config".format(model_name)] = model_config tokenizer_data["{}_max_length".format(model_name)] = w.shape[0] return tokenizer_data, model_options ================================================ FILE: comfy/text_encoders/longcat_image.py ================================================ import re import numbers import torch from comfy import sd1_clip from comfy.text_encoders.qwen_image import Qwen25_7BVLITokenizer, Qwen25_7BVLIModel import logging logger = logging.getLogger(__name__) QUOTE_PAIRS = [("'", "'"), ('"', '"'), ("\u2018", "\u2019"), ("\u201c", "\u201d")] QUOTE_PATTERN = "|".join( [ re.escape(q1) + r"[^" + re.escape(q1 + q2) + r"]*?" + re.escape(q2) for q1, q2 in QUOTE_PAIRS ] ) WORD_INTERNAL_QUOTE_RE = re.compile(r"[a-zA-Z]+'[a-zA-Z]+") def split_quotation(prompt): matches = WORD_INTERNAL_QUOTE_RE.findall(prompt) mapping = [] for i, word_src in enumerate(set(matches)): word_tgt = "longcat_$##$_longcat" * (i + 1) prompt = prompt.replace(word_src, word_tgt) mapping.append((word_src, word_tgt)) parts = re.split(f"({QUOTE_PATTERN})", prompt) result = [] for part in parts: for word_src, word_tgt in mapping: part = part.replace(word_tgt, word_src) if not part: continue is_quoted = bool(re.match(QUOTE_PATTERN, part)) result.append((part, is_quoted)) return result class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.max_length = 512 def tokenize_with_weights(self, text, return_word_ids=False, **kwargs): parts = split_quotation(text) all_tokens = [] for part_text, is_quoted in parts: if is_quoted: for char in part_text: ids = self.tokenizer(char, add_special_tokens=False)["input_ids"] all_tokens.extend(ids) else: ids = self.tokenizer(part_text, add_special_tokens=False)["input_ids"] all_tokens.extend(ids) if len(all_tokens) > self.max_length: all_tokens = all_tokens[: self.max_length] logger.warning(f"Truncated prompt to {self.max_length} tokens") output = [(t, 1.0) for t in all_tokens] # Pad to max length self.pad_tokens(output, self.max_length - len(output)) return [output] class LongCatImageTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__( embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_7b", tokenizer=LongCatImageBaseTokenizer, ) self.longcat_template_prefix = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n" self.longcat_template_suffix = "<|im_end|>\n<|im_start|>assistant\n" def tokenize_with_weights(self, text, return_word_ids=False, **kwargs): skip_template = False if text.startswith("<|im_start|>"): skip_template = True if text.startswith("<|start_header_id|>"): skip_template = True if text == "": text = " " base_tok = getattr(self, "qwen25_7b") if skip_template: tokens = super().tokenize_with_weights( text, return_word_ids=return_word_ids, disable_weights=True, **kwargs ) else: prefix_ids = base_tok.tokenizer( self.longcat_template_prefix, add_special_tokens=False )["input_ids"] suffix_ids = base_tok.tokenizer( self.longcat_template_suffix, add_special_tokens=False )["input_ids"] prompt_tokens = base_tok.tokenize_with_weights( text, return_word_ids=return_word_ids, **kwargs ) prompt_pairs = prompt_tokens[0] prefix_pairs = [(t, 1.0) for t in prefix_ids] suffix_pairs = [(t, 1.0) for t in suffix_ids] combined = prefix_pairs + prompt_pairs + suffix_pairs tokens = {"qwen25_7b": [combined]} return tokens class LongCatImageTEModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__( device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options, ) def encode_token_weights(self, token_weight_pairs, template_end=-1): out, pooled, extra = super().encode_token_weights(token_weight_pairs) tok_pairs = token_weight_pairs["qwen25_7b"][0] count_im_start = 0 if template_end == -1: for i, v in enumerate(tok_pairs): elem = v[0] if not torch.is_tensor(elem): if isinstance(elem, numbers.Integral): if elem == 151644 and count_im_start < 2: template_end = i count_im_start += 1 if out.shape[1] > (template_end + 3): if tok_pairs[template_end + 1][0] == 872: if tok_pairs[template_end + 2][0] == 198: template_end += 3 if template_end == -1: template_end = 0 suffix_start = None for i in range(len(tok_pairs) - 1, -1, -1): elem = tok_pairs[i][0] if not torch.is_tensor(elem) and isinstance(elem, numbers.Integral): if elem == 151645: suffix_start = i break out = out[:, template_end:] if "attention_mask" in extra: extra["attention_mask"] = extra["attention_mask"][:, template_end:] if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]): extra.pop("attention_mask") if suffix_start is not None: suffix_len = len(tok_pairs) - suffix_start if suffix_len > 0 and out.shape[1] > suffix_len: out = out[:, :-suffix_len] if "attention_mask" in extra: extra["attention_mask"] = extra["attention_mask"][:, :-suffix_len] if extra["attention_mask"].sum() == torch.numel( extra["attention_mask"] ): extra.pop("attention_mask") return out, pooled, extra def te(dtype_llama=None, llama_quantization_metadata=None): class LongCatImageTEModel_(LongCatImageTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(device=device, dtype=dtype, model_options=model_options) return LongCatImageTEModel_ ================================================ FILE: comfy/text_encoders/lt.py ================================================ from comfy import sd1_clip import os from transformers import T5TokenizerFast from .spiece_tokenizer import SPieceTokenizer import comfy.text_encoders.genmo import torch import comfy.utils import math import itertools class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) #pad to 128? class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) def ltxv_te(*args, **kwargs): return comfy.text_encoders.genmo.mochi_te(*args, **kwargs) class Gemma3_Tokenizer(): def state_dict(self): return {"spiece_model": self.tokenizer.serialize_model()} def tokenize_with_weights(self, text, return_word_ids=False, image=None, llama_template=None, skip_template=True, **kwargs): self.llama_template = "system\nYou are a helpful assistant.\nuser\n{}\nmodel\n" self.llama_template_images = "system\nYou are a helpful assistant.\nuser\n\n{}\n\nmodel\n" if image is None: images = [] else: samples = image.movedim(-1, 1) total = int(896 * 896) scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) s = comfy.utils.common_upscale(samples, width, height, "area", "disabled").movedim(1, -1) images = [s[:, :, :, :3]] if text.startswith(''): skip_template = True if skip_template: llama_text = text else: if llama_template is None: if len(images) > 0: llama_text = self.llama_template_images.format(text) else: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) text_tokens = super().tokenize_with_weights(llama_text, return_word_ids) if len(images) > 0: embed_count = 0 for r in text_tokens: for i, token in enumerate(r): if token[0] == 262144 and embed_count < len(images): r[i] = ({"type": "image", "data": images[embed_count]},) + token[1:] embed_count += 1 return text_tokens class Gemma3_12BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer = tokenizer_data.get("spiece_model", None) special_tokens = {"": 262144, "": 106} super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1024, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data) class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_12b", tokenizer=Gemma3_12BTokenizer) class Gemma3_12BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata self.dtypes = set() self.dtypes.add(dtype) super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed): tokens_only = [[t[0] for t in b] for b in tokens] embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device) comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5) return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106]) # 106 is class DualLinearProjection(torch.nn.Module): def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, device=None, operations=None): super().__init__() self.audio_aggregate_embed = operations.Linear(in_dim, out_dim_audio, bias=True, dtype=dtype, device=device) self.video_aggregate_embed = operations.Linear(in_dim, out_dim_video, bias=True, dtype=dtype, device=device) def forward(self, x): source_dim = x.shape[-1] x = x.movedim(1, -1) x = (x * torch.rsqrt(torch.mean(x**2, dim=2, keepdim=True) + 1e-6)).flatten(start_dim=2) video = self.video_aggregate_embed(x * math.sqrt(self.video_aggregate_embed.out_features / source_dim)) audio = self.audio_aggregate_embed(x * math.sqrt(self.audio_aggregate_embed.out_features / source_dim)) return torch.cat((video, audio), dim=-1) class LTXAVTEModel(torch.nn.Module): def __init__(self, dtype_llama=None, device="cpu", dtype=None, text_projection_type="single_linear", model_options={}): super().__init__() self.dtypes = set() self.dtypes.add(dtype) self.compat_mode = False self.text_projection_type = text_projection_type self.gemma3_12b = Gemma3_12BModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None) self.dtypes.add(dtype_llama) operations = self.gemma3_12b.operations # TODO if self.text_projection_type == "single_linear": self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device) elif self.text_projection_type == "dual_linear": self.text_embedding_projection = DualLinearProjection(3840 * 49, 4096, 2048, dtype=dtype, device=device, operations=operations) def enable_compat_mode(self): # TODO: remove from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector operations = self.gemma3_12b.operations dtype = self.text_embedding_projection.weight.dtype device = self.text_embedding_projection.weight.device self.audio_embeddings_connector = Embeddings1DConnector( split_rope=True, double_precision_rope=True, dtype=dtype, device=device, operations=operations, ) self.video_embeddings_connector = Embeddings1DConnector( split_rope=True, double_precision_rope=True, dtype=dtype, device=device, operations=operations, ) self.compat_mode = True def set_clip_options(self, options): self.execution_device = options.get("execution_device", self.execution_device) self.gemma3_12b.set_clip_options(options) def reset_clip_options(self): self.gemma3_12b.reset_clip_options() self.execution_device = None def encode_token_weights(self, token_weight_pairs): token_weight_pairs = token_weight_pairs["gemma3_12b"] out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs) out = out[:, :, -torch.sum(extra["attention_mask"]).item():] out_device = out.device if comfy.model_management.should_use_bf16(self.execution_device): out = out.to(device=self.execution_device, dtype=torch.bfloat16) if self.text_projection_type == "single_linear": out = out.movedim(1, -1).to(self.execution_device) out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6) out = out.reshape((out.shape[0], out.shape[1], -1)) out = self.text_embedding_projection(out) if self.compat_mode: out_vid = self.video_embeddings_connector(out)[0] out_audio = self.audio_embeddings_connector(out)[0] out = torch.concat((out_vid, out_audio), dim=-1) extra = {} else: extra = {"unprocessed_ltxav_embeds": True} elif self.text_projection_type == "dual_linear": out = self.text_embedding_projection(out) extra = {"unprocessed_ltxav_embeds": True} return out.to(device=out_device, dtype=torch.float), pooled, extra def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed): return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed) def load_sd(self, sd): if "model.layers.47.self_attn.q_norm.weight" in sd: return self.gemma3_12b.load_sd(sd) else: sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "text_embedding_projection.": "text_embedding_projection."}, filter_keys=True) if len(sdo) == 0: sdo = sd missing_all = [] unexpected_all = [] for prefix, component in [("text_embedding_projection.", self.text_embedding_projection)]: component_sd = {k.replace(prefix, ""): v for k, v in sdo.items() if k.startswith(prefix)} if component_sd: missing, unexpected = component.load_state_dict(component_sd, strict=False, assign=getattr(self, "can_assign_sd", False)) missing_all.extend([f"{prefix}{k}" for k in missing]) unexpected_all.extend([f"{prefix}{k}" for k in unexpected]) if "model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.2.attn1.to_q.bias" not in sd: # TODO: remove ww = sd.get("model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.0.attn1.to_q.bias", None) if ww is not None: if ww.shape[0] == 3840: self.enable_compat_mode() sdv = comfy.utils.state_dict_prefix_replace(sd, {"model.diffusion_model.video_embeddings_connector.": ""}, filter_keys=True) self.video_embeddings_connector.load_state_dict(sdv, strict=False, assign=getattr(self, "can_assign_sd", False)) sda = comfy.utils.state_dict_prefix_replace(sd, {"model.diffusion_model.audio_embeddings_connector.": ""}, filter_keys=True) self.audio_embeddings_connector.load_state_dict(sda, strict=False, assign=getattr(self, "can_assign_sd", False)) return (missing_all, unexpected_all) def memory_estimation_function(self, token_weight_pairs, device=None): constant = 6.0 if comfy.model_management.should_use_bf16(device): constant /= 2.0 token_weight_pairs = token_weight_pairs.get("gemma3_12b", []) m = min([sum(1 for _ in itertools.takewhile(lambda x: x[0] == 0, sub)) for sub in token_weight_pairs]) num_tokens = sum(map(lambda a: len(a), token_weight_pairs)) - m num_tokens = max(num_tokens, 642) return num_tokens * constant * 1024 * 1024 def ltxav_te(dtype_llama=None, llama_quantization_metadata=None, text_projection_type="single_linear"): class LTXAVTEModel_(LTXAVTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, text_projection_type=text_projection_type, model_options=model_options) return LTXAVTEModel_ def sd_detect(state_dict_list, prefix=""): for sd in state_dict_list: if "{}text_embedding_projection.audio_aggregate_embed.bias".format(prefix) in sd: return {"text_projection_type": "dual_linear"} if "{}text_embedding_projection.weight".format(prefix) in sd or "{}text_embedding_projection.aggregate_embed.weight".format(prefix) in sd: return {"text_projection_type": "single_linear"} return {} def gemma3_te(dtype_llama=None, llama_quantization_metadata=None): class Gemma3_12BModel_(Gemma3_12BModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(device=device, dtype=dtype, model_options=model_options) return Gemma3_12BModel_ ================================================ FILE: comfy/text_encoders/lumina2.py ================================================ from comfy import sd1_clip from .spiece_tokenizer import SPieceTokenizer import comfy.text_encoders.llama from comfy.text_encoders.lt import Gemma3_Tokenizer import comfy.utils class Gemma2BTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer = tokenizer_data.get("spiece_model", None) special_tokens = {"": 107} super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data) def state_dict(self): return {"spiece_model": self.tokenizer.serialize_model()} class Gemma3_4BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer = tokenizer_data.get("spiece_model", None) special_tokens = {"": 262144, "": 106} super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, disable_weights=True, tokenizer_data=tokenizer_data) class LuminaTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer) class NTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_4b", tokenizer=Gemma3_4BTokenizer) class Gemma2_2BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Gemma3_4BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): llama_quantization_metadata = model_options.get("llama_quantization_metadata", None) if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B_Vision, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) def process_tokens(self, tokens, device): embeds, _, _, embeds_info = super().process_tokens(tokens, device) comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5) return embeds class LuminaModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel): super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options) def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b"): if model_type == "gemma2_2b": model = Gemma2_2BModel elif model_type == "gemma3_4b": model = Gemma3_4BModel elif model_type == "gemma3_4b_vision": model = Gemma3_4B_Vision_Model class LuminaTEModel_(LuminaModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model) return LuminaTEModel_ ================================================ FILE: comfy/text_encoders/mt5_config_xl.json ================================================ { "d_ff": 5120, "d_kv": 64, "d_model": 2048, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "dense_act_fn": "gelu_pytorch_tanh", "initializer_factor": 1.0, "is_encoder_decoder": true, "is_gated_act": true, "layer_norm_epsilon": 1e-06, "model_type": "mt5", "num_decoder_layers": 24, "num_heads": 32, "num_layers": 24, "output_past": true, "pad_token_id": 0, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 250112 } ================================================ FILE: comfy/text_encoders/newbie.py ================================================ import torch import comfy.model_management import comfy.text_encoders.jina_clip_2 import comfy.text_encoders.lumina2 class NewBieTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.gemma = comfy.text_encoders.lumina2.Gemma3_4BTokenizer(embedding_directory=embedding_directory, tokenizer_data={"spiece_model": tokenizer_data["gemma_spiece_model"]}) self.jina = comfy.text_encoders.jina_clip_2.JinaClip2Tokenizer(embedding_directory=embedding_directory, tokenizer_data={"spiece_model": tokenizer_data["jina_spiece_model"]}) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["gemma"] = self.gemma.tokenize_with_weights(text, return_word_ids, **kwargs) out["jina"] = self.jina.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): raise NotImplementedError def state_dict(self): return {} class NewBieTEModel(torch.nn.Module): def __init__(self, dtype_gemma=None, device="cpu", dtype=None, model_options={}): super().__init__() dtype_gemma = comfy.model_management.pick_weight_dtype(dtype_gemma, dtype, device) self.gemma = comfy.text_encoders.lumina2.Gemma3_4BModel(device=device, dtype=dtype_gemma, model_options=model_options) self.jina = comfy.text_encoders.jina_clip_2.JinaClip2TextModel(device=device, dtype=dtype, model_options=model_options) self.dtypes = {dtype, dtype_gemma} def set_clip_options(self, options): self.gemma.set_clip_options(options) self.jina.set_clip_options(options) def reset_clip_options(self): self.gemma.reset_clip_options() self.jina.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_gemma = token_weight_pairs["gemma"] token_weight_pairs_jina = token_weight_pairs["jina"] gemma_out, gemma_pooled, gemma_extra = self.gemma.encode_token_weights(token_weight_pairs_gemma) jina_out, jina_pooled, jina_extra = self.jina.encode_token_weights(token_weight_pairs_jina) return gemma_out, jina_pooled, gemma_extra def load_sd(self, sd): if "model.layers.0.self_attn.q_norm.weight" in sd: return self.gemma.load_sd(sd) else: return self.jina.load_sd(sd) def te(dtype_llama=None, llama_quantization_metadata=None): class NewBieTEModel_(NewBieTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["llama_quantization_metadata"] = llama_quantization_metadata super().__init__(dtype_gemma=dtype_llama, device=device, dtype=dtype, model_options=model_options) return NewBieTEModel_ ================================================ FILE: comfy/text_encoders/omnigen2.py ================================================ from transformers import Qwen2Tokenizer from comfy import sd1_clip import comfy.text_encoders.llama import os class Qwen25_3BTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_3b", tokenizer=Qwen25_3BTokenizer) self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n' def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs): if llama_template is None: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs) class Qwen25_3BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class Omnigen2Model(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options) def te(dtype_llama=None, llama_quantization_metadata=None): class Omnigen2TEModel_(Omnigen2Model): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(device=device, dtype=dtype, model_options=model_options) return Omnigen2TEModel_ ================================================ FILE: comfy/text_encoders/ovis.py ================================================ from transformers import Qwen2Tokenizer import comfy.text_encoders.llama from comfy import sd1_clip import os import torch import numbers class Qwen3Tokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen3_2b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=284, pad_token=151643, tokenizer_data=tokenizer_data) class OvisTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_2b", tokenizer=Qwen3Tokenizer) self.llama_template = "<|im_start|>user\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background: {}<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): if llama_template is None: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) return tokens class Ovis25_2BModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options) class OvisTEModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="qwen3_2b", clip_model=Ovis25_2BModel, model_options=model_options) def encode_token_weights(self, token_weight_pairs, template_end=-1): out, pooled = super().encode_token_weights(token_weight_pairs) tok_pairs = token_weight_pairs["qwen3_2b"][0] count_im_start = 0 if template_end == -1: for i, v in enumerate(tok_pairs): elem = v[0] if not torch.is_tensor(elem): if isinstance(elem, numbers.Integral): if elem == 4004 and count_im_start < 1: template_end = i count_im_start += 1 if out.shape[1] > (template_end + 1): if tok_pairs[template_end + 1][0] == 25: template_end += 1 out = out[:, template_end:] return out, pooled, {} def te(dtype_llama=None, llama_quantization_metadata=None): class OvisTEModel_(OvisTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if dtype_llama is not None: dtype = dtype_llama if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, dtype=dtype, model_options=model_options) return OvisTEModel_ ================================================ FILE: comfy/text_encoders/pixart_t5.py ================================================ import os from comfy import sd1_clip import comfy.text_encoders.t5 import comfy.text_encoders.sd3_clip from comfy.sd1_clip import gen_empty_tokens from transformers import T5TokenizerFast class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): def __init__(self, **kwargs): super().__init__(**kwargs) def gen_empty_tokens(self, special_tokens, *args, **kwargs): # PixArt expects the negative to be all pad tokens special_tokens = special_tokens.copy() special_tokens.pop("end") return gen_empty_tokens(special_tokens, *args, **kwargs) class PixArtT5XXL(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding class PixArtTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) def pixart_te(dtype_t5=None, t5_quantization_metadata=None): class PixArtTEModel_(PixArtT5XXL): def __init__(self, device="cpu", dtype=None, model_options={}): if t5_quantization_metadata is not None: model_options = model_options.copy() model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata if dtype_t5 is not None: dtype = dtype_t5 super().__init__(device=device, dtype=dtype, model_options=model_options) return PixArtTEModel_ ================================================ FILE: comfy/text_encoders/qwen25_tokenizer/merges.txt ================================================ #version: 0.2 Ġ Ġ ĠĠ ĠĠ i n Ġ t ĠĠĠĠ ĠĠĠĠ e r ĠĠ Ġ o n Ġ a r e a t s t e n o r Ġt h Ċ Ċ Ġ c l e Ġ s i t a n a r a l Ġth e ; Ċ Ġ p Ġ f o u Ġ = i s ĠĠĠĠ ĠĠĠ in g e s Ġ w i on e d i c Ġ b Ġ d e t Ġ m Ġ o ĉ ĉ r o a s e l c t n d Ġ in Ġ h en t i d Ġ n a m ĠĠĠĠĠĠĠĠ ĠĠĠ Ġt o Ġ re - - Ġ { Ġo f o m ) ;Ċ i m č Ċ Ġ ( i l / / Ġa nd u r s e Ġ l e x Ġ S a d Ġ " c h u t i f * * Ġ } e m o l ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ t h ) Ċ Ġ{ Ċ Ġ g i g i v , Ċ c e o d Ġ v at e Ġ T a g a y Ġ * o t u s Ġ C Ġ st Ġ I u n u l u e Ġ A o w Ġ ' e w Ġ < at ion ( ) Ġf or a b or t u m am e Ġ is p e t r c k â Ģ Ġ y i st -- -- . 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W o ck Ġ -- T ext Ġo p an k Ġit s Ġb ack [ " Ġne ed Ġc l Ġs ub Ġl a ( ( . " O bject Ġst art f ile ( self n er e y Ġus er Ġ ent ĠC om it s ĠC on ou ble ow er it em ver y ĠW e lic k Ġ Q ph p t tp ' : ic s Ġu nder Ġ* Ċ . L ) ; ic es Ġre g ) čĊ ĉ public S S Ġth en re at i ous . G e k ire ct he ck cri pt n ing ĠU n Ġm ay ĠW h B o I tem str uct . st re am ib le lo at Ġor g u nd s um _ in .. / _ M Ġh ow r ite ' Ċ T o w w Ġpe ople ind ex . n ht tp ( m ect or Ġin d Ġj av ] ,Ċ ĠH e _ st f ul o le ) {Ċ Ġsh ould op y el p i er _ name ers on I ON ot e Ġt est Ġb et rr or ul ar ã Ģ Ġ Ð b s t ing Ġm ake T r Ġa fter ar get R O olum n r c _ re def ine Ġr ight r ight d ay Ġl ong [ ] ( p t d con d ĠP ro Ġre m ption s v id . g Ġ ext Ġ __ ' )Ċ p ace m p Ġm in st ance a ir a ction w h t ype ut il a it < ? I C t ext Ġp h Ġf l . 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á»ijn Ġ ảnh à¹ĥà¸Ļ à¸ģาร ĠQu á»ijc Ġk ế ׳ ×IJ Ġס ×Ļ Ġy êu ãģ® ãģĭ ĠÄij ẹ ĠÄijẹ p Ġch ức Ġy ıl ĠTür kiye d é ĠÙĤ اÙĦ Ġd á»ĭch ĠolduÄŁ u Ġch á»įn Ġت Ùħ หà¸Ļ ึà¹Īà¸ĩ ãģķãĤĮ ãģŁ Ġph áp ìĽ Ķ Ġti á»ģn ãģĹ ãģ¾ãģĹãģŁ Ġש ׾×IJ ÙĦ Ø© Ġ׾פ ׳×Ļ Ġ×ij ×Ļת ĠH Ãł ĠØŃ Øª ĠØŃت Ùī Ġ×¢ ×ķ×ĵ Ġn ó Ġth áng à¹Ģลืà¸Ń à¸ģ ר ×Ķ Ġt Äĥng Ġcá i Ġtri á»ĥn Ġ×IJ×ķת ×ķ ìłģ ìĿ¸ ĠC ông Ġ׾×Ķ ×Ļ×ķת Ġг ода и Ñİ Ġب عض Ġ à¸ģาร èī¯ ãģĦ ÙĪ ت Ġli ên ĠÐĿ о ĠÐĿ е çļĦ ãģª ĠÙħ ت ĠÑĤак же ĠкоÑĤоÑĢ Ñĭе Ġ×Ļ ×ĵ×Ļ Ġtr á»įng ãĤµ ãĤ¤ãĥĪ ìłģ ìľ¼ë¡ľ Ġt áºŃp Ġש ׾×Ļ íķĺ ê²Į Ġt Ãłi ĠÐ ¯ Ġr á»ĵi ا Ùĥ Ġth Æ°Æ¡ng Ġ×Ķ ×ĸ×Ķ ĠÙĪ ÙħÙĨ à¸Ĺีà¹Ī มี Ġcu á»Ļc Ġbü yük ãģ¨ ãģĭ Ġ×ij ×Ļ×ķתר Ġl ần Ġgö re Ġtr ợ Ġ×ĺ ×ķ×ij ÑĤÑĮ ÑģÑı Ġth á»ijng Ġ׼ ש Ġti êu Ġ×ŀ×IJ ×ķ×ĵ Ø Ľ k Äħ Ġ à¹ĥà¸Ļ Ġv ấn Ġש ׾×ķ ĠÄij á»ģu Ùģ ت Ġê²ĥ ìĿ´ Ġh óa ĠاÙĦع اÙħ ĠÙĬ ÙĪÙħ к ой Ġbi á»ĩt ÑģÑĤ о Ġ×Ķ ×Ļ×ķ à¸Ĺีà¹Ī à¸Īะ Ġ×ĵ ×Ļ Ġ×IJ ×ļ Ġá n ص ÙĪر Ġtr ÃŃ ĠÐŁÑĢ о Ġl á»±c ãģĹãģ¦ ãģĦãģ¾ãģĻ Ġb Ãłi Ġ×ĸ ×IJת Ġb áo à¸ļ à¸Ļ ĠëĮĢ íķľ Ġti ế Ġtiế ng Ġb ên ãģķãĤĮ ãĤĭ s ión Ġt ìm ×¢ ×ķ m é ни Ñı ãģ» ãģ© Ġà¹Ģà¸ŀ ราะ ب Ø© Ġë¶ Ħ Ġ×IJ ×ĸ à¸Ĺ à¹Īาà¸Ļ ת ×Ŀ Ġth êm Ġho ạt y ı ×ĸ ×ķ Ġgi á»Ŀ Ġb án à¸Ĥ าย Ñĩ а Ġ à¹Ĩ ĠاÙĦÙħ ت ĠоÑĩ енÑĮ Ġb ất Ġtr ẻ ÑĤ ÑĢ ĠØ£ ÙĨÙĩ ĠØ« Ùħ Ġ׼ ×ŀ×Ķ Ġkh ó Ġr ằng ĠÙĪ ÙģÙĬ ни й Ġho Ãłn t ó Ġ×IJ שר ĠìĥĿ ê°ģ Ñģ а Ġ׼ ×ijר ĠÑįÑĤ ом lar ının Ġch Æ°a з и Ġd ẫn ĠÐļ ак ج ÙĪ ĠбÑĭ ло ĠÙĬ ت n ı ÅĤ am ĠÙĪÙĩ ÙĪ ×ij ×ķ п и ר ת Ġqu á»ijc ж д ĠÄij Æ¡n Ùĥت ب Ġm ắt ระ à¸ļ ระà¸ļ à¸ļ ĠÙĥ اÙĨت Ġth ân สิà¸Ļ à¸Ħà¹īา ×Ĵ ×Ļ Ġph Æ°Æ¡ng à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī ĠìĦ ± ĠC ác Ġ×Ķ×ŀ ×ķ ĠÑĤ ем Ġ×ĵ ×ķ à¸Ńะ à¹Ħร Ġv Äĥn ãģª ãģ®ãģ§ ĠN á»Ļi Ġ×¢ ×ķ ãĤīãĤĮ ãĤĭ Ġs áng Ġgö ster ãģĵãģ¨ ãĤĴ Ġtaraf ından Ġм а ĠпоÑģл е Ġ׳ ×Ļת Ġ׳×Ļת ף Ġл еÑĤ Ġ׾ ׳×ķ Ñģ Ñģ Ġ×Ļ ×ķ п е ĠÙĪ ÙĦÙĥ ĠÙĪÙĦÙĥ ÙĨ Ġngo Ãłi ĠÄij á»ĭa r zÄħd dz iaÅĤ ĠÙħ ر иÑĤÑĮ ÑģÑı Ġ×IJ×Ĺר ×Ļ Ġ׾ ׼׾ à¸Ĥ à¹īà¸Ńม à¸Ĥà¹īà¸Ńม ูล Ġб ол Ġбол ее جÙħ ع л еÑĤ Ġl á»ĭch ĠÙħ Ø«ÙĦ Ġ그리 ê³ł Ġth ứ ĠdeÄŁ il ÙĪ ØŃ Ġש׾ ×ļ ĠÙħ ØŃÙħد Ġn ếu ĠÄij á»ķi Ġv ừa Ġm á»įi Ġо ни Ġl úc ĠÙĬ ÙĥÙĪÙĨ ì§ Ī Ġש׾ ׳×ķ ĠÐĶ о Ġש ׳×Ļ ล ิ ×IJ פשר Ġs ức ê¶ Į Ġ ứng à¹Ħมà¹Ī มี Ø·ÙĦ ب ĠÑĩ ем Ġch uyên Ġth ÃŃch Ġ×ķ ×Ļ íķ © ĠÙħ صر д о ĠÄij ất Ġch ế à¸Ĭ ืà¹Īà¸Ń Ġìĭ ł ĠØ¥ ذا Ġر ئÙĬس Ġש ×Ļש Ġgiả m Ñģ ка lar ında Ġs ợ ĠtÃŃ ch ĠÙĦ ÙĥÙĨ Ġب Ùħ ×¢ ×ķ×ij ×¢×ķ×ij ×ĵ ÅĤÄħ cz ları na Ġש ×Ŀ ĠÙĦ ت Ġש×Ķ ×ķ×IJ t ów Ġëĭ¤ 른 ĠØ£ Ùĥثر ãģ® ãģ§ãģĻ ׼ ×Ļ×Ŀ ĠolduÄŁ unu ãģĭ ãģª ãĤĤ ãģĨ ÙĬ ØŃ Ġnh ìn Ġngh á»ĩ ãģ«ãģª ãģ£ãģ¦ п а Ġquy ết ÙĦ ÙĤ t á Ġlu ôn ĠÄij ặc Ġ×IJ ר Ġtu á»ķi s ão ìĻ ¸ ر د ĠبÙĩ ا Ġ×Ķ×Ļ ×ķ×Ŀ ×ķ ×ķ×Ļ ãģ§ãģĻ ãģŃ ĠÑĤ ого Ġth ủ ãģĹãģŁ ãģĦ ر ÙĤ Ġb ắt г Ñĥ Ġtá» Ń ÑĪ а Ġ à¸Ľà¸µ Ġ×Ķ×IJ ×Ŀ íı ¬ ż a Ġ×IJת ×Ķ Ġn á»Ļi Ġph ÃŃ ĠÅŁek ilde Ġl á»Ŀi d ıģı Ġ׼×IJ ף Ġt üm Ġm ạnh ĠM ỹ ãģĿ ãĤĵãģª Ġnh á»ı ãģª ãģĮãĤī Ġb ình ı p à¸ŀ า ĠÄij ánh ĠÙĪ ÙĦ ר ×ķת Ġ×IJ ×Ļ×ļ Ġch uyá»ĥn Ùĥ ا ãĤĮ ãĤĭ à¹ģม à¹Ī ãĤĪ ãģı ĠÙĪ ÙĤد íĸ Īëĭ¤ Ġn Æ¡i ãģ«ãĤĪ ãģ£ãģ¦ Ġvi ết Ġà¹Ģà¸ŀ ืà¹Īà¸Ń ëIJĺ ëĬĶ اد ÙĬ ĠÙģ Ø¥ÙĨ ì¦ Ŀ ĠÄij ặt Ġh Æ°á»Ľng Ġx ã Ġönem li ãģł ãģ¨ Ġm ẹ Ġ×ij ×Ļ Ġ×ĵ ×ijר Ġv áºŃt ĠÄij ạo Ġdá»± ng ĠÑĤ ом ĠÙģÙĬ Ùĩا Ġج ÙħÙĬع Ġthu áºŃt st ÄĻp Ġti ết Ø´ ÙĬ Ġе Ñīе ãģĻãĤĭ ãģ¨ ĠmÃł u ĠÑįÑĤ ого Ġv ô ĠÐŃ ÑĤо Ġth áºŃt Ġn ữa Ġbi ến Ġn ữ Ġ׾ ׼×Ŀ ×Ļ ×Ļף Ġس ت ĠÐŀ ÑĤ Ġph ụ ê¹Į ì§Ģ Ġ׾ ×ļ Ġk ỳ à¹ĥ à¸Ħร Ġg ây ĠÙĦ ÙĦÙħ Ġtụ c ت ÙĬÙĨ Ġtr ợ Ġ׾ פ×Ļ Ġb á»ij ĠÐļ а ĠÄij ình ow Äħ s ında Ġkhi ến s ız Ġк огда ס ׾ ĠбÑĭ л à¸Ļ à¹īà¸Ńย обÑĢаР· Ġê²ĥ ìĿ´ëĭ¤ ëĵ¤ ìĿĢ ãģ¸ ãģ® Ġà¹Ģม ืà¹Īà¸Ń Ġph ục Ġ׊׾ק Ġh ết ĠÄij a à¹Ģà¸Ķà¹ĩ à¸ģ íĺ ķ l ÃŃ ê¸ ī Ġع دد ĠÄij á»ĵ Ġg ần Ġ×Ļ ×ķ×Ŀ Ġs Ä© ÑĢ Ñıд Ġquy á»ģn Ġ×IJ ׾×IJ Ùĩ Ùħا ׳ ×Ļ×Ķ ׾ ×ķת Ġ×Ķר ×ij×Ķ Ġti ên Ġal ın Ġd á»ħ 人 ãģĮ но Ñģ л ÑģÑı ĠÄij Æ°a ส าว иÑĢов ан Ġ×ŀס פר ×Ĵ ף Ġki ến ĠÐ ¨ p é б Ñĥ ов ой б а ĠØ¥ ÙĦا ×IJ ׾×Ļ Ġx ây Ġb ợi Ġש ×ķ 人 ãģ® ק ×Ļ×Ŀ à¹Ģà¸Ķ ืà¸Ńà¸Ļ Ġkh á Ġ×ķ ׾×Ķ ×ĵ ×ķת Ġ×¢ ×ij×ķר Ġبش ÙĥÙĦ ĠÙĩÙĨا Ùĥ ÑĤ ÑĢа Ġ íķĺëĬĶ ร à¸Ńà¸ļ owa ÅĤ h é Ġdi á»ħn Ġ×Ķ ׼׾ ĠØ£ س Ġch uyá»ĩn ระ à¸Ķัà¸ļ ĠNh ững Ġ×IJ ×Ĺת ĠØŃ ÙĪÙĦ л ов ׳ ר Ġ×ķ ׳ Ġch Æ¡i Ġiç inde ÑģÑĤв Ñĥ Ġph á»ij ĠÑģ Ñĥ ç§ģ ãģ¯ Ġch ứng Ġv á»±c à¹ģ à¸Ń Ġl áºŃp Ġtừ ng å°ij ãģĹ ĠNg uy ĠNguy á»ħn ĠÙģÙĬ Ùĩ Ġб а ×Ļ ×Ļת Ġ×ľ×¢ ש×ķת Ġ×ŀ ׼ Ġnghi á»ĩm Ġм ного Ġе е ëIJĺ ìĸ´ Ġl ợi Ġ׾ ׾×IJ Ġ׼ ף Ġch ÃŃ ãģ§ ãģ® ×Ĺ ×ķ ש ×ķ×Ŀ Ġ×ŀ ר ĠÐĶ лÑı Å ģ Ġ׼×IJ שר ĠM á»Ļt ĠÙĪاÙĦ ت ĠìĿ´ 룰 ÅŁ a Ġchi ến Ġaras ında Ġ×ij ×IJתר ãģķãĤĮ ãģ¦ãģĦãĤĭ Ø´ ÙĥÙĦ Ġt ượng Ġت ت ĠC ó Ġb á»ı Ġtá»ī nh Ġkh ÃŃ ĠпÑĢ оÑģÑĤ ĠпÑĢоÑģÑĤ о ĠÙĪ ÙĤاÙĦ Ġgi áo ĠN ếu ×IJ ×ŀר ×¢×ł×Ļ ×Ļף íİ ¸ Ùĩد Ùģ ĠB á»Ļ Ġb Ãłn Ġng uyên Ġgü zel ส าย ì² ľ ×ŀ ×ķר Ġph ân ס פק ק ×ij׾ ĠاÙĦÙħ تØŃ ĠاÙĦÙħتØŃ Ø¯Ø© ائ د Ġ×IJ ×ŀר Ġki ÅŁi ì¤ Ģ Ġtr uyá»ģn ĠÙĦ Ùĩا ĠÐľ а à¸ļริ ษ à¸ļริษ ั à¸ļริษั à¸Ĺ Ġש ׳×Ļ×Ŀ Ġмен Ñı ÅŁ e Ġdi á»ĩn Ġ×IJ׳ ×Ĺ׳×ķ k ü Ġc á»ķ Ġm á»Ĺi w ä Ùħ ÙĬ Ġhi á»ĥu ëĭ ¬ Ġ×Ķ ×Ĺ׾ Ġt ên Ġki á»ĩn ÙĨ ÙĤÙĦ Ġv á»ĩ ×ĵ ת ĠÐłÐ¾ÑģÑģ ии л Ñĥ ĠاÙĦع ربÙĬØ© ĠØ· رÙĬÙĤ Ġ×Ķ×ij ×Ļת Ñģ еÑĢ Ġм не ä u Ġtri á»ĩu ĠÄij ủ Ġר ×ij ت ÙĩÙħ à¸ĭ ี Ġì§Ģ ê¸Ī li ÅĽmy د عÙħ ãģł ãĤįãģĨ Ñģки е Ġh á»ıi Ġק ×ķ ÑĢÑĥ Ñģ ÙĨ ظر ãģ® ãĤĤ Ġ×Ķ ׼×Ļ ĠìĽ IJ ÙĪ Ùĩ ĠÙĪ Ùİ ĠB ạn п лаÑĤ Ġ×ŀ ×ŀש лÑİ Ð± ĠнÑĥж но Ġth Æ° ãģ µ ãģı ãĤīãģĦ ر Ø´ ר ×ķ×Ĺ ĠÙĬ تÙħ Ġצר ×Ļ×ļ Ġph á ม à¸Ńà¸ĩ Ġ×ij×IJ ×ķפף Ġcả nh Ġíķľ ëĭ¤ Ġ×Ķ×ŀ ת à¸ķà¹Īาà¸ĩ à¹Ĩ มี à¸ģาร Ñģки Ñħ ĠÐĴ Ñģе Ġا ÙĪ ج ÙĬ ãģĵãģ¨ ãģ¯ Ġd Ãłi Ġh á»ĵ èĩªåĪĨ ãģ® à¹Ħ หà¸Ļ ëĵ¤ ìĿĦ ĠV Äĥn Ġд аж Ġдаж е Ñĭ ми лаÑģ ÑĮ ÙĬ ÙĪÙĨ ÙĨ ÙĪ c ó ãģĹãģ¦ ãģĦãģŁ ãģł ãģĭãĤī طاÙĦ ب Ġc á»Ńa п ÑĢоÑģ ãģªãģ© ãģ® รุ à¹Īà¸Ļ Ġchi ếc л Ñĭ ĠÑıвлÑı еÑĤÑģÑı Ġn á»ķi ãģ® ãģĬ Ġ×IJת ×Ŀ ĠëķĮ문 ìĹIJ à¸ģล าà¸ĩ ĠbaÅŁ ka ìĦ Ŀ ĠÑĨ ел Ùģ ÙĤ ãģ«ãĤĪ ãĤĭ ÙĤ ا Ġçı kar Ġcứ u Ø· ا Ġש ת à¹Ĥ à¸Ħ Ġ×ŀ ׾ Ġ×Ķ פר Ġг де ĠØ® Ø· åīį ãģ« c jÄĻ Ġ׊ש×ķ×ij ר×Ĵ ×¢ Ġkho ảng ĠÄij á»Ŀi ĠÐł е Ġо на Ġ×IJ ׳×ķ ãģ® ãģ« ĠاÙĦØ° ÙĬÙĨ кÑĥ п ãĤµ ãĥ¼ãĥ ãĤµãĥ¼ãĥ ĵ ãĤµãĥ¼ãĥĵ ãĤ¹ в ал г е Ġgi ữa ĠKh ông ĠâĹ ĭ à¸ģล ุà¹Īม ĠÙħÙĨ Ø° à¸Ń à¹Īาà¸Ļ ĠÑģп оÑģоб ĠÄij á»Ļi Ġdi ÄŁer Ġ à¸ĸà¹īา Ùħ Ø«ÙĦ Ġ×Ķ×IJ ×Ļ Ġد ÙĪÙĨ ÙĬر اÙĨ Ñī и بÙĨ اء ĠØ¢ خر ظ Ùĩر Ġ×ij ׼ ĠاÙĦÙħ ع ãĥ Ĵ Ġt ất Ġm ục ĠdoÄŁ ru ãģŁ ãĤī Ġס ×ķ Ġx ác ร à¸Ń ĠcÄĥ n Ġон л Ġонл айн Ġk ý Ġch ân Ġ à¹Ħมà¹Ī اØŃ Ø© r án ׳×Ļ ×Ļ×Ŀ Ġ×ij ף ĠÐ ĸ à¸ķร à¸ĩ д Ñĭ Ġs ắc ÙĦ ت ãĥŃ ãĥ¼ ĠÙĦ ÙĨ Ġר ×ķ Ġd Æ°á»Ľi à¹Ģ à¸ĺ à¹Ģà¸ĺ à¸Ń e ÄŁi Ġ×ķ ש ĠÙĦ Ø£ Ġg ặp Ġc á»ij ãģ¨ ãģ¦ãĤĤ رÙĪ س Ġ׾×Ķ ×Ļ Ġë³ ¸ ä¸Ĭ ãģĴ Ġm ức Ñħ а Ġìŀ ¬ à¸ī ัà¸Ļ ÑĢÑĥ ж Ġaç ık ÙĪ اÙĦ Ġ×ĸ ×ŀף 人 ãģ¯ ع ÙĬÙĨ Ñı Ñħ Ġ×Ĵ×ĵ ×ķ׾ ר ×ķ×ij g ó ëĿ¼ ê³ł Ġark adaÅŁ ÙĨ شر Ġгод Ñĥ ĠболÑĮ ÑĪе ãģ¡ãĤĩ ãģ£ãģ¨ Ġcâ u Ġs át íĶ ¼ Ġti ến íķ´ ìķ¼ ĠÙĪ Ø£ÙĨ à¸Ļ าà¸Ļ Ġ×ij×IJ×ŀ 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á»Ń รà¹Īาà¸ĩ à¸ģาย üz ü æĹ¥æľ¬ ãģ® ê³¼ ìłķ ש ×Ļ×IJ ĠìŀĪ ê³ł ×ij ×ķ׾ ìķ ħ ĠÙĪاÙĦ ا ĠÐĽ и ĠвÑģ Ñij Ġużytk ow ×Ĺ ×ķ׾ ر Ùģض Ġson uç ãģĦ ãģ¾ãģĽãĤĵ ìĤ¬ ìĹħ ëĪ Ħ ÑĤ ек Ġud ziaÅĤ л ез Ġ×Ķ×Ļ ×Ļת×Ļ ãĤīãĤĮ ãģ¦ Ùħس ؤÙĪÙĦ ر ار ÑĤ ан ĠÄij Ãło Ġר ×ķ×ij Ġ×ijש×ij ×Ļ׾ ä»ĬåĽŀ ãģ¯ ãĤ¸ ãĥ¥ Ġ×¢ ×ijר ãģĽ ãģ¦ п олÑĮ ak lı Ġk ÃŃnh د ت лож ение ĠاÙĦÙħ ص ĠاÙĦÙħص رÙĬ à¸Īริà¸ĩ à¹Ĩ ĠاÙĦشر ÙĥØ© ĠÄij á»ı ãĥĽ ãĥĨ ãĥĽãĥĨ ãĥ« Ñį кон Ñįкон ом ĠÙĪ عÙĨ Ġת ׳ Ġ×ª×ł ×IJ×Ļ ĠاÙĦدÙĪÙĦ ÙĬØ© Ġì§Ģ ìĹŃ ãģ§ãģĻ ãģĭ Ġв аÑĢи ĠваÑĢи анÑĤ ĠاÙĦع رب ел а Ġt Æ°á»Ľng sk Äħ Ġm ặc ส ัà¸ģ ãĥĵ ãĥ¼ Ġ×ij ×Ĵ׾ Ġ×ij×Ĵ׾ ׾ ãĥķãĤ¡ ãĥ³ ×ij ×Ļצ ×ij×Ļצ ×ķ×¢ ли ÑģÑĤ à¸Ł ุ à¸Łà¸¸ à¸ķ à¸Łà¸¸à¸ķ à¸ļà¸Ńล à¸Ŀ à¹Īาย ìŀIJ ìĿĺ Ġس ÙĪÙģ Ġש ×Ķת Ġê± ¸ ×¢ ×ij×ķ×ĵ ãģĻãĤĭ ãģĵãģ¨ãģĮ ĠÑĩа ÑģÑĤÑĮ ãĤ¢ ãĥ¡ãĥª ãĤ¢ãĥ¡ãĥª ãĤ« Ġtak ım Ġs Ỽ ĠsỼ m שר ×Ķ è¨Ģ ãģĨ л ан ì» ¤ ׼ ׳×Ķ ÙĪÙģ ÙĬ íĹ Ī lu ÄŁu ĠëĮĢ íķ´ Ġ׾×ij ×Ļת Ġ×Ķר×IJש ×ķ׳×Ķ ص Ùħ Ġsö yled Ġsöyled i à¸Ľ าà¸ģ Ġard ından ãģĪ ãģŁ à¸Ĺัà¹Īว à¹Ħà¸Ľ Ġ׳×ķס ×£ б олÑĮ ãĤĵãģ§ãģĻ ãģijãģ© ĠлиÑĪ ÑĮ Ġ×ij ×IJ×Ļ ĠбÑĭ ÑģÑĤÑĢо ส ัà¸Ļ Ġ×ij פ׳×Ļ л еÑĩ ĠاÙĦØ® بر Ġsó c Ġth ú Ġп ÑıÑĤ ãģĬ é¡ĺ ãģĬé¡ĺ ãģĦ ÑĤ ин ãģ«ãģ¤ãģĦãģ¦ ãģ¯ פ ף Ġдв ÑĥÑħ à¸į ีà¹Ī à¸įีà¹Ī à¸Ľ à¸įีà¹Īà¸Ľ ุ à¸įีà¹Īà¸Ľà¸¸ à¹Īà¸Ļ оп еÑĢ ĠاÙĦب شر ĠاÙĦÙħ اÙĦ ıyor uz تØŃ ÙħÙĬÙĦ à¸ģ ะ éĸĵ ãģ« ×Ĺ ×ķש ĠNg uyên ãģĦãģ¦ ãģĦãĤĭ дÑĥ ÑĪ ש פע ÑĪ Ñĥ å®Ł éļĽãģ« ĠÑĢай он ĠCh á»ī ÙĨ صر Ġìļ ´ Ġìļ´ ìĺģ Ġ×Ķ×ĵ ×Ļף ØŃد د ر ز ĠاÙĦد Ùħ ĠPh áp ÑĤ ÑģÑı è¦ĭ ãģĪ Ġti á»ĥu Ġs á»Ńa а ÑİÑĤÑģÑı ĠB á Ġ×ķ ׼׾ Ð ĸ ÑĪ им ìĿ´ ëĬĶ л ев d ık Ġprés ente Ġara ç صد ÙĤ Ġпом ог ĠاÙĦشر ÙĤ ĠÙĪاÙĦ Ø°ÙĬ رÙĬ ا ×ij ׳×ķת Ġng á»ĵi ר ×ķפ ר×ķפ ×IJ Ġth ấp ãĤĦ ãģ¯ ãĤĦãģ¯ ãĤĬ ĠاÙĦج دÙĬدة éĿŀ常 ãģ« ÙĬÙĦ ÙĬ ìª ½ تع اÙħÙĦ ãģł ãģ¨æĢĿãģĦãģ¾ãģĻ Ùħ Ùħ иÑĤе ли ãĤµãĤ¤ ãĤº اد ات ĠاÙĦÙħ اÙĦÙĬØ© Ùĥات ب к ли веÑĢ Ñħ ни Ñĩ Ġ×ľ×¢ ×ij×ķ×ĵ ׾ ×Ļ×Ķ ØŃ Ùİ ãĤ¤ ãĥĻ ãĤ¤ãĥĻ ãĥ³ãĥĪ Ġת ×Ĵ×ķ×ij×ķת ÑĦ он ĠдÑĢÑĥг ие ×IJ ×ĸ×ķר Ġper ò ìķ ŀ åĢŁ ãĤĬ ר צ×Ļ ×IJ ×ĸ алÑĮ нÑĭÑħ Ġê²ĥ ìľ¼ë¡ľ ĠпÑĢав о ĠاÙĦØ£ رض à¹Ģà¸Ĺ à¸Ħ à¹Ģà¸Ĺà¸Ħ à¹Ĥà¸Ļ à¹Ģà¸Ĺà¸Ħà¹Ĥà¸Ļ à¹Ĥล à¹Ģà¸Ĺà¸Ħà¹Ĥà¸Ļà¹Ĥล ย à¹Ģà¸Ĺà¸Ħà¹Ĥà¸Ļà¹Ĥลย ี צ ר×Ļ ĠÐļ Ñĥ ıl ma 決 ãĤģ ا ÙĪ Ġ×ĵ ק×ķת à¸Ħร ู ĠÙħست ÙĪÙī à¸Ľ 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ä¹ħãģĹãģ¶ ãĤĬ Ġк аÑĤ ĠкаÑĤ ал ĠкаÑĤал ог ส à¹īม ĠÑĢ ей ĠÑĢей ÑĤ ĠÑĢейÑĤ инг Ġth uyá»ģn ĠاÙĦÙħ ÙĤدس esp ère ãģ«åħ¥ ãģ£ãģŁ หมาย à¹Ģลà¸Ĥ ת×Ĺ×ķש ת à¸Ļ à¹Īะ Ġpe ÅĤ ĠpeÅĤ ne Ġpé rd Ġpérd ida หม วà¸Ķ หมวà¸Ķ หมูà¹Ī иÑĩеÑģк ÑĥÑİ çµĤ ãĤı çµĤãĤı ãģ£ãģŁ Ġ×Ĵ ×ķ×Ĵ׾ à¸Ĺำ à¸Ħวาม à¸Ĺำà¸Ħวาม สะà¸Ńาà¸Ķ Hot éis Ġз аÑĢ ĠзаÑĢ егиÑģÑĤ ĠзаÑĢегиÑģÑĤ ÑĢи ĠзаÑĢегиÑģÑĤÑĢи ÑĢова ĠÑģ обÑĭÑĤи ĠÑģобÑĭÑĤи Ñı Ġ×ĸ ׼×IJ ÙħÙĨظ ÙĪÙħØ© Ġ×Ķ×ŀ צ Ġ×Ķ×ŀצ ×Ļ×IJ×ķת Ùħ ÙĥÙĪÙĨ ÙħÙĥÙĪÙĨ ات ä¸ĬãģĮ ãĤĭ Ġm ÄĻ ĠmÄĻ sk หรืà¸Ń à¹Ģà¸Ľà¸¥à¹Īา ëĤ ® Ġnok tas Ġnoktas ı ĠболÑĮÑĪ им ĠлÑĥÑĩ ÑĪиÑħ Ø´Ùĩ ÙĬد à¸Ńำ à¸Ļ à¸Ńำà¸Ļ วย à¸Ńำà¸Ļวย à¸Ħวาม à¸Ńำà¸Ļวยà¸Ħวาม สะà¸Ķวà¸ģ Ġе в Ġев ÑĢ ĠевÑĢ оп ĠевÑĢоп ей à¸ī าย ìĦ Ń Ùħ Ùģا ÙħÙģا ÙĪض ÙħÙģاÙĪض ات ë¹ Į 赤 ãģ¡ãĤĥãĤĵ ĠÑĥдал оÑģÑĮ ĠÐ¥ оÑĤ ĠХоÑĤ Ñı przedsiÄĻbior c ĠH ôm íķĺìĺĢ ìĬµëĭĪëĭ¤ Ġн аг Ġнаг ÑĢÑĥз ĠнагÑĢÑĥз к Ġ×ij×Ļ׳ ׾×IJ×ķ×ŀ×Ļ Ġê°ĢëĬ¥ íķľ ĠH ữu à¸Ń ุà¸Ķ à¸Ńุà¸Ķ ม ת ×ķפ ת×ķפ ×¢×Ķ Ġmi ÅĤo ĠmiÅĤo ÅĽci ksi Äħż ksiÄħż ka ĠاÙĦÙĦ عبة à¸ī าà¸ģ สะ สม ×ŀ תר ×ŀתר ×Ĺש Ġlég ère Ġ׾צ פ Ġ׾צפ ×Ļ×Ķ ĠиÑģÑĤоÑĢ иÑı Ġ ãĥĪãĥ© 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ï¤ µ ï¤ ¼ ï¥ Ģ ï¥ ij ï¥ Ĵ ï¥ ķ ï¥ ĺ ï¥ Ļ ï¥ « ï¥ ¬ ï¥ ° ï ¥¿ ï¦ ĭ ï¦ ı ï¦ Ķ ï¦ ĸ ï¦ ĺ ï¦ Ľ ï¦ ł ï¦ ® ï¦ ¯ ï¦ º ï¦ » ï¦ ¾ ï§ Ĩ ï§ ĸ ï§ Ľ ï§ ŀ ï§ Ł ï§ § ï§ ³ ï§ º ï§ ½ ï¨ ĥ ï¨ ļ ï¨ ¢ ï© Ł ï¬ ¤ ï¬ ¬ ï¬ ¼ ïŃ Ĵ ïŃ ķ ïŃ Ľ ïŃ Ŀ ïŃ ŀ ïŃ Ł ïŃ ¤ ïŃ § ïŃ ¨ ïŃ ® ïŃ ° ïŃ ± ïŃ · ïŃ ¹ ïŃ » ï® Ģ ï® ĥ ï® Ħ ï® ħ ï® į ï® Ĵ ï® ĵ ï® ķ ï® ¦ ï® ® ï® ° ï¯ ĵ ï¯ ľ ï¯ © ï¯ ª ï¯ ¬ ï¯ Ń ï¯ ® ï¯ · ï¯ ¹ ï¯ » ï¯ ¼ ï° ĥ ï° Į ï° IJ ï° ĺ ï° Ļ ï° ľ ï° ŀ ï° ¢ ï° ® ï° ° ï° ¼ ï° ¿ ï± Ģ ï± ģ ï± Ī ï± ĭ ï± ı ï± Ń ï² Ģ ï² ĩ ï² Ī ï² ĭ ï² İ ï² Ĵ ï² ľ ï² ł ï² ¬ ï² » ï³ ĩ ï³ Ķ ï³ £ ï³ « ï´ ĺ ï´ ° ï´ ½ ï ¶ ï¶ ° ï¸ ĸ ï¸ ´ ï¸ ¹ ï¹ į ï¹ Ĺ ï¹ ¢ ï¹ ¤ ï¹ © ï¹ ± ï¾ ° ï¿ Ĥ ï¿ ® ðIJĮ ° ðIJĮ ¹ ðIJĮ º ðIJĮ ½ ðIJį Ĥ ðIJį ĥ ðIJį Ħ ðIJ İ ðIJİ ¹ ðIJ¤ Ĥ ðIJ¤ į ðIJ¤ ı ðIJ¤ ĵ ðIJŃ ī ðIJŃ į ðIJ° ĩ ðIJ° ° ðij Ĥ ðijĤ Ħ ðij ĺ ðijĺ ģ ðĴ Ģ ðĴĢ ¸ ðĴ ģ ðĴģ º ðĴ Ħ ðĴĦ · ðĴ Ĭ ðĴĬ ij ðĴ ĭ ðĴĭ Ĺ ð ĴĮ ðĴĮ ¨ ðĵĥ ¢ ðĵĥ ° ðĸ ł ðĸł ļ ðĿĦ ĥ ðĿĦ ħ ðĿĦ ķ ðĿĦ Ļ ðĿĦ ± ðĿĦ ´ ðĿĦ ¹ ðĿħ İ ðĿħ ª ðĿĨ £ ðĿĨ ³ ðĿĨ ¹ ðĿĩ Ĭ ðĿĩ Ĺ ðĿĩ ļ ðĿĩ ľ ðĿĩ ł ðĿIJ ī ðĿIJ ĸ ðĿIJ ĺ ðĿIJ £ ðĿIJ ± ðĿij Ĭ ðĿij Ń ðĿij ¼ ðĿij ½ ðĿĴ ° ðĿĴ · ðĿĴ ¿ ðĿĵ ģ ðĿĵ ĭ ðĿĵ İ ðĿĵ Ĵ ðĿ ĵĺ ðĿĵ ¢ ðĿĵ ¦ ðĿĵ « ðĿĵ ¿ ðĿĶ İ ðĿĶ ± ðĿĶ ´ ðĿĶ · ðĿĶ ¸ ðĿĶ ½ ðĿķ Ĥ ðĿķ ĥ ðĿķ ĭ ðĿķ ı ðĿķ IJ ðĿķ ¥ ðĿķ ´ ðĿķ º ðĿĸ IJ ðĿĸ Ľ ðĿĸ Ŀ ðĿĸ ŀ ðĿĹ © ðĿĹ ³ ðĿĹ ½ ðĿĺ Ĭ ðĿĺ ĭ ðĿĺ Ķ ðĿĺ ± ðĿĺ ´ ðĿĺ ¿ ðĿĻ Ĵ ðĿĻ Ŀ ðĿĻ Ł ðĿĻ ¬ ðĿĻ Ń ðĿĻ » ðĿĻ ¾ ðĿļ Ī ðĿļ ĭ ðĿļ ij ðĿļ Ł ðĿļ ł ðĿļ £ ðĿĽ ½ ðĿľ Ĥ ðĿľ Ķ ðĿľ Ļ ðŁ Ģ ðŁĢ Ħ ðŁĦ ² ðŁĦ ¶ ðŁħ IJ ðŁħ ĸ ðŁħ ļ ðŁħ Ľ ðŁħ ¦ ðŁħ ¶ ðŁħ » ðŁħ ¼ ðŁĨ ĥ ðŁĨ Ĩ ðŁĨ İ ðŁĪ ¯ ðŁĪ ² ðŁĪ ¹ ðŁĮ ĩ ðŁĮ ĵ ðŁį ĺ ðŁİ ij ðŁİ ¿ ðŁı ı ðŁı Ĵ ðŁı © ðŁı ¯ ðŁIJ Ģ ðŁij Ŀ ðŁĴ ¹ ðŁĴ º ðŁĵ Ł ðŁĵ ª ðŁĵ ¼ ðŁĶ Ģ ðŁĶ Ĥ ðŁĶ ĥ ðŁĶ ĩ ðŁĶ ĵ ðŁĶ ¢ ðŁĶ ¤ ðŁĶ © ðŁķ ĸ ðŁķ ļ ðŁķ ľ ðŁķ Ŀ ðŁķ ŀ ðŁķ ł ðŁķ ¢ ðŁķ ³ ðŁĸ ĩ ðŁĸ ij ðŁĸ ¶ ðŁĹ ģ Ñ ¨ Ú İ á¡ Į Ḡ° ẠĢ á¼ ® á½ Ŀ âĦ ¬ âļ § ⼠¤ ã³ ¬ êĻ ĭ ê¸ ij ëĶ ī ëĹ į ë¡ ij ë¯ ij ë» ħ ë¼ Ŀ ìĦ IJ ìī ¡ ìĭ ² ìı ± ìĹ ¤ ìĿ © ìĿ ¿ ìŁ Ļ ìł ° ì¥ ī íĬ Ń íķ ® ï® ı ðŁħ ± ðŁĨ Ĵ ðŁķ ĭ É ĺ Ê ĵ Õ ĥ à´ ´ འħ áĨ º áĪ Ĭ áĪ ¨ áĪ ¾ áī IJ áĮ ĥ áĮ ½ áĶ Ń áł Ĥ áł ¬ ᨠ¸ á© ĭ ᶠı á¾ Ķ á¿ IJ á¿ ļ âĻ Ļ âļ Ĥ âļ Ĺ â¡ ¢ ⤠¦ ëĸ ° ë¤ 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================================================ FILE: comfy/text_encoders/qwen_image.py ================================================ from transformers import Qwen2Tokenizer from comfy import sd1_clip import comfy.text_encoders.llama import os import torch import numbers class Qwen25_7BVLITokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=3584, embedding_key='qwen25_7b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) class QwenImageTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_7b", tokenizer=Qwen25_7BVLITokenizer) self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, **kwargs): skip_template = False if text.startswith('<|im_start|>'): skip_template = True if text.startswith('<|start_header_id|>'): skip_template = True if prevent_empty_text and text == '': text = ' ' if skip_template: llama_text = text else: if llama_template is None: if len(images) > 0: llama_text = self.llama_template_images.format(text) else: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) key_name = next(iter(tokens)) embed_count = 0 qwen_tokens = tokens[key_name] for r in qwen_tokens: for i in range(len(r)): if r[i][0] == 151655: if len(images) > embed_count: r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:] embed_count += 1 return tokens class Qwen25_7BVLIModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class QwenImageTEModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) def encode_token_weights(self, token_weight_pairs, template_end=-1): out, pooled, extra = super().encode_token_weights(token_weight_pairs) tok_pairs = token_weight_pairs["qwen25_7b"][0] count_im_start = 0 if template_end == -1: for i, v in enumerate(tok_pairs): elem = v[0] if not torch.is_tensor(elem): if isinstance(elem, numbers.Integral): if elem == 151644 and count_im_start < 2: template_end = i count_im_start += 1 if out.shape[1] > (template_end + 3): if tok_pairs[template_end + 1][0] == 872: if tok_pairs[template_end + 2][0] == 198: template_end += 3 out = out[:, template_end:] extra["attention_mask"] = extra["attention_mask"][:, template_end:] if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]): extra.pop("attention_mask") # attention mask is useless if no masked elements return out, pooled, extra def te(dtype_llama=None, llama_quantization_metadata=None): class QwenImageTEModel_(QwenImageTEModel): def __init__(self, device="cpu", dtype=None, model_options={}): if llama_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata if dtype_llama is not None: dtype = dtype_llama super().__init__(device=device, dtype=dtype, model_options=model_options) return QwenImageTEModel_ ================================================ FILE: comfy/text_encoders/qwen_vl.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from typing import Optional, Tuple import math from comfy.ldm.modules.attention import optimized_attention_for_device def process_qwen2vl_images( images: torch.Tensor, min_pixels: int = 3136, max_pixels: int = 12845056, patch_size: int = 14, temporal_patch_size: int = 2, merge_size: int = 2, image_mean: list = None, image_std: list = None, ): if image_mean is None: image_mean = [0.48145466, 0.4578275, 0.40821073] if image_std is None: image_std = [0.26862954, 0.26130258, 0.27577711] batch_size, height, width, channels = images.shape device = images.device # dtype = images.dtype images = images.permute(0, 3, 1, 2) grid_thw_list = [] img = images[0] factor = patch_size * merge_size h_bar = round(height / factor) * factor w_bar = round(width / factor) * factor if h_bar * w_bar > max_pixels: beta = math.sqrt((height * width) / max_pixels) h_bar = max(factor, math.floor(height / beta / factor) * factor) w_bar = max(factor, math.floor(width / beta / factor) * factor) elif h_bar * w_bar < min_pixels: beta = math.sqrt(min_pixels / (height * width)) h_bar = math.ceil(height * beta / factor) * factor w_bar = math.ceil(width * beta / factor) * factor img_resized = F.interpolate( img.unsqueeze(0), size=(h_bar, w_bar), mode='bilinear', align_corners=False ).squeeze(0) normalized = img_resized.clone() for c in range(3): normalized[c] = (img_resized[c] - image_mean[c]) / image_std[c] grid_h = h_bar // patch_size grid_w = w_bar // patch_size grid_thw = torch.tensor([1, grid_h, grid_w], device=device, dtype=torch.long) pixel_values = normalized grid_thw_list.append(grid_thw) image_grid_thw = torch.stack(grid_thw_list) grid_t = 1 channel = pixel_values.shape[0] pixel_values = pixel_values.unsqueeze(0).repeat(2, 1, 1, 1) patches = pixel_values.reshape( grid_t, temporal_patch_size, channel, grid_h // merge_size, merge_size, patch_size, grid_w // merge_size, merge_size, patch_size, ) patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8) flatten_patches = patches.reshape( grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size ) return flatten_patches, image_grid_thw class VisionPatchEmbed(nn.Module): def __init__( self, patch_size: int = 14, temporal_patch_size: int = 2, in_channels: int = 3, embed_dim: int = 3584, device=None, dtype=None, ops=None, ): super().__init__() self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.in_channels = in_channels self.embed_dim = embed_dim kernel_size = [temporal_patch_size, patch_size, patch_size] self.proj = ops.Conv3d( in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False, device=device, dtype=dtype ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = hidden_states.view( -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size ) hidden_states = self.proj(hidden_states) return hidden_states.view(-1, self.embed_dim) def rotate_half(x): x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb_vision(q, k, cos, sin): cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float() q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class VisionRotaryEmbedding(nn.Module): def __init__(self, dim: int, theta: float = 10000.0): super().__init__() self.dim = dim self.theta = theta def forward(self, seqlen: int, device) -> torch.Tensor: inv_freq = 1.0 / (self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float, device=device) / self.dim)) seq = torch.arange(seqlen, device=inv_freq.device, dtype=inv_freq.dtype) freqs = torch.outer(seq, inv_freq) return freqs class PatchMerger(nn.Module): def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2, device=None, dtype=None, ops=None): super().__init__() self.hidden_size = context_dim * (spatial_merge_size ** 2) self.ln_q = ops.RMSNorm(context_dim, eps=1e-6, device=device, dtype=dtype) self.mlp = nn.Sequential( ops.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype), nn.GELU(), ops.Linear(self.hidden_size, dim, device=device, dtype=dtype), ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.ln_q(x).reshape(-1, self.hidden_size) x = self.mlp(x) return x class VisionAttention(nn.Module): def __init__(self, hidden_size: int, num_heads: int, device=None, dtype=None, ops=None): super().__init__() self.hidden_size = hidden_size self.num_heads = num_heads self.head_dim = hidden_size // num_heads self.scaling = self.head_dim ** -0.5 self.qkv = ops.Linear(hidden_size, hidden_size * 3, bias=True, device=device, dtype=dtype) self.proj = ops.Linear(hidden_size, hidden_size, bias=True, device=device, dtype=dtype) def forward( self, hidden_states: torch.Tensor, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, cu_seqlens=None, optimized_attention=None, ) -> torch.Tensor: if hidden_states.dim() == 2: seq_length, _ = hidden_states.shape batch_size = 1 hidden_states = hidden_states.unsqueeze(0) else: batch_size, seq_length, _ = hidden_states.shape qkv = self.qkv(hidden_states) qkv = qkv.reshape(batch_size, seq_length, 3, self.num_heads, self.head_dim) query_states, key_states, value_states = qkv.reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) if position_embeddings is not None: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin) query_states = query_states.transpose(0, 1).unsqueeze(0) key_states = key_states.transpose(0, 1).unsqueeze(0) value_states = value_states.transpose(0, 1).unsqueeze(0) lengths = cu_seqlens[1:] - cu_seqlens[:-1] splits = [ torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) ] attn_outputs = [ optimized_attention(q, k, v, self.num_heads, skip_reshape=True) for q, k, v in zip(*splits) ] attn_output = torch.cat(attn_outputs, dim=1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output class VisionMLP(nn.Module): def __init__(self, hidden_size: int, intermediate_size: int, device=None, dtype=None, ops=None): super().__init__() self.gate_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype) self.up_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype) self.down_proj = ops.Linear(intermediate_size, hidden_size, bias=True, device=device, dtype=dtype) self.act_fn = nn.SiLU() def forward(self, hidden_state): return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) class VisionBlock(nn.Module): def __init__(self, hidden_size: int, intermediate_size: int, num_heads: int, device=None, dtype=None, ops=None): super().__init__() self.norm1 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype) self.norm2 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype) self.attn = VisionAttention(hidden_size, num_heads, device=device, dtype=dtype, ops=ops) self.mlp = VisionMLP(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops) def forward( self, hidden_states: torch.Tensor, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, cu_seqlens=None, optimized_attention=None, ) -> torch.Tensor: residual = hidden_states hidden_states = self.norm1(hidden_states) hidden_states = self.attn(hidden_states, position_embeddings, cu_seqlens, optimized_attention) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class Qwen2VLVisionTransformer(nn.Module): def __init__( self, hidden_size: int = 3584, output_hidden_size: int = 3584, intermediate_size: int = 3420, num_heads: int = 16, num_layers: int = 32, patch_size: int = 14, temporal_patch_size: int = 2, spatial_merge_size: int = 2, window_size: int = 112, device=None, dtype=None, ops=None ): super().__init__() self.hidden_size = hidden_size self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.window_size = window_size self.fullatt_block_indexes = [7, 15, 23, 31] self.patch_embed = VisionPatchEmbed( patch_size=patch_size, temporal_patch_size=temporal_patch_size, in_channels=3, embed_dim=hidden_size, device=device, dtype=dtype, ops=ops, ) head_dim = hidden_size // num_heads self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2) self.blocks = nn.ModuleList([ VisionBlock(hidden_size, intermediate_size, num_heads, device, dtype, ops) for _ in range(num_layers) ]) self.merger = PatchMerger( dim=output_hidden_size, context_dim=hidden_size, spatial_merge_size=spatial_merge_size, device=device, dtype=dtype, ops=ops, ) def get_window_index(self, grid_thw): window_index = [] cu_window_seqlens = [0] window_index_id = 0 vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size for grid_t, grid_h, grid_w in grid_thw: llm_grid_h = grid_h // self.spatial_merge_size llm_grid_w = grid_w // self.spatial_merge_size index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) index_padded = index_padded.reshape( grid_t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size, ) index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( grid_t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size, ) seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) index_padded = index_padded.reshape(-1) index_new = index_padded[index_padded != -100] window_index.append(index_new + window_index_id) cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_size * self.spatial_merge_size + cu_window_seqlens[-1] cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() window_index = torch.cat(window_index, dim=0) return window_index, cu_window_seqlens def get_position_embeddings(self, grid_thw, device): pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h, device=device).unsqueeze(1).expand(-1, w) hpos_ids = hpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) hpos_ids = hpos_ids.permute(0, 2, 1, 3).flatten() wpos_ids = torch.arange(w, device=device).unsqueeze(0).expand(h, -1) wpos_ids = wpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) wpos_ids = wpos_ids.permute(0, 2, 1, 3).flatten() pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) pos_ids = torch.cat(pos_ids, dim=0) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size, device) return rotary_pos_emb_full[pos_ids].flatten(1) def forward( self, pixel_values: torch.Tensor, image_grid_thw: Optional[torch.Tensor] = None, ) -> torch.Tensor: optimized_attention = optimized_attention_for_device(pixel_values.device, mask=False, small_input=True) hidden_states = self.patch_embed(pixel_values) window_index, cu_window_seqlens = self.get_window_index(image_grid_thw) cu_window_seqlens = torch.tensor(cu_window_seqlens, device=hidden_states.device) cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) position_embeddings = self.get_position_embeddings(image_grid_thw, hidden_states.device) seq_len, _ = hidden_states.size() spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size hidden_states = hidden_states.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1) hidden_states = hidden_states[window_index, :, :] hidden_states = hidden_states.reshape(seq_len, -1) position_embeddings = position_embeddings.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1) position_embeddings = position_embeddings[window_index, :, :] position_embeddings = position_embeddings.reshape(seq_len, -1) position_embeddings = torch.cat((position_embeddings, position_embeddings), dim=-1) position_embeddings = (position_embeddings.cos(), position_embeddings.sin()) cu_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]).cumsum( dim=0, dtype=torch.int32, ) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) for i, block in enumerate(self.blocks): if i in self.fullatt_block_indexes: cu_seqlens_now = cu_seqlens else: cu_seqlens_now = cu_window_seqlens hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now, optimized_attention=optimized_attention) hidden_states = self.merger(hidden_states) return hidden_states ================================================ FILE: comfy/text_encoders/sa_t5.py ================================================ from comfy import sd1_clip from transformers import T5TokenizerFast import comfy.text_encoders.t5 import os class T5BaseModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json") super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True) class T5BaseTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) class SAT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer) class SAT5Model(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): super().__init__(device=device, dtype=dtype, model_options=model_options, name="t5base", clip_model=T5BaseModel, **kwargs) ================================================ FILE: comfy/text_encoders/sd2_clip.py ================================================ from comfy import sd1_clip import os class SD2ClipHModel(sd1_clip.SDClipModel): def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): if layer == "penultimate": layer="hidden" layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=True, model_options=model_options) class SD2ClipHTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='clip_h', tokenizer_data=tokenizer_data) class SD2Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer) class SD2ClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="h", clip_model=SD2ClipHModel, **kwargs) ================================================ FILE: comfy/text_encoders/sd2_clip_config.json ================================================ { "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 49407, "hidden_act": "gelu", "hidden_size": 1024, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 16, "num_hidden_layers": 24, "pad_token_id": 1, "projection_dim": 1024, "torch_dtype": "float32", "vocab_size": 49408 } ================================================ FILE: comfy/text_encoders/sd3_clip.py ================================================ from comfy import sd1_clip from comfy import sdxl_clip from transformers import T5TokenizerFast import comfy.text_encoders.t5 import torch import os import comfy.model_management import logging import comfy.utils class T5XXLModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None) if t5xxl_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = t5xxl_quantization_metadata model_options = {**model_options, "model_name": "t5xxl"} super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) def t5_xxl_detect(state_dict, prefix=""): out = {} t5_key = "{}encoder.final_layer_norm.weight".format(prefix) if t5_key in state_dict: out["dtype_t5"] = state_dict[t5_key].dtype quant = comfy.utils.detect_layer_quantization(state_dict, prefix) if quant is not None: out["t5_quantization_metadata"] = quant return out class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data) class SD3Tokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.clip_g.untokenize(token_weight_pair) def state_dict(self): return {} class SD3ClipModel(torch.nn.Module): def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}): super().__init__() self.dtypes = set() if clip_l: self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options) self.dtypes.add(dtype) else: self.clip_l = None if clip_g: self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) self.dtypes.add(dtype) else: self.clip_g = None if t5: dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) self.t5_attention_mask = t5_attention_mask self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask) self.dtypes.add(dtype_t5) else: self.t5xxl = None logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5)) def set_clip_options(self, options): if self.clip_l is not None: self.clip_l.set_clip_options(options) if self.clip_g is not None: self.clip_g.set_clip_options(options) if self.t5xxl is not None: self.t5xxl.set_clip_options(options) def reset_clip_options(self): if self.clip_l is not None: self.clip_l.reset_clip_options() if self.clip_g is not None: self.clip_g.reset_clip_options() if self.t5xxl is not None: self.t5xxl.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_l = token_weight_pairs["l"] token_weight_pairs_g = token_weight_pairs["g"] token_weight_pairs_t5 = token_weight_pairs["t5xxl"] lg_out = None pooled = None out = None extra = {} if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: if self.clip_l is not None: lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) else: l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) if self.clip_g is not None: g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) if lg_out is not None: cut_to = min(lg_out.shape[1], g_out.shape[1]) lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1) else: lg_out = torch.nn.functional.pad(g_out, (768, 0)) else: g_out = None g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) if lg_out is not None: lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) out = lg_out pooled = torch.cat((l_pooled, g_pooled), dim=-1) if self.t5xxl is not None: t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) t5_out, t5_pooled = t5_output[:2] if self.t5_attention_mask: extra["attention_mask"] = t5_output[2]["attention_mask"] if lg_out is not None: out = torch.cat([lg_out, t5_out], dim=-2) else: out = t5_out if out is None: out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device()) if pooled is None: pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) return out, pooled, extra def load_sd(self, sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: return self.clip_g.load_sd(sd) elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: return self.clip_l.load_sd(sd) else: return self.t5xxl.load_sd(sd) def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_quantization_metadata=None, t5_attention_mask=False): class SD3ClipModel_(SD3ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): if t5_quantization_metadata is not None: model_options = model_options.copy() model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options) return SD3ClipModel_ ================================================ FILE: comfy/text_encoders/spiece_tokenizer.py ================================================ import torch import os class SPieceTokenizer: @staticmethod def from_pretrained(path, **kwargs): return SPieceTokenizer(path, **kwargs) def __init__(self, tokenizer_path, add_bos=False, add_eos=True, special_tokens=None): self.add_bos = add_bos self.add_eos = add_eos self.special_tokens = special_tokens import sentencepiece if torch.is_tensor(tokenizer_path): tokenizer_path = tokenizer_path.numpy().tobytes() if isinstance(tokenizer_path, bytes): self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) else: if not os.path.isfile(tokenizer_path): raise ValueError("invalid tokenizer") self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) def get_vocab(self): out = {} for i in range(self.tokenizer.get_piece_size()): out[self.tokenizer.id_to_piece(i)] = i return out def __call__(self, string): if self.special_tokens is not None: import re special_tokens_pattern = '|'.join(re.escape(token) for token in self.special_tokens.keys()) if special_tokens_pattern and re.search(special_tokens_pattern, string): parts = re.split(f'({special_tokens_pattern})', string) result = [] for part in parts: if not part: continue if part in self.special_tokens: result.append(self.special_tokens[part]) else: encoded = self.tokenizer.encode(part, add_bos=False, add_eos=False) result.extend(encoded) return {"input_ids": result} out = self.tokenizer.encode(string) return {"input_ids": out} def decode(self, token_ids, skip_special_tokens=False): if skip_special_tokens and self.special_tokens: special_token_ids = set(self.special_tokens.values()) token_ids = [tid for tid in token_ids if tid not in special_token_ids] return self.tokenizer.decode(token_ids) def serialize_model(self): return torch.ByteTensor(list(self.tokenizer.serialized_model_proto())) ================================================ FILE: comfy/text_encoders/t5.py ================================================ import torch import math from comfy.ldm.modules.attention import optimized_attention_for_device import comfy.ops class T5LayerNorm(torch.nn.Module): def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): super().__init__() self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) self.variance_epsilon = eps def forward(self, x): variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.variance_epsilon) return comfy.ops.cast_to_input(self.weight, x) * x activations = { "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), "relu": torch.nn.functional.relu, } class T5DenseActDense(torch.nn.Module): def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): super().__init__() self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) # self.dropout = nn.Dropout(config.dropout_rate) self.act = activations[ff_activation] def forward(self, x): x = self.act(self.wi(x)) # x = self.dropout(x) x = self.wo(x) return x class T5DenseGatedActDense(torch.nn.Module): def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): super().__init__() self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) # self.dropout = nn.Dropout(config.dropout_rate) self.act = activations[ff_activation] def forward(self, x): hidden_gelu = self.act(self.wi_0(x)) hidden_linear = self.wi_1(x) x = hidden_gelu * hidden_linear # x = self.dropout(x) x = self.wo(x) return x class T5LayerFF(torch.nn.Module): def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations): super().__init__() if gated_act: self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) else: self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) # self.dropout = nn.Dropout(config.dropout_rate) def forward(self, x): forwarded_states = self.layer_norm(x) forwarded_states = self.DenseReluDense(forwarded_states) # x = x + self.dropout(forwarded_states) x += forwarded_states return x class T5Attention(torch.nn.Module): def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations): super().__init__() # Mesh TensorFlow initialization to avoid scaling before softmax self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device) self.num_heads = num_heads self.relative_attention_bias = None if relative_attention_bias: self.relative_attention_num_buckets = 32 self.relative_attention_max_distance = 128 self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype) @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets //= 2 relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) # now relative_position is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = relative_position < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) ) relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) return relative_buckets def compute_bias(self, query_length, key_length, device, dtype): """Compute binned relative position bias""" context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] relative_position = memory_position - context_position # shape (query_length, key_length) relative_position_bucket = self._relative_position_bucket( relative_position, # shape (query_length, key_length) bidirectional=True, num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) return values.contiguous() def forward(self, x, mask=None, past_bias=None, optimized_attention=None): q = self.q(x) k = self.k(x) v = self.v(x) if self.relative_attention_bias is not None: past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype) if past_bias is not None: if mask is not None: mask = mask + past_bias else: mask = past_bias out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask) return self.o(out), past_bias class T5LayerSelfAttention(torch.nn.Module): def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations): super().__init__() self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations) self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) # self.dropout = nn.Dropout(config.dropout_rate) def forward(self, x, mask=None, past_bias=None, optimized_attention=None): output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention) # x = x + self.dropout(attention_output) x += output return x, past_bias class T5Block(torch.nn.Module): def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations): super().__init__() self.layer = torch.nn.ModuleList() self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations)) self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations)) def forward(self, x, mask=None, past_bias=None, optimized_attention=None): x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) x = self.layer[-1](x) return x, past_bias class T5Stack(torch.nn.Module): def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations): super().__init__() self.block = torch.nn.ModuleList( [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] ) self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) # self.dropout = nn.Dropout(config.dropout_rate) def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) intermediate = None optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True) past_bias = None if intermediate_output is not None: if intermediate_output < 0: intermediate_output = len(self.block) + intermediate_output for i, l in enumerate(self.block): x, past_bias = l(x, mask, past_bias, optimized_attention) if i == intermediate_output: intermediate = x.clone() x = self.final_layer_norm(x) if intermediate is not None and final_layer_norm_intermediate: intermediate = self.final_layer_norm(intermediate) return x, intermediate class T5(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.num_layers = config_dict["num_layers"] model_dim = config_dict["d_model"] inner_dim = config_dict["d_kv"] * config_dict["num_heads"] self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations) self.dtype = dtype self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype) def get_input_embeddings(self): return self.shared def set_input_embeddings(self, embeddings): self.shared = embeddings def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs): if input_ids is None: x = embeds else: x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: x = torch.nan_to_num(x) #Fix for fp8 T5 base return self.encoder(x, attention_mask=attention_mask, **kwargs) ================================================ FILE: comfy/text_encoders/t5_config_base.json ================================================ { "d_ff": 3072, "d_kv": 64, "d_model": 768, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "dense_act_fn": "relu", "initializer_factor": 1.0, "is_encoder_decoder": true, "is_gated_act": false, "layer_norm_epsilon": 1e-06, "model_type": "t5", "num_decoder_layers": 12, "num_heads": 12, "num_layers": 12, "output_past": true, "pad_token_id": 0, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 32128 } ================================================ FILE: comfy/text_encoders/t5_config_xxl.json ================================================ [File too large to display: 509 B] ================================================ FILE: comfy/text_encoders/t5_old_config_xxl.json ================================================ { "d_ff": 65536, "d_kv": 128, "d_model": 1024, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "dense_act_fn": "relu", "initializer_factor": 1.0, "is_encoder_decoder": true, "is_gated_act": false, "layer_norm_epsilon": 1e-06, "model_type": "t5", "num_decoder_layers": 24, "num_heads": 128, "num_layers": 24, "output_past": true, "pad_token_id": 0, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 32128 } ================================================ FILE: comfy/text_encoders/t5_pile_config_xl.json ================================================ { "d_ff": 5120, "d_kv": 64, "d_model": 2048, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 2, "dense_act_fn": "gelu_pytorch_tanh", "initializer_factor": 1.0, "is_encoder_decoder": true, "is_gated_act": true, "layer_norm_epsilon": 1e-06, "model_type": "umt5", "num_decoder_layers": 24, "num_heads": 32, "num_layers": 24, "output_past": true, "pad_token_id": 1, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 32128 } ================================================ FILE: comfy/text_encoders/t5_tokenizer/special_tokens_map.json ================================================ { "additional_special_tokens": [ "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "" ], "eos_token": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false }, "pad_token": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false }, "unk_token": { "content": "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false } } ================================================ FILE: comfy/text_encoders/t5_tokenizer/tokenizer.json ================================================ { "version": "1.0", "truncation": null, "padding": null, "added_tokens": [ { "id": 0, 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"special": true } }, "additional_special_tokens": [ "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "" ], "clean_up_tokenization_spaces": true, "eos_token": "", "extra_ids": 100, "legacy": false, "model_max_length": 512, "pad_token": "", "sp_model_kwargs": {}, "tokenizer_class": "T5Tokenizer", "unk_token": "" } ================================================ FILE: comfy/text_encoders/umt5_config_base.json ================================================ { "d_ff": 2048, "d_kv": 64, "d_model": 768, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "dense_act_fn": "gelu_pytorch_tanh", "initializer_factor": 1.0, "is_encoder_decoder": true, "is_gated_act": true, "layer_norm_epsilon": 1e-06, "model_type": "umt5", "num_decoder_layers": 12, "num_heads": 12, "num_layers": 12, "output_past": true, "pad_token_id": 0, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 256384 } ================================================ FILE: comfy/text_encoders/umt5_config_xxl.json ================================================ { "d_ff": 10240, "d_kv": 64, "d_model": 4096, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "dense_act_fn": "gelu_pytorch_tanh", "initializer_factor": 1.0, "is_encoder_decoder": true, "is_gated_act": true, "layer_norm_epsilon": 1e-06, "model_type": "umt5", "num_decoder_layers": 24, "num_heads": 64, "num_layers": 24, "output_past": true, "pad_token_id": 0, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 256384 } ================================================ FILE: comfy/text_encoders/wan.py ================================================ from comfy import sd1_clip from .spiece_tokenizer import SPieceTokenizer import comfy.text_encoders.t5 import os class UMT5XXlModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_xxl.json") super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) class UMT5XXlTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer = tokenizer_data.get("spiece_model", None) super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0, tokenizer_data=tokenizer_data) def state_dict(self): return {"spiece_model": self.tokenizer.serialize_model()} class WanT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="umt5xxl", tokenizer=UMT5XXlTokenizer) class WanT5Model(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs) def te(dtype_t5=None, t5_quantization_metadata=None): class WanTEModel(WanT5Model): def __init__(self, device="cpu", dtype=None, model_options={}): if t5_quantization_metadata is not None: model_options = model_options.copy() model_options["quantization_metadata"] = t5_quantization_metadata if dtype_t5 is not None: dtype = dtype_t5 super().__init__(device=device, dtype=dtype, model_options=model_options) return WanTEModel ================================================ FILE: comfy/text_encoders/z_image.py ================================================ [File too large to display: 2.6 KB] ================================================ FILE: comfy/utils.py ================================================ """ This file is part of ComfyUI. Copyright (C) 2024 Comfy This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ import torch import math import struct import ctypes import os import comfy.memory_management import safetensors.torch import numpy as np from PIL import Image import logging import itertools from torch.nn.functional import interpolate from tqdm.auto import trange from einops import rearrange from comfy.cli_args import args import json import time import threading import warnings MMAP_TORCH_FILES = args.mmap_torch_files DISABLE_MMAP = args.disable_mmap if True: # ckpt/pt file whitelist for safe loading of old sd files class ModelCheckpoint: pass ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint" def scalar(*args, **kwargs): return None scalar.__module__ = "numpy.core.multiarray" from numpy import dtype from numpy.dtypes import Float64DType def encode(*args, **kwargs): # no longer necessary on newer torch return None encode.__module__ = "_codecs" torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode]) logging.info("Checkpoint files will always be loaded safely.") # Current as of safetensors 0.7.0 _TYPES = { "F64": torch.float64, "F32": torch.float32, "F16": torch.float16, "BF16": torch.bfloat16, "I64": torch.int64, "I32": torch.int32, "I16": torch.int16, "I8": torch.int8, "U8": torch.uint8, "BOOL": torch.bool, "F8_E4M3": torch.float8_e4m3fn, "F8_E5M2": torch.float8_e5m2, "C64": torch.complex64, "U64": torch.uint64, "U32": torch.uint32, "U16": torch.uint16, } def load_safetensors(ckpt): import comfy_aimdo.model_mmap f = open(ckpt, "rb", buffering=0) model_mmap = comfy_aimdo.model_mmap.ModelMMAP(ckpt) file_size = os.path.getsize(ckpt) mv = memoryview((ctypes.c_uint8 * file_size).from_address(model_mmap.get())) header_size = struct.unpack(" 0: message = e.args[0] if "HeaderTooLarge" in message: raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt)) if "MetadataIncompleteBuffer" in message: raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt)) raise e else: torch_args = {} if MMAP_TORCH_FILES: torch_args["mmap"] = True pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args) if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: if len(pl_sd) == 1: key = list(pl_sd.keys())[0] sd = pl_sd[key] if not isinstance(sd, dict): sd = pl_sd else: sd = pl_sd return (sd, metadata) if return_metadata else sd def save_torch_file(sd, ckpt, metadata=None): if metadata is not None: safetensors.torch.save_file(sd, ckpt, metadata=metadata) else: safetensors.torch.save_file(sd, ckpt) def calculate_parameters(sd, prefix=""): params = 0 for k in sd.keys(): if k.startswith(prefix): w = sd[k] params += w.nelement() return params def weight_dtype(sd, prefix=""): dtypes = {} for k in sd.keys(): if k.startswith(prefix): w = sd[k] dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel() if len(dtypes) == 0: return None return max(dtypes, key=dtypes.get) def state_dict_key_replace(state_dict, keys_to_replace): for x in keys_to_replace: if x in state_dict: state_dict[keys_to_replace[x]] = state_dict.pop(x) return state_dict def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False): if filter_keys: out = {} else: out = state_dict for rp in replace_prefix: replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) for x in replace: w = state_dict.pop(x[0]) out[x[1]] = w return out def transformers_convert(sd, prefix_from, prefix_to, number): keys_to_replace = { "{}positional_embedding": "{}embeddings.position_embedding.weight", "{}token_embedding.weight": "{}embeddings.token_embedding.weight", "{}ln_final.weight": "{}final_layer_norm.weight", "{}ln_final.bias": "{}final_layer_norm.bias", } for k in keys_to_replace: x = k.format(prefix_from) if x in sd: sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) resblock_to_replace = { "ln_1": "layer_norm1", "ln_2": "layer_norm2", "mlp.c_fc": "mlp.fc1", "mlp.c_proj": "mlp.fc2", "attn.out_proj": "self_attn.out_proj", } for resblock in range(number): for x in resblock_to_replace: for y in ["weight", "bias"]: k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) if k in sd: sd[k_to] = sd.pop(k) for y in ["weight", "bias"]: k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) if k_from in sd: weights = sd.pop(k_from) shape_from = weights.shape[0] // 3 for x in range(3): p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] return sd def clip_text_transformers_convert(sd, prefix_from, prefix_to): sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32) tp = "{}text_projection.weight".format(prefix_from) if tp in sd: sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp) tp = "{}text_projection".format(prefix_from) if tp in sd: sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous() return sd UNET_MAP_ATTENTIONS = { "proj_in.weight", "proj_in.bias", "proj_out.weight", "proj_out.bias", "norm.weight", "norm.bias", } TRANSFORMER_BLOCKS = { "norm1.weight", "norm1.bias", "norm2.weight", "norm2.bias", "norm3.weight", "norm3.bias", "attn1.to_q.weight", "attn1.to_k.weight", "attn1.to_v.weight", "attn1.to_out.0.weight", "attn1.to_out.0.bias", "attn2.to_q.weight", "attn2.to_k.weight", "attn2.to_v.weight", "attn2.to_out.0.weight", "attn2.to_out.0.bias", "ff.net.0.proj.weight", "ff.net.0.proj.bias", "ff.net.2.weight", "ff.net.2.bias", } UNET_MAP_RESNET = { "in_layers.2.weight": "conv1.weight", "in_layers.2.bias": "conv1.bias", "emb_layers.1.weight": "time_emb_proj.weight", "emb_layers.1.bias": "time_emb_proj.bias", "out_layers.3.weight": "conv2.weight", "out_layers.3.bias": "conv2.bias", "skip_connection.weight": "conv_shortcut.weight", "skip_connection.bias": "conv_shortcut.bias", "in_layers.0.weight": "norm1.weight", "in_layers.0.bias": "norm1.bias", "out_layers.0.weight": "norm2.weight", "out_layers.0.bias": "norm2.bias", } UNET_MAP_BASIC = { ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), ("input_blocks.0.0.weight", "conv_in.weight"), ("input_blocks.0.0.bias", "conv_in.bias"), ("out.0.weight", "conv_norm_out.weight"), ("out.0.bias", "conv_norm_out.bias"), ("out.2.weight", "conv_out.weight"), ("out.2.bias", "conv_out.bias"), ("time_embed.0.weight", "time_embedding.linear_1.weight"), ("time_embed.0.bias", "time_embedding.linear_1.bias"), ("time_embed.2.weight", "time_embedding.linear_2.weight"), ("time_embed.2.bias", "time_embedding.linear_2.bias") } def unet_to_diffusers(unet_config): if "num_res_blocks" not in unet_config: return {} num_res_blocks = unet_config["num_res_blocks"] channel_mult = unet_config["channel_mult"] transformer_depth = unet_config["transformer_depth"][:] transformer_depth_output = unet_config["transformer_depth_output"][:] num_blocks = len(channel_mult) transformers_mid = unet_config.get("transformer_depth_middle", None) diffusers_unet_map = {} for x in range(num_blocks): n = 1 + (num_res_blocks[x] + 1) * x for i in range(num_res_blocks[x]): for b in UNET_MAP_RESNET: diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) num_transformers = transformer_depth.pop(0) if num_transformers > 0: for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) for t in range(num_transformers): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) n += 1 for k in ["weight", "bias"]: diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) i = 0 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) for t in range(transformers_mid): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) for i, n in enumerate([0, 2]): for b in UNET_MAP_RESNET: diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) num_res_blocks = list(reversed(num_res_blocks)) for x in range(num_blocks): n = (num_res_blocks[x] + 1) * x l = num_res_blocks[x] + 1 for i in range(l): c = 0 for b in UNET_MAP_RESNET: diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) c += 1 num_transformers = transformer_depth_output.pop() if num_transformers > 0: c += 1 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) for t in range(num_transformers): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) if i == l - 1: for k in ["weight", "bias"]: diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) n += 1 for k in UNET_MAP_BASIC: diffusers_unet_map[k[1]] = k[0] return diffusers_unet_map def swap_scale_shift(weight): shift, scale = weight.chunk(2, dim=0) new_weight = torch.cat([scale, shift], dim=0) return new_weight MMDIT_MAP_BASIC = { ("context_embedder.bias", "context_embedder.bias"), ("context_embedder.weight", "context_embedder.weight"), ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"), ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"), ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"), ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"), ("x_embedder.proj.bias", "pos_embed.proj.bias"), ("x_embedder.proj.weight", "pos_embed.proj.weight"), ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"), ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"), ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"), ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"), ("pos_embed", "pos_embed.pos_embed"), ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), ("final_layer.linear.bias", "proj_out.bias"), ("final_layer.linear.weight", "proj_out.weight"), } MMDIT_MAP_BLOCK = { ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"), ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"), ("context_block.attn.proj.bias", "attn.to_add_out.bias"), ("context_block.attn.proj.weight", "attn.to_add_out.weight"), ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"), ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"), ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"), ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"), ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"), ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"), ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"), ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"), ("x_block.attn.proj.bias", "attn.to_out.0.bias"), ("x_block.attn.proj.weight", "attn.to_out.0.weight"), ("x_block.attn.ln_q.weight", "attn.norm_q.weight"), ("x_block.attn.ln_k.weight", "attn.norm_k.weight"), ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"), ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"), ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"), ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"), ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"), ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"), ("x_block.mlp.fc2.bias", "ff.net.2.bias"), ("x_block.mlp.fc2.weight", "ff.net.2.weight"), } def mmdit_to_diffusers(mmdit_config, output_prefix=""): key_map = {} depth = mmdit_config.get("depth", 0) num_blocks = mmdit_config.get("num_blocks", depth) for i in range(num_blocks): block_from = "transformer_blocks.{}".format(i) block_to = "{}joint_blocks.{}".format(output_prefix, i) offset = depth * 64 for end in ("weight", "bias"): k = "{}.attn.".format(block_from) qkv = "{}.x_block.attn.qkv.{}".format(block_to, end) key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) qkv = "{}.context_block.attn.qkv.{}".format(block_to, end) key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset)) key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset)) key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) k = "{}.attn2.".format(block_from) qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end) key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) for k in MMDIT_MAP_BLOCK: key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) map_basic = MMDIT_MAP_BASIC.copy() map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift)) map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift)) for k in map_basic: if len(k) > 2: key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) else: key_map[k[1]] = "{}{}".format(output_prefix, k[0]) return key_map PIXART_MAP_BASIC = { ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"), ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"), ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"), ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"), ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"), ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"), ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"), ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"), ("x_embedder.proj.weight", "pos_embed.proj.weight"), ("x_embedder.proj.bias", "pos_embed.proj.bias"), ("y_embedder.y_embedding", "caption_projection.y_embedding"), ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"), ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"), ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"), ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"), ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"), ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"), ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"), ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"), ("t_block.1.weight", "adaln_single.linear.weight"), ("t_block.1.bias", "adaln_single.linear.bias"), ("final_layer.linear.weight", "proj_out.weight"), ("final_layer.linear.bias", "proj_out.bias"), ("final_layer.scale_shift_table", "scale_shift_table"), } PIXART_MAP_BLOCK = { ("scale_shift_table", "scale_shift_table"), ("attn.proj.weight", "attn1.to_out.0.weight"), ("attn.proj.bias", "attn1.to_out.0.bias"), ("mlp.fc1.weight", "ff.net.0.proj.weight"), ("mlp.fc1.bias", "ff.net.0.proj.bias"), ("mlp.fc2.weight", "ff.net.2.weight"), ("mlp.fc2.bias", "ff.net.2.bias"), ("cross_attn.proj.weight" ,"attn2.to_out.0.weight"), ("cross_attn.proj.bias" ,"attn2.to_out.0.bias"), } def pixart_to_diffusers(mmdit_config, output_prefix=""): key_map = {} depth = mmdit_config.get("depth", 0) offset = mmdit_config.get("hidden_size", 1152) for i in range(depth): block_from = "transformer_blocks.{}".format(i) block_to = "{}blocks.{}".format(output_prefix, i) for end in ("weight", "bias"): s = "{}.attn1.".format(block_from) qkv = "{}.attn.qkv.{}".format(block_to, end) key_map["{}to_q.{}".format(s, end)] = (qkv, (0, 0, offset)) key_map["{}to_k.{}".format(s, end)] = (qkv, (0, offset, offset)) key_map["{}to_v.{}".format(s, end)] = (qkv, (0, offset * 2, offset)) s = "{}.attn2.".format(block_from) q = "{}.cross_attn.q_linear.{}".format(block_to, end) kv = "{}.cross_attn.kv_linear.{}".format(block_to, end) key_map["{}to_q.{}".format(s, end)] = q key_map["{}to_k.{}".format(s, end)] = (kv, (0, 0, offset)) key_map["{}to_v.{}".format(s, end)] = (kv, (0, offset, offset)) for k in PIXART_MAP_BLOCK: key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) for k in PIXART_MAP_BASIC: key_map[k[1]] = "{}{}".format(output_prefix, k[0]) return key_map def auraflow_to_diffusers(mmdit_config, output_prefix=""): n_double_layers = mmdit_config.get("n_double_layers", 0) n_layers = mmdit_config.get("n_layers", 0) key_map = {} for i in range(n_layers): if i < n_double_layers: index = i prefix_from = "joint_transformer_blocks" prefix_to = "{}double_layers".format(output_prefix) block_map = { "attn.to_q.weight": "attn.w2q.weight", "attn.to_k.weight": "attn.w2k.weight", "attn.to_v.weight": "attn.w2v.weight", "attn.to_out.0.weight": "attn.w2o.weight", "attn.add_q_proj.weight": "attn.w1q.weight", "attn.add_k_proj.weight": "attn.w1k.weight", "attn.add_v_proj.weight": "attn.w1v.weight", "attn.to_add_out.weight": "attn.w1o.weight", "ff.linear_1.weight": "mlpX.c_fc1.weight", "ff.linear_2.weight": "mlpX.c_fc2.weight", "ff.out_projection.weight": "mlpX.c_proj.weight", "ff_context.linear_1.weight": "mlpC.c_fc1.weight", "ff_context.linear_2.weight": "mlpC.c_fc2.weight", "ff_context.out_projection.weight": "mlpC.c_proj.weight", "norm1.linear.weight": "modX.1.weight", "norm1_context.linear.weight": "modC.1.weight", } else: index = i - n_double_layers prefix_from = "single_transformer_blocks" prefix_to = "{}single_layers".format(output_prefix) block_map = { "attn.to_q.weight": "attn.w1q.weight", "attn.to_k.weight": "attn.w1k.weight", "attn.to_v.weight": "attn.w1v.weight", "attn.to_out.0.weight": "attn.w1o.weight", "norm1.linear.weight": "modCX.1.weight", "ff.linear_1.weight": "mlp.c_fc1.weight", "ff.linear_2.weight": "mlp.c_fc2.weight", "ff.out_projection.weight": "mlp.c_proj.weight" } for k in block_map: key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k]) MAP_BASIC = { ("positional_encoding", "pos_embed.pos_embed"), ("register_tokens", "register_tokens"), ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"), ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"), ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"), ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"), ("cond_seq_linear.weight", "context_embedder.weight"), ("init_x_linear.weight", "pos_embed.proj.weight"), ("init_x_linear.bias", "pos_embed.proj.bias"), ("final_linear.weight", "proj_out.weight"), ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift), } for k in MAP_BASIC: if len(k) > 2: key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) else: key_map[k[1]] = "{}{}".format(output_prefix, k[0]) return key_map def flux_to_diffusers(mmdit_config, output_prefix=""): n_double_layers = mmdit_config.get("depth", 0) n_single_layers = mmdit_config.get("depth_single_blocks", 0) hidden_size = mmdit_config.get("hidden_size", 0) key_map = {} for index in range(n_double_layers): prefix_from = "transformer_blocks.{}".format(index) prefix_to = "{}double_blocks.{}".format(output_prefix, index) for end in ("weight", "bias"): k = "{}.attn.".format(prefix_from) qkv = "{}.img_attn.qkv.{}".format(prefix_to, end) key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) k = "{}.attn.".format(prefix_from) qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end) key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) block_map = { "attn.to_out.0.weight": "img_attn.proj.weight", "attn.to_out.0.bias": "img_attn.proj.bias", "norm1.linear.weight": "img_mod.lin.weight", "norm1.linear.bias": "img_mod.lin.bias", "norm1_context.linear.weight": "txt_mod.lin.weight", "norm1_context.linear.bias": "txt_mod.lin.bias", "attn.to_add_out.weight": "txt_attn.proj.weight", "attn.to_add_out.bias": "txt_attn.proj.bias", "ff.net.0.proj.weight": "img_mlp.0.weight", "ff.net.0.proj.bias": "img_mlp.0.bias", "ff.net.2.weight": "img_mlp.2.weight", "ff.net.2.bias": "img_mlp.2.bias", "ff_context.net.0.proj.weight": "txt_mlp.0.weight", "ff_context.net.0.proj.bias": "txt_mlp.0.bias", "ff_context.net.2.weight": "txt_mlp.2.weight", "ff_context.net.2.bias": "txt_mlp.2.bias", "ff.linear_in.weight": "img_mlp.0.weight", # LyCoris LoKr "ff.linear_in.bias": "img_mlp.0.bias", "ff.linear_out.weight": "img_mlp.2.weight", "ff.linear_out.bias": "img_mlp.2.bias", "ff_context.linear_in.weight": "txt_mlp.0.weight", "ff_context.linear_in.bias": "txt_mlp.0.bias", "ff_context.linear_out.weight": "txt_mlp.2.weight", "ff_context.linear_out.bias": "txt_mlp.2.bias", "attn.norm_q.weight": "img_attn.norm.query_norm.weight", "attn.norm_k.weight": "img_attn.norm.key_norm.weight", "attn.norm_added_q.weight": "txt_attn.norm.query_norm.weight", "attn.norm_added_k.weight": "txt_attn.norm.key_norm.weight", } for k in block_map: key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) for index in range(n_single_layers): prefix_from = "single_transformer_blocks.{}".format(index) prefix_to = "{}single_blocks.{}".format(output_prefix, index) for end in ("weight", "bias"): k = "{}.attn.".format(prefix_from) qkv = "{}.linear1.{}".format(prefix_to, end) key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4)) block_map = { "norm.linear.weight": "modulation.lin.weight", "norm.linear.bias": "modulation.lin.bias", "proj_out.weight": "linear2.weight", "proj_out.bias": "linear2.bias", "attn.norm_q.weight": "norm.query_norm.weight", "attn.norm_k.weight": "norm.key_norm.weight", "attn.to_qkv_mlp_proj.weight": "linear1.weight", # Flux 2 "attn.to_out.weight": "linear2.weight", # Flux 2 } for k in block_map: key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) MAP_BASIC = { ("final_layer.linear.bias", "proj_out.bias"), ("final_layer.linear.weight", "proj_out.weight"), ("img_in.bias", "x_embedder.bias"), ("img_in.weight", "x_embedder.weight"), ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"), ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"), ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"), ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"), ("txt_in.bias", "context_embedder.bias"), ("txt_in.weight", "context_embedder.weight"), ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"), ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"), ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"), ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"), ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"), ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"), ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"), ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"), ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), ("pos_embed_input.bias", "controlnet_x_embedder.bias"), ("pos_embed_input.weight", "controlnet_x_embedder.weight"), } for k in MAP_BASIC: if len(k) > 2: key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) else: key_map[k[1]] = "{}{}".format(output_prefix, k[0]) return key_map def z_image_to_diffusers(mmdit_config, output_prefix=""): n_layers = mmdit_config.get("n_layers", 0) hidden_size = mmdit_config.get("dim", 0) n_context_refiner = mmdit_config.get("n_refiner_layers", 2) n_noise_refiner = mmdit_config.get("n_refiner_layers", 2) key_map = {} def add_block_keys(prefix_from, prefix_to, has_adaln=True): for end in ("weight", "bias"): k = "{}.attention.".format(prefix_from) qkv = "{}.attention.qkv.{}".format(prefix_to, end) key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) block_map = { "attention.norm_q.weight": "attention.q_norm.weight", "attention.norm_k.weight": "attention.k_norm.weight", "attention.to_out.0.weight": "attention.out.weight", "attention.to_out.0.bias": "attention.out.bias", "attention_norm1.weight": "attention_norm1.weight", "attention_norm2.weight": "attention_norm2.weight", "feed_forward.w1.weight": "feed_forward.w1.weight", "feed_forward.w2.weight": "feed_forward.w2.weight", "feed_forward.w3.weight": "feed_forward.w3.weight", "ffn_norm1.weight": "ffn_norm1.weight", "ffn_norm2.weight": "ffn_norm2.weight", } if has_adaln: block_map["adaLN_modulation.0.weight"] = "adaLN_modulation.0.weight" block_map["adaLN_modulation.0.bias"] = "adaLN_modulation.0.bias" for k, v in block_map.items(): key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, v) for i in range(n_layers): add_block_keys("layers.{}".format(i), "{}layers.{}".format(output_prefix, i)) for i in range(n_context_refiner): add_block_keys("context_refiner.{}".format(i), "{}context_refiner.{}".format(output_prefix, i)) for i in range(n_noise_refiner): add_block_keys("noise_refiner.{}".format(i), "{}noise_refiner.{}".format(output_prefix, i)) MAP_BASIC = [ ("final_layer.linear.weight", "all_final_layer.2-1.linear.weight"), ("final_layer.linear.bias", "all_final_layer.2-1.linear.bias"), ("final_layer.adaLN_modulation.1.weight", "all_final_layer.2-1.adaLN_modulation.1.weight"), ("final_layer.adaLN_modulation.1.bias", "all_final_layer.2-1.adaLN_modulation.1.bias"), ("x_embedder.weight", "all_x_embedder.2-1.weight"), ("x_embedder.bias", "all_x_embedder.2-1.bias"), ("x_pad_token", "x_pad_token"), ("cap_embedder.0.weight", "cap_embedder.0.weight"), ("cap_embedder.1.weight", "cap_embedder.1.weight"), ("cap_embedder.1.bias", "cap_embedder.1.bias"), ("cap_pad_token", "cap_pad_token"), ("t_embedder.mlp.0.weight", "t_embedder.mlp.0.weight"), ("t_embedder.mlp.0.bias", "t_embedder.mlp.0.bias"), ("t_embedder.mlp.2.weight", "t_embedder.mlp.2.weight"), ("t_embedder.mlp.2.bias", "t_embedder.mlp.2.bias"), ] for c, diffusers in MAP_BASIC: key_map[diffusers] = "{}{}".format(output_prefix, c) return key_map def repeat_to_batch_size(tensor, batch_size, dim=0): if tensor.shape[dim] > batch_size: return tensor.narrow(dim, 0, batch_size) elif tensor.shape[dim] < batch_size: return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size) return tensor def resize_to_batch_size(tensor, batch_size): in_batch_size = tensor.shape[0] if in_batch_size == batch_size: return tensor if batch_size <= 1: return tensor[:batch_size] output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) if batch_size < in_batch_size: scale = (in_batch_size - 1) / (batch_size - 1) for i in range(batch_size): output[i] = tensor[min(round(i * scale), in_batch_size - 1)] else: scale = in_batch_size / batch_size for i in range(batch_size): output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] return output def resize_list_to_batch_size(l, batch_size): in_batch_size = len(l) if in_batch_size == batch_size or in_batch_size == 0: return l if batch_size <= 1: return l[:batch_size] output = [] if batch_size < in_batch_size: scale = (in_batch_size - 1) / (batch_size - 1) for i in range(batch_size): output.append(l[min(round(i * scale), in_batch_size - 1)]) else: scale = in_batch_size / batch_size for i in range(batch_size): output.append(l[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]) return output def convert_sd_to(state_dict, dtype): keys = list(state_dict.keys()) for k in keys: state_dict[k] = state_dict[k].to(dtype) return state_dict def safetensors_header(safetensors_path, max_size=100*1024*1024): with open(safetensors_path, "rb") as f: header = f.read(8) length_of_header = struct.unpack(' max_size: return None return f.read(length_of_header) ATTR_UNSET={} def resolve_attr(obj, attr): attrs = attr.split(".") for name in attrs[:-1]: obj = getattr(obj, name) return obj, attrs[-1] def set_attr(obj, attr, value): obj, name = resolve_attr(obj, attr) prev = getattr(obj, name, ATTR_UNSET) if value is ATTR_UNSET: delattr(obj, name) else: setattr(obj, name, value) return prev def set_attr_param(obj, attr, value): # Clone inference tensors (created under torch.inference_mode) since # their version counter is frozen and nn.Parameter() cannot wrap them. if (not torch.is_inference_mode_enabled()) and value.is_inference(): value = value.clone() return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) def set_attr_buffer(obj, attr, value): obj, name = resolve_attr(obj, attr) prev = getattr(obj, name, ATTR_UNSET) persistent = name not in getattr(obj, "_non_persistent_buffers_set", set()) obj.register_buffer(name, value, persistent=persistent) return prev def copy_to_param(obj, attr, value): # inplace update tensor instead of replacing it attrs = attr.split(".") for name in attrs[:-1]: obj = getattr(obj, name) prev = getattr(obj, attrs[-1]) prev.data.copy_(value) def get_attr(obj, attr: str): """Retrieves a nested attribute from an object using dot notation. Args: obj: The object to get the attribute from attr (str): The attribute path using dot notation (e.g. "model.layer.weight") Returns: The value of the requested attribute Example: model = MyModel() weight = get_attr(model, "layer1.conv.weight") # Equivalent to: model.layer1.conv.weight Important: Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when accessing nested model objects under `ModelPatcher.model`. """ attrs = attr.split(".") for name in attrs: obj = getattr(obj, name) return obj def bislerp(samples, width, height): def slerp(b1, b2, r): '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' c = b1.shape[-1] #norms b1_norms = torch.norm(b1, dim=-1, keepdim=True) b2_norms = torch.norm(b2, dim=-1, keepdim=True) #normalize b1_normalized = b1 / b1_norms b2_normalized = b2 / b2_norms #zero when norms are zero b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 #slerp dot = (b1_normalized*b2_normalized).sum(1) omega = torch.acos(dot) so = torch.sin(omega) #technically not mathematically correct, but more pleasing? res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) #edge cases for same or polar opposites res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] return res def generate_bilinear_data(length_old, length_new, device): coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") ratios = coords_1 - coords_1.floor() coords_1 = coords_1.to(torch.int64) coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 coords_2[:,:,:,-1] -= 1 coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") coords_2 = coords_2.to(torch.int64) return ratios, coords_1, coords_2 orig_dtype = samples.dtype samples = samples.float() n,c,h,w = samples.shape h_new, w_new = (height, width) #linear w ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) coords_1 = coords_1.expand((n, c, h, -1)) coords_2 = coords_2.expand((n, c, h, -1)) ratios = ratios.expand((n, 1, h, -1)) pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h, w_new, c).movedim(-1, 1) #linear h ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) return result.to(orig_dtype) def lanczos(samples, width, height): #the below API is strict and expects grayscale to be squeezed samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1) images = [Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] result = torch.stack(images) return result.to(samples.device, samples.dtype) def common_upscale(samples, width, height, upscale_method, crop): orig_shape = tuple(samples.shape) if len(orig_shape) > 4: samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1]) samples = samples.movedim(2, 1) samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1]) if crop == "center": old_width = samples.shape[-1] old_height = samples.shape[-2] old_aspect = old_width / old_height new_aspect = width / height x = 0 y = 0 if old_aspect > new_aspect: x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) elif old_aspect < new_aspect: y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2) else: s = samples if upscale_method == "bislerp": out = bislerp(s, width, height) elif upscale_method == "lanczos": out = lanczos(s, width, height) else: out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) if len(orig_shape) == 4: return out out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width)) return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width)) def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap)) cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap)) return rows * cols @torch.inference_mode() def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", downscale=False, index_formulas=None, pbar=None): dims = len(tile) if not (isinstance(upscale_amount, (tuple, list))): upscale_amount = [upscale_amount] * dims if not (isinstance(overlap, (tuple, list))): overlap = [overlap] * dims if index_formulas is None: index_formulas = upscale_amount if not (isinstance(index_formulas, (tuple, list))): index_formulas = [index_formulas] * dims def get_upscale(dim, val): up = upscale_amount[dim] if callable(up): return up(val) else: return up * val def get_downscale(dim, val): up = upscale_amount[dim] if callable(up): return up(val) else: return val / up def get_upscale_pos(dim, val): up = index_formulas[dim] if callable(up): return up(val) else: return up * val def get_downscale_pos(dim, val): up = index_formulas[dim] if callable(up): return up(val) else: return val / up if downscale: get_scale = get_downscale get_pos = get_downscale_pos else: get_scale = get_upscale get_pos = get_upscale_pos def mult_list_upscale(a): out = [] for i in range(len(a)): out.append(round(get_scale(i, a[i]))) return out output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device) for b in range(samples.shape[0]): s = samples[b:b+1] # handle entire input fitting in a single tile if all(s.shape[d+2] <= tile[d] for d in range(dims)): output[b:b+1] = function(s).to(output_device) if pbar is not None: pbar.update(1) continue out = output[b:b+1].zero_() out_div = torch.zeros([s.shape[0], 1] + mult_list_upscale(s.shape[2:]), device=output_device) positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)] for it in itertools.product(*positions): s_in = s upscaled = [] for d in range(dims): pos = max(0, min(s.shape[d + 2] - overlap[d], it[d])) l = min(tile[d], s.shape[d + 2] - pos) s_in = s_in.narrow(d + 2, pos, l) upscaled.append(round(get_pos(d, pos))) ps = function(s_in).to(output_device) mask = torch.ones([1, 1] + list(ps.shape[2:]), device=output_device) for d in range(2, dims + 2): feather = round(get_scale(d - 2, overlap[d - 2])) if feather >= mask.shape[d]: continue for t in range(feather): a = (t + 1) / feather mask.narrow(d, t, 1).mul_(a) mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a) o = out o_d = out_div for d in range(dims): o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) o.add_(ps * mask) o_d.add_(mask) if pbar is not None: pbar.update(1) out.div_(out_div) return output def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar) def model_trange(*args, **kwargs): if not comfy.memory_management.aimdo_enabled: return trange(*args, **kwargs) pbar = trange(*args, **kwargs, smoothing=1.0) pbar._i = 0 pbar.set_postfix_str(" Model Initializing ... ") _update = pbar.update def warmup_update(n=1): pbar._i += 1 if pbar._i == 1: pbar.i1_time = time.time() pbar.set_postfix_str(" Model Initialization complete! ") elif pbar._i == 2: #bring forward the effective start time based the the diff between first and second iteration #to attempt to remove load overhead from the final step rate estimate. pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time) pbar.set_postfix_str("") _update(n) pbar.update = warmup_update return pbar PROGRESS_BAR_ENABLED = True def set_progress_bar_enabled(enabled): global PROGRESS_BAR_ENABLED PROGRESS_BAR_ENABLED = enabled PROGRESS_BAR_HOOK = None def set_progress_bar_global_hook(function): global PROGRESS_BAR_HOOK PROGRESS_BAR_HOOK = function # Throttle settings for progress bar updates to reduce WebSocket flooding PROGRESS_THROTTLE_MIN_INTERVAL = 0.1 # 100ms minimum between updates PROGRESS_THROTTLE_MIN_PERCENT = 0.5 # 0.5% minimum progress change class ProgressBar: def __init__(self, total, node_id=None): global PROGRESS_BAR_HOOK self.total = total self.current = 0 self.hook = PROGRESS_BAR_HOOK self.node_id = node_id self._last_update_time = 0.0 self._last_sent_value = -1 def update_absolute(self, value, total=None, preview=None): if total is not None: self.total = total if value > self.total: value = self.total self.current = value if self.hook is not None: current_time = time.perf_counter() is_first = (self._last_sent_value < 0) is_final = (value >= self.total) has_preview = (preview is not None) # Always send immediately for previews, first update, or final update if has_preview or is_first or is_final: self.hook(self.current, self.total, preview, node_id=self.node_id) self._last_update_time = current_time self._last_sent_value = value return # Apply throttling for regular progress updates if self.total > 0: percent_changed = ((value - max(0, self._last_sent_value)) / self.total) * 100 else: percent_changed = 100 time_elapsed = current_time - self._last_update_time if time_elapsed >= PROGRESS_THROTTLE_MIN_INTERVAL and percent_changed >= PROGRESS_THROTTLE_MIN_PERCENT: self.hook(self.current, self.total, preview, node_id=self.node_id) self._last_update_time = current_time self._last_sent_value = value def update(self, value): self.update_absolute(self.current + value) def reshape_mask(input_mask, output_shape): dims = len(output_shape) - 2 if dims == 1: scale_mode = "linear" if dims == 2: input_mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1])) scale_mode = "bilinear" if dims == 3: if len(input_mask.shape) < 5: input_mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1])) scale_mode = "trilinear" mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode) if mask.shape[1] < output_shape[1]: mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]] mask = repeat_to_batch_size(mask, output_shape[0]) return mask def upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out): hi, wi = img_size_in ho, wo = img_size_out # if it's already the correct size, no need to do anything if (hi, wi) == (ho, wo): return mask if mask.ndim == 2: mask = mask.unsqueeze(0) if mask.ndim != 3: raise ValueError(f"Got a mask of shape {list(mask.shape)}, expected [b, q, k] or [q, k]") txt_tokens = mask.shape[1] - (hi * wi) # quadrants of the mask txt_to_txt = mask[:, :txt_tokens, :txt_tokens] txt_to_img = mask[:, :txt_tokens, txt_tokens:] img_to_img = mask[:, txt_tokens:, txt_tokens:] img_to_txt = mask[:, txt_tokens:, :txt_tokens] # convert to 1d x 2d, interpolate, then back to 1d x 1d txt_to_img = rearrange (txt_to_img, "b t (h w) -> b t h w", h=hi, w=wi) txt_to_img = interpolate(txt_to_img, size=img_size_out, mode="bilinear") txt_to_img = rearrange (txt_to_img, "b t h w -> b t (h w)") # this one is hard because we have to do it twice # convert to 1d x 2d, interpolate, then to 2d x 1d, interpolate, then 1d x 1d img_to_img = rearrange (img_to_img, "b hw (h w) -> b hw h w", h=hi, w=wi) img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear") img_to_img = rearrange (img_to_img, "b (hk wk) hq wq -> b (hq wq) hk wk", hk=hi, wk=wi) img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear") img_to_img = rearrange (img_to_img, "b (hq wq) hk wk -> b (hk wk) (hq wq)", hq=ho, wq=wo) # convert to 2d x 1d, interpolate, then back to 1d x 1d img_to_txt = rearrange (img_to_txt, "b (h w) t -> b t h w", h=hi, w=wi) img_to_txt = interpolate(img_to_txt, size=img_size_out, mode="bilinear") img_to_txt = rearrange (img_to_txt, "b t h w -> b (h w) t") # reassemble the mask from blocks out = torch.cat([ torch.cat([txt_to_txt, txt_to_img], dim=2), torch.cat([img_to_txt, img_to_img], dim=2)], dim=1 ) return out def pack_latents(latents): latent_shapes = [] tensors = [] for tensor in latents: latent_shapes.append(tensor.shape) tensors.append(tensor.reshape(tensor.shape[0], 1, -1)) latent = torch.cat(tensors, dim=-1) return latent, latent_shapes def unpack_latents(combined_latent, latent_shapes): if len(latent_shapes) > 1: output_tensors = [] for shape in latent_shapes: cut = math.prod(shape[1:]) tens = combined_latent[:, :, :cut] combined_latent = combined_latent[:, :, cut:] output_tensors.append(tens.reshape([tens.shape[0]] + list(shape)[1:])) else: output_tensors = [combined_latent] return output_tensors def detect_layer_quantization(state_dict, prefix): for k in state_dict: if k.startswith(prefix) and k.endswith(".comfy_quant"): logging.info("Found quantization metadata version 1") return {"mixed_ops": True} return None def convert_old_quants(state_dict, model_prefix="", metadata={}): if metadata is None: metadata = {} quant_metadata = None if "_quantization_metadata" not in metadata: scaled_fp8_key = "{}scaled_fp8".format(model_prefix) if scaled_fp8_key in state_dict: scaled_fp8_weight = state_dict[scaled_fp8_key] scaled_fp8_dtype = scaled_fp8_weight.dtype if scaled_fp8_dtype == torch.float32: scaled_fp8_dtype = torch.float8_e4m3fn if scaled_fp8_weight.nelement() == 2: full_precision_matrix_mult = True else: full_precision_matrix_mult = False out_sd = {} layers = {} for k in list(state_dict.keys()): if k == scaled_fp8_key: continue if not k.startswith(model_prefix): out_sd[k] = state_dict[k] continue k_out = k w = state_dict.pop(k) layer = None if k_out.endswith(".scale_weight"): layer = k_out[:-len(".scale_weight")] k_out = "{}.weight_scale".format(layer) if layer is not None: layer_conf = {"format": "float8_e4m3fn"} # TODO: check if anyone did some non e4m3fn scaled checkpoints if full_precision_matrix_mult: layer_conf["full_precision_matrix_mult"] = full_precision_matrix_mult layers[layer] = layer_conf if k_out.endswith(".scale_input"): layer = k_out[:-len(".scale_input")] k_out = "{}.input_scale".format(layer) if w.item() == 1.0: continue out_sd[k_out] = w state_dict = out_sd quant_metadata = {"layers": layers} else: quant_metadata = json.loads(metadata["_quantization_metadata"]) if quant_metadata is not None: layers = quant_metadata["layers"] for k, v in layers.items(): state_dict["{}.comfy_quant".format(k)] = torch.tensor(list(json.dumps(v).encode('utf-8')), dtype=torch.uint8) return state_dict, metadata def string_to_seed(data): crc = 0xFFFFFFFF for byte in data: if isinstance(byte, str): byte = ord(byte) crc ^= byte for _ in range(8): if crc & 1: crc = (crc >> 1) ^ 0xEDB88320 else: crc >>= 1 return crc ^ 0xFFFFFFFF def deepcopy_list_dict(obj, memo=None): if memo is None: memo = {} obj_id = id(obj) if obj_id in memo: return memo[obj_id] if isinstance(obj, dict): res = {deepcopy_list_dict(k, memo): deepcopy_list_dict(v, memo) for k, v in obj.items()} elif isinstance(obj, list): res = [deepcopy_list_dict(i, memo) for i in obj] else: res = obj memo[obj_id] = res return res def normalize_image_embeddings(embeds, embeds_info, scale_factor): """Normalize image embeddings to match text embedding scale""" for info in embeds_info: if info.get("type") == "image": start_idx = info["index"] end_idx = start_idx + info["size"] embeds[:, start_idx:end_idx, :] /= scale_factor ================================================ FILE: comfy/weight_adapter/__init__.py ================================================ [File too large to display: 1004 B] ================================================ FILE: comfy/weight_adapter/base.py ================================================ [File too large to display: 12.3 KB] ================================================ FILE: comfy/weight_adapter/boft.py ================================================ [File too large to display: 6.8 KB] ================================================ FILE: comfy/weight_adapter/bypass.py ================================================ [File too large to display: 16.2 KB] ================================================ FILE: comfy/weight_adapter/glora.py ================================================ import logging from typing import Callable, Optional import torch import torch.nn.functional as F import comfy.model_management from .base import WeightAdapterBase, weight_decompose class GLoRAAdapter(WeightAdapterBase): name = "glora" def __init__(self, loaded_keys, weights): self.loaded_keys = loaded_keys self.weights = weights @classmethod def load( cls, x: str, lora: dict[str, torch.Tensor], alpha: float, dora_scale: torch.Tensor, loaded_keys: set[str] = None, ) -> Optional["GLoRAAdapter"]: if loaded_keys is None: loaded_keys = set() a1_name = "{}.a1.weight".format(x) a2_name = "{}.a2.weight".format(x) b1_name = "{}.b1.weight".format(x) b2_name = "{}.b2.weight".format(x) if a1_name in lora: weights = ( lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale, ) loaded_keys.add(a1_name) loaded_keys.add(a2_name) loaded_keys.add(b1_name) loaded_keys.add(b2_name) return cls(loaded_keys, weights) else: return None def calculate_weight( self, weight, key, strength, strength_model, offset, function, intermediate_dtype=torch.float32, original_weight=None, ): v = self.weights dora_scale = v[5] old_glora = False if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: rank = v[0].shape[0] old_glora = True if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: if ( old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1] ): pass else: old_glora = False rank = v[1].shape[0] a1 = comfy.model_management.cast_to_device( v[0].flatten(start_dim=1), weight.device, intermediate_dtype ) a2 = comfy.model_management.cast_to_device( v[1].flatten(start_dim=1), weight.device, intermediate_dtype ) b1 = comfy.model_management.cast_to_device( v[2].flatten(start_dim=1), weight.device, intermediate_dtype ) b2 = comfy.model_management.cast_to_device( v[3].flatten(start_dim=1), weight.device, intermediate_dtype ) if v[4] is not None: alpha = v[4] / rank else: alpha = 1.0 try: if old_glora: lora_diff = ( torch.mm(b2, b1) + torch.mm( torch.mm( weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2 ), a1, ) ).reshape( weight.shape ) # old lycoris glora else: if weight.dim() > 2: lora_diff = torch.einsum( "o i ..., i j -> o j ...", torch.einsum( "o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1, ), a2, ).reshape(weight.shape) else: lora_diff = torch.mm( torch.mm(weight.to(dtype=intermediate_dtype), a1), a2 ).reshape(weight.shape) lora_diff += torch.mm(b1, b2).reshape(weight.shape) if dora_scale is not None: weight = weight_decompose( dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function, ) else: weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(self.name, key, e)) return weight def _compute_paths(self, x: torch.Tensor): """ Compute A path and B path outputs for GLoRA bypass. GLoRA: f(x) = Wx + WAx + Bx - A path: a1(a2(x)) - modifies input to base forward - B path: b1(b2(x)) - additive component Note: Does not access original model weights - bypass mode is designed for quantized models where weights may not be accessible. Returns: (a_out, b_out) """ v = self.weights # v = (a1, a2, b1, b2, alpha, dora_scale) a1 = v[0] a2 = v[1] b1 = v[2] b2 = v[3] alpha = v[4] dtype = x.dtype # Cast dtype (weights should already be on correct device from inject()) a1 = a1.to(dtype=dtype) a2 = a2.to(dtype=dtype) b1 = b1.to(dtype=dtype) b2 = b2.to(dtype=dtype) # Determine rank and scale # Check for old vs new glora format old_glora = False if b2.shape[1] == b1.shape[0] == a1.shape[0] == a2.shape[1]: rank = a1.shape[0] old_glora = True if b2.shape[0] == b1.shape[1] == a1.shape[1] == a2.shape[0]: if old_glora and a2.shape[0] == x.shape[-1] and x.shape[-1] == x.shape[-1]: pass else: old_glora = False rank = a2.shape[0] if alpha is not None: scale = alpha / rank else: scale = 1.0 # Apply multiplier multiplier = getattr(self, "multiplier", 1.0) scale = scale * multiplier # Use module info from bypass injection, not input tensor shape is_conv = getattr(self, "is_conv", False) conv_dim = getattr(self, "conv_dim", 0) kw_dict = getattr(self, "kw_dict", {}) if is_conv: # Conv case - conv_dim is 1/2/3 for conv1d/2d/3d conv_fn = (F.conv1d, F.conv2d, F.conv3d)[conv_dim - 1] # Get module's stride/padding for spatial dimension handling module_stride = kw_dict.get("stride", (1,) * conv_dim) module_padding = kw_dict.get("padding", (0,) * conv_dim) kernel_size = getattr(self, "kernel_size", (1,) * conv_dim) in_channels = getattr(self, "in_channels", None) # Ensure weights are in conv shape # a1, a2, b1 are always 1x1 kernels if a1.ndim == 2: a1 = a1.view(*a1.shape, *([1] * conv_dim)) if a2.ndim == 2: a2 = a2.view(*a2.shape, *([1] * conv_dim)) if b1.ndim == 2: b1 = b1.view(*b1.shape, *([1] * conv_dim)) # b2 has actual kernel_size (like LoRA down) if b2.ndim == 2: if in_channels is not None: b2 = b2.view(b2.shape[0], in_channels, *kernel_size) else: b2 = b2.view(*b2.shape, *([1] * conv_dim)) # A path: a2(x) -> a1(...) - 1x1 convs, no stride/padding needed, a_out is added to x a2_out = conv_fn(x, a2) a_out = conv_fn(a2_out, a1) * scale # B path: b2(x) with kernel/stride/padding -> b1(...) 1x1 b2_out = conv_fn(x, b2, stride=module_stride, padding=module_padding) b_out = conv_fn(b2_out, b1) * scale else: # Linear case if old_glora: # Old format: a1 @ a2 @ x, b2 @ b1 a_out = F.linear(F.linear(x, a2), a1) * scale b_out = F.linear(F.linear(x, b1), b2) * scale else: # New format: x @ a1 @ a2, b1 @ b2 a_out = F.linear(F.linear(x, a1), a2) * scale b_out = F.linear(F.linear(x, b2), b1) * scale return a_out, b_out def bypass_forward( self, org_forward: Callable, x: torch.Tensor, *args, **kwargs, ) -> torch.Tensor: """ GLoRA bypass forward: f(x + a(x)) + b(x) Unlike standard adapters, GLoRA modifies the input to the base forward AND adds the B path output. Note: Does not access original model weights - bypass mode is designed for quantized models where weights may not be accessible. Reference: LyCORIS GLoRAModule._bypass_forward """ a_out, b_out = self._compute_paths(x) # Call base forward with modified input base_out = org_forward(x + a_out, *args, **kwargs) # Add B path return base_out + b_out def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor: """ For GLoRA, h() returns the B path output. Note: GLoRA's full bypass requires overriding bypass_forward() since it also modifies the input to org_forward. This h() is provided for compatibility but bypass_forward() should be used for correct behavior. Does not access original model weights - bypass mode is designed for quantized models where weights may not be accessible. Args: x: Input tensor base_out: Output from base forward (unused, for API consistency) """ _, b_out = self._compute_paths(x) return b_out ================================================ FILE: comfy/weight_adapter/loha.py ================================================ [File too large to display: 12.6 KB] ================================================ FILE: comfy/weight_adapter/lokr.py ================================================ import logging from typing import Optional import torch import torch.nn.functional as F import comfy.model_management from .base import ( WeightAdapterBase, WeightAdapterTrainBase, weight_decompose, factorization, ) class LokrDiff(WeightAdapterTrainBase): def __init__(self, weights): super().__init__() ( lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale, ) = weights self.use_tucker = False if lokr_w1_a is not None: _, rank_a = lokr_w1_a.shape[0], lokr_w1_a.shape[1] rank_a, _ = lokr_w1_b.shape[0], lokr_w1_b.shape[1] self.lokr_w1_a = torch.nn.Parameter(lokr_w1_a) self.lokr_w1_b = torch.nn.Parameter(lokr_w1_b) self.w1_rebuild = True self.ranka = rank_a if lokr_w2_a is not None: _, rank_b = lokr_w2_a.shape[0], lokr_w2_a.shape[1] rank_b, _ = lokr_w2_b.shape[0], lokr_w2_b.shape[1] self.lokr_w2_a = torch.nn.Parameter(lokr_w2_a) self.lokr_w2_b = torch.nn.Parameter(lokr_w2_b) if lokr_t2 is not None: self.use_tucker = True self.lokr_t2 = torch.nn.Parameter(lokr_t2) self.w2_rebuild = True self.rankb = rank_b if lokr_w1 is not None: self.lokr_w1 = torch.nn.Parameter(lokr_w1) self.w1_rebuild = False if lokr_w2 is not None: self.lokr_w2 = torch.nn.Parameter(lokr_w2) self.w2_rebuild = False self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) @property def w1(self): if self.w1_rebuild: return (self.lokr_w1_a @ self.lokr_w1_b) * (self.alpha / self.ranka) else: return self.lokr_w1 @property def w2(self): if self.w2_rebuild: if self.use_tucker: w2 = torch.einsum( "i j k l, j r, i p -> p r k l", self.lokr_t2, self.lokr_w2_b, self.lokr_w2_a, ) else: w2 = self.lokr_w2_a @ self.lokr_w2_b return w2 * (self.alpha / self.rankb) else: return self.lokr_w2 def __call__(self, w): w1 = self.w1 w2 = self.w2 # Unsqueeze w1 to match w2 dims for proper kron product (like LyCORIS make_kron) for _ in range(w2.dim() - w1.dim()): w1 = w1.unsqueeze(-1) diff = torch.kron(w1, w2) return w + diff.reshape(w.shape).to(w) def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor: """ Additive bypass component for LoKr training: efficient Kronecker product. Uses w1/w2 properties which handle both direct and decomposed cases. For create_train (direct w1/w2), no alpha scaling in properties. For to_train (decomposed), alpha/rank scaling is in properties. Args: x: Input tensor base_out: Output from base forward (unused, for API consistency) """ # Get w1, w2 from properties (handles rebuild vs direct) w1 = self.w1 w2 = self.w2 # Multiplier from bypass injection multiplier = getattr(self, "multiplier", 1.0) # Get module info from bypass injection is_conv = getattr(self, "is_conv", False) conv_dim = getattr(self, "conv_dim", 0) kw_dict = getattr(self, "kw_dict", {}) # Efficient Kronecker application without materializing full weight # kron(w1, w2) @ x can be computed as nested operations # w1: [out_l, in_m], w2: [out_k, in_n, *k_size] # Full weight would be [out_l*out_k, in_m*in_n, *k_size] uq = w1.size(1) # in_m - inner grouping dimension if is_conv: conv_fn = (F.conv1d, F.conv2d, F.conv3d)[conv_dim - 1] B, C_in, *spatial = x.shape # Reshape input for grouped application: [B * uq, C_in // uq, *spatial] h_in_group = x.reshape(B * uq, -1, *spatial) # Ensure w2 has conv dims if w2.dim() == 2: w2 = w2.view(*w2.shape, *([1] * conv_dim)) # Apply w2 path with stride/padding hb = conv_fn(h_in_group, w2, **kw_dict) # Reshape for cross-group operation hb = hb.view(B, -1, *hb.shape[1:]) h_cross = hb.transpose(1, -1) # Apply w1 (always 2D, applied as linear on channel dim) hc = F.linear(h_cross, w1) hc = hc.transpose(1, -1) # Reshape to output out = hc.reshape(B, -1, *hc.shape[3:]) else: # Linear case # Reshape input: [..., in_m * in_n] -> [..., uq (in_m), in_n] h_in_group = x.reshape(*x.shape[:-1], uq, -1) # Apply w2: [..., uq, in_n] @ [out_k, in_n].T -> [..., uq, out_k] hb = F.linear(h_in_group, w2) # Transpose for w1: [..., uq, out_k] -> [..., out_k, uq] h_cross = hb.transpose(-1, -2) # Apply w1: [..., out_k, uq] @ [out_l, uq].T -> [..., out_k, out_l] hc = F.linear(h_cross, w1) # Transpose back and flatten: [..., out_k, out_l] -> [..., out_l * out_k] hc = hc.transpose(-1, -2) out = hc.reshape(*hc.shape[:-2], -1) return out * multiplier def passive_memory_usage(self): return sum(param.numel() * param.element_size() for param in self.parameters()) class LoKrAdapter(WeightAdapterBase): name = "lokr" def __init__(self, loaded_keys, weights): self.loaded_keys = loaded_keys self.weights = weights @classmethod def create_train(cls, weight, rank=1, alpha=1.0): out_dim = weight.shape[0] in_dim = weight.shape[1] # Just in_channels, not flattened with kernel k_size = weight.shape[2:] if weight.dim() > 2 else () out_l, out_k = factorization(out_dim, rank) in_m, in_n = factorization(in_dim, rank) # w1: [out_l, in_m] mat1 = torch.empty(out_l, in_m, device=weight.device, dtype=torch.float32) # w2: [out_k, in_n, *k_size] for conv, [out_k, in_n] for linear mat2 = torch.empty( out_k, in_n, *k_size, device=weight.device, dtype=torch.float32 ) torch.nn.init.kaiming_uniform_(mat2, a=5**0.5) torch.nn.init.constant_(mat1, 0.0) return LokrDiff((mat1, mat2, alpha, None, None, None, None, None, None)) def to_train(self): return LokrDiff(self.weights) @classmethod def load( cls, x: str, lora: dict[str, torch.Tensor], alpha: float, dora_scale: torch.Tensor, loaded_keys: set[str] = None, ) -> Optional["LoKrAdapter"]: if loaded_keys is None: loaded_keys = set() lokr_w1_name = "{}.lokr_w1".format(x) lokr_w2_name = "{}.lokr_w2".format(x) lokr_w1_a_name = "{}.lokr_w1_a".format(x) lokr_w1_b_name = "{}.lokr_w1_b".format(x) lokr_t2_name = "{}.lokr_t2".format(x) lokr_w2_a_name = "{}.lokr_w2_a".format(x) lokr_w2_b_name = "{}.lokr_w2_b".format(x) lokr_w1 = None if lokr_w1_name in lora.keys(): lokr_w1 = lora[lokr_w1_name] loaded_keys.add(lokr_w1_name) lokr_w2 = None if lokr_w2_name in lora.keys(): lokr_w2 = lora[lokr_w2_name] loaded_keys.add(lokr_w2_name) lokr_w1_a = None if lokr_w1_a_name in lora.keys(): lokr_w1_a = lora[lokr_w1_a_name] loaded_keys.add(lokr_w1_a_name) lokr_w1_b = None if lokr_w1_b_name in lora.keys(): lokr_w1_b = lora[lokr_w1_b_name] loaded_keys.add(lokr_w1_b_name) lokr_w2_a = None if lokr_w2_a_name in lora.keys(): lokr_w2_a = lora[lokr_w2_a_name] loaded_keys.add(lokr_w2_a_name) lokr_w2_b = None if lokr_w2_b_name in lora.keys(): lokr_w2_b = lora[lokr_w2_b_name] loaded_keys.add(lokr_w2_b_name) lokr_t2 = None if lokr_t2_name in lora.keys(): lokr_t2 = lora[lokr_t2_name] loaded_keys.add(lokr_t2_name) if ( (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None) ): weights = ( lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale, ) return cls(loaded_keys, weights) else: return None def calculate_weight( self, weight, key, strength, strength_model, offset, function, intermediate_dtype=torch.float32, original_weight=None, ): v = self.weights w1 = v[0] w2 = v[1] w1_a = v[3] w1_b = v[4] w2_a = v[5] w2_b = v[6] t2 = v[7] dora_scale = v[8] dim = None if w1 is None: dim = w1_b.shape[0] w1 = torch.mm( comfy.model_management.cast_to_device( w1_a, weight.device, intermediate_dtype ), comfy.model_management.cast_to_device( w1_b, weight.device, intermediate_dtype ), ) else: w1 = comfy.model_management.cast_to_device( w1, weight.device, intermediate_dtype ) if w2 is None: dim = w2_b.shape[0] if t2 is None: w2 = torch.mm( comfy.model_management.cast_to_device( w2_a, weight.device, intermediate_dtype ), comfy.model_management.cast_to_device( w2_b, weight.device, intermediate_dtype ), ) else: w2 = torch.einsum( "i j k l, j r, i p -> p r k l", comfy.model_management.cast_to_device( t2, weight.device, intermediate_dtype ), comfy.model_management.cast_to_device( w2_b, weight.device, intermediate_dtype ), comfy.model_management.cast_to_device( w2_a, weight.device, intermediate_dtype ), ) else: w2 = comfy.model_management.cast_to_device( w2, weight.device, intermediate_dtype ) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) if v[2] is not None and dim is not None: alpha = v[2] / dim else: alpha = 1.0 try: lora_diff = torch.kron(w1, w2).reshape(weight.shape) if dora_scale is not None: weight = weight_decompose( dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function, ) else: weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(self.name, key, e)) return weight def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor: """ Additive bypass component for LoKr: efficient Kronecker product application. Note: Does not access original model weights - bypass mode is designed for quantized models where weights may not be accessible. Args: x: Input tensor base_out: Output from base forward (unused, for API consistency) Reference: LyCORIS functional/lokr.py bypass_forward_diff """ # FUNC_LIST: [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d] FUNC_LIST = [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d] v = self.weights # v[0]=w1, v[1]=w2, v[2]=alpha, v[3]=w1_a, v[4]=w1_b, v[5]=w2_a, v[6]=w2_b, v[7]=t2, v[8]=dora w1 = v[0] w2 = v[1] alpha = v[2] w1_a = v[3] w1_b = v[4] w2_a = v[5] w2_b = v[6] t2 = v[7] use_w1 = w1 is not None use_w2 = w2 is not None tucker = t2 is not None # Use module info from bypass injection, not weight dimension is_conv = getattr(self, "is_conv", False) conv_dim = getattr(self, "conv_dim", 0) kw_dict = getattr(self, "kw_dict", {}) if is_conv else {} if is_conv: op = FUNC_LIST[conv_dim + 2] else: op = F.linear # Determine rank and scale rank = w1_b.size(0) if not use_w1 else w2_b.size(0) if not use_w2 else alpha scale = (alpha / rank if alpha is not None else 1.0) * getattr( self, "multiplier", 1.0 ) # Build c (w1) if use_w1: c = w1.to(dtype=x.dtype) else: c = w1_a.to(dtype=x.dtype) @ w1_b.to(dtype=x.dtype) uq = c.size(1) # Build w2 components if use_w2: ba = w2.to(dtype=x.dtype) else: a = w2_b.to(dtype=x.dtype) b = w2_a.to(dtype=x.dtype) if is_conv: if tucker: # Tucker: a, b get 1s appended (kernel is in t2) if a.dim() == 2: a = a.view(*a.shape, *([1] * conv_dim)) if b.dim() == 2: b = b.view(*b.shape, *([1] * conv_dim)) else: # Non-tucker conv: b may need 1s appended if b.dim() == 2: b = b.view(*b.shape, *([1] * conv_dim)) # Reshape input by uq groups if is_conv: B, _, *rest = x.shape h_in_group = x.reshape(B * uq, -1, *rest) else: h_in_group = x.reshape(*x.shape[:-1], uq, -1) # Apply w2 path if use_w2: hb = op(h_in_group, ba, **kw_dict) else: if is_conv: if tucker: t = t2.to(dtype=x.dtype) if t.dim() == 2: t = t.view(*t.shape, *([1] * conv_dim)) ha = op(h_in_group, a) ht = op(ha, t, **kw_dict) hb = op(ht, b) else: ha = op(h_in_group, a, **kw_dict) hb = op(ha, b) else: ha = op(h_in_group, a) hb = op(ha, b) # Reshape and apply c (w1) if is_conv: hb = hb.view(B, -1, *hb.shape[1:]) h_cross_group = hb.transpose(1, -1) else: h_cross_group = hb.transpose(-1, -2) hc = F.linear(h_cross_group, c) if is_conv: hc = hc.transpose(1, -1) out = hc.reshape(B, -1, *hc.shape[3:]) else: hc = hc.transpose(-1, -2) out = hc.reshape(*hc.shape[:-2], -1) return out * scale ================================================ FILE: comfy/weight_adapter/lora.py ================================================ [File too large to display: 13.1 KB] ================================================ FILE: comfy/weight_adapter/oft.py ================================================ [File too large to display: 11.0 KB] ================================================ FILE: comfy/windows.py ================================================ import ctypes import logging import psutil from ctypes import wintypes import comfy_aimdo.control psapi = ctypes.WinDLL("psapi") kernel32 = ctypes.WinDLL("kernel32") class PERFORMANCE_INFORMATION(ctypes.Structure): _fields_ = [ ("cb", wintypes.DWORD), ("CommitTotal", ctypes.c_size_t), ("CommitLimit", ctypes.c_size_t), ("CommitPeak", ctypes.c_size_t), ("PhysicalTotal", ctypes.c_size_t), ("PhysicalAvailable", ctypes.c_size_t), ("SystemCache", ctypes.c_size_t), ("KernelTotal", ctypes.c_size_t), ("KernelPaged", ctypes.c_size_t), ("KernelNonpaged", ctypes.c_size_t), ("PageSize", ctypes.c_size_t), ("HandleCount", wintypes.DWORD), ("ProcessCount", wintypes.DWORD), ("ThreadCount", wintypes.DWORD), ] def get_free_ram(): #Windows is way too conservative and chalks recently used uncommitted model RAM #as "in-use". So, calculate free RAM for the sake of general use as the greater of: # #1: What psutil says #2: Total Memory - (Committed Memory - VRAM in use) # #We have to subtract VRAM in use from the comitted memory as WDDM creates a naked #commit charge for all VRAM used just incase it wants to page it all out. This just #isn't realistic so "overcommit" on our calculations by just subtracting it off. pi = PERFORMANCE_INFORMATION() pi.cb = ctypes.sizeof(pi) if not psapi.GetPerformanceInfo(ctypes.byref(pi), pi.cb): logging.warning("WARNING: Failed to query windows performance info. RAM usage may be sub optimal") return psutil.virtual_memory().available committed = pi.CommitTotal * pi.PageSize total = pi.PhysicalTotal * pi.PageSize return max(psutil.virtual_memory().available, total - (committed - comfy_aimdo.control.get_total_vram_usage())) ================================================ FILE: comfy_api/feature_flags.py ================================================ [File too large to display: 1.9 KB] ================================================ FILE: comfy_api/generate_api_stubs.py ================================================ [File too large to display: 2.8 KB] ================================================ FILE: comfy_api/input/__init__.py ================================================ [File too large to display: 278 B] ================================================ FILE: comfy_api/input/basic_types.py ================================================ [File too large to display: 256 B] ================================================ FILE: 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